“IDENTIFICACIÓN DE METABOLITOS PRODUCTOS DE LA

1

INSTITUTO TECNOLÓGICO DE TEPIC

“IDENTIFICACIÓN DE METABOLITOS PRODUCTOS DE LA FERMENTACIÓN COLÓNICA DE LA FRACCIÓN INDIGESTIBLE DE ALIMENTOS

FRECUENTEMENTE CONSUMIDOS POR ESCOLARES DE TEPIC, NAYARIT”

TESIS

por:

MCA. VICTOR MANUEL ZAMORA GASGA

DOCTORADO EN CIENCIAS

EN ALIMENTOS

Director:

Dra. SONIA GUADALUPE SÁYAGO AYERDI

Co-Director:

Dra. MARÍA GUADALUPE FLAVIA LOARCA PIÑA

Tepic, Nayarit, México Marzo de 2017

i

CONTENIDO

Pág.

LISTA DE CUADROS v

LISTA DE FIGURAS vii

Capítulo 1. INTRODUCCIÓN 1

Capítulo 2. ANTECEDENTES 3

2.1 La dieta en México y el desarrollo de enfermedades 3

2.1.1 La dieta y el síndrome metabólico 4

2.1.2 La dieta y su relación con la obesidad 5

2.1.3 La dieta y su relación con la resistencia a la insulina 7

2.1.4 La dieta y su relación con el cáncer de colon 7

2.2 Digestión y absorción de los nutrientes de la dieta 8

2.3 Fracción Indigestible 10

2.4 El colón y la microbiota intestinal 12

2.4.1 Fermentación colónica 13

2.5 Metabolitos derivados de la fermentación colónica de carbohidratos. 14

2.5.1 Ácidos grasos de cadena corta (AGCC) 15

2.5.2 Principales AGCC y sus efectos en la salud. 16

2.6 Proteínas de la dieta y las bacterias proteolíticas 19

2.6.1 Productos de la fermentación de proteínas 19

2.7 Grasas dietéticas 21

2.8 Metabolitos microbianos derivados de polifenoles dietéticos 22

2.8.1 Metabolitos de polifenoles y su impacto sobre la salud intestinal 24

ii

Capítulo 3. JUSTIFICACIÓN 26

Capítulo 4. HIPÓTESIS 27

Capítulo 5. OBJETIVOS 28

5.1 Objetivo general 28

5.2 Objetivos específicos 28

Capítulo 6. METODOLOGÍA GENERAL 29

6.1 Primera Etapa 29

6.1.1 Análisis de los alimentos frecuentemente consumidos 29

6.1.1.1 Sujetos de estudio 30

6.1.1.2 Evaluación dietética 30

6.1.1.3 Datos antropométricos 31

6.1.1.4 Identificación de los patrones dietéticos en escolares 31

6.2 Segunda Etapa 33

6.2.1 Obtención de las muestras 33

6.2.2 Cuantificación de la fracción indigestible total, soluble e insoluble 33

6.2.3 Digestión gastrointestinal in vitro: Aislamiento de la fracción indigestible 35

6.2.4 Caracterización nutricional, compuestos bioactivos y actividad antioxidante en la

fracción indigestible de los menús 36

6.2.4.1 Cuantificación del Almidón Resistente (AR) 36

6.2.4.2 Análisis de los compuestos antioxidante en la fracción indigestible de los menús 36

6.2.4.3 Análisis de la capacidad antioxidante en las fracciones indigestibles 38

6.3 Tercera Etapa 40

6.3.1 Fermentación colonica in vitro de la fracción indigestible 40

iii

6.3.2 Análisis de componentes antioxidantes asociados en los extractos de la

fermentación 41

6.3.3 Análisis de metabolitos en la fermentación colonica in vitro de la fracción

indigestible de los menús 41

6.3.3.1 Micro extracción en fase solida de los compuestos volátiles en los extractos de

fermentación 42

6.3.3.2 Cromatografía de gases acoplada a espectrometría de masas 42

6.4 Análisis estadístico 43

Capítulo 7. RESULTADOS Y DISCUSIÓN 45

7.1 Patrones dietéticos, perfil nutricional e índice de masa corporal en escolares

mexicanos: Un estudio transversal 45

Resumen 45

7.2 Perfil de metabolitos en un sistema in vitro de fermentación colónica humana en tres

menús consumidos por escolares mexicanos durante el desayuno 63

Resumen 63

7.3 Fermentación colónica humana in vitro de la fracción indigestible aislada de menús

consumidos durante la comida: Impacto en el perfil de los metabolitos intestinales 91

Resumen 91

7.4 Efectos de los alimentos frecuentemente consumidos por escolares mexicanos en la

cena sobre el metabolismo microbiano y la capacidad antioxidante durante la

fermentación colónica in vitro 120

Resumen 120

7.5 Los metabolitos intestinales se asocian al pH y cambios en la actividad antioxidante

durante la fermentación colónica in vitro de productos procesados mexicanos de maíz. 146

Resumen 146

iv

Capítulo 8. CONCLUSIONES 175

Capítulo 9. BIBLIOGRAFÍA 179

v

LISTA DE CUADROS Pág.

Cuadro 2.1 Prevalencia de diabetes (%) en adultos por diagnóstico previo en Encuestas

Nacionales de Salud (2000 a 2012). 7

Cuadro 2.2 Características diferenciales de la fracción indigestible y la fibra dietética. 11

Cuadro 2.3 Bacterias anaerobias predominantes en el colon y sus principales productos de

fermentación 12

Cuadro 2.4 Principales ácidos grasos de cadena corta (AGCC): Sus acciones y mecanismos

ajústalo a la izquierda para que se lea más fácil 17

Cuadro 2.5 Precursores y metabolitos polifenólicos derivados de la fermentación colónica

vitro después de 5 y 24 h de cinco muestras de alimentos. 23

Cuadro 6.1 Definiciones de los grupos de alimentos utilizados para evaluar las dietas de los

escolares del municipio de Tepic 32

Table 1 Multiple regression analysis models exploring the association of principal

components (PC) with weight and body mass index (BMI). 53

Table 2 Characteristics of Mexican schoolchildren by dietary pattern groups. 57

Table 1 Chemical composition and antioxidant compounds content, and antioxidant capacity

in the indigestible fraction (IF) isolated from breakfast menus1 73

Table 2. Short-chain fatty acids (SCFAs, mmol L-1) production at 12, 24, 48 and 72 h of in

vitro fermentation of blank, raffinose and indigestible fraction isolated from

breakfast menus (MM-B; Modified Mexican Breakfast, TM-B; Traditional Mexican

Breakfast, AM-B; Alternative Mexican Breakfast) 1. 77

Table SI. Volatile compounds characterization of in vitro colonic fermentation extracts of

blank, rafinose and indigestible fraction isolated from breakfast menus (MM-D;

Modified Mexican diet, TM-D; Traditional Mexican Diet, AM-D; Alternative

Mexican diet) analyzed by SPME–GC/MS. 89

vi


in the indigestible fraction (IF) isolated from lunch menus1 100


vitro fermentation of blank, raffinose and indigestible fraction isolated from lunch

menus (MM-L; Modified Mexican Lunch, TM-L; Traditional Mexican Lunch, AM-

L; Alternative Mexican Lunch) 1. 104


blank, rafinose and indigestible fraction isolated from Lunch menus (MM-L;

Modified Mexican diet, TM-L; Traditional Mexican Diet, AM-L; Alternative

Mexican diet) analyzed by SPME–GC/MS (mmol/L). 118


in the indigestible fraction (IF) isolated from dinner menus1 127


vitro fermentation of blank, raffinose and indigestible fraction isolated from dinner

menus (MM-D; Modified Mexican Dinner, TM-D; Traditional Mexican Dinner,

AM-D; Alternative Mexican Dinner) 1. 132


blank, rafinose and indigestible fraction isolated from dinner menus (MM-D;

Modified Mexican diet, TM-D; Traditional Mexican Diet, AM-D; Alternative

Mexican diet) analyzed by SPME–GC/MS. 144

Table 2. Production of short-chain fatty acids (SCFAs, mmol L-1) at 12, 24 and 48 h of in

vitro fermentation of blank, raffinose and indigestible fraction isolated from Istmo

Totopo (IT), baked corn tortilla (BCT) and traditional corn tortilla (TCT) *. 160


blank, rafinose and indigestible fraction isolated from Istmo Totopo (IF-IT), Baked

corn tortilla (IF-BCT) and traditional corn tortilla (IF-TCT) analyzed by SPME–

GC/MS (mmol/L) 173

vii

LISTA DE FIGURAS

Figura 2.1 Distribución porcentual de las defunciones registradas por principales causas de

muerte según sexo, hombre (░) y mujer (▓) en el Estado de Nayarit, 2009 4

Figura 2.2 Tendencias en las prevalencias de sobrepeso (□), obesidad I (░), obesidad II ( ▓

), y obesidad III (■) en adultos en el periodo 2000 a 2012. 6

Figura 2.3 Prevalencia de sobrepeso (░) y obesidad (▓) en niños y niñas de 5-11 años de

edad (1999 a 2012) en México. Fuente:Romero-Martínez y cols. (2013). 6

Figura 2.4 Las localizaciones relativas de la digestión y absorción de nutrientes en el tracto

gastrointestinal saludable. 10

Figura 2.5 Rutas de los metabolitos producidos por la interacción entre la microbiota y

metaboloma humano simplificado. 14

Figura 2.6 Alimentación cruzada por la microbiota del colon. 16

Figura 2.7 Posibles mecanismos propuestos para la prevención del cáncer por los

polifenoles dietéticos 25

Figure 1 Principal component analysis (PCA) plots. (A) Loading plots for different food

groups and (B) PCA scores plot for Mexican schoolchildren of the four principal

components. 51

Figure 2 Principal component analysis (PCA) plots. (A) Loading plots for different food

groups and (B) PCA scores plot for Mexican schoolchildren (PC 5, 6, and 7). 52

Figure 3 Mean percentage energy contribution from each food group according to dietary

pattern groups: (■) Modified Mexican Diet (n=312), (░) Traditional Mexican

Diet (n=190) and (■) Alternative Mexican Diet (n=222). 55

Figure 1 Changes in a) pH kinetic plot, b) DPPH Antiradical activity plot and c) FRAP

chelating activity plot in the extracts during in vitro colonic fermentation from (–

viii

) blank, (··♦··) raffinose, and indigestible fraction isolated from breakfast menus:

(-■-) Modified Mexican Breakfast, MM-B, (-▲-) Traditional Mexican

Breakfast, TM-B, and (-●-) Alternative Mexican Breakfast, AM-B at different at

different fermentation times. 75

Figure 2 Identification of Microbial metabolic pattern between the in vitro colonic

fermentation extracts of blank, raffinose and indigestible fraction isolated from

breakfast menus at different fermentation times using principal component

analysis (PCA): Loading scatter plot for a) PC1 vs. PC2; b) PC3 vs. PC4 and

PC5 vs. PC6. 79

Figure 3 Multiple regression analysis models exploring the association of microbial

metabolic pattern (Component scores) with: a) pH Values, b) DPPH antiradical

activity and c) FRAP chelating activity. 81

Figure 4 Dendrogram of hierarchical cluster analysis based on the microbial metabolic

profiles (component score) in colonic fermentation extracts of blank, raffinose,

and indigestible fraction isolated from breakfast menus (MM-B; Modified

Mexican Breakfast, TM-B; Traditional Mexican Breakfast, AM-B; Alternative

Mexican Breakfast) at different fermentation times: 12 h (T12), 24 h (T24), 48 h

(T48) and 72 h (T72). 83



) blank, (··♦··) raffinose, and indigestible fraction isolated from lunch menus: (-■-

) Modified Mexican Lunch, MM-L, (-▲-) Traditional Mexican Lunch, TM-L,

and (-●-) Alternative Mexican Lunch, AM-L at different at different

fermentation times. 102

Figure 2 Cumulative concentration (%) of volatile gut metabolites by chemical groups

between the in vitro colonic fermentation extracts of blank, rafinose and

indigestible fraction isolated from Modified Mexican Lunch (MM-L),

ix

Traditional Mexican Lunch (TM-L), and Alternative Mexican Lunch (AM-L) at

12 and 72 h. 106

Figure 3 Principal components plots (a) metabolites production “PC Loadings” (b) and

sample classification “PC scores” (%) during in vitro fermentation from

indigestible fraction isolated lunch menus (MM-L; Modified Mexican Lunch,

TM-L; Traditional Mexican Lunch, AM-L; Alternative Mexican Lunch). 107



) blank, (··♦··) raffinose, and indigestible fraction isolated from dinner menus: (-

■-) Modified Mexican Dinner, MM-D, (-▲-) Traditional Mexican Dinner, TM-

D, and (-●-) Alternative Mexican Dinner, AM-D at different at different

fermentation times. 130


between the in vitro colonic fermentation extracts of blank, rafinose and

indigestible fraction isolated from Modified Mexican Dinner (MM-D),

Traditional Mexican Dinner (TM-D), and Alternative Mexican Dinner (AM-D)

at 12 and 72 h. 134

Figure 3 Principal components plots (a) metabolites production “PC Loadings” (b) and


indigestible fraction isolated dinner menus (MM-D; Modified Mexican Dinner,

TM-D; Traditional Mexican Dinner, AM-D; Alternative Mexican Dinner). 135

Figure 4. Pearson’s R correlations between gut microbial metabolites, pH values and

antioxidant capacity (DPPH antiradical activity and FRAP chelating activity) in

extracts obtained during in vitro colonic fermentation of indigestible fraction

isolated from dinner menus (▲Increase, ▼decrease and ▬ non-correlation,

p<0.05). 136

Figure 1 Changes during in vitro colonic fermentation of (-○-) blank, (-●-) raffinose

(positive control), and indigestible fraction isolated from (-�-) Istmo Totopo, (-

x

◊-) Baked corn tortilla and (-□-) Traditional corn tortilla: a) pH values plot b)

DPPH Antiradical activity plot and b) FRAP chelating activity plot. 158


during in vitro colonic fermentation extracts of blank, raffinose and indigestible

fraction isolated from Istmo Totopo (IT), Baked corn tortilla (BCT) and

traditional corn tortilla (TCT) at 12 and 48 h. 162

Figure 3 Principal components plots A) metabolites production “PC Loadings” and B)


indigestible fraction isolated from Istmo Totopo (IT), Baked corn tortilla (BCT)

and Traditional Corn Tortilla (TCT). 163

Figure 4. Pearson’s R correlations between gut microbial metabolites and pH values and

antioxidant capacity (DPPH antiradical activity and FRAP chelating activity) in

extracts obtained during in vitro colonic fermentation of blank, raffinose and

indigestible fraction isolated from corn products 165

Capítulo 1. INTRODUCCIÓN

1


El estilo de vida en la población mexicana ha sido objeto de grandes cambios, principalmente en

los patrones de actividad física y dieta (Abell y cols., 2008). Ambos, han contribuido a la creciente

prevalencia de obesidad, diabetes tipo II y la enfermedad cardiovascular (ECV) (Denova-Gutiérrez

y cols., 2011; Denova Gutiérrez y cols., 2010 ; Romero-Polvo y cols., 2012). México, fue uno de

los países con mayor prevalencia de sobrepeso infantil en 2010 y obesidad entre los adultos

(OECD, 2012.). Esta epidemia tiene una alta tasa de crecimiento en los niños, dando como

resultado una alta prevalencia de sobrepeso y obesidad entre los escolares de todo el país. La

Encuesta Nacional de Salud y Nutrición (ENSANUT) del 2006 menciona que en Nayarit existió

una prevalencia de sobrepeso-obesidad en escolares del 31 % (Olaiz-Fernández y cols., 2006). En

Nayarit las principales causas de muerte son las enfermedades istémicas del corazón y la diabetes

mellitus en la población adulta (INEGI, 2009); posiblemente, derivado del incremento en el

sobrepeso y obesidad de la población en los últimos años. Xu y cols. (2006), mencionan que para

generar información acerca de la relación entre estos problemas de salud pública y la dieta, se han

realizado análisis nutricionales de los alimentos y su impacto en la población. La ingesta de

semillas enteras, carencia de minerales, alimentos de alto índice glucémico y carga glucémica, se

han relacionado con estas enfermedades (Liu y cols., 2009b). Sin embargo, estos análisis se han

centrado en nutrientes individuales o alimentos específicos y, las interacciones entre

macronutrientes y micronutrientes pueden pasar por alto. De manera cotidiana la gente consume

una variedad de alimentos en complejas combinaciones de nutrientes. Para llevar a cabo una

investigación epidemiológica y nutricional que más se acerque al modelo de la experiencia

humana, los investigadores han propuesto el estudio de los hábitos alimentarios en lugar de

alimentos aislados, dando un enfoque que puede ayudar a entender con más precisión la relación

entre el consumo de alimentos y la propagación o la prevención de enfermedades crónicas

(Hoffmann y cols., 2004). Es Importante mencionar que, en México, se tienen una gran diversidad

gastronómica, donde cada Estado presenta costumbres y tradiciones culinarias únicas. Sin lugar a

dudas, varios aspectos de la dieta juegan un papel clave en la salud de la población, aspectos tales

como el contenido de grasas, carbohidratos, fibra, etc. Uno de los componentes que ha sido

ampliamente estudiado, es la fibra dietética (FD) (carbohidratos indigestibles), sin embargo, tan


2

sólo es uno de los componentes indigestibles de los alimentos, ya que existen otros componentes

que pueden no ser totalmente digeridos, a estos componentes se le conoce como fracción

indigestible (FI) y poco se conoce de los efectos en la salud de estos compuestos. El estudio de los

carbohidratos indigestible se ha enfocado tan sólo en su en la cuantificación en alimentos

individuales y hay poca información acerca de su contenido en mezclas de alimentos tal cual se

consumen sobre todo en la población mexicana. Diversos estudios han demostrado los efectos

positivos de la FD en la regulación de la respuesta glucémica, la capacidad de promover la

saciedad, la actividad prebiótica y como portador de compuestos antioxidantes (Costabile y cols.,

2008; Kim y cols., 2009). Estudios de metabolómica han demostrado que los beneficios en la

salud de la ingesta de FD, están mediados por los metabolitos producidos durante su fermentación

en el colon (Martin y cols., 2009), entre los efectos potenciales para la salud se encuentra la

producción de ácidos grasos de cadena corta (AGCC) y compuestos antioxidantes asociados a la

fibra (Hernandez-Salazar y cols., 2010). Sin embargo, no todos los metabolitos producidos durante

la fermentación colónica de la fraccipon indigestible (no sólo FD), puede estar asociados con

beneficios para la salud, y algunos de ellos pueden tener efectos adversos. Por ejemplo, el

amoníaco producto de la fermentación de proteínas, en bajas concentraciones altera la mucosa

intestinal, promueve el crecimiento de células tumorales, y facilita contraer infecciones virales

(Lin y Visek, 1991). Por tanto, es importante conocer y estudiar la dieta de la población tal como

es ingerida para tener un panorama más amplio de los efectos saludables y adversos que los

patrones dietéticos ejercen en los individuos.

Capítulo 2. ANTECEDENTES

3


2.1 La dieta en México y el desarrollo de enfermedades

Como en muchos otros países en desarrollo, la transición epidemiológica en México se caracteriza

por una reducción de las enfermedades infecciosas y por un rápido aumento de las enfermedades

crónicas no transmisibles (Rivera y cols., 2002). Entre estas últimas, la obesidad, enfermedades

cardiovasculares (ECV), diabetes tipo II y cáncer, son los problemas de salud pública más

importantes a las que los mexicanos tienen que hacer frente en la actualidad (Olaiz y cols., 2006).

Basado en datos de varias encuestas nacionales representativas, los estudios epidemiológicos han

mostrado cambios importantes en los patrones de consumo de alimentos en los hogares en México

(Rivera y cols., 2002). Estos cambios pueden contribuir a la creciente epidemia de enfermedades

crónicas relacionadas con la nutrición. En la Figura 2.1 se muestra las principales causas de

muerte en el estado de Nayarit. Durante un corto tiempo, se ha observado un notable incremento

en el consumo de grasas, azúcares refinados y bebidas carbonatadas, así como una importante

reducción en la ingesta de frutas y verduras. Los estudios también muestran una disminución de la

actividad física recreativa, un mayor acceso a los alimentos ricos en energía y de bajo costo, así

como, una baja ingesta de micronutrientes (Hernández y cols., 2003; Rivera y cols., 2002). Los

grupos de alimentos y los análisis de nutrientes aisladas se han utilizado para describir el binomio

dieta-enfermedad. Sin embargo, los individuos y las poblaciones no consumen los nutrientes o

alimentos aislados, sino diferentes alimentos de los diferentes grupos, en combinaciones o de

acuerdo a patrones, que existen por cuestiones culturales y económicos importantes (Hu, 2002).

Los estudios de población han demostrado una asociación entre diferentes patrones alimentarios

con diversos marcadores antropométricos y bioquímicos de la obesidad además de la asociación

con el desarrollo de las enfermedades crónicas (Montonen y cols., 2005; Newby y cols., 2004;

Newby y cols., 2003).


4

Figura 2.1 Distribución porcentual de las defunciones registradas por principales causas de muerte

según sexo, hombre (░) y mujer (▓) en el Estado de Nayarit, 2009. Fuente: INEGI (2011)

2.1.1 La dieta y el síndrome metabólico

El síndrome metabólico (SM) es una “condición” en la que se conjugan distintos padecimientos en

el individuo como, obesidad central, dislipidemia, hiperglucemia, e hipertensión (Alberti y cols.,

2005; Reaven, 1988). Aproximadamente un tercio de los adultos en México presentan esta

condición (Aguilar-Salinas y cols., 2004), donde algunos estudios han relacionado que una mayor

ingesta de cereales integrales y frutas y verduras en la dieta diaria puede derivar en una menor

prevalencia de SM (Esmaillzadeh y cols., 2004; Sahyoun y cols., 2006). La evidencia es menos

constante en el consumo de harinas refinadas, donde los resultados han sido un tanto

contradictorios, ya que algunos estudios han reportado una asociación positiva entre la ingesta de

harinas refinadas con el SM, mientras que otros no encontraron ninguna relación. Por último, la

ingesta de refrescos y bebidas azucaradas se han asociado positivamente con el SM (Denova-

0 10 20 30 40 50

Lasdemáscausas

Enfermedadesdelhígado

Enfermedadesrespiratoriasinferiores

Accidesntesdetransporte

Enfermedadescerebrovasculares

Enfermedadesisquémicasdelcorazón

Diabetesmellitus

Tumores(neoplasias)malignos

Accidentesdetransporte


5

Gutiérrez y cols., 2010; Dhingra y cols., 2007). La evaluación de los hábitos alimentarios, en el

que varios alimentos o nutrientes son combinados en una variable compuesta, puede arrojar una

luz más clara sobre la relación dieta-enfermedad (Schwerin y cols., 1981; Slattery y cols., 1998),

no solo en estudios de campo sino también como parte de las metodologías del análisis de

alimentos en un laboratorio.

2.1.2 La dieta y su relación con la obesidad

La obesidad se ha convertido en una enfermedad cada vez más frecuente en todo el mundo, donde

al menos mil millones de personas presentan sobrepeso y ~ 300 millones de personas son obesas

(WHO, 2008). En México, la ENSANUT, 2006 reveló que un 70% de los adultos presentan

sobrepeso o son obesos (Olaiz-Fernández y cols., 2006) y en el 2012 según la ENSANUT

(Romero-Martínez y cols., 2013) se reportó un 71.1%. Donde las mujeres presentan mayor

tendencia hacia la obesidad y los hombres mayor tendencia hacia el sobrepeso (Ver Figura 2.2).

La obesidad del adulto se ha relacionado con numerosas enfermedades crónicas y genera altos

costos al Gobierno en salud pública. Además, la creciente prevalencia de la obesidad plantea

numerosas implicaciones negativas para la salud física y económica de muchas sociedades, y se

menciona que niños con sobrepeso u obesidad tienden al desarrollo de estas enfermedades en su

etapa adulta (Ford y Mokdad, 2008).

0

10

20

30

40

50

60

70

80

2000 2006 2012 2000 2006 2012 2000 2006 2012

Total Hombres Mujeres

Porcen

taje(%

)


6

Figura 2.2 Tendencias en las prevalencias de sobrepeso (□), obesidad I (░), obesidad II ( ▓ ), y

obesidad III (■) en adultos en el periodo 2000 a 2012. Fuente:Romero-Martínez y cols. (2013).

En la Figura 2.3 se muestra el incremento del sobrepeso y obesidad en escolares (5-11 años)

mexicanos entre los periodos de 1999 al 2012. A pesar de considerables esfuerzos en

investigación, la etiología nutricional de la obesidad sigue siendo poco clara y controversial,

especialmente en relación con las funciones desempeñadas por las grasas dietéticas (Ebbeling y

cols., 2007; McMillan-Price y cols., 2006; Willett, 1998) y carbohidratos (Acheson, 2010). Sin

embargo, estos factores de la dieta por sí solos probablemente explican sólo una parte del efecto de

la dieta sobre la obesidad. Los hábitos alimentarios generales parecen afectar a la salud más que el

consumo de alimentos y nutrientes individuales.

Este enfoque también proporciona una imagen más precisa del consumo de alimentos en la vida

cotidiana que los estudios de nutrientes aislados, produciendo resultados que ofrecen una mayor

comprensión de los cambios dietéticos saludables y recomendaciones de salud pública (van Dam y

cols., 2002)

Figura 2.3 Prevalencia de sobrepeso (░) y obesidad (▓) en niños y niñas de 5-11 años de edad

(1999 a 2012) en México. Fuente:Romero-Martínez y cols. (2013).

0

5

10

15

20

25

30

35

40

1999 2006 2012 1999 2006 2012

Niños Niñas

Porcen

taje(%

)


7

2.1.3 La dieta y su relación con la resistencia a la insulina

La insulinoresistencia (IR) se asocia con múltiples trastornos metabólicos y aumenta el riesgo de

desarrollo de diabetes tipo II y ECV (Baxter y cols., 2006). La prevalencia mundial de diabetes ha

aumentado considerablemente en los últimos años, debido a estilos de vida cada vez menos

saludables, incluyendo dietas más desbalanceadas (Rivera y cols., 2002). En el Cuadro 2.1 se

muestra la prevalencia de diabetes mellitus en adultos mexicanos por diagnóstico previo entre los

años 2000 al 2012. En los EE.UU., la Encuesta Nacional de Examen de Salud y Nutrición

(National Health and Nutrition Examination Survey) encontró que 52.1% de los adolescentes

obesos (entre 12-19 años) presentaban IR (Lee y cols., 2006). Sin embargo, la prevalencia de IR en

niños y adolescentes mexicanos no está bien documentada.

La dieta parece desempeñar un papel clave en el desarrollo de IR. Hasta la fecha, varios aspectos

de la dieta, tales como la ingesta de grasas, cierto tipo de carbohidratos, fibra dietética, el índice

glucémico de los alimentos, la carga glucémica se han relacionado con la IR (Liu y cols., 2009a).

Estudios previos han encontrado que en los adultos un patrón alimentario caracterizado por un

consumo predominante de frutas, verduras, granos enteros, productos lácteos bajos en grasa y un

bajo consumo de grasas saturadas se asocia con un menor riesgo de IR (Baxter y cols., 2006;

Esmaillzadeh y cols., 2007). Por el contrario, se ha demostrado que los hábitos alimentarios con

altos índices glucémicos (carbohidratos fácilmente digeribles) que son bajos en granos enteros

aumentan el riesgo de IR (McNaughton y cols., 2008).

Cuadro 2.1 Prevalencia de diabetes (%) en adultos por diagnóstico previo en Encuestas

Nacionales de Salud (2000 a 2012).

Edad (años) 20 a 29 30 a 39 40 a 49 50 a 59 60 a 69 70+ Total

ENSA 2000 0.04 2.2 5.4 10 17.8 9.9 4.6

ENSANUT 2006 0.04 3.4 10.6 16.8 19.7 17.2 7.3

ENSANUT 2012 1.6 3.1 9.4 19.4 26.3 20 9.2

Fuente: Romero-Martínez y cols. (2013).

2.1.4 La dieta y su relación con el cáncer de colon


8

Otra de las enfermedades que hoy en día se relacionan con la dieta son algunos tipos de cáncer,

donde, el más comúnmente diagnosticados en los Estados Unidos es el cáncer colorrectal (CCR),

el cual es la tercera causa de mortalidad por cáncer en México (Campos-Vega y cols., 2010). Esta

enfermedad está definida como una neoplasia maligna a partir del crecimiento de un tumor en el

epitelio intestinal como resultado de la replicación no controlada de células dañinas que tienen la

capacidad de destruir las células adyacentes o propagar la metástasis a otros tejidos. Los factores

ambientales son de gran importancia en la relación entre la dieta y cáncer (Bingham, 1997). Las

dietas de la mayoría de los países emergentes son más altas en almidón y carbohidratos en

comparación con los países occidentales que son más altas en grasas animales y proteínas (Scott y

cols., 2012). Las diferencias en los hábitos alimenticios entre los países han aportado alguna

información útil en cuanto a cuáles son los principales factores en la asociación con el CCR. La

industrialización ha tenido un papel importante que desempeñar en el aumento de la incidencia del

CCR en los países no occidentales. En los años 50’s y 60’s en comparación con el Reino Unido,

Japón tenía un alto consumo de cereales y menor consumo de productos cárnicos en casi 60

g/persona al día y al principio de la década de 90’s esto comenzó a cambiar de forma paralela a la

ingesta de cereales, la cual cayó, mientras el consumo de carne aumentó y al mismo tiempo se

observó un incremento en la incidencia de CCR, en casi un 30% en el país Nipon en los siguientes

30 años (Key y cols., 2002). Sin embargo, los estudios epidemiológicos que vinculan la dieta y el

cáncer a menudo carecen de un marcador específico que está fuertemente asociado con el cáncer y

la modulación de la dieta por separado. Más recientemente, los vínculos entre los metabolitos

producidos a partir de la dieta y ciertos componentes de los alimentos han mostrado una

asociación con la incidencia de cáncer (Rose y cols., 2007). Los patrones dietéticos se

correlacionan positivamente e incluye la ingesta de grasas y proteínas, en concreto, de productos

cárnicos y cárnicos procesados (Rose y cols., 2007). Diversas revisiones de epidemiología y

estudios prospectivos han encontrado asociaciones entre el aumento del consumo específicamente

de proteínas de carne roja, procesada y CCR (Larsson y Wolk, 2006; Lee y cols., 2004; Norat y

cols., 2002; Sandhu y cols., 2001). La principal diferencia entre la carne roja y la carne blanca son

los niveles de hemoglobina, y más concretamente el hierro contenidos en la misma. Donde

muchos estudios han centrado el contenido de hemoglobina como un factor de riesgo para CCR a

través de la participación en la formación catalítica de compuestos de N-nitrogenados (de Vogel y

cols., 2008; Klenow y cols., 2009).

2.2 Digestión y absorción de los nutrientes de la dieta


9

Como se mencionó anteriormente, la dieta; tiene un gran impacto en el desarrollo de enfermedades

que afectan la salud de la población y sin duda el proceso de digestión de los nutrientes juega un

rol importante en este sentido. Las funciones de motilidad y secreción del sistema gastrointestinal

están reguladas para permitir una digestión y absorción óptima de los nutrientes presentes en los

alimentos ingeridos. La digestión de los alimentos se puede dividir en mecánica y química (Ver

Figura 2.4). La digestión mecánica se inicia con la masticación en la cavidad oral y continua en el

estómago gracias a las potentes contracciones de la zona caudal. Todos ellos permiten la división

de alimento hasta convertirlo en partículas de muy pequeño tamaño (2 mm) antes de su

vaciamiento hacia el intestino delgado. Esta división mecánica da lugar a un gran aumento en la

superficie de contacto del alimento, lo que favorece la actuación de las enzimas hidrolíticas y otras

sustancias, facilitando la digestión química (Martínez de Victoria Muñoz y cols., 2010).

La digestión química la realizan enzimas hidrolíticas presentes en la luz gastrointestinal y en el

epitelio mucosal. Estas enzimas son secretadas por distintas glándulas a la luz intestinal, donde

ejercen su función (digestión luminal), o bien se asocian a la membrana del polo apical de los

enterocitos (borde en cepillo) (digestión de membrana) (Martínez de Victoria Muñoz y cols.,

2010). Los procesos de digestión comienzan en la cavidad bucal y continúan en el estómago, pero

es en el intestino delgado donde adquieren una mayor relevancia, de hecho, las alteraciones en los

procesos de digestión sólo aparecen por malfuncionamiento de los mecanismos digestivos y de su

regulación en este segmento. Una vez terminada la digestión, los productos resultantes deben

atravesar la barrera intestinal hasta llegar al medio interno, sangre o linfa de los capilares que

irrigan las vellosidades intestinales y que se localizan en la lámina propia. Para llegar al torrente

sanguíneo o linfático, los productos de la digestión deben cruzar la capa no agitada de líquido en el

lumen, el glicocáliz, en estrecho contacto con la membrana apical del enterocito que también debe

pasar, el citoplasma enterocitario, la membrana basolateral, el espacio intercelular, la membrana

basal (Martínez de Victoria Muñoz y cols., 2010).

El transporte (absorción) de las sustancias luminales hasta el medio interno puede realizarse por

diversos mecanismos que se ponen en marcha dependiendo de sus propiedades fisicoquímicas.

Estos mecanismos son pinocitosis, difusión simple o facilitada y transporte activo. Sin embargo

existe una porción de los alimentos que no es digerida ni absorbida en el intestino delgado,

conocida como fracción indigestible, que puede alcanzar el colon y ser utilizada como sustrato por

la microflora colónica, produciendo metabolitos que han sido involucrados con la salud del

huésped (Saura-Calixto y cols., 2000).


10

Figura 2.4 Las localizaciones relativas de la digestión y absorción de nutrientes en el tracto gastrointestinal saludable. Fuente: Jeejeebhoy (2002)

2.3 Fracción Indigestible

La fracción indigestible (FI) se define como la parte de los “alimentos vegetales” que no se digiere

ni se absorben en el intestino delgado, llegando al colon, donde es un sustrato para la microflora

fermentativa (Saura-Calixto y cols., 2000). Como tal, comprende no sólo fibra dietética, sino


11

también otros compuestos de probada resistencia a la acción de enzimas digestivas tales como

almidón resistente (AR), el cual escapa de la digestión y absorción en el intestino delgado humano

en los individuos sanos (Asp y Björck, 1992; Englyst y cols., 1982) y, otras sustancias alimenticias

no incluidas en la definición de fibra dietética como proteínas resistentes, oligosacáridos, ciertos

compuestos fenólicos, etc (Cummings y Englyst, 1991). Estos compuestos además, pueden ser

fermentados por la microbiota del colon, y presentar efectos fisiológicos similares a los de la fibra

dietética (Saura-Calixto y cols., 2000). En el Cuadro 2.2 se observan las principales diferencias

entre los conceptos de fracción indigestible y fibra dietética. Este concepto surge como una

alternativa al concepto de fibra dietética; que considera en su metodología la eliminación

enzimática de la proteína y almidón.

El método para la determinación de FI combina en un solo procedimiento las metodologías para

fibra dietética (Mañas y cols., 1994) y el análisis de almidón resistente (Goñi y cols., 1996). Vale

la pena señalar que, en el método de fracción indigestible, las muestras se analizan tal como se

ingieren (fresca, hervida o frita) y las condiciones analíticas (pH, temperatura, tiempos de

incubación) están cerca de las condiciones fisiológicas. Desde un punto de vista fisiológico los

valores de la fracción indigestible representan cantidades más estrechas de las sustancias

indigestibles que podrían alcanzar el colon.

Cuadro 2.2 Características diferenciales de la fracción indigestible y la fibra dietética.

Fracción indigestible Fibra dietética

Definición Principales componentes de los alimentos indigeribles

Restringido a PNA + lignina

Condiciones analíticas Fisiológicas No fisiológicas

Preparación de la muestra Como se come Molidos y hervidos

Componentes de los residuos gravimétricos Los incluidos en la definición

PNA, Lignina y otros más (AR, PR TC)

Digestibilidad del almidón No alterada Modificada artificialmente

PNA: Polisacáridos no amiláceos, AR: Almidón resistente, PR: Proteína resistente, TC: Taninos

condensados. Fuente: (Saura-Calixto y cols., 2000)


12

2.4 El colón y la microbiota intestinal

El colon o intestino grueso humano es de aproximadamente 150 cm de largo con un área de

superficie (no seccionada) de 1300 cm2. La microbiota colónica consiste en varios cientos de

especies (Ver Cuadro 2.3) y, aunque hay diferencias interindividuales significativas ciertas

especies bacterianas están presentes constantemente en la mayoría de los individuos (Sekelja y

cols., 2010). La microbiota es un componente importante y constituye entre el 40 y 45% de sólidos

fecales en personas que consumen dietas occidentales (Stephen y Cummings 1980; Cabotaje et al

1990).

Cuadro 2.3 Bacterias anaerobias predominantes en el colon y sus principales productos de fermentación Productos de fermentación

Bacteria Descripción

# (Log 10/g BS heces) Nutrición C2 C3 C4 Láctico Succínico

Bacteroides Bacilos Gram (-)

11 3 Principalmente sacarolíticas, algunas especies proteolíticas

+ + - - +

Eubacteria Bacilos Gram (+)

10 7 Principalmente sacarolíticas, algunas especies proteolíticas

+ - + + -

Bifidobacteria Bacilos Gram (+)

10 2 Sacarolíticas

+ - - + -

Lactobacilos Bacilos Gram (+)

9 6 Sacarolíticas

- - - + -

Ruminococos Bacilos Gram (+)

10 2 Sacaroíiticas

+ - - - -

Peptococos Bacilos Gram (+)

10 0 Proteolíticas

+ - + + -

Peptostrep-tococos

Bacilos Gram (+)

10 1 Especies sacarolíticas y proteolíticas

+ - - + -

Clostridios Bacilos Gram (+)

9 8 Especies sacarolíticas y proteolíticas

+ + + + -


13

C2=Ác. Acético, C3= Ác. Propiónico, C4= Ác. Butírico. Adaptado de: Cummings y Macfarlane

(1991)

Los análisis de genes que codifican para la fracción 16S del rRNA amplificados directamente

junto con estudios de metagenómica han ayudado a definir los filotipos que son más abundantes

dentro de la microbiota fecal humana (Qin y cols., 2010; Tap y cols., 2009; Walker y cols., 2010).

Los filos bacterianos dominantes en adultos sanos son Firmicutes, Bacteroidetes y Actinobacteria,

normalmente con menor abundancia de Verrucomicrobia y Proteobacteria. Dos de las especies

bacterianas más dominantes en el intestino grueso de los individuos que consumen una dieta

occidental son las especies productoras de butirato, Faecalibacterium prausnitzii y Eubacterium

rectale (Flint y cols., 2012a), que conforman alrededor de 8% y 4% de la microbiota,

respectivamente. Este grupo de bacterias existen en condiciones relativamente estables con

respecto a los factores ambientales tales como la disponibilidad de nutrientes, pH, temperatura,

potencial óxido-reducción y el grado de anaerobiosis. La microbiota del colon está en contacto

íntimo con su huésped y toma parte en un gran número de procesos metabólicos mismos que se

dan a partir de la fermentación colónica de los componentes indigestibles de los alimentos.

2.4.1 Fermentación colónica

En el hombre, el intestino grueso recibe el material del íleon que ya ha sido digerido, los

contenidos se mezclan y son retenidos durante 6-12 h en el ciego y en el colon ascendente. A partir

de entonces, la digesta se expulsa y se pasa a través del colon transverso para su almacenamiento y

alcanza el colon descendente y su eventual elimnación. El tiempo promedio de retención del

material que entra al colon hasta su excreción es de aproximadamente 60 h en individuos del

Reino Unido (Cummings y Macfarlane, 1991). La cantidad total de material que entra en el colon

cada día es de 1.5 kg aproximadamente, mientras que el peso promedio de las heces, en el Reino

Unido, es de 120 g/d (Cummings y Macfarlane, 1991). Por lo tanto, el intestino grueso es un

sistema abierto, con nutrientes que fluyen desde el ciego, bacterias, productos metabólicos de éstas

y la fracción no utilizada en esta sección termina siendo excretada en forma de heces. La hidrólisis

anaeróbica de los carbohidratos y proteínas por bacterias se conoce convencionalmente como

fermentación colónica. En el hombre, los productos finales principales son los AGCC, ácido

acético, propiónico y butírico, los gases de H2, y CO2, amoniaco, aminas, fenoles y energía, que las

bacterias utilizan para el crecimiento y el mantenimiento de la función celular (Scott y cols.,

2012). En la Figura 2.5 se muestran las rutas metabólicas de los metabolitos producidos por la

microbiota. Desde el punto de vista del anfitrión, los productos finales de las reacciones de


14

fermentación son importantes, ya que se absorben en el intestino e influyen en muchos aspectos

del metabolismo.

Figura 2.5 Rutas de los metabolitos producidos por la interacción entre la microbiota y metaboloma humano simplificado. Adaptado de: Hamer y cols. (2012).

2.5 Metabolitos derivados de la fermentación colónica de carbohidratos.

Muchos carbohidratos presentes en alimentos derivados de plantas son digeridos lentamente o no

digeridos por el intestino delgado, lo que los hace disponibles a la fermentación por la microflora

del intestino grueso. Aproximadamente 40 g de carbohidratos provenientes de la dieta alcanzan el

colon cada día (Cummings y Englyst, 1991). Los principales carbohidratos son almidones

resistentes (AR), polisacáridos no amiláceos (PNA) y oligosacáridos, aunque algunos

monosacáridos y disacáridos (por ejemplo, alcoholes de azúcar). Estos carbohidratos son

degradados por las bacterias del colon a través de vías que conducen a la formación de ácido

acético, succínico, propiónico butírico y fórmico, ácido láctico, etanol, hidrógeno y CO2,

dependiendo de cada cepa y especie (Russell y cols., 2013). La formación de ácido butírico se


15

produce en ciertas bacterias Firmicutes, ya sea a través de la enzima quinasa del ácido butírico, (en

muchas especies de Clostridium y Coprococcus), o por la vía del butiril CoA: Acetato CoA

transferasa (Louis y Flint, 2009). Las bacterias productoras de ácido butírico como Roseburia,

Eubacterium rectale, Eubacterium hallii y Faecalibacterium prausnitzii, absorben directamente el

ácido acético externo y lo convierte a butírico (Louis y cols., 2010). El ácido acético es producido

por la mayoría de los anaerobios, incluyendo bacterias acetógenas que son capaces de realizar

acetogénesis reductiva de ácido fórmico o hidrógeno más CO2. Los productores de ácido succínico

y propiónico pertenecen al filo Bacteroidetes, pero también incluyen algunos Firmicutes. El ácido

láctico puede ser formado por muchos grupos, pero en general se convierte en ácido acético,

propiónico o butírico de un subconjunto de bacterias que utilizan ácido láctico (Flint y cols.,

2012b). La formación de los gases como H2 y CO2 varía entre especies en cultivos puros; en la

comunidad mixta encontrada en el colon estos productos son parcialmente convertidos a ácido

acético, metano y sulfuros de hidrógeno (Nakamura y cols., 2010).

2.5.1 Ácidos grasos de cadena corta (AGCC)

En la fermentación colonica ocurre un gran número de complejas interacciones derivadas de la

alimentación cruzada entre los microorganismos del colon (Ver Figura 2.6). La presencia de

carbohidratos indigestibles y la producción de AGCC pueden alterar positivamente la fisiología

del colon. Varios estudios sobre la microbiota intestinal han mostrado que la producción de AGCC

ocurre en el orden siguiente acético > propiónico > butírico, en proporciones molares de

aproximadamente 60:20:20, principalmente fermentados en el colon proximal y distal (Topping y

Clifton, 2001).


16

Figura 2.6 Alimentación cruzada por la microbiota del colon. Adaptado: Flint y cols. (2008)

Un aumento en la síntesis de AGCC también crea un entorno más ácido en el intestino, lo cual es

importante in vivo en términos de resistencia a la colonización contra patógenos (Roberfroid,

2000). La producción de AGCC se ve afectada por muchos factores, incluyendo la fuente de

sustrato, en particular, la composición química del sustrato fermentable, la cantidad de sustrato

disponible, su forma física (por ejemplo, tamaño de partícula, la solubilidad, la asociación con

complejos indigeribles tales como lignina), la composición de las especies de bacterias de la

microbiota, factores ecológicos (interacciones competitivas y de cooperación entre los diferentes

grupos de bacterias) y el tiempo de tránsito intestinal (Huazano-García y López, 2013). Los

AGCC se absorben rápidamente en el ciego y en el colon proximal y, se excretan en las heces sólo

del 5% al 10%. Los principales AGCC (Ácido acético, propiónico y butírico), son absorbidos en

diferentes regiones del colon. Una vez absorbidos, los AGCC se metabolizan en tres sitios

principales en el cuerpo: 1) células del epitelio en el colon proximal, donde el ácido butírico es un

sustrato importante para el mantenimiento de la energía de la microbiota; 2) las células del hígado

que metabolizan residuos de ácido butírico y propiónico usados para la gluconeogénesis, de novo

lipognénesis, donde 50 a 70% de ácido acético también es absorbido en hígado, y 3) células

musculares que generan energía a partir de la oxidación de los residuos de ácido acético (Van Loo

y cols., 1995).

2.5.2 Principales AGCC y sus efectos en la salud.

Tal como se ha mencionado anteriormente, los principales AGCC producidos en el colon son: el

ácido acético, propiónico y butírico. En el Cuadro 2.4 se presentan algunos de los efectos en la

salud de estos AGCC. El ácido acético es el principal AGCC producido en el colon, éste se

absorbe fácilmente y se transporta al hígado, y por lo tanto es el que menos se metaboliza (Cook y

Sellin, 1998). La presencia de acetil-CoA sintetasa en el citosol de tejido adiposo y glándulas

mamarias permite utilizar el ácido acético para la lipogénesis una vez que entra en la circulación

sistémica. El ácido acético es el sustrato primario para la síntesis de colesterol. También puede ser

absorbido y utilizado por los tejidos periféricos (Wong y cols., 2006). Por otro lado, el ácido

propiónico se produce a través de dos vías principales: 1) por la fijación de CO2 para formar acido

succínico, el cual posteriormente es descarboxilado (la "ruta del ácido dicarboxílico") y 2) se

forma ácido láctico y acrilato (la "vía de acrilato") (Cummings y Macfarlane, 1991). El ácido

propiónico es también un sustrato para la gluconeogénesis hepática y se ha informado de que este


17

ácido inhibe la síntesis de colesterol en el tejido hepático (Cheng y Lai, 2000). Por último, el ácido

butírico es el combustible preferido por las células epiteliales del colon, pero también juega un

papel importante en la regulación de la proliferación y la diferenciación celular (Topping y

Clifton, 2001). El ácido butírico es el AGCC más importante en el metabolismo de los

colonocitos, donde se metaboliza del 70 % al 90 % de ácido butírico. Éste AGCC se utiliza

preferentemente sobre el ácido propiónico y acético en una proporción de 90:30:50 (Cook y Sellin,

1998).

Cuadro 2.4 Principales ácidos grasos de cadena corta (AGCC): Sus acciones y mecanismos ajústalo a la izquierda para que se lea más fácil AGCC Sitio de acción Principio de acciones / Efectos y mecanismos propuestos.

Ácido acético Hígado,

músculo y

otros tejidos

periféricos

Metabolismo: El principal sustrato para la síntesis de colesterol

Ácido

propiónico

El tejido

adiposo y el

hígado

Metabolismo: El principal sustrato para la gluconeogénesis

(especialmente en rumiantes), inhibe HMG-CoA → disminución

de la síntesis hepática de colesterol

Anti-inflamatorios: En el intestino bajo modula la actividad de la

ciclooxigenasa, estimula GPCR41 y GPCR43; inhibe NF-κB a

través de PPARγ

Antimicrobiano: Inhibe la expresión de los genes que facilitan la

invasión y la penetración del epitelio intestinal por Salmonella

typhimurium

Mejora de la sensibilidad a la insulina: Inhibe la lipólisis y

favorece la lipogénesis en el tejido adiposo viceral (TAV) y

suprime la producción de ácidos grasos (AG) en el hígado →

bajos niveles de AG en el hígado y el plasma → menor estado

inflamatoria → reduce la resistencia a la insulina causada por la

interacción AG-inflamación

Saciedad: Promueve la saciedad a través de complejo

neuronal, endocrino, paracrino, autocrino ; influye en

Continúa Cuadro 2.4


18

la producción de hormonas adipocineticas, tales como la leptina,

una hormona anorexígenica potente.

Ácido butírico

Colonocitos Anticarcinogénicos: Inhibe las histonas deacetilasas (HDAC) →

hiperacetilación de histonas y mejora de la accesibilidad de los

factores de transcripción al ADN nucleosomal → regula la

expresión génica y la función celular; aumenta la actividad de la

enzima desintoxicante glutatión-S-transferasa, inhibe la

migración de células tumorales mediante la inhibición de la

expresión del factor de deterioro más acelerado (FDA) y

activación de metaloproteinasas pro-metastásicas; inhibe la

angiogénesis inducida por el tumor mediante la modulación de

las proteínas relacionadas con la angiogénesis, el factor de

crecimiento endotelial vascular (FCEV) y factor inducible de

hipoxia (FIH-1α).

Anti-inflamatorios: Suprime la activación de NF-kappa B a

través de la inhibición de la HDAC, la inhibición de la

producción de interferón-γ y / o señalización, y la regulación al

alza de receptores activados por el proliferador de peroxisomas

(PPARγ); actúa como una molécula de señalización a través de

GPR41 y GPR43

Refuerzo de las líneas de defensa del colon: Aumenta la

expresión de genes MUC2 → estimula la síntesis

de mucina; aumenta factor trébol intestinal (ITF o TFF3)

secretado por las células caliciformes intestinales

Saciedad: vía GLP-1 o el péptido YY

Adaptado de: Vipperla y O'Keefe (2012)

Aproximadamente el 95% del ácido butírico producido por las bacterias del colon es transportado

a través del epitelio, pero las concentraciones en sangre portal por lo general no son detectables

como resultado de una rápida utilización (Pryde y cols., 2002). La producción de ácido butírico

también puede ocurrir a través de la utilización de otros productos de la fermentación tales como



19

ácido acético o ácido láctico que pueden actuar como precursores del ácido butírico en la

alimentación cruzada de la microbiota colónica.

2.6 Proteínas de la dieta y las bacterias proteolíticas

Diariamente al colon llegan a diario, aproximadamente entre 12 a 18 g de proteína, esto es,

proteínas residuales de la dieta y enzimas secretadas en el intestino delgado. La proporción de

proteínas residuales de la dieta en el colon (alrededor de 10% de ingesta de proteínas) dependen de

la cantidad y tipo de proteína consumida (Cummings, 1997). El colon es un sitio activo de rotación

de proteínas que proporciona nitrógeno para el crecimiento de las bacterias sacarolíticas además de

aportar aminoácidos para la fermentación por especies sacarolíticas (Cummings y Macfarlane,

1991). Las bacterias proteolíticas predominantes identificaaos en las heces humanas son especies

de Bacteroides (especialmente del grupo B. fragilis (Macfarlane, 1995), así como Clostridium

perfringens, propionibacterias, estreptococos, bacilos y estafilococos (Macfarlane y cols., 1986).

La fermentación de aminoácidos como una fuente de energía se produce en el colon distal, donde

las fuentes de hidratos de carbono se agotan y el pH luminal es casi neutro (Hamer y cols., 2012).

Entre las bacterias fecales las especies de Bacteroides poseen actividad peptidasa muy fuerte

(Wallace y McKain, 1997) y también predominan cuando el pH es de alrededor de 6.5 (Walker y

cols., 2005), como en el colon distal. Además, varios grupos de bacterias fermentan

preferentemente aminoácidos y poseen sólo una débil actividad sacarolíticas como peptococos,

acidaminococci, veillonella, y algunas fusobacterias, eubacterias y clostridios (Macfarlane y

Macfarlane, 1997).

2.6.1 Productos de la fermentación de proteínas

La diversidad de los metabolitos resultado de la fermentación de proteínas es mayor comparados a

los metabolitos producidos por la fermentación de carbohidratos. La vía principal de la

fermentación de aminoácidos en el colon humano es la desaminación, dando lugar a la producción

de AGCC y amoniaco (Cummings y Macfarlane, 1991). Alrededor del 30% del sustrato se

convierte en ácido acético (principal AGCC), propiónico y butírico y los ácidos grasos de cadena

ramificada (AGCR) isobutírico, 2-metilbutiríco e isovaleríco. Estos AGCR están formados


20

principalmente de los aminoácidos de cadena ramificada valina, isoleucina y leucina,

respectivamente (Macfarlane y Macfarlane, 1997; Rasmussen y cols., 1988), y se utilizan a

menudo como marcadores fecales para la fermentación de proteínas. Concentraciones de amoníaco

fecal van del 12 a 30 mM en los seres humanos y pueden aumentar con un alto consumo de

proteínas (Hughes y cols., 2000). La mayor parte del amoníaco producido se absorbe rápidamente,

se metaboliza en el hígado produciendo urea, la cual se excreta en la orina. El amoníaco altera la

morfología de los tejidos intestinales, y puede actuar como un promotor de tumores en el intestino.

La desaminación bacteriana de los aminoácidos aromáticos conduce a la producción de los

compuestos fenólicos, p-cresol, fenilpropioníco (a partir de tirosina), fenilacético (a partir de

fenilalanina), indol propiónico e indol acético (a partir de triptófano). Las bacterias involucradas

en esta conversión incluyen especies de los géneros Clostridium, Bacteroides, Enterobacter,

Bifidobacterium y Lactobacillus. Las concentraciones de compuestos fenólicos son más altas en el

colon distal en comparación con el colon proximal (Macfarlane, 1995). Los fenoles se absorben

rápidamente en el colon, son metabolizados en el hígado y se excreta principalmente como p-

cresol en la orina (Macfarlane, 1995).

La descarboxilación de aminoácidos y péptidos conduce a la formación de una gran variedad de

aminas. El género Clostridio está vinculado a la producción de aminas, las Bifidobacterias y

Bacteroides contribuyen a su producción (Cummings y Macfarlane, 1991). Su significado

toxicológico no se entiende bien, pero las aminas podrían actuar como precursores en la formación

de nitrosaminas. Las nitrosaminas son conocidos carcinogénicos y se puede detectar en las heces

humanas. La formación de nitrosaminas gástricas ha sido bien descrita en los seres humanos y la

participación de la microbiota se ha demostrado mediante la comparación de ratas libre de

gérmenes (LG) y ratas convencionales (Massey y cols., 1988). Sin embargo, la mayoría de

bacterias capaces de producir nitrosaminas son especies aeróbicas tales como Escherichia,

Pseudomonas, Proteus, Klebsiella, sin embargo son prácticamente indetectables en el colon

(Suzuki y Mitsuoka, 1984). El sulfuro de hidrógeno (H2S) es producido por las bacterias

reductoras de sulfato (BPS, por ejemplo, Desulfovibrio spp.) a partir de azufre inorgánico de la

dieta y aminoácidos sulfatados (metionina, cisteína, cistina y taurina). Las altas concentraciones de

sulfuro en el colon pueden estar relacionadas con la enfermedad inflamatoria intestinal y colitis

ulcerosa (Cummings, 1997). La evidencia a partir de experimentos en animales LG demuestra

claramente el papel de la microbiota en la generación de estos metabolitos de proteínas. La

alimentación con una dieta alta en proteína en ratas convencionales produjo un aumento de ácidos

grasos de cadena ramificada (AGCR) en tejido del ciego, pero no en ratas LG (Lhoste y cols.,

1996). En un estudio parecido, Wikoff y cols. (2009) no detectaron fenoles e indoles, pero las


21

concentraciones más altas de los aminoácidos aromáticos se detectó en la sangre de los ratones LG

en comparación con los ratones convencionales.

2.7 Grasas dietéticas

La grasa de la dieta se absorbe principalmente en el intestino delgado humano, pero un estudio

reciente mostró que el 7% de los ácidos grasos de la dieta que llevan la etiqueta 13C se excreta en

las heces (Gabert y cols., 2011). Pocos estudios han investigado el efecto de la grasa de la dieta

sobre la microbiota humana y los metabolitos. El consumo de una dieta alta en grasa (DAG) en

comparación con dieta bajas en grasa (DBG) redujo significativamente las concentraciones de

AGCC en la heces, incluyendo las concentraciones de butirato y los recuentos de Bifidobacterias

(Brinkworth y cols., 2009). Sin embargo, con el fin de crear las dietas equivalentes de energía, la

dieta DAG fue baja en hidratos de carbono, mientras que la dieta DBG fue significativamente

mayor en hidratos de carbono digeribles y fibra por lo que no quedó claro si tal efecto se debió a la

presencia de los carbohidratos en mayor o menor concentración. El análisis de la microbiota de

ratones alimentados con una dieta AG para inducir la obesidad durante 12 semanas y, a

continuación un cambio a una dieta normal durante otras 10 semanas indicaron que la composición

de la microbiota del grupo de ratones obesos en la DAG era diferente, pero no fue diferente a la

del grupo de control después de 10 semanas con una dieta normal (Zhang y cols., 2012). Por lo

tanto la fuerte respuesta de la microbiota a la dieta alta en grasas puede ser reversible, lo que

indica que la microbiota responde a la dieta y no es asociada al fenotipo obeso. Los ratones obesos

eran todavía significativamente más pesados que el grupo de control, incluso después de 10

semanas de nuevo con la dieta normal. Las dietas altas en grasa causan una inflamación de bajo

grado en el intestino, debido al aumento de los niveles plasmáticos de marcadores de la

inflamación, y los niveles más altos de lipopolisacárido (LPS) (Cani y cols., 2007a). Por otro

ladoLos estudios en modelos animales han demostrado que el efecto de una dieta alta en grasa, y

libre de carbohidratos puede ser mitigado en varios parámetros de la salud mediante la adición de

prebióticos, tales como fructooligosacáridos (FOS) (Cani y cols., 2007b; Dewulf y cols., 2011) o

arabinoxilanos (Neyrinck y cols., 2011). La suplementación de la dieta AG con FOS restaura el

número de Bifidobacteria spp. mejorando la tolerancia a la glucosa y reduciendo la expresión de

citoquinas pro-inflamatorias (Cani y cols., 2007b). El consumo de una DAG condujo a una

reducción significativa en números de las especies de Roseburia, que fueron restaurados ya sea por

la administración de suplementos como arabinoxilano (Neyrinck y cols., 2011) o por quitina-

glucano de hongos (Neyrinck y cols., 2012). Los números de Bifidobacteria no solo no disminuye

en DAG en este caso, sino también se mejorara siguiendo la administración de suplementos de


22

arabinoxilano, y una reducción en los niveles de marcadores inflamatorios circulantes,

interleucina-6 (IL-6) y la proteína quimio atrayente de monocitos 1 (MCP-1) fue observado

(Neyrinck y cols., 2011). Los cambios positivos en los niveles de marcadores inflamatorios

circulantes, que aumentan con DAG, puede ser debido a la mejora de las funciones de barrera

intestinal mediada por el receptor de GLP-2 (Cani y cols., 2009), lo que se demostró el efecto

positivo de las DAG cuando son complementadas con FOS y arabinoxilano (Neyrinck y cols.,

2011).

2.8 Metabolitos microbianos derivados de polifenoles dietéticos El creciente interés en los polifenoles ha sido provocado por cientos de estudios que han asociado

las dietas ricas en polifenoles a varios efectos sobre la salud en los seres humanos (Cardona y

cols., 2013). Los polifenoles han sido reconocidos como promotores de la salud debido a su

actividad antioxidante. Sin embargo, en los últimos años la bioactividad de estos fotoquímicos se

ha relacionado con su biodisponibilidad y el catabolismo en los seres humanos y, se está poniendo

de manifiesto que los efectos biológicos no deben ser atribuidos a los compuestos nativos

presentes en los alimentos, sino a sus metabolitos producidos en diversos compartimentos internos

en el cuerpo humano (Manach y cols., 2005). El intestino delgado se considera una pieza clave en

la absorción y el metabolismo de polifenoles. Los polifenoles dietéticos son modificados y

absorbidos en el intestino delgado antes de entrar en la circulación sistémica, sin embargo, la

mayoría no son absorbidos eficientemente, alcanzando así el colon. En el colon, la microbiota

humana induce transformaciones drásticas que son muy diferentes de las ejercidas por las enzimas

intestinales y hepáticas. El colon alberga un ecosistema microbiano altamente complejo que

interactúa simbióticamente con el anfitrión y funciona como un biorreactor con un potencial

metabólico prácticamente ilimitado (Walle, 2004). La microbiota intestinal lleva a cabo reacciones

químicas para modificar esqueletos fenólicos y permite la absorción de una gama de metabolitos

de menor peso. Enzimas microbianas pueden hidrolizar glucósidos, glucurónidos, sulfatos, amidas,

ésteres y lactonas, además provocar la escisión de anillos, reacciones de reducción,

descarboxilación, desmetilación, y deshidroxilación (Monagas y cols., 2010). En el Cuadro 2.5 se

muestra los precursores polifenólicos y sus metabolitos derivados de la fermentación colónica in

vitro de distintos alimentos. De este modo, la biodisponibilidad de los polifenoles está empezando

a ser reconsiderada y ha sido objeto de estudio. Como un ejemplo, los ácidos clorogénicos y

flavan-3-oles producen sus espectros únicos de metabolitos colónicos, que se excretan en la orina


23

en cantidades sustanciales correspondientes al 29% de la ingesta de café y aproximadamente 71%

después de consumo de té verde (Manach y cols., 2004).

Cuadro 2.5 Precursores y metabolitos polifenólicos derivados de la fermentación colónica vitro después de 5 y 24 h de cinco muestras de alimentos.

Alimento Precursor fenólico Metabolito 5h 24h Chocolate negro Epicatequina, catequina, dímeros

de procianidina tipo b, trímeros de procianidina tipo b, tetrámeros de procianidina tipo b.

5-(3´,4´-dihidroxifenil)-ү-valerolactona

D D

(3,4-dihidroxifenil) ácido acético

D D

Ácido protocatéico D D ácido hidroxibenzoico D D ácido salicílico

D ND

Jugo de naranja Hexósido de delfinidina; hexósido de malonil cianidina; cianidina hexósido de dioxaloil; Hexósido de cianidina ; soforósido de cianidina, rutinósido de delfinidina ; hexósido de cuomaroil; hexósido feruloil; hexósido de ácido sináptico; hexósido de kaempferol; Narirutina; didimina; eriocitrina; diosmina, hesperidina.

Ácido dihidroferúlico D D ácido sinápico D D Ácido protocatéico D D

Salvado de Avena Ácido ferúlico; derivado de ácido ferúlico, ácido sinápico; avenantramida A; avenantramida B, ácido salicílico

Ácido dihidroferúlico D D Ácido dihidrosinápico D D

Café Ácido ferúlico; ácidos coumaroilquínico; ácidos cafeoilquínico; ácidos feruloilquínico; ácidos dicafeoilquínico, ácido salicílico

Ácido caféico D D

Ácido dihidroferúlico D D Ácido quínico D D ácido dihidrocaféico

D

D

ácido ferúlico D D



24

ácido protocatéquico D D ácido hidroxibenzoico D D

Frambuesas Hexósido de pelargonidina; Hexósido de cianidina, rutinósido de cianidina ; soforósido de cianidina ; cianidina sambubiósido ramnósido; coumaroil hexósido; cafeoil hexósido; ácido salicílico.

Ácido protocatéico D D Ácido benzoico D D

D: Detectado, ND: No detectado. Adaptado de Dall'Asta y cols. (2012)

2.8.1 Metabolitos de polifenoles y su impacto sobre la salud intestinal

Los metabolitos de los polifenoles dietéticos pueden presentar un efecto positivo en el

mantenimiento de la salud intestinal o la prevención de enfermedades como cáncer. En la Figura

2.7 se resumen los mecanismos propuestos para la prevención de cáncer de colon a partir de los

polifenoles dietéticos. Diversos estudios han demostrado los efectos que presentan los polifenoles

en la salud. Queipo-Ortuño y cols. (2012) llevaron a cabo un estudio de intervención humana y

encontraron que el consumo regular de polifenoles del vino tinto generó una disminución

significativa en los niveles plasmáticos de la presión arterial, triglicéridos y colesterol de

lipoproteínas de alta densidad. Monagas y cols. (2009), observaron que los ácidos fenólicos

dihidroxilados (ácido 3,4 – dihidroxifenil propiónico, ácido 3-hidroxifenilpropiónico y ácido 3,4-

dihidroxifenil) derivan de metabolismo microbiano de proantocianidinas presentando marcadas

propiedades antiinflamatorias in vitro, reduciendo la secreción de TNF-α (factor de necrosis

tumoral alfa), IL-1β (Interleucina 1β) y IL-6 (interleucina 6) en células mononucleares de sangre

periférica de sujetos sanos. Se ha sugerido que estos metabolitos microbianos podrían estar entre la

nueva generación de agentes terapéuticos para el tratamiento de las enfermedades

inmunoinflamatorias tales como la aterosclerosis (Monagas y cols., 2009), así como para la

amortiguación de la respuesta inflamatoria a antígenos bacterianos, que puede tener implicaciones

para enfermedades crónicas inflamatorias o autoinmunes tales como la enfermedad inflamatoria

intestinal (Tuohy y cols., 2012). Larrosa y cols. (2009) filtraron los diferentes catabolitos

microbianos de los polifenoles para evaluar su potencial antiinflamatorio in vitro, encontraron que

el ácido hidrocaféico, dihidroxifenil acético e hidroferúlico redujo la producción de prostaglandina

E2 (promotora importante de la carcinogénesis) en al menos un 50% en células de fibroblastos de

colon estimuladas con interleucina 1 beta (IL-1β). Estos resultados sugieren que los alimentos que


25

contienen precursores de ácido hidrocaféico (procianidinas, derivados de ácidos hidroxicinámicos,

etc) como la alcachofa, cacao, manzanas y fresas podrían ejercer actividad antiinflamatoria y

reducir la inflamación intestinal en los seres humanos.

Figura 2.7 Posibles mecanismos propuestos para la prevención del cáncer por los polifenoles dietéticos. Adaptado: Cardona y cols. (2013)

Queda claro que existe mucha bibliografía acerca del efecto de los metabolitos proveniente de

alimentos específicos en la salud, sin embargo, poco o nada se conoce de los efectos de

metabolitos generados a partir de la ingesta de patrones dietéticos de una población. Precisamente

éste es el enfoque del presente trabajo, específicamente en la salud de colon.

Capítulo 3. JUSTIFICACIÓN

26

Capítulo 3. JUSTIFICACIÓN

Las modificaciones en la actividad física y los hábitos alimentarios puede ser un factor que

contribuye a la prevalencia de sobrepeso y obesidad en la población infantil. La prevalencia de

sobrepeso y obesidad desde la infancia es un factor importante para el desarrollo de enfermedades

no transmisibles entre la población. Existe poca información de los patrones dietéticos actuales

presentes en la población infantil. Por otro lado, componentes de la fracción indigestible de los

alimentos, específicamente la fibra dietética ha asociada con una gran cantidad de beneficios

fisiológicos y existen una gran cantidad de estudios acerca de la composición, estructura y

componente asociados a la fibra dietética en alimentos aislados (un solo alimento), pero los

individuos y las poblaciones no consumen nutrientes o alimentos aislados, sino diferentes

alimentos de los diversos grupos, en combinaciones o patrones dietéticos. El interés en el papel de

la microbiota colónica humana en la salud o la enfermedad ha aumentado constantemente. A

través del proceso de fermentación, las bacterias del colon producen una amplia gama de

compuestos que pueden tener importantes implicaciones en los procesos fisiológicos de colon.

Debido a la inaccesibilidad del colon humano, los estudios in situ son difíciles y es necesaria más

información para comprender los procesos metabólicos de la microbiota. La mayoría de los

estudios que presentan este enfoque han evaluado sólo un número limitado de compuestos

orgánicos volátiles. Mayormente ácidos grasos de cadena corta (AGCC) han sido analizados a

causa de los efectos beneficiosos para el huésped, mientras que otros informaron sobre la medición

de compuestos que contienen azufre o compuestos fenólicos (fenol, indol y escatol), como

marcadores de la fermentación de proteínas. Sin embargo, poco a poco se comienza a creer que

cada metabolito tiene una importancia en la actividad biológica de todo el extracto de

fermentación. De aquí la importancia de la caracterización de los extractos que en este trabajo

proponemos.

Capítulo 4. HIPÓTESIS

27

Capítulo 4. HIPÓTESIS

La fermentación colónica de la fracción indigestible de alimentos consumidos durante el

desayuno, comida y cena producen distintos perfiles de metabolitos microbianos relacionados

directamente con los grupos alimenticios que se consumen con frecuencia en la dieta,

promoviendo cambios en pH y en el estado antioxidante afectand el estado de salud–enfermedad

de los individuos. Por lo anterior, debido a la poca información de los patrones dietéticos en la

población infantil nayarita y poco conocimiento de los efectos fisiológicos que se tenga a partir de

la ingesta de los alimentos típicos de la región, es necesario llevar a cabo un estudio que se

enfoque en estos aspectos.

Capítulo 5. OBJETIVOS

28

Capítulo 5. OBJETIVOS

5.1 Objetivo general

Determinar el perfil de metabolitos microbianos productos de la fermentación colónica de la

fracción indigestible de alimentos frecuentemente consumidos por escolares de Tepic, Nayarit.

5.2 Objetivos específicos

• Evaluar los patrones dietéticos mediante análisis multivariado y determinar los alimentos

de mayor consumo en escolares de entre 9 y 12 años del municipio de Tepic, Nayarit.

• Preparar los alimentos de mayor consumo y someterlos a un proceso de digestión

gastrointestinal in vitro para cuantificar y aislar la fracción indigestible.

• Determinar la composición nutricional y actividad antioxidante de la fracción indigestible

aisladas de los alimentos frecuentemente consumidos por los escolares.

• Realizar la fermentación colónica in vitro de la fracción indigestible e identificar los

metabolitos producidos en este proceso, así como determinar su capacidad antioxidante

Capítulo 6. METODOLOGÍA GENERAL

29


Este estudio se dividió en tres etapas.

Durante la primera etapa se llevó a cabo la evaluación de una encuesta nutricional (trabajo previo)

realizada a alumnos de primaria entre 9-12 años de edad (n=724). A partir de la encuesta se realizó

el análisis de los alimentos frecuentemente consumidos, con herramientas estadísticas

multivariadas. Los alimentos con mayor consumo fueron elaborados en el laboratorio siguiendo

las condiciones culinarias propias de la región. Estos alimentos fueron sometidos a un proceso de

digestión in vitro para cuantificar y aislar la FI.

En la segunda etapa las FI aisladas fueron caracterizados parcialmente, mediante análisis de

composición química (proteínas, carbohidratos, lípidos, cenizas, humedad, almidon resistente,

compuestos bioactivos (polifenoles extraíbles y no extraíbles: taninos condensados y taninos

hidrolizables) y actividad antioxidante (AOX: DPPH, ABTS y FRAP).

La tercera etapa consistió en la simulación de la fermentación colónica in vitro de las FI aisladas y

identificación de los metabolitos producidos durante este proceso con técnicas de cromatografía de

gases acoplada a espectrometría de masas. Así mismo se determinó la capacidad antioxidante en

los extractos en los tiempos 12, 24, 48 y 72 h de fermentación.

A continuación, se describe más ampliamente la metodología que fue llevada a cabo.

6.1 Primera Etapa

6.1.1 Análisis de los alimentos frecuentemente consumidos

Este trabajo partió de una encuesta previa, realizada a escolares de escuelas primarias de la ciudad

de Tepic, Nayarit, México en el 2012. La encuesta nutricional fue para evaluar el consumo de

alimentos durante 48 h, dividiendo cada día en cinco tiempos de ingesta: Desayuno, almuerzo o

recreo, comida, merienda y cena. Los datos de estas encuestas fueron llevados al programa DIAL

“Alce Ingeniería” (Ortega y cols., 2011), diseñado para calcular, programar y modificar cualquier


30

tipo de dieta. Con la información de este programa se obtuvo el perfil nutricional de los

macronutirentes (carbohidratos, lípidos y proteínas) de los 5 tiempos de ingesta. Se eligieron los

tres tiempos de ingesta con el mayor contenido energético durante el día.

6.1.1.1 Sujetos de estudio

Once escuelas públicas (10 urbanos y 1 zona suburbana) en el municipio de Tepic, Nayarit,

México fueron seleccionados al azar para participar en la encuesta. Un total de 724 escolares entre

las edades de 9 a 12 años (302 niños y 422 niñas) participaron en este estudio. Se obtuvieron la

ingesta diaria de alimentos y los datos antropométricos.

6.1.1.2 Evaluación dietética

La ingesta dietética se midió utilizando recordatorios de alimentos de 24 h en dos días no

consecutivos, donde se evaluaron las ingestas calóricas de cinco tiempos de ingesta; desayuno,

almuerzo, comida, merienda y cena. Los recordatorios de alimentos tienen las ventajas de

conceder baja presión en el encuestado, los tiempos de administración son relativamente corto y,

por lo tanto, de presentan una alta tasa de respuesta; no alteran la ingesta habitual del encuestado,

y permiten la cuantificación de los parámetros dietéticos (McPherson y cols., 2000).

Los padres ayudaron a sus hijos a completar los recordatorios de alimentos. Se registraron los

tipos de alimentos que se consumen en detalle al entrevistador; por ejemplo, el tipo de grasa con el

que se cocinó el alimento, nombre de la marca, o los constituyentes de platos combinados, etc., y

en relación con su cantidad o volumen comúnmente utilizado en casa o de otras medidas (platos,

vasos, cucharas, etc.) que se utilizaron. Las ingestas de los alimentos y nutrientes se calcularon

utilizando el software de análisis de la dieta DIAL versión 1.0 (Ortega y cols., 2012). La base de

datos de los alimentos en el software fueron modificadas utilizando tablas y recetas de alimentos

mexicanos tradicionales (De Chávez y Solano, 2010; Pérez y cols., 2008), así mismo se utilizaron

las tablas de composición química presente en las etiquetas de los productos comerciales.


31

6.1.1.3 Datos antropométricos

Personal capacitado midió la altura y el peso de los escolares, dichas mediciones se realizaron por

triplicado. La altura se midió utilizando un estadiómetro (Seca, 213, Hamburgo Alemania). El

peso se registró en una balanza electrónica (Seca, 803, Hamburgo Alemania) con una capacidad

de 150 kg. Los sujetos se encontraban descalzos y vestían ropas ligeras durante las mediciones. El

índice de masa corporal (IMC) fue calculado como el cociente entre el peso y el cuadro de la

estatura (Kg·m-2). Este índice se utilizó para clasificar a los escolares teniendo en cuenta un valor

de IMC <percentil 5 como bajo peso, con un percentil entre 5 a 85 como peso normal, los que

presentaron percentiles > 85 < 95 con sobrepeso y obesos a los que presentaron percentiles ≥ 95

(Barlow y Committee, 2007).

6.1.1.4 Identificación de los patrones dietéticos en escolares

Un total de 495 alimentos consumidos por los participantes fueron clasificados en 13 grupos de

acuerdo a los grupos de alimentos identificados por el software DIAL versión 1.0 (Ver Cuadro

6.1). Se realizó un análisis de componentes principales (ACP) para determinar la energía con la

que cada grupo de alimentos contribuyó a los factores extraídos. Los factores se calcularon sin

rotación y el número de factores extraídos se basaron en valores propios > 1.0 (criterio de Kaiser),

la identificación de un punto de quiebre en el gráfico de sedimentación, y la interpretabilidad (Hair

y Anderson, 2010).

Se calcularon las puntuaciones factoriales para cada individuo. Un análisis de regresión múltiple se

utilizó para determinar la relación entre los pesos, IMC y las puntuaciones de los factores

encontrados en el ACP. Además, a partir de las puntuaciones factoriales se clasificó a los escolares

en tres dietas utilizando el análisis de conglomerados con la técnica de agrupamiento K-medias,

con el objetivo de identificar los grupos de alimentos con más alto consumo de calorías y

determinar la relación entre la dieta y el sobrepeso/obesidad.


32

Cuadro 6.1 Definiciones de los grupos de alimentos utilizados para evaluar las dietas de los escolares del municipio de Tepic

Grupo Alimenticio Tipo de Alimento

Cereals Cereales para el desayuno, harina de trigo, tostadas, pan blanco, pan,

galletas, pan dulce, pasta, granos de maíz, tortilla de maíz, arroz, avena.

Legumbres Soja, lentejas, garbanzos, frijoles refritos, frijoles cocidos, chicharos, ejote.

Vegetales Zanahoria, la verdolaga, tomate verde, rábano, pimiento verde, pepino, papa,

nopal, col blanca, cilantro, chile, cebolla blanca, calabaza, calabaza, brócoli.

Frutas Uva, pera, toronja, tamarindo, sandía, plátano, pitahaya, piña, pasas, naranja,

papaya, nanchi, melón, mango, manzana, limón.

Leche y productos

lácteos

Leche entera, la leche sin lactosa, leche evaporada, leche luz, yogur, batidos

de leche, crema, queso.

Carne y derivados

Carne de res, pollo, cerdo, salchicha, tocino, vientre, jamón, hígado, costilla

de res, salchichas de pavo, salchicha de cerdo, pierna de cerdo, mollejas de

pollo.

Pescados y mariscos Peces sierra, salmón, sardinas, pulpo, pargo, ostras, peces marlin, camarón

seco, camarón, atún.

Huevo Claras de huevo y huevo entero

Dulces Azúcar, piruletas, edulcorantes, pasteles, chocolate en polvo, barras de

chocolate, mermelada, caramelo gomoso, chicle, dulces con chile.

Aceites y grasas Aceite de maíz, aceite de girasol, aceite de oliva, manteca de cerdo,

mantequilla.

Bebidas Agua, café, té, refrescos, jugos naturales y procesados.

Botanas Frituras de maíz, palomitas de maíz, papas fritas, semillas de calabaza, maní,

aceitunas.

Salsas y

condimentos

Puré de tomate, la pimienta, el orégano, la salsa picante, salsa inglesa,

pipián, el vinagre, la hoja de laurel, el caldo de pollo, menta, comino, clavo,

ajo, sal.


33

6.2 Segunda Etapa 6.2.1 Obtención de las muestras

De cada una de las tres dietas formadas por el análisis multivariado de los datos, se eligieron los

alimentos con mayor frecuencia de consumo en los tres principales tiempos de ingesta (Mayor

aporte de energía durante el día), y se formaron un total de 9 menús. Tomando en cuenta la

descripción dada por los entrevistados o bajo criterios típicos de la región, la elaboración de los

alimentos fue estandarizada en el laboratorio para fines científicos. Las materias primas para la

preparación de los alimentos fueron adquiridos en un supermercado local del municipio de Tepic.

Los alimentos de cada menú fueron preparados, mezclados y homogenizados utilizando un

procesador de alimentos (Nutribullet, NB-101B, China). Inmediatamente después de ser

elaborados, se determinó el contenido de humedad de las muestras por el método 925.10 de

AOAC (1990). Las muestras homogenizadas fueron colocados en bolsas con cierre hermético y

almacenadas a -80º C. Las muestras congeladas fueron secadas a vacío en una liofilizadora

(FreeZone 6, Labconco, USA). Posteriormente fueron reducidas de tamaño en un molino (Ika,

M20, USA) y tamizadas utilizando una malla con tamaño de 0.05 mm hasta obtener muestras

homogéneas, que fueron colocadas en recipientes herméticos y finalmente fueron almacenadas a -

20º C para los posteriores análisis.

6.2.2 Cuantificación de la fracción indigestible total, soluble e insoluble

Para la determinación de la fracción indigestible en las muestras se utilizó la metodología descrita

por Saura-Calixto y cols. (2000). El método emula la digestión de los alimentos en el tracto

gastrointestinal. Digestión gástrica: Se pesaron 300 mg de muestra en base seca en tubos de

centrifuga, se adicionó 10 mL de regulador de HCl-KCL (pH1.5). Se ajustó el pH de los tubos a

1.5 y se adicionaron 0.2 mL de un solución de pepsina (P-7000, Sigma Aldrich, St Louis Missouri,

USA) y buffer HCL-KCL 0.2 M (300 mg/mL). La mezcla se incubó a 40º C por 1 h con agitación

constante (80 rpm). Digestión intestinal: Se agregaron 4.5 mL de regulador de fosfato 0.1 M y se

ajustó el pH de cada tubo a 7.5. Se adicionó 1 mL de solución de pancreatina (P-1750, Sigma

Aldrich) y buffer de fosfato 0.1 M (5 mg/mL), 1 mL de solución de lipasa (L-3126, Sigma


34

Aldrich) y buffer de fosfato 0.1 M (7 mg/mL) y 2 mL de una solución de extracto de bilis y buffer

de fosfatos (7 mg / mL) a cada tubo y se incubó a 37º C por 6 h con agitación constante (80 rpm).

Posteriormente, para la hidrólisis del almidón, se agregaron 9 mL de regulador tris maleato 0.1 M

y se verificó que el pH sea de 6.9 para adicionar i mL de solución de α-amilasa (A-3176, Sigma

Aldrich) y búfer tris-Maleato (120 mg/mL) y se incubó a 37º C por 16 h con agitación constante

(80 rpm). Terminado este tiempo se adicionaron 10 mL de regulador acetato de sodio y se ajustó el

pH a 4.75. A esta mezcla se le adicionaron amiloglucosidasa (A-9913, Sigma Aldrich) y se

incubaron a 60º C por 45 min. Concluidas las reacciones enzimáticas, se procedió a centrifugar las

muestras durante 15 min a 3000 rpm, recuperando los sobrenadantes. Por otro lado, los residuos

fueron lavados con 5 mL y nuevamente fueron centrifugados, recolectando los sobrenadantes en

vasos de precipitado de 100 mL. Los tubos con el residuo se colocaron durante 16 h en la estufa de

105º C para cuantificar gravimétricamente la FI insoluble de las muestras. El sobrenadante se

colocó en membrandas de diálisis (D9652-100ft avg. Ancho de 33 mm, 12400 Da, Sigma Aldrich)

previamente tratadas y lavadas con agua hirviendo durante 5 min (aproximadamente 20 cm de

cada bolsa de diálisis) y se dializó contra agua corriente por 48 h a temperatura de 25º C para

eliminar los componentes digeridos. Enseguida se transfirió el contenido de la bolsa de diálisis a

un matraz volumétrico de 50 mL y se aforó con agua destilada. Se tomaron 17 mL del volumen

anterior y se colocó en tubos de centrifuga de polipropileno con tapa, al que se le adicionó 1 mL de

ácido sulfúrico (H2SO4). Los tubos se llevaron a un baño de agua en ebullición durante 90 min.

Terminada la hidrolisis ácida, se colocaron 2 mL de la solución anterior (muestra tratada con

H2SO4) a tubos de ensaye que contenían 1 mL de reactivo DNS (ácido dinitrosalicilico) y 0.5 mL

de solución NaOH 3.9 M. Los tubos fueron mezclados en vortex. Posteriormente se preparó una

curva con glucosa como estándar (0 a 2 mg/mL). Finalmente se calentó 15 minutos en un baño de

agua a ebullición, y posterior a esto, se adicionaron 15 mL de agua destilada, para cuantificar la FI

soluble como azucares reductores con ácido dinitrosalicilico en un espectrofotómetro (Jenway,

6715, USA) a 530 nm.

La FI total se determinó como la suma de la FI insoluble más la FI soluble.


35

6.2.3 Digestión gastrointestinal in vitro: Aislamiento de la fracción indigestible

Cada muestra liofilizada se llevó a un proceso de digestión gastrointestinal in vitro. Esta técnica

simula las condiciones gastrointestinales fisiológicas, tomando en cuenta la acción enzimática, los

cambios de pH y la temperatura del cuerpo humano. La finalidad fue determinar la fracción que no

es digerida ni absorbida en el intestino delgado conocida como fracción indigestible (FI),

cuantificarla y aislarla para utilizarla como sustrato de la fermentación colónica. Para cuantificar la

FI se siguió la metodología descrita por Saura-Calixto y cols. (2000). Con el fin de incrementar la

cantidad de FI aislada se seguió la metodología de Saura-Calixto y cols. (2000), con las

modificaciones realizadas por Tabernero y cols. (2011). Se pesaron 9 g de cada alimento

liofilizado, los cuales fueron digeridos enzimáticamente en pasos subsecuentes simulando

condiciones fisiológica: Se utilizaron 0.6 mL de una solución de pepsina (P-7000, Sigma Aldrich)

y buffer HCL-KCL 0.2 M (300 mg/mL). Se ajustó el pH de los tubos a 1.5 y se incubó 40º C por 1

h. Posteriormente, se realizó una hidrólisis con 3 mL de solución de pancreatina (P-1750, Sigma

Aldrich) y buffer de fosfato 0.1 M (5 mg/mL). La hidrólisis de las grasas se realizó con 3 mL de

solución de lipasa (L-3126, Sigma Aldrich) y buffer de fosfato 0.1M (7 mg/mL). Además, 3 mL

de una solución de extracto de bilis y buffer de fosfatos (17.5 mg / mL) fueron colocados a cada

tubo; el pH de las muestras fue ajustado a 7.5, e incubado a 37º C por 6 h. Para la hidrólisis del

almidón, se utilizaron 3 mL de una solución con α-amilasa (A-3176, Sigma Aldrich) y búfer Tris-

Maleato (120 mg/mL) ajustando el pH de las muestras a 6.9 e incubando a 37º C por 16 h.

Finalmente 300 µL de amiloglucosidasa (A-9913, Sigma Aldrich, USA) fueron colocados en cada

tubo y se incubaran a 60º C por 45 minutos. Terminado la hidrólisis enzimática las muestras se

transfirieron a tubos de diálisis (D9652-100ft avg. Anchura plana de 47 mm, 12400 Da, Sigma

Aldrich) y permanecieron por 48 h a temperatura a 25º C para eliminar los componentes digeridos.

El retenido fue colectado, concentrado en rota vapor a 60º C y liofilizado por 72 h, y se utilizó

para los análisis posteriores: Caracterizaión de la fracción indigestible y fermentación colónica in

vitro.


36

6.2.4 Caracterización nutricional, compuestos bioactivos y actividad antioxidante en la

fracción indigestible de los menús

Las fracciones indigestibles (FI) aisladas fueron caracterizadas mediante la cuantificación de

almidón resistente, la determinación de compuestos fenólicos, taninos condensados e hidrolizables

y análisis de capacidad antioxidante por los métodos FRAP, ABTS y DPPH.

6.2.4.1 Cuantificación del Almidón Resistente (AR)

El AR presente en las muestras fue determinado por el método propuestos por por Goñi y cols

(1996) con algunas modificaciones. En este trabajo se realizó la determinación a partir de 25 mg

de fracción indigestible total, los cuales fueron pesados en tubos de centrifuga, se agregaron 3 mL

de agua destilada y 3 mL de KPH 4 M, la mezcla se mantuvo con agitación constante durante 30

min a temperatura ambiente. Se agregaron 5.5 mL de HCl 2M y 3 mL de buffer de acetato de

sodio, se ajustó el pH a 4.75, se adicionaron 80 µL de amiloglucosidasa (A-9913, Sigma Aldrich,

USA) en proporción 3.8 buffer de acetato de sodio a 1 de enzima. La mezcla fue incubada a 60º C

por 45 min con agitación constante. Las muestras fueron centrifugadas a 3000 rpm por 15 min y se

recolectó el sobrenadante en un matraz aforado de 50 mL.

Para la cuantificación del AR, se tomaron 500 µL de muestras y se mezcló con 1 mL de reactivo

del reactivo GOD-POD, leyendo la absorbancia a 505 nm, utilizando una curva de glucosa entre 0-

100 mg/L. La siguiente formula se empleó para el cálculo.

% AR = glucosa µgml ∗ 0.001 ∗ 50 ∗ 0.9peso muestra (mg) ∗ 100

6.2.4.2 Análisis de los compuestos antioxidante en la fracción indigestible de los menús

Obtención de extractos


37

Para estos análisis se realizó una extracción acuosa-orgánica de acuerdo con la metodología

propuesta por Pérez-Jiménez y cols. (2009). En tubos de centrifuga se pesaron 100 mg de FI

aislada y se añadieron 5 mL de una solución metabólica acidificada (metanol:agua, 50:50 v/v, HCl

2 N). Los tubos se agitaron en un orbital a temperatura ambiente durante 1 h. Los tubos se

centrifugaron a 6000 rpm durante 10 min a 4 ° C y los sobrenadantes se recuperaron en un matraz

de 25 mL . A los residuos se adicionaron 5 ml de una solución acetona-agua (70:30 v/v) y los

tubos nuevamente fueron agitados en un orbital a temperatura ambiente durante 1 h. Los tubos se

centrifugaron a 6000 rpm durante 10 min a 4 ° C y los sobrenadantes se recuperaron y mezclaron

con los sobrenadantes anteriormente recuperados. La mezcla se aforó a 10 mL con una solución

metanol/agua/HCl - acetona/agua (50:50 v/v). Los extractos fueron usados para determinar el

contenido polifenoles extraíbles (PE) y actividad antioxidante, con los métodos FRAP, ABTS, y

DPPH. Por otro lado, el residuo fue utilizado para cuantificar polifenoles no extraíbles (PNE):

Polifenoles hidrolizables (PH) y taninos condensados en las muestras.

Polifenoles extraíbles (PE) en los extractos de las fracciones indigestibles

El contenido de PE en los extractos fue determinado con el reactivo Folin-Ciocalteu utilizando la

metodología propuesta por Montreau (1972) con algunas modificaciones realizadas por Alvarez-

Parrilla y cols. (2010a). Se utilizaron 250 µL de extracto y fueron mezclados con 1000 µL de una

solución de carbonato de sodio (75 g/L), y 1250 µL del reactivo Folin–Ciocalteu (100mL/L) en

tubos de ensaye. Los tubos fueron homogenizados usando un agitador vortex. La solución fue

incubada a 50º C bajo completa obscuridad por 15 min. Pasado el tiempo de incubación, los tubos

se dejaron enfriar a temperatura ambiente y 270 µL de esta mezcla fueron colocados en pocillos de

microplacas. La absorbancia fue medida a 750 nm usando un lector de microplaca con múltiple

detección (Biotek, Synergy HT, Winooski VT, USA), utilizando el programa Gen5. Los resultados

fueron expresados en equivalente de ácido galico (EAG; mg/ g extracto bs), usando una curva

estándar de ácido gálico (0–0.2 mg/mL).

Polifenoles no extraíbles (PNE) en los extractos de las fracciones indigestibles


38

Como se mencionó anteriormete, los residuos fueron utilizados para determinar el contenido de

PNE en las FI aisladas.

Para los polifenoles hidrolizables (PH), los residuos fueron tratados con 4 mL de metanol

absoluto y 0.4 ml de ácido sulfúrico (H2SO4) concentrado a 85 °C por 20 h (Hartzfeld y cols.,

2002). Las muestras fueron centrifugadas (3000 rpm por 10 min), con la recuperación de los

sobrenadantes. Se realizó un lavado con 4 mL de agua destilada, el volumen final fue ajustado a 10

mL con agua destilada. El contenido plifenoles fue determinado de acuerdo al ensayo con el

reactivo de Folin-Ciocalteu, con las modificaciones realizadas por Alvarez-Parrilla y cols. (2010a),

cuya técnica fue descrita anteriormente.

Para los taninos condensados los residuos fueron tratados con 4 mL HCl/butanol/FeCl3 (5:95, v/v)

a 100º C por 3 h (Reed y cols., 1982). Las muestras fueron centrifugadas (3000 rpm por 10 min),

con la recuperación de los sobrenadantes. Se realizarán un lavado con 4 mL de HCL/butanol/FeCl3

(5:95, v/v) y el volumen final fue ajustado a 10 mL. Las muestras de TC fueron leídas a en un

espectrofotómetro a 555 nm y se compararon con vaina de algarrobo (Ceratonia siliquia) como

estándar de proantocianidinas (Nestle, Ldt., Vers-Chezl-les Blanes, Suiza). Los resultados se

expresaron como gramos equivalentes de taninos hidrolizables por gramos de FI inicial en base

humeda.

6.2.4.3 Análisis de la capacidad antioxidante en las fracciones indigestibles

Actividad Quelante: Poder antioxidante reductor del hierro (FRAP)

El ensayo FRAP se realizó de acuerdo a lo descrito por Alvarez-Parrilla y cols. (2010b). Una

solución de trabajo fue preparada por la mezcla de 25 mL Buffer de acetato (0.3 M, pH 3.6), 2.5

mL de solución de 2,4,6-tripiridil-s-Triazina (TPTZ) (10 mM TPTZ en 40 mM HCl), y 2.5 mL de

solución que contiene FeCl3 6H2O 20 mM. La solución de trabajo fue incubada a 37º C hasta antes

de ser usada. Se tomaron 24 µL de los extractos y se dejaron reaccionar con 180 µL de la solución

de trabajo FRAP. Las lecturas de absorbancia se tomaron después de 30 min de reacción

utilizando un lector de microplacas de detección múltiple (Biotek, Synergy HT, Winooski VT,


39

EE.UU.) con el programa Gen5. El incremento en la absorbancia es debido a la reducción de Fe3+

por los antioxidantes y la subsecuente formación de un complejo colorido TPTA-Fe2+ medido a

una absorbancia de 595 nm. Los resultados fueron expresados en Equivalentes Trolox (ET; mM /g

FI bs) con una curva estándar usada entre 8.125x10-3 - 0.13 mM Trolox.

Actividad antiradical: Ensayo 1,1-Difenill-2-picril hidrazilo (DPPH)

El ensayo de DPPH fue realizado de acuerdo con el método propuesto por Prior y cols. (2005) con

algunas modificaciones. La solución madre se preparó disolviendo 49.29 mg DPPH con 25 ml de

metanol y después se almacenó a -20°C hasta su uso. La solución de trabajo se obtuvo mediante la

mezcla de 950 µl de solución madre con 25 ml de metanol para obtener una solución de DPPH

190 µM. Se tomaron 30 µl del extracto de la muestra y se dejaron reaccionar con 200 µl de la

solución de trabajo DPPH 190 µM La absorbancia fue medida a 517 nm, despues de 10 min de

reacción, utilizando un lector de microplacas de detección múltiple (Biotek, Synergy HT,

Winooski VT, EE.UU.) con el software Gen5. Se realizó una curva estándar entre 37,5 a 600 µM

de Trolox y los resultados se expresan en equivalentes de Trolox (ET; mM / g de FI bs).

Actividad antiradical: Ensayo del radical 2,2'-azino-bis(3-etilbenzotiazolina-acido-6-

sulfónico ( ABTS•+)

La capacidad de los extractos para neutralizar el radical ABTS•+ fue analizada utilizando la

metodología propuesta por Re y cols. (1999) con algunas modificaciones. Los radicales catiónicos

de ABTS+ fueron producidos por la reacción entre 7 Mm del reactivo ABTS y 2.45mM de

persulfato de potasio disueltos en buffer de fosfatos (0.1 M, pH 7.4). La solución fue almacenada

en total oscuridad a temperatura ambiente durante 12 a16 h con agitación constante. Antes de su

uso, la solución se diluyó con buffer de fosfatos, hasta obtener una absorbancia de 0.7 ± 0.01 a una

longitud de onda de 734 nm. La actividad antiradical se evaluó mediante la mezcla de 20 µl de los

extractos con 255 µl de ABTS a una temperatura de 30°C durante 7 minutos utilizando un lector


40

de microplacas de detección múltiple (Biotek, Synergy HT) con Gen5 software. La disminución en

la absorbancia se midió a 734 nm. Se utilizó una curva estándar entre 37.5 a 600 µM de Trolox y

los resultados se expresan en equivalentes de Trolox (ET; µM / g de extracto bs).

6.3 Tercera Etapa

6.3.1 Fermentación colonica in vitro de la fracción indigestible

La FI aislada de cada muestra fueron llevadas a un sistema de fermentación colónica in vitro por

lotes, con la finalidad de evaluar la producción de metabolitos. El método utilizado fue adaptado

según la propuesta de Campos-Vega y cols. (2009). El proceso de fermentación in vitro fue

evaluado por triplicado para cada conjunto de alimentos y fue realizado a una temperatura 37º C

utilizando un baño de agua. El inóculo fecal fue proporcionado por cinco escolares nayaritas sanos

entre 9-12 años de edad, con el consentimiento de sus padres, los cuales afirmaron que los

escolares no consumieron antibióticos por al menos 3 meses antes del ensayo y que no presentaron

historial de enfermedades gastrointestinales recientes. En tubos de centrifuga estériles con

capacidad de 15 mL, fueron colocados 9 mL de medio de cultivo basal que contenía los siguientes

componentes: 2 g/L de agua peptonada, 2 g/L de extracto de levadura, 0.1 g/L de NaCl, 0.04 g/L

K2HPO4, 0.04 g/L KH2PO4, 0.01 g/L de MgSO4.7H2O, 0.01 g/L de CaCl2.2H2O, 2 g/L de

NaHCO3, 0.5 g/L de clorhidrato de L-cisteína, 0.5 g/L de sales biliares, 2 mL de tween 80, 0.2 g/L

de hematina (diluida en 5 mL de NaOH 1M) y 10 µL Vitamina K1. Los tubos sellados se

mantuvieron bajo condiciones anaerobias mediante el flujo (7.5 L/h) de una mezcla de gases que

contenía H2-CO2-N2 (10:10:80 v/v), libre de oxigeno (O2) por 12 h. El inóculo fecal fue preparado

con la mezcla de 10 g (2 g de heces por donador, 5 donadores) de heces frescas y 90 mL de buffer

de fosfato de sodio 0.1 M y se homogeneizaron en un sistema digital de alta velocidad (IKA-Ultra-

Turrax, T18, EE.UU.) durante 1 min a 6000 rpm. Los tubos con medio de cultivo basal fueron

inoculados con 1 mL de solución de heces fecales y con 100 mg de fracción indigestible aislada de

cada alimento. La rafinosa fue utilizada como un control de azúcar fermentable; también fue


41

utilizado un blanco negativo, el cual correspondió al medio de cultivo más inoculo fecal. La

fermentación de ambos controles fue bajo las mismas condiciones de las muestras. Las muestras se

agitaron en vórtex durante 30 s y se determinó el pH al tiempo cero. Posteriormente se colocaron

en un baño de agua a 37ºC. Debido a que el sistema de fermentación fue por lotes, se realizaron

tres fermentaciones utilizando las fracciones indigestibles obtenidas de alimentos consumidos

durante el desayuno (Lote 1), la comida (lote 2) y la cena (lote 3). Dichas fermentaciones fueron

realizadas con inoculos diferentes provenientes de donadores distintos, pero que cumplían con las

características antes mencionadas. Durante la fermentación, el pH, la actividad antioxidantes

(DPPH y FRAP) y la producción de metabolitos orgánicos volátiles de las muestras fueron

evaluados al tiempo 12, 24, 48 y 72 h. Terminado los tiempos, los tubos de la fermentación fueron

centrifugados (Hermle Z 323 K; Wehingen, Alemania) a 4500 rpm durante 15 min a 4°C y los

sobrenadantes se dividieron en dos para realizar los siguientes análisis: A) Análisis del perfil

metabolitos por cromatografía de gases; B) Análisis de actividad antioxidante asociados a los

extractos. Los sobrenadantes fueron almacenados a -80º C hasta sus análisis.

6.3.2 Análisis de componentes antioxidantes asociados en los extractos de la fermentación

Loss sobrenadantes obtenidas después de las 12, 24, 48 y 72 h fueron utlizados para evaluar la

capacidad antioxidante por los métodos DPPH y FRAP. Las metodologías descritas en el apartado

6.1.4.5, fueron empleadas en la determinación de la capacidad antioxidante. El trolox fue utilizado

como estándar disuelto en medio de cultivo basal. Los datos fueron expresados como mMol de ET

/ g de fracción indigestible bs.

6.3.3 Análisis de metabolitos en la fermentación colonica in vitro de la fracción indigestible

de los menús

El análisis de los metabolitos productos de la fermentación de la FI de los alimentos se realizó para

determinar cuantitativamente la concentración de los tres principales ácidos grasos de cadena corta


42

(ácido acético, propiónico, butírico) y semi-cuantitativamente el total de la fracción volátil

presente en los extractos. Loa anterior fue realizado utilizando la siguiente metodología:

6.3.3.1 Micro extracción en fase solida de los compuestos volátiles en los extractos de

fermentación

La micro extracción en fase sólida (MEFS) fue la técnica utilizada para la extracción-

concentración de compuestos volátiles y se realizó de acuerdo con la metodología propuesta por

Zamora-Gasga y cols. (2014). Se pesaron 500 mg de sobrenadantes en viales (20 mL). Los viales

se taparon y colocaron en el automuestreador (VT-32-20, Gerstel MPS2). La extracción se realizó

con una fibra compuesta por polidimetilsiloxano-divinilbenceno-carboxeno (PDMS/DVB/CAR)

de 2 cm. Las muestras se agitaron (250 rpm) a 45 °C durante 5 min. A continuación, la fibra

PDMS/DVB/CAR fue expuesta al espacio de cabeza, y las muestras se agitaron durante 120 min,

250 rpm a 45 °C. Posteriormente, la fibra se insertó en el puerto de inyección del equipo de

cromatografía de gases (CG) para la desorción térmica (240 ° C durante 10 min) y el análisis por

espectrometría de masas (EM). Las extracciones se realizaron por triplicado en cada tiempo de

fermentación, con un total de 60 viales en cada lote de fermentación (Un total de tres lotes). Los

análisis se realizaron en cinco corridas de doce viales por corrida y el orden de los viales fue

aleatorizado.

6.3.3.2 Cromatografía de gases acoplada a espectrometría de masas

Las muestras fueron analizadas por cromatografía de gases (CG) acoplada a espectrometría de

masas (EM) con ionización de electrones con un sistema Agilent Technologies 7890ª, utilizando

un detector selectivo de masa (Agilent Technologies 5975C VL) equipado con un MPS2 XL

automuestreador (Gerstel). La separación de las muestras se llevó acabo con una columna capilar

DB-5MS (60 m x 250 m X 0,25 micras; Agilent J & W) usando gas helio como acarreador a una

velocidad de flujo de 1 mL / min. El inyector del equipo de CG fue utilizado a una temperatura de

250°C. La fuente de EM y el cuádruplo se mantuvieron a temperaturas de 230°C y 150°C,

respectivamente. El inyector se utilizó en el modo de división máxima (splitless). La temperatura


43

del horno comenzó a 40ºC por 5 min y fue programada como sigue: 5ºC / min de 40 a 200ºC, se

mantuvo a 200ºC por 2 min, más 20ºC / min hasta alcanzar 230ºC y se mantuvo a 230ºC por 15

min. El tiempo total de análisis fue de 55.5 min. La cuantificación de los AGCC en las muestras se

obtuvo a través de curvas de calibración de los ácidos acético, propiónico y butírico. Uan tentativa

identificación de los componentes volátiles se realizó mediante la comparación de los espectros de

masas de las muestras con la biblioteca de sistema de datos del programa MSD ChemStation

(Agilent G1701EAversion E.02.00.493). Se calculó la concentración relativa de todos los

metabolitos de fermentación frente al ácido acético (AGCC más volátil) como patrón interno y los

resultados se expresaron en mmol L-1 de extracto. Con el fin de comparar las diferencias en el

perfil metabólico, las concentraciones (mmol L-1) fueron estandarizadas y expresadas como

porcentajes de concentraciones relativas en cada metabolito utilizando la siguiente formula:

!" % = !" !!! !" !"#" !"#$%&'(#&(!" !!!!"#$% !" !"#$%&'(#&)) ∗ 100

Donde CR es la concentración relativa de cada metabolito

6.4 Análisis estadístico

Todos los experimentos fueron realizados por triplicado, cada alimento fue considerado como

unidad experimental. En la primera etapa, la elección de los alimentos fue realizada con un análisis

de conglomerados k-medias vía componentes principales, utilizando el programa STATISTICA,

versión 10.0 (Statsoft. Inc. 1984-2010, Tulsa, OK, EE.UU.). La caracterización nutricional,

compuestos bioactivos y actividad antioxidante de las menus y de la fracción indigestible se

analizó con un diseño de mediciones repetidas. Donde se determinó los efectos de las tres dietas

formadas (DM, DT y DA) y los tres tiempos de ingesta (desayuno, comida y cena). En la segunda

etapa, la fermentación in vitro se determinó con la combinación de un diseño factorial con

bloques. Donde los factores fueron la dieta (DM, DT y DA) y los tiempos de fermentación (12, 24

48 y 72). Cada bloque está formado por el tiempo de ingesta (desayuno, comida y cena) y las

variables de respuesta fueron, los cambios de pH, y la actividad antioxidante en los extractos. Para

la última etapa de este trabajo, se realizará un análisis de componentes principales (PCA) de las


44

concentraciones (%) de los compuestos volátiles en base a los valores medios de los triplicados

utilizando el programa STATISTICA, versión 10.0 En este contexto, los casos serán las diferentes

FI de las tres dietas (DM, DT y DA), los controles positivo y negativo en cada tiempo de

fermentación (12, 24, 48 y 72 h), las variables serán los compuestos volátiles identificados y el

valor de entrada en la matriz será la concentración relativa del compuesto volátil. Los resultados

obtenidos de las determinaciones de cada etapa fueron analizados utilizando el programa

estadístico STATISTICA (Stat Soft, Inc. 1994-2010) Versión 10.0 mediante un análisis de

varianza (ANOVA), para determinar si existió efecto significativo en los los factores y una prueba

Fisher LSD se realizó para la comparación de medias con una significancia del 5% (α=0.05).

Capítulo 7. RESULTADOS Y DISCUSIÓN

45


7.1 Patrones dietéticos, perfil nutricional e índice de masa corporal en escolares mexicanos:

Un estudio transversal

Resumen

México es uno de los veinte países con mayor prevalencia de sobrepeso y obesidad en el mundo.

Este problema también ocurre en la población infantil mexicana. La prevalencia de sobrepeso y

obesidad en niños mexicanos en edad escolar aumentó del 26,9% al 34,4% durante el período

1999-2012 y el 25% de los niños tiene sobrepeso (2). La dieta juega un papel importante en el

desarrollo de este trastorno. Tradicionalmente, la dieta mexicana se basa en maíz, frijoles y chiles,

que se cocinan en diferentes platos típicos, dependiendo de la región, pero han sido cada vez más

reemplazados por comidas listas para comer, comidas rápidas y bebidas endulzadas (4).

Los patrones dietéticos seguidos por los niños preadolescentes tienen influencia en su estado

nutricional no solo durante la infancia, sino también durante su adolescencia, así como, en la edad

adulta. Pocos estudios han examinado la asociación entre los patrones dietéticos y las

enfermedades no transmisibles en la población escolar mexicana, y hoy en día, está demostrado

que las dietas altas en azúcares simples pueden aumentar el riesgo de resistencia a la insulina en

niños y adolescentes (7). Por otra parte, la presión arterial diastólica, la glucosa y las

concentraciones de triglicéridos se han asociado positivamente con la ingesta de refrescos /

bebidas azucaradas y con la ingesta de grasas altas en grasas (6). Sin embargo, estos estudios se

enfocan principalmente en el área central de México y debido a la amplia diversidad culinaria del

país, se necesitan estudios complementarios en diferentes regiones.

La información sobre la relación entre los patrones dietéticos, el perfil nutricional y el índice de

masa corporal se puede utilizar para planificar una intervención nutricional eficaz para preparar las

pautas dietéticas para los niños. El objetivo de este capítulo fue identificar los patrones dietéticos

globales en la población escolar (nueve a doce años) del estado de Nayarit y determinar la relación

entre los patrones de alimentación, obesidad, perfil nutricional y tipos de alimentos consumidos.


46

“Dietary patterns, nutritional profile, and body mass index in Mexican schoolchildren: A

cross-sectional study”

Victor Manuel Zamora-Gasga1, Efigenia Montalvo-González1, Guadalupe Flavia Loarca-Piña2,

Alejandra Martina Chacón-López1, Juscelino Tovar3, Sonia Guadalupe Sáyago-Ayerdi 1*

1Instituto Tecnológico de Tepic, Laboratorio Integral de Investigación en Alimentos,

División de Estudios de Posgrado, Av Instituto Tecnológico No 2595, Col Lagos del Country CP

63175, Tepic, Nayarit México.

2 Programa de Posgrado en Alimentos del Centro de la República, Facultad de Química,

Universidad Autónoma de Querétaro, Centro Universitario, Cerro de las Campanas S/N, CP

76010, Santiago de Querétaro, Querétaro México.

3Food for Health Science Centre.Lund University, MediconVillage. Lund, SE-223 81. Sweden

*Corresponding author, e-mail: [email protected]

Tel.: +52 311 211 94 00 ext 328

Summary

Childhood obesity is a serious public health problem in Mexico. In this study, childhood-specific

dietary patterns and their relationship with overweight-obesity prevalence, nutrient profiles and


47

types of foods consumed were studied. A descriptive cross-sectional study of 724 randomly

selected schoolchildren between 9 to 12 years old from Nayarit State, Mexico was performed.

Data on anthropometric characteristics and food intake were recorded. Seven dietary patterns and

three specific diets were identified by multivariate analysis. A dietary pattern characterized by

high legume, snack and low beverage intake was negatively associated with weight and body mass

index. The overall overweight and obesity prevalence was 20.2% and 20.6%, respectively. Diet

type significantly influenced (p<0.05) protein, carbohydrates and fat intake but did not show

correlation with the overweight-obesity status. Simple sugars, candies, pastries and sweetened

beverages appeared in all dietary patterns. Dietary patterns in countries with a wide gastronomic

diversity should be considered to design preventive nutrition intervention programs.

Keywords: Feeding, obesity, schoolchildren, energy Intake, food habits

Resumen

La obesidad infantil es un problema de salud pública en México. En este trabajo, se estudiaron los

patrones dietéticos de escolares mexicanos y su relación con la prevalencia de sobrepeso-obesidad,

el perfil de nutrientes y los principales grupos de alimentos consumidos. Se llevó a cabo un estudio

transversal descriptivo donde participaron 724 escolares seleccionados al azar de entre 9 a 12 años

del Estado de Nayarit, México evaluándose las características antropométricas y la ingesta de

alimentos. Siete patrones dietéticos y tres dietas específicas fueron identificados mediante análisis

multivariado. Un patrón de dieta caracterizada por una alta ingesta de legumbres y aperitivos junto

con una baja ingesta de bebidas azucaradas se asoció negativamente con el peso e índice de masa

corporal. La prevalencia de sobrepeso y obesidad fue del 20.2 y 20.6%, respectivamente. El tipo

de dieta influyó significativamente (p <0.05) en la ingesta de proteínas, hidratos de carbono y el

consumo de grasas, pero no mostró correlación con la condición de sobrepeso-obesidad. Los

azúcares simples, dulces, postres y bebidas endulzadas aparecieron en todos los patrones

dietéticos. Los patrones dietéticos en los países con una gran diversidad gastronómica podrían ser

considerados de interés para diseñar programas de intervención nutricional preventiva.

Palabras clave: Alimentación, obesidad, escolares, ingesta de energía, hábitos alimenticios

Introduction

Mexico is among the twenty countries with the highest overweight and obesity prevalence in the

world. At least 69.3% of Mexican adults are overweight, and 27.2% are obese (1). This problem

also occurs in the Mexican childhood population. Overweight and obesity prevalence in school-


48

aged Mexican children increased from 26.9% to 34.4% during the 1999 – 2012 period and 25% of

children are currently overweight (2). The rapid increase in the prevalence of overweight and

obesity has prompted many explanations, including genetic predisposition, altered energy

requirements, endocrine disruptors, and social, and economic factors (3). In this sense, it is clear

that diet plays an important role in the development of this disorder. Traditionally, the Mexican

diet was based on corn, beans, and chili peppers, which are cooked in different typical dishes,

depending on the region, but they have been increasingly replaced by ready to eat meals, fast

foods, as well as, commercial carbonated and sugar sweetened beverages (4). Probably, a

combination of lower real prices of food, changes in relative prices of different types of food and

increased availability of highly processed, energy-dense, micronutrient-poor foods is responsible

for such a drastic dietary change (5). In fact, Mexican schoolchildren have a high intake of fat,

sugar (sucrose and fructose), soft drinks, and processed foods (6). The dietary patterns followed by

preteen children had influence in their nutritional health not only during childhood, but also;

during their teen years, as well as, in adulthood. Few studies have examined the association

between dietary patterns and non-communicable diseases in the Mexican school population, and

nowadays; is highly demonstrated that diets high in simple sugars may increase the risk of insulin

resistance in children and adolescents (7). On the other hand, diastolic blood pressure, glucose,

and triglycerides concentrations have been positively associated with the intake of soft

drinks/sweetened beverages, and with high-fat dairy intake (6). However, these studies were

focused mainly in the central area of Mexico and due to the extensive culinary diversity in the

country, complementary studies in different regions are needed. Information about the relationship

between dietary patterns, nutritional profile, and body mass index can be used to plan an efficient

nutritional intervention to prepare dietary guidelines for children. The objective of this work was

to identify the overall dietary patterns in the schoolchildren population (nine to twelve years old)

in Nayarit State, and to determine the relationship between those dietary patterns, obesity,

nutritional profile, and types of foods consumed.

Materials and Methods.

Study population

Public Education Services of Nayarit State (SEPEN), case report form and informed consent form

approved the protocol of this descriptive cross-sectional study. Eleven public schools in the city of

Tepic, Nayarit State, Mexico were selected randomly, 724 schoolchildren (9-12 years old, 302

boys and 422 girls) participated in the study. Daily food intake and anthropometric data were


49

obtained. Informed consent was obtained from children and from their parents or tutors. The

following data were collected: grade, gender, age, weight, height, and place of living.

Dietary assessment

Dietary intake was measured using two-day non-consecutive food records, where five meal food

intakes were evaluated: breakfast, mid-morning meal, lunch, afternoon snack and dinner. The

foods consumed were recorded in detail in order to obtain brand name and declared composition

of commercial items, constituents of mixed dishes, etc. The assessments of quantity/volume,

common household or other measures (plates, cups, spoons, etc.) were used. Nutrient intakes were

calculated using the DIAL dietary analysis software (8). The DIAL food database was expanded

adding the food composition of traditional Mexican foods and recipes (9, 10).

Dietary patterns identification

A total of 495 food items consumed by the participants were classified into 13 groups according to

the DIAL software as follows:

1) Cereals; breakfast cereals, wheat flour, toast, white bread, bread, biscuits, sweet bread, paste,

corn kernels, corn tortilla, rice, oats. 2) Legumes; soybeans, lentils, chickpeas, refried beans,

boiled beans, green beans, pea. 3) Vegetables; carrot, purslane, green tomato, radish, green pepper,

cucumber, potato, cactus, white cabbage, cilantro, chilli, white onion, squash, pumpkin, broccoli.

4) Fruits; Grape, pear, grapefruit, tamarind, watermelon, banana, dragon fruit, pineapple, raisins,

orange, papaya, nanchi, melon, mango, apple, lemon. 5) Milk and dairy products; whole milk,

lactose-free milk, evaporated milk, light milk, yogurt, milkshakes, cream, cheese. 6) Meat; beef,

chicken, pork, sausage, bacon, belly, ham, liver, beef rib, turkey sausage, pork sausage, pork leg,

chicken gizzards. 7) Fish and seafood; sawfish, salmon, sardines, octopus, snapper, oysters,

marlin, dry shrimp, fresh shrimp and tuna. 8) Egg; whole and white egg. 9) Sugars, sweets, and

pastries; sucrose, fructose, lollipops, artificial sweeteners, cakes, chocolate powder, chocolate bar,

jam, gummy candy, gum, candy with chili. 10) Oils and fats; corn oil, sunflower oil, olive oil, lard,

butter. 11) Beverages; purified water, coffee, tea, soda, natural and processed juices. 12) Snacks;

corn chips, popcorn, potato chips, pumpkin seeds, peanuts, olive. 13) Sauces and seasoning;

tomato puree, pepper, oregano, hot sauce, Worcestershire sauce, “pipian”, vinegar, bay leaf,

chicken broth, mint, cumin, cloves, garlic, salt.

Food group energy intakes (in kcal) were divided by the total energy intake in order to derive their

relative energy contribution to the overall diet. In order to reduce data dimensionality a principal


50

component analysis (PCA) was performed to determine the energy contributed by each food group

to the extracted principal components (PC). Dietary patterns were extracted based on the

correlation matrix. PC were calculated without rotation and the number of PC was based on

eigenvalues >1.0, identification of a break point in the scree plot, and interpretability. PC scores

for each individual were calculated. Multiple regression analysis was used to test the relation

between weight, BMI and PC score. Results were reported for the final model as standardized beta

coefficient (b) and level of statistical significance (p<0.05). Furthermore, k-means cluster analysis

(KCA) with PC score was used to classify students in three diet types and to reveal meaningful

dietary patterns, as well as, to identify food groups with higher caloric intake and to determine the

relationship between diet and overweight-obesity status. Three-cluster solution was derived

through KCA with a maximum number of iterations equal to ten. Initial cluster centers were

obtained with sort distances and taking observations at constant intervals. Three clusters were

selected because they were best interpretable and to check whether PCA and KCA extracted

comparable patterns.

Anthropometric data

Trained personnel measured height and weight in scholars. Measurements were done in triplicate

and the values were an averaged. Height was measured using a stadimeter (SECA, Model 213,

Hamburg Germany) and recorded to the nearest 0.1 cm. Weight was recorded to the nearest 0.1 kg

using an electronic scale (SECA, Model 803, Hamburg Germany) with a capacity of 150 kg.

Subjects were shoeless and wore light cloths. Body mass index (BMI) was calculated and subjects

were classified as follows: BMI<5th percentile, underweight, 5 to 85th percentile, normal weight,

> 85th percentile, overweight and ≥95th percentile obese (11).

Statistical analysis

Statistica Release version 8.0 (StatSoft. Inc., Tulsa, OK, USA) was used for data analysis.

Continuous variables are presented as mean values ± standard deviation, and categorical variables

are shown as absolute frequencies. Contingency tables with calculation of χ2 test evaluated

associations between the categorical variables, while one-way analysis of variance was applied for

evaluating the associations between groups. P values < 0.05 were considered significant.

Results

Factors of schoolchildren’s dietary behaviors


51

Thirteen dietary patterns emerged from PCA, but only seven had Eigenvalues > 1. This analysis

explained 70.25 % of the variance in food group intake. Figures 1A and 2A show the PCA-

loading matrix for the seven dietary patterns in the studied population.

Figure 1 Principal component analysis (PCA) plots. (A) Loading plots for different food groups

and (B) PCA scores plot for Mexican schoolchildren of the four principal components.

As higher absolute values indicate that the food variable contributes more to the development of

the principal component (PC), dietary patterns showed the following characteristics: a pattern high

in meat, oils and fats, low in cereals, milk and dairy products (PC 1);a pattern rich in vegetables,

sauces, and seasonings, and low in sugar, sweets, pastries, oils, and fats (PC 2); a pattern

-4 -3 -2 -1 0 1 2 3PC 1

-6

-4

-2

0

2

4

6

PC 2

Cereals

Legumes

Vegetables

Fruits

Milk and dairy products

Meat

Fish and seafoods

Eggs

Sugars, sweets and pastry Oils and fats

Beverages

Snacks

Sauces and seasoning

-1.0 -0.5 0.0 0.5 1.0PC 1

VE: 13.15%

-1.0

-0.5

0.0

0.5

1.0

PC 2

VE:

11.

14%

Cereals

Legumes

Vegetables

Fruits


Meat

Fish and seafoods

Eggs

Sugars, sweets and pastry Oils and fats

Beverages

Snacks


CerealsLegumesVegetables

Fruits

Milk and dairyMeat

Fish and seafoods

Eggs

Sugars, sweets and pastry

Oils and fats

Beverages

Snacks


-1.0 -0.5 0.0 0.5 1.0PC 3

VE: 10.40%

-1.0

-0.5

0.0

0.5

1.0

PC 4

VE:

9.9

1%

CerealsLegumesVegetables

Fruits

Milk and dairyMeat

Fish and seafoods

Eggs


Oils and fats

Beverages

Snacks


-5 -4 -3 -2 -1 0 1 2 3PC 3

-4

-2

0

2

4

6

PC 4


52

characterized by high consumption of cereals, low consumption of vegetables, fruits, sugar,

sweets, and pastries (PC3); a pattern characterized by high consumption of beverages and snacks,

and low intake of egg (PC 4); and a pattern that represented important vegetables, fish, and

seafood consumption (PC 5), legumes and beverages (PC 6), and high consumption of legumes

and snacks, and low intake of beverages (PC 7). PCA was performed to obtain PC scores for each

child subject. PC scores were used in the multiple regression and cluster analysis.

Figure 2 Principal component analysis (PCA) plots. (A) Loading plots for different food groups

and (B) PCA scores plot for Mexican schoolchildren (PC 5, 6, and 7).

Associations between PC´s and weight or BMI were then evaluated with multiple regression

analysis; the models are presented in Table 1. PC 5 was positively associated with weight

Cereals

Legumes

Vegetables

Fruits

Milk and dairy productsMeat

Fish and seafoods

Eggs


Oils and fats

Beverages

SnacksSauces and seasoning

-1.0 -0.5 0.0 0.5 1.0PC 5

CV: 9.33%

-1.0

-0.5

0.0

0.5

1.0

PC 6

VE:

8.2

7%

Cereals

Legumes

Vegetables

Fruits

Milk and dairy productsMeat

Fish and seafoods

Eggs


Oils and fats

Beverages

SnacksSauces and seasoning

-4 -2 0 2 4 6 8PC 5

-4

-3

-2

-1

0

1

2

3

4

5

PC 6

-4 -3 -2 -1 0 1 2 3 4 5 6PC 7

-4

-3

-2

-1

0

1

2

3

PC 1

Cereals

Legumes

Vegetables

Fruits


Meat

Fish and seafoods

Eggs


Oils and fats

BeveragesSnacks


-1.0 -0.5 0.0 0.5 1.0

PC 7 VE: 8.05%

-1.0

-0.5

0.0

0.5

1.0

PC 1

VE:

13.

15%

Cereals

Legumes

Vegetables

Fruits


Meat

Fish and seafoods

Eggs


Oils and fats

BeveragesSnacks



53

(standardized β-coefficient = 0.124, p< 0.01) and BMI (standardized β -coefficient = 0.117, p<

0.01). PC 7 was negatively associated with weight (standardized β -coefficient = -0.077, p = 0.04)

and with BMI (standardized β -coefficient = -0.073, p = 0.04). PC 4 was negatively associated

with BMI (standardized β -coefficient = -0.064 p = 0.01).

Table 1 Multiple regression analysis models exploring the association of principal components

(PC) with weight and body mass index (BMI).

Variables* Weight BMI

Standardized β-coeficient P Standardized β-coeficient P

Age (years) -0.046 0.21 -0.041 0.27

PC 1 -0.001 0.97 0.011 0.76

PC 2 0.048 0.19 0.069 0.06

PC 3 -0.009 0.81 -0.019 0.60

PC 4 -0.064 0.08 -0.103 0.01

PC 5 0.124 < 0.01 0.117 <0.01

PC 6 -0.054 0.14 -0.037 0.31

PC 7 -0.077 0.04 -0.073 0.04

* PC description; 1, high in meat, oils and fats and low in cereals, milk and dairy products; 2, rich

in vegetables, sauces and seasoning and poor of sugar, sweet, pastry and oils and fats; 3, high

consumption of cereals and low consumption of vegetables, fruits, sugar, sweets and pastry; 4,

high consumption of beverages and snacks and low intake of eggs; 5, vegetables, fish and seafood

consumption; 6, legumes and beverage intake; 7: high consumption in legumes and snacks and

low intake of beverages.

Schoolchildren diets

Using cluster analysis, the schoolchildren were divided into three categories: Cluster 1 with 312

(43.1%) subjects, cluster 2 with 190 (26.2%) and Cluster 3 with the remaining 222 (30.7%). The

PC scores are presented in Figure 1B and 2B. They were used to classify Mexican schoolchildren

into three groups. PC 1, 3 and 4, obtained the highest score in Cluster 1 (n=312), which represents

a diet high in meat, oils, cereals, snacks and beverages. Thus, Cluster 1 was labeled as “Modified

Mexican Diet”. PC 2, 6 and 7, had the highest score in cluster 2 (n=190), and was named as

“Traditional Mexican Diet” with large intake of legumes, vegetables, snacks, sauces and


54

seasonings. The cluster 3 (n=222) had a high score in PC 5, with increased consumption of fish

and vegetables named as an “Alternative Mexican Diet”.

Figure 3 shows the mean energy supplied by each food group to the three diet types. In general,

the cereal group was the greatest energy source followed by meat, milk, and dairy products.

Significant differences (p<0.05) were found between each diet for these three food groups. The

higher intake of legumes was found in “Traditional Mexican Diet” (8% intake) with significant

differences (p<0.05) compared with the other diets. In “Alternative Mexican Diet”, milk and dairy

products represented the second most important energy source (24%), just below the cereals group

(26%). Energy intake from meat was more frequent than fish and seafood (8 to 17 % vs 0.5 to 2%

respectively) in all diets. The energy intake from sugars, sweets, and pastries was higher in

“Alternative Mexican Diet” (9.8%), showing significant differences (p<0.05) with “Modified and

Traditional Mexican Diets” (5.3 and 4.5% respectively), although these types of foods were

presented in all the diets. In the “Modified Mexican Diet”, beverages accounted for about 12% of

total energy intake, being significantly different (p<0.05) from the other diets. The highest levels

of fruit and vegetables intakes were observed in the “Alternative and Traditional Mexican Diets”

(4.8% and 2.7%, respectively), values that were significantly different (p< 0.05) compared with

the “Modified Mexican Diet”. Sauces and seasoning intake showed significant differences

between “Traditional Mexican Diet” (3% intake) and “Modified and Alternative Mexican Diets”

(2% intake). No significant differences (p >0.05) were observed in eggs, snacks, oil and fats

intakes among the different diet types.


55

Figure 3 Mean percentage energy contribution from each food group according to dietary pattern groups: (■) Modified Mexican Diet (n=312), (░)

Traditional Mexican Diet (n=190) and (■) Alternative Mexican Diet (n=222).

Values are mean ± standard error calculated using ANOVA test (p <0.05). Means in each food group not sharing the same letter are significantly

different (p< 0.05).


56

Basic characteristics of Mexican schoolchildren Characteristics of schoolchildren by dietary

pattern groups are shown in Table 2. Broadly, the prevalence of overweight was 20.2% and of

obesity was 20.6%. In girls, 2.6% showed low weight, 59.7% had normal weight, 19.4% were

overweight, and 18.2% were obese. In boys, 3.3% had low weight, 51.7% had normal weight,

21.2% were overweight, and 23.8% were obese. Sex, school grade, living place and nutritional

state did not show association with diet type (p>0.05), 43% of the whole schoolchildren sample,

40.5% of girls and 56.7% of boys respectively, consumed the “Modified Mexican Diet”. Four in

ten children with overweight or obesity consumed the “Modified Mexican Diet” and four in ten

children with low weight consumed the “Traditional Mexican Diet”. No significant differences (p

>0.05) were found between age, BMI and total energy intake. However, significant differences

were found in protein, carbohydrates, and fat intakes among the various diet types (p<0.05). Mean

energy intake in the whole-cohort of schoolchildren was 1864.80 ± 492 kcal/day. Carbohydrates

represented 50% of total energy intake (234.9 ± 72.57 g/day), protein 14% (65.51 ± 20.77 g/day),

and fat 36% (73.67± 23.66 g/day). Meals with more total energy contribution in the day were

breakfast 23% (467.44 ± 243 kcal/day), mid-morning meal 28% (568.42 ± 290 kcal/day) and

dinner 23% (452.33 ± 225 kcal/day).

Discussion

In this study, specific dietary patterns were identified in a cohort of Mexican school children living

in Nayarit State using a combination of PCA and cluster analyses and the association of each PC

with weight and BMI was evaluated. Seven dietary patterns emerged and three diet types were

identified: “Modified Mexican”, “Traditional Mexican” and “Alternative Mexican”. Positive and

negative associations between weight and BMI, and dietary pattern intake were also observed.

Interestingly, elevated BMI was associated to PC 5 (fish, moderate vegetable, oils and fats, and

low consumption of legumes, meat and sauces and seasoning), something that does not agree

completely with the generally accepted beneficial impact of vegetables or fish on health (12). This

apparent paradox may be explained by the occurrence of interactions between different food

groups in the PC. Also, both “Modified” and “Alternative” Mexican diets imply a relatively low

legume intake (3%) compared with the “Traditional Mexican Diet”, where legumes constitute 8%

of the daily intake. Thus, the two dietary patterns characterized by a low intake of legumes and

high intake of oils and fats may lead to the elevated BMI values recorded. Additionally, the form

of preparation of different dishes, both at household level and away from home, may have

influence on body mass and composition of schoolchildren.


57

Table 2 Characteristics of Mexican schoolchildren by dietary pattern groups.

* Categorical variables are presented as frequencies and percentages in parentheses for diet. P

values calculated using Chi square test. † Continuous variables are presented as mean ± standard

error. p values calculated using ANOVA test.

Modified Traditional Alternative p value

n Categorical Variables*

312 (43.09) 190 (26.4) 222( 30.66) Sex

0.102

Female 171 (40.52) 109 (25.83) 142 (33.65) Male 141 (46.69) 81 (26.82) 80 (26.49) School grade

0.060

4th 75 (36.95) 65 (32.029 63 (31.03) 5th 123 (44.09) 62 (22.22) 94 (33.69) 6th 114 (47.11) 63 (26.03) 65 (26.86) Place of living

0.226

Urban 296 (43.02) 177 (25.73) 215 (31.25) Rural 16 (44.44) 13 (36.11) 7 (19.44) Nutritional state

0.700

Low weight 6 (28.57) 9 (42.86) 6 (28.57) Normal weight 177 (43.38) 105 (25.74) 126 (30.88) Overweight 61 (41.78) 39 (26.71) 46 (31.51) Obese 68 (45.64) 37 (24.83) 44 (29.53)

Continuous variables†

Age (years) 10.66 ± 0.05 10.74 ± 0.07 10.79 ± 0.06 0.318 BMI (kg/m2) 20.13 ± 0.23 19.87 ± 0.31 20.19 ± 0.28 0.727

Total Energy intake (kcal/d) 1900.29 ± 26.99 1846.26 ± 36.36 1830.79 ± 33.77 0.228

Carbohydrate intake (g) 242.15 ± 4.13 224.06 ± 5.45 234.07 ± 4.60 0.025

% Energy intake 50.89 ± 0.43 48.20 ± 0.56 51.32 ± 0.45 <0.001 Protein intake

(g) 64.31 ± 1.09 69.10 ± 1.36 64.11 ± 1.59 0.021 % Energy intake 13.59 ± 0.14 15.16 ± 0.17 14.05 ± 0.19 <0.001 Fat intake

(g) 74.94 ± 1.30 74.85 ± 1.72 70.89 ± 1.63 0.109 % Energy intake 35.52 ± 0.36 36.64 ± 0.49 34.63 ± 0.39 0.007


58

Previous studies have suggested a negative influence of away-from-home food consumption on

obesity among all age groups, due to high energy, saturated and total fat content (13). A high

intake of legumes and snacks, together with low sweet beverage consumption were associated

with decreased weight and BMI. Legumes (eg, beans, lentils, chickpeas) have low fat content and

are good sources of dietary fiber and protein, and provide important levels of slowly digestible

carbohydrates (14). Several epidemiological studies have shown that dietary patterns

corresponding to high legume intake are associated with protection against heart disease (15). We

observed an association between high intake of snacks and low BMI. The energy intake from

snacks in this study ranged between 4 and 6 % total energy. This value was lower than those found

in an investigation in American children (more than 27% of total energy), although in that study

desserts and sweetened beverages were considered as snack (16). Evidently, the criteria used for

classifying different items in food groups may be of importance when establishing comparisons

with other populations. Additionally, the high consumption of whole milk and dairy products

could be associated with the lipid profile showed by Mexican children. A decrease in whole milk

intake accompanied by an increase in sugar-sweetened beverages could be contributing to obesity

development. Nonetheless, consumption of milk and dairy products low in fat with added

micronutrients could reduce obesity risk (17). Fruits and vegetables have been considered rich

sources of some essential dietary micronutrients, polyphenols and dietary fiber. Although fruits

and vegetables intake in this study may be considered low, it was not associated with increased

BMI values. On the other hand, it has long been recommended reducing the intake of red meat,

mainly due to its high content of saturated fatty acids; however, we found no relationship between

this food group and increased weight or BMI values. In this sense, lean red meat can lead to

decreased levels of ghrelin (a hormone that stimulates hunger) and increased secretion of the

hunger-reducing hormones glucagon-like peptide-1 (GLP-1) and peptide YY (PYY) (18). In the

present study no relationship between overweight-obesity status and diet types was found.

Multiple factors influence overweight and obesity status in children, including mothers’ obesity,

low physical activity, skipping breakfast, habitual overeating, father’s overweight and mother’s

age over 40 years (3). Recently, the R230C variant of ABCA1 (cholesterol transporter) was

associated with high circulating triglyceride and reduced high-density lipoprotein cholesterol

levels in overweight/obese Mexican schoolchildren (19). Those observations suggest a genetic

factor associated with the development of obesity in this population, although larger studies are

needed to validate this finding (19). BMI has been linked to the development of non-transmissible

diseases in childhood and children with a BMI ≥ 85th percentile and waist circumference ≥ 88.08


59

cm have greater risk for prehypertension and metabolic syndrome (20). Interestingly, we did not

find a significant correlation between overweight-obesity status and the type of diets. However, we

found here that the diet type influenced the nutrient intake profile and the intake of different food

groups. Importantly, the observed differences in intake of certain food groups on the various diets

were of a reasonable magnitude (i.e. “Modified Mexican” 5.7 % higher for beverages; “Traditional

Mexican” 8 % higher in legumes, and “Alternative Mexican” 8.5 % higher in milk and dairy

products). If such differences persist after childhood they are likely to have important

consequences for the children’s health outcomes, given that dietary habits have been shown to

track over the life course. Considering this, it is important that children develop skills at early life,

which enable them to choose healthy foods in less structured environments, such as away from

home or off school setting, so that these practices can continue into adulthood (21). In Australian

schoolchildren, consumption of vegetables and fruits was higher in school days compared to non-

school days, due to the existence of school/based interventions encouraging fruit and vegetable

breaks and/or providing subsidized fruit and vegetables (22). In recent years, healthy diets and

physical activity have been promoted in Mexican education system, programs that have

contributed to control the prevalence of obesity in schoolchildren (23). However, these actions

have been conducted mainly in central Mexico, specifically, Mexico City. The information on

dietary patterns identified in this work can be used to plan a nutritional intervention and prepare

healthy dietary guidelines for specific regions, and possibly to take into account at a larger

national scale, considering the diversity in foods preparations. As far as we know, this study is the

first to examine the association between dietary patterns and overweight-obesity occurrence in

schoolchildren in western Mexico. The combination of several multivariate techniques proposed in

this work allowed to improve understanding of relationships of obesity and dietary patterns.

Several limitations need to be considered in the interpretation of present data. Besides the

relatively small sample size, dietary recalls are known to leave margin for inaccuracy. Moreover,

the cross-sectional design (two-day recall) of the study also limits the interpretation of our findings

in terms of its temporality. The classification into 13 food groups may have generalized the effects

of dietary patterns; a larger number of food groups could provide more specific information on the

influence of food items. The dietary pattern approach can create difficulties in replicating results in

other populations. Nevertheless, this approach is useful for enhancing our understanding of the

complex dietary variables involved in the development of non-communicable diseases. Further

interventional and/or longitudinal studies are necessary to confirm here-presented findings.


60

Conclusion

The use of a combination of cluster analysis via principal component analysis gave seven dietary

factors and emerged three different diets: Traditional, Modified and Alternative Mexican Diets. A

dietary pattern characterized by high legume, snack and low beverage intake was negatively

associated with weight and BMI, suggesting that the Traditional Mexican dietary pattern could

lead to reduced prevalence of overweight and obesity among schoolchildren. An unclear

relationship between overweight-obesity status and diet types was found. The macronutrient intake

profile of schoolchildren is dependent on the type of diet consumed. The energy intake from

sugars, sweets, pastries and sweetened beverages appears in all dietary patterns. New policies for

the combat of overweight and obesity prevalence in Mexican schoolchildren should be consider,

including the design of nutrition intervention program customized to the dietary patterns of each

geographic zone.

Acknowledgements Special thanks to the children participants and their parents, to Public

Education Services of Nayarit State (SEPEN) and to all the teachers who readily consented to

carry out the study during school hours. VMZ-G acknowledges CONACYT, Mexico, for a

scholarship (Registration number: 253795).

References

1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S,

Abbafati C, Abera SF. Global, regional, and national prevalence of overweight and obesity in

children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease

Study 2013. Lancet. 2014;384(9945):766-81.

2. Romero-Martínez M, Shamah-Levy T, Franco-Núñez A, Villalpando S, Cuevas-Nasu L,

Gutiérrez J, Rivera-Dommarco J. Encuesta Nacional de Salud y Nutrición 2012. Salud Publica

Mex. 2013;55.

3. Vanhala M, Korpelainen R, Tapanainen P, Kaikkonen K, Kaikkonen H, Saukkonen T,

Keinänen-Kiukaanniemi S. Lifestyle risk factors for obesity in 7-year-old children. Obes Res Clin

Pract 2009;3(2):99-107.

4. Astudillo O. Country in Focus: Mexico's growing obesity problem. Lancet Diabetes

Endocrinol. 2014;2(1):15-6.


61

5. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in

developing countries. Nutr Rev. 2012;70(1):3-21.

6. Perichart-Perera O, Balas-Nakash M, Rodríguez-Cano A, Muñoz-Manrique C, Monge-Urrea

A, Vadillo-Ortega F. Correlates of dietary energy sources with cardiovascular disease risk markers

in Mexican school-age children. J Am Diet Assoc. 2010;110(2):253-60.

7. Romero-Polvo A, Denova-Gutiérrez E, Rivera-Paredez B, Castañón S, Gallegos-Carrillo K,

Halley-Castillo E, Borges G, Flores M, Salmerón J. Association between Dietary Patterns and

Insulin Resistance in Mexican Children and Adolescents. Ann Nutr Metab. 2012;61(2):142-50.

8. Ortega R, López-Sobaler A, Andrés P, Requejo A, Aparicio Vizuete A. DIAL software for

assessing diets and food calculations. 2.16 ed. Madrid, España: Departamento de Nutrición (UCM)

and Alce Ingeniería, S.L; 2012.

9. Pérez AB, Palacios G, Castro B. Sistema mexicano de alimentos equivalentes. 3 ed. México,

DF: Fomento de Nutrición y salud AC; 2008.

10. De Chávez MM, Solano JAL. Composición de alimentos: valor nutritivo de los alimentos de

mayor consumo. Mexico DF: McGraw-Hill; 2010.

11. Barlow SE, Committee atE. Expert Committee Recommendations Regarding the Prevention,

Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report.

Pediatrics. 2007;120(Supplement 4):S164-S92.

12. Trichopoulou A, Martínez-González MA, Tong TY, Forouhi NG, Khandelwal S, Prabhakaran

D, Mozaffarian D, de Lorgeril M. Definitions and potential health benefits of the Mediterranean

diet: views from experts around the world. BMC medicine. 2014;12(1):1.

13 Gorgulho BM, Fisberg RM, Marchioni DML. Nutritional quality of major meals consumed

away from home in Brazil and its association with the overall diet quality. Prev Med.

2013;57(2):98-101.

14. Sáyago-Ayerdi SG, Tovar J, Zamora-Gasga VM, Bello-Pérez LA. Starch digestibility and

predicted glycaemic index (pGI) in starchy foods consumed in Mexico. Starch - Stärke.

2014;66(1-2):91-101.


62

15. Clifton PM. Legumes and Cardiovascular Disease. In: Watson RR, Preedy VR, editors.

Bioactive Foods in Promoting Health: Fruits and Vegetables. Oxford, UK: Academic Press; 2009.

p. 449.

16. Rivera JA, Barquera S, González-Cossío T, Olaiz G, Sepúlveda J. Nutrition Transition in

Mexico and in Other Latin American Countries. Nutr Rev. 2004;62:S149-S57.

17. Rosado JL, Garcia OP, Ronquillo D, Hervert-Hernández D, Caamaño MdC, Martínez G,

Gutiérrez J, García S. Intake of milk with added micronutrients increases the effectiveness of an

energy-restricted diet to reduce body weight: a randomized controlled clinical trial in Mexican

women. J Am Diet Assoc. 2011;111(10):1507-16.

18. Batterham RL, Heffron H, Kapoor S, Chivers JE, Chandarana K, Herzog H, Le Roux CW,

Thomas EL, Bell JD, Withers DJ. Critical role for peptide YY in protein-mediated satiation and

body-weight regulation. Cell Metab 2006;4(3):223-33.

19. Gamboa-Meléndez MA, Galindo-Gómez C, Juárez-Martínez L, Gómez FE, Diaz-Diaz E,

Ávila-Arcos MA, Ávila-Curiel A. Novel Association of the R230C Variant of the ABCA1 Gene

with High Triglyceride Levels and Low High-density Lipoprotein Cholesterol Levels in Mexican

School-age Children with High Prevalence of Obesity. Arch Med Res. 2015;46(6):495-501.

20. Perichart-Perera O, Balas-Nakash M, Schiffman-Selechnik E, Barbato-Dosal A, Vadillo-

Ortega F. Obesity increases metabolic syndrome risk factors in school-aged children from an

urban school in Mexico City. J Am Diet Assoc. 2007;107(1):81-91.

21. Lake AA, Adamson AJ, Craigie AM, Rugg-Gunn AJ, Mathers JC. Tracking of dietary intake

and factors associated with dietary change from early adolescence to adulthood: the ASH30 study.

Obes Facts. 2009;2(3):157-65.

22. Grimes CA, Riddell LJ, Nowson CA. Nutrient and core and non-core food intake of Australian

schoolchildren differs on school days compared to non-school days. Appetite. 2014;83(0):104-11.

23. Safdie M, Lévesque L, González-Casanova I, Salvo D, Islas A, Hernández-Cordero S,

Bonvecchio A, Rivera JA. Promoting healthful diet and physical activity in the Mexican school

system for the prevention of obesity in children. Salud Publica Mex. 2013;55(3):S357-S73


63

7.2 Perfil de metabolitos en un sistema in vitro de fermentación colónica humana en tres

menús consumidos por escolares mexicanos durante el desayuno

Resumen

La obesidad infantil se ha asociado con numerosos resultados negativos en el estado de salud físio

y psicológico. La mayoría de las pautas dietéticas están de acuerdo en la importancia de una

correcta distribución de las calorías durante el día, lograda principalmente por el consumo regular

de comida, almuerzo y cena (US-HHS, 2005). En México, la epidemia de obesidad entre escolares

ha resultado en políticas federales y estatales que mejoran los entornos alimentarios en las escuelas

(Levy et al., 2012). A menudo se afirma que el desayuno es la comida más importante del día.

Varios informes apoyan la asociación entre el consumo de desayuno y su calidad nutricional con

un riesgo reducido de desarrollar síndrome metabólico (Odegaard et al., 2013). Sin embargo,

teniendo en cuenta que los nutrientes o los alimentos rara vez se comen aislados, los estudios de

los patrones dietéticos o menú completo tienen un mayor número de ventajas sobre el estudio de

un único nutriente o enfoque de un solo alimento, considerando la aparición de interacciones

bioquímicas sinérgicas o antagonistas entre nutrientes, así como la existencia de diferentes fuentes

alimenticias del mismo nutriente (Barbaresko et al., 2013). La mayor parte de la información

disponible sobre los patrones dietéticos del desayuno y la salud en los escolares proviene

principalmente de estudios epidemiológicos (Karatzi et al., 2014). Sin embargo, la información

acerca de la composición de menús enteros identificados en diferentes patrones dietéticos es

escasa.

Probablemente, la combinación de análisis epidemiológico y estudios in vitro de los menús del

desayuno puedan contribuir a una mejor comprensión de los efectos que los alimentos pueden

promover en la salud. Los procesos fermentativos que tienen lugar en el intestino grueso humano

han suscitado mucho interés en las últimas décadas. Los diferentes metabolitos derivados de la

fermentación parecen tener una importante actividad fisiológica en los niveles tanto colónico como

periférico (Flint, 2016). En este capítulo se evaluó el perfil de metabolitos obtenido después de la

fermentación colónica in vitro de la fracción indigerible (FI) aislada de tres menús de desayuno

consumidos por escolares mexicanos. Se prestó especial atención en los metabolitos asociados con

cambios en el pH y la capacidad antioxidante (AOX). También se evaluó la composición general,

contenido de polifenoles extraíbles y no extraíbles, y AOX de los FI´s aisladas.


64

Metabolites profile in an in vitro human colonic fermentation of three menus consumed by

Mexican schoolchildren at breakfast

Victor Manuel Zamora-Gasga1, Montalvo-González Efigenia1, Guadalupe Loarca-Piña2, Pedro

Alberto Vázquez-Landaverde3, Juscelino Tovar 4, Sonia G Sáyago-Ayerdi1*

1Instituto Tecnológico de Tepic, Laboratorio Integral de Investigación en Alimentos,

División de Estudios de Posgrado, Av Instituto Tecnológico No 2595, Col Lagos del Country CP

63175, Tepic, Nayarit México.

2 Programa de Posgrado en Alimentos del Centro de la República, Facultad de Química,

Universidad Autónoma de Querétaro, Centro Universitario, Cerro de las Campanas S/N, CP

76010, Santiago de Querétaro, Querétaro México.

3 Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada del Instituto Politécnico

Nacional, Unidad Querétaro, Cerro Blanco No. 141. Col Colinas del Cimatario, CP 76090,

Santiago de Querétaro, Querétaro México.

4 Food for Health Science Centre. Lund University, Medicon Village. Scheelevägen 2. Lund, SE-

223 81. Sweden

*Corresponding author, [email protected]

Tel.: +52 311211-940


65

Highlights

• Indigestible fraction (IF) was isolated and characterized from food consumed at breakfast

• Effects of three isolated IF on gut metabolism in vitro was analyzed

• Traditional breakfast has a higher condensed and hydrolysable tannins content.

• Short chain fatty acid production was low during IF-breakfast fermentation

• Metabolite patterns were associated with pH and antioxidant capacity at fermentation

Graphical Abstract


66

Abstract

Transitional food led changes in diets and lifestyles that promote childhood obesity, and Mexico is

an example of this epidemic. Breakfast is considered the most important meal of the day and the

study of a whole menu had higher number of advantages over single nutrients. The aim of this

work was to evaluate the metabolite profile of the indigestible fraction (IF) by in vitro human

colonic fermentation isolated from three different breakfast menus consumed by Mexican

schoolchildren in Tepic, State Nayarit Mexico. The menus were named as: modified, traditional,

and Alternative Mexican Breakfast (MM-B, TM-B, AM-B, respectively). The IF in all breakfast

were about 5.78 g/100 g wet basis (wb), and TM-B had 1.1 g/100 g wb for soluble IF, where beans

were included. The indigestible protein was relatively high (≈21 %) and this could contribute with

negative implications. Fermentation of menus showed a positive changed in pH only in TM-B,

which can be related with poor or low fermentation of the indigestible carbohydrates. TM-B

showed a greater pH decreased and also present a lower short chain fatty acid (SCFA) production,

but a greater relative concentration of hexanoic, heptanoic and octanoic acids. Besides, 55 volatile

compounds were detected by SPME-GC-MS and three principal components (PC) were identified.

PC1 was influenced by higher SCFA production and low fatty acid esters production. Fatty acid

ester “less than eight carbon atoms” and volatile organic acids production (PC2) resulted in a

decrease on pH and an increase on antioxidant capacity (AOX). This result suggested that total

metabolites present in the intestinal medium could affect the pH and antioxidant status in the

colon. Fermentation extracts of IF isolated from traditional breakfast presents a fermentation-time-

dependent beneficial metabolic pattern.

Keywords:

Breakfast Menus, Indigestible fraction, Antioxidant capacity, Gut metabolites.

Abbreviations:

AM-B, alternative Mexican breakfast; AOX, antioxidant capacity; CGC/MS, gas

chromatography–mass spectrometry; DP, dietary pattern; HCA, Hierarchical cluster analysis; HS-

SPME, headspace solid-phase micro-extraction; IF, Indigestible fraction; MM-B, modified


67

Mexican breakfast; PCA, Principal component analysis; SCFA, short chain fatty acid; TM-B;

traditional Mexican breakfast.

1 Introduction

Childhood obesity has been associated with numerous negative health and psychological

outcomes. Its surge has been explained as consequence of globalization-related changes in diet and

lifestyle that promote positive energy balance (Malik et al., 2013). Most dietary guidelines agree

on the importance of a correct distribution of calories throughout the day, achieved principally by

a regular meal consumption of breakfast, lunch, and dinner (US-DHHS and USDA, 2015). In

Mexico, obesity epidemic among schoolchildren has resulted in federal and state policies that

improve school food environments (Levy et al., 2012). It is often stated that breakfast is the most

important meal of the day. Several reports support the association between breakfast consumption

and its nutritional quality with a reduced risk to develop metabolic syndrome (Odegaard et al.,

2013). However, taking into account that nutrients or foods are rarely eaten isolated, studies of

dietary patterns or whole menu have a higher number of advantages over the single-nutrient or

single-food approach, by considering the occurrence of synergistic or antagonistic biochemical

interactions among nutrients, as well as the existence of different food sources of the same nutrient

(Barbaresko et al., 2013). Most available information on breakfast dietary patterns (DP) and health

in schoolchildren is derived principally from epidemiological studies (Karatzi et al., 2014).

However, information is scare about the composition of whole menus identified in different

dietary patterns. Probably, the combination of epidemiological and in vitro analysis of breakfast

menus may contribute to a better understanding of the effects that food can promote on health. The

fermentative processes that take place in the human large intestine have raised much interest in the

last decades. Different fermentation-derived metabolites appear to have to important physiological

activity at both colonic and peripheral levels (Flint, 2016). In this work we evaluated the

metabolites profile obtained after in vitro colonic fermentation of the indigestible fraction (IF)

isolated from three breakfast menus consumed by Mexican schoolchildren in Tepic, State Nayarit

Mexico. Particular attention was paid to metabolites associated with changes in pH and

antioxidant capacity (AOX). The general composition, extractable and non-extractable

polyphenols content, and AOX of the isolated IFs were also evaluated


68

2. Material and methods

2.1 Preparation of breakfast menus

Data sources on DP and food frequency consumption at breakfast were obtained from a nutritional

survey carried out on eleven public schools in Tepic (Nayarit State, western Mexico). These data

are being reported elsewhere (Zamora-Gasga et al., 2017). Frequently consumed foods in each DP

were used to create three breakfast menus, which comprised by the following foods: a) Modified

Mexican Breakfast (MM-B) (≈394 g total weight): one scrambled egg, three corn tortillas (90 g),

chocolate milk (whole milk 264 mL, cane sugar 7 g, chocolate powder 13 g); b) Traditional

Mexican Breakfast (TM-B) (≈ 478 g total weight): one scrambled egg, three corn tortillas (90 g),

refried beans (83 g), chocolate milk (whole milk 264 mL, cane sugar 6g, chocolate powder 11 g);

c) Alternative Mexican Breakfast (AM-B) (≈370 g total weight): one scrambled egg, two corn

tortillas (60 g), chocolate milk (whole milk 256 mL, cane sugar 10g, chocolate powder 18 g).

Individual ingredients were purchased from the local supermarket and menus were prepared in the

laboratory kitchen according to traditional regional customs. Recently prepared menus were

homogenized in a food processor (NB-101B, Nutribullet, China), frozen (-80 °C), freeze-dried

(FreeZone 6, Labconco, USA), ground, sieved through a mesh size of 500 µm, and stored at -20

°C until analysis. Each menu was prepared in triplicate.

2.2 Quantification and isolation of indigestible fraction (IF) in menus

The IF quantification was evaluated according to Saura-Calixto et al. (2000), a method that

simulates the physiological situation in the upper gastrointestinal tract. The method was run at

preparative scale, according to the modifications proposed by Tabernero et al. (2011). Insoluble

(IIF) was considered as the digestion residues pelleted by centrifugation, while those retained by

dialysis represented soluble IF (SIF); the sum of both fractions equals total IF (TIF). TIF was

collected, freeze-dried, milled (IKA M20, USA), sieved (500-micron mesh), and stored in seal

bags at -20 °C.


69

2.2.2 Chemical composition of total indigestible fraction

Moisture content, ash, protein and fat were analyzed according to AOAC (1990) 925.10, 923.03,

920.87 and 920.39 methods, respectively. Total carbohydrates were evaluated by the phenol-

sulfuric method (Dubois et al., 1956). Resistant starch was assessed following a multi-enzymatic

digestion protocol (Goñi et al., 1996).

2.2.3 Antioxidant compounds analysis in the total indigestible fraction

TIF samples (250 mg) from the different menus were extracted with aqueous-organic solution

according to the methodology proposed by Pérez-Jiménez et al. (2009). Total, soluble polyphenols

(TSP) in extractable fraction were determined with the Folin–Ciocalteu’s reagent (Montreau,

1972) using a 96-well microplate reader (Biotek, Synergy HT, Winooski VT, USA) with Gen5

software, and the results were expressed as gallic acid equivalents (g GAE/ 100g IF). The residues

of the extraction were treated for non-extractable polyphenols quantification (Condensed tannins

and hydrolyzable polyphenols). Condensed tannins (CT) were assessed by the method proposed by

Reed et al. (1982); results were expressed as CT equivalents/ 100 g IF, using a carob pod

(Ceratonia siliqua) proanthocyanidin standard. Hydrolyzable polyphenols (HP) were evaluated by

Hartzfeld et al. (2002) method and results were expressed as g GAE/100 g IF.

2.2.4 Antioxidant capacity (AOX) analyses

AOX of the extractable fraction obtained above was analyzed. AOX methodologies were slightly

modified to a microplate reader (Biotek, Synergy HT, Winooski VT, USA) with Gen5 software.

1,1-Diphenyl-2-picryl hydrazyl (DPPH) antiradical activity assay was performed according to the

method of Prior et al. (2005). DDPH radical-scavenging activities were expressed as Trolox (6-

hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic) equivalent (TE; mmol /g IF db). Ferric

reducing antioxidant power (FRAP) assay was performed as described by Benzie et al. (1996).

Results are expressed as mmol of Trolox equivalents (TE: mmol /g IF db).

2.3 In vitro colonic fermentation by human microflora


70

The assays were preformed using a pool of fresh fecal samples collected from five healthy

schoolchildren (9–12 years), apparently free from gastrointestinal diseases and who did not receive

antibiotic treatment during the previous 3 months. The fermentation process was developed

according to Campos Vega et al. (2009). TIF of breakfast menus was fermented at 37 °C under

anaerobic conditions. Two different controls were also conducted in parallel: a) raffinose, used as

a fermentable sugar reference that produce SCFA was incubated in medium with faeces inoculum,

and b) the faecal suspension was incubated without addition of substrate, serving as negative

control. All incubations were performed in triplicate; samples were collected at 12, 24, 48 and

72 h. and centrifuged (Hermle Z 323 K; Wehingen, Germany) (3500 × g,15 min, 4 °C).

Supernatants were placed into a 20 mL vial sealed with a magnetic cap with a poly-tetra-fluoro-

ethylene (PTFE)/silicon septum. The vials were immediately stored at −80 °C in order to minimize

any deteriorating changes in the volatile components of the samples until they were processed.

2.4 Metabolites characterization by HS-SPME- GC/MS

Supernatants from the colonic fermentation were characterized by Gas Chromatography–Mass

Spectrometry (CGC/MS) using a headspace solid-phase micro-extraction (HS-SPME) technique

according to Zamora-Gasga et al. (2015); incubation with polydimethylsiloxane-divinylbenzene-

carboxen “PDMS/DVB/CAR” fiber for 120 min, 250 rpm at 45 °C; the fiber was inserted into the

injection port of the GC system for thermal desorption (240 °C for 10 min) for GC/MS analysis.

The extractions were performed in triplicate. Five runs with twelve vials / run were performed and

the tests order was completely randomized. The volatile constituents were analyzed with an

Agilent 5975C VL mass selective detector coupled to an Agilent 7890A gas chromatograph

(Agilent Technologies, Inc., Santa Clara, CA), equipped with a DB-5MS capillary column (60 m

X 250 µm X 0.25 µm; Agilent). Sample quantification was obtained by means of acetic, propionic

and butyric acid standard curves. Tentative identification of the volatile components was done

comparing the mass spectra of the samples with the data system library MSD ChemStation

software (Agilent G1701EAversion E.02.00.493). Relative concentration of all fermentation

metabolites versus acetic acid as internal standard was calculated and the results were expressed in

mmol L-1.


71

2.5 Statistical analysis

Data were subjected to one-way ANOVA/Fisher's LSD test (Levene's test, p≥0.05, Shapiro–Wilk

W test, p≥0.05, n = 9) and to independent-samples Kruskal–Wallis non-parametric test/Multiple

comparisons of mean ranks for all test groups (Levene's test, p<0.05, Shapiro–Wilk W test,

p<0.05, n = 9). Two-way ANOVA with a post hoc Fisher's LSD test was used to determine the

effect of the substrate type and fermentation time on pH changes and antioxidant capacity

(Substrate type × fermentation time). Principal component analysis (PCA) of fermentation

metabolites was performed based on the mean values of triplicates. Components were calculated

without rotation and the number of extracted factors were based on eigenvalues >1.0 and

explained variance (%) >70. A secondary analysis based on multiple lineal regression analysis was

used to identify which metabolic profiling (Component score) where associated with pH and

antioxidant capacity (DPPH and FRAP methods). In order to identify which samples (blank,

raffinose, and samples IF) showed similar metabolic profiles, a hierarchical cluster analysis (HCA)

was realized. Single Linkage amalgamation method of clustering and the Euclidean distance

measure were used. All analyzes were performed using STATISTICA software, version 10.0

(StatSoft. Inc. 1984–2007, Tulsa, OK, USA).

3. Results and Discussion

3.1 Characterization of indigestible fraction in breakfast menus

Moisture, IF content, and chemical composition of complete menus are shown in Table 1. No

significant differences in moisture content were observed between menus; this probably reflects

the similar volume amount of milk included in the various menus (≈250 mL). Small but significant

differences (p<0.05) in IF content were found between menus (6.11, 6.22 and 5.03g /100 g wet

basis (wb) for MM-B, TM-B and AM-B respectively). The insoluble IF (IIF) contents in MM-B

and TM-B (5.60, 5.13 g/100 g wb) were not significantly different. TM-B presented the highest

Soluble IF (SIF) value (1.10 g/100 g menu wb). It is worth noting that these menus included

refried beans. Cooked beans are good sources of dietary fiber (DF), with contents as high as 59


72

mg/g dry basis (DB) insoluble DF (Dueñas et al., 2016). Ash content in the IFs showed significant

differences (p<0.05), with values ranging from 0.19 to 0.25 g/100 g menus wb. Has been reported

that some minerals can be poorly absorbed in the human small intestine because of the presence of

certain inhibitory compounds like phytates, CT, and DF (present in cereal- and legumes-

containing breakfast), although they may be absorbed in colon (Raes et al., 2014). On the other

hand, TM-B and MM-B exhibited the highest indigestible carbohydrate content, with 0.89 g / 100

g menu wb, which was about three times higher (p<0.05) than in AM-B (0.34 g / 100 g menu wb).

Similarly, the resistant starch (RS) content was significantly (p<0.05) higher in IF isolated from

TM-B, with values that were 30 and 60% higher than in MM-B and AM-B, respectively. These

results could be attributed to the presence of appreciable levels of indigestible carbohydrates, as

DF and RS, in corn tortilla and beans (Sáyago Ayerdi et al., 2014). IF of menus showed a high

IF fat content of 3.30, 3.32 and 3.24 g/100 g menu wb, in MM-B, TM-B and AM-B, respectively.

The ratio between fat/indigestible carbohydrate content in menus was 9.5, 4.5 and 3.7 w/w (AM-

B, MM-B and TM-B respectively). Protein content was relatively high (1.30, 1.42 and 1.07 g/100

g menu wb for MM-B, TM-B and AM-B, respectively) with significant differences (p<0.05).

These values may have negative implications to the intestinal health in schoolchildren, as diets

high in protein and low in indigestible carbohydrates have been reported to result in decreased

colonic production of fecal cancer-protective metabolites (e.g. butyric acid) and increased

concentrations of hazardous compounds (e.g. N-nitroso compounds) (Russell et al., 2011).

3.2 Antioxidant compounds in the indigestible fraction

The levels of TSP, HP, and CT present in IF of the studied menus are summarized in Table 1. TSP

associated to IF were released in the aqueous-organic extraction system and showed chelating

AOX, but no DPPH radical scavenging activity was observed (Table 1). Chelating activity was

higher (21.25 mmol TE/g IF DW) in extracts from IF isolated from MM-B menus and lower in

TM-B (16.82 mmol TE/g IF DW). Besides polyphenols, other constituents present in IF from the

breakfasts may affect AOX. The presence of egg yolk components like phosvitin, phospholipids,

carotenoids and free aromatic amino acids may contribute to the overall antioxidant capacity of the

samples (Remanan et al., 2014); however; the concentration of these compounds in IF from food

mixtures has not been studied. TM-B showed a high CT content (2.15 g/100 g IF DW),


73

significantly greater than in MM-B (0.57 g/100 g IF Dw), although not different from the level

measured in IF from AM-B (0.92 g/100 g IF DW). TM-B exhibited the highest HP content (2.15

g/100 g IF DW) among the studied breakfasts. The presence of beans in TM-B could be largely

responsible for the high non-extractable polyphenols contents, as they are abundant in this legume

(Ramírez-Jiménez et al., 2015).

Table 1 Chemical composition and antioxidant compounds content, and antioxidant capacity in

the indigestible fraction (IF) isolated from breakfast menus1

Parameter Menus2

MM-B TM-B AM-B Moisture content (g/100g menu ) 73.26 ± 0.37a 74.13 ± 0.10a 73.62 ± 0.40a IF (g/100g menu wb) Insoluble IF 5.60 ± 0.22b 5.13 ± 0.21ab 4.57 ± 0.10a Soluble IF 0.51 ± 0.06a 1.10 ± 0.14b 0.46 ± 0.06a Total IF 3 6.11 ± 0.24b 6.22 ± 0.35b 5.03 ± 0.09a IF Nutritional Composition (g/ 100g menu wb) Ash 0.25 ± 0.01a 0.24 ± 0.01a 0.19 ± 0.01b Indigestible Carbohydrates 0.73 ± 0.01a 0.89 ± 0.07a 0.34 ± 0.05b Resistant Starch 0.008 ± 0.001a 0.012 ± 0.001b 0.004 ± 0.001c Lipids 3.30 ± 0.04a 3.32 ± 0.18a 3.24 ± 0.04a Proteins 4 1.30 ± 0.05a 1.42 ± 0.09a 1.07 ± 0.02b IF Antioxidant Compounds (g/ 100g IF DB) Total soluble polyphenols 1.16 ± 0.03a 0.94 ± 0.02ab 0.93 ± 0.02b Condensed Tannins 0.57 ± 0.02a 2.15 ± 0.13b 0.92 ± 0.04ba Hydrolyzable polyphenols 1.57 ± 0.01a 2.15 ± 0.02c 1.78 ± 0.02b IF Antioxidant Capacity (mmol TE/g FI DB) DPPH ND ND ND FRAP 21.25 ± 1.24b 16.82 ± 0.16a 19.23 ± 0.15ab

1Values are mean ± standard error (n = 3); Wet basis (wb); Dry basis (db); No Detected (ND).

Means in rows marked with different letters indicate significant difference (p <0.05). 2 Menus;


74

MM-B: Modified Mexican Breakfast, TM-B: Traditional Mexican Breakfast, AM-B: Alternative

Mexican Breakfast. 3 Total IF = Sum of soluble IF + insoluble IF. 4 Conversion factor: % N x 6.25.

Also, interactions between indigestible proteins and polyphenols presents in food matrix may

avoid the absorption of the polyphenols in the small intestine, increasing its content in the IF

(Mullen et al., 2009).

3.3 Changes in pH and antioxidant capacity during in vitro colonic fermentation

The progress of the fermentation process was followed measuring pH changes in each sample

during 72 h (Figure 1a). Significant differences (p < 0.05) were found among the substrates. The

largest decrease in pH was raffinose, but among the IF from breakfast menus was observed for

TM-B after 72 h (pH= 6.07). The pH variations between 0 and 72 h of fermentation were -0.02,

0.48, and -0.07 for MM-B, TM-B, and AM-B menus, respectively. Only the TM-B menu showed

a positive change in pH. Such increased pH values can be related to poor or limited fermentation

of the indigestible carbohydrates present in the substrate (Kettle et al., 2015). AOX evaluated by

DPPH and FRAP assays (Figure 1b and c), showed time- dependence and significant differences

(p <0.05). It is noteworthy that DPPH activity (less than 40 mmol TE/g IF) was detected in

fermentation extracts but not in aqueous-organic extracts. In terms of FRAP, MM-B (16.31 mmol

TE/g IF DW) showed significant differences respect to TM-B (8.13 mmol TE/g IF db) and AM-B

(10.79 mmol TE/g IF DW) at 48 h. However, the AM-B menu had the highest FRAP activity at 72

h (10.85 mmol TE/g IF DW). Microbial metabolites can influence AOX in fermentation extracts.

It has been reported that the presence of 4-vinylguaiacol (microbial metabolite of ferulic acid)

decreases AOX of fermentation extracts from arabino-xylanoligosaccharides (Snelders et al.,

2014). Recently, in vitro antioxidant effects of urolithin A (colonic metabolite of ellagic acid) on

hepatocellular carcinomas HepG2 5 cells were demonstrated (Wang et al., 2015).


75

Figure 1 Changes in a) pH kinetic plot, b) DPPH Antiradical activity plot and c) FRAP chelating activity plot in the extracts

during in vitro colonic fermentation from (–) blank, (··♦··) raffinose, and indigestible fraction isolated from breakfast menus: (-

■-) Modified Mexican Breakfast, MM-B, (-▲-) Traditional Mexican Breakfast, TM-B, and (-●-) Alternative Mexican

Breakfast, AM-B at different at different fermentation times, Values are means ± SEM (n=3). *Significant difference using

Two-way ANOVA/Fisher's LSD test (Samples ×Time interaction, p<0.05). *For DPPH and FRAP , the blank was subtracted of

the samples.


76

3.4 Production of short chain fatty acids (SCFA) during in vitro fermentation

The SCFA production at 12, 24, 48 and 72 h of in vitro fermentation of IF isolated from breakfast

menus are showed in Table 2. At 24 h, AM-B produced the greatest acetate concentration (47.23

mmol/L), and was statistically different (p < 0.05) from the other breakfast menus. Propionic acid

concentrations were very similar (p > 0.05) between MM-B and TM-B (28.5 and 28.9 mmol/L

respectively) at 24 h. and AM-B showed the highest concentration with 35.7 mmol/L. Regarding

butyric acid, no significant differences (p>0.05) were found between fermented IF from breakfast

menus and the assay blank. Low production of SCFA may be related to the proportions of

macronutrients present in IF from the breakfast menus. A recent study suggested that the

magnitude of SCFA production during in vitro fermentation is dependent on the diet of the fecal

donor (Yang et al., 2016).

Interestingly, the substrate with lowest carbohydrate concentration (AM-B menu) showed the

highest production of SCFA. It seems also contradictory that TM-B showed a larger pH decrease

and lower SCFA production. Hypothetically, it can be thus proposed that other metabolites may be

involved in the pH decrease observed during fermentation of IF from TM-B menu. In this sense,

the volatile organic compounds produced during the fermentation are shown in Supplementary

Table SI. A greater relative concentration of hexanoic acid (ID: 31), heptanoic acid (ID: 34) and

octanoic acid (ID: 41) was recorded in the IF fermentation medium from TM-B compared to the

other breakfast menus, particularly after 48 and 72 h. Therefore, reduced pH values may be

attributed to the production of non-SCFA organic acids (Sagar et al., 2015).


77

Table 2. Short-chain fatty acids (SCFAs, mmol L-1) production at 12, 24, 48 and 72 h of in vitro fermentation of blank,

raffinose and indigestible fraction isolated from breakfast menus (MM-B; Modified Mexican Breakfast, TM-B; Traditional

Mexican Breakfast, AM-B; Alternative Mexican Breakfast) 1.

SCFA/ fermentation

time

Blank Raffinose IF- Breakfast Menus

MM-B TM-B AM-B

Acetic acid 12 h 6.05 ± 0.75a 205.05 ± 25b 2.39 ± 0.08a 12.93 ± 0.69a 6.41 ± 0.87a 24 h ND 273.92 ± 33.41c 19.88 ± 2.27a 7.45 ± 0.07a 47.23 ± 0.30b 48 h 11.28 ± 6.22a 572.54 ± 24.63b 19.89 ± 2.13a 5.89 ± 0.36a 11.63 ± 1.07a 72 h ND 254.75 ± 12.48b 20.32 ± 8.69a ND 17.40 ± 6.22a Propionic acid 12 h 4.18 ± 0.62a 44.17 ± 0.89b ND ND ND 24 h ND 89.01 ± 2.61c 28.50 ± 3.15a 28.91 ± 1.13a 35.76 ± 2.30b 48 h ND 134.33 ± 8.10c 27.13 ± 1.73b ND 18.38 ± 0.45a 72 h ND 103.88 ± 2.46 ND ND ND Butyric acid

12 h 0.78 ± 0.17a 25.97 ± 5.63b 0.75 ± 0.13a 1.52 ± 0.11a 0.67 ± 0.12a 24 h 0.44 ± 0.09a 36.94 ± 7.92b 3.41 ± 1.00a 4.79 ± 0.24a 4.57 ± 0.30a 48 h 0.78 ± 0.18a 63.55 ± 7.57b 3.49 ± 0.29a 1.85 ± 0.24a 3.86 ± 0.76a 72 h ND 34.92 ± 1.32 ND ND ND

*The values are reported in mmol/L produced per 100 mg substrate as mean ± SEM of three replicate; Different lowercase

letters indicate significant differences in rows among substrates for a time (p < 0.05). No Detected (ND).


78

3.5 Volatile compounds during the in vitro colonic fermentation

A total of 55 volatile compounds were detected in fermentation extracts of blank samples,

raffinose standard and IFs isolated from the different breakfast menus (See Supporting

Information Table SI). PCA was performed to determine microbial metabolic pattern of

fermentation in the samples at different fermentation times. Three principal components (PC) were

obtained (Eigenvalues > 1) that explained 71.31% of the total variance. Loading scatter plot for the

three principal components are shown in Figure 2. PC 1, 2, and 3 explained 44.75, 15.30, and 11.2

% respectively, of all variance from the gut metabolites production. PC1 on the positive axis

(Figure 2a) was highly influenced by different microbial metabolites, mainly SCFA (See

Supporting Information Table SI); butyric acid, Benzaldehyde, 4-methyl-, acetic acid, propionic

acid, butanoic acid, 3-methyl-, and ethanol (ID: 20, 23, 12, 16, 24 and 2 respectively). Increased

concentrations of these compounds were registered during in vitro colonic fermentation. On the

other hand, PC1 negative axis comprises compounds that were produced at relatively low levels

during the fermentation process, mainly medium to long chain fatty acid esters (e.g. nonanoic acid,

methyl ester, ID:19; nonanoic acid, ethyl ester, ID:21; dodecanoic acid, ethyl ester ID:36;

tridecanoic acid, methyl ester, ID:39; decanoic acid, methyl ester ID:25; decanoic acid, ethyl ester,

ID:26; pentadecanoic acid, methyl ester, ID:46), indole (ID:49), and disulfide, dimethyl (ID:5).

Regarding PC2 (Figure2a), some fatty acid esters with less than eight carbon atoms: butanoic

acid, methyl ester, ID:4; hexanoic acid, methyl ester (ID: 8); hexanoic acid, ethyl ester (ID:9);

heptanoic acid, methyl ester (ID:10); heptanoic acid, ethyl ester (ID:14) and volatile organic acids

(e.g. hexanoic acid “ID:31”, heptanoic acid “ID:34” and octanoic acid “ID:34”) were located in

the positive axis (high production). However, the volatile compounds on the negative axis (low

production) were trimethylamine, dimethyl trisulfide and indole (ID: 1, 11 and 49 respectively). In

PC 3 (Figure 2b), increased production is noticed for propionic acid (ID:16), butyric acid, (ID:20),

and butanoic acid, 3-methyl- (ID:24) and a low production observed for trimethylamine (ID:1),

trichloromethane (ID:3), pyridine, 2,4,6-trimethyl- (ID:13), phenol (ID:38), and phenol, 4-methyl-

(ID:40).


79

Figure 2 Identification of Microbial metabolic pattern between the in vitro colonic fermentation extracts of blank, raffinose and

indigestible fraction isolated from breakfast menus at different fermentation times using principal component analysis (PCA):

Loading scatter plot for a) PC1 vs. PC2; b) PC3 vs. PC4 and PC5 vs. PC6 (Detailed of identification number for each volatile

compound are outlined in the Supporting Information Table SI).


80

3.6 Correlation analysis between microbial metabolic pattern, pH and antioxidant capacity

Multiple regression analysis was conducted to test the relationship between microbial metabolic

pattern (component scores) pH, DPPH, and FRAP in fermentation extracts of IF isolated from

breakfast menus at different times (Figure 3). The linear regression model was significant

(p<0.05) for data with high values in the coefficients determined, indicating a proper fit of the data

with the model (Multiple R2 values= 0.91, 0.88, and 0.62 for pH, DPPH, and FRAP, respectively).

The results showed that the three components were significantly (p < 0.05) associated with the

decrease in pH values. The largest predicted decrease in pH was observed with the fermentative

metabolic pattern characterized by a high production of fatty acid esters and volatile organic acids,

with a low production of trimethylamine, dimethyl, trisulfide and indole (PC 2). Also, three PC

scores were associated with increased antioxidant capacity. Increase in predicted DPPH was

observed with increasing production of PC 2 members (Coefficient = 27.02), followed by

production of metabolites included in PC1 (high SCFA production and low fatty acid esters

production, Coefficient = 23.87) and PC3 (high propionic and butyric production and low

production of protein metabolites, Coefficient = 22.88). Finally, for chelating activity, increased

FRAP values were observed with increased scores of PC2 and PC3., while PC1 coefficient was

not significant (p>0.05). These results suggest that overall metabolite composition of the luminal

medium may affect pH and antioxidant status of the colon. They also highlight the importance of

assessing the microbial metabolic pattern instead of individual metabolites identified during IF

fermentation isolated from complete food. Actually, volatile compounds (in particular SCFAs as

pentanoic and hexanoic acids) have been proposed as potential noninvasive biomarkers, which

may reflect gut dysbiosis, and thus capable of differentiating subjects in health and disease

(Covington et al., 2013). Additionally, some compounds identified in this work have been

previously reported as harmful to health. For instance, experimental and clinical studies have

shown trimethylamine-N-oxide (formed in the liver from trimethylamine), phenol and p-cresol as

strong predictors of coronary artery disease and Crohn's disease (Trøseid et al., 2015; Walton et

al., 2013).


81

Figure 3 Multiple regression analysis models exploring the association of microbial metabolic pattern (Component scores)

with: a) pH Values, b) DPPH antiradical activity and c) FRAP chelating activity. *Model fit was adjusted to: Y = β0 + β1*X1 +

β2*X2 + β3*X3, where, Y is response variable (pH, DPPH and FRAP), β0 is the intercept, β1, β2, β3, are the coefficients and X1, X2,

X3 are the Component Score (CS1, CS2 and CS3).


82

3.7 Hierarchical cluster analysis (HCA)

Dendrogram of component scores (metabolic profile) from different fermentation times of IF

isolated from breakfast menus are presented in Figure 4. The HCA led to three clusters, indicating

that the production of volatile compounds by gut microbiota was different between the formed

clusters. The analysis clearly group raffinose (Cluster I) and blank samples (Cluster III) at all

times. In addition, Cluster I presented component positive scores for PC 1 (dark color), indicating

that the cluster follows a metabolic profile characterized by high SCFA production and low fatty

acid esters (medium to long chain) production. Cluster II was formed by fermentation extracts of

IF isolated from all breakfast menus. However, it is noted that TM-M (at 48 and 72 h) showed the

highest values (dark color) of the scores for PC2 (high production of C<8 fatty acid esters and

volatile organic acids). PC2 was associated with decreased pH and increased AOX. Additionally,

the remaining samples were characterized by light colors, indicating that they are not associated

with health benefits. HCA results indicated that in vitro colonic fermentation results in

considerable differences in the metabolic pattern depending on the IF samples and fermentation

time.


83

Figure 4 Dendrogram of hierarchical cluster analysis based on the microbial metabolic profiles (component score) in colonic

fermentation extracts of blank, raffinose, and indigestible fraction isolated from breakfast menus (MM-B; Modified Mexican

Breakfast, TM-B; Traditional Mexican Breakfast, AM-B; Alternative Mexican Breakfast) at different fermentation times: 12 h

(T12), 24 h (T24), 48 h (T48) and 72 h (T72).


84

4. Conclusions

This study shows that IF isolated from three breakfast menus consumed by Mexican

schoolchildren have different chemical composition. TM-B had non-extractable polyphenols, and

indigestible carbohydrates (DF) as main bioactive compounds. It is being increasingly shown that

the fermentative metabolism of ingested food creates a microbial metabolome that has marked

impact on host health. In this work, PCA allowed the identification of metabolic patterns that are

associated with beneficial health effects (decreased colonic pH and increased antioxidant capacity

values). Compounds associated with different diseases were also identified and associated with

harmful effects (increased pH and decreased AOX). Hierarchical cluster analysis showed that

fermentation extracts of IF isolated from a traditional Mexican breakfast presents a fermentation

time-dependent beneficial metabolic pattern. Further work evaluating the colonic fermentative

patterns of foods combined in complete realistic regimens are needed to understand the impact of

diet on intestinal and general health status.

Acknowledgments: Zamora-Gasga, VM; acknowledge the fellowship to CONACYT- Registration

number: 253795) and Sáyago-Ayerdi, SG; would also like to acknowledge the financial support to

PROMEP –ITTEP-PTC-003.

5. References

AOAC. (1990). Association of Official Agricultural Chemists. Oficial Methods of Analysis. 15th

Edition.

Barbaresko, J., Koch, M., Schulze, M. B., & Nöthlings, U. (2013). Dietary pattern analysis and

biomarkers of low-grade inflammation: a systematic literature review. Nutrition reviews,

71(8), 511-527.

Benzie, I. F., & Strain, J. (1996). The ferric reducing ability of plasma (FRAP) as a measure of

“antioxidant power”: the FRAP assay. Analytical biochemistry, 239(1), 70-76.

Campos-Vega, R., Reynoso-Camacho, R., Pedraza Aboytes, G., Acosta Gallegos, J., Guzman -

Maldonado, S., Paredes-Lopez, O., Oomah, B., & Loarca Piña, G. (2009). Chemical


85

composition and in vitro polysaccharide fermentation of different beans (Phaseolus vulgaris

L.). Journal of Food science, 74(7), T59-T65.

Covington, J. A., Westenbrink, E. W., Ouaret, N., Harbord, R., Bailey, C., O'Connell, N., Cullis,

J., Williams, N., Nwokolo, C. U., & Bardhan, K. D. (2013). Application of a novel tool for

diagnosing bile acid diarrhoea. Sensors, 13(9), 11899-11912.

Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P., & Smith, F. (1956). Colorimetric method

for determination of sugars and related substances. Analytical chemistry, 28(3), 350-356.

Dueñas, M., Sarmento, T., Aguilera, Y., Benitez, V., Mollá, E., Esteban, R. M., & Martín-

Cabrejas, M. A. (2016). Impact of cooking and germination on phenolic composition and

dietary fibre fractions in dark beans (Phaseolus vulgaris L.) and lentils (Lens culinaris L.).

LWT-Food Science and Technology, 66, 72-78.

Flint, H. J. (2016). Gut microbial metabolites in health and disease. Gut microbes, 7(3), 187-188.

Goñi, I., Garcia-Diz, L., Mañas, E., & Saura-Calixto, F. (1996). Analysis of resistant starch: a

method for foods and food products. Food chemistry, 56(4), 445-449.

Hartzfeld, P. W., Forkner, R., Hunter, M. D., & Hagerman, A. E. (2002). Determination of

hydrolyzable tannins (gallotannins and ellagitannins) after reaction with potassium iodate.

Journal of Agricultural and Food Chemistry, 50(7), 1785-1790.

Karatzi, K., Moschonis, G., Barouti, A.-A., Lionis, C., Chrousos, G. P., & Manios, Y. (2014).

Dietary patterns and breakfast consumption in relation to insulin resistance in children. The

Healthy Growth Study. Public health nutrition, 17(12), 2790-2797.

Kettle, H., Louis, P., Holtrop, G., Duncan, S. H., & Flint, H. J. (2015). Modelling the emergent

dynamics and major metabolites of the human colonic microbiota. Environmental

microbiology, 17(5), 1615-1630.


86

Levy, T. S., Ruán, C. M., Castellanos, C. A., Coronel, A. S., Aguilar, A. J., & Humarán, I. M. G.

(2012). Effectiveness of a diet and physical activity promotion strategy on the prevention of

obesity in Mexican school children. BMC public health, 12(1), 1.

Malik, V. S., Willett, W. C., & Hu, F. B. (2013). Global obesity: trends, risk factors and policy

implications. Nature Reviews Endocrinology, 9(1), 13-27.

Montreau, F. (1972). Sur le dosage des composés phénoliques totaux dans les vins par la methode

Folin-Ciocalteau. Connaiss. Vigne Vin., 24, 397-404.

Mullen, W., Borges, G., Donovan, J. L., Edwards, C. A., Serafini, M., Lean, M. E., & Crozier, A.

(2009). Milk decreases urinary excretion but not plasma pharmacokinetics of cocoa flavan-3-

ol metabolites in humans. The American journal of clinical nutrition, 89(6), 1784-1791.

Odegaard, A. O., Jacobs, D. R., Steffen, L. M., Van Horn, L., Ludwig, D. S., & Pereira, M. A.

(2013). Breakfast frequency and development of metabolic risk. Diabetes Care, 36(10), 3100-

3106.

Pérez-Jiménez, J., Arranz, S., & Saura-Calixto, F. (2009). Proanthocyanidin content in foods is

largely underestimated in the literature data: An approach to quantification of the missing

proanthocyanidins. Food Research International, 42(10), 1381-1388.

Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of

antioxidant capacity and phenolics in foods and dietary supplements. Journal of agricultural

and food chemistry, 53(10), 4290-4302.

Raes, K., Knockaert, D., Struijs, K., & Van Camp, J. (2014). Role of processing on

bioaccessibility of minerals: Influence of localization of minerals and anti-nutritional factors

in the plant. Trends in Food Science & Technology, 37(1), 32-41.

Ramírez-Jiménez, A. K., Reynoso-Camacho, R., Tejero, M. E., León-Galván, F., & Loarca-Piña,

G. (2015). Potential role of bioactive compounds of Phaseolus vulgaris L. on lipid-lowering

mechanisms. Food Research International, 76, Part 1, 92-104.


87

Reed, J. D., McDowell, R. T., Van Soest, P. J., & Horvath, P. R. (1982). Condensed tannins: a

factor limiting the use of cassava forage. Journal of the Science of Food and Agriculture,

33(3), 213-220.

Remanan, M., & Wu, J. (2014). Antioxidant activity in cooked and simulated digested eggs. Food

& function, 5(7), 1464-1474.

Russell, W. R., Gratz, S. W., Duncan, S. H., Holtrop, G., Ince, J., Scobbie, L., Duncan, G.,

Johnstone, A. M., Lobley, G. E., & Wallace, R. J. (2011). High-protein, reduced-carbohydrate

weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. The

American journal of clinical nutrition, 93(5), 1062-1072.

Sagar, N. M., Cree, I. A., Covington, J. A., & Arasaradnam, R. P. (2015). The interplay of the gut

microbiome, bile acids, and volatile organic compounds. Gastroenterology research and

practice, 2015.

Saura-Calixto, F., García-Alonso, A., Goni, I., & Bravo, L. (2000). In vitro determination of the

indigestible fraction in foods: an alternative to dietary fiber analysis. Journal of Agricultural

and food Chemistry, 48(8), 3342-3347.

Sáyago-Ayerdi, S. G., Tovar, J., Zamora-Gasga, V. M., & Bello-Pérez, L. A. (2014). Starch

digestibility and predicted glycaemic index (pGI) in starchy foods consumed in Mexico.

Starch/Stärke, 66(1-2), 91-101.

Snelders, J., Olaerts, H., Dornez, E., Van de Wiele, T., Aura, A.-M., Vanhaecke, L., Delcour, J.

A., & Courtin, C. M. (2014). Structural features and feruloylation modulate the fermentability

and evolution of antioxidant properties of arabinoxylanoligosaccharides during in vitro

fermentation by human gut derived microbiota. Journal of Functional Foods, 10, 1-12.

Tabernero, M., Venema, K., Maathuis, A. J., & Saura-Calixto, F. D. (2011). Metabolite production

during in vitro colonic fermentation of dietary fiber: analysis and comparison of two

European diets. Journal of agricultural and food chemistry, 59(16), 8968-8975.


88

Trøseid, M., Ueland, T., Hov, J., Svardal, A., Gregersen, I., Dahl, C., Aakhus, S., Gude, E.,

Bjørndal, B., & Halvorsen, B. (2015). Microbiota dependent metabolite trimethylamine

N oxide is a ssociated with disease severity and survival of patients with chronic heart

failure. Journal of internal medicine, 277(6), 717-726.

US-DHHS and USDA. (2015). United State Department of Health and Human Services and

United State Department of Agriculture. 2015-2020 Dietary Guidelines for Americans. (Vol.

8th ed). Washington, DC.

Walton, C., Fowler, D. P., Turner, C., Jia, W., Whitehead, R. N., Griffiths, L., Dawson, C.,

Waring, R. H., Ramsden, D. B., & Cole, J. A. (2013). Analysis of volatile organic compounds

of bacterial origin in chronic gastrointestinal diseases. Inflammatory bowel diseases, 19(10),

2069-2078.

Wang, Y., Qiu, Z., Zhou, B., Liu, C., Ruan, J., Yan, Q., Liao, J., & Zhu, F. (2015). In vitro

antiproliferative and antioxidant effects of urolithin A, the colonic metabolite of ellagic acid,

on hepatocellular carcinomas HepG2 cells. Toxicology in vitro, 29(5), 1107-1115.

Yang, J., & Rose, D. J. (2016). The impact of long-term dietary pattern of fecal donor on in vitro

fecal fermentation properties of inulin. Food & Function.

Zamora-Gasga, V., Montalvo-González, E., Loarca-Piña, M., Chacón-López, M., Tovar, J., &

Sáyago-Ayerdi, S. (2017). Dietary patterns, nutritional profile, and body mass index in

Mexican schoolchildren: A cross-sectional study. Archivos Latinoamericanos de Nutrición.

Zamora-Gasga, V. M., Loarca-Piña, G., Vázquez-Landaverde, P. A., Ortiz-Basurto, R. I., Tovar,

J., & Sáyago-Ayerdi, S. G. (2015). In vitro colonic fermentation of food ingredients isolated

from Agave tequilana Weber var. azul applied on granola bars. LWT-Food Science and

Technology, 60(2), 766-772.


89

Table SI. Volatile compounds characterization of in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from breakfast menus (MM-D; Modified Mexican diet, TM-D;

Traditional Mexican Diet, AM-D; Alternative Mexican diet) analyzed by SPME–GC/MS.

ID RT Volatil Name Chemical Group

T12 T24 T48 T72 VC (%) Blank Raffinos

e MM-D TM-D AM-D Blank Raffinose MM-D TM-D AM-D Blank Raffinos

e MM-D TM-D AM-D Blank Raffinose MM-D TM-D AM-D

1 6.044 Trimethylamine Amines 37.20 0.00 24.23 16.08 21.81 24.96 0.00 26.32 12.88 49.53 33.73 0.00 12.83 8.26 27.33 61.47 0.00 18.51 18.56 28.48 77.79

2 8.475 Ethanol Alcohols and polyols 57.50 73.02 8.19 67.40 3.77 29.88 159.40 94.63 51.17 116.34 0.00 177.80 16.00 49.58 20.82 110.80 122.17 34.39 78.81 0.00 83.20

3 10.985 Trichloromethane Alkyl halides 21.18 12.36 9.37 8.56 9.41 14.18 15.55 10.50 19.30 17.46 0.00 0.00 0.00 0.00 0.00 41.05 11.72 13.80 17.86 14.31 82.71

4 12.265 Butanoic acid, methyl ester Fatty Acyls 6.87 12.15 43.68 42.09 21.33 0.00 8.26 159.80 150.40 33.66 6.69 10.52 177.65 255.49 69.02 13.14 16.81 154.62 350.23 127.92 117.57

5 14.404 Disulfide, dimethyl Organic disulfides 0.00 0.00 11.06 38.50 26.21 0.00 0.00 29.38 23.27 30.45 4.71 0.00 3.50 4.29 15.48 0.00 0.00 7.78 26.72 3.72 114.80

6 16.547 Pentanoic acid, methyl ester Fatty Acyls 6.97 0.00 71.48 102.68 78.62 4.23 3.80 327.28 311.20 171.65 13.16 4.52 312.07 414.73 196.23 17.02 4.32 406.83 637.63 395.29 109.26

7 19.273 Pentanoic acid, ethyl ester Fatty Acyls 0.00 0.00 6.81 7.80 12.07 0.00 0.00 33.23 25.32 12.75 4.16 0.00 24.54 30.05 13.75 0.00 0.00 19.06 49.35 17.15 108.82

8 20.744 Hexanoic acid, methyl ester Fatty Acyls 11.87 4.32 23.72 36.38 16.56 10.76 0.00 183.41 149.71 31.89 63.35 5.42 250.85 1720.24 576.83 57.49 13.03 1351.8

4 2292.4

5 864.02 172.62

9 23.233 Hexanoic acid, ethyl ester Fatty Acyls 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.90 6.83 0.00 4.60 0.00 9.27 69.40 14.81 0.00 0.00 43.55 89.11 60.85 175.92

10 24.813 Heptanoic acid, methyl ester Fatty Acyls 0.00 0.00 0.00 64.33 0.00 34.70 0.00 376.35 362.08 0.00 297.16 0.00 759.18 4367.54

3657.04 250.51 0.00 4653.8

8 7933.9

4 5154.6

8 168.99

11 26.242 Dimethyl trisulfide Organic trisulfides 0.00 0.00 117.05 442.44 441.73 0.00 0.00 291.65 179.48 212.46 15.39 0.00 0.00 10.44 115.96 0.00 0.00 0.00 50.80 0.00 155.94

12 26.247 Acetic Acid Organic acid 6.05 176.44 2.39 15.30 8.52 0.00 381.06 30.83 10.11 40.38 7.52 451.15 16.50 18.37 19.00 0.00 393.48 13.55 4.09 11.60 182.91

13 26.399 Pyridine, 2,4,6-trimethyl- Pyridines and derivatives 66.24 0.00 0.00 36.24 25.00 60.69 0.00 11.81 12.60 7.41 61.69 0.00 28.83 24.83 41.47 207.55 0.00 28.07 42.56 50.65 131.15

14 26.980 Heptanoic acid, ethyl ester Fatty Acyls 0.00 0.00 4.94 8.25 9.52 0.00 0.00 40.30 25.72 15.06 23.28 0.00 34.14 238.07 96.39 12.34 0.00 189.39 368.41 272.10 163.27

15 28.571 Octanoic acid, methyl ester Fatty Acyls 31.77 7.97 566.71 920.55 593.61 49.08 7.51 1522.92

1821.15 734.62 76.73 8.80 1179.3

3 1943.9

3 1642.2

3 85.87 11.65 2063.50

3163.46

1979.41 102.20

16 29.758 Propionic Acid Organic acid 4.19 59.68 0.00 6.88 0.00 0.00 89.01 39.63 33.19 41.67 1.61 117.83 23.64 2.11 22.05 0.00 138.52 0.00 0.00 0.00 144.27

17 30.670 Octanoic acid, ethyl ester Fatty Acyls 4.18 0.00 19.21 30.30 35.46 4.68 0.00 170.39 83.29 40.55 6.90 0.00 48.70 86.91 38.52 9.40 0.00 65.73 137.07 63.42 112.90

18 31.626 Benzene, 1,3-bis(1,1-dimethylethyl)- Benzene and substituted derivatives 29.61 62.27 31.49 48.19 48.37 23.56 68.51 76.31 75.19 81.71 25.94 64.67 60.74 87.12 62.61 46.70 70.31 73.53 112.68 104.23 39.01

19 32.327 Nonanoic acid, methyl ester Fatty Acyls 13.34 0.00 116.39 155.26 183.59 12.96 0.00 203.80 239.01 189.41 12.22 0.00 141.92 194.69 321.22 20.20 0.00 303.48 280.34 202.30 87.54

20 33.352 Butiric Acid Organic acid 0.78 25.71 0.75 1.51 0.67 0.44 36.59 3.38 4.74 4.53 0.77 52.34 3.45 1.83 3.81 0.00 60.85 0.00 0.24 1.41 180.89

21 34.563 Nonanoic acid, ethyl ester Fatty Acyls 0.00 0.00 10.91 15.18 30.26 0.00 0.00 26.52 19.07 20.41 0.00 0.00 13.10 18.95 16.19 0.00 0.00 17.84 26.25 0.00 101.25

22 35.387 Acetophenone Benzene and substituted derivatives 33.95 0.00 85.37 34.17 98.99 21.14 0.00 108.65 0.00 102.57 32.22 0.00 136.65 0.00 127.21 44.36 0.00 139.74 42.49 116.66 92.89

23 35.572 Benzaldehyde, 4-methyl- Benzene and substituted derivatives 0.00 42.67 0.00 0.00 0.00 0.00 57.85 0.00 0.00 0.00 0.00 88.71 0.00 0.00 0.00 0.00 39.16 0.00 0.00 0.00 219.73

24 35.924 Butanoic acid, 3-methyl- Fatty Acyls 0.00 88.26 0.00 0.00 0.00 0.00 159.96 90.99 82.31 56.95 9.25 135.71 18.34 0.00 57.56 0.00 224.96 0.00 0.00 53.32 132.33

25 37.004 Decanoic acid, methyl ester Fatty Acyls 47.39 30.68 5726.77

5042.27

5764.08 45.75 47.56 6795.2

3 6546.7

5 4911.7

0 45.16 48.88 4594.18

4488.24

8579.37 97.90 58.02 9226.7

4 4938.2

0 5803.1

5 88.51

26 39.951 Decanoic acid, ethyl ester Fatty Acyls 0.00 0.00 332.79 307.44 344.83 0.00 0.00 626.55 338.67 441.59 0.00 0.00 340.84 318.17 343.73 0.00 0.00 399.27 430.54 113.88 93.33

27 40.452 Pentanoic acid Lipids and lipid-like molecules 0.00 0.02 0.00 6.38 0.03 0.00 0.00 0.00 5.59 0.00 0.04 0.00 0.41 6.92 0.00 0.00 0.00 0.06 8.43 0.00 203.12

28 40.987 Methyl 8-methyl-decanoate Fatty Acyls 0.00 0.00 11.81 15.64 25.66 0.00 0.00 19.34 20.84 17.26 0.00 0.00 16.57 23.77 0.00 0.00 0.00 25.69 0.00 24.42 107.67

29 43.242 Undecanoic acid, methyl ester Fatty Acyls 0.00 0.00 161.03 124.58 261.70 0.00 0.00 176.46 153.70 227.89 0.00 0.00 125.06 113.15 415.44 0.00 0.00 253.69 162.73 183.11 99.94

30 45.763 Decanoic acid, propyl ester Fatty Acyls 0.00 0.00 0.00 27.30 13.00 0.00 0.00 33.81 0.00 29.10 0.00 0.00 31.62 33.52 49.66 0.00 0.00 75.48 77.18 0.00 137.16

31 46.644 Hexanoic acid Fatty Acyls 20.53 150.65 0.00 0.00 0.00 25.80 179.41 0.00 0.00 0.00 180.54 206.85 12.97 124.38 279.26 15.82 441.89 35.87 253.72 0.00 130.45

32 46.897 Benzenepropanoic acid, methyl ester Fatty Acyls 0.00 0.00 27.66 25.72 27.00 0.00 0.00 76.60 75.74 47.32 0.00 0.00 117.21 148.41 99.45 33.71 0.00 148.44 171.44 110.48 105.39

33 48.546 Dodecanoic acid, methyl ester Fatty Acyls 114.10 26.53 3150.45

2591.42

3732.87 83.11 26.86 3350.5

5 2475.3

7 3611.2

9 65.66 24.89 2496.96

2137.17

3555.92 135.18 22.67 4175.4

9 2915.9

0 2550.8

8 84.96


90

34 50.311 Heptanoic acid Fatty Acyls 0.00 99.11 0.00 0.00 0.00 0.00 23.70 0.00 0.00 17.56 223.48 85.92 0.00 309.19 221.85 80.82 163.19 0.00 748.87 218.46 164.04

35 50.449 Benzenemethanol, 4-methyl- Benzene and substituted derivatives 18.61 26.82 11.01 10.53 19.30 19.45 0.00 17.91 0.00 21.52 14.88 32.50 40.12 38.61 25.68 57.36 0.00 15.87 36.43 0.00 75.47

36 50.589 Dodecanoic acid, ethyl ester Fatty Acyls 11.01 0.00 114.55 106.14 111.59 7.76 0.00 182.77 109.46 145.00 0.00 0.00 96.40 134.26 133.24 8.55 0.00 115.48 158.58 122.06 83.76

37 51.104 Methyl 10-methyl-dodecanoate Fatty Acyls 0.00 0.00 13.35 10.94 20.95 5.12 0.00 18.72 12.89 20.72 0.00 0.00 15.78 15.72 28.45 12.15 0.00 33.37 0.00 23.34 91.70

38 51.372 Phenol Benzene and substituted derivatives

1139.60 314.26 673.60 707.71 813.83 922.3

5 609.44 973.59 884.62 1180.02

1407.16 404.40 1124.6

9 865.91 1289.40

1998.63 1171.46 1348.8

3 1412.7

5 1683.2

8 39.84

39 52.143 Tridecanoic acid, methyl ester Fatty Acyls 8.52 0.00 61.01 53.97 70.22 6.28 0.00 63.93 67.69 62.55 10.92 0.00 55.88 49.30 104.26 9.45 0.00 84.25 64.38 54.01 80.22

40 53.496 Phenol, 4-methyl- Benzene and substituted derivatives 98.09 73.24 86.32 68.48 78.07 103.0

6 66.73 103.76 93.14 103.01 294.09 77.57 136.25 158.60 169.34 422.56 89.16 225.63 453.71 345.76 74.66

41 53.717 Octanoic Acid Fatty Acyls 15.72 34.27 0.00 13.52 7.65 17.19 42.81 21.93 15.73 18.94 42.32 48.39 33.72 25.24 0.00 0.00 66.99 68.98 40.99 40.42 73.76

42 55.739 Methyl tetradecanoate Fatty Acyls 136.93 20.07 927.09 902.18 683.39 96.66 22.51 1015.69

1236.79 883.32 74.96 30.40 1123.4

8 1005.3

5 1483.7

7 141.46 28.80 1287.30

1060.94 886.56 79.18

43 55.989 Methyl myristoleate Fatty Acyls 38.87 5.32 225.70 193.58 324.91 26.14 6.71 217.00 206.21 270.32 17.40 0.00 147.48 96.05 296.95 28.24 0.00 251.75 128.26 123.92 85.41

44 56.328 Nonanoic acid Fatty Acyls 0.00 19.79 0.00 0.00 0.00 0.00 13.81 0.00 0.00 0.00 0.00 22.75 0.00 0.00 18.12 15.61 21.33 20.82 0.00 0.00 142.48

45 58.068 Methyl 13-methyltetradecanoate Fatty Acyls 8.69 0.00 67.33 55.68 0.00 12.27 0.00 67.26 83.30 65.38 10.45 0.00 0.00 60.90 82.14 16.88 0.00 122.52 85.41 37.74 99.07

46 58.489 Pentadecanoic acid, methyl ester Fatty Acyls 13.05 0.00 24.79 24.77 21.90 14.22 0.00 28.54 33.22 72.07 11.88 0.00 42.01 37.73 45.06 14.03 0.00 35.62 33.91 45.13 76.03

47 58.836 n-Decanoic acid Fatty Acyls 0.00 22.88 0.00 0.00 15.29 0.00 28.85 0.00 0.00 0.00 0.00 29.40 42.41 25.29 0.00 0.00 22.53 34.05 0.00 65.96 132.00

48 59.506 Phenol, 2,4-bis(1,1-dimethylethyl) Benzene and substituted derivatives 19.88 19.62 12.77 0.00 14.61 22.92 22.86 19.34 14.05 24.13 19.96 32.69 20.02 30.23 22.25 50.43 20.72 0.00 20.14 21.49 52.10

49 60.928 Indole Indoles and derivatives 573.25 136.14 684.85 563.83 659.64 415.94 113.85 635.20 538.83 664.10 366.17 97.76 483.55 425.13 640.96 446.87 130.70 543.16 471.55 571.74 42.54

50 62.524 9-Hexadecenoic acid, methyl ester, (Z)- Fatty Acyls 43.82 14.74 189.49 25.81 28.16 4.45 0.00 0.00 82.13 40.06 33.83 0.00 0.00 22.14 58.69 44.66 0.00 0.00 36.16 36.69 131.31

51 62.715 Methyl hexadec-9-enoate Fatty Acyls 0.00 0.00 0.00 144.68 181.80 34.73 12.88 101.23 249.57 240.02 0.00 0.00 143.58 100.90 292.95 48.04 24.07 0.00 122.98 142.12 103.18

52 63.095 Hexadecanoic acid, methyl ester Fatty Acyls 41.48 16.76 0.00 90.16 73.66 33.50 16.21 174.68 179.37 135.53 41.36 24.34 239.29 235.56 265.63 48.85 37.08 0.00 194.92 207.15 88.45

53 64.859 Hexadecanoic acid, ethyl ester Fatty Acyls 0.00 0.00 0.00 12.83 39.24 0.00 0.00 45.36 40.64 36.12 0.00 0.00 49.92 54.66 40.84 0.00 0.00 45.75 35.07 46.10 99.33

54 67.496 9-Octadecenoic acid, methyl ester, (E)- Fatty Acyls 366.22 0.00 560.31 320.40 399.65 184.0

6 161.29 552.22 706.60 717.44 290.24 0.00 782.84 480.17 952.85 219.72 185.38 578.32 470.42 590.14 61.02

55 68.442 Ethyl Oleate Fatty Acyls 0.00 0.00 102.09 0.00 98.02 0.00 0.00 131.61 169.89 105.33 0.00 0.00 135.80 0.00 159.67 0.00 0.00 0.00 253.77 205.64 123.89

ID; Identification, RT; retentiton time, VC; Variation coefficient


91

7.3 Fermentación colónica humana in vitro de la fracción indigestible aislada de menús

consumidos durante la comida: Impacto en el perfil de los metabolitos intestinales

Resumen

El tracto intestinal humano es el hogar de un ecosistema diverso y esencial de bacterias, conocidas

como micrbiota intestinal. Hoy en día, las interacciones huésped-microbiota se consideran un

componente fundamental subyacente a la salud y la enfermedad [1]. Así, la microbiota intestinal

genera una amplia gama de compuestos bioactivos únicos que son el resultado de la

transformación bacteriana de componentes dietéticos indigestibles. Algunos metabolitos

bacterianos (ácidos grasos de cadena corta, AGCC) desempeñan un papel vital en el

mantenimiento de la inmunidad del huésped y la homeostasis metabólica [2]. Sin embargo, los

metabolitos asociados a la disbiosis (trimetilamina, TMA), se consideran factores causales en las

enfermedades cardiovasculares [3]. La espectrometría de masas se ha convertido en una poderosa

herramienta para estudios de perfil metabólico debido a su amplio rango dinámico, capacidades

cuantitativas reproducibles y su capacidad para analizar muestras de complejidad molecular

significativa [4]. La formación de metabolitos microbianos está fuertemente influenciada por la

composición intestinal y la ingesta dietética, en particular de los carbohidratos, proteínas y grasas

dietéticas no digeribles [6]. Las enfermedades inflamatorias relacionadas con la dieta se han

convertido en serios problemas de salud pública para la sociedad occidental, incluyendo a México.

Las alteraciones en la dieta han dado lugar a un cambio en la microbiota del intestino que se ha

demostrado desempeña un papel importante en trastornos complejos como las enfermedades

inflamatorias intestinales [9]. Hipoteticamente pensamos que los metabolitos de bajo peso

molecular producidos por la microbiota intestinal influyen en las actividades biológicas en el colón

y que a su vez dependen de los componentes no digeribles de los alimentos consumidos durante

las comidas. Por lo tanto, en el presente capítulo se investigó el papel del perfil de metabolitos

microbianos sobre la actividad antioxidante y los valores de pH de durante la fermentación in vitro

de la fracción indigestible aislada de alimentos consumidos por escolares durante la comida


92

In vitro human colonic fermentation of indigestible fraction isolated from lunch menus:

Impact on the gut metabolites and antioxidant capacity

Abstract

Scope:

We tested the hypothesis that metabolites produced by intestinal microbiota are influenced by

indigestible fraction (IF) isolated from three lunch menus: Modified Mexican Lunch (MM-L),

Traditional Mexican Lunch (TM-L) and Alternative Mexican Lunch (AM-L).

Methods and results:

IF-Lunch menus were isolated after withstanding in vitro gastrointestinal digestion and total

soluble polyphenols (TSP), condensed tannins (CT) hydrolysable tannins and antioxidant capacity

(AOX: DPPH and FRAP) were evaluated. AOX, pH and bacterial metabolites profile changes

were monitored during in vitro colonic fermentation. Differences between lunch menus were

found for IF, TSP, CT and FRAP values (p<0.05). TM-L had the highest TSP and CT content

(0.84 and 1.89 g/100g db). TM-L and AM-L showed the highest FRAP activity. Changes on pH

and AOX during fermentation were time and substrate dependent (p<0.05). Butyric acid levels

showed no significant differences between lunch menus and blank (p>0.05). Besides, 57 volatile

compounds were detected by SPME-GC-MS. Trimethylamine, phenol and indole were associated

with increased pH and decreased AOX.

Conclusion:

Our study represents a new contribution on the potential effects of food habits on bacterial

metabolites production. Only through food complex matrix study will we improve our

understanding of the effects of diet on colon health promotion.


93

Keywords:

Lunch Menus, Indigestible fraction, Antioxidant capacity, Gut metabolites.

Graphical abstract

1

23

4

5

67

8

910

11

121314

15

16

1718

19

2021

22

23

2425 26

27

28

29

30

31

32

3334

35

36

3738

39

40

4142

43

44

45

4647

48

49

50

51

52

5354

555657

-1.0 -0.5 0.0 0.5 1.0Metabolic profile A

Explained Variance: 34.81%

-1.0

-0.5

0.0

0.5

1.0

Met

aboli

c pro

file

BE

xpla

ined

Var

iance

: 17.1

8%

1

23

4

5

67

8

910

11

121314

15

16

1718

19

2021

22

23

2425 26

27

28

29

30

31

32

3334

35

36

3738

39

40

4142

43

44

45

4647

48

49

50

51

52

5354

555657

12-Blank

12-Raffinose

12-MM-L

12-TM-L

12-AM-L

24-Blank

24-Raffinose

24-MM-L

24-TM-L

24-AM-L

48-Blank

48-Raffinose

48-MM-L

48-TM-L

48-AM-L

72-Blank

72-Raffinose

72-MM-L

72-TM-L

72-AM-L

-10 -8 -6 -4 -2 0 2 4 6 8 10Metabolic profile A

-4

-2

0

2

4

6

Met

abo

lic

pro

file

B

12-Blank

12-Raffinose

12-MM-L

12-TM-L

12-AM-L

24-Blank

24-Raffinose

24-MM-L

24-TM-L

24-AM-L

48-Blank

48-Raffinose

48-MM-L

48-TM-L

48-AM-L

72-Blank

72-Raffinose

72-MM-L

72-TM-L

72-AM-L

Dietary Lunch indigestible fraction - Gut microbiota

Bacterial metbolites profile and AOX


94

1 Introduction

The human intestinal tract is home to a diverse and essential ecosystem of microbes. Commensal

microorganisms establish the intestinal metabolic system by expressing genes that outnumber ours

by more than 100-fold [1] Thus, gut microbiota generates a wide range of unique bioactive

compounds which are the result of bacterial transformation of indigestible dietary components.

Some bacterial metabolites (short-chain fatty acids, SCFA) play a vital role in maintenance of host

immunity and metabolic homeostasis [2]. However, dysbiosis-associated metabolites (e.g.

trimethylamine), are considered causative factors in cardiovascular diseases [3]. The presence of

these compounds, retained in the non-solid phase of faeces that has led to the use of faecal water to

determine the potentially toxic environment to which the lumen is exposed [4, 5]. Mass

spectrometry has become a powerful tool for metabolic profile studies due to its wide dynamic

range, reproducible quantitative capabilities and its ability to analyze samples of significant

molecular complexity [6]. Data analysis involves the mining of high-density spectral data

generated from biofluids such as blood plasma or fecal water measured against a set of reference

biochemical signatures using computer pattern recognition algorithms [7]. Bacterial metabolite

formation is strongly influenced by gut composition and dietary intake [8]. Diet-related

inflammatory conditions such as obesity, type 2 diabetes, cardiovascular disease, chronic kidney

disease and autoimmune diabetes have become in serious public health problems for Western

society, including Mexico [9]. The modern western diet is one environmental factor that has

changed with increased overall caloric intake, and changes in the relative amounts of dietary

components [10]. These dietary alterations have resulted in a shift in the composite gut microbiota,

from both a structure and function perspective, that have been shown to play a role in complex

disorders such as inflammatory bowel diseases [11]. We tested the hypothesis that low molecular

weight metabolites produced by intestinal microbiota is influenced by the indigestible components

of whole foods consumed at Mexican lunch. Additionally, we investigated the role of microbial

metabolites profile on antioxidant activity and pH values of extracts at different fermentation

times.



95

2.1 Lunch food preparation

Data sources on dietary patterns and food frequency consumption at lunch were obtained from a

nutritional survey carried out on eleven public schools in a Western city of Mexico (Tepic, Nayarit

State). Frequently consumed foods in each DP were used to create three lunch menus. Lunch

menus were named and comprised by the following foods; Modified Mexican Lunch (MM-L):

Soda (350 mL), three corn tortillas (90 g), refried beans (60 g) and roast beef (96 g). Traditional

Mexican Lunch (TM-L): three corn tortillas (90 g), refried beans (86 g), water (350 mL), roast

beef (96 g) and chambray onion (18 g). Alternative Mexican Lunch (AM-L): three fish ceviche

tostadas and soda (300 mL). Individual foods were purchased in a local supermarket and were

prepared in the laboratory kitchen according to traditional customs of the region. After prepared,

each menu, it was homogenized in a food processor (NB-101B, Nutribullet, China), frozen (-80

°C), freeze-dried (FreeZone 6, Labconco, USA), grounded, sieved with a mesh size of 500 µm,

and stored at -20 °C until analysis. Each menu preparation was performed by triplicate. Moisture

content of complete lunch menus, was analyzed according to AOAC [12] 925.10 methods.


A digestion procedure that mimicking the physiological situation in the upper tract (stomach and

small intestine) was utilized for indigestible fraction (IF) quantification [13]. In order to increase

the amount of isolated IF the follow amendments were realized. Briefly, 9 g of freeze-dried

samples was incubated with: a) pepsin (0.6 mL of a 300 mg/mL solution in 0.2 M HCl-KCl buffer,

pH 1.5, 40 °C, 1 h, P-7000, Sigma Aldrich, USA), b) pancreatin (3 mL of a 5 mg/ mL solution in

0.1 M phosphate buffer; pH 7.5, 37 °C, 6 h, P-1750, Sigma Aldrich, USA), c) a-amylase (3 mL of

a 120 mg/mL solution in 0.1 M Tris-maleate buffer, pH 6.9, 37 °C, 16 h, A-3176, Sigma Aldrich)

and amyloglucosidase 300 µL (pH 4.5, 60 °C 45 min, A-9913, Sigma Aldrich). Sample were

transferred into dialysis tubes (D9527 -30.48 m avg. Flat width 43 mm, 14000 Da, Sigma Aldrich)

and dialyzed against water for 48 h at 25 °C to remove digested compounds [14]. Total IF

(Dialysis retained) was collected, freeze-dried, milled (IKA M20, USA) and sieved using a 500-

micron mesh, and placed in bags with seal, for storage at -20 ° C until subsequent analyses.



96

Samples (250 mg) of total IF isolated from lunch menus were extracted by aqueous-organic

solution [15]. Total soluble polyphenols (TSP) in extractable fraction were determined with the

Folin–Ciocalteu’s reagent [16] using a 96-well microplate reader (Biotek, Synergy HT, Winooski

VT, USA) with Gen5 software, and the results were expressed in g of gallic acid equivalents (g

GAE/ 100g IF). The residues of the extraction were treated for non-extractable polyphenols

quantification (Condensed tannins and hydrolyzable polyphenols). Condensed tannins (CT) were

quantified and results were expressed as CT equivalents/ 100 g IF, using a carob pod (Ceratonia

siliqua) proanthocyanidin standard [17]. Hydrolyzable polyphenols (HP) were evaluated by a

previously reported methodology [18] and results were expressed as g GAE/100 g IF.


AOX was evaluated on the extractable fraction obtained before, and methodologies were slightly


1,1-Diphenyl-2-picryl hydrazyl (DPPH) antiradical activity assay was evaluated [19] and results

were expressed as Trolox (6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic) equivalent (TE;

mmol /g IF db). Ferric reducing antioxidant power (FRAP) assay was performed [20]. Results are

expressed as mmol of Trolox equivalents (TE) /g IF db.


Total IF isolated from three lunch menus were fermented in a batch culture system with pre-

conditioned nutritive medium and under strict anaerobic conditions at 37 °C [21]. In parallel, two

different controls were conducted under the applied conditions: a) Raffinose was incubated in

medium with faeces inoculum as a fermentable sugar reference, and b) the faecal suspension was

incubated without substrate as a negative control. All incubations were performed in triplicate and

samples and controls were collected at 12, 24, 48 and 72 h. The tubes obtained at each time of

fermentation were centrifuged (Hermle Z 323 K; Wehingen, Germany) (3500 × g ,15 min, 4 °C).

Supernatants were divided into two parts; metabolite profile analysis and antioxidant capacity

assays and were immediately stored at −80 °C until analysis.



97

Supernatants (500 mg) were placed into a 20 mL vial sealed with a magnetic cap with a poly-tetra-

fluoro-ethylene (PTFE)/silicon septum. HS-SPME method was used for the extraction and

concentration of volatile compounds [22]; incubation with polydimethylsiloxane-divinylbenzene-

carboxen “PDMS/DVB/CAR” fiber for 120 min, 250 rpm at 45 °C; the fiber was inserted into the

injection port of the GC system for thermal desorption (240 °C for 10 min) for GC/MS analysis.

The extractions were performed in triplicate. Five runs with twelve vials / run were performed and

the tests order was completely randomized. The volatile constituents were analyzed with an

Agilent 5975C VL mass selective detector coupled to an Agilent 7890A gas chromatograph

(Agilent Technologies, Inc., Santa Clara, CA), equipped with a DB-5MS capillary column (60 m

X 250 µm X 0.25 µm; Agilent). Sample quantification was obtained by means of acetic, propionic

and butyric acid standard curves. Tentative identification of the volatile components was done

comparing the mass spectra of the samples with the data system library MSD ChemStation

software (Agilent G1701EAversion E.02.00.493). Relative concentration of all fermentation

metabolites versus acetic acid as internal standard was calculated and the results were expressed in

mmol L-1.

Antioxidant capacity (AOS) during in vitro colonic fermentation

Changes in the AOX of the sample extracts during in vitro colonic fermentation at 12, 24 and 48 h

were measured by the methods described above.


Results are expressed as mean ± SEM (n=3). Univariate testing of differences for lunch menus

was processed by ANOVA/Fisher's Least Significant Difference test. Besides, Multivariate

analysis was applied for metabolites profile interpretation. Principal component analysis (PCA)

was used to determine patterns in relative metabolites production. Finally, Pearson´s correlation

coefficients were used to evaluate the relationship between individual metabolites and changes in

pH and AOX during in vitro colonic fermentation. All analyzes were performed using

STATISTICA software, version 10.0 (StatSoft. Inc. 1984–2007, Tulsa, OK, USA). A significance

level of α = 0.05 was used in data analysis.


98

3. Results

3.1 Content and nutritional composition of indigestible fraction (IF) in lunch menus

The results shown in Table 1 indicate that the moisture content in TM-L and AM-L were higher

than MM-L (p<0.05), which follows that in these food mixtures are less solid components

available for gastrointestinal digestion. The increase in moisture content in TM-L could be

attributed to solubilisation of components by water addition and consequently, as a dilution effect

[23]. Meanwhile, AM-L was prepared with raw fish and raw vegetables that could increase the

amount of water and decrease the amount of soluble solids by the same dilution effect. On the

other hand, total IF content is the fraction resistant (comprising dietary fiber, resistant starch,

resistant protein, resistant lipids, certain polyphenols, and other associated compounds) to

gastrointestinal digestion and serves as substrate for the gut microbiota [13]. In this parameters,

significant differences (p<0.05) between in MM-L (5.35 g/100g wb) and TM-L (5.46 g/100g wb)

vs AM-L (2.39 g/100g wb) were found (Table 1). The main sources of dietary fiber are cereals,

legumes such as beans and lentils, vegetables, and fruits that are considered to form an important

part of a balanced healthy diet [24]. Insoluble IF was the main fraction of total IF, since it

represented from 89% in MM-L to 92% in TM-L (Table 1). Disruption of the natural matrix or

types of microstructural changes in lunch menus imparted by processing and during food ingestion

could be relevant in matrix-nutrient interactions, and their effect on composition and content of

indigestible fraction [25]. The soluble IF constituted 42% in AM-L (0.99g/100g wb) and these

values are the highest found for the three lunch menus (p<0.05). Soluble IF is associated with

viscous polysaccharides such as pectin and gums, which decrease assimilation of nutrients in

particular has been observed that these components have a postprandial glucose-lowering effect

and promote changes in the gut microbiota composition [26, 27].


At present, there is scarce literature on phenolic compounds associated with indigestible fraction.

Because of the role of phenolic compounds in food complex matrix, total soluble polyphenols

(TSP), hydrolysable tannins (HT), and condensed tannins (CT) were studied in IF of lunch menus


99

and the results are shown in Table 1. The HT were the most abundant phenolic compounds in all

samples followed by CT and TSP.


100


the indigestible fraction (IF) isolated from lunch menus1

Parameter Menus2

MM-L TM-L AM-L

Moisture content (g/100g menu )

IF (g/100g menu wb) 74.30 ± 0.39a 81.74 ± 0.13b 82.90 ± 0.14c

Insoluble IF 4.79 ± 0.29a 5.07 ± 0.38a 1.39 ± 0.12b

Soluble IF 0.56 ± 0.03a 0.39 ± 0.04a 0.99 ± 0.09b

Total IF 3 5.35 ± 0.32a 5.46 ± 0.42a 2.39 ± 0.20b

IF Antioxidant Compounds (g/ 100g IF db)

Total soluble polyphenols 0.75 ± 0.01a 0.84 ± 0.01b 0.68 ± 0.01c

Condensed Tannins 1.41 ± 0.06a 1.89 ± 0.05b 0.70 ± 0.02c

Hydrolyzable polyphenols 2.24 ± 0.13a 1.99 ± 0.05a 2.18 ± 0.00a

IF Antioxidant Capacity (mmol TE/g IF db)

DPPH ND ND ND

FRAP 8.05 ± 0.25a 10.83 ± 0.24b 10.28 ± 0.12b 1Values are mean ± standard error (n = 3); Wet basis (wb); Dry basis (db); No Detected (ND).


MM-L: Modified Mexican Lunch, TM-L: Traditional Mexican Lunch, AM-L: Alternative

Mexican Lunch. 3 Total IF = Sum of soluble IF + insoluble IF.

The TSP, expressed as gallic acid equivalent, ranged from 0.68 to 0.84 g/100g sample db, with

statistically significant differences (p<0.05) between all the samples. TM-L showed the highest

TPC followed by MM-L and AM-L. The amount of beans in TM-L is 43% higher than in MM-L,

so this legume could be responsible for the observed differences. Hydroxybenzoic compounds

(protocatechuic acid, p-Hydroxybenzoic aldehyde and p-Hydroxybenzoic acid) were the most

abundant phenolic class detected (approximately 90% of TSP) in dietary fiber of cooked beans


101

[28]. With reference to CT, the highest (p<0.05) content was found in TM-L (1.89g/100g db),

followed by MM-L (1.41 g/100g db). In AM-L, CT (proanthocyanidins) were not detected. Fruits

have been found to be major sources of proanthocyanidins in the diet, besides of legumes and

cereals such as sorghum and barley, but they are not detectable in corn [29] and Mexican

vegetables[30]. Hydrolyzable polyphenols were a quantitatively important fraction of polyphenols

in all samples (approximately 50% for MM-L and TM-L and up to 75% in the case of the AM-L).

Quercetin has been reported as the individual constituent of HP identified in vegetables as onion

and cucumber consumed in Europe [31] and which were also used in AM-L preparation. In the

colon, the proanthocyanidins are catabolized by the gut microflora into a series of beneficial

metabolites such as phenyl valerolactone, phenylacetic and phenylpropionic acids [32].

Meanwhile, HT are metabolized to gallic acid, pyrogallol, phloroglucinol and finally to acetate and

butyrate via several bacterial enzymes [33] TSP linked to the IF were released in the aqueous-

organic extraction and it showed FRAP activity but DPPH activity was not observed (Table 1).

FRAP activity was higher in IF extracts isolated for TM-L and AM-L (10.83 and 10.28 mmol

TE/g IF db) and lower in MM (8.05 mmol TE/g IF db). These results suggest that antioxidant

mechanism in extracts can be as chelating agent and not as a free radical neutralizer [34]. In this

sense, polyphenols present in a mixture can interact with indigestible components (proteins, lipids

and carbohydrates), and these interactions can affect the total antioxidant capacity of extracts [35].

3.3 Changes in pH and antioxidant capacity from in vitro colonic fermentation extracts

The progress of the fermentation was followed by measuring pH changes in each sample for 72 h

(Figure 1). Raffinose control showed a significant drop (p < 0.05) of at least 3 units in their pH

values after 72 h, which is most probably caused by the fermentation end products (mainly SCFA).

After fermentation, TM-L showed a decrease in pH value (p<0.05), but the magnitude of the

reduction in pH value was markedly different depending on fermentation time. The highest pH

difference (pHto – pHt) was 0.29 unit found at 48 h. A significant increase in pH values during

MM-L and AM-L fermentation (similar to control without substrate) was observed. Differential in

pH between 0 and 72 h of fermentation was -0.11 for MM-L and -0,05 for AM-L menu. The pH of

the colon is potentially a key factor in determining the species composition and metabolic outputs

of the human intestinal.


102

Figure 1 Changes in a) pH kinetic plot, b) DPPH Antiradical activity plot and c) FRAP chelating activity plot in the extracts

during in vitro colonic fermentation from (–) blank, (··♦··) raffinose, and indigestible fraction isolated from lunch menus: (-■-)

Modified Mexican Lunch, MM-L, (-▲-) Traditional Mexican Lunch, TM-L, and (-●-) Alternative Mexican Lunch, AM-L at

different at different fermentation times, Values are means ± SEM (n=3). *Significant difference using Two-way

ANOVA/Fisher's LSD test (Samples ×Time interaction, p<0.05). For DPPH and FRAP, the blank was subtracted of the

samples.

Fermentation Time (h)

0 12 24 48 72

DP

PH

Ant

ioxi

dant

Act

ivity

(mM

TE

/g s

ubst

rate

db)

0

50

100

150

200

Fermentation time (h)

0 12 24 48 72

FR

AP

Ant

ioxi

dant

Act

ivity

(m

M T

E /g

sub

stra

te d

b)

0

5

10

15

20

25


0 12 24 48 72

pH

3.0

4.0

5.0

6.0

7.0

8.0**** **** ****


103

Bacteroides spp. are generally less able than many dominant Firmicutes to tolerate the presence of

short chain fatty acids at pH 5.5 limiting propionate formation and enhancing butyrate production

by the community at pH values around 5.5 compared with 6.5-6.8[36]. For bacteria that use the

butyryl-CoA:acetate-CoA-transferase route (Roseburia and Faecalibacterium prausnitzii), acetate

consumption and butyrate production are reported to increase at mildly acidic pH (5.5) compared

with near neutral pH (6.7) [37]. On the other hand, AOX evaluated by DPPH and FRAP assays

(Figure 1b and c), showed time dependent and significant differences (p <0.05) between samples.

It is noteworthy that DPPH activity (less than 35 mmol TE/g IF) was detected in fermentation

extracts at 72 h but not DPPH activity was found in aqueous-organic extracts (Table 1). In FRAP,

AM-L showed higher values (7.47 mmol TE/g IF DW) respect to MM-L (4.68 mmol TE/g IF db),

TM-L (2.43 mmol TE/g IF DW) and even that positive control (4.25 mmol TE/g IF DW) at 72 h.

Conversely, TM presented the lowest values for this AOX activity during all fermentation times.

Differences between the AOX values found by the two methods evaluated suggest that the

antioxidant mechanism of the gut metabolites is mainly as free radical neutralizers and secondarily

as chelating agents. This antioxidant mechanism was also observed in natural powdered

seasonings extracts after gastrointestinal and colonic digestion. The authors concluded that gut

metabolites present greater high free radical scavenging ability but not with significant reducing

properties [38]. . Simple soluble compounds as phenylacetic, phenylpropionic, benzoic acid

derivatives and branched-chain fatty acids (isobutyrate, 2-methylbutyrate and isovalerate) with

potential antioxidant properties can thereby be generated, mainly by amino acids and phenolic gut

metabolism [34, 39]. The combination of ascorbic acid (dietary antioxidant) with the colonic

microbiota metabolite 3,4-dihydroxyphenylacetic acid showed a potent synergic antioxidant effect

[40]. Also, 10-hydroxy-cis-12-octadecenoic acid (gut microbial metabolite of dietary linoleic acid

with immunomodulatory activity) has been associated with improved antioxidant/detoxifying

effects in a murine intestinal epithelial cell line (MODE-K)[41]

3.4 Production of SCFA

The concentrations of SCFA at 12, 24 48 and 72 h of fermentation are present in Table 2. Acetic

propionic and butyric acid concentrations in lunch menus were significantly lower than raffinose

(p<0.05). Acetate was the most abundant SCFA produced by all samples.

7. RESULTADOS Y DISCUSIÓN: CAPÍTULO 4

104


raffinose and indigestible fraction isolated from lunch menus (MM-L; Modified Mexican Lunch, TM-L; Traditional Mexican

Lunch, AM-L; Alternative Mexican Lunch) 1.

SCFA/ fermentation time

Blank Raffinose

IF- Lunch Menus MM-M TM-M AM-M

Acetic acid

12 h 3.88 ± 0.93a 198.28 ± 10.05b 16.96 ± 1.28a 32.25 ± 1.54a 8.55 ± 0.26a 24 h ND 224.34 ± 3.94b 10.17 ± 0.52a 33.12 ± 2.59a 22.14 ± 3.60a 48 h 3.40 ± 0.02a 909.71 ± 81.27b 36.95 ± 1.93a 13.95 ± 1.41a 39.42 ± 8.46a

72 h 7.67 ± 1.94a 237.79 ± 8.35c 68.12 ± 8.85b 16.28 ±

2.92ab 20.84 ± 1.00ab

Propionic acid 12 h ND 30.02 ± 0.19c 3.75 ± 0.49a 7.35 ± 0.58b 2.38 ± 0.25a 24 h ND 23.78 ± 1.33c 5.18 ± 0.22a 17.44 ± 0.16b 16.30 ± 0.57b 48 h 1.85 ± 0.12a 82.95 ± 1.86d 19.72 ± 2.11c 6.82 ± 0.72b 17.35 ± 0.77c 72 h ND 28.53 ± 2.33c 12.91 ± 1.24b 4.74 ± 1.22a 5.29 ± 1.19a Butyric acid

12 h 0.60 ± 0.08a 36.22 ± 8.44b 1.08 ± 0.16a 1.97 ± 0.28a 1.32 ± 0.26a 24 h 0.39 ± 0.04a 17.80 ± 1.74b 2.81 ± 0.32a 5.14 ± 0.87a 1.99 ± 0.34a 48 h 0.92 ± 0.19a 47.99 ± 4.45b 2.45 ± 0.44a 1.35 ± 0.20a 3.42 ± 0.33a 72 h 1.13 ± 0.23a 19.36 ± 1.34b 1.82 ± 0.16a 1.46 ± 0.19a 1.41 ± 0.23a


letters indicate significant differences in rows among substrates for a time (p < 0.05).


105

IF lunch menus vs blank were not significantly different (p > 0.05) throughout the fermentation

assay at 12, 24 and 48 h. At 72 h, MM-L produced the greatest acetic acid concentration (68.12

mmol/L), and was statistically different (p < 0.05) from blank (Without substrate). Propionic acid

concentrations were fermentation time and substrate dependent (p > 0.05). TM-L produced the

highest propionate concentration after 12 h of fermentation and was statistically different (p <

0.05) from the other samples MM-L and AM-L. Propionate production remained low for MM-L

between 6-12 h bu an incresead at 48 and 72 h was observed (19.72 and 12.91 mmol/L

respectively). For AM-L the times with greater production of propionic acid were at 24 and 48 h

but it was not significantly different (p>0.05) from TM-L and AMD respectively. In Butyric acid

levels showed no significant differences between lunch menus and values were similar to blank.

The low production of AGCC in lunch menus (Mainly made with food of animal origin, meat and

fish.) suggests that the isolated fractions could have a low indigestible carbohydrate content and a

high indigestible protein and lipids content. This could have negative implications on the health of

individuals. The fermentation of proteinaceous substrates have been considered potentially

detrimental for host health and have been implicated in development of noncommunicable

diseases [42, 43] and the pathogenesis of large intestinal diseases [44]. Based on greater intake

levels of fiber through fruit and vegetables, vegan and vegetarian diets were associated with higher

production levels of SCFA, whereas the animal-based diet supported much lower levels [45]. Our

results highlight the importance of low levels of dietary fiber in lunch menus frequently consumed

by schoolchildren Mexican population. A recent study show that fiber intake is low and added

sugar and saturated fat intakes are higher than recommended for >50% of the Mexican population

aged ≥1 year [46].

3.5 Production of volatile gut metabolites

The kinetic analysis by GC-MS/SPME of the volatile metabolites produced by fecal microbiota at

the time points 12, 24, 48 and 72 h showed different metabolic profiles in relation to the different

substrates utilized (See Supporting Information Table SI). A total of 57 different metabolites

belonging to the families of amines, alcohol and polyols, alkyl halides, fatty acid ester, benzene

and substituted derivatives, organic disulfides, carbonyl compounds, organic trisulfides, pyridines

and derivatives, organic acid, alkanes, naphthalenes, benzothiazoles, indoles and derivatives and


106

SCFA were detected (Figure 2). No changes were observed in the metabolites profile of raffinose

at 12 and 72 h, which indicates that fermentation would occur over a longer portion of the colon.

Organic acids and SCFA were the most abundant compounds found in this sample. Nevertheless,

is noticeable, a decrease of organic trisulfides, indoles and derivatives and an increase in the

production of fatty acid esters and organic acid for lunch menus at 72 h. Until now, few reports on

medium chain fatty acids (MCFAs) related to colonic health have been published. Interestingly,

the MCFAs (pentanoic, hexanoic, heptanoic, octanoic and nonanoic acid were identified as the

most discriminatory metabolites between healthy controls and patients with inflammatory bowel

disease [47]. MCFAs were shown to activate the peroxisome proliferator activated receptor

(PPAR)-γ protein that regulates fatty acid storage and glucose metabolism [48]. Fatty acid esters

were the principal metabolites during in vitro colonic fermentation of the samples. These

compounds were identified in patients who presented with chronic diseases of the gastrointestinal

tract, especially in Crohn's disease [49].

Figure 2 Cumulative concentration (%) of volatile gut metabolites by chemical groups between

the in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from

Modified Mexican Lunch (MM-L), Traditional Mexican Lunch (TM-L), and Alternative Mexican

Lunch (AM-L) at 12 and 72 h. Detailed of identification number for each volatile compound are

outlined in the Supporting Information Table SI.

12 hours of fermentation

Blank Raffinose MM-D TM-D AM-D

Cum

ula

tive

conce

ntr

atio

n (

%)

0

20

40

60

80

100



Cum

ulat

ive

conc

entr

atio

n (%

)

0

20

40

60

80

100

Amines Alcohols and polyols Alkyl halides Fatty acid ester Benzene and substituted derivatives Organic disulfides Carbonyl compounds Organic trisulfides Pyridines and derivatives Organic acid Alkanes Naphthalenes Benzothiazoles Indoles and derivatives SCFA
















107

Secondly, a statistical descriptive approach (PCA) showed that the fermentation of different corn

products for 12, 24, 48 and 72 h clearly affects the fecal metabolic fingerprint (Figure 3). The first

two principal components (PC) explained above 50% of the variation in the data (Figure 3a).

These components separate the volatiles with potentially healthy effects presenting positive

Eigenvectors in PC1 and in PC2 (eg, acetic, propionic, butyric acid, organic acids and) from those

volatiles with potentially Harmful effects presenting negative Eigenvectors on PC1 and on PC2

(e.g. phenol, phenol, 4-methyl-, trimethylamine, dimethyl disulfide, indole and fatty acid esters).

In fact, a good separation was observed for raffinose and blank at all fermentation times (Figure

3b). In lunch menus, it was notable that short fermentation times (12 and 24 h) promote a

metabolic profile similar to blank. However, long fermentation times (48 and 72 h) the samples

followed the metabolic profile B (with potentially healthy effects), specifically in MM-L and TM-

L at 72 h. For its part, raffinose was the substrate with the healthiest metabolites profile

particularly at 48 h of fermentation. In this work, the importance of improving diets with high

indigestible carbohydrates content was manifested in gut metabolic profile produced during in

vitro colonic fermentation.

Figure 3 Principal components plots (a) metabolites production “PC Loadings” (b) and sample

classification “PC scores” (%) during in vitro fermentation from indigestible fraction isolated

lunch menus (MM-L; Modified Mexican Lunch, TM-L; Traditional Mexican Lunch, AM-L;

1

23

4

5

67

8

910

11

121314

15

16

1718

19

2021

22

23

2425 26

27

28

29

30

31

32

3334

35

36

3738

39

40

4142

43

44

45

4647

48

49

50

51

52

5354

555657

-1.0 -0.5 0.0 0.5 1.0Metabolic profile A

Explained Variance: 34.81%

-1.0

-0.5

0.0

0.5

1.0

Met

abol

ic p

rofil

e B

Expl

aine

d V

aria

nce:

17.

18%

1

23

4

5

67

8

910

11

121314

15

16

1718

19

2021

22

23

2425 26

27

28

29

30

31

32

3334

35

36

3738

39

40

4142

43

44

45

4647

48

49

50

51

52

5354

555657

12-Blank

12-Raffinose

12-MM-L

12-TM-L

12-AM-L

24-Blank

24-Raffinose

24-MM-L

24-TM-L

24-AM-L

48-Blank

48-Raffinose

48-MM-L

48-TM-L

48-AM-L

72-Blank

72-Raffinose

72-MM-L

72-TM-L

72-AM-L

-10 -8 -6 -4 -2 0 2 4 6 8 10Metabolic profile A

-4

-2

0

2

4

6

Met

abol

ic p

rofil

e B

12-Blank

12-Raffinose

12-MM-L

12-TM-L

12-AM-L

24-Blank

24-Raffinose

24-MM-L

24-TM-L

24-AM-L

48-Blank

48-Raffinose

48-MM-L

48-TM-L

48-AM-L

72-Blank

72-Raffinose

72-MM-L

72-TM-L

72-AM-L


108

Alternative Mexican Lunch). Detailed of identification number for each volatile compound are

outlined in the Table SI


109

3.6 Correlation between gut metabolites, pH and antioxidant activity

To the best of our knowledge, this is first time to report the relationship between metabolic profile

and biological activity (pH and AOX) during the in vitro colonic fermentation of indigestible

fraction isolated from lunch menus frequently consumed by a schoolchildren Mexican population.

The pH values during in vitro colonic fermentation was positive correlated (p<0.05) with

trimethylamine, pyridine 2,4,6-trimethyl-, benzene 1,2-dichloro-, acetophenone, dodecanoic acid

ethyl ester, phenol, phenol 4-methyl-, indole and 1H-Indole 3-methyl- and negative correlated for

acetic acid, nonanal, propionic acid, benzene, 1,3-bis(1,1-dimethylethyl)-, butyric acid, butanoic

acid 3-methyl-, pentanoic acid, hexanoic acid and benzothiazole (Figure 4). A significant positive

correlations were found between DPPH antiradical activity and SCFA, butanoic acid 3-methyl-,

pentanoic acid, acetophenone, hexanoic acid and negative correlation was found for

trimethylamine, dimethyl trisulfide, dodecanoic acid ethyl ester, phenol, phenol 4-methyl-,

pentadecanoic acid methyl ester, indole and 1H-Indole 3-methyl-. Interestingly, SCFA, nonanal,

butanoic acid 3-methyl- and organic acid carried significant positive correlations with FRAP

chelating activity and fatty acid esters were negatively correlated with this activity. Some

metabolites with negative effects in pH and AOX found in this work (e.g. trimethylamine as

precursor of trimethylamine N-oxide and indole as precursor of indoxyl sulfate) have been

associated with the development of atherosclerosis and chronic kidney disease [50, 51].


110

Figure 4. Pearson’s R correlations between gut microbial metabolites, pH values and antioxidant

capacity (DPPH antiradical activity and FRAP chelating activity) in extracts obtained during in


111

vitro colonic fermentation of indigestible fraction isolated from lunch menus (▲Increase,

▼decrease and ▬ non-correlation, p<0.05).

5. Conclusions

Our study represents a new contribution on the in vitro potential effects of food habits on bacterial

metabolites production. IF-Lunch menus had extractable and non-extractable polyphenols, as main

bioactive compounds, but they do not contribute to an anti-radical AOX. Although the differences

in IF content, the magnitude of SCFA production in lunch menus was similar to blank (without

substrate). That could be related to the low contribution of dietary fiber in Mexican diet. PCA

allowed the identification of metabolic patterns that were associated with beneficial health effects

(Low pH and high AOX). Bacterial metabolites as trimethylamine, fatty acid esters and indole

were associated with increased pH and decreased AOX in the fermentation extracts. Bacterial

metabolites profile was dependent on fermentation time and substrate. In addition, the approach

presented here, could be used as predictive model to easily assess the indigestible components

effects coming from the food intake. Finally, we are convinced that only through food complex

matrix study will we improve our understanding of the effects of diet on colon health promotion.

Author contributions

SA-SG designed the study, supported the experimental work, and contributed to the drafting of the

manuscript and its revisions. ZG-VM conducted most of the experimental work and contributed to

the drafting of the manuscript. CC-AP contributed to the experimental work and helped revise the

manuscript; VL-PA helped design the study and supported the experimental work. T-J and LP-

MGF contributed to the revision of the manuscript and contributed to the final version

Acknowledgements The authors thank the schoolchildren who donated stool voluntarily for

participating in this study. Zamora-Gasga, VM; acknowledge the fellowship to CONACYT-

Registration number: 253795) and Sáyago-Ayerdi, SG; would also like to acknowledge the

financial support to PROMEP –ITTEP-PTC-003.


112

Conflict of interest statement

The authors have declared no conflict of interest.

5. References

[1] Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., et al., Host-gut microbiota metabolic

interactions. Science 2012, 336, 1262-1267.

[2] Byrne, C., Chambers, E., Morrison, D., Frost, G., The role of short chain fatty acids in appetite

regulation and energy homeostasis. International Journal of Obesity 2015, 39, 1331-1338.

[3] Trøseid, M., Ueland, T., Hov, J., Svardal, A., et al., Microbiota�dependent metabolite

trimethylamine�N�oxide is associated with disease severity and survival of patients with

chronic heart failure. Journal of internal medicine 2015, 277, 717-726.

[4] Vandeputte, D., Falony, G., Vieira-Silva, S., Tito, R. Y., et al., Stool consistency is strongly

associated with gut microbiota richness and composition, enterotypes and bacterial growth rates.

Gut 2015, gutjnl-2015-309618.

[5] Weir, T. L., Manter, D. K., Sheflin, A. M., Barnett, B. A., et al., Stool microbiome and

metabolome differences between colorectal cancer patients and healthy adults. PloS one 2013, 8,

e70803.

[6] Nicholson, J. K., Holmes, E., Wilson, I. D., Gut microorganisms, mammalian metabolism and

personalized health care. Nature Reviews Microbiology 2005, 3, 431-438.

[7] Barnes, S., Benton, H. P., Casazza, K., Cooper, S. J., et al., Training in metabolomics research.

II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway

analysis, applications of metabolomics and its future. Journal of Mass Spectrometry 2016, 51, 535-

548.

[8] Flint, H. J., Duncan, S. H., Scott, K. P., Louis, P., Links between diet, gut microbiota

composition and gut metabolism. Proceedings of the Nutrition Society 2015, 74, 13-22.


113

[9] Popkin, B. M., Adair, L. S., Ng, S. W., Global nutrition transition and the pandemic of obesity

in developing countries. Nutrition reviews 2012, 70, 3-21.

[10] Seth, A., Metabolic syndrome and obesity in childhood and adolescence. The Indian journal

of medical research 2016, 143, 120.

[11] Leone, V., Chang, E. B., Devkota, S., Diet, microbes, and host genetics: the perfect storm in

inflammatory bowel diseases. Journal of Gastroenterology 2013, 48, 315-321.

[12] AOAC, Association of Official Agricultural Chemists. Oficial Methods of Analysis. 15th

Edition. 1990.

[13] Saura-Calixto, F., García-Alonso, A., Goni, I., Bravo, L., In vitro determination of the

indigestible fraction in foods: an alternative to dietary fiber analysis. Journal of Agricultural and

food Chemistry 2000, 48, 3342-3347.

[14] Tabernero, M., Venema, K., Maathuis, A. J., Saura-Calixto, F. D., Metabolite production

during in vitro colonic fermentation of dietary fiber: analysis and comparison of two European

diets. Journal of agricultural and food chemistry 2011, 59, 8968-8975.

[15] Pérez-Jiménez, J., Arranz, S., Saura-Calixto, F., Proanthocyanidin content in foods is largely

underestimated in the literature data: An approach to quantification of the missing

proanthocyanidins. Food Research International 2009, 42, 1381-1388.

[16] Montreau, F., Sur le dosage des composés phénoliques totaux dans les vins par la methode

Folin-Ciocalteau. Connaiss. Vigne Vin. 1972, 24, 397-404.

[17] Reed, J. D., McDowell, R. T., Van Soest, P. J., Horvath, P. R., Condensed tannins: a factor

limiting the use of cassava forage. Journal of the Science of Food and Agriculture 1982, 33, 213-

220.

[18] Hartzfeld, P. W., Forkner, R., Hunter, M. D., Hagerman, A. E., Determination of

hydrolyzable tannins (gallotannins and ellagitannins) after reaction with potassium iodate. Journal

of Agricultural and Food Chemistry 2002, 50, 1785-1790.


114

[19] Prior, R. L., Wu, X., Schaich, K., Standardized methods for the determination of antioxidant

capacity and phenolics in foods and dietary supplements. Journal of agricultural and food

chemistry 2005, 53, 4290-4302.

[20] Benzie, I. F., Strain, J., The ferric reducing ability of plasma (FRAP) as a measure of

“antioxidant power”: the FRAP assay. Analytical biochemistry 1996, 239, 70-76.

[21] Campos�Vega, R., Reynoso�Camacho, R., Pedraza�Aboytes, G., Acosta�Gallegos, J., et

al., Chemical composition and in vitro polysaccharide fermentation of different beans (Phaseolus

vulgaris L.). Journal of Food science 2009, 74, T59-T65.

[22] Zamora-Gasga, V. M., Loarca-Piña, G., Vázquez-Landaverde, P. A., Ortiz-Basurto, R. I., et

al., In vitro colonic fermentation of food ingredients isolated from Agave tequilana Weber var.

azul applied on granola bars. LWT-Food Science and Technology 2015, 60, 766-772.

[23] Schmiele, M., Mascarenhas, M. C. C. N., da Silva Barretto, A. C., Pollonio, M. A. R., Dietary

fiber as fat substitute in emulsified and cooked meat model system. LWT-Food Science and

Technology 2015, 61, 105-111.

[24] Grundy, M. M.-L., Edwards, C. H., Mackie, A. R., Gidley, M. J., et al., Re-evaluation of the

mechanisms of dietary fibre and implications for macronutrient bioaccessibility, digestion and

postprandial metabolism. British Journal of Nutrition 2016, 116, 816-833.

[25] Norton, J., Espinosa, Y. G., Watson, R., Spyropoulos, F., Norton, I., Functional food

microstructures for macronutrient release and delivery. Food & function 2015, 6, 663-678.

[26] Kovatcheva-Datchary, P., Nilsson, A., Akrami, R., Lee, Ying S., et al., Dietary Fiber-Induced

Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella. Cell

Metabolism 2015, 22, 971-982.

[27] Flores-Silva, P. C., Bello-Pérez, L. A., Rodriguez-Ambriz, S. L., Osorio-Diaz, P., In vitro

colonic fermentation and glycemic response of high fiber gluten-free snacks in rats. Journal of

Functional Foods 2017, 28, 59-63.


115

[28] Dueñas, M., Sarmento, T., Aguilera, Y., Benitez, V., et al., Impact of cooking and

germination on phenolic composition and dietary fibre fractions in dark beans (Phaseolus vulgaris

L.) and lentils (Lens culinaris L.). LWT-Food Science and Technology 2016, 66, 72-78.

[29] Serrano, J., Puupponen�Pimiä, R., Dauer, A., Aura, A. M., Saura�Calixto, F., Tannins:

current knowledge of food sources, intake, bioavailability and biological effects. Molecular

nutrition & food research 2009, 53, S310-S329.

[30] Hervert-Hernández, D., García, O. P., Rosado, J. L., Goñi, I., The contribution of fruits and

vegetables to dietary intake of polyphenols and antioxidant capacity in a Mexican rural diet:

Importance of fruit and vegetable variety. Food Research International 2011, 44, 1182-1189.

[31] Pérez-Jiménez, J., Saura-Calixto, F., Macromolecular antioxidants or non-extractable

polyphenols in fruit and vegetables: Intake in four European countries. Food Research

International 2015, 74, 315-323.

[32] Zhang, L., Wang, Y., Li, D., Ho, C.-T., et al., The absorption, distribution, metabolism and

excretion of procyanidins. Food & function 2016, 7, 1273-1281.

[33] Marín, L., Miguélez, E. M., Villar, C. J., Lombó, F., Bioavailability of dietary polyphenols

and gut microbiota metabolism: antimicrobial properties. BioMed research international 2015,

2015.

[34] Valdés, L., Cuervo, A., Salazar, N., Ruas-Madiedo, P., et al., The relationship between

phenolic compounds from diet and microbiota: impact on human health. Food & function 2015, 6,

2424-2439.

[35] Jakobek, L., Interactions of polyphenols with carbohydrates, lipids and proteins. Food

chemistry 2015, 175, 556-567.

[36] Chung, W. S. F., Walker, A. W., Louis, P., Parkhill, J., et al., Modulation of the human gut

microbiota by dietary fibres occurs at the species level. BMC biology 2016, 14, 1.


116

[37] Kettle, H., Louis, P., Holtrop, G., Duncan, S. H., Flint, H. J., Modelling the emergent

dynamics and major metabolites of the human colonic microbiota. Environmental microbiology

2015, 17, 1615-1630.

[38] Del Pino-García, R., González-SanJosé, M. L., Rivero-Pérez, M. D., García-Lomillo, J.,

Muñiz, P., Total antioxidant capacity of new natural powdered seasonings after gastrointestinal

and colonic digestion. Food Chemistry 2016, 211, 707-714.

[39] Davila, A.-M., Blachier, F., Gotteland, M., Andriamihaja, M., et al., Intestinal luminal

nitrogen metabolism: role of the gut microbiota and consequences for the host. Pharmacological

research 2013, 68, 95-107.

[40] Noguer, M., Cerezo, A. B., Moyá, M. L., Troncoso, A. M., García-Parrilla, M. C., Synergism

effect between phenolic metabolites and endogenous antioxidants in terms of antioxidant activity.

Advances in Chemical Engineering and Science 2014, 4, 258.

[41] Bergamo, P., Luongo, D., Miyamoto, J., Cocca, E., et al., Immunomodulatory activity of a gut

microbial metabolite of dietary linoleic acid, 10-hydroxy-cis-12-octadecenoic acid, associated with

improved antioxidant/detoxifying defences. Journal of Functional Foods 2014, 11, 192-202.

[42] Organ, C. L., Otsuka, H., Bhushan, S., Wang, Z., et al., Choline Diet and Its Gut Microbe–

Derived Metabolite, Trimethylamine N-Oxide, Exacerbate Pressure Overload–Induced Heart

Failure. Circulation: Heart Failure 2016, 9, e002314.

[43] Boulangé, C. L., Neves, A. L., Chilloux, J., Nicholson, J. K., Dumas, M.-E., Impact of the gut

microbiota on inflammation, obesity, and metabolic disease. Genome medicine 2016, 8, 1.

[44] Louis, P., Hold, G. L., Flint, H. J., The gut microbiota, bacterial metabolites and colorectal

cancer. Nature reviews Microbiology 2014, 12, 661-672.

[45] De Filippis, F., Pellegrini, N., Vannini, L., Jeffery, I. B., et al., High-level adherence to a

Mediterranean diet beneficially impacts the gut microbiota and associated metabolome. Gut 2015,

gutjnl-2015-309957.


117

[46] López-Olmedo, N., Carriquiry, A. L., Rodríguez-Ramírez, S., Ramírez-Silva, I., et al., Usual

intake of added sugars and saturated fats is high while dietary fiber is low in the Mexican

population. The Journal of Nutrition 2016, 146, 1856S-1865S.

[47] De Preter, V., Machiels, K., Joossens, M., Arijs, I., et al., Faecal metabolite profiling

identifies medium-chain fatty acids as discriminating compounds in IBD. Gut 2014, gutjnl-2013-

306423.

[48] Liberato, M. V., Nascimento, A. S., Ayers, S. D., Lin, J. Z., et al., Medium chain fatty acids

are selective peroxisome proliferator activated receptor (PPAR) γ activators and pan-PPAR partial

agonists. PLoS One 2012, 7, e36297.

[49] Walton, C., Fowler, D. P., Turner, C., Jia, W., et al., Analysis of volatile organic compounds

of bacterial origin in chronic gastrointestinal diseases. Inflammatory bowel diseases 2013, 19,

2069-2078.

[50] Poesen, R., Windey, K., Evenepoel, P., De Preter, V., et al., Nephrology, Dialysis,

Transplantation, Springer International 2014, pp. 49-50.

[51] Bäckhed, F., Meat-metabolizing bacteria in atherosclerosis. Nature medicine 2013, 19, 533-

534.


118

Table SI. Volatile compounds characterization of in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from Lunch menus (MM-L; Modified Mexican diet, TM-L; Traditional Mexican Diet, AM-

L; Alternative Mexican diet) analyzed by SPME–GC/MS (mmol/L).

ID RT Volatile Name Chemical Group

T12 T24 T48 T72

VC (%) Blank Raffinose MM-L TM-L AM-L Blank Raffinose MM-L TM-L AM-L Blank Raffinose MM-L TM-L AM-L Blank Raffinose MM-L TM-L AM-L

1 6.001 Trimethylamine Amines 18.90 0.00 24.78 8.08 23.34 21.39 0.00 19.44 22.15 28.16 36.34 0.00 10.83 5.94 16.82 24.64 0.00 8.09 9.51 9.79 74.03

2 8.359 Ethanol Alcohols and polyols 23.29 29.76 0.00 0.00 25.46 27.54 0.00 36.26 105.87 0.00 0.00 140.27 16.91 17.49 0.00 0.00 4.80 12.92 0.00 0.00 168.28

3 9.229 Methyl propionate Carboxylic acids and derivatives 0.00 4.16 4.80 0.00 5.40 0.00 0.00 6.18 5.24 5.56 0.00 6.58 0.00 5.08 4.94 0.00 0.00 8.24 7.04 4.05 88.19

4 10.754 Trichloromethane Alkyl halides 6.66 12.35 13.95 7.71 8.92 0.00 0.00 0.00 0.00 0.00 13.52 10.33 4.55 3.70 8.63 18.15 5.50 8.31 4.89 4.60 80.50

5 12.074 Butanoic acid, methyl ester Fatty Acyls 9.12 10.08 15.14 20.82 13.64 0.00 11.39 37.23 64.07 37.46 7.65 19.00 25.37 54.57 48.62 14.02 13.21 107.67 97.49 38.26 91.69

6 13.918 Toluene Benzene and substituted derivatives 0.00 9.29 0.00 0.00 0.00 13.19 9.39 12.97 0.00 0.00 0.00 0.00 69.14 0.00 0.00 32.04 20.80 0.00 0.00 0.00 201.46

7 14.254 Disulfide, dimethyl Organic disulfides 16.38 0.00 42.66 20.99 27.81 22.34 0.00 41.91 75.68 77.19 6.67 0.00 5.58 3.83 105.29 0.00 0.00 25.81 5.30 4.55 126.00

8 15.244 1-Butanol Alcohols and polyols 0.00 0.00 0.00 3.46 6.55 0.00 0.00 0.00 51.66 10.27 0.00 44.85 13.50 0.00 14.84 0.00 13.44 34.64 0.00 0.00 163.81

9 16.364 Pentanoic acid, methyl ester Fatty Acyls 0.00 3.96 29.82 27.60 9.55 3.83 0.00 109.60 113.49 124.28 9.09 3.42 89.91 107.20 248.38 9.91 0.00 225.73 145.57 147.00 111.99

10 19.121 Pentanoic acid, ethyl ester Fatty Acyls 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.42 7.02 8.50 0.00 0.00 5.76 6.02 14.03 0.00 0.00 11.08 8.83 0.00 134.95

11 20.057 Heptanal Carbonyl compounds 0.00 0.00 19.23 5.24 13.41 4.53 0.00 13.32 8.18 16.04 7.47 9.70 6.05 0.00 0.00 5.08 0.00 0.00 0.00 0.00 114.19

12 20.561 Hexanoic acid, methyl ester Fatty Acyls 0.00 3.68 54.95 28.92 15.28 5.16 0.00 177.68 150.64 76.85 6.80 0.00 212.63 373.80 615.36 10.67 3.64 666.64 541.71 364.33 135.25

13 23.045 Hexanoic acid, ethyl ester Fatty Acyls 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.89 6.26 4.68 0.00 0.00 9.08 13.47 22.69 0.00 0.00 24.23 22.00 0.00 153.67

14 24.537 Heptanoic acid, methyl ester Fatty Acyls 4.37 0.00 61.80 21.44 11.30 8.51 3.62 200.64 128.16 54.79 9.53 0.00 347.03 1042.80 934.45 10.30 6.09 1218.92 1224.46 824.62 149.03

15 25.883 Dimethyl trisulfide Organic trisulfides 392.74 0.00 674.84 283.24 461.07 176.20 0.00 133.60 183.95 157.68 37.76 0.00 39.85 28.03 25.67 34.12 0.00 11.86 13.03 17.21 139.52

16 26.235 Pyridine, 2,4,6-trimethyl- Pyridines and derivatives 0.00 0.00 14.85 0.00 17.69 10.86 0.00 19.99 14.48 6.11 23.21 0.00 13.57 9.48 0.00 20.28 0.00 16.01 12.43 14.73 84.55

17 26.247 Acetic Acid Organic acid 3.88 198.28 16.96 32.25 8.55 0.00 224.34 10.17 33.12 22.14 3.40 909.71 36.95 13.95 39.42 7.67 237.79 68.12 16.28 20.84 216.12

18 26.806 Heptanoic acid, ethyl ester Fatty Acyls 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.68 5.48 0.00 0.00 0.00 21.05 36.51 39.91 0.00 0.00 55.38 45.89 0.00 170.50

19 28.059 Benzene, 1,2-dichloro- Benzene and substituted derivatives 4.04 0.00 0.00 0.00 5.41 7.82 0.00 0.00 0.00 4.27 3.47 0.00 0.00 0.00 3.31 13.68 0.00 0.00 0.00 4.61 154.95

20 28.156 Nonanal Carbonyl compounds 0.00 14.41 0.00 0.00 8.69 0.00 5.84 8.97 6.09 8.82 5.31 7.78 10.59 0.00 5.51 0.00 6.05 0.00 0.00 0.00 103.27

21 28.347 Octanoic acid, methyl ester Fatty Acyls 13.72 0.00 86.80 27.27 23.45 16.32 0.00 317.15 44.12 22.14 13.70 0.00 191.20 256.09 115.67 9.78 9.95 509.08 231.47 192.11 132.63

22 29.758 Propionic Acid Organic acid 0.00 30.02 3.75 7.35 2.38 0.00 23.78 5.18 17.44 16.30 1.85 82.95 19.72 6.82 17.35 0.00 28.53 12.91 4.74 5.29 131.04

23 29.837 Benzaldehyde Benzene and substituted derivatives 24.76 14.25 15.26 14.39 14.91 27.57 17.30 19.43 21.93 26.25 12.57 9.45 25.37 13.23 17.64 20.57 16.34 18.96 19.78 12.97 27.86

24 31.456 Benzene, 1,3-bis(1,1-dimethylethyl)- Benzene and substituted derivatives 12.35 45.89 38.32 19.93 30.68 16.59 44.10 42.50 23.73 29.89 22.69 26.36 23.09 13.29 33.12 15.85 27.98 26.96 13.37 32.85 38.30

25 32.114 Nonanoic acid, methyl ester Fatty Acyls 6.46 0.00 8.61 15.18 10.86 5.67 0.00 43.00 20.89 9.46 12.05 0.00 23.64 49.15 10.09 7.40 0.00 57.56 40.39 20.20 101.70

26 32.534 1,3-Benzenediol, 4-ethyl- Benzene and substituted derivatives 8.36 10.28 0.00 0.00 7.21 6.27 7.25 11.58 9.71 7.95 9.34 5.75 8.21 0.00 4.98 5.39 5.50 9.60 7.14 6.16 50.88

27 33.352 Butiric Acid Organic acid 0.60 36.22 1.08 1.97 1.32 0.39 17.80 2.81 5.14 1.99 0.92 47.99 2.45 1.35 3.42 1.13 19.36 1.82 1.46 1.41 173.57

28 34.275 Tetradecane Alkanes 4.34 0.00 0.00 8.83 0.00 0.00 0.00 7.71 7.24 0.00 5.33 0.00 5.94 0.00 0.00 0.00 4.59 0.00 3.90 0.00 133.47


119

29 34.948 Acetophenone Benzene and substituted derivatives 14.44 0.00 24.87 18.28 32.43 0.00 0.00 30.85 19.45 25.63 13.18 0.00 16.60 13.33 19.66 11.10 0.00 16.52 17.08 23.32 69.88

30 35.264 Butanoic acid, 3-methyl- Fatty Acyls 0.01 108.85 0.01 37.86 25.28 7.33 146.97 29.92 101.24 28.09 10.67 220.10 74.63 0.01 68.61 11.02 121.66 23.40 25.10 33.03 109.68

31 36.508 Decanoic acid, methyl ester Fatty Acyls 21.91 13.54 38.02 163.08 48.06 25.11 11.00 421.92 201.01 44.65 11.92 5.58 333.60 734.07 65.29 22.04 12.90 347.78 101.49 58.63 141.36

32 39.159 Pentanoic acid Fatty Acyls 10.36 248.72 0.00 27.58 10.31 7.25 181.39 86.76 99.01 55.33 95.71 346.57 53.30 3.78 64.68 32.60 181.55 88.95 33.25 31.94 110.25

33 41.611 Benzeneacetic acid, methyl ester Fatty Acyls 0.00 0.00 0.00 0.00 12.16 0.00 0.00 0.00 6.49 8.42 0.00 0.00 6.44 0.00 8.83 0.00 0.00 14.93 15.62 10.65 135.84

34 42.901 Undecanoic acid, methyl ester Fatty Acyls 0.00 0.00 0.00 8.26 4.67 0.00 0.00 14.20 9.62 3.33 0.00 0.00 11.35 21.15 3.38 0.00 0.00 9.93 6.66 0.00 130.95

35 45.850 Hexanoic acid Fatty Acyls 9.34 267.33 0.00 33.11 49.17 17.47 151.41 88.20 79.45 64.43 115.46 278.26 76.85 40.89 91.40 22.30 149.34 122.69 79.07 64.47 84.27

36 46.504 Benzenepropanoic acid, methyl ester Fatty Acyls 7.82 0.00 17.69 34.91 10.66 8.50 8.87 48.55 57.92 36.94 5.32 0.00 51.38 154.43 171.88 0.00 8.86 114.92 174.03 67.93 119.38

37 47.379 Naphthalene, 1,2,3,4-tetrahydro-2,5,8-trimethyl- Naphthalenes 4.53 8.28 0.00 0.00 6.88 0.00 10.58 3.75 0.00 5.17 4.28 4.35 0.00 0.00 5.44 0.00 0.00 0.00 0.00 0.00 126.09

38 49.117 Naphthalene, 1,2,3,4-tetrahydro-1,5,7-trimethyl- Naphthalenes 0.00 9.27 0.00 6.52 5.17 0.00 11.66 0.00 8.08 6.57 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00 4.89 0.00 137.15

39 49.715 Benzothiazole Benzothiazoles 6.41 12.42 0.00 6.32 11.19 5.83 6.43 0.00 14.61 8.75 6.58 13.95 0.00 0.00 9.23 4.21 12.31 0.00 7.88 11.63 71.96

40 50.056 Heptanoic acid Fatty Acyls 0.00 52.66 0.00 8.72 0.00 0.00 31.80 30.95 26.83 15.32 81.41 41.66 32.56 37.72 67.08 17.57 42.28 71.10 109.68 76.48 83.87

41 50.392 Dodecanoic acid, methyl ester Fatty Acyls 80.39 17.86 69.69 168.18 82.87 71.49 19.05 363.19 178.19 32.92 87.81 16.32 177.25 520.52 65.12 38.65 12.62 264.47 164.95 75.24 103.84

42 50.402 Dodecanoic acid, ethyl ester Fatty Acyls 9.32 0.00 7.71 11.94 8.00 8.00 0.00 13.76 13.24 0.00 7.35 0.00 17.01 22.88 7.12 0.00 0.00 20.82 13.06 0.00 91.45

43 50.604 Butylated Hydroxytoluene Benzene and substituted derivatives 6.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 31.62 0.00 34.04 23.07 26.29 23.62 40.23 32.33 34.94 124.10

44 51.061 Phenol Benzene and substituted derivatives 368.57 96.38 196.23 129.69 171.32 244.05 48.00 209.99 156.85 280.20 259.99 81.19 127.33 97.34 119.77 326.98 102.56 144.93 130.66 287.92 49.76

45 52.001 Tridecanoic acid, methyl ester Fatty Acyls 5.26 0.00 0.00 7.89 0.00 4.42 0.00 13.51 7.24 0.00 5.14 0.00 6.09 21.40 0.00 0.00 0.00 11.54 11.57 4.64 120.07

46 53.245 Phenol, 4-methyl- Benzene and substituted derivatives 436.02 132.04 683.53 469.78 286.25 680.36 97.15 862.39 613.76 551.39 703.92 124.38 805.90 687.64 926.73 441.14 91.65 1045.23 883.43 701.78 52.27

47 54.056 Tridecanoic acid, 12-methyl-, methyl ester Fatty Acyls 6.83 0.00 6.99 16.03 6.54 5.13 0.00 28.42 19.18 0.00 7.19 0.00 11.38 32.56 0.00 6.25 0.00 22.18 15.70 4.83 104.29

48 55.570 Methyl tetradecanoate Fatty Acyls 120.96 9.04 0.00 162.39 76.18 100.26 12.86 283.31 157.88 17.06 156.96 11.75 114.39 425.00 41.76 50.71 16.86 275.64 235.61 57.77 98.59

49 55.776 Methyl myristoleate Fatty Acyls 23.88 0.00 25.15 66.23 19.89 19.43 0.00 136.85 47.39 6.09 42.43 0.00 21.84 90.73 9.30 9.20 0.00 51.18 26.96 10.85 114.73

50 57.892 Pentadecanoic acid, methyl ester Fatty Acyls 0.00 0.00 12.21 9.55 7.20 6.17 0.00 29.14 9.81 0.00 15.04 0.00 14.46 23.60 7.78 6.51 0.00 25.79 15.39 9.06 93.22

51 58.738 Phenol, 2,4-bis(1,1-dimethylethyl) Benzene and substituted derivatives 9.12 11.13 6.24 7.54 9.05 13.55 8.68 13.84 10.53 10.48 14.70 8.70 11.42 13.24 10.03 16.32 10.22 0.00 13.23 13.20 33.89

52 60.703 Indole Indoles and derivatives 517.58 15.37 710.36 574.97 748.05 418.15 9.87 515.45 506.84 428.95 498.38 15.56 323.85 282.89 312.23 201.33 14.63 272.80 233.68 341.79 64.48

53 61.599 1H-Indole, 3-methyl- Indoles and derivatives 8.47 0.00 5.41 12.20 11.08 14.02 0.00 13.13 11.22 7.26 20.24 0.00 6.08 24.34 10.41 6.56 0.00 32.16 0.00 6.99 90.46

54 62.324 11-Hexadecenoic acid, methyl ester Fatty Acyls 19.63 0.00 0.00 9.81 7.04 9.62 0.00 21.55 9.49 0.00 10.57 4.21 7.21 0.00 8.63 0.00 0.00 0.00 10.87 0.00 112.67

55 62.516 Methyl hexadec-9-enoate Fatty Acyls 14.07 0.00 0.00 58.88 24.96 17.17 3.62 75.55 59.05 7.88 34.94 6.34 20.30 86.88 22.56 13.80 0.00 46.69 34.83 29.11 92.24

56 62.902 Hexadecanoic acid, methyl ester Fatty Acyls 16.37 4.86 16.04 33.05 26.94 23.63 8.00 56.34 35.49 9.75 38.82 0.00 22.03 109.29 24.37 21.24 8.48 50.21 83.48 30.98 87.15

57 67.380 9-Octadecenoic acid, methyl ester, (E)- Fatty Acyls 115.70 0.00 19.81 186.68 150.24 116.63 51.58 340.93 233.64 92.30 275.45 62.53 114.41 271.03 157.61 0.00 75.49 169.70 284.33 209.37 68.08 ID; Identification, RT; retentiton time, VC; Variation coefficient


120

7.4 Efectos de los alimentos frecuentemente consumidos por escolares mexicanos en la cena

sobre el metabolismo microbiano y la capacidad antioxidante durante la fermentación

colónica in vitro

Resumen

El interés en el papel de la microbiota colónica humana en la salud o la enfermedad ha aumentado

constantemente. A través del proceso de fermentación, las bacterias del colon producen una amplia

gama de compuestos que pueden tener importantes implicaciones en los procesos fisiológicos de

colon (Nicholson y cols., 2012; Russell y cols., 2013b). Debido a la inaccesibilidad del colon

humano, los estudios in situ son difíciles y es necesaria más información para comprender los

procesos metabólicos de la microbiota. La mayoría de los estudios que presentan este enfoque han

evaluado sólo un número limitado de compuestos orgánicos volátiles. Sin embargo, poco a poco se

comienza a creer que cada metabolito tiene una importancia en la actividad biológica de todo el

extracto de fermentación. Es así que Beyer-Sehlmeyer y cols. (2003) y Campos-Vega y cols.

(2012), proponen que los efectos quimioprotectores beneficiosos en cultivos de células de cáncer

de colon podrían atribuirse a las diferencias en la magnitud de las actividades biológicas de

diferentes compuestos presentes en los extractos completos. De este modo, proponemos la micro

extracción en fase solida (MEFS) junto con la cromatografía de gases acoplada a espectrometría de

masas (GC-MS) para proveer un perfil completo de metabolitos microbianos volátiles. Hasta la

fecha, sólo unos pocos estudios han tratado de generar una visión más completa del perfil

metabólico de la microbiota utilizando heces fecales humanas como principal objeto de estudio

(De Preter y cols., 2009; Garner y cols., 2009). De acuerdo con nuestro conocimiento, ha habido

pocos intentos para determinar la composición volátil, producto del metabolismo microbiano de la

fracción indigestible de los alimentos consumidos en la cena. Por lo tanto, en este capítulo se

porpone la diferenciación de los compuestos volátiles obtenidos por MEFS/CG-EM/ACP de

diferentes tiempos de fermentación de los componentes indigestibles de mezclas de alimentos

complejos que nos permitirá idetntificar los metabolitos responsables de los cambios en el pH y

actividad antioxidante durante la fermentación colonica in vitro de la fracción indigestible aislada

de alimentos frecuentemente consumidos por escolares durante la cena.


121

Effects of foods frequently eaten by Mexican schoolchildren at dinner on microbial

metabolism and antioxidant capacity during in vitro colonic fermentation

1 Introduction

Diet is one of the most influential lifestyle factors contributing to rise of inflammatory diseases

and autoimmunity in both developed and developing countries (Richards, Yap, McLeod, Mackay,

& Mariño, 2016). In particular, Western diet, including high-fat and cholesterol, high-protein,

high-sugar, and excess salt intake, promote obesity, metabolic syndrome, and cardiovascular

disease (Manzel et al., 2014). Actually, Mexican population follow these trends, wherein the

dietary fiber intake is low and added sugar and saturated fat intakes are higher than recommended

for >50% of the population aged ≥1 year (López-Olmedo et al., 2016). However, it has been

suggested that dietary polyphenol intake in a rural Mexican Diet is similar to those determined in a

healthy diet reference “Spanish Mediterranean diet” (Hervert-Hernández, García, Rosado, & Goñi,

2011). Recently evidence shown that traditional Mexican diet (based on corn, beans, and chili pep)

modestly improved insulin sensitivity under conditions of weight stability in healthy women of

Mexican descent (Santiago-Torres et al., 2016) and decreases the glucose intolerance and

biochemical abnormalities caused by a sucrose-enriched high-fat diet in rats (Avila�Nava et al.,

2017). Several studies show that long-term and shorter term dietary variation influences gut

microbiota composition and determines its metabolic profile (Kaakoush et al., 2017; Rajilić�

Stojanović, Heilig, Tims, Zoetendal, & Vos, 2013). Since many microbial products have been

shown to influence the development of diseases and the maintenance of health status of the colon

(Levy, Blacher, & Elinav, 2017; Richards et al., 2016; Yasmin et al., 2015); In this sense,

interindividual variation in microbial profiles of the intestine in humans can lead to differences in

disease risk (Conlon & Bird, 2014). Given the importance of the diet on gut metabolism-host

health, our understanding of how different sources of indigestible components from western diet

could be allow the microbial profiles to be modulated (Flint, Duncan, Scott, & Louis, 2015).

Previously, we investigated dietary patterns in Mexican schoolchildren from Tepic, State Nayarit

Mexico but the effects of foods in this dietary pattern on the gut metabolism are unknown.

Therefore, the aims were to quantify and isolate the indigestible fraction (IF) in three dinner


122

menus consumed by Mexican schoolchildren and to assess how the gut metabolite profile could be

affected by indigestible components of this menus in an in vitro colonic fermentation model.

Relationships between the abundance of microbial metabolites and changes in pH, antioxidant

capacity (AOX) values were also examined.


2.1 Dinner food preparation

Data sources on dietary patterns (DP) and food frequency consumption at dinner were obtained

from a nutritional survey carried out on eleven public schools in a Western city of Mexico (Tepic,

Nayarit State). Frequently consumed foods in each DP were used to create three dinner menus.

Dinner menus were named and comprised by the following foods; Modified Mexican Dinner

(MM-D): Soda (357 mL) and three roasted meat tacos with cabbage and green sauce. Traditional

Mexican Dinner (TM-D): Whole milk (250 mL) and three bean and cheese tacos. Alternative

Mexican Dinner (AM-D): Chocolate milk (whole milk 300 mL, cane sugar 8 g, chocolate powder

16 g) and a piece of sweet bread. Individual foods were purchased in a local supermarket and were

prepared in the laboratory kitchen according to traditional customs of the region. After prepared,

each menu, it was homogenized in a food processor (NB-101B, Nutribullet, China), frozen (-80

°C), freeze-dried (FreeZone 6, Labconco, USA), grounded, sieved with a mesh size of 500 µm,

and stored at -20 °C until analysis. Each menu preparation was performed by triplicate. Moisture

content of complete dinner menus, was analyzed according to AOAC (1990) 925.10 methods.


A digestion procedure that mimicking the physiological situation in the upper tract (stomach and

small intestine) was utilized for indigestible fraction (IF) quantification (Saura-Calixto, García-

Alonso, Goni, & Bravo, 2000). In order to increase the amount of isolated IF the follow

amendments were realized (Tabernero, Venema, Maathuis, & Saura-Calixto, 2011). Insoluble IF

was considered as the digestion residues pelleted by centrifugation, while those retained by

dialysis represented soluble IF; the sum of both fractions equals total IF. Total, IF was collected,


123

freeze-dried, milled (IKA M20, USA), sieved (500-micron mesh), and stored in seal bags at -20

°C.


Samples (250 mg) of total IF isolated from dinner menus were extracted by aqueous-organic

solution (Pérez-Jiménez, Arranz, & Saura-Calixto, 2009). Total soluble polyphenols (TSP) in

extractable fraction were determined with the Folin–Ciocalteu’s reagent (Montreau, 1972) using a

96-well microplate reader (Biotek, Synergy HT, Winooski VT, USA) with Gen5 software, and the

results were expressed in g of gallic acid equivalents (g GAE/ 100g IF). The residues of the

extraction were treated for non-extractable polyphenols quantification (Condensed tannins and

hydrolyzable polyphenols). Condensed tannins (CT) were quantified and results were expressed as

CT equivalents/ 100 g IF, using a carob pod (Ceratonia siliqua) proanthocyanidin standard (Reed,

McDowell, Van Soest, & Horvath, 1982). Hydrolyzable polyphenols (HP) were evaluated by a

previously reported methodology (Hartzfeld, Forkner, Hunter, & Hagerman, 2002) and results

were expressed as g GAE/100 g IF.


AOX was evaluated on the extractable fraction obtained before, and methodologies were slightly


1,1-Diphenyl-2-picryl hydrazyl (DPPH) antiradical activity assay was evaluated (Prior, Wu, &

Schaich, 2005). Ferric reducing antioxidant power (FRAP) assay was performed (Benzie & Strain,

1996). Results AOX were expressed as Trolox (6-hydroxy-2,5,7,8-tetramethylchromane-2-

carboxylic) equivalent (TE; mmol /g IF db).


Total IF isolated from three dinner menus were fermented in a batch culture system with pre-

conditioned nutritive medium and under strict anaerobic conditions at 37 °C (Campos�Vega et

al., 2009). In parallel, two different controls were conducted under the applied conditions: a)

Raffinose was incubated in medium with faeces inoculum as a fermentable sugar reference, and b)


124

the faecal suspension was incubated without substrate as a negative control. All incubations were

performed in triplicate and samples and controls were collected at 12, 24, 48 and 72 h. The tubes

obtained at each time of fermentation were centrifuged (Hermle Z 323 K; Wehingen, Germany)

(3500 × g ,15 min, 4 °C). Supernatants were divided into two parts; metabolite profile analysis and

antioxidant capacity assays and were immediately stored at −80 °C until analysis.


Supernatants (500 mg) were placed into a 20 mL vial sealed with a magnetic cap with a poly-tetra-

fluoro-ethylene (PTFE)/silicon septum. HS-SPME method was used for the extraction and

concentration of volatile compounds (Zamora-Gasga et al., 2015); incubation with

polydimethylsiloxane-divinylbenzene-carboxen “PDMS/DVB/CAR” fiber for 120 min, 250 rpm at

45 °C; the fiber was inserted into the injection port of the GC system for thermal desorption (240

°C for 10 min) for GC/MS analysis. The extractions were performed in triplicate. Five runs with

twelve vials / run were performed and the tests order was completely randomized. The volatile

constituents were analyzed with an Agilent 5975C VL mass selective detector coupled to an

Agilent 7890A gas chromatograph (Agilent Technologies, Inc., Santa Clara, CA), equipped with a

DB-5MS capillary column (60 m X 250 µm X 0.25 µm; Agilent). Sample quantification was

obtained by means of acetic, propionic and butyric acid standard curves. Tentative identification of

the volatile components was done comparing the mass spectra of the samples with the data system

library MSD ChemStation software (Agilent G1701EAversion E.02.00.493). Relative

concentration of all fermentation metabolites versus acetic acid as internal standard was calculated

and the results were expressed in mmol L-1.

Antioxidant capacity (AOS) during in vitro colonic fermentation

Changes in the AOX of the sample extracts during in vitro colonic fermentation at 12, 24 and 72h

h were measured by the methods described above (paragraph 2.2.4)



125

Results are expressed as mean ± SEM (n=3). Univariate testing of differences for dinner menus

was processed by ANOVA/Fisher's Least Significant Difference test. Besides, Multivariate

analysis was applied for metabolites profile interpretation. Principal component analysis (PCA)

was used to determine patterns in relative metabolites production. Finally, Pearson´s correlation

coefficients were used to evaluate the relationship between individual metabolites and changes in

pH and AOX during in vitro colonic fermentation. All analyzes were performed using

STATISTICA software, version 10.0 (StatSoft. Inc. 1984–2007, Tulsa, OK, USA). A significance

level of α = 0.05 was used in data analysis.

3. Results

3.1 Content and nutritional composition of indigestible fraction (IF) in dinner menus

The results shown in Table 1 indicate that the moisture content in MM-D (80.03 g/100g wb) was

about five and ten percentage points higher than TMD and AM-D, respectively (p<0.05). which

follows that in this dinner menu are less solid components available for gastrointestinal digestion.

Complete digestion time depends on food structure including bolus liquid consistency; liquids are

digested faster than semisolids or cellular structures (Sensoy, 2014). The increase in moisture

content in TM-D could be attributed to soda addition and consequently as a dilution effect. On the

other hand, colonic fermentation of indigestible might act on the gut to cause symptoms have been

associated with irritable bowel syndrome (Gibson, Varney, Malakar, & Muir, 2015). In total IF

content, significant differences (p<0.05) between in MM-D (2.69 g/100g wb), AM-D (4.17 g/100g

wb) and TM-D (6.95 g/100g wb) were found (Table 1). TM-D presented the highest total IF

content that can be attributed to the presence of beans in the menu. Inclusion of black beans with a

typical Western-style meal attenuates postprandial insulin in adults with metabolic síndrome and

this effect was partly explained by the fiber content (Reverri et al., 2015). Insoluble IF was the

main fraction of total IF, since it represented from 73.2% in MM-D to 90.9% in TM-D. AM-D

showed the highest. Soluble IF content soluble indigestible fraction of the three menus (p<0.05).

Soluble IF constitute s the 5.57, 9.06 and 26.8 % of Total IF in MM-D, TM-D and AM-D

respectively. Some of the important properties of soluble IF components (e.g. β-glucan, pectins,

guar gum, arabinoxylans, and inulins) are water solubility, ability to form viscous solutions, high


126

fermentability in colon and has a greater hypolipidemic effect compared with Insoluble IF

(Surampudi, Enkhmaa, Anuurad, & Berglund, 2016). Interactions among food matrix may

generate different physical properties in dinner menus and thus modifying IF content. In

accordance with Zhang, Maladen, Campanella, and Hamaker (2010) electrostatic interactions

among carboxyl groups, possessing negative charge, of the free fatty acids and the poly-ionic

protein are the basis for ternary complex with starch (e.g. TM-D dinner: Corn tortilla and bean

starch, Cheese protein and vegetable oil interactions); this complex could decrease the

macronutrients digestibility and finally increase the indigestible component ratio (Parada &

Santos, 2016).


Total, soluble polyphenols (TSP), hydrolysable tannins (HT), and condensed tannins (CT) were

studied in IF of dinner menus and the results are shown in Table 1. The TSP content of MM-D

(0.83 g/100g IF db) did not show significant differences (p<0.05) with AM-D (0.78 g/100g IF db)

and TM-D (0.85 g/100g IF db). High molecular weight compounds such as condensed tannins

(proanthocyanidins) and hydrolyzable polyphenols are covalently attached to dietary fiber or

proteins and can be released into the colon by gut fermentation (Bohn, 2014). In CT, the highest

(p<0.05) content was found in TM-D (2.22 g/100g IF db), followed by AM-D (1.00 g/100g db).

The presence of beans in TM-L and cocoa in AM-D could be responsible for the observed

differences. After ingestion, a small amount of flavan-3-ols or proanthocyanidins oligomers are

absorbed in the small intestine. Cocoa and common beans procyanidins (Possibly present in TM-D

and AM-D) have shown effects for preventing loss of gut barrier function and epithelial

inflammation, which are critical steps in the pathogenesis of metabolic endotoxemia, inflammatory

bowel disease and colon cancer (Bitzer et al., 2015; Moreno-Jiménez et al., 2015). The role of gut

microflora in the catabolism of proanthocyanidins has been evaluated. The microbial derived

phenylvalerolactone (5-(3´,4´-dihydroxyphenyl)-ϒ-valerolactone) and phenolic acids (flavan-3-ol

monomers) were the predominant metabolites of cocoa procyanidins in blood and urine (Ottaviani,

Kwik-Uribe, Keen, & Schroeter, 2012). Hydrolyzable tannins (HT) values ranged from 1.58 to

2.24 g/100g sample db, with statistically significant differences (p<0.05) between all the samples.

HT were a quantitatively important fraction of polyphenols in all samples (approximately 43% for


127

AM-D and TM-D and up to 60% in the case of the MM-D). In the colon, HT are metabolized to

gallic acid, pyrogallol, phloroglucinol and finally to acetate and butyrate via several bacterial

enzymes (Marín, Miguélez, Villar, & Lombó, 2015). TSP linked to the IF were released in the

aqueous-organic extraction and it showed FRAP activity but DPPH activity was observed only for

TM-D (2.17 g/100g IF db). FRAP activity was higher in IF extracts isolated for TM-D and AM-D

(11.29 and 12.25 mmol TE/g IF db) and lower in MM (9.66 mmol TE/g IF db). These results

suggest that antioxidant mechanism in extracts can be as chelating agent and not as a free radical

neutralizer (Valdés et al., 2015).


the indigestible fraction (IF) isolated from dinner menus1

Parameter Menus2

MM-D TM-D AM-D

Moisture content (g/100g menu ) 80.03 ± 0.24c 75.62 ± 0.56b 71.57 ± 0.09a

IF (g/100g menu wb)

Insoluble IF 2.54 ± 0.10a 6.32 ± 0.14b 3.04 ± 0.08a

Soluble IF 0.15 ± 0.04a 0.63 0.03b 1.12 ± 0.07c

Total IF 3 2.69 ± 0.14a 6.95 ± 0.16c 4.17 ± 0.03b

IF Antioxidant Compounds (g/ 100g IF db)

Total soluble polyphenols 8.36 ± 0.27 ab 7.81 ± 0.17 a 8.57 ± 0.21 b

Condensed Tannins 0.48 ± 0.02a 2.22 ± 0.02c 1.00 ± 0.05b

Hydrolyzable polyphenols 2.08 ± 0.03a 2.24 ± 0.02 b 1.58 ± 0.05c

IF Antioxidant Capacity (mmol TE/g IF db)

DPPH ND 2.17 ± 0.44 a ND

FRAP 9.66 ± 0.27 a 11.29 ± 0.43 b 12.25 ± 0.33b 1Values are mean ± standard error (n = 3); Wet basis (wb); Dry basis (db); No Detected (ND).


MM-D: Modified Mexican Dinner, TM-D: Traditional Mexican Dinner, AM-D: Alternative

Mexican Dinner. 3 Total IF = Sum of soluble IF + insoluble IF.


128

3.3 Changes in pH and antioxidant capacity from in vitro colonic fermentation extracts

The pH data were collected for 72 h during in vitro colonic fermentation of IF isolated from three

dinner menus (Figure 1). After fermentation, TM-D showed greatest decrease in pH value

(p<0.05) of dinner menus, but the magnitude of the reduction in pH value was markedly different

depending on fermentation time (pH ∆0h – 72h = 0.45 units). Aversely, AM-D presented a

significant increase in the pH value at 72 (0.35 units). Mean of pH levels were significantly lower

(p<0.05) in Raffinose control (pH ∆0h – 72h = 2.55 units) compared with dinner menus at 72 h

(p<0.05) which is most probably caused by the fermentation end products (mainly SCFA). In a

clinical trial, colonic pH levels were correlated positively with irritable bowel syndrome symptoms

severity, but higher levels of SCFAs and lower pH values are associated with decreased colon

transit time and positively with stool frequency (Ringel-Kulka et al., 2015). Colonic pH can

influence the formation of the gut metabolites. In vitro incubation was observed that the formation

of lactate is highest under slightly acidic conditions (pH 5·9), but, low pH (<5·2) lactate utilization

was strongly inhibited, resulting in lactate accumulation (Belenguer et al., 2007). Beside, low pH

(5.5) tends to increase acetate uptake and butyrate production while near neutral pH (6.7) has the

opposite effect (Kettle, Louis, Holtrop, Duncan, & Flint, 2015). In this in vitro study, determined

values demonstrated a varied antioxidant capacity (DPPH and FRAP), which differs from

substrate and fermentation time (Figure 1B and C). Figure 1B shows that the scavenging effects

of raffinose on DPPH radical were proportional to fermentation time and the values were higher

(p<0.05) of the menus from the 24 h to 72 h (28.35 to 94.31 mmol TE/gIF, respectively). Dinner

menus were more active at 72 h and there were no significant differences (p>0.05) between their

values (mean of 28 mmol TE/gIF). TM-D has the highest activity DPPH at 12 and a dramatic

decrease was observed from 48 h (From 20.33 to 3.41 mmol TE/g IF, respectively). Besides, in

FRAP, the AOX of AM-D at 12 h was similar to raffinose control (p>0.05) but significantly

greater than MM-D and TM-D (p<0.05). At 72 h, TM-D did not show FRAP activity but AM-D

and MM-D showed similar activity (4.13 and 4.69 mmol TE/g IF, respectively). Differences

between the AOX values found by the two methods evaluated suggest that the antioxidant

mechanism of the gut metabolites is mainly as free radical neutralizers and secondarily as


129

chelating agents. Typically, dietary polyphenols are compounds known for their antioxidant

capacity.


130

Figure 1 Changes in a) pH kinetic, b) DPPH Antiradical activity and c) FRAP chelating activity in the extracts during in vitro

colonic fermentation from (–) blank, (··♦··) raffinose, and indigestible fraction isolated from dinner menus: (-■-) Modified

Mexican Dinner, MM-D, (-▲-) Traditional Mexican Dinner, TM-D, and (-●-) Alternative Mexican Dinner, AM-D at different

at different fermentation times, Values are means ± SEM (n=3). *Significant difference using Two-way ANOVA/Fisher's LSD

test (Samples ×Time interaction, p<0.05). For DPPH and FRAP, the blank was subtracted of the samples.


0 12 24 48 72

DPP

H A

ntio

xidan

t Act

ivity

(mM

TE/

g su

bstra

te d

b)

0

20

40

60

80

100

120* * * *


0 12 24 48 72

FRAP

Anti

oxida

nt Ac

tivity

(m

M T

E /g

subs

trate

db)

0

2

4

6

8

10

12

14

16

18 * * * *


0 12 24 48 72

pH

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0 * * * *


131

In this sense, corn tortillas (important element in MM-D and TM-D menus) have a high content of

ferulic acid, carotenoids and anthocyanin’s that might lie in the colon and exert a direct antioxidant

effect (Bello-Perez, Osorio-Díaz, Agama-Acevedo, & Gonzalez-Soto, 2016). In addition,

microbial metabolites of fermentation may exert an antioxidant effect. Phenylacetic,

phenylpropionic, benzoic acid derivatives and branched-chain fatty acids (isobutyrate, 2-

methylbutyrate and isovalerate) with potential antioxidant properties can thereby be generated,

mainly by amino acids and phenolic gut metabolism (Davila et al., 2013; Valdés et al., 2015). For

its part, ellagic acid (Constituent of hydrolysable tannins) is largely metabolized by the colon

microbiota, giving rise to urolithin A (3,8-dihydroxy-6H-dibenzopyran-6-one) and its

monohydroxylated analog known as urolithin B (González-Barrio, Edwards, & Crozier, 2011)

3.4 Production of SCFA

The concentrations of SCFA at 12, 24 48 and 72 h of fermentation are present in Table 2. Acetic

propionic and butyric acid concentrations in dinner menus were significantly lower than raffinose

(p<0.05). Acetate was the most abundant SCFA produced by dinner menus at 72 h (From 20.13 in

TM-D to 48.17 mmol/L in AM-D). In propionic and butyric acid concentrations, IF dinner menus

vs blank were not significantly different (p > 0.05) throughout the all fermentation times except in

AM-D at 72 h (28.75 for propionic and 3.15 3.15 mmol/L for butyric acid). The low production of

AGCC in dinner menus (Mainly made with food of animal origin) suggests that the isolated

fractions could have a low indigestible carbohydrate content and a high indigestible protein and

lipids content. This could have negative implications on the health of individuals. An animal-based

diet increased the abundance of bile-tolerant microorganisms, including Alistipes, Bilophila, and

Bacteroides species and decreased the abundance of species specialized in the utilization of

polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus bromii) (David et al., 2014).

In contrast to the above, a higher stimulation of SCFA production in AM-D could be explained by

the presence of resistant starch in bread and consecutively cross-feeding between butyric acid-

producing bacteria and Bifidobacterium species (Rios-Covian, Gueimonde, Duncan, Flint, &

Clara, 2015).


132


raffinose and indigestible fraction isolated from dinner menus (MM-D; Modified Mexican Dinner, TM-D; Traditional Mexican

Dinner, AM-D; Alternative Mexican Dinner) 1.

SCFA/ fermentation time

Blank Raffinose IF- Dinner Menus

MM-D TM-D AM-D Acetic acid 12 h ND 184.71 ± 19.14 ND ND ND 24 h ND 240.20 ± 0.67b ND 5.44 ± 0.50a 5.02 ± 0.51a 48 h ND 193.79 ± 5.83b 5.97 ± 1.07a 2.96 ± 0.81a 10.98 ± 0.54a 72 h 1.68 ± 0.38a 405.97 ± 6.24d 29.57 ± 2.91b 20.13 ± 1.07b 48.17 ± 1.55c Propionic acid 12 h ND 82.28 ± 10.42 ND ND ND 24 h ND 102.06 ± 6.39b ND 17.02 ± 1.30a ND 48 h ND 93.68 ± 11.53b 2.05 ± 0.35a 3.50 ± 0.79a 8.32 ± 1.09a 72 h ND 92.31 ± 0.71c 5.08 ± 1.67a 2.76 ± 0.44a 28.75 ± 1.73b Butyric acid

12 h 1.20 ± 0.13a 18.10 ± 2.09b ND 0.83 ± 0.05a 0.70 ± 0.06a 24 h 1.23 ± 0.37a 21.09 ± 2.23b ND 1.33 ± 0.16a 0.31 ± 0.03a 48 h 0.54 ± 0.04a 15.30 ± 2.76b 0.73 ± 0.03a 0.97 ± 0.20a 1.41 ± 0.12a 72 h 0.42 ± 0.08a 9.46 ± 0.69c 0.60 ± 0.04ab 0.22 ± 0.05a 3.15 ± 0.11b


letters indicate significant differences in rows among substrates for a time (p < 0.05).


133

In a clinical trial, the intake of whole-grain and fiber-rich rye bread versus refined wheat bread

does not differentiate intestinal microbiota composition and main sugar-converting butyrate

producers were similarly associated (Lappi et al., 2013). However, studies on chemical

composition in IF are needed to identify foods with prebiotic potential in the Mexican diet.



the time points 12, 24, 48 and 72 h showed different metabolic profiles in relation to the different


belonging to the families of amines, alcohol and polyols, alkanes, alkyl halides, benzene and

substituted derivatives, bensothiazoles, varbonyl compounds, fatty acid esters, indoles and

derivatives, organic acid, organic disulfides, organic phosphoric acids, organic trisulfides and

SCFA were detected (Figure 2). At 12 h, organic trisulfides was the most abundant compounds

found in MM-D. The fermentation of proteinaceous substrates have been considered potentially

detrimental for host health and have been implicated in development of noncommunicable

diseases (Boulangé, Neves, Chilloux, Nicholson, & Dumas, 2016; Organ et al., 2016) and the

pathogenesis of large intestinal diseases (Louis, Hold, & Flint, 2014). Notably, in MM-D, Organic

acid production was increased at 72 h. The formation of organic acid as hexanoic acid could

explain the decrease in SCFA in this sample. Hexanoic acid formation occurs through a carboxylic

acid chain elongation process, which uses reverse β-oxidation of acetic and/or n-butyric acid, and

ethanol or lactic acid as an electron donor (de Araújo Cavalcante, Leitão, Gehring, Angenent, &

Santaella, 2016). Besides, important changes were observed in the metabolites profile of dinner

menus at 12 and 72 h, which indicates that presence of fatty acid esters would occur over a longer

portion of the colon, especially in TM-D and AM-D. These compounds were identified in patients

who presented with chronic diseases of the gastrointestinal tract, especially in Crohn's disease

(Walton et al., 2013).


134

Figure 2 Cumulative concentration (%) of volatile gut metabolites by chemical groups between

the in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from

Modified Mexican Dinner (MM-D), Traditional Mexican Dinner (TM-D), and Alternative

Mexican Dinner (AM-D) at 12 and 72 h. Detailed of identification number for each volatile

compound are outlined in the Supporting Information Table SI†.

Secondly, a statistical descriptive approach (PCA) showed that the fermentation of different corn

products for 12, 24, 48 and 72 h clearly affects the fecal metabolic fingerprint (Figure 3). The first

two principal components (PC) explained above 50% of the variation in the data (Figure 3A).

These components separate the volatiles with potentially healthy effects presenting positive

Eigenvectors in PC1 (eg, acetic, propionic and butyric acid,) from those volatiles with potentially

harmful effects presenting positive Eigenvectors on PC2 (e.g. dimethyl disulfide, dimethyl

trisulfide, indole and fatty acid esters). In fact, a good separation was observed for raffinose and

blank at all fermentation times (Figure 3b). In dinner menus, it was notable that MM-D promote a

harmful metabolic profile During the first 48 h of fermentation. TM-D and AM-D in intermediate

and long fermentation times (24, 48 and 72 h) the samples do not follow identified patterns. In this

work, the importance of improving diets with high indigestible carbohydrates content was

manifested in gut metabolic profile produced during in vitro colonic fermentation.

12 h of fermentation


Cum

ulat

ive

Con

cent

ratio

n (%

)

0

20

40

60

80

100

72h of fermentation


Cum

ulat

ive

Con

cent

ratio

n (%

)

0

20

40

60

80

100

Amines Alcohols and polyolsAlkanesAlkyl halidesBenzene and substituted derivativesBenzothiazolesCarbonyl compoundsFatty acid estersIndoles and derivativesOrganic acidOrganic disulfidesOrganic phosphoric acidsOrganic trisulfidesSCFA


135

Figure 3 Principal components plots (a) metabolites production “PC Loadings” (b) and sample

classification “PC scores” (%) during in vitro fermentation from indigestible fraction isolated

dinner menus (MM-D; Modified Mexican Dinner, TM-D; Traditional Mexican Dinner, AM-D;

Alternative Mexican Dinner). Detailed of identification number for each volatile compound are

outlined in the Table SI†.


The pH values during in vitro colonic fermentation was positive correlated (p<0.05) with

trimethylamine, phenol, phenol 4-methyl-, pentadecanoic acid, ethyl esther, phenol, 2,4-bis(1,1-

dimethylethyl) and indole negative correlated for SCFA and pentanoic acid, (Figure 4). A

significant positive correlations were found between DPPH and FRAP AOX and SCFA and

pentanoic acid and negative correlation with was found mainly for phenol and phenol 4-methyl.

Some metabolites with negative effects in pH and AOX found in this work (e.g. trimethylamine as

precursor of trimethylamine N-oxide and indole as precursor of indoxyl sulfate) have been

associated with the development of atherosclerosis and chronic kidney disease (Bäckhed, 2013;

Poesen et al., 2014).

1

2

3

45

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28 29

30

31

32

33

34

35

36

37

38

39

40

41

42

4344

45 46

47

48

49

50

51

52

53

54

-1.0 -0.5 0.0 0.5 1.0PC 1

Variance Explained: 30.27%

-1.0

-0.5

0.0

0.5

1.0PC

2V

aria

nce

Expl

aine

d: 1

8.66

% 1

2

3

45

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28 29

30

31

32

33

34

35

36

37

38

39

40

41

42

4344

45 46

47

48

49

50

51

52

53

54

12-Blank

12-Raffinose

12-MM-D

12-TM-D12-AM-D

24-Blank24-Raffinose

24-MM-D

24-TM-D

24-AM-D

48-Blank

48-Raffinose

48-MM-D

48-TM-D

48-AM-D 72-Blank

72-Raffinose72-MM-D

72-TM-D

72-AM-D

-10 -8 -6 -4 -2 0 2 4 6 8PC 1

-6

-4

-2

0

2

4

6

8

PC 2

12-Blank

12-Raffinose

12-MM-D

12-TM-D12-AM-D

24-Blank24-Raffinose

24-MM-D

24-TM-D

24-AM-D

48-Blank

48-Raffinose

48-MM-D

48-TM-D

48-AM-D 72-Blank

72-Raffinose72-MM-D

72-TM-D

72-AM-D

A) B)


136

Figure 4. Pearson’s R correlations between gut microbial metabolites, pH values and antioxidant

capacity (DPPH antiradical activity and FRAP chelating activity) in extracts obtained during in

vitro colonic fermentation of indigestible fraction isolated from dinner menus (▲Increase,

▼decrease and ▬ non-correlation, p<0.05).

pH DPPH FRAPTrimethylamineAcetic acid, methyl esterEthanolMethyl propionateTrichloromethaneButanoic acid, methyl esterDisulfide, dimethylButanoic acid, ethyl esterPentanoic acid, methyl esterBenzene, 1,3-dimethyl-Pentanoic acid, ethyl esterHexanoic acid, methyl ester2-Heptanone, 4-methyl-Hexanoic acid, ethyl esterHeptanoic acid, methyl esterDimethyl trisulfideAcetic AcidHeptanoic acid, ethyl esterDodecaneBenzene, 1,3-dichloro-Octanoic acid, methyl esterPropanoic acidBenzaldehydeOctanoic acid, ethyl esterNonanoic acid, methyl ester1,3-Benzenediol, 4-ethyl-Butanoic acidNonanoic acid, ethyl esterAcetophenoneDecanoic acid, methyl esterPentanoic acidDecanoic acid, ethyl esterUndecanoic acid, methyl esterHexanoic acidDodecanoic acid, methyl esterBenzothiazoleHeptanoic acidDodecanoic acid, ethyl esterButylated HydroxytoluenePhenolTridecanoic acid, methyl esterPhenol, 4-methyl-Octanoic AcidMethyl tetradecanoateMethyl myristoleateTributyl phosphatePentadecanoic acid, methyl esterPhenol, 2,4-bis(1,1-dimethylethyl)IndoleDodecanoic acidMethyl hexadec-9-enoateHexadecanoic acid, methyl esterHexadecanoic acid, ethyl esterEthyl Oleate


137

5. Conclusions

This study represents a new contribution on the in vitro potential effects of food habits on bacterial

metabolites production. IF-Dinner menus had extractable and non-extractable polyphenols, as

main bioactive compounds, but they do not contribute to an anti-radical AOX. Although the

differences in IF content, the magnitude of SCFA production in dinner menus was similar to blank

(without substrate) except for the AM-D menú which may contain resistant starch in IF

composition. That could be related to the low contribution of dietary fiber in Mexican diet. PCA

allowed the identification of metabolic patterns that were associated with beneficial health effects

(Low pH and high AOX). MM-D microbial metaboitos may cause adverse effects on the health of

the population. Bacterial metabolites as phenol and phenol 4-methyl were associated with

increased pH and decreased AOX in the fermentation extracts. More studies are needed to confirm

our results.

Acknowledgements The authors thank the schoolchildren who donated stool voluntarily for

participating in this study. Zamora-Gasga, VM; acknowledge the fellowship to CONACYT-

Registration number: 253795).

Conflict of interest statement

The authors have declared no conflict of interest.

5. References

AOAC. (1990). Association of Official Agricultural Chemists. Oficial Methods of Analysis. 15th

Edition.

Avila�Nava, A., Noriega, L. G., Tovar, A. R., Granados, O., Perez�Cruz, C., Pedraza�

Chaverri, J., & Torres, N. (2017). Food combination based on a pre�hispanic Mexican diet

decreases metabolic and cognitive abnormalities and gut microbiota dysbiosis caused by a

sucrose�enriched high�fat diet in rats. Molecular nutrition & food research, 61(1).


138

Bäckhed, F. (2013). Meat-metabolizing bacteria in atherosclerosis. Nature medicine, 19(5), 533-

534.

Belenguer, A., Duncan, S. H., Holtrop, G., Anderson, S. E., Lobley, G. E., & Flint, H. J. (2007).

Impact of pH on lactate formation and utilization by human fecal microbial communities.

Applied and environmental microbiology, 73(20), 6526-6533.

Bello-Perez, L. A., Osorio-Díaz, P., Agama-Acevedo, E., & Gonzalez-Soto, R. A. (2016).

Functional and Beneficial Properties of Corn Tortilla Functional Properties of Traditional

Foods (pp. 139-155): Springer.

Benzie, I. F., & Strain, J. (1996). The ferric reducing ability of plasma (FRAP) as a measure of

“antioxidant power”: the FRAP assay. Analytical biochemistry, 239(1), 70-76.

Bitzer, Z. T., Glisan, S. L., Dorenkott, M. R., Goodrich, K. M., Ye, L., O’Keefe, S. F., . . . Neilson,

A. P. (2015). Cocoa procyanidins with different degrees of polymerization possess distinct

activities in models of colonic inflammation. The Journal of Nutritional Biochemistry, 26(8),

827-831. doi: http://dx.doi.org/10.1016/j.jnutbio.2015.02.007

Bohn, T. (2014). Dietary factors affecting polyphenol bioavailability. Nutrition reviews, 72(7),

429-452.

Boulangé, C. L., Neves, A. L., Chilloux, J., Nicholson, J. K., & Dumas, M.-E. (2016). Impact of

the gut microbiota on inflammation, obesity, and metabolic disease. Genome medicine, 8(1), 1.

Campos�Vega, R., Reynoso�Camacho, R., Pedraza�Aboytes, G., Acosta�Gallegos, J.,

Guzman�Maldonado, S., Paredes�Lopez, O., . . . Loarca�Piña, G. (2009). Chemical


L.). Journal of Food science, 74(7), T59-T65.

Conlon, M. A., & Bird, A. R. (2014). The impact of diet and lifestyle on gut microbiota and

human health. Nutrients, 7(1), 17-44.


139

David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., . . .

Fischbach, M. A. (2014). Diet rapidly and reproducibly alters the human gut microbiome.

Nature, 505(7484), 559-563.

Davila, A.-M., Blachier, F., Gotteland, M., Andriamihaja, M., Benetti, P.-H., Sanz, Y., & Tomé,

D. (2013). Intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences

for the host. Pharmacological research, 68(1), 95-107.

de Araújo Cavalcante, W., Leitão, R. C., Gehring, T. A., Angenent, L. T., & Santaella, S. T.

(2016). Anaerobic fermentation for n-caproic acid production: A review. Process Biochemistry.

Flint, H. J., Duncan, S. H., Scott, K. P., & Louis, P. (2015). Links between diet, gut microbiota

composition and gut metabolism. Proceedings of the Nutrition Society, 74(01), 13-22.

Gibson, P. R., Varney, J., Malakar, S., & Muir, J. G. (2015). Food components and irritable bowel

syndrome. Gastroenterology, 148(6), 1158-1174. e1154.

González-Barrio, R., Edwards, C. A., & Crozier, A. (2011). Colonic catabolism of ellagitannins,

ellagic acid, and raspberry anthocyanins: in vivo and in vitro studies. Drug Metabolism and

Disposition, 39(9), 1680-1688.

Hartzfeld, P. W., Forkner, R., Hunter, M. D., & Hagerman, A. E. (2002). Determination of


Journal of Agricultural and food Chemistry, 50(7), 1785-1790.

Hervert-Hernández, D., García, O. P., Rosado, J. L., & Goñi, I. (2011). The contribution of fruits

and vegetables to dietary intake of polyphenols and antioxidant capacity in a Mexican rural

diet: Importance of fruit and vegetable variety. Food Research International, 44(5), 1182-1189.

doi: http://dx.doi.org/10.1016/j.foodres.2010.09.021

Kaakoush, N. O., Martire, S. I., Raipuria, M., Mitchell, H. M., Nielsen, S., Westbrook, R. F., &

Morris, M. J. (2017). Alternating or continuous exposure to cafeteria diet leads to similar shifts

in gut microbiota compared to chow diet. Molecular nutrition & food research, 61(1).


140

Kettle, H., Louis, P., Holtrop, G., Duncan, S. H., & Flint, H. J. (2015). Modelling the emergent

dynamics and major metabolites of the human colonic microbiota. Environmental

microbiology, 17(5), 1615-1630.

Lappi, J., Salojärvi, J., Kolehmainen, M., Mykkänen, H., Poutanen, K., de Vos, W. M., & Salonen,

A. (2013). Intake of whole-grain and fiber-rich rye bread versus refined wheat bread does not

differentiate intestinal microbiota composition in Finnish adults with metabolic syndrome. The

Journal of Nutrition, 143(5), 648-655.

Levy, M., Blacher, E., & Elinav, E. (2017). Microbiome, metabolites and host immunity. Current

Opinion in Microbiology, 35, 8-15.

López-Olmedo, N., Carriquiry, A. L., Rodríguez-Ramírez, S., Ramírez-Silva, I., Espinosa-

Montero, J., Hernández-Barrera, L., . . . Rivera, J. A. (2016). Usual intake of added sugars and

saturated fats is high while dietary fiber is low in the Mexican population. The Journal of

Nutrition, 146(9), 1856S-1865S.

Louis, P., Hold, G. L., & Flint, H. J. (2014). The gut microbiota, bacterial metabolites and

colorectal cancer. Nature Reviews Microbiology, 12(10), 661-672.

Manzel, A., Muller, D. N., Hafler, D. A., Erdman, S. E., Linker, R. A., & Kleinewietfeld, M.

(2014). Role of “Western diet” in inflammatory autoimmune diseases. Current allergy and

asthma reports, 14(1), 404.

Marín, L., Miguélez, E. M., Villar, C. J., & Lombó, F. (2015). Bioavailability of dietary

polyphenols and gut microbiota metabolism: antimicrobial properties. BioMed research

international, 2015.

Montreau, F. (1972). Sur le dosage des composés phénoliques totaux dans les vins par la methode

Folin-Ciocalteau. Connaiss. Vigne Vin., 24, 397-404.

Moreno-Jiménez, M. R., Cervantes-Cardoza, V., Gallegos-Infante, J. A., González-Laredo, R. F.,

Estrella, I., García-Gasca, T. d. J., . . . Rocha-Guzmán, N. E. (2015). Phenolic composition


141

changes of processed common beans: Their antioxidant and anti-inflammatory effects in

intestinal cancer cells. Food Research International, 76, 79-85.

Organ, C. L., Otsuka, H., Bhushan, S., Wang, Z., Bradley, J., Trivedi, R., . . . Hazen, S. L. (2016).

Choline Diet and Its Gut Microbe–Derived Metabolite, Trimethylamine N-Oxide, Exacerbate

Pressure Overload–Induced Heart Failure. Circulation: Heart Failure, 9(1), e002314.

Ottaviani, J. I., Kwik-Uribe, C., Keen, C. L., & Schroeter, H. (2012). Intake of dietary

procyanidins does not contribute to the pool of circulating flavanols in humans. The American

journal of clinical nutrition, 95(4), 851-858.

Parada, J., & Santos, J. L. (2016). Interactions between Starch, Lipids, and Proteins in Foods:

Microstructure Control for Glycemic Response Modulation. Critical reviews in food science

and nutrition, 56(14), 2362-2369.

Pérez-Jiménez, J., Arranz, S., & Saura-Calixto, F. (2009). Proanthocyanidin content in foods is



Poesen, R., Windey, K., Evenepoel, P., De Preter, V., Verbeke, K., & Meijers, B. (2014). The

influence of chronic kidney disease on the gut microbial metabolism. Paper presented at the

Nephrology, Dialysis, Transplantation.

Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of

antioxidant capacity and phenolics in foods and dietary supplements. Journal of Agricultural


Rajilić�Stojanović, M., Heilig, H. G., Tims, S., Zoetendal, E. G., & Vos, W. M. (2013). Long�

term monitoring of the human intestinal microbiota composition. Environmental microbiology,

15(4), 1146-1159.


142

Reed, J. D., McDowell, R. T., Van Soest, P. J., & Horvath, P. R. (1982). Condensed tannins: a

factor limiting the use of cassava forage. Journal of the Science of Food and Agriculture, 33(3),

213-220.

Reverri, E. J., Randolph, J. M., Steinberg, F. M., Kappagoda, C. T., Edirisinghe, I., & Burton-

Freeman, B. M. (2015). Black beans, fiber, and antioxidant capacity pilot study: examination of

whole foods vs. functional components on postprandial metabolic, oxidative stress, and

inflammation in adults with metabolic syndrome. Nutrients, 7(8), 6139-6154.

Richards, J. L., Yap, Y. A., McLeod, K. H., Mackay, C. R., & Mariño, E. (2016). Dietary

metabolites and the gut microbiota: an alternative approach to control inflammatory and

autoimmune diseases. Clinical & Translational Immunology, 5(5), e82.

Ringel-Kulka, T., Choi, C. H., Temas, D., Kim, A., Maier, D. M., Scott, K., . . . Ringel, Y. (2015).

Altered colonic bacterial fermentation as a potential pathophysiological factor in irritable bowel

syndrome. The American journal of gastroenterology.

Rios-Covian, D., Gueimonde, M., Duncan, S. H., Flint, H. J., & Clara, G. (2015). Enhanced

butyrate formation by cross-feeding between Faecalibacterium prausnitzii and Bifidobacterium

adolescentis. FEMS microbiology letters, 362(21), fnv176.

Santiago-Torres, M., Kratz, M., Lampe, J. W., Tapsoba, J. D. D., Breymeyer, K. L., Levy, L., . . .

Neuhouser, M. L. (2016). Metabolic responses to a traditional Mexican diet compared with a

commonly consumed US diet in women of Mexican descent: a randomized crossover feeding

trial. The American journal of clinical nutrition, 103(2), 366-374.

Saura-Calixto, F., García-Alonso, A., Goni, I., & Bravo, L. (2000). In vitro determination of the

indigestible fraction in foods: an alternative to dietary fiber analysis. Journal of Agricultural


Sensoy, I. (2014). A review on the relationship between food structure, processing, and

bioavailability. Critical reviews in food science and nutrition, 54(7), 902-909.


143

Surampudi, P., Enkhmaa, B., Anuurad, E., & Berglund, L. (2016). Lipid Lowering with Soluble

Dietary Fiber. Current atherosclerosis reports, 18(12), 75.

Tabernero, M., Venema, K., Maathuis, A. J., & Saura-Calixto, F. D. (2011). Metabolite production


diets. Journal of Agricultural and food Chemistry, 59(16), 8968-8975.

Valdés, L., Cuervo, A., Salazar, N., Ruas-Madiedo, P., Gueimonde, M., & González, S. (2015).

The relationship between phenolic compounds from diet and microbiota: impact on human

health. Food & function, 6(8), 2424-2439.

Walton, C., Fowler, D. P., Turner, C., Jia, W., Whitehead, R. N., Griffiths, L., . . . Cole, J. A.

(2013). Analysis of volatile organic compounds of bacterial origin in chronic gastrointestinal

diseases. Inflammatory bowel diseases, 19(10), 2069-2078.

Yasmin, A., Butt, M. S., Afzaal, M., van Baak, M., Nadeem, M. T., & Shahid, M. Z. (2015).

Prebiotics, gut microbiota and metabolic risks: Unveiling the relationship. Journal of

Functional Foods, 17, 189-201. doi: http://dx.doi.org/10.1016/j.jff.2015.05.004


J., & Sáyago-Ayerdi, S. G. (2015). In vitro colonic fermentation of food ingredients isolated

from Agave tequilana Weber var. azul applied on granola bars. LWT-Food Science and

Technology, 60(2), 766-772.

Zhang, G., Maladen, M., Campanella, O. H., & Hamaker, B. R. (2010). Free fatty acids

electronically bridge the self-assembly of a three-component nanocomplex consisting of

amylose, protein, and free fatty acids. Journal of Agricultural and food Chemistry, 58(16),

9164-9170.


144

Table SI. Volatile compounds characterization of in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from dinner menus (MM-D; Modified Mexican diet, TM-D; Traditional Mexican Diet, AM-D;

Alternative Mexican diet) analyzed by SPME–GC/MS.

ID RT Volatil Name Chemical Group T12 T24 T48 T72 VC (%) Blank Raffinose MM-D TM-D AM-D Blank Raffinose MM-D TM-D AM-D Blank Raffinose MM-D TM-D AM-D Blank Raffinose MM-D TM-D AM-D

1 6.02 Amines Trimethylamine 17.91 0.00 28.77 14.38 29.97 11.25 0.00 12.80 8.24 28.33 19.65 0.00 18.49 4.92 24.00 45.05 0.00 10.11 4.02 11.43 84.45

2 7.41 Carboxylic acids and derivatives Acetic acid, methyl ester 0.00 0.00 0.00 19.78 23.72 0.00 0.00 0.00 22.35 34.89 0.00 0.00 0.00 19.71 42.97 4.03 0.00 34.29 14.50 7.27 127.59

3 8.36 Alcohols and polyols Ethanol 0.00 35.05 93.49 0.00 117.13 0.00 28.46 71.25 56.29 117.25 48.85 98.12 50.32 0.00 122.75 95.10 93.99 71.29 0.00 70.44 74.02

4 9.30 Carboxylic acids and derivatives Methyl propionate 3.80 3.70 0.00 4.79 5.16 0.00 4.76 0.00 12.39 9.87 4.24 4.51 0.00 5.58 12.78 0.00 10.42 9.25 3.69 0.00 89.27

5 10.92 Alkyl halides Trichloromethane 17.55 11.06 0.00 8.20 0.00 10.10 10.78 0.00 14.12 17.62 15.41 18.80 0.00 13.21 15.40 28.89 20.99 10.55 4.80 0.00 75.26

6 12.21 Fatty Acyls Butanoic acid, methyl ester 13.17 7.17 5.50 32.19 41.35 12.77 38.46 6.21 82.69 63.04 10.04 11.13 0.00 26.25 73.49 15.44 9.65 24.31 17.46 48.43 88.78

7 14.23 Organic disulfides Disulfide, dimethyl 135.14 0.00 199.66 173.63 148.02 0.00 0.00 106.90 133.76 163.97 63.17 0.00 15.80 6.50 41.54 32.00 0.00 19.01 17.09 22.86 109.28

8 14.94 Fatty Acyls Butanoic acid, ethyl ester 0.00 0.00 96.60 49.38 44.78 0.00 0.00 101.63 7.70 7.01 0.00 0.00 40.29 5.01 11.18 0.00 0.00 3.43 0.00 69.12 153.41

9 16.40 Fatty Acyls Pentanoic acid, methyl ester 12.24 3.68 15.04 86.12 32.18 8.76 0.00 30.25 139.41 167.17 17.55 0.00 12.63 132.47 302.25 32.69 4.98 114.83 205.02 99.51 117.86

10 18.60 Benzene and substituted derivatives Benzene, 1,3-dimethyl- 5.83 0.00 6.35 3.96 5.85 4.08 4.08 5.36 5.62 4.39 0.00 3.39 0.00 4.57 7.45 8.99 9.69 8.88 0.00 3.87 64.19

11 19.15 Fatty Acyls Pentanoic acid, ethyl ester 0.00 0.00 0.00 149.88 107.01 0.00 0.00 302.09 10.26 14.81 0.00 0.00 260.79 11.52 43.64 0.00 0.00 16.05 29.93 793.46 216.72

12 20.56 Fatty Acyls Hexanoic acid, methyl ester 14.17 0.00 8.42 52.57 21.64 49.25 0.00 27.25 273.36 42.32 164.77 6.59 50.62 1085.23 78.63 211.31 0.00 940.18 1065.90 21.68 176.91

13 21.30 Carbonyl compounds 2-Heptanone, 4-methyl- 3.72 0.00 10.09 5.70 8.68 0.00 4.81 8.80 0.00 5.01 4.67 4.24 3.94 0.00 4.99 7.81 7.89 0.00 0.00 4.30 79.09

14 23.08 Fatty Acyls Hexanoic acid, ethyl ester 0.00 0.00 76.74 87.38 34.88 0.00 0.00 324.84 13.50 8.28 9.35 0.00 629.83 45.45 37.24 13.93 0.00 70.70 100.33 573.19 183.93

15 24.59 Fatty Acyls Heptanoic acid, methyl ester 15.21 0.00 7.89 76.37 43.20 114.80 0.00 51.18 902.36 146.15 510.74 0.00 183.23 7048.82 169.21 634.37 13.46 8426.79 9204.20 76.37 215.83

16 26.00 Organic trisulfides Dimethyl trisulfide 502.99 0.00 1350.18 955.66 590.14 0.00 0.00 555.46 477.72 603.53 65.98 0.00 42.11 8.90 193.20 64.85 0.00 29.04 27.88 51.02 138.32

17 26.38 SCFA Acetic Acid 0.00 184.72 0.00 0.00 0.00 0.00 240.20 0.00 5.44 5.02 0.00 193.79 5.97 2.96 10.98 1.68 405.97 29.57 20.13 48.17 191.14

18 26.86 Fatty Acyls Heptanoic acid, ethyl ester 0.00 0.00 103.97 34.82 36.13 5.11 0.00 595.93 32.52 8.05 31.02 0.00 1883.68 293.14 21.98 44.96 0.00 710.49 756.77 764.87 177.75

19 27.45 Alkanes Dodecane 0.00 0.00 5.77 4.60 4.98 0.00 0.00 4.89 3.29 4.14 0.00 4.70 4.39 0.00 3.96 0.00 3.90 0.00 0.00 3.89 94.98

20 28.03 Benzene and substituted derivatives Benzene, 1,3-dichloro- 0.00 0.00 12.00 13.37 0.00 0.00 0.00 7.66 10.09 14.97 0.00 0.00 5.82 0.00 10.74 0.00 0.00 6.86 6.73 5.65 113.58

21 28.51 Fatty Acyls Octanoic acid, methyl ester 31.33 10.33 0.00 674.68 501.52 114.96 0.00 7.80 1031.37 1016.15 299.41 0.00 23.14 1957.45 1459.89 439.78 5.43 2625.29 2202.15 263.12 129.91

22 29.49 SCFA Propanoic acid 0.00 82.29 0.00 0.00 0.00 0.00 102.06 0.00 17.02 0.00 0.00 93.68 2.05 3.50 8.32 0.00 92.31 5.08 2.76 28.76 169.42

23 30.02 Benzene and substituted derivatives Benzaldehyde 26.98 18.36 24.41 26.29 26.40 23.32 21.91 14.22 19.57 0.00 22.19 31.97 14.09 11.81 16.55 55.60 0.00 0.00 0.00 21.36 70.28

24 30.55 Fatty Acyls Octanoic acid, ethyl ester 0.00 0.00 138.90 578.83 364.07 5.42 0.00 66.18 0.00 47.41 15.24 0.00 164.57 0.00 121.96 33.02 0.00 214.37 127.10 348.92 141.75

25 32.20 Fatty Acyls Nonanoic acid, methyl ester 11.79 0.00 0.00 117.22 136.02 0.00 0.00 0.00 218.52 301.83 44.85 0.00 5.15 224.52 465.74 53.64 0.00 193.85 113.38 63.71 130.53

26 32.70 Benzene and substituted derivatives 1,3-Benzenediol, 4-ethyl- 13.25 3.90 0.00 24.90 0.00 4.86 6.29 15.72 23.86 25.92 6.30 9.29 6.68 12.13 10.11 4.45 8.96 10.20 4.83 6.66 76.30

27 33.39 SCFA Butanoic acid 1.20 18.10 0.00 83.00 0.70 1.23 21.09 0.00 1.33 0.31 0.54 15.30 0.73 0.97 1.41 0.43 9.46 0.61 0.22 3.15 235.52

28 34.41 Fatty Acyls Nonanoic acid, ethyl ester 0.00 0.00 50.56 38.95 51.21 0.00 0.00 26.23 17.03 21.40 0.00 0.00 24.29 14.69 46.22 10.61 0.00 21.30 11.31 45.26 98.58

29 35.14 Benzene and substituted derivatives Acetophenone 12.47 0.00 197.71 46.25 43.02 0.00 0.00 145.34 49.95 44.22 15.02 0.00 96.48 32.74 33.28 36.99 0.00 91.68 0.00 35.20 119.74

30 36.69 Fatty Acyls Decanoic acid, methyl ester 40.71 39.16 15.01 3159.65 4109.32 32.96 4.54 14.64 3172.16 4468.53 52.76 4.76 24.62 6513.80 9647.04 85.27 24.86 491.03 1879.75 2228.69 148.77

31 39.24 Fatty Acyls Pentanoic acid 20.48 185.54 7.94 16.85 0.00 23.04 160.60 9.93 33.14 0.00 29.85 246.82 7.56 38.28 30.80 20.30 180.94 45.16 0.00 22.37 137.33

32 39.80 Fatty Acyls Decanoic acid, ethyl ester 0.00 0.00 287.55 1155.40 1382.79 0.00 0.00 123.20 210.62 372.26 0.00 0.00 123.46 217.17 852.44 0.00 0.00 65.67 137.04 1687.66 155.09


145

33 43.04 Fatty Acyls Undecanoic acid, methyl ester 0.00 0.00 0.00 59.60 116.55 0.00 0.00 0.00 98.97 211.90 0.00 0.00 0.00 131.07 310.56 6.82 0.00 19.78 53.70 60.85 157.74

34 45.97 Fatty Acyls Hexanoic acid 33.52 32.22 0.00 0.00 0.00 46.51 34.24 11.07 0.00 0.00 112.52 71.02 20.20 255.38 0.00 152.78 38.91 310.87 155.66 0.00 140.96

35 48.45 Fatty Acyls Dodecanoic acid, methyl ester 110.98 34.31 0.00 1578.77 4941.14 111.89 8.76 0.00 2675.44 7864.62 145.25 15.33 0.00 3410.43 9299.80 180.01 16.24 348.87 1634.76 2865.39 155.70

36 49.92 Benzothiazoles Benzothiazole 6.92 5.33 21.42 25.30 0.00 5.83 5.74 18.21 7.04 0.00 7.70 0.00 0.00 7.18 0.00 10.78 17.27 12.06 16.98 20.14 85.40

37 50.22 Fatty Acyls Heptanoic acid 18.24 15.57 0.00 7.95 0.00 74.56 22.46 33.82 34.20 0.00 268.61 35.67 0.00 686.81 0.00 341.28 51.79 784.48 598.19 0.00 169.34

38 50.45 Fatty Acyls Dodecanoic acid, ethyl ester 17.72 0.00 127.86 379.57 801.69 11.76 0.00 64.52 93.90 278.78 19.52 0.00 54.63 107.89 473.15 47.88 0.00 49.14 110.18 866.25 148.50

39 50.70 Benzene and substituted derivatives Butylated Hydroxytoluene 41.27 28.58 9.21 70.91 47.37 32.81 17.56 9.23 39.89 0.00 44.14 22.15 19.80 44.63 35.12 57.76 29.84 60.33 36.83 0.00 60.38

40 51.14 Benzene and substituted derivatives Phenol 177.85 20.91 168.73 82.06 64.76 176.89 32.34 134.11 76.30 111.24 157.18 41.68 268.82 84.17 218.92 207.88 65.83 156.51 59.69 119.36 56.59

41 52.04 Fatty Acyls Tridecanoic acid, methyl ester 8.61 0.00 0.00 27.21 43.65 6.35 0.00 0.00 51.30 65.23 19.21 0.00 0.00 68.06 63.36 10.74 0.00 12.40 36.37 22.88 112.03

42 53.30 Benzene and substituted derivatives Phenol, 4-methyl- 398.84 86.17 384.39 305.84 262.02 370.28 115.45 434.07 337.66 400.50 583.56 115.41 684.37 468.19 321.18 903.69 165.89 834.26 377.03 254.97 56.90

43 53.58 Fatty Acyls Octanoic Acid 0.00 12.94 0.00 0.00 9.89 0.00 0.00 0.00 0.00 10.84 0.00 0.00 0.00 0.00 15.00 28.70 33.98 19.65 35.76 12.36 135.53

44 55.62 Fatty Acyls Methyl tetradecanoate 106.13 7.54 23.83 517.15 698.56 101.85 7.09 39.23 951.70 921.04 216.28 12.85 41.00 1260.18 975.61 198.24 16.21 274.45 776.63 357.54 108.58

45 55.86 Fatty Acyls Methyl myristoleate 18.99 4.22 13.64 183.92 83.29 19.69 0.00 21.25 190.76 278.51 36.13 0.00 18.88 200.10 389.79 37.70 0.00 75.13 85.17 0.00 131.85

46 56.22 Organic phosphoric acids and derivatives Tributyl phosphate 3.58 4.06 0.00 8.22 7.68 4.16 0.00 0.00 0.00 0.00 4.70 0.00 0.00 0.00 0.00 11.06 17.35 13.26 0.00 5.44 130.05

47 58.12 Fatty Acyls Pentadecanoic acid, methyl ester 15.84 0.00 0.00 19.53 24.53 8.45 0.00 7.43 31.07 38.79 21.84 0.00 0.00 32.65 28.08 13.43 0.00 23.28 25.81 11.88 84.60

48 58.76 Benzene and substituted derivatives Phenol, 2,4-bis(1,1-dimethylethyl) 16.70 0.00 0.00 12.00 9.94 0.00 0.00 0.00 10.00 11.15 16.83 0.00 0.00 19.23 15.33 19.13 0.00 0.00 13.58 12.53 98.08

49 60.75 Indoles and derivatives Indole 483.61 27.41 867.43 926.11 869.79 253.76 21.94 583.60 560.05 691.16 304.05 18.17 532.96 517.63 631.22 624.72 49.76 557.18 308.95 401.66 61.69

50 62.23 Fatty Acyls Dodecanoic acid 0.00 0.00 0.00 11.75 16.24 0.00 0.00 0.00 34.97 31.10 10.00 16.36 0.00 47.13 58.41 12.85 83.09 28.40 55.39 25.45 111.06

51 62.53 Fatty Acyls Methyl hexadec-9-enoate 0.00 0.00 14.22 104.63 0.00 0.00 0.00 14.43 78.49 152.16 0.00 0.00 14.51 105.05 146.72 0.00 0.00 0.00 71.38 55.14 139.52

52 62.94 Fatty Acyls Hexadecanoic acid, methyl ester 29.88 0.00 11.34 26.49 67.59 21.08 0.00 12.11 83.42 115.46 28.71 0.00 14.58 132.46 137.12 41.57 0.00 117.26 101.51 52.96 96.64

53 64.73 Fatty Acyls Hexadecanoic acid, ethyl ester 0.00 0.00 25.64 29.75 41.68 0.00 0.00 26.00 0.00 27.05 0.00 0.00 23.46 22.10 40.75 0.00 0.00 21.69 19.42 26.03 99.16

54 68.36 Fatty Acyls Ethyl Oleate 84.55 0.00 98.53 72.52 217.24 0.00 0.00 90.83 0.00 203.76 81.61 0.00 100.84 0.00 252.13 209.45 0.00 201.05 142.85 145.54 91.81

ID;Identification,RT;retentitontimeVC;Variationcoefficient


146

7.5 Los metabolitos intestinales se asocian al pH y cambios en la actividad antioxidante

durante la fermentación colónica in vitro de productos procesados mexicanos de maíz.

Resumen

La principal fuente de carbohidratos en la mayoría de las dietas es el almidón, que es el

carbohidrato de almacenamiento de legumbres, hortalizas de raíz y cereales como maíz.1 México

tiene el mayor consumo per cápita de maíz en el mundo, que se consume principalmente como

tortilla y otros productos de maíz nixtamalizados.2 Existe una creciente evidencia que muestra

asociaciones entre patrones dietéticos tradicionales que incluían productos de maíz y la

disminución en el riesgo de enfermedades crónicas no transmisibles en la población mexicana.3,4

La presencia de polifenoles, almidón resistente (AR) y polisacáridos no amiláceos (PNA) en los

productos de maíz hacen que estos alimentos puedan ser considerados como nutracéuticos.5

Recientes investigaciones han reportado una relación inversa entre el consumo de fibra

(incluyendo RS), los polifenoles dietéticos y el riesgo de obesidad y diabetes asociados con

cambios en la microbiota intestinal.6 La microbiota intestinal puede producir metabolitos dañinos

asociados con enfermedades o compuestos beneficiosos que protegen contra la enfermedad. La

microbiota intestinal ejerce un papel primordial promoviendo la fermentación de fibra soluble y

RS para la producción de ácidos grasos de cadena corta (AGCC), principalmente ácido acético,

propiónico y butírico.7 Además, los compuestos nitrogenados que tienen el potencial de promover

el cáncer.8 Claramente, la dieta influye en el perfil de los metabolitos intestinales y estos juegan un

papel importante en la salud del huésped. Sin embargo, no existen datos sobre la fermentación

colónica de componentes indigestibles aislados de productos de maíz mexicanos. En este sentido,

se han utilizado modelos de fermentación fecal in vitro para evaluar la producción de metabolitos

intestinales debido a que estos resultados son difíciles de medir in vivo.9 La microextracción en

fase sólida (MEFS) seguida de cromatografía de gases-espectrometría de masas (CG- EM) se han

utilizado para analizar el perfil de los metabolitos intestinales.10 Por lo anterior, el objetivo de este

capítulo fue determinar las correlaciones entre los cambios en el pH, los valores de la actividad

antioxidante y la concentración de metabolitos intestinales formados durante la fermentación

colónica in vitro de la fracción indigestible aislada de tres productos de maíz: Totopos del istmo,

tortilla de maíz horneadas y una tortilla de maíz tradicional.


147

Gut metabolites are associated to pH and antioxidant activity changes during in vitro colonic

fermentation of Mexican processed corn products.

Abstract

Diet is a major factor driving the composition and metabolism of the colonic microbiota. In

Mexico, nixtamalized corn products are widely consumed. However, there are no reports

addressing the production of metabolites during the colonic fermentation of their indigestible

fraction (IF). The aim of this work was to determine changes in antioxidant capacity (AOX: DPPH

and FRAP), pH values, short chain fatty acid (SCFA) concentration and relative metabolite

production during in vitro colonic fermentation of IF isolated from three corn products: Istmo

Totopos (IT), baked corn tortilla (BCT), and traditional corn tortilla (TCT). IFs were isolated after

withstanding a simulated in vitro gastrointestinal digestion, characterized and used as substrate for

in vitro colonic fermentation using a human fecal batch culture model. Fermentation extracts were

characterized by gas chromatography–mass spectrometry analysis using a headspace solid-phase

micro extraction technique (SPME-GC-MS.) Significant differences (p<0.05) were found on pH

values and AOX of the samples and a time dependence was observed. TCT and BCT presented the

highest DPPH values after 24 h of fermentation (60.21 and 53.21 mmol TE/g IF, respectively).

Butyric acid production was higher (p<0.05) from TCT (331.96 mmol·L-1 at 48h) than from a

fermentable reference substrate (raffinose: 144.93 mmol·L-1). Besides, 46 volatile compounds

were detected by SPME-GC-MS and two principal components were identified. Changes in

bacterial metabolites profile were found after 12 and 48 h of fermentation, except for IT.

Trimethylamine, phenol and phenol 4-methyl- were associated with increased pH and decreased

AOX. Corn products may have potential beneficial effects on gut health.


148

Graphical Abstract

1 Introduction

Our diet is composed of a variety of dietary macronutrients- carbohydrates, proteins, fats and

fibers. Starch is the main is storage carbohydrate in dried legumes, root vegetables and cereals,

such as corn, and represents the major source of carbohydrates in most diets.1 Mexico has the

highest per capita consumption of corn, which is mainly eaten as corn tortilla (325 g of tortilla per

person/ per day) and others nixtamalized corn products.2 Growing evidence shows associations

between traditional dietary patterns that include corn products and decreased risk of chronic non-

communicable diseases in the Mexican population.3, 4 The presence of polyphenols (PP), resistant

starch (RS) and non-starch polysaccharides (NSP) in corn products make these foods good

candidates to qualify as nutraceuticals or functional foods, promoting health benefits.5 Recent

research has revealed an inverse relationship between consumption of dietary fiber (including RS)

and dietary PP and the risk of obesity and diabetes, a relationship that seems associated with

changes in the gut microbiota.6 The gut microbiota can either produce harmful metabolites

associated with certain pathologies or beneficial compounds that protect against disease. The

microbiota exerts a primordial role in the fermentation of soluble dietary fiber and RS that leads to

the production of short-chain fatty acids (SCFA), mainly acetic, propionic, and butyric acids.7 In


149

addition, nitrogenous compounds (fermentation products of resistant proteins), particularly N-

nitrous, are potential cancer promoters and exert carcinogenic effects via DNA alkylation, which

can cause mutations.8 Clearly, diet has marked influence on gut metabolites profile and this has an

important impact on the host health. It is noteworthy, however, that despite the relative abundance

of corn products in the Mexican diet, no data are available on the colonic fermentation of their

indigestible components. In this sense, in vitro fecal fermentation models have been used to

evaluate the production of gut metabolites, since these outcomes are difficult to measure in vivo.9

Headspace solid-phase microextraction (HS-SPME) followed by Gas Chromatography–Mass

Spectrometry (GC-MS) have been used to analyze gut metabolites profile, given its better matrix

clean-up features, relatively inexpensiveness, as well as its fats operation and easy automation

possibilities.10 In view of all the above, in the present study, an in vitro gastrointestinal digestion

model was used to quantify and isolate the indigestible fraction (IF) in three nixtamalized corn

products of ample consumption in Mexico: Istmo Totopo (IT), baked corn tortilla (BCT) and

traditional corn tortilla (TCT). The main objective was to submit the isolated IF to in vitro colonic

fermentation, to determine the correlations between changes in pH, antioxidant capacity (AOX)

values and gut metabolites concentration.

2 Materials and methods

2.1 Raw material

Three corn products consumed popularly in Mexico were utilized in this study: Istmo Totopos

(IT), baked corn tortilla (BCT), and traditional corn tortilla (TCT). IT were supplied by the

National Center for Genetic Resources (Jalisco, México) and were traditionally prepared with a

corn breed, ‘Zapalote Chico’, cultivated in Oaxaca, Mexico. BCT (Saníssimo, Corporativo Bimbo,

S.A. de C.V., Mexico DF, Mexico) and TCT (Maseca®, Gruma, S.A.B. de C.V., Nuevo Leon,

Mexico) were purchased from the local market. Each sample was homogenized in a food

processor (NB-101B, Nutribullet, China), frozen (-80 °C), freeze-dried (FreeZone 6, Labconco,

USA), milled, sieved with a mesh size of 500 µm and stored at -20 °C until analysis.

2.2 Chemical composition of corn products


150

Moisture, ash, protein and fat contents were analyzed according to AOAC (1990)11 methods

925.10, 923.03, 920.87 and 920.39, respectively. Additionally, resistant starch (RS) was assessed

following a multienzymatic digestion protocol12 using pepsin (P-7000, Sigma Aldrich, St Louis

Missouri, USA), α-amylase (A-3176, Sigma Aldrich), and amyloglucosidase (A-9913, Sigma

Aldrich).

2.3 Simulated in vitro gastrointestinal digestion

2.3.1 Quantification and isolation of indigestible fraction

A digestion procedure that mimics the physiological situation in the upper digestive tract (stomach

and small intestine) was utilized for the quantification of indigestible fraction (IF).13 In order to

increase the yield of isolated IF, certain preparative adjustments were included. Briefly, 9 g of

freeze-dried samples were sequentially incubated with: a) pepsin (0.6 mL of a 300 mg/mL solution

in 0.2 M HCl-KCl buffer, pH 1.5, 40 °C, 1 h, P-7000, Sigma Aldrich, USA), b) pancreatin (3 mL

of a 5 mg/mL solution in 0.1 M phosphate buffer; pH 7.5, 37 °C, 6 h, P-1750, Sigma Aldrich,

USA), c) alpha-amylase (3 mL of a 120 mg/mL solution in 0.1 M Tris-maleate buffer, pH 6.9, 37

°C, 16 h, A-3176, Sigma Aldrich). Samples were transferred into dialysis tubes (D9527 -30.48 m

avg. Flat width 43 mm, 14000 Da, Sigma Aldrich) and dialyzed against tap water for 48 h at 25 °C

to remove the digestion-released compounds.14 Total IF (dialysis retentate) was collected, freeze-

dried, milled (IKA M20, USA) and sieved using a 500 µm mesh, and placed in bags with seal,

which were stored at -20 ° C until subsequent analyses.


Total IF isolated from the corn products was fermented in a batch culture system with pre-

conditioned nutritive medium and under strict anaerobic conditions, at 37 °C.15 Fresh faecal

samples were collected (maximum 2 h after defecation) from four healthy adults (25–35 years)

who declared no gastrointestinal diseases and no intake of antibiotics at least 3 months before the

beginning of the study. The medium was adjusted to pH 7 using HCl and reduced in an anaerobic

chamber for 12 h prior to the fermentation. A 1:10 (w/v) dilution of the faecal samples with 0.1

mol/L, pH 7 phosphate buffer was prepared and homogenized in a digital high-speed homogenizer


151

system (IKA-Ultra-Turrax, T18, USA; 1 min, 6000 rpm). The resulting faecal suspension (1 mL)

was distributed in disposable tubes (containing 9 mL nutritive medium) and 0.1 g of the isolated

total IF from each corn product were added. In parallel, two different controls were incubated

under the same conditions: a) rafinosse was incubated in medium with faeces inoculum as a

fermentable indigestible sugar reference, and b) the faecal suspension was incubated without any

additional substrate, and used as a negative control. All incubations were performed in triplicate

and both samples and controls were analyzed after 12, 24, and 48 h of fermentation. Changes in

pH values were measured at each time point. The material obtained at each time of fermentation

was centrifuged (Hermle Z 323 K; Wehingen, Germany) at 3500 × g for 15 min at 4 °C.

Supernatants were divided into two parts: one was used for metabolite profile analysis and the

other was for AOX assays. Samples were always kept at −80 °C until analysis.

2.4.1 Metabolite profile analysis

Headspace solid-phase microextraction (HS-SPME) conditions

Fermentation supernatants (500 mg) were placed into a 20 mL vial sealed with a magnetic cap

with a poly-tetra-fluoro-ethylene (PTFE)/silicon septum. HS-SPME method was used for the

extraction and concentration of volatile compounds.16 Briefly, a 2 cm polydimethylsiloxane-

divinylbenzene-carboxen (PDMS/DVB/CAR) solid phase microextraction (SPME) fiber was used.

Sample vials were placed into a Gerstel MPS2Autosampler. The samples were stirred (250 rpm) at

45 °C for 5 min. The DVB/CAR/PDMS fiber was then exposed to the headspace, and the samples

were stirred for 120 min (250 rpm at 45 °C). The fiber was then inserted into the injection port of

the GC system for thermal desorption (240 °C for 10 min) for GC/MS analysis. The extractions

were performed in triplicate. Five runs with twelve vials / run were performed and the tests order

was completely randomized.

Gas chromatography–mass spectrometry (GC–MS) analysis

The volatile constituents were analyzed with an Agilent 5975C VL mass selective detector

coupled to an Agilent 7890A gas chromatograph (Agilent Technologies, Inc., Santa Clara, CA),

equipped with a DB-5MS capillary column (60 m X 250 µm X 0.25 µm; Agilent). Helium (flow


152

rate, 1 mL/min) was used as the carrier gas. The injector temperature was 250 °C. The oven

temperature program was 40°C for 5 min, increased by 5°C /min to 200 °C and was maintained for

2 min, then increased by 20°C/min to 230°C and held for 15 min. Mass spectra were recorded at

70 eV in electron impact (EI) ionization mode. The temperature of the quadrupole mass detector

and ion source were 150 and 230°C, respectively. The injector was used in the splitless mode.

SCFA were quantified using standard curves of acetic, propionic and butyric acids. Tentative

identification of the volatile components was done by comparing the mass spectra of the samples

with the data system library MSD ChemStation software (Agilent G1701EAversion E.02.00.493).

Relative concentration of all fermentation metabolites versus acetic acid as internal standard was

calculated and the results were expressed in mmol/L extract.

2.4.2 Antioxidant capacity (AOX) during in vitro colonic fermentation

Changes in the AOX of the sample extracts during in vitro colonic fermentation at 12, 24 and 48 h

were measured by two methods: a) DPPH• radical scavenging activity: 1,1-Diphenyl-2-picryl

hydrazyl (DPPH) radical scavenging activity assay was determinate in aqueous-organic extracts.

Extract or Trolox standard (30 µL) were reacted with 200 µL of DPPH solution (190 µM) and

absorbance readings at 517 nm were taken after 10 min reaction using a multi-detection microplate

reader (Biotek, Synergy HT,Winooski VT, USA) with Gen5 software. Trolox (37.5 - 600 µM) was

used as the standard for the calibration curve, and the DDPH radical-scavenging activities were

expressed as Trolox equivalent (TE; mmol/g dry basis).17 b) Ferric-ion reducing antioxidant

power: The stock solutions included 0.3 M acetate buffer (0.3 M acetic acid and 0.3 M sodium

acetate), pH 3.6, 10 mM TPTZ (2, 4, 6-tripyridyl-s-triazine) solution in 40 mM HCl and 20 mM

ferric chloride (FeCl3.6H2O) solution. The fresh working solution was prepared in a 10:1:1 ratio

prior to use and heated to 37oC in water bath before use. Briefly, 24 µL sample extracts or Trolox

standard were added to each well of a 96-well microplate and was dispensed 180 µL of FRAP

solution using multi-channel dispenser. Absorbance at 595 nm was taken after 30 min of reaction

using microplate reader (Biotek, Synergy HT, Winooski VT, USA) with Gen5 software. The

standard curve was used between 8.125x10-3 - 0.13 mmol Trolox and results are expressed in

Trolox equivalent (TE; mmol/g dry matter).18


153


Results are expressed as mean ± SEM (n=3). Univariate testing of differences for corn products

was processed by one-way ANOVA/Fisher's Least Significant Difference test. Besides,

Multivariate analysis was applied for metabolites profile interpretation. Principal component

analysis (PCA) was used to determine patterns in relative metabolites production. Finally,

Pearson´s correlation coefficients were used to evaluate the relationship between individual

metabolites and changes in pH and AOX during in vitro colonic fermentation. All analyzes were

performed using STATISTICA software, version 10.0 (StatSoft. Inc. 1984–2007, Tulsa, OK,

USA). A significance level of α = 0.05 was used in data analysis.

3 Results and Discussion

3.1 Chemical composition

Table 1 shows the chemical composition of the different nixtamalized corn products. Differences

were found among the products (p<0.05). BCT exhibited the highest ash content of the three corn

samples, followed by IT and TCT (3.39, 2.40 and 1.35 g/100g db, respectively). The type of corn

(agronomic varieties) and calcium salts used as additives in the formulation of BCT could

influence the high value recorded for this sample.19 Regarding fat content, BCT and IT did not

exhibit appreciable difference (p>0.05), but TCT had a significantly lower content. The maize

cultivar employed can play an important role in these results; ‘Zapalote chico’ race is

characterized by high oil content and flotation index, and a low pericarp content.20 Fat content in

corn tortilla prepared with different nixtamalization procedures (3.41 to 4.60 g/100g db) have been

previously shown to have higher fat values than those reported here.21 On the other hand, the three

samples exhibited different protein contents (p<0.05); with TCT showing the highest level (8.91

g/100g db). Those differences in protein content may be attributed to temperature differences

during the hydrothermal treatment (nixtamalization); moisture content and time of processing

greatly influence the salt-soluble protein content of corn kernels.22

3.1.1 Resistant starch (RS) and indigestible fraction (IF) in corn products


154

Starch is another important component in corn products. Starches can be classified into three types

according to their digestibility: rapidly digestible, slowly digestible, and resistant starch (RS).23 RS

is considered a bioactive compound forming part of the indigestible fraction. RS, insoluble and

soluble IF contents in corn products are presented in Table 1. RS content in TCT was the highest

(5.2 g/100 g) although not significantly different (p>0.05) from the level in IT (4.34 g/100g db).

These values were similar to those reported in corn tortilla made with kernels of different

endosperm (floury, intermediate, and vitreous) stored for 96 h at 4 °C (4.9, 4.4 and 5.6 g/100 g

respectively).24 The variations in RS contents may be related to starch structural changes

(intermolecular associations between amylose and amylopectin, amylose and lipids or amylose-

protein complexes) occurring during processing. In addition, storage of corn products potentiates

the formation of retrograded RS fractions .25

Table 1 Chemical composition and indigestible fraction content from three Mexican corn

products: Istmo Totopos (IT), Baked corn tortilla (BCT) and traditional corn tortilla (TCT (g/100g

db) 1

Parameter Mexican Corn products

IT BCT TCT

Chemical composition

Moisture 2 4.67 ± 0.13a 4.93 ± 0.06a 49.58 ± 0.72b

Ash 2.40 ± 0.02b 3.39 ± 0.04c 1.35 ± 0.05a

Fat 2.36 ± 0.12a 2.44 ± 0.03a 0.7 ± 0.03b

Protein 3 7.20 ± 0.13a 8.26 ± 0.29b 8.91 ± 0.04c

Available Starch 74.64 ± 2.03a 77.05 ± 0.28ab 78.02 ± 0.89b

Resistant Starch 4.34 ± 0.78a 3.2 ± 0.03b 5.2 ± 0.1a

Indigestible Fraction (IF) content

Total IF 4 27.22 ± 0.11a 25.54 ± 3.51a 25.39 ± 0.28a

Soluble IF 2.89 ± 0.60a 2.05 ± 0.16a 2.13 ± 0.48a

Insoluble IF 24.34 ± 0.50a 24.49 ± 3.41a 23.262 ± 0.78a


155

1Values are mean ± standard error (n = 3); Dry basis (db). Means in rows marked with different

letters indicate significant difference (p <0.05). 2Expressed in wet basis. 3Conversion factor: % N x

6.25. 4 Total IF = Sum of soluble IF + insoluble IF.

Considering a 30 g serving, IT would supply 1.24 g RS/portion), which is almost twice RS supply

than in TCT (0.78 g/portion), a consequence of dilution effect by differences in moisture content.

(4.67 g/100 g wb for IT and 49.58 g/100 g wb for TCT). RS reaches the colon were it is fermented

producing short-chain fatty acids (SCFA), especially butyric acid), limiting glucose absorption in

the small intestine, causing a decrease in the rate of starch hydrolysis and reducing the total caloric

intake.5 IF content consists in food components that being unavailable for digestion in the small

intestine, pass into the colon where are also used as a substrate for fermentation by the gut

microbiota. IF is classified as: soluble IF, that comprises monosaccharides, disaccharides, and

oligosaccharides, and insoluble IF, which includes RS, indigestible protein, indigestible fat,

polyphenols, and non-starch polysaccharides (NSP: cellulose, hemicelluloses and lignin).13 The

studied corn products did not show differences in their soluble, insoluble and total IF contents

(p>0.05). Total IF content may be modified during the nixtamalization process; overcooking

caused by high temperatures allows softening and removal of the pericarp in corn kernels,

resulting in decreased indigestible carbohydrates contents.26 Besides, soluble to insoluble IF ratio

in corn products can be also affected by the temperature during processing. High cooking

temperatures produce pirodextrinization of starch and NSP, increasing the content of indigestible

soluble carbohydrates with concomitant decrease in the insoluble indigestible carbohydrate

fractions.21 Despite the lack of differences in IF content, present results support the idea of that

corn products as appreciable sources of dietary fiber (DF) in Mexican diets. DF exerts various

physiological functions associated whit human health, as it contributes to normal postprandial

glycemic/insulinemic responses, reduce blood cholesterol levels and may provide protective

effects against colon cancer.27, 28

3.3 pH and antioxidant capacity (AOX) change during in vitro colonic fermentation

3.3.1 pH Change


156

Figure 1 A shows the pH values of the fermentation cultures at the beginning and end of the

incubation period. Changes in pH provide an overview of the process of fermentation. Starting pH

values ranged approximately between 7.05 and 7.3 before fermentation, and decreased in the IF-

containing samples already after 12 h fermentation, a change that is related to the production of

SCFA (Table 2). Predominant SCFA (acetic, propionic and butyric acids) have pKa values around

4.8 and their production upon carbohydrate fermentation decreases the intestinal lumen pH.29 The

pH values in corn products extracts were higher compared with the control sample (raffinose) at

all-time points (p < 0.05), indicating a relatively lower fermentability of IF. The pH significantly

declined from 7.05 to 4.83 during the fermentation of TCT (0 to 12 h), and then showed a slight

increase at 24, 48 and 72 h. This behavior was similar in the IT and BCT preparations. The

approximately 2 pH units decrease recorded during fermentation of the corn samples is indicative

of potential beneficial action of these products on the host health. The pH lowering effect has been

used as a marker of colonic cancer preventive power, impact on gut microbiota composition and

ability to prevent overgrowth of pH-sensitive pathogenic bacteria such as Enterobacteriaceae and

Clostridia.30, 31

3.3.2 DPPH radical scavenging activity and Ferrous ion chelating activity

Oxidative stress is involved in both inflammation, the process of initiation and progression of

different pathologies, such as colorectal cancer.32 In this in vitro study, AOX values showed

differences, depending on the substrate and time of fermentation (Figure 1B and C). Figure 1B

shows that the scavenging effects of raffinose on DPPH radical were proportional to fermentation

time. TCT and BCT preparations were more active after 24 h (60.1 and 53.2 mmol TE/g IF,

respectively), followed by a decline of about 50% at 48 h. IT exerted the highest DPPH

antioxidant activity at 12 h followed by a marked decrease from 24 h on (from 48.47 to 22.78

mmol TE/g IF, respectively). According to the FRAP antioxidant activity assay the various corn

products showed similar AOX after being fermented for 12 h, but their values were significantly

greater than those recorded for the raffinose reference (p<0.05). After 24 h of fermentation, FRAP

values of BCT preparation increased significantly (p<0.05), reaching the maximum value of the

three corn products, followed by a reduction up at the end of the experiment (48 h), maintaining a

value of 10.93 mmol TE/g IF. FRAP values of IT decreased by 82.1% in the 12 to 48 h


157

fermentation period. Typically, the in vitro antioxidant action of foods is limited to liberation of

antioxidant components during the gastrointestinal digestion. Corn tortillas have a high content of

ferulic acid, carotenoids and anthocyanins that might reach the colon and exert a direct antioxidant

effect assigned to radical scavenging or metal ion chelation.5, 33


158

Figure 1 Changes during in vitro colonic fermentation of (-○-) blank, (-●-) raffinose (positive control), and indigestible fraction

isolated from (-�-) Istmo Totopo, (-◊-) Baked corn tortilla and (-□-) Traditional corn tortilla: a) pH values plot b) DPPH

Antiradical activity plot and b) FRAP chelating activity plot. Values are means ± SEM (n=3). For DPPH and FRAP, the values

are reported after subtraction of blank sample (no added substrate) at the same time point. *Significant difference using Two-

Way ANOVA/Fisher's LSD test (Samples ×Time interaction, p<0.05)


0 12 24 48

pH

3.0

4.0

5.0

6.0

7.0

8.0 * * *


0 12 24 48

DPP

H A

ntio

xidan

t Act

ivity

(mM

TE/

g su

bstra

te d

b)

10

20

30

40

60

70

0

50

* * *


0 12 24 48

FRA

P A

ntio

xidan

t Act

ivity

(m

M T

E /g

subs

trate

db)

0

5

10

15

20

25* * *

*

A) B) C)


159

Also, RS can stimulate the fermentative production of butyric acid and this metabolite may also

exert in vitro and in vivo antioxidant effect.34 Pre-clinical study has shown that oxidative stress in

the colonic mucosa can be beneficially modulated by butyric acid, increasing glutathione (GSH)

production and decreasing the formation of reactive oxygen species.35 Therefore, the effect that

fermentable indigestible components exert on the growth of various colonic microbial strains and

the production of gut metabolites with AOX activity stress the impact that the type of ingested

food has on human health.

3.4 Short Chain Fatty Acids (SCFA): Profile During Fermentation

The concentrations of SCFA measured at 12, 24 and 48 h of fermentation are shown in Table 2.

Acetic and propionic acid concentrations in the corn products preparations were significantly

lower than those registered using raffinose as substrate (p<0.05). However, butyric acid levels

after 24 and 48 h were higher in TCT than in the raffinose control (p<0.05). The concentrations of

acetic, propionic, and butyric acids in TCT were significantly higher than IT and BCT (p<0.05) at

48 h. The molar proportions of the different SCFA varied throughout the fermentation of the corn

products. At 12 h, raffinose exhibited a 54: 34: 13 molar ratio for acetic, propionic and butyric

acids, respectively; this profile resembled that found at 48 h (55: 32: 13).

But, for the IT substrate, a 41: 34: 25 molar ratio was found after 12 h, and a 34: 23: 43 proportion

was detected at the end of the 48 h period. These results indicate an increase in the production of

butyric acid and a decrease of acetic and propionic acid as the fermentation proceeds. The same

behavior was observed for the BCT and TCT substrates. This can be considered beneficial, given

that butyric acid stimulate apoptosis of colorectal carcinoma cells.36 The stimulation of butyric

acid production by IF from corn products may be explained by the cross-feeding between butyric

acid-producing bacteria and Bifidobacterium species. The ability of Faecalibacterium prausnitzii,

for instance, to increase butyric acid levels in the fermentation medium is explained by its higher

affinity for the substrate and it´s ability to the partially consume the acetic acid produced by

Bifidobacterium strains.37 Bifidobacterium longum, Bifidobacterium catenulatum,

Bifidobacterium bifidum, Lactobacillus gasseri and Lactobacillus salivarius were the main species

involved in SCFA production (mainly acetic acid).38


160

Table 2. Production of short-chain fatty acids (SCFAs, mmol L-1) at 12, 24 and 48 h of in vitro fermentation of blank, raffinose

and indigestible fraction isolated from Istmo Totopo (IT), baked corn tortilla (BCT) and traditional corn tortilla (TCT) *.

Metabolite / fermentation time Substrate

Blank Raffinose IT BCT TCT Acetic acid

12 h 27.54 ± 1.55aA 367.51 ± 2.53cA 145.10 ± 1.11bA 199.77 ± 47.51bA 333.90 ± 49.4cA 24 h 35.86 ± 2.64aA 729.15 ± 28.80eB 230.37 ± 19.77bB 308.48 ± 13.91cB 468.55 ± 12.33dB 48 h 19.09 ± 9.62aA 611.85 ± 4.17dC 188.58 ± 5.10bAB 171.73 ± 18.83bA 498.00 ± 18.60cB

Propionic acid 12 h 9.03 ± 4.60aA 232.04 ± 1.56cA 120.45 ± 0.51bA 150.07 ± 30.64bA 176.33 ± 15.01bcA

24 h ND 421.44 ± 38.43dB 135.29 ± 12.71bA 160.72 ± 25.04bA 284.98 ± 10.40cB 48 h 4.12 ± 4.12aA 362.17 ± 58.37dB 123.93 ± 9.13bA 135.72 ± 9.64bA 293.75 ± 6.15cB

Butyric acid 12 h 3.46 ± 1.84aA 86.19 ± 0.14bA 89.90 ± 4.51bA 145.88 ± 1.96bA 141.44 ± 59bA

24 h 7.51 ± 7.51aA 109.61 ± 22.81bA 174.53 ± 20.79bB 178.53 ± 29.19bAB 363.01 ± 13.18cB 48 h 3.88 ± 1.95aA 144.93 ± 27.30bA 235.21 ± 47.6bB 236.58 ± 30.03bB 331.96 ± 9.34dB

*The values are reported in mmoL· L-1 produced per 100 mg substrate as mean ± SEM of three replicates; Different lowercase letters indicate significant differences in rows among substrates for a time. Different capital letters in columns indicate significant differences between times for a same substrate (Two-way ANOVA and Fisher LSD Test, p< 0.05). ND: Not detected.


161

The composition of the indigestible fractions, particularly their content of RS, could be involved in

the SCFA profile produced by fermentation of the corn product substrates. Recently the

manipulation of the gut microbiota and SCFA production using RS was related with protection

against colitis-associated colorectal cancer in rats.39



the time points 12, 24, and 48 h showed different metabolic profiles in relation to the different


belonging to the families of alcohols and polyols, alkanes, amines, benzene and substituted

derivatives, carbonyl compounds, ethers, fatty acid esters, indoles and derivatives, olefins, organic

acids, organic disulfides, organic trisulfides and SCFA were detected (Figure 2). No changes were

observed in the relative concentrations of organic acids, SCFA and indole during the fermentation

of IT, indicating that this substrate is steadily fermented, which suggests that fermentation of this

substrate would occur over a longer portion of the colon. Structural differences in the indigestible

carbohydrates present in the samples could favor the maintenance of the bacteria and their

metabolites.40

Nevertheless, it is noticeable that as the fermentation process the fermentation of TCT and BCT

went on the concentration of organic acids decreased and the production of metabolites from the

indole group increased. Indole and derivatives are products of the metabolism of aminoacids by

bacteria including Bacteroides thetaiotaomicron, B. eggerthii, B. ovatus, B. fragilis,

Parabacteroides distasonis, Clostridium bartlettii, and Eubacterium hallii.41 The products of the

fermentation of amino acids, such as phenol and p-cresol, are considered toxic to the intestine.42

However, indole (metabolite synthesized from tryptophan) is able to modulate the hormone GLP-1

secretion by L-enteroendocrine cells43 and could be involved in development and neural

function.44


162

Figure 2 Cumulative concentration (%) of volatile gut metabolites by chemical groups during in vitro colonic fermentation

extracts of blank, raffinose and indigestible fraction isolated from Istmo Totopo (IT), Baked corn tortilla (BCT) and traditional

corn tortilla (TCT) at 12 and 48 h. Detailed of identification number for each volatile compound are outlined in the Table SI†.


Blank Raffinose Istmo Totopo Baked Tortilla Corn Tortilla

Cum

ulativ

e co

ncen

tratio

n (%

)

0

20

40

60

80

100


Blank Raffinose Istmo Totopo Baked Tortilla Corn Tortilla

Cum

ulativ

e co

ncen

tratio

n (%

)

0

20

40

60

80

100

Alcohols and polyolsAlkanes Amines Benzene and substituted derivatives Carbonyl compounds EthersFatty acid ester Indoles and derivatives Olefins Organic acid Organic disulfides*0 Organic trisulfidesSCFA

IT BCT TCT IT BCT TCT


163

Using statistical descriptive approach (PCA), here it is shown that the fermentation of different

corn products for 12, 24, and 48 h clearly affects the fecal metabolic fingerprint (Figure 3). The

first two principal components (PC) explained nearly 45% of the variation in the data (Figure

3A).These components separate the volatiles that are generally obtained from the fermentative

metabolism of carbohydrates with positive Eigenvectors in PC1 and negative Eigenvectors in PC2

(e.g., butyric acid, ethanol, organic acids and fatty acid esters) from those volatiles that are most

prevalent as products from protein metabolism with low Eigenvectors on PC1 and positive

Eigenvectors on PC2 (e.g. phenol, phenol, 4-methyl-, trimethylamine and dimethyl trisulfide). In

fact, a good separation of the 12 h time point from the other fermentation points was observed for

all samples, most evidently in the case of TCT (Figure 3B). Notably, the increase of butyric acid

represented the greatest variation registered during fermentation of the corn products. No other

SCFA was found to be significantly modified, suggesting that butyric acid was the main final

product from the microbial degradation of the IF isolated from corn products, especially in TCT.

Figure 3 Principal components plots A) metabolites production “PC Loadings” and B) sample

classification “PC scores” (%) during in vitro fermentation from indigestible fraction isolated from

Istmo Totopo (IT), Baked corn tortilla (BCT) and Traditional Corn Tortilla (TCT).

Trim ethy lam ine

Ethy l ether

Hexane

Ethanol

Butanoic acid, m ethy l ester

Disulfide, dim ethy l

Toluene

2,4-Dim ethy l-1-heptene

Pentanoic acid, m ethy l ester

Heptanal

Acetic Acid

Dim ethy l trisulfide

Propionic Acid

Benzene, 1,4-dichloro-

Benzaldehy de

Butiric Acid

Nonanal

Butanoic acid, 3-m ethy l-

Octanoic acid, m ethy l ester

Pentanoic acid Acetophenone

Benzaldehy de, 4-m ethy l-

2-Nonenal, (E)-

DecanalNonanoic acid, m ethy l ester

Pentanoic acid, 4-m ethy l-

Benzene, 1,3-bis(1,1-dim ethy lethy l)-

Hexanoic acidDecanoic acid, m ethy l ester

Heptanoic acid

Phenol

Benzenem ethanol, 4-m ethy l-

Phenol, 4-m ethy l-

Octanoic Acid

Dodecanoic acid, m ethy l ester

Nonanoic acid

Buty lated Hy droxy toluene

n-Decanoic acid

2-Propenoic acid, tridecy l ester

Indole

Diethy l Phthalate

Tetradecanal

9-Hexadecenoic acid, m ethy l ester, (Z)-

Hexadecanoic acid, m ethy l ester

Dibuty l phthalate

9,12-Octadecadienoic acid (Z,Z)-, m ethy l ester

-1.0 -0.5 0.0 0.5 1.0PC 1 (25.55%)

-1.0

-0.5

0.0

0.5

1.0

PC 2

(17.

88%

)

Trim ethy lam ine

Ethy l ether

Hexane

Ethanol

Butanoic acid, m ethy l ester

Disulfide, dim ethy l

Toluene

2,4-Dim ethy l-1-heptene

Pentanoic acid, m ethy l ester

Heptanal

Acetic Acid

Dim ethy l trisulfide

Propionic Acid

Benzene, 1,4-dichloro-

Benzaldehy de

Butiric Acid

Nonanal

Butanoic acid, 3-m ethy l-

Octanoic acid, m ethy l ester

Pentanoic acid Acetophenone

Benzaldehy de, 4-m ethy l-

2-Nonenal, (E)-

DecanalNonanoic acid, m ethy l ester

Pentanoic acid, 4-m ethy l-

Benzene, 1,3-bis(1,1-dim ethy lethy l)-

Hexanoic acidDecanoic acid, m ethy l ester

Heptanoic acid

Phenol

Benzenem ethanol, 4-m ethy l-

Phenol, 4-m ethy l-

Octanoic Acid

Dodecanoic acid, m ethy l ester

Nonanoic acid

Buty lated Hy droxy toluene

n-Decanoic acid

2-Propenoic acid, tridecy l ester

Indole

Diethy l Phthalate

Tetradecanal

9-Hexadecenoic acid, m ethy l ester, (Z)-

Hexadecanoic acid, m ethy l ester

Dibuty l phthalate

9,12-Octadecadienoic acid (Z,Z)-, m ethy l ester 12-B

12-R

12-IT

12-BCT

12-TCT

24-B

24-R

24-IT

24-BCT 24-TCT

48-B

48-R

48-IT

48-BCT

48-TCT

-6 -4 -2 0 2 4 6 8PC1 (25.55%)

-6

-4

-2

0

2

4

6PC

2 (1

7.88

%)

12-B

12-R

12-IT

12-BCT

12-TCT

24-B

24-R

24-IT

24-BCT 24-TCT

48-B

48-R

48-IT

48-BCT

48-TCT

A) B)


164


To the best of our knowledge, this is first study reporting the relationship between metabolic

profile and biological activity (pH and AOX) during the in vitro colonic fermentation of IF

isolated on processed corn products. pH values during in vitro colonic fermentation were

positively correlated (p<0.05) with trimethylamine, dimethyl trisulfide, benzaldehyde, phenol and

4-methyl-phenol and inversely correlated with ethanol, 2,4-dimethyl-1-heptene, propionic acid,

butyric acid, acetophenone, benzene 1,3-bis(1,1-dimethylethyl)- and nonanoic acid (Figure 4).

Significant positive correlations were found between DPPH antiradical activity and ethanol, 2,4-

Dimethyl-1-heptene, acetic acid, propionic acid, butyric acid, nonanal, acetophenone, and 1,3-

bis(1,1-dimethylethyl)-benzene, while it correlated negatively with trimethylamine, dimethyl

trisulfide, phenol and 4-methyl-phenol production. Interestingly, acetic acid, propionic acid,

ethanol, and nonanal, exhibited significant positive correlations with FRAP chelating activity,

whereas trimethylamine, phenol, and indole were negatively correlated with this activity. In this

work, some metabolites showed negative relationship with pH and AOX values (e.g.

trimethylamine and 4-methyl-phenol), and they have been recently associated with the

development of cardiovascular diseases and genotoxic effects on colonic epithelial cells.45, 46


165

Figure 4. Pearson’s R correlations between gut microbial metabolites and pH values and

antioxidant capacity (DPPH antiradical activity and FRAP chelating activity) in extracts obtained

during in vitro colonic fermentation of blank, raffinose and indigestible fraction isolated from corn

products

In vitro Gut metabolite pH DPPH FRAPTrimethylamineEthyl etherHexaneEthanolButanoic acid, methyl esterDisulfide, dimethylToluene2,4-Dimethyl-1-heptenePentanoic acid, methyl esterHeptanalAcetic AcidDimethyl trisulfidePropionic AcidBenzene, 1,4-dichloro-BenzaldehydeButiric AcidNonanalButanoic acid, 3-methyl-Octanoic acid, methyl esterPentanoic acidAcetophenoneBenzaldehyde, 4-methyl-2-Nonenal, (E)-DecanalNonanoic acid, methyl esterPentanoic acid, 4-methyl-Benzene, 1,3-bis(1,1-dimethylethyl)-Hexanoic acidDecanoic acid, methyl esterHeptanoic acidPhenolBenzenemethanol, 4-methyl-Phenol, 4-methyl-Octanoic AcidDodecanoic acid, methyl esterNonanoic acidButylated Hydroxytoluenen-Decanoic acid2-Propenoic acid, tridecyl esterIndoleDiethyl PhthalateTetradecanal9-Hexadecenoic acid, methyl ester, (Z)-Hexadecanoic acid, methyl esterDibutyl phthalate9,12-Octadecadienoic acid (Z,Z)-, methyl ester

p Va

lue

-1-0.8-0.6-0.4-0.200.20.40.60.81Pe

arso

n's c

orre

latio

n co

effic

ient

<0.001<0.01<0.05

<0.001<0.01<0.05>0.05

>0.05

>0.05


166

4. Conclusion

This contribution shows the changes in of bacterial metabolites, pH and AOX values during in

vitro colonic fermentation of IF isolated from three corn products. Chemical composition and RS

content was different among maize products. The IF content was the same but the magnitude of

SCFA production, particularly butyric acid, during in vitro fermentation was dependent on

fermentation time and substrate. Bacterial metabolites as trimethylamine, phenol and 4-methyl-

phenol were associated with increased pH and decreased AOX in the fermentation extracts. In

general terms, TCT and IT could be perceived as foods with potential health effects on colon

potential. In addition, the approach followed here, may be used as predictive model to assess the

metabolic implications of food substrates present in the traditional Mexican diet. Only through the

study of foods frequently consumed by a population will we improve our understanding of the

effects of diet on colon health promotion.

Acknowledgments: Zamora-Gasga, VM; acknowledge the fellowship to Consejo Nacional de

Ciencia y Tecnología (CONACYT- Registration number: 253795) and Sáyago-Ayerdi, SG; would

also like to acknowledge the financial support to PROMEP –ITTEP-PTC-003.

5. References

1. J. Cummings and A. Stephen, Carbohydrate terminology and classification, Eur. J. Clin. Nutr.,

2007, 61, S5-S18.

2. I. Rojas-Molina, E. Gutierrez-Cortez, A. Palacios-Fonseca, L. Baños, J. Pons-Hernández, S.

Guzmán-Maldonado, P. Pineda-Gomez and M. Rodríguez, Study of structural and thermal

changes in endosperm of quality protein maize during traditional nixtamalization process,

Cereal Chem., 2007, 84, 304-312.


167

3. M. Santiago-Torres, M. Kratz, J. W. Lampe, J. D. D. Tapsoba, K. L. Breymeyer, L. Levy, A.

Villaseñor, C.-Y. Wang, X. Song and M. L. Neuhouser, Metabolic responses to a traditional

Mexican diet compared with a commonly consumed US diet in women of Mexican descent: a

randomized crossover feeding trial, Am. J. Clin. Nutr., 2016, 103, 366-374.

4. S. Rodríguez-Ramírez, V. Mundo-Rosas, A. García-Guerra and T. Shamah-Levy, Dietary

patterns are associated with overweight and obesity in Mexican school-age children, Arch.

Latinoam. Nutr., 2011, 61, 270.

5. L. A. Bello-Perez, P. Osorio-Díaz, E. Agama-Acevedo and R. A. Gonzalez-Soto, in Functional

Properties of Traditional Foods, Springer, 2016, pp. 139-155.

6. W.-J. Lee and K. Hase, Gut microbiota-generated metabolites in animal health and disease, Nat.

Chem. Biol., 2014, 10, 416-424.

7. J. Slavin, Fiber and prebiotics: mechanisms and health benefits, Nutrients, 2013, 5, 1417-1435.

8. P. Louis, G. L. Hold and H. J. Flint, The gut microbiota, bacterial metabolites and colorectal

cancer, Nature Rev. Microbiol., 2014, 12, 661-672.

9. A. N. Payne, A. Zihler, C. Chassard and C. Lacroix, Advances and perspectives in in vitro

human gut fermentation modeling, Trends Biotechnol., 2012, 30, 17-25.

10. F. Bianchi, M. Dall’Asta, D. Del Rio, A. Mangia, M. Musci and F. Scazzina, Development of

a headspace solid-phase microextraction gas chromatography–mass spectrometric method for

the determination of short-chain fatty acids from intestinal fermentation, Food Chem., 2011,

129, 200-205.

11. AOAC, Association of Official Agricultural Chemists. Oficial Methods of Analysis. 15th

Edition. Washington, D.C., USA., 1990.

12. I. Goñi, L. Garcia-Diz, E. Mañas and F. Saura-Calixto, Analysis of resistant starch: a method

for foods and food products, Food Chem., 1996, 56, 445-449.


168

13. F. Saura-Calixto, A. García-Alonso, I. Goni and L. Bravo, In vitro determination of the

indigestible fraction in foods: an alternative to dietary fiber analysis, J. Agric. Food Chem.,

2000, 48, 3342-3347.

14. M. Tabernero, K. Venema, A. J. Maathuis and F. D. Saura-Calixto, Metabolite production


diets, J. Agric. Food Chem., 2011, 59, 8968-8975.

15. R. Campos�Vega, R. Reynoso�Camacho, G. Pedraza�Aboytes, J. Acosta�Gallegos, S.

Guzman�Maldonado, O. Paredes�Lopez, B. Oomah and G. Loarca�Piña, Chemical


L.), J. Food Sci., 2009, 74, T59-T65.

16. V. M. Zamora-Gasga, G. Loarca-Piña, P. A. Vázquez-Landaverde, R. I. Ortiz-Basurto, J.

Tovar and S. G. Sáyago-Ayerdi, In vitro colonic fermentation of food ingredients isolated from

Agave tequilana Weber var. azul applied on granola bars, LWT-Food Sci. Technol., 2015, 60,

766-772.

17. R. L. Prior, X. Wu and K. Schaich, Standardized methods for the determination of antioxidant

capacity and phenolics in foods and dietary supplements, J. Agric. Food Chem., 2005, 53,

4290-4302.

18. E. Alvarez-Parrilla, L. A. De La Rosa, P. Legarreta, L. Saenz, J. Rodrigo-García and G. A.

González-Aguilar, Daily consumption of apple, pear and orange juice differently affects plasma

lipids and antioxidant capacity of smoking and non-smoking adults, Int. J. Food Sci. Nutr.,

2010, 61, 369-380.

19. M. Ş. Argun and İ. S. Doğan, Effects of Varying Nixtamalization Conditions on the Calcium

Absorption and Pasting Properties of Dent and Flint Corn Flours, J. Food Process. Eng. ,

2016.

20. Y. Salinas Moreno, F. Aragón Cuevas, C. Ybarra Moncada, J. Aguilar Villarreal, B. Altunar

López and E. Sosa Montes, Caracterización física y composición química de razas de maíz de


169

grano azul/morado de las regiones tropicales y subtropicales de Oaxaca, Rev. Fitotec. Mex.,

2013, 36, 23-31.

21. L. A. Bello-Perez, P. C. Flores-Silva, E. Agama-Acevedo, J. de Dios Figueroa-Cardenas, J. A.

Lopez-Valenzuela and O. H. Campanella, Effect of the nixtamalization with calcium carbonate

on the indigestible carbohydrate content and starch digestibility of corn tortilla, J. Cereal Sci.,

2014, 60, 421-425.

22. S. Odjo, P. Malumba, J. Dossou, S. Janas and F. Béra, Influence of drying and hydrothermal

treatment of corn on the denaturation of salt-soluble proteins and color parameters, J. Food

Eng., 2012, 109, 561-570.

23. P. Raigond, R. Ezekiel and B. Raigond, Resistant starch in food: a review, J. Sci. Food Agr.,

2015, 95, 1968-1978.

24. P. Osorio-Díaz, E. Agama-Acevedo, L. A. Bello-Pérez, J. J. Islas-Hernández, N. O. Gomez-

Montiel and O. Paredes-López, Effect of endosperm type on texture and in vitro starch

digestibility of maize tortillas, LWT-Food Sci. Technol., 2011, 44, 611-615.

25. A. Flores-Morales, M. Jiménez-Estrada and R. Mora-Escobedo, Determination of the

structural changes by FT-IR, Raman, and CP/MAS 13C NMR spectroscopy on retrograded

starch of maize tortillas, Carbohydr. Polym., 2012, 87, 61-68.

26. G. Mendez-Montealvo, M. Sánchez-Rivera, O. Paredes-López and L. A. Bello-Perez, Thermal

and rheological properties of nixtamalized maize starch, Int. J. Biol. Macromolec., 2006, 40,

59-63.

27. Y. Ai and J. l. Jane, Macronutrients in Corn and Human Nutrition, Compr. Rev. Food Sci.

Food Saf., 2016.

28. R. Reynoso-Camacho, G. Guerrero-Villanueva, J. de Dios Figureueroa, M. A. Gallegos-

Corona, S. Mendoza, G. Loarca-Piña and M. Ramos-Gomez, Anticarcinogenic effect of corn


170

tortilla against 1, 2-dimethylhydrazine (dmh)-induced colon carcinogenesis in Sprague–Dawley

rats, Plant. Foods Hum. Nutr., 2015, 70, 146-152.

29. J. Verspreet, B. Damen, W. F. Broekaert, K. Verbeke, J. A. Delcour and C. M. Courtin, A

Critical Look at Prebiotics Within the Dietary Fiber Concept, Annu. Rev. Food Sci. Technol.,

2016, 7, 167-190.

30. S. R. Sarbini and R. A. Rastall, Prebiotics: metabolism, structure, and function, Funct. Food

Rev., 2011, 3, 93-106.

31. J. Zimmer, B. Lange, J.-S. Frick, H. Sauer, K. Zimmermann, A. Schwiertz, K. Rusch, S.

Klosterhalfen and P. Enck, A vegan or vegetarian diet substantially alters the human colonic

faecal microbiota, Eur. J. Clin. Nutr., 2012, 66, 53-60.

32. M. A. Azcarate-Peril, M. Sikes and J. M. Bruno-Barcena, The intestinal microbiota,

gastrointestinal environment and colorectal cancer: A putative role for probiotics in prevention

of colorectal cancer?, Am. J. Physiol. Gastrointest. Liver Physiol., 2011, DOI:

10.1152/ajpgi.00110.2011.

33. C. de la Parra, S. O. Serna Saldivar and R. H. Liu, Effect of processing on the phytochemical

profiles and antioxidant activity of corn for production of masa, tortillas, and tortilla chips, J.

Agric. Food Chem., 2007, 55, 4177-4183.

34. M. W. Bourassa, I. Alim, S. J. Bultman and R. R. Ratan, Butyrate, neuroepigenetics and the

gut microbiome: Can a high fiber diet improve brain health?, Neurosci. Lett., 2016, 625, 56-63.

35. H. M. Hamer, D. M. A. E. Jonkers, A. Bast, S. A. L. W. Vanhoutvin, M. A. J. G. Fischer, A.

Kodde, F. J. Troost, K. Venema and R.-J. M. Brummer, Butyrate modulates oxidative stress in

the colonic mucosa of healthy humans, Clin. Nutr., 2009, 28, 88-93.

36. A. Han, N. Bennett, A. MacDonald, M. Johnstone, J. Whelan and D. R. Donohoe, Inactivation

of Pyruvate Dehydrogenase Complex Mediates The Selective Effects of Butyrate on Colorectal

Cancer Cells, FASEB J., 2016, 30, 42.48-42.48.


171

37. D. Rios-Covian, M. Gueimonde, S. H. Duncan, H. J. Flint and G. Clara, Enhanced butyrate

formation by cross-feeding between Faecalibacterium prausnitzii and Bifidobacterium

adolescentis, FEMS Microbiol. Lett. , 2015, 362, fnv176.

38. E. Puertollano, S. Kolida and P. Yaqoob, Biological significance of short-chain fatty acid

metabolism by the intestinal microbiome, Curr. Opin. Clin. Nutr. Metab. Care, 2014, 17, 139-

144.

39. Y. Hu, R. K. Le Leu, C. T. Christophersen, R. Somashekar, M. A. Conlon, X. Q. Meng, J. M.

Winter, R. J. Woodman, R. McKinnon and G. P. Young, Manipulation of the gut microbiota

using resistant starch is associated with protection against colitis-associated colorectal cancer in

rats, Carcinogenesis, 2016, 37, 366-375.

40. B. R. Hamaker and Y. E. Tuncil, A perspective on the complexity of dietary fiber structures

and their potential effect on the gut microbiota, J. Mol. Biol., 2014, 426, 3838-3850.

41. W. R. Russell, L. Hoyles, H. J. Flint and M.-E. Dumas, Colonic bacterial metabolites and

human health, Curr. Opin. Microbiol., 2013, 16, 246-254.

42. C. Walton, D. P. Fowler, C. Turner, W. Jia, R. N. Whitehead, L. Griffiths, C. Dawson, R. H.

Waring, D. B. Ramsden and J. A. Cole, Analysis of volatile organic compounds of bacterial

origin in chronic gastrointestinal diseases, Inflamm. Bowel Dis., 2013, 19, 2069-2078.

43. C. Chimerel, E. Emery, D. K. Summers, U. Keyser, F. M. Gribble and F. Reimann, Bacterial

metabolite indole modulates incretin secretion from intestinal enteroendocrine L cells, Cell

Rep., 2014, 9, 1202-1208.

44. J.-H. Lee, T. K. Wood and J. Lee, Roles of indole as an interspecies and interkingdom

signaling molecule, Trends Microbiol., 2015, 23, 707-718.

45. A. Wilson, C. McLean and R. B. Kim, Trimethylamine-N-oxide: a link between the gut

microbiome, bile acid metabolism, and atherosclerosis, Curr. Opin. Lipidol., 2016, 27, 148-

154.


172

46. M. Andriamihaja, A. Lan, M. Beaumont, M. Audebert, X. Wong, K. Yamada, Y. Yin, D.

Tomé, C. Carrasco-Pozo, M. Gotteland, X. Kong and F. Blachier, The deleterious metabolic

and genotoxic effects of the bacterial metabolite p-cresol on colonic epithelial cells, Free Radic.

Biol. Med., 2015, 85, 219-227.


173

Table SI. Volatile compounds characterization of in vitro colonic fermentation extracts of blank, rafinose and indigestible fraction isolated from Istmo Totopo (IF-IT), Baked corn tortilla (IF-BCT) and traditional corn tortilla (IF-

TCT) analyzed by SPME–GC/MS (mmol/L)

ID RT 12 24 48

VC (%) Volatile Name Chemical Group Blank Raffinose IF-IT IF-BCT IF-TCT Blank Raffinose IF-IT IF-BCT IF-TCT Blank Raffinose IF-IT IF-BCT IF-TCT

1 5.960 Trimethylamine Amines 20.33 0.00 0.00 0.00 0.00 21.29 0.00 0.00 0.00 0.00 66.31 0.00 0.00 0.00 0.00 248.81

2 6.252 Ethyl ether Ethers 38.96 88.01 77.03 68.81 59.68 0.00 0.00 30.35 24.37 49.55 0.00 0.00 0.00 0.00 0.00 111.46

3 7.082 Hexane Alkanes 0.00 0.00 0.00 0.00 0.00 0.00 41.46 0.00 39.80 73.02 33.21 0.00 0.00 0.00 45.29 155.73

4 7.906 Ethanol Alcohols and polyols 0.00 86.33 0.00 168.88 220.16 0.00 187.16 227.31 222.99 301.79 0.00 187.65 0.00 168.50 191.25 80.57

5 11.280 Butanoic acid, methyl ester Fatty Acyls 0.00 0.00 34.67 29.07 82.99 0.00 0.00 36.08 23.92 64.55 0.00 0.00 41.45 0.00 48.96 112.87

6 12.885 Disulfide, dimethyl Organic disulfides 37.13 0.00 0.00 0.00 0.00 38.34 0.00 0.00 0.00 0.00 0.00 0.00 50.46 0.00 81.26 184.95

7 13.083 Toluene Benzene and substituted derivatives 0.00 0.00 0.00 0.00 473.50 295.15 251.42 407.64 449.17 226.80 246.74 0.00 273.54 0.00 281.99 92.15

8 14.499 2,4-Dimethyl-1-heptene Olefins 0.00 0.00 32.64 32.86 30.57 0.00 0.00 19.84 30.90 31.38 0.00 21.39 22.11 28.00 32.81 76.41

9 15.022 Pentanoic acid, methyl ester Fatty acid ester 0.00 0.00 0.00 0.00 19.70 0.00 0.00 30.43 0.00 23.47 0.00 0.00 42.56 0.00 27.25 154.32

10 18.079 Heptanal Carbonyl compounds 0.00 0.00 23.92 0.00 20.79 0.00 0.00 25.11 0.00 37.00 0.00 19.56 0.00 0.00 28.10 131.94

11 20.028 Acetic Acid SCFA 27.54 367.51 145.10 199.77 333.90 35.86 729.15 230.37 308.48 468.55 19.09 611.85 188.58 171.73 498.00 74.19

12 21.978 Dimethyl trisulfide Organic trisulfides 115.60 0.00 0.00 0.00 0.00 752.69 0.00 0.00 0.00 0.00 46.50 0.00 26.15 29.55 153.32 257.96

13 22.223 Propionic Acid SCFA 9.03 232.04 120.45 150.07 176.33 0.00 421.44 135.29 160.72 284.98 4.12 362.17 123.93 135.72 293.75 72.39

14 23.182 Benzene, 1,4-dichloro- Benzene and substituted derivatives 0.00 24.80 24.38 26.66 33.52 30.65 30.20 29.23 29.59 33.14 29.55 18.85 26.46 23.13 26.74 31.47

15 23.624 Benzaldehyde Benzene and substituted derivatives 54.76 0.00 0.00 0.00 0.00 35.03 0.00 0.00 0.00 0.00 0.00 42.84 0.00 0.00 0.00 211.33

16 24.351 Butiric Acid SCFA 3.46 86.19 89.90 145.88 141.44 7.51 109.61 174.53 178.53 363.01 3.88 144.93 235.21 236.58 331.96 72.77

17 25.180 Nonanal Carbonyl compounds 34.57 19.24 34.73 0.00 28.75 0.00 25.31 0.00 20.85 41.89 0.00 33.80 20.79 31.82 38.56 68.96

18 25.410 Butanoic acid, 3-methyl- Organic acid 0.00 44.41 0.00 30.15 0.00 0.00 53.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 213.63

19 25.521 Octanoic acid, methyl ester Fatty acid ester 0.00 0.00 0.00 0.00 43.01 0.00 0.00 35.06 26.84 60.28 50.52 0.00 0.00 0.00 29.39 135.32

20 26.948 Pentanoic acid Organic acid 0.00 0.00 0.00 112.19 202.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 276.19

21 27.127 Acetophenone Benzene and substituted derivatives 0.00 0.00 34.46 33.07 30.83 0.00 0.00 0.00 35.96 32.09 0.00 0.00 0.00 44.77 36.00 112.15

22 27.425 Benzaldehyde, 4-methyl- Benzene and substituted derivatives 35.67 29.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.01 26.67 24.78 21.14 133.22

23 27.554 2-Nonenal, (E)- Carbonyl compounds 19.78 0.00 54.96 75.03 27.84 28.11 0.00 51.13 26.51 36.31 0.00 0.00 31.78 43.13 45.03 77.68

24 28.251 Decanal Carbonyl compounds 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 33.22 22.77 0.00 74.81 0.00 0.00 238.94

25 28.506 Nonanoic acid, methyl ester Fatty acid ester 0.00 0.00 54.63 37.81 76.24 0.00 0.00 135.26 60.92 117.97 0.00 0.00 152.58 0.00 114.26 113.46

26 28.544 Pentanoic acid, 4-methyl- Organic acid 0.00 0.00 0.00 0.00 0.00 0.00 20.52 0.00 0.00 0.00 0.00 24.34 180.50 0.00 0.00 309.18

27 28.960 Benzene, 1,3-bis(1,1-dimethylethyl)- Benzene and substituted derivatives 48.12 62.17 103.04 106.87 91.25 34.60 86.78 88.07 120.09 126.03 36.10 106.08 106.85 175.75 148.27 41.22

28 29.461 Hexanoic acid Organic acid 0.00 34.12 25.18 26.34 0.00 0.00 31.24 28.23 0.00 26.67 45.68 39.29 43.29 33.83 166.13 120.42

29 31.270 Decanoic acid, methyl ester Fatty acid ester 0.00 0.00 0.00 0.00 26.02 0.00 0.00 0.00 0.00 22.46 0.00 0.00 0.00 0.00 21.45 207.93

30 31.876 Heptanoic acid Organic acid 0.00 22.05 22.30 0.00 0.00 0.00 0.00 0.00 0.00 29.72 67.19 46.06 0.00 0.00 0.00 168.02

31 32.368 Phenol Benzene and substituted derivatives 100.74 0.00 0.00 0.00 0.00 573.21 0.00 0.00 0.00 0.00 574.45 0.00 0.00 0.00 0.00 241.34


174

32 32.569 Benzenemethanol, 4-methyl- Benzene and substituted derivatives 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39.14 61.28 50.75 0.00 211.14

33 34.080 Phenol, 4-methyl- Benzene and substituted derivatives 0.00 0.00 0.00 0.00 0.00 33.92 0.00 0.00 0.00 0.00 165.71 0.00 0.00 0.00 0.00 323.52

34 34.197 Octanoic Acid Organic acid 38.81 78.71 70.52 78.21 61.30 23.13 75.23 61.76 85.55 0.00 0.00 49.47 0.00 42.30 0.00 72.92

35 36.240 Dodecanoic acid, methyl ester Fatty acid ester 22.49 0.00 24.43 22.31 45.09 25.46 0.00 48.22 45.57 54.50 32.02 0.00 81.30 0.00 76.78 83.59

36 36.432 Nonanoic acid Organic acid 221.55 334.35 372.36 394.35 320.70 118.46 257.73 275.88 291.24 189.57 81.55 396.79 506.23 238.43 163.01 41.32

37 36.594 Butylated Hydroxytoluene Benzene and substituted derivatives 28.03 32.62 23.63 24.98 20.76 30.82 35.83 21.87 20.79 24.78 0.00 35.05 0.00 0.00 20.06 56.98

38 38.697 n-Decanoic acid Organic acid 33.28 67.65 44.38 53.88 43.05 0.00 33.26 34.41 39.92 38.36 0.00 92.07 86.22 0.00 31.27 69.81

39 40.245 2-Propenoic acid, tridecyl ester Fatty acid ester 108.18 242.24 179.61 201.88 174.85 203.06 361.73 247.22 254.43 290.62 205.51 518.10 350.42 461.24 332.36 40.60

40 40.705 Indole Indoles and derivatives 2043.97 292.58 1032.86 752.14 890.08 2200.81 418.91 1714.30 1184.04 1769.78 2161.86 410.78 1913.45 2101.52 1571.39 50.59

41 41.408 Diethyl Phthalate Benzene and substituted derivatives 41.95 60.31 24.19 50.62 44.89 23.19 34.07 28.78 42.89 40.58 46.51 43.83 46.48 54.60 36.83 25.48

42 42.649 Tetradecanal Carbonyl compounds 0.00 0.00 0.00 0.00 0.00 0.00 29.60 0.00 0.00 0.00 0.00 47.18 24.20 23.00 0.00 182.69

43 44.168 9-Hexadecenoic acid, methyl ester, (Z)- Fatty acid ester 0.00 0.00 22.01 20.52 35.32 20.01 0.00 44.48 25.41 39.96 27.86 0.00 50.19 0.00 49.06 84.69

44 44.321 Hexadecanoic acid, methyl ester Fatty acid ester 19.71 0.00 31.11 27.19 32.21 0.00 0.00 30.52 26.04 28.02 28.42 0.00 34.36 31.87 35.72 64.75

45 47.872 Dibutyl phthalate Benzene and substituted derivatives 70.82 0.00 59.48 58.92 57.16 67.19 121.81 82.80 78.74 0.00 80.06 66.93 75.02 74.54 75.69 46.95

46 48.073 9,12-Octadecadienoic acid (Z,Z)-, methyl ester Fatty acid ester 0.00 0.00 25.80 28.42 53.26 0.00 0.00 47.00 55.91 84.21 0.00 0.00 49.22 22.04 78.27 101.41

ID; Identification, RT; retentiton time, VC; Variation coefficient

Capítulo 8. CONCLUSIONES

175


En este trabajo se evaluó el impacto de los componentes indigestibles de alimentos frecuentemente

consumidos y sus posibles efectos en el mantenimiento de la salud del colon en una población de

escolares que viven en Tepic, Nayarit, México.

El uso de una combinación de análisis de conglomerados (AC) y el análisis de componentes

principales (ACP) determinó siete factores dietéticos y tres dietas diferentes: Dieta Tradicional

Mexicana, Dieta Modificada Mexicana y Dieta Alternativa Mexicana.

Un patrón dietético caracterizado por una alta ingesta de leguminosas, bocadillos y bajas ingesta

bebidas se asoció negativamente con el peso y el IMC, lo que sugiere que el patrón dietético

tradicional mexicano podría conducir a la reducción de la prevalencia de sobrepeso y obesidad

entre los escolares.

No se encontró una relación clara entre el estatus de sobrepeso-obesidad y los tipos de dieta.

El perfil de ingesta de macronutrientes de los escolares depende del tipo de dieta consumida. El

consumo de energía de azúcares, dulces, pasteles y bebidas endulzadas aparece en todos los

patrones dietéticos.

Se deben considerar nuevas políticas para el combate de la prevalencia de sobrepeso y obesidad en

escolares mexicanos, incluyendo el diseño de un programa de intervención nutricional

personalizado tomando en cuenta los patrones dietéticos de cada zona geográfica.

En cuanto a la digestión gastrointestinal y fermentación colínica in vitro de los alimentos

frecuentemente consumidos por los escolares en las dietas previamente identificadas, se obtuvo

que las fracciones indigestibles (FI) aisladas de los tres menús del desayuno presentaron una

composición química variada.

TM-B mostró el mayor contenido polifenoles no extraíbles, y carbohidratos no digeribles (DF)

como principales compuestos bioactivos. Estos compuestos podrían influir en el metabolismo


176

fermentativo al ingerir estos alimentos modificando el perfil de metabolitos microbianos que tiene

un impacto marcado en la salud del huésped.

En este trabajo, ACP permitió la identificación de patrones metabólicos que se asocian con efectos

beneficiosos para la salud (disminución del pH del colon y aumento de los valores de la capacidad

antioxidante). También se identificaron compuestos asociados con diferentes enfermedades que

además se relacionaron con potenciales efectos nocivos (aumento del pH y disminución de

capacidad antioxidante “AOX”) durante la fermentación colónica in vitro.

El análisis de agrupamiento jerárquico mostró que los extractos de fermentación de las FI´s

aisladas de un desayuno mexicano tradicional presentan un patrón metabólico beneficioso

dependiente del tiempo de fermentación.

Para entender el impacto de la dieta sobre el estado de salud intestinal, se necesitan trabajos

adicionales que evalúen los patrones fermentativos colónicos de los alimentos combinados en

regímenes realistas completos.

Nuestro estudio representa una nueva contribución sobre los efectos potenciales in vitro de los

hábitos alimentarios en la producción de metabolitos bacterianos. Las FI´s aisladas de menús de la

comida presentaron una apreciable cantidad de polifenoles extraíbles y no extraíbles, como

principales compuestos bioactivos, pero no contribuyen a un AOX anti-radical.

Aunque existieron diferencias en el contenido de FI, la magnitud de la producción de ácidos

grasos de cadena corta (AGCC) en los menús de de las comidas fueron similares al blanco (sin

sustrato). Eso puede estar relacionado con la baja contribución de la fibra dietética en la dieta

mexicana. ACP permitió la identificación de patrones metabólicos que se asociaron con efectos

beneficiosos para la salud (pH bajo y alto AOX).

Los metabolitos bacterianos como trimetilamina, ésteres de ácidos grasos e indol se asociaron con

el aumento del pH y la disminución de AOX en los extractos de fermentación. El perfil de los

metabolitos bacterianos fue dependiente del tiempo de fermentación y del sustrato. El enfoque


177

presentado aquí, podría ser utilizado como modelo predictivo para evaluar fácilmente los efectos

de los componentes indigestibles provienente de la ingesta de alimentos comunes en la dieta.

Los menús de las cenas mostraron diferencias importantes y similitudes con los resultados

obtenidos durante el desayuno y la comida. La fracción indigestible de la dieta tradicional presentó

el mayor contenido de polifenoles extraíbles y no extraíbles, como principales compuestos

bioactivos, con una ligera contribución en la AOX anti-radical. A pesar de las diferencias en el

contenido de FI, la magnitud de la producción de AGCC en los menús de cena fueron bajos, y

similares a los encontrados en los otros tiempos de ingesta evaluados. El menú de la dieta

alternativa puede contener almidón resistente en la composición de FI lo que favorecíó la

producción de AGCC. Fue apreciable la relación entre el bajo contenido de AGCC y la baja

contribución de la fibra dietética en la todas las dietas mexicanas.

El ACP permitió la identificación de patrones metabólicos que se asociaron con efectos

beneficiosos para la salud (pH bajo y alto AOX). Siendo los AGCC los que se relacionaron con

efectos benéficos en los extractos de fermentación. Sin embargo, los metaboitos microbianos del

menú de la Dieta Modificada Mexicana fueron mayormente relacionados a la fermentación de

proteínas (principalmente trisulfidos) lo que sugiere posibles efectos adversos en la salud de la

población.

Los metabolitos bacterianos como el fenol y el fenol 4-metilo se asociaron con el aumento del pH

y la disminución de AOX en los extractos de fermentación, estos metabolitos estuvieron presentes

durante las distintas femrentaciones de los alimentos consumidos durante el desayuno, comida y la

cena. Sin embargo, son necesarios estudios in vivo para relacionar nuestros resultados con los

posibles efectos de la dieta sobre la salud intestinal

Por último, la fermentación de alimentos procesadores de maíz, mostraron una relación directa

entre los metabolitos bacterianos, el pH y los valores de AOX durante la fermentación colónica in

vitro. La composición química y el contenido de AR fueron diferentes entre los productos de maíz.

A diferencias de los menús, y a pesar de que el contenido de FI fue el mismo, la magnitud de la

producción de AGCC, particularmente de ácido butírico, durante la fermentación in vitro,

dependió del tiempo de fermentación y del sustrato. Los metabolitos bacterianos como la


178

trimetilamina, el fenol y el 4-metilfenol se asociaron con el aumento del pH y la disminución de

AOX en los extractos de fermentación, siendo estos compuestos relacionados negativamente

efectos saludables en todas las muestras analizadas durante el desarrollo de este trabajo.

En términos generales, la tortilla tradicional de maíz y el totopo del istmo de Tehuantepec podrían

percibirse como alimentos con efectos potenciales para la salud intestinal debido a la alta

producción de ácido butírico lo que sugiere una relación con el contenido de AR.

Finalmente, este estudio demuestra que sólo a través del análisis de los alimentos consumidos con

frecuencia por una población, se logrará mejorar nuestra comprensión de los efectos de la dieta en

la promoción de la salud del colon.

Capítulo 9. BIBLIOGRAFÍA

179

Capítulo 9. BIBLIOGRAFÍA Abell, G. C., Cooke, C. M., Bennett, C. N., Conlon, M. A., y McOrist, A. L. 2008. Phylotypes

related to Ruminococcus bromii are abundant in the large bowel of humans and increase in

response to a diet high in resistant starch. FEMS microbiology ecology, 66(3), 505-515.

Acheson, K. J. 2010. Carbohydrate for weight and metabolic control: where do we stand?

Nutrition, 26(2), 141-145.

Aguilar-Salinas, C. A., Rojas, R., Gómez-Pérez, F. J., Valles, V., Rı ́os-Torres, J. M., Franco, A.,

Olaiz, G., Rull, J. A., y Sepúlveda, J. 2004. High prevalence of metabolic syndrome in

Mexico. Archives of medical research, 35(1), 76-81.

Alberti, K., Zimmet, P., y Shaw, J. 2005. The metabolic syndrome--a new worldwide definition.

Lancet, 366(9491), 1059.

Alvarez-Parrilla, E., de la Rosa, L. A., Amarowicz, R., y Shahidi, F. 2010a. Antioxidant activity of

fresh and processed Jalapeno and Serrano peppers. Journal of Agricultural and Food

Chemistry, 59(1), 163-173.

Alvarez-Parrilla, E., De La Rosa, L. A., Legarreta, P., Saenz, L., Rodrigo-García, J., y González-

Aguilar, G. A. 2010b. Daily consumption of apple, pear and orange juice differently

affects plasma lipids and antioxidant capacity of smoking and non-smoking adults.

International Journal of Food Sciences and Nutrition, 61(4), 369-380.

AOAC. 1990. Association of Official Analisys Chemists. Oficial Methods of Analysis. 15th

Edition.

Asp, N.-G., y Björck, I. 1992. Resistant starch. Trends in Food Science & Technology, 3(0),

111-114.

Barlow, S. E., y Committee, a. t. E. 2007. Expert Committee Recommendations Regarding the

Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity:

Summary Report. Pediatrics, 120(Supplement 4), S164-S192.

Baxter, A. J., Coyne, T., y McClintock, C. 2006. Dietary patterns and metabolic syndrome-a

review of epidemiologic evidence. Asia Pacific journal of clinical nutrition, 15(2), 134.


180

Bingham, S. 1997. Meat, starch and non-starch polysaccharides, are epidemiological and

experimental findings consistent with acquired genetic alterations in sporadic colorectal

cancer? Cancer letters, 114(1), 25-34.

Bradford, M. M. 1976. A rapid and sensitive method for the quantitation of microgram quantities

of protein utilizing the principle of protein-dye binding. Analytical biochemistry, 72(1),

248-254.

Brinkworth, G. D., Noakes, M., Clifton, P. M., y Bird, A. R. 2009. Comparative effects of very

low-carbohydrate, high-fat and high-carbohydrate, low-fat weight-loss diets on bowel

habit and faecal short-chain fatty acids and bacterial populations. British Journal of

Nutrition, 101(10), 1493-1502.

Campos-Vega, R., Guevara-Gonzalez, R. G., Guevara-Olvera, B. L., Dave Oomah, B., y Loarca-

Piña, G. 2010. Bean (Phaseolus vulgaris L.) polysaccharides modulate gene expression in

human colon cancer cells (HT-29). Food Research International, 43(4), 1057-1064.

Campos-Vega, R., Reynoso-Camacho, R., Pedraza-Aboytes, G., Acosta-Gallegos, J. A., Guzman-

Maldonado, S. H., Paredes-Lopez, O., Oomah, B. D., y Loarca-Piña, G. 2009. Chemical

Composition and In vitro Polysaccharide Fermentation of Different Beans (Phaseolus

vulgaris L.). Journal of Food Science, 74(7), T59-T65.

Cani, P., Amar, J., Iglesias, M. A., Poggi, M., Knauf, C., Bastelica, D., Neyrinck, A. M., Fava, F.,

Tuohy, K. M., y Chabo, C. 2007a. Metabolic endotoxemia initiates obesity and insulin

resistance. Diabetes, 56(7), 1761-1772.

Cani, P., Neyrinck, A., Fava, F., Knauf, C., Burcelin, R., Tuohy, K., Gibson, G., y Delzenne, N.

2007b. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-

induced diabetes in mice through a mechanism associated with endotoxaemia.

Diabetologia, 50(11), 2374-2383.

Cani, P. D., Possemiers, S., Van de Wiele, T., Guiot, Y., Everard, A., Rottier, O., Geurts, L.,

Naslain, D., Neyrinck, A., y Lambert, D. M. 2009. Changes in gut microbiota control

inflammation in obese mice through a mechanism involving GLP-2-driven improvement

of gut permeability. Gut, 58(8), 1091-1103.


181

Cardona, F., Andrés-Lacueva, C., Tulipani, S., Tinahones, F. J., y Queipo-Ortuño, M. I. 2013.

Benefits of polyphenols on gut microbiota and implications in human health. The Journal

of nutritional biochemistry, 24(8), 1415-1422.

Cook, S., y Sellin, J. 1998. Review article: short chain fatty acids in health and disease. Alimentary

pharmacology & therapeutics, 12(6), 499-507.

Costabile, A., Klinder, A., Fava, F., Napolitano, A., Fogliano, V., Leonard, C., Gibson, G. R., y

Tuohy, K. M. 2008. Whole-grain wheat breakfast cereal has a prebiotic effect on the

human gut microbiota: a double-blind, placebo-controlled, crossover study. Br J Nutr,

99(1), 110-120.

Cummings, J. 1997. Carbohydrate and protein digestion: the substrate available for fermentation.

En The large intestine in nutrition and disease., Brussels, Belgium: Danone Institute. pp.:

15–42

Cummings, J. H., y Englyst, H. N. 1991. What is dietary fibre? Trends in Food Science and

Technology, 2, 99–103.

Cummings, J. H., y Macfarlane, G. T. 1991. The control and consequences of bacterial

fermentation in the human colon. Journal of Applied Microbiology, 70(6), 443-459.

Cheng, H.-H., y Lai, M.-H. 2000. Fermentation of resistant rice starch produces propionate

reducing serum and hepatic cholesterol in rats. The Journal of nutrition, 130(8), 1991-

1995.

Dall'Asta, M., Calani, L., Tedeschi, M., Jechiu, L., Brighenti, F., y Del Rio, D. 2012. Identification

of microbial metabolites derived from in vitro fecal fermentation of different polyphenolic

food sources. Nutrition, 28(2), 197-203.

De Chávez, M. M., y Solano, J. A. L. 2010. Composición de alimentos: valor nutritivo de los

alimentos de mayor consumo. Mexico DF. pp.

de Vogel, J., van-Eck, W. B., Sesink, A. L., Jonker-Termont, D. S., Kleibeuker, J., y van der Meer,

R. 2008. Dietary heme injures surface epithelium resulting in hyperproliferation,

inhibition of apoptosis and crypt hyperplasia in rat colon. Carcinogenesis, 29(2), 398-403.

Denova-Gutiérrez, E., Castañón, S., Talavera, J. O., Flores, M., Macías, N., Rodríguez-Ramírez,

S., Flores, Y. N., y Salmerón, J. 2011. Dietary patterns are associated with different


182

indexes of adiposity and obesity in an urban Mexican population. The Journal of nutrition,

141(5), 921-927.

Denova-Gutiérrez, E., Talavera, J. O., Huitrón-Bravo, G., Méndez-Hernández, P., y Salmerón, J.

2010. Sweetened beverage consumption and increased risk of metabolic syndrome in

Mexican adults. Public health nutrition, 13(6), 835.

Denova Gutiérrez, E., Castañón, S., Talavera, J. O., Gallegos Carrillo, K., Flores, M.,

Dosamantes Carrasco, D., Willett, W. C., y Salmerón, J. 2010. Dietary patterns are

associated with metabolic syndrome in an urban Mexican population. The Journal of

nutrition, 140(10), 1855-1863.

Dewulf, E. M., Cani, P. D., Neyrinck, A. M., Possemiers, S., Holle, A. V., Muccioli, G. G.,

Deldicque, L., Bindels, L. B., Pachikian, B. D., y Sohet, F. M. 2011. Inulin-type fructans

with prebiotic properties counteract GPR43 overexpression and PPARγ-related

adipogenesis in the white adipose tissue of high-fat diet-fed mice. The Journal of

nutritional biochemistry, 22(8), 712-722.

Dhingra, R., Sullivan, L., Jacques, P. F., Wang, T. J., Fox, C. S., Meigs, J. B., D’Agostino, R. B.,

Gaziano, J. M., y Vasan, R. S. 2007. Soft drink consumption and risk of developing

cardiometabolic risk factors and the metabolic syndrome in middle-aged adults in the

community. Circulation, 116(5), 480-488.

Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P., y Smith, F. 1956. Colorimetric method for

determination of sugars and related substances. Analytical chemistry, 28(3), 350-356.

Ebbeling, C. B., Leidig, M. M., Feldman, H. A., Lovesky, M. M., y Ludwig, D. S. 2007. Effects of

a Low–Glycemic Load vs Low-Fat Diet in Obese Young Adults. JAMA: the journal of the

American Medical Association, 297(19), 2092-2102.

Englyst, H., Wiggins, H. S., y Cummings, J. H. 1982. Determination of the non-starch

polysaccharides in plant foods by gas-liquid chromatography of constituent sugars as

alditol acetates. Analyst, 107(1272), 307-318.

Esmaillzadeh, A., Kimiagar, M., Mehrabi, Y., Azadbakht, L., Hu, F. B., y Willett, W. C. 2007.

Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women.

The American journal of clinical nutrition, 85(3), 910-918.


183

Esmaillzadeh, A., Mirmiran, P., y Azizi, F. 2004. Whole-grain consumption and the metabolic

syndrome: a favorable association in Tehranian adults. European journal of clinical

nutrition, 59(3), 353-362.

Flint, H. J., Bayer, E. A., Rincon, M. T., Lamed, R., y White, B. A. 2008. Polysaccharide

utilization by gut bacteria: potential for new insights from genomic analysis. Nature

Reviews Microbiology, 6(2), 121-131.

Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P., y Forano, E. 2012a. Microbial degradation of

complex carbohydrates in the gut. Gut Microbes, 3(4), 289-306.

Flint, H. J., Scott, K. P., Louis, P., y Duncan, S. H. 2012b. The role of the gut microbiota in

nutrition and health. Nature Reviews Gastroenterology and Hepatology, 9(10), 577-589.

Ford, E. S., y Mokdad, A. H. 2008. Epidemiology of obesity in the Western Hemisphere. Journal

of Clinical Endocrinology & Metabolism, 93(11 Supplement 1), s1-s8.

Gabert, L., Vors, C., Louche Pélissier, C., Sauvinet, V., Lambert Porcheron, S., Drai, J., Laville,

M., Désage, M., y Michalski, M. C. 2011. 13C tracer recovery in human stools after

digestion of a fat rich meal labelled with [1, 1, 1 13C3] tripalmitin and [1, 1, 1 13C3]

triolein. Rapid Communications in Mass Spectrometry, 25(19), 2697-2703.

Goñi, I., García-Diz, L., Mañas, E., y Saura-Calixto, F. 1996. Analysis of resistant starch: a

method for foods and food products. Food Chemistry, 56(4), 445-449.

Hair, J. F., y Anderson, R. E. 2010. Multivariate data analysis. pp.

Hamer, H. M., De Preter, V., Windey, K., y Verbeke, K. 2012. Functional analysis of colonic

bacterial metabolism: relevant to health? American Journal of Physiology-Gastrointestinal

and Liver Physiology, 302(1), G1-G9.

Hartzfeld, P. W., Forkner, R., Hunter, M. D., y Hagerman, A. E. 2002. Determination of


Journal of Agricultural and Food Chemistry, 50(7), 1785-1790.

Hernandez-Salazar, M., Osorio-Diaz, P., Loarca-Pina, G., Reynoso-Camacho, R., Tovar, J., y

Bello-Perez, L. A. 2010. In vitro fermentability and antioxidant capacity of the

indigestible fraction of cooked black beans (Phaseolus vulgaris L.), lentils (Lens culinaris

L.) and chickpeas (Cicer arietinum L.). J Sci Food Agric, 90(9), 1417-1422.


184

Hernández, B., Haene, J. d., Barquera, S., Monterrubio, E., Rivera, J., Shamah, T., Sepúlveda, J.,

Haas, J., y Campirano, F. 2003. Factors associated with physical activity among Mexican

women of childbearing age. Revista Panamericana de Salud Pública, 14(4), 235-245.

Hoffmann, K., Schulze, M. B., Schienkiewitz, A., Nothlings, U., y Boeing, H. 2004. Application

of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J

Epidemiol, 159(10), 935-944.

Hu, F. B. 2002. Dietary pattern analysis: a new direction in nutritional epidemiology. Current

opinion in lipidology, 13(1), 3-9.

Huazano-García, A., y López, M. G. 2013. Metabolism of Short Chain Fatty Acids in the Colon

and Faeces of Mice After a Supplementation of Diets with Agave Fructans. En Lipid

Metabolism R. Valenzuela-Baez (Ed.): InTech. pp.: 163-182

Hughes, R., Magee, E., y Bingham, S. 2000. Protein degradation in the large intestine: relevance

to colorectal cancer. Current issues in intestinal microbiology, 1(2), 51-58.

INEGI. 2011. “Estadísticas a propósito del día de muertos” datos de Nayarit. Instituto Nacional de

Estadística y Geografía.

Jeejeebhoy, K. N. 2002. Short bowel syndrome: a nutritional and medical approach. Canadian

Medical Association Journal, 166(10), 1297-1302.

Key, T. J., Allen, N. E., Spencer, E. A., y Travis, R. C. 2002. The effect of diet on risk of cancer.

Lancet, 360(9336), 861-868.

Kim, H., Stote, K. S., Behall, K. M., Spears, K., Vinyard, B., y Conway, J. M. 2009. Glucose and

insulin responses to whole grain breakfasts varying in soluble fiber, beta-glucan: a dose

response study in obese women with increased risk for insulin resistance. Eur J Nutr,

48(3), 170-175.

Klenow, S., Pool-Zobel, B. L., y Glei, M. 2009. Influence of inorganic and organic iron

compounds on parameters of cell growth and survival in human colon cells. Toxicology in

vitro, 23(3), 400-407.

Larrosa, M., Luceri, C., Vivoli, E., Pagliuca, C., Lodovici, M., Moneti, G., y Dolara, P. 2009.

Polyphenol metabolites from colonic microbiota exert anti inflammatory activity on

different inflammation models. Molecular nutrition & food research, 53(8), 1044-1054.


185

Larsson, S. C., y Wolk, A. 2006. Meat consumption and risk of colorectal cancer: A meta

analysis of prospective studies. International Journal of Cancer, 119(11), 2657-2664.

Lee, D.-H., Anderson, K. E., Harnack, L. J., Folsom, A. R., y Jacobs, D. R. 2004. Heme iron, zinc,

alcohol consumption, and colon cancer: Iowa Women's Health Study. Journal of the

National Cancer Institute, 96(5), 403-407.

Lee, J. M., Okumura, M. J., Davis, M. M., Herman, W. H., y Gurney, J. G. 2006. Prevalence and

Determinants of Insulin Resistance Among US Adolescents A population-based study.

Diabetes Care, 29(11), 2427-2432.

Lhoste, E. F., Catala, I., Fiszlewicz, M., Gueugneau, A., Popot, F., Vaissade, P., Corring, T., y

Szylit, O. 1996. Influence of caecal microflora and of two dietary protein levels on the

adaptation of the exocrine pancreas: comparative study in germ-free and conventional rats.

British Journal of Nutrition, 75(03), 433-444.

Lin, H.-C., y Visek, W. J. 1991. Colon Mucosal Cell Damage by Ammonia in Rats. The Journal of

nutrition, 121(6), 887-893.

Liu, E., McKeown, N. M., Newby, P., Meigs, J. B., Vasan, R. S., Quatromoni, P. A., D'Agostino,

R. B., y Jacques, P. F. 2009a. Cross-sectional association of dietary patterns with insulin-

resistant phenotypes among adults without diabetes in the Framingham Offspring Study.

British Journal of Nutrition, 102(4), 576.

Liu, E., McKeown, N. M., Newby, P. K., Meigs, J. B., Vasan, R. S., Quatromoni, P. A.,

D'Agostino, R. B., y Jacques, P. F. 2009b. Cross-sectional association of dietary patterns

with insulin-resistant phenotypes among adults without diabetes in the Framingham

Offspring Study. Br J Nutr, 102(4), 576-583.

Louis, P., y Flint, H. J. 2009. Diversity, metabolism and microbial ecology of butyrate producing

bacteria from the human large intestine. FEMS microbiology letters, 294(1), 1-8.

Louis, P., Young, P., Holtrop, G., y Flint, H. J. 2010. Diversity of human colonic butyrate

producing bacteria revealed by analysis of the butyryl CoA: acetate Co A transferase

gene. Environmental microbiology, 12(2), 304-314.

Macfarlane, G., Cummings, J., y Allison, C. 1986. Protein degradation by human intestinal

bacteria. Journal of general microbiology, 132(6), 1647-1656.


186

Macfarlane, G. T., y Macfarlane, S. 1997. Human colonic microbiota: ecology, physiology and

metabolic potential of intestinal bacteria. Scandinavian Journal of Gastroenterology-

Supplements, 32(222), 3-9.

Macfarlane, S., Macfarlane, GT. 1995. Proteolysis and amino acid fermentation. En Human

colonic bacteria-role in nutrition, physiology and pathology M. G. Gibson GR (Ed.): CRC

Press Inc. pp.: 75–100

Manach, C., Scalbert, A., Morand, C., Rémésy, C., y Jiménez, L. 2004. Polyphenols: food sources

and bioavailability. The American journal of clinical nutrition, 79(5), 727-747.

Manach, C., Williamson, G., Morand, C., Scalbert, A., y Rémésy, C. 2005. Bioavailability and

bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies. The

American journal of clinical nutrition, 81(1), 230S-242S.

Mañas, E., Bravo, L., y Saura-Calixto, F. 1994. Sources of error in dietary fibre analysis. Food

Chemistry, 50(4), 331-342.

Martin, F. P., Sprenger, N., Yap, I. K., Wang, Y., Bibiloni, R., Rochat, F., Rezzi, S., Cherbut, C.,

Kochhar, S., Lindon, J. C., Holmes, E., y Nicholson, J. K. 2009. Panorganismal gut

microbiome-host metabolic crosstalk. Proteome Res, 8(4), 2090-2105.

Martínez de Victoria Muñoz, E., Mañas-Almendros, M., y Yago Torregrosa, M. D. (2010).

Fisiología de la Digestión. In Á. Gil Hernández (Ed.), Tratado de Nutrición). Mexico,

D.F.: Médica Panamericana.

Massey, R., Key, P., Mallett, A., y Rowland, I. 1988. An investigation of the endogenous

formation of apparent total N-nitroso compounds in conventional microflora and

germ-free rats. Food and chemical toxicology, 26(7), 595-600.

McMillan-Price, J., Petocz, P., Atkinson, F., O'Neill, K., Samman, S., Steinbeck, K., Caterson, I.,

y Brand-Miller, J. 2006. Comparison of 4 diets of varying glycemic load on weight loss

and cardiovascular risk reduction in overweight and obese young adults: a randomized

controlled trial. Archives of Internal Medicine, 166(14), 1466.

McNaughton, S. A., Mishra, G. D., y Brunner, E. J. 2008. Dietary patterns, insulin resistance, and

incidence of type 2 diabetes in the Whitehall II Study. Diabetes Care, 31(7), 1343-1348.


187

McPherson, R. S., Hoelscher, D. M., Alexander, M., Scanlon, K. S., y Serdula, M. K. 2000.

Dietary Assessment Methods among School-Aged Children: Validity and Reliability.

Preventive Medicine, 31(2), S11-S33.

Monagas, M., Khan, N., Andrés-Lacueva, C., Urpi-Sarda, M., Vázquez-Agell, M., Lamuela-

Raventos, R. M., y Estruch, R. 2009. Dihydroxylated phenolic acids derived from

microbial metabolism reduce lipopolysaccharide-stimulated cytokine secretion by human

peripheral blood mononuclear cells. British Journal of Nutrition, 102(2), 201.

Monagas, M., Urpi-Sarda, M., Sánchez-Patán, F., Llorach, R., Garrido, I., Gómez-Cordovés, C.,

Andres-Lacueva, C., y Bartolomé, B. 2010. Insights into the metabolism and microbial

biotransformation of dietary flavan-3-ols and the bioactivity of their metabolites. Food &

Function, 1(3), 233-253.

Montonen, J., Knekt, P., Härkänen, T., Järvinen, R., Heliövaara, M., Aromaa, A., y Reunanen, A.

2005. Dietary patterns and the incidence of type 2 diabetes. American Journal of

Epidemiology, 161(3), 219-227.

Montreau, F. 1972. Sur le dosage des composés phénoliques totaux dans les vins par la methode

Folin-Ciocalteau. Connaiss Vigne Vin, 24, 397-404.

Nakamura, N., Lin, H. C., McSweeney, C. S., Mackie, R. I., y Gaskins, H. R. 2010. Mechanisms

of microbial hydrogen disposal in the human colon and implications for health and

disease. Food Science and Technology, 1.

Newby, P., Muller, D., y Tucker, K. L. 2004. Associations of empirically derived eating patterns

with plasma lipid biomarkers: a comparison of factor and cluster analysis methods. The

American journal of clinical nutrition, 80(3), 759-767.

Newby, P. K., Muller, D., Hallfrisch, J., Qiao, N., Andres, R., y Tucker, K. L. 2003. Dietary

patterns and changes in body mass index and waist circumference in adults. The American

journal of clinical nutrition, 77(6), 1417-1425.

Neyrinck, A. M., Possemiers, S., Druart, C., Van de Wiele, T., De Backer, F., Cani, P. D.,

Larondelle, Y., y Delzenne, N. M. 2011. Prebiotic effects of wheat arabinoxylan related to

the increase in bifidobacteria, Roseburia and Bacteroides/Prevotella in diet-induced obese

mice. PloS one, 6(6), e20944.


188

Neyrinck, A. M., Possemiers, S., Verstraete, W., De Backer, F., Cani, P. D., y Delzenne, N. M.

2012. Dietary modulation of clostridial cluster XIVa gut bacteria ( Roseburia

spp.) by chitin–glucan fiber improves host metabolic alterations induced by high-fat diet

in mice. The Journal of nutritional biochemistry, 23(1), 51-59.

Norat, T., Lukanova, A., Ferrari, P., y Riboli, E. 2002. Meat consumption and colorectal cancer

risk: Dose response meta analysis of epidemiological studies. International Journal of

Cancer, 98(2), 241-256.

OECD. 2012. Overweight and obesity at age 15 by gender. Organisation for Economic Co-

operation and Development - Social Policy Division - Directorate of Employment, Labour

and Social Affairs. Consultado: 2012 [http://www.oecd.org/els/social/family/database].

Olaiz-Fernández, G., Rivera-Dommarco, J., Shamah-Levy, T., Rojas, R., Villalpando-Hernández,

S., Hernández-Avila, M., y Sepúlveda-Amor, J. 2006. Encuesta Nacional de Salud y

Nutricion 2006 I. N. d. S. Pública. Cuernavaca, México. pp.

Olaiz, G., Rivera, J., Shamah T, Rojas R, Villalpando S, Hernandez M, Sepúlveda, J., Hass, J., y

Campirano, F. 2006. Encuesta Nacional de Salud y Nutrición 2006. Cuernavaca (México):

Instituto Nacional de Salud Pública.

Ortega, R., López-Sobaler, A., Andrés, P., Requejo, A., Aparicio-Vizuete, A., y Molinero, L.

(2011). DIAL, Software for assessing diets and food calculations. . In Departamento de

Nutrición (UCM) y Alce Ingeniería, S.L. ).

Ortega, R., López-Sobaler, A., Andrés, P., Requejo, A., y Aparicio Vizuete, A. (2012). DIAL

software for assessing diets and food calculations. . In 2.16 ed.). Madrid, España:

Departamento de Nutrición (UCM) and Alce Ingeniería, S.L.

Pérez-Jiménez, J., Arranz, S., y Saura-Calixto, F. 2009. Proanthocyanidin content in foods is



Pérez, A. B., Palacios, G., y Castro, B. 2008. Sistema mexicano de alimentos equivalentes3 ed.

México, DF. pp.


189

Prior, R. L., Wu, X., y Schaich, K. 2005. Standardized methods for the determination of

antioxidant capacity and phenolics in foods and dietary supplements. Journal of

Agricultural and Food Chemistry, 53(10), 4290-4302.

Pryde, S. E., Duncan, S. H., Hold, G. L., Stewart, C. S., y Flint, H. J. 2002. The microbiology of

butyrate formation in the human colon. FEMS microbiology letters, 217(2), 133-139.

Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N.,

Levenez, F., y Yamada, T. 2010. A human gut microbial gene catalogue established by

metagenomic sequencing. Nature, 464(7285), 59-65.

Queipo-Ortuño, M. I., Boto-Ordóñez, M., Murri, M., Gomez-Zumaquero, J. M., Clemente-

Postigo, M., Estruch, R., Diaz, F. C., Andrés-Lacueva, C., y Tinahones, F. J. 2012.

Influence of red wine polyphenols and ethanol on the gut microbiota ecology and

biochemical biomarkers. The American journal of clinical nutrition, 95(6), 1323-1334.

Rasmussen, H., Holtug, K., y Mortensen, P. 1988. Degradation of amino acids to short-chain fatty

acids in humans: an in vitro study. Scandinavian journal of gastroenterology, 23(2), 178-

182.

Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M., y Rice-Evans, C. 1999. Antioxidant

activity applying an improved ABTS radical cation decolorization assay. Free Radical

Biology and Medicine, 26(9), 1231-1237.

Reaven, G. M. 1988. Role of insulin resistance in human disease. Diabetes, 37(12), 1595-1607.

Reed, J. D., McDowell, R. T., Van Soest, P. J., y Horvath, P. R. 1982. Condensed tannins: a factor

limiting the use of cassava forage. Journal of the Science of Food and Agriculture, 33(3),

213-220.

Rivera, J. A., Barquera, S., Campirano, F., Campos, I., Safdie, M., y Tovar, V. 2002.

Epidemiological and nutritional transition in Mexico: rapid increase of non-communicable

chronic diseases and obesity. Public health nutrition, 5(1A), 113-122.

Roberfroid, M. B. 2000. Fructo-oligosaccharide malabsorption: benefit for gastrointestinal

functions. Current Opinion in Gastroenterology, 16(2), 173-177.

Romero-Martínez, M., Shamah-Levy, T., Franco-Núñez, A., Villalpando, S., Cuevas-Nasu, L.,

Gutiérrez, J., y Rivera-Dommarco, J. 2013. Encuesta Nacional de Salud y Nutrición 2012.

Salud Publica Mex, 55.


190

Romero-Polvo, A., Denova-Gutiérrez, E., Rivera-Paredez, B., Castañón, S., Gallegos-Carrillo, K.,

Halley-Castillo, E., Borges, G., Flores, M., y Salmerón, J. 2012. Association between

Dietary Patterns and Insulin Resistance in Mexican Children and Adolescents. Annals of

Nutrition and Metabolism, 61(2), 142-150.

Rose, D. J., DeMeo, M. T., Keshavarzian, A., y Hamaker, B. R. 2007. Influence of dietary fiber on

inflammatory bowel disease and colon cancer: importance of fermentation pattern.

Nutrition reviews, 65(2), 51-62.

Russell, W. R., Hoyles, L., Flint, H. J., y Dumas, M.-E. 2013. Colonic bacterial metabolites and

human health. Current opinion in microbiology.

Sahyoun, N. R., Jacques, P. F., Zhang, X. L., Juan, W., y McKeown, N. M. 2006. Whole-grain

intake is inversely associated with the metabolic syndrome and mortality in older adults.

The American journal of clinical nutrition, 83(1), 124-131.

Sandhu, M. S., White, I. R., y McPherson, K. 2001. Systematic Review of the Prospective Cohort

Studies on Meat Consumption and Colorectal Cancer Risk A Meta-Analytical Approach.

Cancer Epidemiology Biomarkers & Prevention, 10(5), 439-446.

Saura-Calixto, F., Garcia-Alonso, A., Goni, I., y Bravo, L. 2000. In vitro Determination of the

Indigestible Fraction in Foods: An Alternative to Dietary Fiber Analysis. Journal of

Agricultural and Food Chemistry, 48(8), 3342-3347.

Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J., y Duncan, S. H. 2012. The influence of diet

on the gut microbiota. Pharmacological Research.

Schwerin, H. S., Stanton, J., Riley, A., Schaefer, A., Leveille, G., Elliott, J., Warwick, K., y Brett,

B. 1981. Food eating patterns and health: a reexamination of the Ten-State and HANES I

surveys. The American journal of clinical nutrition, 34(4), 568-580.

Sekelja, M., Berget, I., Næs, T., y Rudi, K. 2010. Unveiling an abundant core microbiota in the

human adult colon by a phylogroup-independent searching approach. The ISME journal,

5(3), 519-531.

Slattery, M. L., Boucher, K. M., Caan, B. J., Potter, J. D., y Ma, K.-N. 1998. Eating patterns and

risk of colon cancer. American Journal of Epidemiology, 148(1), 4-16.

Suzuki, K., y Mitsuoka, T. 1984. N-nitrosamine formation by intestinal bacteria. IARC scientific

publications(57), 275.


191

Tabernero, M., Venema, K., Maathuis, A. J., y Saura-Calixto, F. D. 2011. Metabolite production

during in vitro colonic fermentation of dietary fiber: analysis and comparison of two

European diets. Journal of Agricultural and Food Chemistry, 59(16), 8968-8975.

Tap, J., Mondot, S., Levenez, F., Pelletier, E., Caron, C., Furet, J. P., Ugarte, E., Muñoz Tamayo,

R., Paslier, D. L., y Nalin, R. 2009. Towards the human intestinal microbiota phylogenetic

core. Environmental microbiology, 11(10), 2574-2584.

Topping, D. L., y Clifton, P. M. 2001. Short-Chain Fatty Acids and Human Colonic Function:

Roles of Resistant Starch and Nonstarch Polysaccharides. Physiological Reviews, 81(3),

1031-1064.

Tuohy, K. M., Conterno, L., Gasperotti, M., y Viola, R. 2012. Up-regulating the human intestinal

microbiome using whole plant foods, polyphenols, and/or fiber. Journal of Agricultural

and Food Chemistry, 60(36), 8776-8782.

van Dam, R. M., Rimm, E. B., Willett, W. C., Stampfer, M. J., y Hu, F. B. 2002. Dietary patterns

and risk for type 2 diabetes mellitus in US men. Annals of Internal Medicine, 136(3), 201-

209.

Van Loo, J., Coussement, P., De Leenheer, L., Hoebregs, H., y Smits, G. 1995. On the presence of

inulin and oligofructose as natural ingredients in the western diet. Critical Reviews in

Food Science & Nutrition, 35(6), 525-552.

Vipperla, K., y O'Keefe, S. J. 2012. The microbiota and its metabolites in colonic mucosal health

and cancer risk. Nutr Clin Pract, 27(5), 624-635.

Walker, A. W., Duncan, S. H., McWilliam Leitch, E. C., Child, M. W., y Flint, H. J. 2005. pH and

Peptide Supply Can Radically Alter Bacterial Populations and Short-Chain Fatty Acid

Ratios within Microbial Communities from the Human Colon. Applied and Environmental

Microbiology, 71(7), 3692-3700.

Walker, A. W., Ince, J., Duncan, S. H., Webster, L. M., Holtrop, G., Ze, X., Brown, D., Stares, M.

D., Scott, P., y Bergerat, A. 2010. Dominant and diet-responsive groups of bacteria within

the human colonic microbiota. The ISME journal, 5(2), 220-230.

Wallace, R. J., y McKain, N. 1997. Peptidase activity of human colonic bacteria. Anaerobe, 3(4),

251-257.


192

Walle, T. 2004. Absorption and metabolism of flavonoids. Free Radical Biology and Medicine,

36(7), 829-837.

WHO. 2008. Global strategy on diet, physical activity, and health. [cited 2013 March 11].

Available from: http://www.who.int/dietphysicalactivity/publications/facts/obesity/en.

Wikoff, W. R., Anfora, A. T., Liu, J., Schultz, P. G., Lesley, S. A., Peters, E. C., y Siuzdak, G.

2009. Metabolomics analysis reveals large effects of gut microflora on mammalian blood

metabolites. Proceedings of the National Academy of Sciences, 106(10), 3698-3703.

Willett, W. C. 1998. Is dietary fat a major determinant of body fat? The American journal of

clinical nutrition, 67(3), 556S-562S.

Wong, J. M., de Souza, R., Kendall, C. W., Emam, A., y Jenkins, D. J. 2006. Colonic health:

fermentation and short chain fatty acids. Journal of clinical gastroenterology, 40(3), 235-

243.

Xu, J., Eilat-Adar, S., Loria, C., Goldbourt, U., Howard, B. V., Fabsitz, R. R., Zephier, E. M.,

Mattil, C., y Lee, E. T. 2006. Dietary fat intake and risk of coronary heart disease: the

Strong Heart Study. Am J Clin Nutr, 84(4), 894-902.


J., y Sáyago-Ayerdi, S. G. 2014. In vitro colonic fermentation of food ingredients

isolated from agave tequilana Weber var. azul applied on granola bars.

LWT-Food Science and Technology.

Zhang, C., Zhang, M., Pang, X., Zhao, Y., Wang, L., y Zhao, L. 2012. Structural resilience of the

gut microbiota in adult mice under high-fat dietary perturbations. The ISME journal.


193

Documents

“IDENTIFICACIÓN DE METABOLITOS PRODUCTOS DE LA