Robert A. Scott, Daniel F. Freitag, Li Li, Audrey Y. Chu, Praveen Surendran, Robin Young, Niels Grarup, Alena Stancakova, Yuning Chen, Tibor V. Varga, Hanieh Yaghootkar, Jian'an Luan, Jing Hua Zhao, Sara M. Willems, Jennifer Wessel, Shuai Wang, Nisa Maruthur, Kyriaki Michailidou, Ailith Pirie, Sven J. van der Lee & 119 others
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.
|Journal||Science Translational Medicine|
|Number of pages||13|
|Publication status||Published - 1 Jun 2016|