Hot topics. Chairmen: S. Liatis (Greece) G. Crepaldi (Italy)

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1 Hot topics Chairmen: S. Liatis (Greece) G. Crepaldi (Italy)

2 Polygenic scores and risk of developing type 2 diabetes M. Vaxillaire (France)

3 MGSD 2015 Meeting, Istanbul, Turkey 7-9 May, 2015 TYPE 2 DIABETES-RELATED POLYGENIC RISK SCORES: Association with fasting glycaemia and incidence of type 2 diabetes Martine Vaxillaire CNRS UMR Genomics and Metabolic Diseases Pasteur Institute of Lille, Lille 2 University, European Genomic Institute for Diabetes (E.G.I.D.), Lille, France martine.vaxillaire@good.ibl.fr

4 Genetic Heterogeneity of Type 2 Diabetes GENE ENVIRONMENT LIFE STYLE HABITS MONOGENIC FORMS POLYGENIC FORMS Epigenetics Intestinal microbiote Interactions. -> Neonatal Diabetes -> Maturity-Onset Diabetes of the Young (MODY) -> Other familial forms (late onset diabetes) Most common forms of Type 2 Diabetes (>95% of cases) Heritability: ~40-70% Vaxillaire et al., Best Pract Res Clin Endocrinol Metab. 2012; Bonnefond A, Froguel P. Cell Metab. 2015, 21(3):

5 Font size according to OR values

6 Legacy of Genome-wide association studies (GWAS) >90 loci T2D FG >40 loci Marullo L, et al. Curr Diab Rep 2014; Bonnefond A, Froguel P, Cell Metab 2015

7 Currently used T2D predictive models include several clinical and biochemical factors FINDRISC, ARIC, EPIC, FHS, Stern model Balkau B, et al At-risk genetic variants Metabolome Clinically used risk factors for T2D Several prediction combined genetic prediction models Age (or risk scores) have been assessed sex in numerous prospective ethnicity cohorts, obesity blood pressure such FPG as FHS, FOS, DPP Study, MPP study, waist EPIC circumference Study, Danish Inter99 2h- glucose Study, fasting CoLaus insulin Study.. and showed significant insulin associations resistance with incidence beta-cell function of physical Type 2 diabetes. inactivity smoking [de alcohol Miguel-Yanes JM, Diab Care 2011; Vassy JL, NAFLD Pediatrics 2012; Lyssenko V & Laakso M, family history 2013; Mühlenbruch K, et al. PloS One 2013; HbA 1c Andersson EA, et al. Diabetes 2013; Vassy HDL cholesterol JL, inflammatory et al. Diabetes 2014; markers Vaxillaire M, et al. Diabetologia triglycerides 2014]. adiponectin liver enzymes fetuin-a

8 Genetic risk scores (GRS) and prediction models for Type 2 diabetes incidence: Data from the French D.E.S.I.R. cohort Two type 2 diabetes-related genotype risk scores associate with variation of fasting plasma glucose and development of impaired glucose homeostasis in the prospective D.E.S.I.R. study. Vaxillaire M, Yengo L, et al. Diabetologia 2014 Main objective: To update and combine the genetic information from the last GWAS Meta-analyses ( ) In the French prospective D.E.S.I.R. Study, a general population of 5,212 participants (Data from an Epidemiological Study on the Insulin Resistance Syndrome) Study design: 12 common loci (both effects) 65 T2D-associated Frequent SNPs (62 loci) (DIAGRAM-MA 2012) To assess the combined, additive genetic effects for two GRS: GRS-1 = 65 SNPs on IFG and T2D incidence in 4,075 individuals non diabetics at baseline follow-up of 8.4 (±1.6) yrs (Cox regression models) 24 FPG-raising Frequent SNPs (MAGIC-MA 2012) GRS-2 = 89 SNPs MAF: Genotyping with the Metabochip (custom iselect-illumina DNA arrays) on quantitative metabolic traits (FPG, FI, HbA1c, HOMA2-B, HOMA2-S, BMI) in 3,927 non diabetic individuals (at baseline and during the 9-yrs follow-up) Voight BF, et al. (2012) The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular and anthropometric traits. PLoS Genet 8:e Morris AP, et al. Nat Genet 2012; Scott RA, et al. Nat Genet 2012 (Linear mixed models)

9 Association of two GRS with longitudinal variation of FPG levels during a 9-year follow-up in the D.E.S.I.R. participants: Data from 3,927 non diabetic D.E.S.I.R. participants (at baseline and during a mean follow-up time of 8.4 years) GRS-1 GRS-2 variables β (SE) p value β (SE) p value Glycemia at inclusion (mmol/l/year) 0,0011 (0,0003) 8, ,0011 (0,0002) 6, Insulinemia (pmol/l/year) 0,0067 (0,0191) 0,73 0,0007 (0,0165) 0,97 HbA1c (% per year) 0,00025 (0,00035) 0,47 0,00025 (0,00030) 0,42 HbA1c (mmol/mol/year) 0,0027 (0,0038) 0,47 0,0027 (0,0032) 0,42 HOMA2-B (per year) -0,0293 (0,0188) 0,12-0,0443 (0,0162) 6, HOMA2-S (per year) 0,0022 (0,0311) 0,94 0,0207 (0,0268) 0,44 BMI (kg/m 2 /year) 0,00027 (0,0009) 0,77-0,00016 (0,0008) 0,83 Les modèles (MML) ont été ajustés à l âge, au sexe, à l IMC à l inclusion, et à la valeur basale à l inclusion du trait analysé. GRS-1: combinaison de 65 SNPs associés au risque de DT2; GRS-2: combinaison de 89 SNPs (plus 24 SNPs associés à la variation de GàJ). p-value < : significative (0.05/20); < p-value < 0.05 : significativité nominale Vaxillaire M, Yengo L, et al. Diabetologia 2014

10 Association of two GRS with the incidence of IFG and T2D in the D.E.S.I.R. Study - Cumulative incidence by tertile of GRS - T2D only IFG + T2D GRS-1: HR= 1,07 [1,03; 1,10], p= 1, HR= 1,03 [1,02; 1,04], p= 4, GRS-2: HR= 1,05 [1,02; 1,08], p= 1, HR= 1,04 [1,03; 1,05], p=1, GRS-1 comprises 65 SNPs; GRS-2 comprises 89 SNPs GRS-1 Cumulative incidence of T2D only AUC Log -T2D rank : test GRS-1: p-value: 0, GRS-2: -4 0,575 p= 0,165 GRS-2 Cumulative incidence of IFG and T2D AUC Log -IFG+T2D rank test : p-value: GRS-1: 0, GRS-2: 0,585 p= 0,0045 n= 148 incident type 2 diabetic cases n= 660 incident IFG and 148 incident T2D cases Vaxillaire M, Yengo L, et al. Diabetologia 2014

11 Metabolomics and metabolic profiling in Type 2 Diabetes: From research to personalized medicine Genes mrnas Proteins

12 Metabolomics and metabolic profiling in Type 2 Diabetes: From research to personalized medicine

13 Non Targeted Metabolomic analysis in middle-age individuals of the French prospective D.E.S.I.R. study Study design: Nested case-control study DESIR Cohort N= 5,212 patients recruited Metabolomic profiling Non-targeted metabolomic profiling of plasma samples from incident diabetics and randomly sampled subjects Sampling Subjects were nondiabetic at baseline Subjects from the DESIR cohort (231 incident diabetes and 778 random samples) Baseline N= 231 incident diabetic cases Follow-up of 9 years N= 836 random samples Prevalent and incident diabetics in random samples were excluded from analysis (778 analyzed) plasma Extraction and derivatization UHPLC-MS/MS (+ESI) GC-MS (+EI) UHPLC-MS/MS (-ESI) Genetic factors, SNP Genotyping Genotyping was performed with Metabochip DNA arrays (custom iselect-illumina genotyping arrays) 65 T2D-associated SNPs (according to DIAGRAMv3 consortium) present on the Metabochip were selected. Using these SNPs, we defined the genotype risk score (GRS) as the sum of alleles known to increase the risk of T2D. Identification of metabolites (n=404) Candidate Biomarkers Clinical, biological risk factors Sex, age, BMI, fasting glucose, blood pressure, Triglycerides, HDLcholesterol, smoking status, waist circumference, family history of T2D, Gamma-Glutamyl transferase, physical activity. Yengo L, et al. Data submitted

14 Twelve Metabolites predict T2D incidence in the D.E.S.I.R. Study Three novel metabolites* are significantly associated with incident T2D Leucine, Valine, Tyrosine, Phenylalanine, 3-methyl-2-oxovalerate, Glycine and Linoleoyl-glycerophosphorylcholine, also known to associate with the risk of type 2 diabetes, showed nominal associations with incident Type 2 diabetes in the present study. Metabolite mannose glucose 1,5-anhydroglucitol (1,5-AG) 1-palmitoylglycerol (1-monopalmitin) 2-hydroxybutyrate (AHB) 3-hydroxyisobutyrate* 1-oleoylglycerol* (1-monoolein) 1-stearoylglycerol (1-monostearin) beta-sitosterol Isovalerylcarnitine* cotinine isoleucine Super-pathway Carbohydrate Carbohydrate Carbohydrate Lipid Amino Acid Amino Acid Lipid Lipid Lipid Amino Acid Xenobiotics Amino Acid Univariate analysis multivariate analysis HR [95% CI] per SD p-value HR [95% CI] per SD p-value 2.18 ( ) 2.59 ( ) 0.66 ( ) 1.55 ( ) 1.54 ( ) 1.51 ( ) 1.51 ( ) 1.46 ( ) 0.49 ( ) 1.53 ( ) 2.24 ( ) 1.48 ( ) ( ) 2.11 ( ) 0.70 ( ) 0.96 ( ) 1.20 ( ) 0.82 ( ) 1.11 ( ) 1.24 ( ) 0.77 ( ) 1.42 ( ) 1.35 ( ) 1.35 ( ) Bonferroni correction (p<10-4 ); Prentice-weighted Cox regression adjusted for age, sex and BMI at baseline Yengo L, et al. Data submitted

15 Cumulative incidence of T2D by tertile of a Metabolomic Risk Score (MRS) Metabolites found to be associated with incident T2D were aggregated into a Metabolomic risk score (MRS), as weighted according to the estimated regression coefficients from a conditional multivariate analysis (when p-val. < 0.5) The MRS was categorized into tertile groups. Kaplan-Meier Curves by tertile of MRS Proportion of incident type 2 diabetes 0,3 0,25 0,2 0,15 0,1 0, Age (years) 1er Tertile (T1) 2nd tertile (T2) 3rd tertile (T3) Average age of onset of T2D First tertile (T1) second tertile (T2) Third tertile (T3) Tertiles of the metabolomic risk score The incidence of T2D was significantly higher in the second (2nd versus 1st: HR=8.10; p= ) and third (3rd versus 1rst: HR=54.95; p= ) tertiles of MRS. On average, individuals in the third MRS tertile group developed T2D at 49 years while diabetes occurred at 66 years in the first tertile group (p= ). Yengo L, et al. Data submitted

16 Interactions of the MRS with known T2D risk factors, Improvement in T2D prediction of MRS over previous models Suggestive interactions between clinical risk factors and 4 of the 12 metabolites identified: Predictive value of identified metabolites on top of known T2D risk factors (M1 < M2 < M3): Interactions between Metabolites and T2D Risk Factor beta-sitosterol HDL beta-sitosterol SBP 3-hydroxyisobutyrate GGT 3-hydroxyisobutyrate Waist 3-hydroxyisobutyrate HDL 3-hydroxyisobutyrate SBP 2-hydroxybutyrate GGT High risk Low risk Sensibility 120% 100% 80% 60% 40% 20% ROC curves -- Clinical factors only (AUC=84.5%) - M1 -- Clinical + GRS (AUC=84.8%) - M2 -- Clinical + GRS + MRS (AUC= 89%) - M3 1-oleoylglycerol Age 0 0,5 1 1,5 2 2,5 3 Hazard Ratio 0% 0% 50% 100% 150% 1-Specificity In these interactions, the associations of the metabolites were Significant improvement in the AROC (M3 vs M2): stronger in the groups at lower risk. except for the interaction +4.26% ( ) between 3-hyroxyisobutyrate and HDL. Strong and significant increase in the risk reclassification (NRI): +76.4% ( ) Yengo L, et al. Data submitted

17 In conclusion Polygenic risk scores based on combined genetic information from T2D risk and fasting glucose variation can help to discriminate middle-aged individuals at risk of developing T2D in a general population. [Vassy JL, et al. Polygenic Type 2 Diabetes prediction at the limit of common variant detection. Diabetes Feb 11, 2014] A new metabolomic risk score showed an improvement in T2D diabetes prediction especially : - in individuals poorly recognized by classic clinical risk factors, - in individuals susceptible to develop T2D at an early age (<50 years). Metabolomic and genetic traits provide complementary information, to be used on top of clinical risk factors for a better identification of early metabolic disturbances preceding the onset of T2D, especially in individuals that are poorly recognized by classical clinical risk factors. Menni C, et al. Diabetes TwinsUK study; Floegel A, et al. Diabetes EPIC-Potsdam study; Walford GA, et al. Diabetes Care FOS study Such novel biomarkers will be useful, and may be required for the development of early interventions for prevention of T2D, involving life style changes and pharmacotherapy. Additional research is needed to understand the potential causality related to metabolomic biomarkers to the onset of T2D.

18 An atlas of Genetic influences on human blood metabolites Shin SY, Fauman EB, Petersen AK, Krumsiek J,. Kastenmüller G, Spector TD, Soranzo N. Nat Genetics 2014, 46(6): Medical and pharmacological relevance of metabolomic associations Ideogram of metabolomic associations (chromosome maps with the locations of 145 loci)

19 Collaborators & Funding Sources Collaborators Loic Yengo Abdelilah Arredouani Amélie Bonnefond Ronan Roussel Mario Falchi Ghislain Rocheleau Abdelali Haoudi Stephane Lobbens Beverley Balkau Michel Marre Philippe Froguel Funding supported by CNRS, FEDER, E.G.I.D., ANR-10-LABX-46 Qatar foundation (QBRI)

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21 Legacy of Genome-wide association studies (GWAS) EFFECT SIZE Rare variants in MODY/ND Linkage studies / Sanger sequencing NGS / Functional studies 28 genes => Rare variants (strong effect) Rare or low-frequency variants in common T2D Large-scale Sanger resequencing / NGS DNA arrays / Functional studies Genes carrying associated variants in aggregation Genes carrying singleassociated variants Frequent SNPs in common T2D 92 genes DNA arrays => Common SNPs (small effect) VARIANT FREQUENCY New avenues for therapeutic strategy in T2D prevention and care Towards a better characterization of different T2D subtypes?

22 Currently used T2D predictive models include several clinical and biochemical factors At-risk genetic variants Metabolome Several combined genetic prediction models (or risk scores) have been assessed in numerous prospective cohorts, such as FHS, FOS, DPP Study, MPP study, EPIC Study, Danish Inter99 Study, CoLaus Study.. and showed significant associations with incidence of Type 2 diabetes. [de Miguel-Yanes JM, Diab Care 2011; Vassy JL, Pediatrics 2012; Lyssenko V & Laakso M, 2013; Mühlenbruch K, et al. PloS One 2013; Andersson EA, et al. Diabetes 2013; Vassy JL, et al. Diabetes 2014; Vaxillaire M, et al. Diabetologia 2014]. Vassy JL, et al. Polygenic Type 2 Diabetes prediction at the limit of common variant detection. Diabetes Feb 11, 2014