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On the Verge of Precision Medicine in Diabetes

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Abstract

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

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References

  1. International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels: International Diabetes Federation; 2021.

    Google Scholar 

  2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183: 109119. https://doi.org/10.1016/j.diabres.2021.109119.

    Article  PubMed  Google Scholar 

  3. Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014;383:1068–83. https://doi.org/10.1016/s0140-6736(13)62154-6.

    Article  CAS  PubMed  Google Scholar 

  4. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60:793–9. https://doi.org/10.1007/s00125-017-4210-x.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6:361–9. https://doi.org/10.1016/s2213-8587(18)30051-2.

    Article  PubMed  Google Scholar 

  6. Ahlqvist E, Prasad RB, Groop L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes. 2020;69:2086–93. https://doi.org/10.2337/dbi20-0001.

    Article  CAS  PubMed  Google Scholar 

  7. Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 2018;15: e1002654. https://doi.org/10.1371/journal.pmed.1002654.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50:559–71. https://doi.org/10.1038/s41588-018-0084-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. DiCorpo D, LeClair J, Cole JB, Sarnowski C, Ahmadizar F, Bielak LF, et al. Type 2 diabetes partitioned polygenic scores associate with disease outcomes in 454,193 individuals across 13 cohorts. Diabetes Care. 2022;45:674–83. https://doi.org/10.2337/dc21-1395.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022;54:560–72. https://doi.org/10.1038/s41588-022-01058-3.

    Article  CAS  PubMed  Google Scholar 

  11. Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J, Tcheandjieu C, et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52:680–91. https://doi.org/10.1038/s41588-020-0637-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021;53:840–60. https://doi.org/10.1038/s41588-021-00852-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Florez JC. Newly identified loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia. 2008;51:1100–10. https://doi.org/10.1007/s00125-008-1025-9.

    Article  CAS  PubMed  Google Scholar 

  14. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38:320–3. https://doi.org/10.1038/ng1732.

    Article  CAS  PubMed  Google Scholar 

  15. Lyssenko V, Lupi R, Marchetti P, Del Guerra S, Orho-Melander M, Almgren P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest. 2007;117:2155–63. https://doi.org/10.1172/jci30706.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. da Silva XG, Loder MK, McDonald A, Tarasov AI, Carzaniga R, Kronenberger K, et al. TCF7L2 regulates late events in insulin secretion from pancreatic islet beta-cells. Diabetes. 2009;58:894–905. https://doi.org/10.2337/db08-1187.

    Article  CAS  Google Scholar 

  17. Srinivasan S, Kaur V, Chamarthi B, Littleton KR, Chen L, Manning AK, et al. TCF7L2 genetic variation augments incretin resistance and influences response to a sulfonylurea and metformin: the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH). Diabetes Care. 2018;41:554–61. https://doi.org/10.2337/dc17-1386.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Udler MS, McCarthy MI, Florez JC, Mahajan A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev. 2019;40:1500–20. https://doi.org/10.1210/er.2019-00088.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hivert MF, Jablonski KA, Perreault L, Saxena R, McAteer JB, Franks PW, et al. Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the Diabetes Prevention Program. Diabetes. 2011;60:1340–8. https://doi.org/10.2337/db10-1119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24. https://doi.org/10.1038/s41588-018-0183-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–13. https://doi.org/10.1038/s41588-018-0241-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Davies MJ, D’Alessio DA, Fradkin J, Kernan WN, Mathieu C, Mingrone G, Management of hyperglycaemia in type 2 diabetes, et al. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2018;2018(61):2461–98. https://doi.org/10.1007/s00125-018-4729-5.

    Article  Google Scholar 

  23. Nathan DM, Buse JB, Kahn SE, Krause-Steinrauf H, Larkin ME, Staten M, et al. Rationale and design of the glycemia reduction approaches in diabetes: a comparative effectiveness study (GRADE). Diabetes Care. 2013;36:2254–61. https://doi.org/10.2337/dc13-0356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pearson ER, Flechtner I, Njølstad PR, Malecki MT, Flanagan SE, Larkin B, et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N Engl J Med. 2006;355:467–77. https://doi.org/10.1056/NEJMoa061759.

    Article  CAS  PubMed  Google Scholar 

  25. Steele AM, Shields BM, Wensley KJ, Colclough K, Ellard S, Hattersley AT. Prevalence of vascular complications among patients with glucokinase mutations and prolonged, mild hyperglycemia. JAMA. 2014;311:279–86. https://doi.org/10.1001/jama.2013.283980.

    Article  CAS  PubMed  Google Scholar 

  26. Kahn SE, Haffner SM, Heise MA, Herman WH, Holman RR, Jones NP, et al. Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N Engl J Med. 2006;355:2427–43. https://doi.org/10.1056/NEJMoa066224.

    Article  CAS  PubMed  Google Scholar 

  27. Pavkov ME, Hanson RL, Knowler WC, Bennett PH, Krakoff J, Nelson RG. Changing patterns of type 2 diabetes incidence among Pima Indians. Diabetes Care. 2007;30:1758–63. https://doi.org/10.2337/dc06-2010.

    Article  PubMed  Google Scholar 

  28. Zhou K, Donnelly L, Yang J, Li M, Deshmukh H, Van Zuydam N, et al. Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol. 2014;2:481–7. https://doi.org/10.1016/s2213-8587(14)70050-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Graham GG, Punt J, Arora M, Day RO, Doogue MP, Duong JK, et al. Clinical pharmacokinetics of metformin. Clin Pharmacokinet. 2011;50:81–98. https://doi.org/10.2165/11534750-000000000-00000.

    Article  CAS  PubMed  Google Scholar 

  30. Kerb R, Brinkmann U, Chatskaia N, Gorbunov D, Gorboulev V, Mornhinweg E, et al. Identification of genetic variations of the human organic cation transporter hOCT1 and their functional consequences. Pharmacogenetics. 2002;12:591–5. https://doi.org/10.1097/00008571-200211000-00002.

    Article  CAS  PubMed  Google Scholar 

  31. Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest. 2007;117:1422–31. https://doi.org/10.1172/jci30558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou K, Donnelly LA, Kimber CH, Donnan PT, Doney ASF, Leese G, et al. Reduced-function SLC22A1 polymorphisms encoding organic cation transporter 1 and glycemic response to metformin: a GoDARTS study. Diabetes. 2009;58:1434–9. https://doi.org/10.2337/db08-0896.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Christensen MMH, Brasch-Andersen C, Green H, Nielsen F, Damkier P, Beck-Nielsen H, et al. The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c. Pharmacogenet Genomics. 2011;21:837–50. https://doi.org/10.1097/FPC.0b013e32834c0010.

    Article  CAS  PubMed  Google Scholar 

  34. Dujic T, Zhou K, Donnelly LA, Tavendale R, Palmer CN, Pearson ER. Association of organic cation transporter 1 with intolerance to metformin in type 2 diabetes: a GoDARTS study. Diabetes. 2015;64:1786–93. https://doi.org/10.2337/db14-1388.

    Article  CAS  PubMed  Google Scholar 

  35. Zhou K, Bellenguez C, Spencer CC, Bennett AJ, Coleman RL, Tavendale R, et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet. 2011;43:117–20. https://doi.org/10.1038/ng.735.

    Article  CAS  PubMed  Google Scholar 

  36. van Leeuwen N, Nijpels G, Becker ML, Deshmukh H, Zhou K, Stricker BH, 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. 2012;55:1971–7. https://doi.org/10.1007/s00125-012-2537-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhou K, Yee SW, Seiser EL, van Leeuwen N, Tavendale R, Bennett AJ, et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet. 2016;48:1055–9. https://doi.org/10.1038/ng.3632.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Florez JC, Jablonski KA, Taylor A, Mather K, Horton E, White NH, et al. The C allele of ATM rs11212617 does not associate with metformin response in the Diabetes Prevention Program. Diabetes Care. 2012;35:1864–7. https://doi.org/10.2337/dc11-2301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li JH, Perry JA, Jablonski KA, Chen L, Srinivasan S, Todd JN, et al. 28-OR: Identification of ancestry-specific alleles in a genome-wide association study (GWAS) for metformin (MET) response in the Diabetes Prevention Program (DPP). Diabetes. 2021;70:28-or. https://doi.org/10.2337/db21-28-OR.

    Article  Google Scholar 

  40. Rena G, Hardie DG, Pearson ER. The mechanisms of action of metformin. Diabetologia. 2017;60:1577–85. https://doi.org/10.1007/s00125-017-4342-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Walford GA, Colomo N, Todd JN, Billings LK, Fernandez M, Chamarthi B, et al. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH): design of a pharmacogenetic resource for type 2 diabetes. PLoS ONE. 2015;10: e0121553. https://doi.org/10.1371/journal.pone.0121553.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li JH, Brenner LN, Kaur V, Figueroa K, Udler MS, Leong A, et al. Genome-wide association analysis identifies ancestry-specific genetic variation associated with medication response in the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH). medRxiv. 2022. https://doi.org/10.1101/2022.01.24.22269036.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Sola D, Rossi L, Schianca GP, Maffioli P, Bigliocca M, Mella R, et al. Sulfonylureas and their use in clinical practice. Arch Med Sci. 2015;11:840–8. https://doi.org/10.5114/aoms.2015.53304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhou K, Donnelly L, Burch L, Tavendale R, Doney AS, Leese G, et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin Pharmacol Ther. 2010;87:52–6. https://doi.org/10.1038/clpt.2009.176.

    Article  CAS  PubMed  Google Scholar 

  45. Holstein A, Plaschke A, Ptak M, Egberts EH, El-Din J, Brockmöller J, et al. Association between CYP2C9 slow metabolizer genotypes and severe hypoglycaemia on medication with sulphonylurea hypoglycaemic agents. Br J Clin Pharmacol. 2005;60:103–6. https://doi.org/10.1111/j.1365-2125.2005.02379.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen L, Li JH, Kaur V, Muhammad A, Fernandez M, Hudson MS, et al. The presence of two reduced function variants in CYP2C9 influences the acute response to glipizide. Diabet Med. 2020;37:2124–30. https://doi.org/10.1111/dme.14176.

    Article  CAS  PubMed  Google Scholar 

  47. Holstein A, Hahn M, Patzer O, Seeringer A, Kovacs P, Stingl J. Impact of clinical factors and CYP2C9 variants for the risk of severe sulfonylurea-induced hypoglycemia. Eur J Clin Pharmacol. 2011;67:471–6. https://doi.org/10.1007/s00228-010-0976-1.

    Article  CAS  PubMed  Google Scholar 

  48. Feng Y, Mao G, Ren X, Xing H, Tang G, Li Q, et al. Ser1369Ala variant in sulfonylurea receptor gene ABCC8 is associated with antidiabetic efficacy of gliclazide in Chinese type 2 diabetic patients. Diabetes Care. 2008;31:1939–44. https://doi.org/10.2337/dc07-2248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Javorsky M, Klimcakova L, Schroner Z, Zidzik J, Babjakova E, Fabianova M, et al. KCNJ11 gene E23K variant and therapeutic response to sulfonylureas. Eur J Intern Med. 2012;23:245–9. https://doi.org/10.1016/j.ejim.2011.10.018.

    Article  CAS  PubMed  Google Scholar 

  50. Gloyn AL, Hashim Y, Ashcroft SJ, Ashfield R, Wiltshire S, Turner RC. Association studies of variants in promoter and coding regions of beta-cell ATP-sensitive K-channel genes SUR1 and Kir6.2 with Type 2 diabetes mellitus (UKPDS 53). Diabet Med. 2001;18:206–12. https://doi.org/10.1046/j.1464-5491.2001.00449.x.

    Article  CAS  PubMed  Google Scholar 

  51. Pearson ER, Donnelly LA, Kimber C, Whitley A, Doney ASF, McCarthy MI, et al. Variation in TCF7L2 influences therapeutic response to sulfonylureas: a GoDARTs study. Diabetes. 2007;56:2178–82. https://doi.org/10.2337/db07-0440.

    Article  CAS  PubMed  Google Scholar 

  52. Dawed AY, Yee SW, Zhou K, van Leeuwen N, Zhang Y, Siddiqui MK, et al. Genome-wide meta-analysis identifies genetic variants associated with glycemic response to sulfonylureas. Diabetes Care. 2021;44:2673–82. https://doi.org/10.2337/dc21-1152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Brown E, Heerspink HJL, Cuthbertson DJ, Wilding JPH. SGLT2 inhibitors and GLP-1 receptor agonists: established and emerging indications. Lancet. 2021;398:262–76. https://doi.org/10.1016/s0140-6736(21)00536-5.

    Article  CAS  PubMed  Google Scholar 

  54. Drucker DJ, Buse JB, Taylor K, Kendall DM, Trautmann M, Zhuang D, et al. Exenatide once weekly versus twice daily for the treatment of type 2 diabetes: a randomised, open-label, non-inferiority study. Lancet. 2008;372:1240–50. https://doi.org/10.1016/s0140-6736(08)61206-4.

    Article  CAS  PubMed  Google Scholar 

  55. Diamant M, Van Gaal L, Stranks S, Northrup J, Cao D, Taylor K, et al. Once weekly exenatide compared with insulin glargine titrated to target in patients with type 2 diabetes (DURATION-3): an open-label randomised trial. Lancet. 2010;375:2234–43. https://doi.org/10.1016/s0140-6736(10)60406-0.

    Article  CAS  PubMed  Google Scholar 

  56. Jones AG, McDonald TJ, Shields BM, Hill AV, Hyde CJ, Knight BA, et al. Markers of β-cell failure predict poor glycemic response to GLP-1 receptor agonist therapy in type 2 diabetes. Diabetes Care. 2016;39:250–7. https://doi.org/10.2337/dc15-0258.

    Article  CAS  PubMed  Google Scholar 

  57. Sathananthan A, Man CD, Micheletto F, Zinsmeister AR, Camilleri M, Giesler PD, et al. Common genetic variation in GLP1R and insulin secretion in response to exogenous GLP-1 in nondiabetic subjects: a pilot study. Diabetes Care. 2010;33:2074–6. https://doi.org/10.2337/dc10-0200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. de Luis DA, Diaz Soto G, Izaola O, Romero E. Evaluation of weight loss and metabolic changes in diabetic patients treated with liraglutide, effect of RS 6923761 gene variant of glucagon-like peptide 1 receptor. J Diabetes Complic. 2015;29:595–8. https://doi.org/10.1016/j.jdiacomp.2015.02.010.

    Article  Google Scholar 

  59. Yu M, Wang K, Liu H, Cao R. GLP1R variant is associated with response to exenatide in overweight Chinese type 2 diabetes patients. Pharmacogenomics. 2019;20:273–7. https://doi.org/10.2217/pgs-2018-0159.

    Article  CAS  PubMed  Google Scholar 

  60. Dawed AY, Mari A, McDonald TJ, Li L, Wang S, Hong M-G, et al. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomized controlled trials. medRxiv. 2022. https://doi.org/10.1101/2022.05.27.22271124.

    Article  Google Scholar 

  61. Ferreira MC, da Silva MER, Fukui RT, do Carmo Arruda-Marques M, Azhar S, Dos Santos RF. Effect of TCF7L2 polymorphism on pancreatic hormones after exenatide in type 2 diabetes. Diabetol Metab Syndr. 2019; 11:10. https://doi.org/10.1186/s13098-019-0401-6.

  62. Scott RA, Freitag DF, Li L, Chu AY, Surendran P, Young R, et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci Transl Med. 2016;8:341ra76. https://doi.org/10.1126/scitranslmed.aad3744.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Deacon CF. Dipeptidyl peptidase-4 inhibitors in the treatment of type 2 diabetes: a comparative review. Diabetes Obes Metab. 2011;13:7–18. https://doi.org/10.1111/j.1463-1326.2010.01306.x.

    Article  CAS  PubMed  Google Scholar 

  64. Scheen AJ. Pharmacokinetics of dipeptidylpeptidase-4 inhibitors. Diabetes Obes Metab. 2010;12:648–58. https://doi.org/10.1111/j.1463-1326.2010.01212.x.

    Article  CAS  PubMed  Google Scholar 

  65. Wilson JR, Shuey MM, Brown NJ, Devin JK. Hypertension and type 2 diabetes are associated with decreased inhibition of dipeptidyl peptidase-4 by sitagliptin. J Endocr Soc. 2017;1:1168–78. https://doi.org/10.1210/js.2017-00312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Javorský M, Gotthardová I, Klimčáková L, Kvapil M, Židzik J, Schroner Z, et al. A missense variant in GLP1R gene is associated with the glycaemic response to treatment with gliptins. Diabetes Obes Metab. 2016;18:941–4. https://doi.org/10.1111/dom.12682.

    Article  CAS  PubMed  Google Scholar 

  67. Űrgeová A, Javorský M, Klimčáková L, Židzik J, Šalagovič J, Hubáček JA, et al. Genetic variants associated with glycemic response to treatment with dipeptidylpeptidase 4 inhibitors. Pharmacogenomics. 2020;21:317–23. https://doi.org/10.2217/pgs-2019-0147.

    Article  CAS  PubMed  Google Scholar 

  68. Zimdahl H, Ittrich C, Graefe-Mody U, Boehm BO, Mark M, Woerle HJ, et al. Influence of TCF7L2 gene variants on the therapeutic response to the dipeptidylpeptidase-4 inhibitor linagliptin. Diabetologia. 2014;57:1869–75. https://doi.org/10.1007/s00125-014-3276-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90. https://doi.org/10.1038/ng.2383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. ’t Hart LM, Fritsche A, Nijpels G, van Leeuwen N, Donnelly LA, Dekker JM, et al. The CTRB1/2 locus affects diabetes susceptibility and treatment via the incretin pathway. Diabetes. 2013;62:3275–81. https://doi.org/10.2337/db13-0227.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Lupsa BC, Inzucchi SE. Use of SGLT2 inhibitors in type 2 diabetes: weighing the risks and benefits. Diabetologia. 2018;61:2118–25. https://doi.org/10.1007/s00125-018-4663-6.

    Article  CAS  PubMed  Google Scholar 

  72. Zimdahl H, Haupt A, Brendel M, Bour L, Machicao F, Salsali A, et al. Influence of common polymorphisms in the SLC5A2 gene on metabolic traits in subjects at increased risk of diabetes and on response to empagliflozin treatment in patients with diabetes. Pharmacogenet Genom. 2017;27:135–42. https://doi.org/10.1097/fpc.0000000000000268.

    Article  CAS  Google Scholar 

  73. Drexel H, Leiherer A, Saely CH, Brandtner EM, Geiger K, Vonbank A, et al. Are SGLT2 polymorphisms linked to diabetes mellitus and cardiovascular disease? 2019. Prospective study and meta-analysis. Biosci Rep. https://doi.org/10.1042/bsr20190299.

  74. Francke S, Mamidi RN, Solanki B, Scheers E, Jadwin A, Favis R, et al. In vitro metabolism of canagliflozin in human liver, kidney, intestine microsomes, and recombinant uridine diphosphate glucuronosyltransferases (UGT) and the effect of genetic variability of UGT enzymes on the pharmacokinetics of canagliflozin in humans. J Clin Pharmacol. 2015;55:1061–72. https://doi.org/10.1002/jcph.506.

    Article  CAS  PubMed  Google Scholar 

  75. Dayeh T, Volkov P, Salö S, Hall E, Nilsson E, Olsson AH, et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 2014;10: e1004160. https://doi.org/10.1371/journal.pgen.1004160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Nilsson E, Jansson PA, Perfilyev A, Volkov P, Pedersen M, Svensson MK, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes. 2014;63:2962–76. https://doi.org/10.2337/db13-1459.

    Article  PubMed  Google Scholar 

  77. García-Calzón S, Perfilyev A, Martinell M, Ustinova M, Kalamajski S, Franks PW, et al. Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes. Sci Transl Med. 2020. https://doi.org/10.1126/scitranslmed.aaz1803.

    Article  PubMed  Google Scholar 

  78. Shah SH, Newgard CB. Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ Cardiovasc Genet. 2015;8:410–9. https://doi.org/10.1161/circgenetics.114.000223.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Walford GA, Ma Y, Clish C, Florez JC, Wang TJ, Gerszten RE. Metabolite Profiles of Diabetes Incidence and Intervention Response in the Diabetes Prevention Program. Diabetes. 2016;65:1424–33. https://doi.org/10.2337/db15-1063.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Li H, He J, Jia W. The influence of gut microbiota on drug metabolism and toxicity. Expert Opin Drug Metab Toxicol. 2016;12:31–40. https://doi.org/10.1517/17425255.2016.1121234.

    Article  CAS  PubMed  Google Scholar 

  81. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528:262–6. https://doi.org/10.1038/nature15766.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannerås-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23:850–8. https://doi.org/10.1038/nm.4345.

    Article  CAS  PubMed  Google Scholar 

  83. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91. https://doi.org/10.1038/s41588-019-0379-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Mercader JM, Ng MCY, Manning AK, Rich SS. Predicting diabetes risk in diverse populations: what next? Lancet Diabetes Endocrinol. 2021;9:808–10. https://doi.org/10.1016/s2213-8587(21)00287-4.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Williams AL, Jacobs SB, Moreno-Macías H, Huerta-Chagoya A, Churchhouse C, Márquez-Luna C, et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature. 2014;506:97–101. https://doi.org/10.1038/nature12828.

    Article  CAS  PubMed  Google Scholar 

  86. Davis BH, Limdi NA. Translational pharmacogenomics: discovery, evidence synthesis and delivery of race-conscious medicine. Clin Pharmacol Ther. 2021;110:909–25. https://doi.org/10.1002/cpt.2357.

    Article  PubMed  Google Scholar 

  87. Shendre A, Dillon C, Limdi NA. Pharmacogenetics of warfarin dosing in patients of African and European ancestry. Pharmacogenomics. 2018;19:1357–71. https://doi.org/10.2217/pgs-2018-0146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide association studies in pharmacogenomics. Clin Pharmacol Ther. 2021;110:637–48. https://doi.org/10.1002/cpt.2349.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7:442–51. https://doi.org/10.1016/s2213-8587(19)30087-7.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Johnson D, Wilke MAP, Lyle SM, Kowalec K, Jorgensen A, Wright GEB, et al. A systematic review and analysis of the use of polygenic scores in pharmacogenomics. Clin Pharmacol Ther. 2022;111:919–30. https://doi.org/10.1002/cpt.2520.

    Article  PubMed  Google Scholar 

  91. Florez JC. Mining the genome for therapeutic targets. Diabetes. 2017;66:1770–8. https://doi.org/10.2337/dbi16-0069.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet. 2000;26:76–80. https://doi.org/10.1038/79216.

    Article  CAS  PubMed  Google Scholar 

  93. Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003;52:568–72. https://doi.org/10.2337/diabetes.52.2.568.

    Article  CAS  PubMed  Google Scholar 

  94. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun. 2015;6:5897. https://doi.org/10.1038/ncomms6897.

    Article  CAS  PubMed  Google Scholar 

  95. Santer R, Calado J. Familial renal glucosuria and SGLT2: from a mendelian trait to a therapeutic target. Clin J Am Soc Nephrol. 2010;5:133–41. https://doi.org/10.2215/cjn.04010609.

    Article  CAS  PubMed  Google Scholar 

  96. Calado J, Soto K, Clemente C, Correia P, Rueff J. Novel compound heterozygous mutations in SLC5A2 are responsible for autosomal recessive renal glucosuria. Hum Genet. 2004;114:314–6. https://doi.org/10.1007/s00439-003-1054-x.

    Article  PubMed  Google Scholar 

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Correspondence to Jose C. Florez.

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JHL is partially supported by an MGH ECOR Fund for Medical Discovery Clinical Research Fellowship Award. JCF is the recipient of NIH Grants R01 GM117163, UM1 DK126185, R01 DK123019, U54 DK118612, and K24 HL157960 relevant to this work.

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Josephine H. Li has no competing interests to declare that are relevant to the content of this article. Jose C. Florez has received speaking honoraria from Novo Nordisk, AstraZeneca, and Merck for research lectures over which he had full control of the content, and has also received consulting honoraria from AstraZeneca and Novo Nordisk.

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Li, J.H., Florez, J.C. On the Verge of Precision Medicine in Diabetes. Drugs 82, 1389–1401 (2022). https://doi.org/10.1007/s40265-022-01774-4

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