TY - JOUR
T1 - A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin
AU - Goswami, S.
AU - Yee, S. W.
AU - Xu, F.
AU - Sridhar, S. B.
AU - Mosley, J. D.
AU - Takahashi, A.
AU - Kubo, M.
AU - Maeda, S.
AU - Davis, R. L.
AU - Roden, D. M.
AU - Hedderson, M. M.
AU - Giacomini, K. M.
AU - Savic, R. M.
N1 - Publisher Copyright:
© 2016 American Society for Clinical Pharmacology and Therapeutics
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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.
AB - 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.
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U2 - 10.1002/cpt.428
DO - 10.1002/cpt.428
M3 - Article
C2 - 27415606
AN - SCOPUS:84990248628
SN - 0009-9236
SP - 537
EP - 547
JO - Clinical Pharmacology and Therapeutics
JF - Clinical Pharmacology and Therapeutics
ER -