The Pharmacogenetics of Type 2 Diabetes: A Systematic Review

The Pharmacogenetics of Type 2 Diabetes: A Systematic Review

1. Nisa M. Maruthur1,2,3⇑, 
2. Matthew O. Gribble2,3, 
3. Wendy L. Bennett1,2,
4. Shari Bolen4,5,6, 
5. Lisa M. Wilson7, 
6. Poojitha Balakrishnan3,
7. Anita Sahu8, 
8. Eric Bass1,7, 
9. W.H. Linda Kao1,2,3 and
10. Jeanne M. Clark1,2,3

+Author Affiliations

1. 1Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD
2. 2Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, MD
3. 3Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
4. 4Center for Health Care Research and Policy, The MetroHealth System, Cleveland, OH
5. 5Division of General Internal Medicine, The MetroHealth System/Case Western Reserve University, Cleveland, OH
6. 6Department of Biostatistics and Epidemiology, Case Western Reserve University, Cleveland, OH
7. 7Department of Health Policy and Management, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
8. 8University of Maryland School of Medicine, Baltimore, MD

1. Corresponding author: Nisa M. Maruthur, maruthur@jhmi.edu.

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, HbA_1c, 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 KCNQ1and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1Aloci. 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.

Footnotes

• This article contains Supplementary Data online athttp://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc13-1276/-/DC1.

• Received May 31, 2013.
• Accepted November 19, 2013.

• © 2014 by the American Diabetes Association.

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