'Lo último en obesidad'. Este es el título del Simposio Internacional que organizamos en la Fundación Ramón Areces los días 1 y 2 de diciembre de 2015. En colaboración con la Fundación General CSIC, reunió a algunos de los mayores expertos en la materia para analizar cómo reducir este grave problema de salud pública.

7. Epigenome-Wide Study Identifies
Novel Methylation Loci
Associated with Body Mass Index
and Waist Circumference
PHDGH CD38
CPT1A
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.

8. 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

10. 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

13. 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

14. “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”

18. 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.

20. 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. Perilipin
polymorphism 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. Obes
Res. 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.

21. 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
Percentweightchange
1_1
2 carrier
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
BMI(Kg/m2)
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
11
and
11
2 carrier
or
2 carrier
2 carrier
and
2 carrier
p = 0.002
p = 0.090
n=315n=137n=318
BMI(Kg/m2)
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
11
and
11
2 carrier
or
2 carrier
2 carrier
and
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.

22. 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.

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

26. 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.

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

31. 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.

32. Weighted Gene c Risk Score (GRS) calculated on
the basis of 63 obesity-associated variants.
Gene cs of Lipid
Lowering Drugs and Diet
Network (GOLDN)
Mul -Ethnic Study of Atherosclerosis
Ranges (minimum to maximum) for ter les 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 ter les of obesity GRS
Ranges (minimum to maximum) for ter les 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.

34. 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)]

35. 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.

36. 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 Mendelian
randomization 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 genes
depending 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.

37. 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 quantification
Reliable dietary assessment methods are crucial when attempting to understand
the links between diet and health
Workpackage 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.

39. © Tufts University, Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging
Jose M Ordovas
Thank you