The Pharmacogenetics of Type 2 Diabetes: A Systematic Review

Abstract

OBJECTIVEWe performed a systematic review to identify which genetic variants predict response to diabetes medications.RESEARCH DESIGN AND METHODSWe performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation. Two investigators reviewed titles, abstracts, and articles independently. Two investigators abstracted data sequentially and evaluated study quality independently. Quality evaluations were based on the Strengthening the Reporting of Genetic Association Studies guidelines and Human Genome Epidemiology Network guidance.RESULTSOf 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). Studies were not standalone randomized controlled trials, and most evaluated patients with diabetes. Significant medication–gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.CONCLUSIONSWe found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics. While high-quality controlled studies with prespecified analyses are still lacking, our results bring the promise of personalized medicine in diabetes one step closer to fruition.

Anti-diabetic activity of stigmasterol from soybean oil by targeting the GLUT4 glucose transporter

“…There are two types of diabetes: type 1 diabetes (insulin-dependent) and type 2 diabetes (non-insulin-dependent). The most common form is type 2 diabetes mellitus (T2DM), which up to 90% of diagnosed DM patients have [4]. …”

Pharmacogenetics in type 2 diabetes: precision medicine or discovery tool?

“…Multiple reports of associations between genetic variants and drug response for any one of the 12 drug classes currently approved for the treatment of type 2 diabetes have been published [2][3][4], although the degree of confidence in the reported results varies greatly. To a large extent, this is due to the limited sample sizes currently available for studies into non-generic drugs, as most such studies are the province of individual pharmaceutical companies, and firm executives may be wary of limiting the marketability of a proprietary compound by attaching a genetic indication.…”

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

“…[4] However, pharmacogenomics faces challenges due to a number of factors: (1) in a complex, multi-factorial disease, involving many genes, there may not be a simple correlation between a mutation in one gene and the subsequent disease phenotype or its response to a particular treatment, and in any event there are many steps and much uncertainty between having a gene mutation and having an altered phenotype; (2) many diseases and many human phenotypes have an environmental component as well as a genetic component and human genetics is blind to these factors, especially the influence of the bacteria in the human gut microbiome and finally; (3) the issue of phenoconversion, where genotypic extensive metabolisers may be converted into phenotypic poor metabolisers by drug administration and thereby confound a pharmacogenomics prediction. [5] Thus, whilst the promise of pharmacogenomics to help deliver personalised medicine is clear, it has recently been shown to have delivered less than was expected in randomised clinical trials in the areas of cardiovascular disease, [6] diabetes [7] and depression. [8] Given the importance of improving the selection of medicines for patients, so as to maximise efficacy and to minimise adverse effects, there is thus clearly a need for new technologies to work in tandem with pharmacogenomics to improve the current personalised or precision medicine paradigm.…”