American Diabetes Association
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Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas

posted on 2021-10-04, 19:06 authored by Adem Y Dawed, Sook Wah Yee, Kaixin Zhou, Nienke van Leeuwen, Yanfei Zhang, Moneeza K Siddiqui, Amy Etheridge, Federico Innocenti, Fei Xu, Josephine H. Li, Joline W Beulens, Amber A van der Heijden, Roderick C Slieker, Yu-Chuan Chang, Josep M. Mercader, Varinderpal Kaur, John S. Witte, Ming Ta Michael Lee, Yoichiro Kamatani, Yukihide Momozawa, Michiaki Kubo, Colin N A Palmer, Jose C. Florez, Monique M. Hedderson, Leen M. ‘t Hart, Kathleen M. Giacomini, Ewan R Pearson, the MetGen Plus Consortium, the DIRECT Consortium
Objective: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction.

Research design and methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white Europeans with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis-eQTLs and functional validation in cellular models. Finally, we examined for a possible drug-drug-gene interactions.

Results: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (p < 5×10-8). The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.39×10−8) lower reduction in HbA1c. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3, 029 human whole blood samples, the C-allele is a cis-eQTL for increased expression of GXYLT1 (β=0.21, p=2.04×10-58). The C-allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (p=4.80×10−8). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) 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, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users, C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor.

Conclusion: 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 when prescribing glucose-lowering drugs.


The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. Funding was in part from the National Institutes of Health, R01-GM117163 to KMG, MMH and JCF. ERP holds a Wellcome New Investigator Award (102820/Z/13/Z). Funding for SUGAR-MGH was provided by NIH/NIDDK R01-DK088214. JHL is supported by NIH/NIDDK T32-DK007028. JCF is supported by NIH/NIDDK K24-DK110550. Geisinger MyCode type 2 diabetes project was supported by the Geisinger Health Plan Quality Pilot Fund (PI: Ming Ta M. Lee).


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