Development of fiber rich powder and effect of supplementation on constipation
hp7301 PROJECT-ABHISHEK JAIN
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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-Astudy of Whole Grain Diet-InducedMetabolic
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:
FactorAnalysis 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 15%)
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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
Factors
Metabolites
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 diet
1,3-D
3-HB .480
3-HP .458
3-PP .479
Acetate .612
Benzoate .733
Butyrate .515
Caffeine .787
Choline .658
Dimethylamine .586
Ethanol .586
Formate .680
Glutamate .929
Glycine .981
Histidine .478
Hypoxanthine
Isoleucine .913
Isovalerate .535
Lactate .919
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Leucine .942
Lysine .480
Maltose .733
Methanol .565
Methylamine .916
NDMA .762
Nicotinate .446
PAG .710
Proline .553
Propionate .815
Ribose .752
Tyrosine .768
Uracil .533
Valerate .712
Valine .895
Xanthine .572
Endotoxin .476
Glucose .873
Succinate .686
Extraction 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 compositionis affectedby 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.
Table 3: Manova Test of between Subjects effect
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Methylamine 3 .340 .797
NDMA 3 4.017 .016
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
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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 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
HierarchicalCluster 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 human
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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
Cluster1
Cluster2
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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.
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
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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.
