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Philippe Froguel, MD, PhD

Apports de la génétique pour la prédiction du risque métabolique et CV. Philippe Froguel, MD, PhD. Genomic Medicine, Hammersmith Hospital Imperial College London , UK. p.froguel@imperial.ac.uk. Genomics and molecular physiology of metabolic diseases CNRS 8090-Institute of Biology

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Philippe Froguel, MD, PhD

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  1. Apports de la génétique pour la prédiction du risque métabolique et CV Philippe Froguel, MD, PhD Genomic Medicine, Hammersmith Hospital Imperial College London, UK p.froguel@imperial.ac.uk Genomics and molecular physiology of metabolic diseases CNRS 8090-Institute of Biology Pasteur Institute, Lille, France

  2. Environment Gene 1 Gene n Gene 2 Gene 14 Gene 3 Gene 13 Gene 4 Gene 12 Gene 5 Gene 11 Gene 6 Gene 10 Low Penetrance Gene 7 Gene 9 Epistatic Interaction between gènes Gene 8 Metabolic and CV diseases = complex multifactorial traits Nature loads the gun but nurture pulls the trigger Steve Humphries, UCL,London disease

  3. 10 000 years ago, hunters became farmers…… “The Shape of Things to Come” Farmer Hunter The Economist, Dec.2003

  4. Are there thrifty genes ?

  5. R. Leibel, personal communication

  6. Quels sont les gènes “candidats” connus du risque CV ? • Gènes modifiant le cholestérol plasmatique (Apo B, LDL R) • Gènes d’apoprotéines (Apo E) • Gènes du métabolisme des lipides: PCSK9 • Gènes du stress oxydatif, inflammation, métabolisme: PON, Adiponectin etc…

  7. The march of technology 1980 single variant (100 SNPs; 103 genotypes) detailed study of individual genes (102 SNPs; 105+ genotypes) 1990 2000 regional studies (104 SNPs; 108 genotypes ) 2006-2007 genome-wide association (3 105 SNPs; 109 genotypes) 3,5 106 SNPs (2007) complete resequencing ?

  8. tag SNPs ~300,000 tag SNPs needed to cover common variation in whole genome in CEU Relative power (%) random SNPs Average inter-marker distance (kb)

  9. GWA: a staged approach

  10. February 2007 Nature, 22nd of February 2007

  11. Nature, February 2007 Science 2007 :

  12. modified from Frayling T, nature reviews genetics, 2007 OR CAP10 T2D gene harvest

  13. T2D gene combined effect in the French population % T2D 14% 25% 40% Number of risk alleles Cauchi et al., 2008 Plos One

  14. Test + Test + Test - Test - Prediction of T2D risk in French subjects using genetic markers Number of risk alleles Number of risk alleles ROC curve Genetic markers represent a reliable test (86%) to detect subjects who will develop T2D AUC = 0.86 What about non-Europeans ? % cases with a positive test % controls with a positive test Cases Controls Cases Controls Cauchi et al., 2008 Plos One

  15. Case/control GWA for complex trait analysis Cases controls

  16. Cases controls General Population GWA in complex traits Case/Control vs Quantitative Trait analyses

  17. Conversion to disease (%) } smaller difference in eventual disease prevalence } large difference in conversion rates Time (years) Interest of Prospective studies in general populations Is it possible to predict disease incidence in general populations ?

  18. Intronic SNP Glucokinase regulatory protein, GCKRP GCKR was revealed as a novel locus for modulating triglyceride levels Saxena et al. Science 316, 1331, 2007 Van Schaftingen, FASEB J, 1994 de la Iglesia, J Biol Chem, 2000 Grimsby et al. J Biol Chem, 2000 Slosberg et al. Diabetes, 2001

  19. at Inclusion: over the 9-years follow-up: P = 8x10-13 P = 9x10-9 Δ% -1.27 [-1.70, -0.84] Δ% -1.43 [-1.81, -1.04] Fasting Glycemia (mmol/l) Fasting Glycemia (mmol/l) n = 1383 n = 2125 n = 855 n= 13 941 observations P = 3x10-7 P = 5x10-5 Δ%-3.90 [-5.72, -2.04] Δ%-4.23 [-5.80, -2.63] Fasting Insulinemia (pmol/l) Fasting Insulinemia (pmol/l) n= 13 915 observations n = 1383 n = 2125 n = 855 P = 5x10-4 P = 1x10-4 Δ%3.69 [1.61, 5.82] Δ%3.41 [1.68, 5.16] Fasting Triglycerides (mmol/l) Fasting Triglycerides (mmol/l) n= 13 955 observations n = 1383 n = 2125 n = 855 Effects of GCKR rs1260326-P446L on the quantitative parameters using the up-to-four repeated measurements over the 9-years follow-up study: Mixte model, repeated measures (0, 3, 6, 9 yrs) (nominal P-val, additive model)

  20. GCKR associates with Hyperglycemia, Type 2 diabetes and Dyslipidemia in the D.E.S.I.R. Population Overall risk (at the end of the study) and Incidence risk    Vaxillaire et al. Diabetes, 2008

  21. Slightly Elevated Blood Glucose (FPG) Increased T2D risk Increased CHD & mortality

  22. Slightly Elevated Blood Glucose (FPG) -30 GCK GCKR Increased T2D risk dyslipidemia Increased CHD & mortality

  23. Slightly Elevated Blood Glucose (FPG) -30 GCK GCKR Increased T2D risk High CRP Increased CHD & mortality dyslipidemia

  24. High CRP Slightly Elevated Blood Glucose (FPG) -30 GCK GCKR Increased T2D risk LEPR HNF1 a IL6R Increased CHD & mortality dyslipidemia

  25. High CRP Slightly Elevated Blood Glucose (FPG) -30 GCK GCKR Increased T2D risk LEPR HNF1 a IL6R Increased CHD & mortality dyslipidemia ?

  26. Unpublished data

  27. High CRP b-cell defects Insulin Resistance Elevated Blood Glucose G6PC2 GCKR Increased T2D risk -30 GCK LEPR HNF1a IL6R ? dyslipidemia Increased CHD & mortality

  28. “A genotype score of nine validated SNPs that are associated with modulation in levels of LDL or HDL cholesterol was an independent risk factor for incident cardiovascular disease. The score did not improve risk discrimination but did modestly improve clinical risk reclassification for individual subjects beyond standard clinical factors.”

  29. CNR1 &Stroke Mortality Characteristics of study subjects: Botnia MDC-CVA NORDIL N (males/females) 4,660 (2,146/2,514) 5,190 (2,140/3,050) 5,152 (2,567/2,585) 15,002 individuals Age (years) 58.2  13.8 57.5  5.9 60.2  6.6 BMI 27 ± 4 26  4 28  4 T2D 34.3% 8.8% 8.6% Smoking, current and former (%) 38.6 31.1 28.4 Mean follow up time 11.1 10.5 4.5 Stroke incident (N) (thrombotic stroke/cerebral haemorrhage) 211 (n.a.) 167 (133/20) 162 (136/19) 540 individuals that experienced stroke incident Stroke mortality (N) 125 13 18

  30. CNR1 &Stroke Mortality: the BOTNIA study rs806381 (-10924A>G) Kaplan-Meyer curves for incidence of stroke mortality in Botnia T2D families (A) and among T2D patients (B) according to the CNR1 rs806381 genotypes. HR = 2.2 1.4-3.5, P = 0.0005 HR = 2.3 1.4-3.9. P = 0.002 The effect of the CNR1 rs806381 genotypes, under a recessive model, on the incidence of stroke mortality was confirmed by Cox survival analysis (adjusted for age, gender and family dependence) during the 11 years of follow-up.

  31. rs806381 AA AG GG CNR1 &Stroke Mortality: the MDC-CVA study rs806381 (-10924A>G) Kaplan-Meyer curves for incidence of stroke mortality in MDC-CVA according to the CNR1 rs806381 genotypes. Survival (%) 100 100 75 75 Survival (%) rs806381 rs806381 AA AA AG AG GG GG 50 50 0 0 2 2 4 4 6 6 8 8 Time (y) Time (y) HR=3.9 [1.0-14.7], P = 0.049

  32. CNR1 &Stroke Mortality: the NORDIL study rs806381 (-10924A>G) Kaplan-Meyer curves for incidence of stroke mortality in NORDIL study according to the CNR1 rs806381 genotypes. HR=3.2 [1.2-12.5], P = 0.003

  33. From Obesity to T2D and to CV diseases metabolic syndrome inflammation abnormal food behaviour FTO MC4R CB1 ENPP1, CB1 insulin resistance early onset obesity Type 2 Diabetes cardiovascular diseases impaired b-cell differentiation, growth.. CDKN2A/B KIR 6.2 GCK ZNT8 HNFs (TCF2) impaired insulin secretion/exocytosis Beta cell mass reduction

  34. The march of technology 1980 single variant (100 SNPs; 103 genotypes) detailed study of individual genes (102 SNPs; 105+ genotypes) 1990 2000 regional studies (104 SNPs; 108 genotypes ) 2006-2007 genome-wide association (3 105 SNPs; 109 genotypes) 2009- 201x? 3,5 106 SNPs (2007) complete resequencing

  35. Resequencing genes amongst the tails of the phenotype distribution ? General Population

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