TY - JOUR
T1 - Genome-wide meta-analysis identifies genetic variants associated with glycemic response to sulfonylureas
AU - Dawed, Adem Y.
AU - Yee, Sook Wah
AU - Zhou, Kaixin
AU - Leeuwen, Nienke van
AU - Zhang, Yanfei
AU - Siddiqui, Moneeza K.
AU - Etheridge, Amy
AU - Innocenti, Federico
AU - Xu, Fei
AU - Li, Josephine H.
AU - Beulens, Joline W.
AU - van der Heijden, Amber A.
AU - Slieker, Roderick C.
AU - Chang, Yu-Chuan
AU - Mercader, Josep M.
AU - Kaur, Varinderpal
AU - Witte, John S.
AU - Lee, Ming Ta Michael
AU - Kamatani, Yoichiro
AU - Momozawa, Yukihide
AU - Kubo, Michiaki
AU - Palmer, Colin N. A.
AU - Florez, Jose C.
AU - Hedderson, Monique M.
AU - ‘t Hart, Leen M.
AU - for MetGen Plus, for the DIRECT Consortium
AU - Giacomini, Kathleen M.
AU - Pearson, Ewan R.
N1 - Funding Information:
Acknowledgments. The authors acknowl edge the following individuals for their contributions in providing information required for the studies: Dana Fraser from Parexel, who completed imputation for the Metformin Response (METRO) cohort; Dilrini Ranatunga from Kaiser Permanente Northern California Division of Research, for providing the phenotype and genotype data for the PMET1 cohort and phenotype data for PMET2 cohort; Sara R. Rashkin Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, for providing advice related to computational coding and analyses required for the PMET cohorts; and Xujia Zhou, University of California, San Francisco, CA, for experimental assistance required for this study. The authors also acknowledge Jennifer L. Aponte, Genomic Medicine, Parexel; Margaret G. Ehm, Target Sciences, GlaxoSmithKline; and Dawn M. Waterworth, Target Sciences, GlaxoSmithKline for their contribution to data analysis and revising the manuscript. The authors also acknowledge clinicalstudydatarequest.com for access to the HARMONY data. Finally, the authors thank all study participants. Funding and Duality of Interest. The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries (EFPIA) companies’ in kind contribution. Funding was in part from the National Institutes of Health (NIH), R01-GM117163, to J.C.F., M.M.H., and K.M.G. E.R.P. holds a Wellcome New Investigator Award (102820/Z/13/Z). Funding for SUGAR-MGH was provided by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, R01-DK088214. J.H.L. is sup- ported by NIDDK, NIH, T32-DK007028. J.C.F. is supported by NIDDK, NIH, K24-DK110550. Geisinger MyCode type 2 diabetes project was supported by the Geisinger Health Plan Quality Pilot Fund (Principal Investigator: M.T.M.L.). E.R.P. has received honoraria for speaking from Lilly and Sanofi. J.C.F. has received honoraria for speaking at scientific conferences from Novo and for consulting from Goldfinch Bio. No other potential conflicts of interest relevant to this article were reported. Author Contributions. A.Y.D, S.W.Y, L.M.‘t.H., K.M.G., and E.R.P. contributed to conception and design of the study. A.Y.D., S.W.Y., N.V.L., Y.Z., M.K.S., A.E., F.I, F.X., J.H.L., R.C.S., and Y.-C.C. contributed to data analysis. S.W.Y., A.E., F.I., J.M.M, V.K., J.S.W., M.T.M.L., Y.K., Y.M., M.K., C.N.A.P., J.C.F., M.M.H., and L.M.‘t.H. contributed to data collection and genotyping. A.Y.D., S.W.Y., K.M.G., and E.R.P. contributed to manuscript writing, with contributions from all authors on the final version. A.Y.D. S.W.Y, K.M.G., and E.R.P are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Funding Information:
The authors acknowledge the following individuals for their contributions in providing information required for the studies: Dana Fraser from Parexel, who completed imputation for the Metformin Response (METRO) cohort; Dilrini Ranatunga from Kaiser Permanente Northern California Division of Research, for providing the phenotype and genotype data for the PMET1 cohort and phenotype data for PMET2 cohort; Sara R. Rashkin Center for Applied Bioinformatics, St. Jude Children?s Research Hospital, Memphis, TN, for providing advice related to computational coding and analyses required for the PMET cohorts; and Xujia Zhou, University of California, San Francisco, CA, for experimental assistance required for this study. The authors also acknowledge Jennifer L. Aponte, Genomic Medicine, Parexel; Margaret G. Ehm, Target Sciences, GlaxoSmithKline; and Dawn M. Waterworth, Target Sciences, GlaxoSmithKline for their contribution to data analysis and revising the manuscript. The authors also acknowledge clinicalstudydatarequest.com for access to the HARMONY data. Finally, the authors thank all study participants. Funding and Duality of Interest. The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union?s Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries (EFPIA) companies? in kind contribution. Funding was in part from the National Institutes of Health (NIH), R01-GM117163, to J.C.F., M.M.H., and K.M.G. E.R.P. holds a Wellcome New Investigator Award (102820/Z/13/Z). Funding for SUGARMGH was provided by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, R01-DK088214. J.H.L. is sup ported by NIDDK, NIH, T32-DK007028. J.C.F. is supported by NIDDK, NIH, K24-DK110550. Geisinger MyCode type 2 diabetes project was supported by the Geisinger Health Plan Quality Pilot Fund (Principal Investigator: M.T.M.L.).
Publisher Copyright:
© 2021 by the American Diabetes Association.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - OBJECTIVE Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA
1c reduction. RESEARCH DESIGN AND METHODS As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA
1c reduction after 12 months of therapy followed by metaanalysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions. RESULTS After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA
1c reduction at a genome-wide scale (P < 5×10
28). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10
28), lower reduction in HbA
1c. Similarly, the C allele was associated with higher glucose trough levels (b = 1.61, P = 0.005) in healthy volunteers in the SUGARMGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (b = 0.21, P = 2.04 × 10
258). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA
1c (P = 4.80 × 10
28). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10
27), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA
1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor. CONCLUSIONS We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.
AB - OBJECTIVE Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA
1c reduction. RESEARCH DESIGN AND METHODS As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA
1c reduction after 12 months of therapy followed by metaanalysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions. RESULTS After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA
1c reduction at a genome-wide scale (P < 5×10
28). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10
28), lower reduction in HbA
1c. Similarly, the C allele was associated with higher glucose trough levels (b = 1.61, P = 0.005) in healthy volunteers in the SUGARMGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (b = 0.21, P = 2.04 × 10
258). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA
1c (P = 4.80 × 10
28). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10
27), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA
1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor. CONCLUSIONS We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.
UR - http://www.scopus.com/inward/record.url?scp=85120862273&partnerID=8YFLogxK
U2 - 10.2337/dc21-1152
DO - 10.2337/dc21-1152
M3 - Article
C2 - 34607834
SN - 0149-5992
VL - 44
SP - 2673
EP - 2682
JO - Diabetes Care
JF - Diabetes Care
IS - 12
ER -