El 29 de marzo de 2016 celebramos un Simposio Internacional sobre el 'Impacto de las ciencias ómicas en la medicina, nutrición y biotecnología'. Organizado por la Fundación Ramón Areces en colaboración con la Real Academia Nacional de Medicina y BioEuroLatina, abordó cómo un mejor conocimiento del genoma humano está permitiendo notables avances hacia una medicina de precisión.

9. 25
26
27
28
29
30
31
32
45-66 67-72 72-86
BMI
Obesity GRS Ranges
255
Casas-Agustench P et al. J Acad Nutr Diet. 2014;114:1954-1966.
263
264
The GOLDN Study
P trend<0.001

10. The field of genomics often struggles to replicate consistently observations of relationships
between genetic variants and health outcomes.

11. Genetic variation does not always affect individual disease risk
directly, but rather the potential is expressed only in the presence
of certain environmental (dietary) conditions.
Therefore, it is likely that at least some of the
disparate genetic association results are due to
differences in population nutrient intake that are
interacting with genes to allow or block their
expression (i.e., LIPC <->HDL-C).
Moreover, Genetic up- or down-regulation of
specific metabolic pathways may also explain
variation in individual requirements for certain
vitamins and minerals (i.e., MTHFR <-> Folic Acid)
Tucker KL, Smith CE, Lai CQ, Ordovas JM. Quantifying diet for nutrigenomic studies. Annu Rev Nutr. 2013;33:349-71.

12. The result of any single diet
and health outcome study
represents the average effect
within a range of dietary
responses due to unmeasured
genetic variation in regulation.
Because of this variability—not
only in individuals, but also in
populations—studies
attempting to replicate an
association between a specific
genetic polymorphism and
health outcomes may or may
not find it, depending on the
overall level of intake of a
moderating dietary factor.

13. Dietary Assessment Methods

14. The Cohorts for Heart and Aging Research in Genomic
Epidemiology (CHARGE) consortium was originally formed with five
well-described longitudinal cohort studies in the United States and Europe to
facilitate GWAS meta-analyses of genetic variation and health. Since then,
further studies have been added, with a recent analysis using data from 15
cohorts with dietary measures.
Cohort Country N Dietary assessment Fat % E SAT FAT %E PUFA %E
ARIC USA 2,980
9,198
IA 66-FFQ 33.2(6.8)
30.5(7.5)
12.2(3.1)
11.2(3.2)
5.1(1.5)
4.5(1.4)
CHS USA 3,222 SA 99-FFQ-NCI
SA 131-FFQ-W
32.3(6.0) 10.3(2.2) 7.4(2.2)
GOLDN USA 1,120 IA NCI 124-DHQ 35.5(6.7) 11.9(2.7) 7.6(2.1)
Rotterdam Netherlands 4,576 SA Food list
IA FFQ
36.3(6.1) 14.4(3.2) 6.9(1.1)
Inchianti Italy 1,100 IA 236-FFQ 31.0(5.1) 10.4(2.2) 3.4(0.7)
Dietary assessment and dietary fat intakes in the Cohorts for Heart and Aging Research in
Genomic Epidemiology (CHARGE) Consortium
Tucker KL, Smith CE, Lai CQ, Ordovas JM. Quantifying diet for nutrigenomic studies. Annu Rev Nutr. 2013;33:349-71.

15. One of those domains is nutrition and dietary
supplements. The measures include specific
questions to define breastfeeding (3), caffeine
intake (13), calcium intake (18), dairy food
servings (2, only milk and cheese), use of dietary
supplements (18), fiber intake (17), fruit and
vegetable intake (9), % energy from fat (16),
selenium (from serum), sugar intake (4), vitamin
D (serum), and total dietary intake (Automated
Self-Administered 24-Hour Recall (ASA24) and
two 24HRs more than a week apart, one on a
weekday and one on a weekend day).
The National Institutes of Health PhenX
(Phenotypes and eXposures) working group,
has developed a tool kit to encourage
standardized questionnaires or methods for
obtaining phenotype data in large projects.
Developed with support for the National
Human Genome Research Institute, the
group began its work to assist consortia in
harmonizing data across studies. We
developed a tool kit in 2006 and continue to
refine it (http://www.phenxtoolkit.org)
Hamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, Hammond JA, Huggins W, Jackman D, Pan H, Nettles DS, Beaty TH, Farrer
LA, Kraft P, Marazita ML, Ordovas JM, Pato CN, Spitz MR, Wagener D, Williams M, Junkins HA, Harlan WR, Ramos EM, Haines J. The PhenX
Toolkit: get the most from your measures. Am J Epidemiol. 2011 Aug 1;174(3):253-60

17. 25
26
27
28
29
30
31
32
45-66 67-72 72-86
BMI
Obesity GRS Ranges
255
Casas-Agustench P et al. J Acad Nutr Diet. 2014;114:1954-1966.
263
264
The GOLDN Study
P trend<0.001

18. 23
24
25
26
27
28
29
30
31
32
33
34
20 30 40 50 60 70 80 90
BMI
Obesity GRS
High SFA
Low SFA
Pinteraction=0.013
Casas-Agustench P et al. J Acad Nutr Diet. 2014;114:1954-1966.

19. H
D
L
L
D
L
H
D
L
L
D
L
H
D
L
L
D
L
H
D
L
L
D
L
TT
CC
CT

20. SCARB1
Zanoni P et al. Science.
2016 Mar
11;351(6278):1166-71.
Voight BF et al. Lancet.
2012 Aug
11;380(9841):572-80.

22. vención con eta
iterránea
www.predimed.es
Illes Balears
Reus
Barcelona
NavarraVitoria
Málaga
Sevilla
Gran Canaria
7,447 participants
n=2,450n=2,543 n=2,454
MedDiet
Extra virgin olive oil
(1L/w)
MedDiet
Nuts (30g/d)
Control diet (low fat)
“American Heart
Association guidelines”

23. • ~56 females
• ~67 years of age
• ~14% current smokers
• ~30 BMI
• ~83% hypertension
• ~50% Type 2 diabetes
• ~72% Dyslipidemia
• ~22% Family History of Premature CVD

24. Incidence of CVD (Stroke, myocardial infarction, CVD death)

25. C>T
TCF7L2
effects

28. 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. Int J Obes. 2010;34:516-23.
• Elevated calorie
intake
• Elevated cytokines
• Impaired sleep
• Higher total and
abdominal obesity

29. Garaulet M, et al. Int J Obes. 2013 Apr;37(4):604-11.

30. Epigenetics

31. 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.

33. PRKCZ

34. Future research combining an understanding of
genetic variation and dietary intake, therefore,
promises to clarify many previously
controversial or conflicting results on diet and
health.
Furthermore, we expect that this research will
lead to more effective personalized nutrition
information that is based on improved
understanding of individual requirements for
specific nutrients or sensitivity to others.
In many cases, however, these
associations may not be identified
because of substantial error in the dietary
assessment. Thus new biomarkers and
technologies are needed.
Besides what and how much we eat is also
important when we eat. Therefore,
chronobiology should join nutrition and
genetics to precisely define personalized
dietary recommendations.
Percentage of implausible reporters by BMI for US women aged 20 to 74 years in
the NHANES (1971-2010). Archer E, Mayo Clin Proc. 2015;90(7):911-26

35. ?
PRESENT
FUTURE
GENOME
Unknown - Known
ENVIRONMENT
Obesogenic - Personalized
OBESITY PREDISPOSITION
Expressed - Controlled

36. Saliva collection for
DNA extraction
Genomic Analysis Interpretation
Personalized AdviceHealthy Aging

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