Epigenome-Wide Study Identifies

Novel Methylation Loci

Associated with Body Mass Index and Waist Circumference

PHDGH CD38CPT1A

AHRR

Carnitine palmitoyltransferase 1A (CPT1A):• This enzyme is essential for fatty acid

oxidation, a multistep process that breaks down (metabolizes) fats and converts them into energy.

• higher methylation status of CPT1A results in decreased expression of the gene, which in turn is negatively correlated with BMI and WC.

• Dietary factors such as intake of long-chain monounsaturated fatty acids have also been shown to regulate CPT1A expression as well as DNA methylation patterns.

Aslibekyan S et al. Obesity.2015 Jul;23(7):1493-501.

Its founding member cohorts include:

• Age, Gene, Environment, Susceptibility Study-- Reykjavik

• Atherosclerosis Risk in Communities Study

• Cardiovascular Health Study

• Framingham Heart Study

• Rotterdam Study

Additional core cohorts include:

• Coronary Artery Risk Development in Young Adults

• Family Heart Study

• Health, Aging, and Body Composition Study

• Jackson Heart Study

• Multi-Ethnic Study of Atherosclerosis

Cohorts for Heart and Aging Research in Genomic Epidemiology (Charge) Consortium

Percentage of implausible reporters by BMI for US women aged 20 to 74 years in the National Health and Nutrition Examination Survey (NHANES) (1971-2010). Physiologically implausible values were determined via the following equation: (reported energy intake/basal metabolic rate) <1.35. Implausible values may be considered “incompatible with life.”

Archer E, Mayo Clin Proc. 2015;90(7):911-26

Patiss. Francoise Papperino’s Beanscene Starbucks

vol. (ml) 52 50 48 27

CQA* (mg) 423 216 93 24

Caff.¶ (mg) 322 205 77 51*Chlorogenic acids; ¶Caffeine

Crozier TWM et al. Food Funct., 2012, 3, 976-984

“SOME people eat as little fat as possible to lose weight and stay healthy, while others avoid carbohydrates. A vegan diet (with no animal products) and the paleo diet (with lots) both have enthusiastic devotees. One popular diet encourages intermittent fasting, another frequent small meals. Who’s right?”Perhaps they all are, according to the new field of “personalized nutrition.”

“This month, an Israeli study of personalized nutrition was heralded by a media frenzy. “This diet study upends everything we thought we knew about ‘healthy’ food,” claimed one headline. The study suggested that dieters may be mistakenly eating a lot of some foods, like tomatoes, that are good for most people, but bad for them. And it raised the possibility that an individualized approach to nutrition could eventually supplant national guidelines meant for the entire public.Personalized medicine has already become well established in clinical practice. We know that the effects of some drugs vary from person to person and that genetic analysis of tumors can help doctors select the best cancer treatment for a particular patient. Despite the recent fanfare, we have also known for a long time that people respond differently to specific foods based on their genes, past health or other

factors…………Despite the hype, personalized nutrition is not ready for practical application in the clinic. But this exciting field of research may help explain why people respond so differently to diet based on biology. In this way, personalized nutrition may build upon, rather than substitute for, national dietary guidelines, providing a common ground for all sides in the “diet war” to declare a truce”

METHOD: We mined the scientific literature to collect GxE interactions from 386 publications for blood lipids, glycemic traits, obesity anthropometrics, vascular measures, inflammation and metabolic syndrome. The CardioGxE catalog is composed of 1187 significant GxEs (in 189 genes) and 13770 with no significant inter-action observed. HIGHLIGHTS: 1) The CardioGxE SNPs showed little overlap with variants identified by main effect GWAS,

indicating the importance of environmental interactions with genetic factors on cardiometabolic traits.

2) These GxE SNPs were enriched in adaptation to climatic and geographical features, with implications on energy homeostasis and response to physical activity.

3) Comparison to gene networks responding to plasma cholesterol-lowering or regression of atherosclerosis showed that GxE genes have a greater role in those responses, particularly through high-energy diets and fat intake, than do GWAS-identified genes for the same traits.

Cusano NE, Kiel DP, Demissie S, Karasik D, Adrienne Cupples L, Corella D, Gao Q, Richardson K, Yiannakouris N, Ordovas JM. A Polymorphism in a gene encoding Perilipin 4 is associated with height but not with bone measures in individuals from the Framingham Osteoporosis Study. Calcif Tissue Int. 2012 Feb;90(2):96-107.

Smith CE, Arnett DK, Corella D, Tsai MY, Lai CQ, Parnell LD, Lee YC, Ordovás JM. Perilipinpolymorphism interacts with saturated fat and carbohydrates to modulate insulin resistance. Nutr Metab Cardiovasc Dis. 2012 May;22(5):449-55.

Deram S, Nicolau CY, Perez-Martinez P, Guazzelli I, Halpern A, Wajchenberg BL, Ordovas JM, Villares SM. Effects of perilipin (PLIN) gene variation on metabolic syndrome risk and weight loss in obese children and adolescents. J Clin Endocrinol Metab. 2008 Dec;93(12):4933-40.

Smith CE, Tucker KL, Yiannakouris N, Garcia-Bailo B, Mattei J, Lai CQ, Parnell LD, Ordovás JM. Perilipin polymorphism interacts with dietary carbohydrates to modulate anthropometric traits in hispanics of Caribbean origin. J Nutr. 2008 Oct;138(10):1852-8.

Perez-Martinez P, Yiannakouris N, Lopez-Miranda J, Arnett D, Tsai M, Galan E, Straka R, Delgado-Lista J, Province M, Ruano J, Borecki I, Hixson J, Garcia-Bailo B, Perez-Jimenez F, Ordovas JM. Postprandial triacylglycerol metabolism is modified by the presence of genetic variation at the perilipin (PLIN) locus in 2 white populations. Am J Clin Nutr. 2008 Mar;87(3):744-52.

Corella D, Qi L, Tai ES, Deurenberg-Yap M, Tan CE, Chew SK, Ordovas JM. Perilipin gene variation determines higher susceptibility to insulin resistance in Asian women when consuming a high-saturated fat, low-carbohydrate diet. Diabetes Care. 2006 Jun;29(6):1313-9.

Jang Y, Kim OY, Lee JH, Koh SJ, Chae JS, Kim JY, Park S, Cho H, Lee JE, Ordovas JM. Genetic variation at the perilipin locus is associated with changes in serum free fatty acids and abdominal fat following mild weight loss. Int J Obes (Lond). 2006 Nov;30(11):1601-8.

Corella D, Qi L, Sorlí JV, Godoy D, Portolés O, Coltell O, Greenberg AS, Ordovas JM. Obese subjects carrying the 11482G>A polymorphism at the perilipin locus are resistant to weight loss after dietary energy restriction. J Clin Endocrinol Metab. 2005 Sep;90(9):5121-6.

Qi L, Tai ES, Tan CE, Shen H, Chew SK, Greenberg AS, Corella D, Ordovas JM. Intragenic linkage disequilibrium structure of the human perilipin gene (PLIN) and haplotype association with increased obesity risk in a multiethnic Asian population. J Mol Med . 2005 Jun;83(6):448-56.

Qi L, Shen H, Larson I, Schaefer EJ, Greenberg AS, Tregouet DA, Corella D, Ordovas JM. Gender-specific association of a perilipin gene haplotype with obesity risk in a white population. ObesRes. 2004 Nov;12(11):1758-65.

Qi L, Corella D, Sorlí JV, Portolés O, Shen H, Coltell O, Godoy D, Greenberg AS, Ordovas JM. Genetic variation at the perilipin (PLIN) locus is associated with obesity-related phenotypes in White women. Clin Genet. 2004 Oct;66(4):299-310.

Weight reduction, low caloric diet and PLIN (11482G>A ) polymorphism in obese subjects

-8

-7

-6

-5

-4

-3

-2

-1

0

1

Baseline 3 Months 6 Months 12 Months

Time on Diet

Perc

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an

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Corella et al. J Clin Endocrinol Metab 90: 5121–5126, 2005 Corella D et al. Diabetes Care 2006 Jun;29(6):1313-9

PLIN (11482G->A/14995A->T) SNPs, Diet and Metabolic Syndrome

n=315n=137n=318

BM

I (K

g/m

2)

26.4

26.2

26.0

25.8

25.6

25.4

25.2

25.0

24.8

WOMEN Global p = 0.007

pTrend = 0.001

PLIN1

PLIN4

11and

11

2 carrieror

2 carrier

2 carrierand

2 carrier

p = 0.002

p = 0.090

n=315n=137n=318

BM

I (K

g/m

2)

26.4

26.2

26.0

25.8

25.6

25.4

25.2

25.0

24.8

WOMEN Global p = 0.007

pTrend = 0.001

PLIN1

PLIN4

11and

11

2 carrieror

2 carrier

2 carrierand

2 carrier

p = 0.002

p = 0.090

Association between PLIN1 (6209T>C) and PLIN4 (11482G>A) polymorphisms and BMI in Women

Qi L, et al. Clin Genet. 2004 Oct;66(4):299-310.

1900

1950

2000

2050

2100

2150

2200

2250

CC CT+TT

Calories/day

98.5

99

99.5

100

100.5

101

101.5

102

102.5

103

103.5

CC CT+TT

Waist Circumf.

Garaulet M, et al. CLOCK gene is implicated in weight reduction in obese patients participating in a dietary

programme based on the Mediterranean diet. Int J Obes. 2010;34:516-23.

Milagro FI et al. Chronobiol Int. 2012 Nov;29(9):1180-94.

13.0 (1.3-124.0)0.026

8.68 (1.73-43.59)0.008

19.3 (2.52-147.48)0.004

OR (95%CI)P-value

Hypermethylation of CLOCK CpG1 is associated to:

Milagro FI et al. Chronobiol Int. 2012 Nov;29(9):1180-94.

Richardson K, et al. PLoS One. 2011 Apr 20;6(4):e17944.

A

G

Weighted Genetic Risk Score (GRS) calculated on the basis of 63 obesity-associated variants.

Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)

Multi-Ethnic Study of Atherosclerosis

Ranges (minimum to maximum) for tertiles 1 through 3 are 44.8 to 66.3, 66.4 to 71.5, and 71.6 to 85.7. Values in parentheses are means of tertiles of obesity GRS

Ranges (minimum to maximum) for tertiles 1 through 3 are 37.6 to 56.3, 56.4 to 62.2, and 62.3 to 83.1

Casas-Agustench P et al. J Acad Nutr Diet. 2014;114:1954-1966.

WeightedGene cRiskScore(GRS)calculatedonthebasisof63obesity-associatedvariants.

Gene csofLipidLoweringDrugsandDietNetwork(GOLDN)

Mul -EthnicStudyofAtherosclerosis

Ranges(minimumtomaximum)forter les1through3are44.8to66.3,66.4to71.5,and71.6to85.7.Valuesinparenthesesaremeansofter lesofobesityGRS

Ranges(minimumtomaximum)forter les1through3are37.6to56.3,56.4to62.2,and62.3to83.1

Casas-AgustenchPetal.JAcadNutrDiet.2014;114:1954-1966.

Exposome: “Encompasses life-course environmental exposures (including lifestyle factors), from the prenatal period onwards.” [Wild, C. P. (2005) Cancer Epidemiol. Biomarkers Prev. 14, 1847–50.]

“The cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes” [G.W. Miller and D.P. Jones. Toxicological Sciences 137, 1–2 (2014)]

TYPES OF NUTRITIONAL BIOMARKERS

Biomarkers of dietary exposure

Different types of biomarkers aimed at assessing dietary intake of different foods, nutrients, non-nutritive components or dietary patterns (recovery biomarkers, concentration biomarkers, recovery biomarkers and predictive biomarkers). Example: Urinary nitrogen as biomarker of protein intake.

Biomarkers of nutritional status

Biomarkers which reflect not only intake but also metabolism of the nutrient (s) and possibly effects from disease processes. Example: Some of the biomarkers of one-carbon metabolism such as homocysteine, which reflect not only nutritional intake, but also metabolic processes. It is important to note that a single biomarker may not reflect the nutritional status of a single nutrient, but may indicate the interactions of several nutrients.

Biomarkers of health/disease Biomarkers related to different intermediate phenotypes of a disease or even to the severity of the disease. Example: plasma concentrations of total cholesterol or triglycerides associated for cardiovascular diseases.

Classification of new omic-based biomarkers

Genetic biomarkers Based on changes in DNA, mainly polymorphisms of a single nucleotide (SNP). Examples: Polymorphisms in the lactase gene (LCT) as proxies of milk consumption in Mendelianrandomization analyses.

Epigenetic biomarkers Biomarkers based on the main epigenetic regulators: DNA methylation, histone modification and non-coding RNAs. Examples: DNA hypermetylation or hypomethylation of specific genesdepending on food intake; Levels of circulating microRNAs associated with several nutrion related diseases.

Transcriptomic biomarkers Biomarkers based on RNA expression (whole transcriptome or differences in expression of selected genes). Example: Differences in the gene expression profile in subjects following a Mediterranean diet in comparison with control subjects.

Proteomic biomarkers Biomarkers based on the study of the proteome. Example: Analysis of the proteome of participants fed control diets with the proteome of participants fed low folate diets.

Lipidomic biomarkers Biomarkers based on the study of the lipidome. Lipidomic profile of human plasma in type 2 diabetic subjects on a high-fat diet versus a high carbohydrate diet.

Metabolomic biomarkers Biomarkers based on the study of the proteome. Example: The 1H NMR urinary profile in subjects following a traditional Mediterranean diet in comparison with the urinary profile of subject on a low fat diet.

Corella D, Ordovás JM. Biomarkers: background, classification and guidelines for applications in nutritional epidemiology. Nutr Hosp. 2015 Feb 26;31 Suppl 3:177-88.

Partners• TNO, Netherlands (the Ben van Ommen and

Marjan van Erk team, coordinator)• Technical University Munchen, Germany (the

Hannelore Daniel team)• Imperial College London, UK (the Gary Frost,

Jimmy Bell and Alex Blakemore teams)• University of Oslo (Norway), the Christian Drevon

team• Wageningen University (Netherlands), the Michael

Müller & Lydia Afman team• University College Dublin (Ireland), the Lorraine

Brennan team• Medical University Varna (Bulgaria), the Diana

Ivanova team• IARC (France), the Augustin Scalbert team• CEINGE (Italy), the Luigi Fontana team• University of Cordoba (Spain), the José Lopez-

Miranda team• NuGO (Netherlands) (Fre Pepping and Ingeborg

van Leeuwen)• ILSI Europe (Belgium) Stephane Vidry and team)• EDI (Germany)• Paprika Bioanalytics BT (Hungary), Ralph Ruehl• VITAS AS (Norway), Thomas Gundersen• Biqualys (Netherlands), Jacques Vervoort• Biocrates (Austria), Rania Kovaiou• University of Alberta (Canada), David Wishart

team• University of Toronto (Canada), Ahmed el Sohemy

team• CSIRO (Australia), Michael Fenech team• University of Auckland (New Zealand), Lynn

Ferguson team• IMDEA (Spain) Jose Ordovas team• TUFTS University (USA), Jose Ordovas team

WP1 Metabolomics based food intake quantificationReliable dietary assessment methods are crucial when attempting to understand the links between diet and healthWorkpackage 1 focuses on identification of novel biomarkers of dietary intake and develops an online database summarising information on dietary biomarkers.WP leader: Lorraine Brennan, UCD.

© Tufts University, Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging

Jose M Ordovas

Thank you