A genetically supported drug repurposing pipeline for diabetes treatment using electronic health recordsResearch in context
Megan M. Shuey,
Kyung Min Lee,
Jacob Keaton,
Nikhil K. Khankari,
Joseph H. Breeyear,
Venexia M. Walker,
Donald R. Miller,
Kent R. Heberer,
Peter D. Reaven,
Shoa L. Clarke,
Jennifer Lee,
Julie A. Lynch,
Marijana Vujkovic,
Todd L. Edwards
Affiliations
Megan M. Shuey
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
Kyung Min Lee
VA Informatics and Computer Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
Jacob Keaton
Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
Nikhil K. Khankari
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
Joseph H. Breeyear
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Nashville VA Medical Center, Nashville, TN, USA
Venexia M. Walker
Medical Research Council, Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol Medical School, UK; Population Health Sciences, University of Bristol, Bristol, UK; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Donald R. Miller
Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA; Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA
Kent R. Heberer
VA Palo Alto Health Care System, Palo Alto, CA, USA; Departments of Medicine and Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
Peter D. Reaven
Phoenix VA Health Care System, Phoenix, AZ, USA; College of Medicine, University of Arizona, Phoenix, AZ, USA
Shoa L. Clarke
Departments of Medicine and Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
Jennifer Lee
VA Palo Alto Health Care System, Palo Alto, CA, USA
Julie A. Lynch
VA Informatics and Computer Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; School of Medicine, University of Utah, Salt Lake City, UT, USA
Marijana Vujkovic
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Corresponding author. Division of Translational Medicine and Human Genetics, Smilow Center for Translational Research, 3400 Civic Center Blvd, Suite 11-134, Philadelphia, PA, USA 19104.
Todd L. Edwards
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Nashville VA Medical Center, Nashville, TN, USA; Corresponding author. Department of Medicine, Division of Epidemiology Vanderbilt Genetics Institute Vanderbilt University Medical Center, 2525 West End Ave suite 600, office 615, Nashville, TN, 37204, USA.
Summary: Background: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. Methods: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug–gene pairs. These drug–gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). Findings: After filtering on sample size, 20 candidate drug–gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: −0.11%, p = 0.01 and −0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10–25). Interpretation: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. Funding: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.
