Identification of Genetic Variation Influencing Metformin Response in a Multi-Ancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP)

Authors

• Josephine H. Li, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.
• James A. Perry, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland.
• Kathleen A. Jablonski, Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, District of Columbia.
• Shylaja Srinivasan, Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of California at San Francisco, San Francisco, California.
• Ling Chen, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.
• Jennifer N. Todd, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.
• Maegan Harden, Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
• Josep M. Mercader, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.
• Qing Pan, Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, District of Columbia.
• Adem Y. Dawed, Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, Scotland, U.K.
• Sook Wah Yee, Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California.
• Ewan R. Pearson, Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, Scotland, U.K.
• Kathleen M. Giacomini, Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California.
• Ayush Giri, Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee.
• Adriana M. Hung, Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
• Shujie Xiao, Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan.
• L Keoki Williams, Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan.
• Paul W. Franks, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmo, Sweden.
• Robert L. Hanson, Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona.
• Steven E. Kahn, Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington.
• William C. Knowler, Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona.
• Toni I. Pollin, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland.
• Jose C. Florez, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.

Document Type

Journal Article

Publication Date

12-16-2022

Journal

Diabetes

DOI

10.2337/db22-0702

Abstract

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (p<9×10-9). In MET, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], p=2.8×10-12). Rs145591055 near OMSR (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], p=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (p(G×T)<1.0×10-4). Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in pre-diabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.

APA Citation

Li, Josephine H.; Perry, James A.; Jablonski, Kathleen A.; Srinivasan, Shylaja; Chen, Ling; Todd, Jennifer N.; Harden, Maegan; Mercader, Josep M.; Pan, Qing; Dawed, Adem Y.; Yee, Sook Wah; Pearson, Ewan R.; Giacomini, Kathleen M.; Giri, Ayush; Hung, Adriana M.; Xiao, Shujie; Williams, L Keoki; Franks, Paul W.; Hanson, Robert L.; Kahn, Steven E.; Knowler, William C.; Pollin, Toni I.; and Florez, Jose C., "Identification of Genetic Variation Influencing Metformin Response in a Multi-Ancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP)" (2022). GW Authored Works. Paper 2086.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2086

Department

Epidemiology