INTERDISCIPLINARY GRADUATE SCHOOL

Human nutrition and Disease -A study of Whole Grain Diet-Induced Metabolic Changes in Human

Doctor of Philosophy

Name: Abhishek Jain

Submitted To: Prof HO Moon-Ho Ringo

Date of submission: 18 November, 2016

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Human nutrition and Disease -A study of Whole Grain Diet-Induced

Metabolic Changes in Human

Introduction

Increasing evidence indicates that changes in the composition of the human gut

microbiota affect host metabolism and are associated with a variety of diseases. Changes in diet

have been shown to rapidly affect the composition of the gut microbiota Furthermore,

microbiota-diet interactions impact host physiology through the generation of a number of

bioactive metabolites For example, short-chain fatty acids(SCFAs),which are generated by

microbial fermentation of dietary polysaccharides in the gut, are an important energy source for

colonocytes and also function as signaling molecules, modulating intestinal inflammation and

metabolism By quantifying the release and consumption of metabolites by the gut microbiota, it

may be possible to elucidate interactions between the gut microbiota and host metabolism This

information would allow identification of diagnostic biomarkers and may provide insight into the

role of the gut microbiota in disease progression. However, gut microbiome and metabolite

composition have been shown to be affected by some other factors such as age, sex, etc.

The aim of this study was to monitor the effect of whole grain diet on human faecal

metabolites. In order to examine the effect of whole grain on human faecal metabolite, samples

from 39 human male subjects of two different age groups (25-30 and more than 50) were

collected. These 39 subjects were divided into 4 different groups based on the percentage of

whole grain in their diets. The description is shown in the table

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Table 1: Description of 39 Human Subjects in this Study

No of subjects Diet Whole grain percentage age

5 Diet 1 0% 25-30

4 >50

5 Diet 2 15% 25-30

5 >50

5 Diet 3 30% 25-30

5 >50

5 Diet 4 45% 25-30

5 >50

Results:

Factor Analysis of Human Faecal Metabolites

42 metabolites were obtained after GC-MS and LC-MS metabolomics profiling of human

faecal samples, collected from 39 subjects. Exploratory factor analysis was performed to reduce

the number of variables and to group the similar metabolites together.

Figure 1 and Table 2 shows the three important factor extracted in this study. Factor I

represents the metabolite separating groups with low percentage of whole grain (i.e. 0% and

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15%) diet from groups with higher percentage of whole grain diet (i.e. 30% and 45%). Factor 2

represents the class of metabolites which shows higher concentration in diet4 (whole grain 45%)

than other diet groups with lower percentage of whole grain. Factor 3 represents the class of

metabolites which shows lower concentration in diet4 (whole grain 45%) than other diet groups

with lower percentage of whole grain.

Table2: Factor Loading based on Factor Analysis for metabolites from 39 human subjects with different percentage of whole grain diet

FactorsMetabolites separate 0%

& 15% whole grain diet

group from 30% and 45%

Metabolites which have

greater concentration in 45% whole

grain diet

Metabolites which shows

lesser concentration in 45% whole

grain diet1,3-D3-HB .4803-HP .4583-PP .479Acetate .612Benzoate .733Butyrate .515Caffeine .787Choline .658Dimethylamine .586Ethanol .586Formate .680Glutamate .929Glycine .981Histidine .478HypoxanthineIsoleucine .913Isovalerate .535Lactate .919

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Leucine .942Lysine .480Maltose .733Methanol .565Methylamine .916NDMA .762Nicotinate .446PAG .710Proline .553Propionate .815Ribose .752Tyrosine .768Uracil .533Valerate .712Valine .895Xanthine .572Endotoxin .476Glucose .873Succinate .686Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization.

Metabolite which belongs to the same category were grouped together and represented by

one significant metabolite of that class. Benzoate, formate, Histidine and ribose all belong to

acidic class of molecules so they are represented by most significant metabolite Benzoate.

Methanol reacts with ammonia for the formation of dimethylamine and methylamine. These

three metabolite are the part of same pathway and therefore represented by most significant

metabolite methylamine.

Similarly, 3-HB, Caffeine, Choline and NDMA are represented by NDMA. The group of

three amino acids Proline Tyrosine and Valine is represented by Valine.

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Figure 1: Factor Analysis of Human Faecal Metabolomic Profile

Manova confirms metabolite composition is affected by diet but not age

Manova was performed to examine the main and interaction effect of diet and age on faecal

metabolite composition. Statistical findings proves that metabolite composition is significantly

different based on diet, F=75,21.79=11.4, p<.005; Wilk’s Ʌ =.000, partial η2=0.975) .On the other

hand age (F=25,7=1,60,p>.005; Wilk’s Ʌ =.148,partial η2=0.85) and interaction between age and

diet (F=75,21.79=1.4,p>.005; Wilk’s Ʌ =.005,partial η2=0.83) could not produce significant effect

on metabolite composition. 

Manova test of between subjects effect results are presented in table 3 to determine the

significant effect of diet, age and interaction of diet and age on individual metabolite

composition.

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Table 3: Manova Test of between Subjects effect

Source Dependent Variable df F Sig.

Diet 3-HP 3 1.631 .202

3-PP 3 4.538 .009

Acetate 3 8.485 .000

Benzoate 3 2.677 .064

Butyrate 3 6.508 .002

Ethanol 3 2.979 .047

Glutamate 3 2.149 .114

Glycerol 3 4.149 .014

Glycine 3 2.838 .054

Isoleucine 3 4.149 .014

Isovalerate 3 1.635 .201

Lactate 3 5.507 .004

Leucine 3 5.406 .004

Lysine 3 9.296 .000

Maltose 3 2.804 .056

Methylamine 3 14.217 .000

NDMA 3 7.645 .001

Nicotinate 3 4.889 .007

Propionate 3 9.654 .000

Uracil 3 16.600 .000

Valerate 3 3.925 .017

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Valine 3 4.978 .006

Xanthine 3 5.808 .003

Endotoxin 3 36.935 .000

Glucose 3 21.309 .000

Age 3-HP 1 3.967 .055

3-PP 1 2.258 .143

Acetate 1 .011 .918

Benzoate 1 5.245 .029

Butyrate 1 3.268 .080

Ethanol 1 1.305 .262

Glutamate 1 .655 .425

Glycerol 1 6.136 .019

Glycine 1 1.039 .316

Isoleucine 1 2.632 .115

Isovalerate 1 .610 .441

Lactate 1 .713 .405

Leucine 1 3.227 .082

Lysine 1 10.812 .003

Maltose 1 2.676 .112

Methylamine 1 .069 .795

NDMA 1 5.358 .027

Nicotinate 1 .000 .991

Propionate 1 .832 .369

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Uracil 1 1.165 .289

Valerate 1 .245 .624

Valine 1 4.747 .037

Xanthine 1 .279 .601

Endotoxin 1 .189 .667

Glucose 1 .023 .880

diet * age 3-HP 3 1.488 .237

3-PP 3 .202 .894

Acetate 3 .477 .701

Benzoate 3 1.651 .198

Butyrate 3 2.815 .055

Ethanol 3 1.333 .281

Glutamate 3 3.640 .023

Glycerol 3 1.326 .284

Glycine 3 3.000 .045

Isoleucine 3 2.252 .102

Isovalerate 3 1.067 .377

Lactate 3 2.343 .092

Leucine 3 3.993 .016

Lysine 3 2.423 .085

Maltose 3 .215 .885

Methylamine 3 .340 .797

NDMA 3 4.017 .016

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Nicotinate 3 2.291 .098

Propionate 3 2.496 .078

Uracil 3 1.977 .138

Valerate 3 1.872 .155

Valine 3 2.304 .096

Xanthine 3 .306 .821

Endotoxin 3 2.441 .083

Glucose 3 .503 .683

The results in the table above shows that the composition of most of the metabolites

except,3-HP, Benzoate, Glycine, Isovalerate and Maltose differs significantly amongst different

dietary group. On contrary to this, only four metabolites Benzoate, Glycerol, Lysine and NDMA

are significantly affected by difference in age group. Similarly, Interaction between diet and age

also shows significant effect only on four metabolites namely Glutamate, glycine, leucine and

NDMA.

Thus we can conclude that metabolite composition is primarily depending on dietary habits

Discriminant Analysis separates human subjects with 0 and 15% whole grain diet from 30

and 45 % Whole grain diet

Discriminant analysis was performed to explore the difference in metabolite composition

within different dietary group and age group. Three statistically significant canonical

discriminant functions with acceptable classification accuracy of 87% separates the dietary

groups based on the difference in faecal metabolite composition. It can be observed from figure

that responses of human with 0% and 15% whole grain diet were clustered together and these

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can be clearly separated from the human with 30% and 45% whole grain diet. Even human with

30% whole grain diet are clustered far apart from human with 45% whole grain diet.

The importance of metabolite discriminating within different dietary group was also

analysed by standardized discriminant function coefficient and it was found that all the tested

metabolite played significant role in separation except acetate.

Discriminant analysis results based on age, group showed insignificant discriminant

function so it can be concluded that metabolite composition in human is not influenced by age

factor.

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Figure 2: Discriminant Analysis results of Different Dietary Groups

Hierarchical Cluster Analysis

Separate Cluster analysis for 39 human subjects (Figure 4) and 25 metabolites (Figure 3)

obtained from factor analysis were performed to confirm the findings of factor analysis and

discriminant analysis results. Most of the compounds present in cluster one and two are similar

to the metabolites explained by factor 1 and 2 in factor analysis. These results show the accuracy

of factor analysis results.

Figure 4 precisely separates human of diet1, diet2 and diet3 from diet4. All the human

with 45% whole grain in their diet are clustered together and differentiated themselves from

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human with lower percentage of whole grain diet. These results confirm the finding of

discriminant analysis done in the previous section of this report.

Figure 3: Cluster Analysis of Metabolites

Yellow - Higher Concentration

Green - Intermediate Concentration

Blue - Lower Concentration

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Cluster 1

Cluster 2

Figure 4: Cluster Analysis of 39 Human Subjects

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Discussion:

In this study, GC-MS and LC-MS based metabolomics was performed on human faecal

samples to analyse whether varying percentage of whole grain in diet and difference in age group

would affect the faecal metabolite composition. We also wanted to investigate whether this

whole grain diet induced faecal metabolites have relationship with any disease in human.

From the statistical analysis of data, it can be clearly seen that 39 human subjects are

distinguished from each other based on the different concentration of whole grain in their diet

but metabolite composition is not significantly affected by age factor.

Specifically, some metabolites like Methylamine, Propionate, Xanthine, Glucose,

Acetate, Endotoxin and Butyrate are present in higher concentration in human eating 45% whole

grain diet. On the other hand, human subjects with 0% and 15% whole grain diet are grouped

together with lower concentration of NDMA, Valine, Leucine, Nicotinate and higher

concentration of 3-PP.

The increasing concentration of methylamine in Diet group 3 and 4 (i.e 30 and 45%

whole grain) gives an important link in relation to human disease. Methylamine are produced

from degradation of amino acids by gut bacteria. If these methylated amines are absorbed in the

blood circulation they might be converted into toxic metabolites such as Hydrogen peroxide and

HCHO both of which are associated with human disease such as diabetes, vascular disorders.

We also observed the elevated level of NDMA in diet groups with 30% and 45% whole grain.

NDMA is Suspected to be human Carcinogen.

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Another group of metabolite consist of Xanthine, Endotoxin, Uracil showed higher

concentration with increasing percentage (i.e. 30 and 45%) of whole grain in the diet. These

metabolite decreases the PH of gut environment which is responsible for death of some

beneficial gram positive and gram negative bacteria of human gut.

The appropriate amount of whole grain in human diet is a debatable issue among human

nutritional scientists. On one hand, Whole grain has variety of health benefits and also an

optimum amount of grain in our diet keeps us away from various health diseases. Whereas,

Human faecal metabolite data from this study clearly suggests that diets containing 30 and 45%

whole grain generate a variety of metabolites which might be associated with human disease

such as Diabetes, Alzheimer's, Celiac.

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