The EPIC-Norfolk Study
A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease.
Science translational medicine 2015 ; 8: 341ra76.
Scott RA, Freitag DF, Li L, Chu AY, Surendran P, Young R, Grarup N, Stančáková A, Chen Y, Varga TV, Yaghootkar H, Luan J, Zhao JH, Willems SM, Wessel J, Wang S, Maruthur N, Michailidou K, Pirie A, van der Lee SJ, Gillson C, Al Olama AA, Amouyel P, Arriola L, Arveiler D, Aviles-Olmos I, Balkau B, Barricarte A, Barroso I, Garcia SB, Bis JC, Blankenberg S, Boehnke M, Boeing H, Boerwinkle E, Borecki IB, Bork-Jensen J, Bowden S, Caldas C, Caslake M, CVD50 consortium, Cupples LA, Cruchaga C, Czajkowski J, den Hoed M, Dunn JA, Earl HM, Ehret GB, Ferrannini E, Ferrières J, Foltynie T, Ford I, Forouhi NG, Gianfagna F, Gonzalez C, Grioni S, Hiller L, Jansson JH, Jørgensen ME, Jukema JW, Kaaks R, Kee F, Kerrison ND, Key TJ, Kontto J, Kote-Jarai Z, Kraja AT, Kuulasmaa K, Kuusisto J, Linneberg A, Liu C, Marenne G, Mohlke KL, Morris AP, Muir K, Müller-Nurasyid M, Munroe PB, Navarro C, Nielsen SF, Nilsson PM, Nordestgaard BG, Packard CJ, Palli D, Panico S, Peloso GM, Perola M, Peters A, Poole CJ, Quirós JR, Rolandsson O, Sacerdote C, Salomaa V, Sánchez MJ, Sattar N, Sharp SJ, Sims R, Slimani N, Smith JA, Thompson DJ, Trompet S, Tumino R, van der A DL, van der Schouw YT, Virtamo J, Walker M, Walter K, GERAD_EC Consortium, Neurology Working Group of the Cohorts for Heart, Aging Research in Genomic Epidemiology (CHARGE) Aging Research in Genomic Epidemiology (CHARGE), Alzheimer’s Disease Genetics Consortium, Pancreatic Cancer Cohort Consortium, European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD) European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD), EPIC-InterAct EPIC-InterAct, Abraham JE, Amundadottir LT, Aponte JL, Butterworth AS, Dupuis J, Easton DF, Eeles RA, Erdmann J, Franks PW, Frayling TM, Hansen T, Howson JM, Jørgensen T, Kooner J, Laakso M, Langenberg C, McCarthy MI, Pankow JS, Pedersen O, Riboli E, Rotter JI, Saleheen D, Samani NJ, Schunkert H, Vollenweider P, O'Rahilly S, CHARGE Consortium, CHD Exome+ Consortium, CARDIoGRAM Exome Consortium, Deloukas P, Danesh J, Goodarzi MO, Kathiresan S, Meigs JB, Ehm MG, Wareham NJ, and Waterworth DM
DOI : 10.1126/scitranslmed.aad3744
PubMed ID : 27252175
PMCID : PMC5219001
URL : https://www.science.org/doi/10.1126/scitranslmed.aad3744
Abstract
Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.