Pharmacogenetics of novel glucose-lowering drugs

The aim of this work was to review studies in which genetic variants were assessed with respect to metabolic response to treatment with novel glucose-lowering drugs: dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i). In total, 22 studies were retrieved from the literature (MEDLINE). Variants of the GLP-1 receptor gene (GLP1R) were associated with a smaller reduction in HbA(1c) in response to DPP-4i. Variants of a number of other genes (KCNQ1, KCNJ11, CTRB1/2, PRKD1, CDKAL1, IL6 promoter region, TCF7L2, DPP4, PNPLA3) have also been related to DPP-4i response, although replication studies are lacking. The GLP1R gene was also reported to play a role in the response to GLP-1 RA, with larger weight reductions being reported in carriers of GLP1R variant alleles. There were variants of a few other genes (CNR1, TCF7L2, SORCS1) described to be related to GLP-1 RA. For SGLT2i, studies have focused on genes affecting renal glucose reabsorption (e.g. SLC5A2) but no relationship between SLC5A2 variants and response to empagliflozin has been found. The relevance of the included studies is limited due to small genetic effects, low sample sizes, limited statistical power, inadequate statistics (lack of gene-drug interactions), inadequate accounting for confounders and effects modifiers, and a lack of replication studies. Most studies have been based on candidate genes. Genome-wide association studies, in that respect, may be a more promising approach to providing novel insights. However, the identification of distinct subgroups of type 2 diabetes might also be necessary before pharmacogenetic studies can be successfully used for a stratified prescription of novel glucose-lowering drugs.

Automated summary

Current studies have identified genetic variants associated with response to DPP-4i, GLP-1 RA, and SGLT2i, but replication studies are lacking. Most studies have been candidate gene-based, indicating that genome-wide association studies may offer more promising insights in the future.