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Abstracts
Personalisierte Ernährung und Einteilung/ Klassifizierung von me-
tabolischen Typen basierend auf genetischen, epigenetischen und
mikrobiologischen Analysen
Personalized nutrition and classification of metabolic types based
on genetics, epigenetics and gut microbiota
Humans vary in their need and response to diet. Genetic dis-
positions, such as single nucleotide polymorphisms (SNPs) are
frequently used for clustering consumers in metabolic types
(metabotypes), according to individual characteristics of energy
extraction from food or risk for metabolic diseases. Results are
then used for individualized concepts for weight management
or weight loss. However, SNPs explain only a minor part of me-
tabolic variability whereas epigenetic regulation of metabolic
enzymes, GI microbiota and lifestyle are of ample importance.
In a pilot study enrolling 37participants under nutritional advice
we analyzed results from a panel of SNPs, epigenetic markers
and microbiota as well as food frequency questionnaires. The
results of this study clearly indicate that epigenetic and micro-
biota markers need to be integrated in the denition of me-
tabotypes. Such improved metabotypes may then enable an
improved guidance for a personalized nutrition.
Keywords: personalized nutrition, metabolic types, SNPs, epi-
genetic, gut microbiota
Menschen unterscheiden sich in ihren Ernährungsbedürfnissen
und deren Stoffwechsel. Genetische Veranlagungen, wie zum
Beispiel Einzelnukleotidpolymophismen (engl. Single nucleotide
polymorphisms SNPs) werden häug verwendet um Patienten
in verschiedene metabolische Typen (metabotypes) einzuteilen,
passierend an den individuellen Eigenschaften wie zum Beispiel
Energieextraktion aus verschiedenen Nahrungsmitteln oder das
genetische Risiko für metabolische Erkrankungen. Diese Eintei-
lungen können weites für ein individuelles Konzept für Gewichts-
management und Gewichtsverlust verwendet werden. Nichts-
destotrotz können SNPs nur einen kleinen Teil der metabolischen
Variabilität erklären, weshalb epigenetische Regulation von En-
zymen, die gastrointestinale Mikrobiota und der Lebensstil von
selber Bedeutung sind. In einer Pilotenstudie mit 37 Teilnehmern
und Ernährungsberatung wurden SNPs, epigenetische Marker,
gastrointestinale Mikrobiota sowie Ernährungsfragebogen analy-
siert. Die Ergebnisse der Studie zeigen deutlich, dass epigenetische
Marker als auch Mikrobiota zu den Analysen der Metabotypes
integriert werden sollte. Diese verbesserten Metabotypes könnten
eine bessere personalisierte Ernährungsberatung ermöglichen.
Schlüsselwörter: Personalisierte Ernährung, metabolische Ty-
pen, SNPs, Epigenetik, Darmmikrobiota
Stephanie Lilja, Diana Gessner, Christina Schnitzler, Nicola Stephanou-Rieser,
Claudia Nichterl, Angelika Pointner, Elena Tomeva, Marlene Remely, Alexander Haslberger
Correspondence
Department of Nutritional Sciences, University of Vienna Doz. Dr. Alexander Haslberger
Department of Nutritional Sciences, University of Vienna
Althanstraße 14, 1090 Wien, Austria
e-mail: alexander.haslberger@univie.ac.at
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INTRODUCTION
Dietary preferences and habits are controlled by socioeco-
nomic, psychological, behavioral and in particular biological
determinants such as hunger, satiety and sensory aspects (5).
Body weight and composition, as well as metabolic rate are
affected by nutrient intake and biochemical pathways regula-
ting nutrient absorption, distribution, metabolism, excretions
and other cellular energy processes (4). Genetic and epigenetic
mechanisms act as key regulators and predispositions may even
forecast the response to a weight loss intervention (3,23). The
eld nutrigenetics offers a new opportunity to evaluate the role
of genes, which determine metabolism, disorders and further
use the predisposition of genes for a personalized nutrition. Ge-
nome wide association studies (GWAS) indicate that particular
gene polymorphisms such as single nucleotide polymorphisms
(SNPs), the most common type of genetic variation, are related
to obesity. SNPs within fat mass and obesity associated genes
(FTO) were shown to increase the Body Mass Index (BMI) by
0.4kg/m²/allele, caused by an increased intake of fat (2, 11). For
example, variants in Melanocortin 4 receptor (MC4R), a gene
activating stress neuropeptides, can be linked with lifestyle,
food intake and eating habits and as well stress. Carriers of
the risk allele have a signicant higher intake of processed food
and fruits according to recent studies (12). Transcription factor
7-like 2 (TCF7L2) is a key regulator of glucose homeostasis and
has been most consistently associated with Diabetes Mellitus 2
(DM2).SNP rs7903146 has been reported to have the highest
effect on development of DM2 (11).
The gene Peroxisome proliferator-activated receptor gamma
(PPARG) encodes a regulator of adipocyte differentiation.
Galbete et al. showed that subjects consuming a high amount
of carbohydrates and carrying the risk allele had an increa-
sed obesity risk (13,14). Fatty acid desaturases (FADS) are
enzymes involved in the metabolism of polyunsaturated fatty
acids (PUFAs). It has been reported that individuals with a
polymorphism in rs174547 within the FADS1 gene have in-
creased triglyceride (TG) levels, decreased high density lipo-
proteins (HDL), cholesterol and an increased coronary artery
disease risk (10). Leptin is an adipocyte-secreted hormone
and regulates energy homeostasis, blood pressure and food
intake. Polymorphisms at the leptin receptor (LEPR) decrease
the beneficial effects of leptin, like reducing appetite and
food intake, and moreover lead to an increased energy me-
tabolism (19). Angiotensin-converting enzyme (ACE) gene
variants are associated with endurance performance, like
swimming, cycling and running, based on lower ACE activi-
ty and increased bradykinin. This mutation results in more
oxygenated blood delivered to the working muscles (8). The
SNP transcription factor AP-2ß (TFAP2B) rs987237 has a
significant association with waist to hip ratio (3). Martinez
et al. showed a higher weight loss in wildtypes with a low
fat and low caloric diet (20,21).
Epigenetics. The main epigenetic mechanisms are DNA
methylation, histone modifications and non coding RNAs
(3). DNA methylations occur mainly in cytosines followed
by guanines (CpGs) by the addition of methyl groups to
the pyrimidine ring in position 5 of cytosine (1). Influenced
by internal and external factors such as diet, lifestyle and
environment, DNA methylations at CpGs are specific and
vary over time within an individual and further may act
transgenerational (1,3,22). CpG methylation can change the
activity of a gene and therefore is able to repress or promote
its expression. Epigenetic markers such as DNA methylation
of specific CPGs are used as predictors for metabolic risks
and predictors for the success of a diet related treatment,
like weight loss or weight maintenance (7). For instance, an
elevated Interleukin 6 (IL6) release in blood is linked with
a decreased gene promoter methylation. High IL6 blood
levels are associated with several inflammatory diseases (8).
Moreover, studies showed a higher IL6 methylation in obese
individuals. Aumueller et al. reported that low IL6 methyla-
tion is associated with a better weight loss (25).
Another promising epigenetic marker associated with meta-
bolism constitutes the long interspersed element 1 (LINE1).
LINE1 is a retrotransposon, which is widely expressed in the
human genome (3) and is associated with genetic instability
and chromosomal abnormalities (22). Usually assessed to
estimate global DNA methylation, LINE1 methylation is rela-
ted to BMI, DM2, insulin resistance, cardiovascular disease,
inflammatory response and cancer (3,9) as well as obesity
and metabolic syndrome (MetS).
Microbiota. The microorganisms in the gut are a highly me-
tabolic active community and are regarded as a regulator
of its host homeostasis. The gut microbiota contains 100
times more genes than human cells. The composition of
the microbiota varies over lifetime with diet as strongest
impact factor (15). Indigestible complex carbohydrates are
a major source for carbon, the main substrate for the gut
microbiota. After their fermentation short chain fatty acids
(SFAs), like acetate, propionate and butyrate are produced
and absorbed via the colon mucosa. SFAs show multiple
health promoting activities and have beneficial effects in
appetite regulation, lipid and glucose metabolism (15,16).
However, an imbalanced gut microbiota affects metabolites
like butyrate and lipopolysaccharides (LPS), which interfe-
re with the host’s epigenetic mechanism and may trigger
pro-inflammator y processes (24,29). GI microbiota have
been grouped in enterotypes according to main bacterial
groups with relevance for metabolic characteristics and di-
scussed critically (30-32) .
OBJECTIVES
The Metabotype-Study was initiated to evaluate a clustering
of participants into four different metabotypes based on
differences in genetics as well as epigenetics and GI – micro-
biota. Analysis of cluster of SNPs as described scientifically
and already used commercially should be complemented with
analysis of epigenetic CpG methylation of metabolic relevant
genes and analysis of gut microbiota composition. The hypo-
thesis that a solely SNP based categorisation of metabotypes
misses important aspects was supported by the outcome of
a comparison of a SNPs analysis and an integrated analysis
of SNPs including epigenetic and microbiota marker.
METHODS
The study population included 37 healthy men and women
from 30 to 60 years of age. Exclusion criteria were chronic
diseases, colitis ulcerosa, supplementation of pre- or pro-
biotics, antibiotic intake and BMI over 30. Blood spots were
used for sample collection of capillary blood. DNA extraction
was conducted with the QIAamp DNA Mini Kit (Qiagen,
Hilden, Germany). SNP analysis was per formed with the
StepOne Plus (Thermo Fisher, Massachusetts, USA) using
TaqMan Mastermix and TaqMan SNP Genotyping Assays
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Figure 1 shows the contribution of the different metabotypes
using SNP analysis.
In total we found 23 balanced types, 7 glyco, 2 protein and
5 fat types. In figure 2 the distribution of different sport
types and physical activity in our study population is shown.
Figure 3 shows the connection between IL-6-methylation
and the amount of Cluster IV. The higher the methylation
of IL-6, the higher the amount of Cluster IV bacteria. Figur e
4 shows the correlation between the different forms of the
TCF7L2-SNP and the amount of Bacteriodetes. The wild type
shows the highest quantity of Bacteriodetes.
Figure 1: Different Metabotypes
Figure 2: Sport types and physical activity
Figure 3: IL6 Methylation and Cluster IV
Figure 4: TCF7L 2 and Bacteroidetes
from Thermo Fisher (Massachusetts, USA). For epigenetic
analyses DNA was bisulfite converted, using the EpiTec bi-
sulfite kit (Qiagen, Hilden, Germany). High resolution mel-
ting curve analysis was conducted to assess LINE1 and IL6
methylation. For quantification analysis of microbiota re-
al-time polymerase chain reaction (PCR) was applied using
TaqMan qPCR and SYBER Green qPCR in a Rotorgene 3000
after DNA extraction of stool samples using QIAamp Fast
DNA Mini Stool Kit.
Metabolic types. For our study we chose 12 SNPs in to-
tal. MC4 R rs17782313, TCF7L2 rs79 03146, IL6 rs1800795,
SLC6A14 r s 201119 8 , FTO rs9939609, PPARG2 rs1801282
have been associated either with BMI and obesity or DM2
(2). Moreover MC4R rs17782313 and LEPR rs9436740 are
linked with satiety, IL6 rs1800795 with weight regain and
SLC6A14 rs2011198 with eating disorder development.
Others like TFAP2B r s9 87 23 7, FADS1 rs174547, and ADRB3
rs4994 as well as FTO rs993609 and TCF7L2 rs7903146 are
reported to correlate to different metabolic types. The ACE
gene is associated with different sport types (19,26). For
the classification of the different metabotypes we focused
solely towards SNPs linked with nutrition and metabolism
(18). As already described by Martinez et al. we gave points
from zero to two for each SNP (2).
RESULTS
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DISCUSSION
Our main findings identify an association between genetics,
epigenetics and gut microbiota variations. Considering the
outcome for the strength sport type, individuals in this group
have a higher amount of Firmicutes in general as well as
Cluster IV and a higher methylation of IL6. A high abundance
of Cluster IV or Firmicutes which is mostly seen in humans
with high BMI (27) is therefore suggested to result in a low
weight loss. Latter was previously reported to be correlated
with a high methylation of IL6. Further, Firmicutes and Clus-
ter IV are associated with increased inflammatory and stress
levels as reported in FFQ. Remely et al. showed that Cluster
IV and Cluster XIVa decreased in people with DM2 after
weight loss suggesting that people with higher BMI exhibit
a higher distribution of these bacterial groups. Our results
could additionally demonstrate this outcome with Cluster IV
(27,28). Furthermore, we observed that the wildtype forms
of TCF7L2 and LEPR were correlated to higher amounts of
Bacteroidetes, which were shown to be higher abundant in
lean individuals. Both SNPs can be associated with obesity as
well as diabetes: Carrying no risk allele portends persons are
more likely lean, thus have a lower risk for obesity and DM2.
This could be underlined by showing that a high amount of
Bacteroidetes tends to correlate with a low risk for obesity,
which has also been reported by previous studies (17). LINE1
is considered to be highly methylated in participants with
high BMI (33), which also emerges in our study. Moreover
we observed a possible interaction of LINE1 methylation with
PPARG2, where heterozygotes were higher methylated, and
consequently had a higher BMI. Controversially, the wildtype
for PPARG2 showed a positive correlation with methylation
of IL6, which could indicate that wildtype carriers have a less
efficiency to lose weight but a lower risk to develop DM2.
This study demonstrates that interactions between genetic as
well as epigenetic variations, the gut microbial composition
and their influences through diet and lifestyle but also phy-
sical activity are relevant for genotype based interventions
bearing an enormous potential in developing personalized
diets based on the genotype (20).
Humans differ in height, weight, activity, cognition, strength,
endurance and their preference for food, due to a wide range
of biological variables. These variables include allelic poly-
morphisms and changes in the epigenomic and also metabo-
lomic landscape due to environmental influences (6). DNA
methylation changes are directly correlated to dietary inter-
ventions, weight loss and regain and further are associated
with the development of diseases e.g. metabolic disorders
(3). With increasing knowledge of gene-diet interactions
for macro-and micronutrients it will be possible to give re-
commendations based on the (epi) genetic make-up (11,23).
CONCLUSION
Metabolic diseases are a central burden for public health
and heath care. There is increasing evidence that genetic,
epigenetic and microbiota aspects contribute to individual
mechanisms, which result in individual pathways for metabo-
lism and energy extraction from food. Genetic dispositions,
such as SNPs are under scientic investigation but already in
commercial use for dening metabolic types (metabotypes).
These metabotypes dene the risk for metabolic diseases,
preferences for energy extraction from food and individuali-
zed concepts for weight management or weight loss. To our
knowledge there is no other study focusing on SNPs and their
classication into metabotypes. Furthermore, other nutritional
recommendations based on genetic disposition do not consider
environmental and nutritional effects on gene regulation. Results
show that SNPs can be clearly attributed to metabotypes. Analysis
of DNA methylation strengthens the outcome. Furthermore gut
microbiota composition shows signicant correlation with SNP
and methylation according to metabotype clustering.
References
1. Caroline Brettfeld et al., “Integration of OMICS Data for Obesi-
ty,” Journal of Diabetes and Obesity, (2015), doi:10.15436/2376-
0949.15.023.
2. Leticia Goni et al., “A Genetic Risk Tool for Obesity Predisposi-
tion Assessment and Personalized Nutrition Implementation Based
on Macronutrient Intake,” Genes and Nutrition 10, no. 1 (2014):
1–10, doi:10.1007/s12263-014- 0445-z.
3. Leticia Goni et al., “Single-Nucleotide Polymorphisms and DNA
Methylation Markers Associated with Central Obesity and Re-
gulation of Body Weight,” Nutrition Reviews 72, no. 11 (2014):
673–9 0, do i:1 0.1111/nure.1214 3.
4. J. Alfredo Martínez, “Perspectives on Personalized Nutrition
for Obesity,” Journal of Nutrigenetics and Nutrigenomics 7, no.
1 (2014): 1–3, doi:10.1159/000365158.
5. .Serge Rezzi et al., “Human Metabolic Phenotypes Link Directly
to Specic Dietary Preferences in Healthy Individuals,” Journal
of Proteome Research 6, no. 11 (2007): 4469–77, doi:10.1021/
pr070431h.
6. Laurent B. Fay and J. Bruce German, “Personalizing Foods: Is
Genotype Necessary?,” Current Opinion in Biotechnology 19, no.
2 (2008): 121–28, doi:10.1016/j.copbio.2008.02.010.
7.J Alfredo Martínez et al., “Epigenetics in Adipose Tissue , Obe-
sity , Weight Loss , and Diabetes 1 , 2,” Diabetes, 2014, 71–81,
doi:10.3945/an.113.004705.71.
8. Alun Jones, Hugh E. Montogomer, and David R Woods,
“Human Performance: A Role for the ACE Genotype,” Exer-
cise and Sport Sciences Reviews 30, no. 4 (2002): 184–90,
doi:10.1097/00003677-200210000-00008.
9.Carolina Ferreira Nicoletti et al., “DNA Methylation and Hy-
droxymethylation Levels in Relation to Two Weight Loss Strategies:
Energy-Restricted Diet or Bariatric Surgery,” Obesity Surgery 26,
no. 3 (2016): 603–11, doi:10.1007/s11695-015-1802-8.
10.Fengqiong Liu et al., “Dietary N-3 Polyunsaturated Fatty
Acid Intakes Modify the Effect of Genetic Variation in Fatty Acid
Desaturase 1 on Coronary Artery Disease,” PLoS ONE 10, no. 4
(2015): 1–10, doi:10.1371/journal.pone.0121255.
11. Hanne Holbæk Jensen and Lesli Hingstrup Larsen, “Dietary
Management and Genetic Predisposition,” Current Nutrition Re-
ports 2, no. 3 (2013): 159–66, doi:10.1007/s13668-013-0050-6.
12. Sunmin Park et al., “Interactions with the MC4R rs17782313
Variant, Mental Stress and Energy Intake and the Risk of Obesity
in Genome Epidemiology Study,” Nutrition & Metabolism 13, no.
1 (2016): 38, doi:10.1186/s12986-016-0096-8.
13. C. Galbete et al., “Pro12Ala Variant of the PPARG2 Gene
Increases Body Mass Index: An Updated Meta-Analysis Encom-
passing 49,092 Subjects,” Obesity 21, no. 7 (2013): 1486–95,
doi:10.1002/oby.20150.
14. Cecilia Galbete et al., “Lifestyle Factors Modify Obesity Risk
Linked to PPARG2 and FTO Variants in an Elderly Population: A
Cross-Sectional Analysis in the SUN Project,” Genes and Nutrition
8, no. 1 (2013): 61–67, doi:10.1007/s12263-012-0296-4.
15. Silke Matysik et al., “Metabolomics of Fecal Samples: A Practi-
cal Consideration,” Trends in Food Science & Technology, no. May
(2016), doi:10.1016/j.tifs.2016.05.011.
16. Yuanyuan Lu et al., “Short Chain Fatty Acids Prevent High-Fat-
JEM Dezember 2017 Vorabdruck
Wissenschaftliche Arbeiten
Diet-Induced Obesity in Mice by Regulating G Protein-Coupled
Receptors and Gut Microbiota,” Scientic Reports 6, no. August
(2016): 37589, doi:10.1038/srep37589.
17. M Arumugam et al., “Enterotypes of the Human Gut Micro-
biome,” Nature 473, no. 7346 (2011): 174– 80, doi:10.1038/na-
ture09944.
18. Liu et al., “Dietary N-3 Polyunsaturated Fatty Acid Intakes
Modify the Effect of Genetic Variation in Fatty Acid Desaturase
1 on Coronary Artery Disease.”
19. E. J. Brandl et al., “Association Study of Polymorphisms in
Leptin and Leptin Receptor Genes with Antipsychotic-Induced
Body Weight Gain,” Progress in Neuro-Psychopharmacology and
Biological Psychiatry 38, no. 2 (2012): 134–41, doi:10.1016/j.
pnpbp.2012.03.001.
20. “Personalized Weight Loss Strategies—the Role of Macronu-
trient Distribution J. Alfredo Martinez, Santiago Navas– Carretero,
Wim H. M. Saris and Arne Astrup,” n.d.
21. Tanja Stocks et al., “TFAP2B-Dietary Protein and Glycemic
Index Interactions and Weight Maintenance after Weight Loss
in the Diogenes Trial,” Human Heredity 75, no. September 2013
(2013): 213 –19, do i:10.1159/000 353591.
22.Carraro et al., “LINE-1 and Inammatory Gene Methylation
Levels Are Early Biomarkers of Metabolic Changes: Association
with Adiposity.”
23. M. J. Moreno-Aliaga et al., “Does Weight Loss Prognosis
Depend on Genetic Make-Up?,” Obesity Reviews 6, no. 2 (2005):
155 – 68, do i :10.1111/j.1467-789X.200 5.0018 0 .x.
24. Marlene Remely et al., “Microbiota and epigenetic regulation
of inammatory mediators; Springer; Methods in Pharmacology
and Toxicology; pp 115-134
25. Eva Aumüller et al., “DNA Methylation on Interleukin- 6 Cor-
relates with Weight Loss in Obese Women”, Jacobs Journals of
Biomarkers, 2, no. 1 (2016).
26. Elina Suviolahti et al., “The SLC6A14 Gene Shows Eviden-
ce of assosiation with obesity” 112, no. 11 (2003), doi:10.1172/
JCI200317491.Introduction.
27. Marlene Remely et al., “Effects of Short Chain Fatty Acid Pro-
ducing Bacteria on Epigenetic Regulation of FFAR3 in Type 2 Dia-
betes and Obesity,” Gene 537, no. 1 (2014): 85–92, doi:10.1016/j.
gene.2013.11.081.
28. Marlene Remely and Alexander G Haslberger, “Molecular
Aspects of Medicine The Microbial Epigenome in Metabolic
Syndrome,” Molecular Aspects of Medicine 54 (2017): 71–77,
doi:10.1016/j.mam.2016.09.003.
29. Blaut M. and Bischoff S.C, “Probiotics and Obesi-
ty” Ann Nutr Metab 2010;57, no. suppl 1 (2010): 20–23,
doi:10.1159/000309 079.
30. Gorvitovskaia A, Holmes SP, Huse SM. Interpreting Prevotella
and Bacteroides as biomarkers of diet and lifestyle. Microbiome.
2016 Apr 12;4:15
31. Knights D, Ward TL, McKinlay CE, Miller H, Gonzalez A, Mc-
Donald D, Knight R. Rethinking "enterotypes". Cell Host Microbe.
2014 Oc t 8;16(4):433-7.
32. Jeffery IB, Claesson MJ, O'Toole PW, Shanahan F. Categori-
zation of the gut microbiota: enterotypes or gradients? Nat Rev
Microbiol. 2012 Sep;10(9):591-2.
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