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Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin

Nature Genetics volume 48, pages 1055–1059 (2016)Cite this article

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Abstract

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 × 10−14) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

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Figure 1: Pharmacogenetic impact of rs8192675 on metformin response in participants of European ancestry.
Figure 2: HbA1c reduction by BMI group and rs8192675 genotype.
Figure 3: Regional plots of the SLC2A2 locus.
Figure 4: Genetic impact of GLUT2 variants on glucose homeostasis in different physiological and pharmacologic states.

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References

  1. Madiraju, A.K. et al. Metformin suppresses gluconeogenesis by inhibiting mitochondrial glycerophosphate dehydrogenase. Nature 510, 542–546 (2014).

    Article  CAS  Google Scholar 

  2. DeFronzo, R.A. & Goodman, A.M. Efficacy of metformin in patients with non-insulin-dependent diabetes mellitus. N. Engl. J. Med. 333, 541–549 (1995).

    Article  CAS  Google Scholar 

  3. UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 352, 854–865 (1998).

  4. Zhou, K. et al. Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol. 2, 481–487 (2014).

    Article  CAS  Google Scholar 

  5. Pawlyk, A.C., Giacomini, K.M., McKeon, C., Shuldiner, A.R. & Florez, J.C. Metformin pharmacogenomics: current status and future directions. Diabetes 63, 2590–2599 (2014).

    Article  CAS  Google Scholar 

  6. Tkáč, I. et al. Pharmacogenomic association between a variant in SLC47A1 gene and therapeutic response to metformin in type 2 diabetes. Diabetes Obes. Metab. 15, 189–191 (2013).

    Article  Google Scholar 

  7. Stocker, S.L. et al. The effect of novel promoter variants in MATE1 and MATE2 on the pharmacokinetics and pharmacodynamics of metformin. Clin. Pharmacol. Ther. 93, 186–194 (2013).

    Article  CAS  Google Scholar 

  8. Jablonski, K.A. et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 59, 2672–2681 (2010).

    Article  CAS  Google Scholar 

  9. Shu, Y. et al. Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics. Clin. Pharmacol. Ther. 83, 273–280 (2008).

    Article  CAS  Google Scholar 

  10. Zhou, K. et al. Reduced-function SLC22A1 polymorphisms encoding organic cation transporter 1 and glycemic response to metformin: a GoDARTS study. Diabetes 58, 1434–1439 (2009).

    Article  CAS  Google Scholar 

  11. van Leeuwen, N. et al. A gene variant near ATM is significantly associated with metformin treatment response in type 2 diabetes: a replication and meta-analysis of five cohorts. Diabetologia 55, 1971–1977 (2012).

    Article  CAS  Google Scholar 

  12. GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group, Wellcome Trust Case Control Consortium 2 & MAGIC investigators. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 (2011).

  13. Zhou, K. et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin. Pharmacol. Ther. 87, 52–56 (2010).

    Article  CAS  Google Scholar 

  14. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352, 837–853 (1998).

  15. Diabetes Prevention Program Research Group. Long-term safety, tolerability, and weight loss associated with metformin in the Diabetes Prevention Program Outcomes Study. Diabetes Care 35, 731–737 (2012).

  16. Diabetes Prevention Program Research Group. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet 374, 1677–1686 (2009).

  17. Kahn, S.E. et al. Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N. Engl. J. Med. 355, 2427–2443 (2006).

    Article  CAS  Google Scholar 

  18. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  Google Scholar 

  19. Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin AC levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    Article  CAS  Google Scholar 

  20. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  21. van de Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).

    Article  Google Scholar 

  22. Kabakchiev, B. & Silverberg, M.S. Expression quantitative trait loci analysis identifies associations between genotype and gene expression in human intestine. Gastroenterology 144, 1488–496 (2013).

    Article  CAS  Google Scholar 

  23. Manz, F. et al. Fanconi–Bickel syndrome. Pediatr. Nephrol. 1, 509–518 (1987).

    Article  CAS  Google Scholar 

  24. Fanconi, G. & Bickel, H. Chronic aminoaciduria (amino acid diabetes or nephrotic-glucosuric dwarfism) in glycogen storage and cystine disease. Helv. Paediatr. Acta 4, 359–396 (1949).

    CAS  PubMed  Google Scholar 

  25. Miller, R.A. et al. Biguanides suppress hepatic glucagon signalling by decreasing production of cyclic AMP. Nature 494, 256–260 (2013).

    Article  CAS  Google Scholar 

  26. Hosokawa, M. & Thorens, B. Glucose release from GLUT2-null hepatocytes: characterization of a major and a minor pathway. Am. J. Physiol. Endocrinol. Metab. 282, E794–E801 (2002).

    Article  CAS  Google Scholar 

  27. Burcelin, R., Dolci, W. & Thorens, B. Glucose sensing by the hepatoportal sensor is GLUT2-dependent: in vivo analysis in GLUT2-null mice. Diabetes 49, 1643–1648 (2000).

    Article  CAS  Google Scholar 

  28. Seyer, P. et al. Hepatic glucose sensing is required to preserve β cell glucose competence. J. Clin. Invest. 123, 1662–1676 (2013).

    Article  CAS  Google Scholar 

  29. Hundal, R.S. et al. Mechanism by which metformin reduces glucose production in type 2 diabetes. Diabetes 49, 2063–2069 (2000).

    Article  CAS  Google Scholar 

  30. Fullerton, M.D. et al. Single phosphorylation sites in Acc1 and Acc2 regulate lipid homeostasis and the insulin-sensitizing effects of metformin. Nat. Med. 19, 1649–1654 (2013).

    Article  CAS  Google Scholar 

  31. Pau, C.T., Keefe, C., Duran, J. & Welt, C.K. Metformin improves glucose effectiveness, not insulin sensitivity: predicting treatment response in women with polycystic ovary syndrome in an open-label, interventional study. J. Clin. Endocrinol. Metab. 99, 1870–1878 (2014).

    Article  CAS  Google Scholar 

  32. McCreight, L.J., Bailey, C.J. & Pearson, E.R. Metformin and the gastrointestinal tract. Diabetologia 59, 426–435 (2016).

    Article  CAS  Google Scholar 

  33. Ait-Omar, A. et al. GLUT2 accumulation in enterocyte apical and intracellular membranes: a study in morbidly obese human subjects and ob/ob and high fat–fed mice. Diabetes 60, 2598–2607 (2011).

    Article  CAS  Google Scholar 

  34. Gong, L., Goswami, S., Giacomini, K.M., Altman, R.B. & Klein, T.E. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet. Genomics 22, 820–827 (2012).

    Article  CAS  Google Scholar 

  35. Bailey, C.J. The current drug treatment landscape for diabetes and perspectives for the future. Clin. Pharmacol. Ther. 98, 170–184 (2015).

    Article  CAS  Google Scholar 

  36. Banda, Y. et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1285–1295 (2015).

    Article  Google Scholar 

  37. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  Google Scholar 

  38. Kooy, A. et al. Long-term effects of metformin on metabolism and microvascular and macrovascular disease in patients with type 2 diabetes mellitus. Arch. Intern. Med. 169, 616–625 (2009).

    Article  CAS  Google Scholar 

  39. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  40. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  41. Sherifali, D., Nerenberg, K., Pullenayegum, E., Cheng, J.E. & Gerstein, H.C. The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis. Diabetes Care 33, 1859–1864 (2010).

    Article  CAS  Google Scholar 

  42. Postmus, I. et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat. Commun. 5, 5068 (2014).

    Article  CAS  Google Scholar 

  43. Mägi, R. & Morris, A.P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

    Article  Google Scholar 

  44. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  45. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  46. Innocenti, F. et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 7, e1002078 (2011).

    Article  CAS  Google Scholar 

  47. Schröder, A. et al. Genomics of ADME gene expression: mapping expression quantitative trait loci relevant for absorption, distribution, metabolism and excretion of drugs in human liver. Pharmacogenomics J. 13, 12–20 (2013).

    Article  Google Scholar 

  48. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    Article  Google Scholar 

  49. Xia, K. et al. seeQTL: a searchable database for human eQTLs. Bioinformatics 28, 451–452 (2012).

    Article  CAS  Google Scholar 

  50. Dahlin, A. et al. Gene expression profiling of transporters in the solute carrier and ATP-binding cassette superfamilies in human eye substructures. Mol. Pharm. 10, 650–663 (2013).

    Article  CAS  Google Scholar 

  51. Liang, X. et al. Metformin is a substrate and inhibitor of the human thiamine transporter, THTR-2 (SLC19A3). Mol. Pharm. 12, 4301–4310 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge G.I. Bell (University of Chicago) for providing the expression vector for SLC2A2 (pSP64T-SLC2A2), and D.L. Minor and F. Findeisen for their guidance in performing oocyte injection and preparing cRNA. For full acknowledgments, see the Supplementary Note.

Author information

Author notes

  1. Kaixin Zhou and Sook Wah Yee: These authors contributed equally to this work.

  2. Kathleen M Giacomini and Ewan R Pearson: These authors jointly directed this work.

Authors and Affiliations

  1. School of Medicine, University of Dundee, Dundee, UK

    Kaixin Zhou, Roger Tavendale, Tanja Dujic, Lisa Logie, Calum Sutherland, Colin N A Palmer & Ewan R Pearson

  2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,

    Sook Wah Yee, Huan-Chieh Chien & Kathleen M Giacomini

  3. Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA

    Eric L Seiser & Federico Innocenti

  4. Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands

    Nienke van Leeuwen & Leen M 't Hart

  5. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

    Amanda J Bennett, Christopher J Groves & Mark I McCarthy

  6. Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

    Ruth L Coleman & Rury R Holman

  7. Department of General Practice, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

    Amber A van der Heijden

  8. Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

    Joline W Beulens & Leen M 't Hart

  9. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands

    Joline W Beulens

  10. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

    Catherine E de Keyser, Albert Hofman & Bruno H Stricker

  11. Latvian Genome Data Base (LGDB), Riga, Latvia

    Linda Zaharenko & Janis Klovins

  12. Latvian Biomedical Research and Study Centre, Riga, Latvia

    Linda Zaharenko, Janis Klovins & Valdis Pirags

  13. Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA

    Daniel M Rotroff & Alison A Motsinger-Reif

  14. Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA

    Daniel M Rotroff

  15. Treant Zorggroep, Location Bethesda, Hoogeveen, the Netherlands

    Mattijs Out & Adriaan Kooy

  16. Bethesda Diabetes Research Centre, Hoogeveen, the Netherlands

    Mattijs Out & Adriaan Kooy

  17. Biostatistics Center, George Washington University, Rockville, Maryland, USA

    Kathleen A Jablonski

  18. Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Ling Chen & Jose C Florez

  19. Faculty of Medicine, Šafárik University, Košice, Slovakia

    Martin Javorský, Jozef Židzik & Ivan Tkáč

  20. Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA

    Albert M Levin

  21. Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA

    L Keoki Williams

  22. Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan, USA

    L Keoki Williams

  23. Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Tanja Dujic & Sabina Semiz

  24. Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Sabina Semiz

  25. RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan

    Michiaki Kubo

  26. Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan

    Shiro Maeda

  27. Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan

    Shiro Maeda

  28. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.,

    John S Witte & Longyang Wu

  29. Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA.,

    John S Witte & Kathleen M Giacomini

  30. Department of Urology, University of California, San Francisco, San Francisco, California, USA

    John S Witte

  31. UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.,

    John S Witte

  32. Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, the Netherlands

    Ron H N van Schaik

  33. Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands

    Coen D A Stehouwer

  34. Faculty of Medicine, University of Latvia, Riga, Latvia

    Valdis Pirags

  35. Department of Endocrinology, Pauls Stradins Clinical University Hospital, Riga, Latvia

    Valdis Pirags

  36. Inspectorate of Healthcare, Heerlen, the Netherlands

    Bruno H Stricker

  37. Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Michael J Wagner

  38. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands

    Leen M 't Hart

  39. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Mark I McCarthy

  40. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK

    Mark I McCarthy

  41. Division of Research, Kaiser Permanente Northern California, Oakland, California, USA

    Monique M Hedderson

  42. Program in Metabolism, Broad Institute, Cambridge, Massachusetts, USA

    Jose C Florez

  43. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA

    Jose C Florez

  44. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA

    Jose C Florez

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  1. Kaixin Zhou
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Consortia

MetGen Investigators

DPP Investigators

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Contributions

Conception and design of the study: E.R.P. and K.M.G.; data analysis: K.Z., S.W.Y., E.L.S., N.v.L., A.A.v.d.H., J.W.B., C.E.d.K., L.Z., D.M.R., M.O., K.A.J., L.C., M.J., A.M.L., L.K.W., T.D. and A.A.M.-R.; data collection and genotyping: S.W.Y., C.S., R.T., A.J.B., C.J.G., R.L.C., L.L., L.K.W., T.D., S.S., M.K., M.M.H., H.-C.C., F.I., S.M., J.S.W., L.W., J.Ž., I.T., A.K., R.H.N.v.S., C.D.A.S., J.K., V.P., A.H., B.H.S., M.J.W., L.M.H., J.C.F., R.R.H., M.I.M. and C.N.A.P.; manuscript writing: E.R.P., K.Z., S.W.Y. and K.M.G. with contributions from all authors on the final version.

Corresponding authors

Correspondence to Kathleen M Giacomini or Ewan R Pearson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Genome-wide screening in the discovery cohort of 1,373 GoDARTS participants.

All 44 independent associations with P < 5.0 × 10−4 (blue line) and 23 independent signals from the next tier (P < 0.001 above the red line) with plausible biological candidacy were followed up in stage 1 replication.

Supplementary Figure 2 The three-stage replication study design flowchart.

Supplementary Figure 3 Association between rs8192675 and baseline HbA1c and on-treatment HbA1c.

The C allele was used as the effect allele in association tests. HbA1c was measured as a percentage.

Supplementary Figure 4 Functional enhancer assay.

(a,b) Luciferase activity in two liver cell lines transfected with reporter construct of the genomic region that include SLC2A2 intron variant rs8192675 (chr3:170724883). The construct (chr3:170724251–170727543) (195 ng) along with Renilla constructs (5 ng) was transiently transfected into Huh-7 and HepaRG liver cell lines for analysis of luciferase activity. Firefly luciferase activity was normalized to Renilla luciferase activity. The methods for cloning and luciferase assays have been previously described by our group (PLoS Genet. 10, e1004648, 2014, and Clin. Pharmacol. Ther. 89, 571–578, 2011). Pooled genomic DNA were used to clone the genomic region using the In-Fusion HD Cloning kit (Clontech) with forward and reverse primers using the following primer sequences: forward (+ strand) GCTCGCTAGCCTCGAGGCAACCAGATAGAATAATAC; reverse (+ strand) CGCCGAGGCCAGATCTGGTTCTCGTCCATGGCAATG. The genomic region was cloned into XhoI- and BglII-digested pGL4.23 using the Infusion HD cloning system (Clontech). The underlined region is the digestion site for XhoI and BglII. The reference allele of rs8192675 (T allele) showed significantly greater luciferase activity than the alternate allele (C allele) (P < 0.05). Data are reported as the relative fold increase compared with the pGL4.23 vector (white bar) containing the SNP rs8192675 (black and light gray bar). Each bar represents the mean ± s.e.m. from three or four replicates from one experiment. The experiments were repeated three times with similar significance and trend. The APOE basal promoter was used as a positive control.

Supplementary Figure 5 Uptake and inhibition studies in Xenopus laevis oocytes expressing human GLUT2 (SLC2A2).

(a–c) Uptake of model substrate (14C-2-deoxyglucose (2-DG)) (a) and metformin (Metf.) (b,c) in Xenopus laevis oocytes expressing GLUT2. (a) At 30 min, uptake of the model substrate is significantly higher than in oocytes injected with saline. In the presence of GLUT2 inhibitor, phloretin (200 μM), GLUT2-mediated uptake of 14C-2-deoxyglucose is inhibited. (b,c) However, uptake of 14C-metformin (at 30 and 60 min) is not significantly different between oocytes injected with saline or GLUT2 and also in the presence of GLUT2 inhibitor, phloretin. (d) Inhibition of GLUT2-mediated uptake of 14C-2-deoxyglucose by phloretin (200 μM) and metformin (30 and 50 mM). Phloretin significantly inhibit GLUT2-mediated uptake of 14C-2-deoxyglucose but not metformin. Xenopus laevis oocytes were purchased from Ecocytes. Capped cRNA was synthesized in vitro from human GLUT2 expression vector (pSP64T) (from G.I. Bell, University of Chicago) linearized using the mMessage mMachine SP6 kit (Ambion). 50 ng of the synthesized cRNA was injected into each oocyte. Modified Barth solution was used as the uptake buffer. DPM, disintegrations per minute, measure of the activity of the source of 14C-2-deoxyglucose radioactivity.

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Supplementary Figures 1–5, Supplementary Tables 1–8 and Supplementary Note. (PDF 2014 kb)

Supplementary Data: First-stage replication within the GoDARTS.

The first-stage replication was performed with three genotyping assays of CardioMetabochip (M), Sequenom (S) and TaqMan (T). Each P value for association was a geometric mean of two P values from the linear regression of HbA1c and the logistic regression of achieving a treatment target of HbA1c (XLSX 19 kb)

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Zhou, K., Yee, S., Seiser, E. et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet 48, 1055–1059 (2016). https://doi.org/10.1038/ng.3632

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