Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas
Authors: Dawed Adem Y. , Yee Sook Wah , Zhou Kaixin , van Leeuwen Nienke , Zhang Yanfei , Siddiqui Moneeza K. , Etheridge Amy , Innocenti Federico , Xu Fei , Li Josephine H. , Beulens Joline W. , van der Heijden Amber A. , Slieker Roderick C. , Chang Yu-Chuan , Mercader Josep M. , Kaur Varinderpal , Witte John S. , Lee Ming Ta Michael , Kamatani Yoichiro , Momozawa Yukihide , Kubo Michiaki , Palmer Colin N.A. , Florez Jose C. , Hedderson Monique M. , ‘t Hart Leen M. , Giacomini Kathleen M. , Pearson Ewan R. ,   DIRECT Consortium,   MetGen Plus, Pearson Ewan , Dawed Adem , Zhou Kaixin , Holman Rury , Coleman Ruth , ‘t Hart Leen , Slieker Roderick , Beulens Joline , van der Heijden Amber , Nijpels Giel , Elders Petra , Rutters Femke , Stricker Bruno , Ahmadizar Fariba , de Keyser Catherine , Koov Adriaan , Out Mattijs , Kloviņš Jānis , Zaharenko Linda , Javorsky Martin , Tkac Ivan , Florez Jose , Giacomini Kathy , Wah Yee Sook , Hedderson Monique , Kubo Michiaki , Motsinger-Reif Alison , Wagner Michael , Semiz Sabina , Dujic Tanja , Christensen Mette , Brøsen Kim , Waterworth Dawn , Ehm Meg , Ma Ronald , Psaty Bruce , Floyd James 
Published: October 2021

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 HbA1c 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 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 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 HbA1c reduction at a genome-wide scale (P &lt; 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 and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (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.