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The Pharmacogenetics of Type 2 Diabetes: A Systematic Review
Authors:Nisa M. Maruthur  Matthew O. Gribble  Wendy L. Bennett  Shari Bolen  Lisa M. Wilson  Poojitha Balakrishnan  Anita Sahu  Eric Bass  W.H. Linda Kao  Jeanne M. Clark
Abstract:

OBJECTIVE

We performed a systematic review to identify which genetic variants predict response to diabetes medications.

RESEARCH DESIGN AND METHODS

We 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.

RESULTS

Of 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.

CONCLUSIONS

We 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.
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