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

Journal name:
Nature Genetics
Volume:
48,
Pages:
1055–1059
Year published:
DOI:
doi:10.1038/ng.3632
Received
Accepted
Published online

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.

At a glance

Figures

  1. Pharmacogenetic impact of rs8192675 on metformin response in participants of European ancestry.
    Figure 1: Pharmacogenetic impact of rs8192675 on metformin response in participants of European ancestry.

    The forest plots show meta-analyses of association test results for metformin-induced change in HbA1c in a total of 10,557 participants from 10 MetGen cohorts. Results from linear regression models with (left) and without (right) adjustment for baseline HbA1c are presented. The x axis represents the impact on metformin-induced HbA1c reduction of each copy of the C allele. HbA1c was measured in percentage.

  2. HbA1c reduction by BMI group and rs8192675 genotype.
    Figure 2: HbA1c reduction by BMI group and rs8192675 genotype.

    Participants were stratified into obese (BMI ≥ 30 kg/m2) and nonobese (BMI < 30 kg/m2) groups. The number of obese and nonobese individuals in each genotype group is noted along the x axis. Error bars, s.e.m.

  3. Regional plots of the SLC2A2 locus.
    Figure 3: Regional plots of the SLC2A2 locus.

    SNPs are plotted by position on chromosome 3 against association with meta-analysis of HbA1c reduction without baseline adjustment (−log10 P) in 7,223 participants (left) and meta-analysis of SLC2A2 expression (−log10 P) in 1,226 liver samples (right). In both plots rs8192675 (purple circle) and its proxies are the top signals. The nonsynonymous SNP rs5400 (arrow) was also nominally associated with HbA1c reduction. Estimated recombination rates (cM/Mb) are plotted in blue to reflect the local LD structure. The SNPs surrounding the most significant SNP, rs8192675, are color coded to reflect their LD with this SNP. This LD was taken from pairwise r2 values from the HapMap CEU data. Genes, the position of exons and the direction of transcription from the UCSC Genome Browser are noted.

  4. Genetic impact of GLUT2 variants on glucose homeostasis in different physiological and pharmacologic states.
    Figure 4: Genetic impact of GLUT2 variants on glucose homeostasis in different physiological and pharmacologic states.

    In patients with the monogenic Fanconi–Bickel syndrome (FBS), the loss-of-function variants led to lower fasting glucose but higher post-prandial glucose; the expression-reducing C allele at rs8192675 was associated with lower HbA1c in normal glycemia state but higher HbA1c in hyperglycemia state (before pharmacological treatment was indicated in patients with type 2 diabetes (T2D)); metformin but not sulfonylurea treatment reversed the genetic impact on HbA1c.

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

  6. The three-stage replication study design flowchart.
    Supplementary Fig. 2: The three-stage replication study design flowchart.
  7. Association between rs8192675 and baseline HbA1c and on-treatment HbA1c.
    Supplementary Fig. 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.

  8. Functional enhancer assay.
    Supplementary Fig. 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.

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

    (ac) 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.

Accession codes

Referenced accessions

Gene Expression Omnibus

References

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Author information

  1. These authors contributed equally to this work.

    • Kaixin Zhou &
    • Sook Wah Yee
  2. These authors jointly directed this work.

    • Kathleen M Giacomini &
    • Ewan R Pearson

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

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

  3. DPP Investigators

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

  5. ACCORD Investigators

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

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 financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Genome-wide screening in the discovery cohort of 1,373 GoDARTS participants. (110 KB)

    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.

  2. Supplementary Figure 2: The three-stage replication study design flowchart. (219 KB)
  3. Supplementary Figure 3: Association between rs8192675 and baseline HbA1c and on-treatment HbA1c. (70 KB)

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

  4. Supplementary Figure 4: Functional enhancer assay. (143 KB)

    (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.

  5. Supplementary Figure 5: Uptake and inhibition studies in Xenopus laevis oocytes expressing human GLUT2 (SLC2A2). (134 KB)

    (ac) 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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  1. Supplementary Text and Figures (2,062 KB)

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

Excel files

  1. Supplementary Data: First-stage replication within the GoDARTS. (20 KB)

    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

Additional data