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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Gene Expression Omnibus

References

  1. 1.

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

  2. 2.

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

  3. 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. 4.

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

  5. 5.

    , , , & Metformin pharmacogenomics: current status and future directions. Diabetes 63, 2590–2599 (2014).

  6. 6.

    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).

  7. 7.

    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).

  8. 8.

    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).

  9. 9.

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

  10. 10.

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

  11. 11.

    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).

  12. 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. 13.

    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).

  14. 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. 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. 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. 17.

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

  18. 18.

    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).

  19. 19.

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

  20. 20.

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

  21. 21.

    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).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

    , & Glucose sensing by the hepatoportal sensor is GLUT2-dependent: in vivo analysis in GLUT2-null mice. Diabetes 49, 1643–1648 (2000).

  28. 28.

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

  29. 29.

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

  30. 30.

    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).

  31. 31.

    , , & 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).

  32. 32.

    , & Metformin and the gastrointestinal tract. Diabetologia 59, 426–435 (2016).

  33. 33.

    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).

  34. 34.

    , , , & Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet. Genomics 22, 820–827 (2012).

  35. 35.

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

  36. 36.

    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).

  37. 37.

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

  38. 38.

    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).

  39. 39.

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

  40. 40.

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

  41. 41.

    , , , & The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis. Diabetes Care 33, 1859–1864 (2010).

  42. 42.

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

  43. 43.

    & GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

  44. 44.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  45. 45.

    , , , & A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

  46. 46.

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

  47. 47.

    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).

  48. 48.

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

  49. 49.

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

  50. 50.

    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).

  51. 51.

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

Download references

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

    • Kaixin Zhou
    •  & Sook Wah Yee

    These authors contributed equally to this work.

    • Kathleen M Giacomini
    •  & Ewan R Pearson

    These authors jointly directed this work.

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

Consortia

  1. MetGen Investigators

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

  2. DPP Investigators

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

  3. ACCORD Investigators

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

Authors

  1. Search for Kaixin Zhou in:

  2. Search for Sook Wah Yee in:

  3. Search for Eric L Seiser in:

  4. Search for Nienke van Leeuwen in:

  5. Search for Roger Tavendale in:

  6. Search for Amanda J Bennett in:

  7. Search for Christopher J Groves in:

  8. Search for Ruth L Coleman in:

  9. Search for Amber A van der Heijden in:

  10. Search for Joline W Beulens in:

  11. Search for Catherine E de Keyser in:

  12. Search for Linda Zaharenko in:

  13. Search for Daniel M Rotroff in:

  14. Search for Mattijs Out in:

  15. Search for Kathleen A Jablonski in:

  16. Search for Ling Chen in:

  17. Search for Martin Javorský in:

  18. Search for Jozef Židzik in:

  19. Search for Albert M Levin in:

  20. Search for L Keoki Williams in:

  21. Search for Tanja Dujic in:

  22. Search for Sabina Semiz in:

  23. Search for Michiaki Kubo in:

  24. Search for Huan-Chieh Chien in:

  25. Search for Shiro Maeda in:

  26. Search for John S Witte in:

  27. Search for Longyang Wu in:

  28. Search for Ivan Tkáč in:

  29. Search for Adriaan Kooy in:

  30. Search for Ron H N van Schaik in:

  31. Search for Coen D A Stehouwer in:

  32. Search for Lisa Logie in:

  33. Search for Calum Sutherland in:

  34. Search for Janis Klovins in:

  35. Search for Valdis Pirags in:

  36. Search for Albert Hofman in:

  37. Search for Bruno H Stricker in:

  38. Search for Alison A Motsinger-Reif in:

  39. Search for Michael J Wagner in:

  40. Search for Federico Innocenti in:

  41. Search for Leen M 't Hart in:

  42. Search for Rury R Holman in:

  43. Search for Mark I McCarthy in:

  44. Search for Monique M Hedderson in:

  45. Search for Colin N A Palmer in:

  46. Search for Jose C Florez in:

  47. Search for Kathleen M Giacomini in:

  48. Search for Ewan R Pearson in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Kathleen M Giacomini or Ewan R Pearson.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5, Supplementary Tables 1–8 and Supplementary Note.

Excel files

  1. 1.

    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

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.3632

Further reading