A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin. Issue 5 (23rd September 2016)

• Record Type: Journal Article
• Authors: Goswami, S; Yee, SW; Xu, F; Sridhar, SB; Mosley, JD; Takahashi, A; Kubo, M; Maeda, S; Davis, RL; Roden, DM; Hedderson, MM; Giacomini, KM; Savic, RM
• Is Part Of: Clinical pharmacology & therapeutics. Volume 100:Issue 5(2016:Nov.)
• Page Start: 537
• Page End: 547
• Publication Date: 2016-09-23
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.428
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : One‐third of type‐2 diabetic patients respond poorly to metformin. Despite extensive research, the impact of genetic and nongenetic factors on long‐term outcome is unknown. In this study we combine nonlinear mixed effect modeling with computational genetic methodologies to identify predictors of long‐term response. In all, 1, 056 patients contributed their genetic, demographic, and long‐term HbA1c data. The top nine variants (of 12, 000 variants in 267 candidate genes) accounted for approximately one‐third of the variability in the disease progression parameter. Average serum creatinine level, age, and weight were determinants of symptomatic response; however, explaining negligible variability. Two single nucleotide polymorphisms (SNPs) in CSMD1 gene (rs2617102, rs2954625) and one SNP in a pharmacologically relevant SLC22A2 gene (rs316009) influenced disease progression, with minor alleles leading to less and more favorable outcomes, respectively. Overall, our study highlights the influence of genetic factors on long‐term HbA1c response and provides a computational model, which when validated, may be used to individualize treatment.