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The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

  • Genetics (AP Morris, Section Editor)
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Abstract

Purpose of Review

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

Recent Findings

Generation of multi-omic data to measure each of the “biologic layers,” developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D.

Summary

Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual’s diabetes subtype for improved detection and management.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14. https://doi.org/10.1016/j.diabres.2009.10.007.

    Article  CAS  PubMed  Google Scholar 

  2. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L. The many faces of diabetes: a disease with increasing heterogeneity. Lancet (London, England). 2014;383(9922):1084–94. https://doi.org/10.1016/s0140-6736(13)62219-9.

    Article  Google Scholar 

  3. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol. 2014;2(1):56–64. https://doi.org/10.1016/S2213-8587(13)70112-8.

    Article  PubMed  Google Scholar 

  4. • McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793–9. https://doi.org/10.1007/s00125-017-4210-x. This article proposes the ‘palette’ model for type 2 diabetes, which centred on a molecular taxonomy that position an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression.

    Article  PubMed  Google Scholar 

  5. McPherson JD, Marra M, Hillier L, Waterston RH, Chinwalla A, Wallis J, et al. A physical map of the human genome. Nature. 2001;409(6822):934–41. https://doi.org/10.1038/35057157.

    Article  CAS  PubMed  Google Scholar 

  6. International HapMap Consortium. The international HapMap project. Nature. 2003;426(6968):789–96. https://doi.org/10.1038/nature02168.

    Article  CAS  Google Scholar 

  7. •• 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 Gen. 2018;50(11):1505–13. https://doi.org/10.1038/s41588-018-0241-6. The most recent T2D genome-wide genetic discovery paper.

    Article  CAS  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(4):559–71. https://doi.org/10.1038/s41588-018-0084-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Magi R, Reschen ME, et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet. 2015;47(12):1415–25. https://doi.org/10.1038/ng.3437.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Merino J, Florez JC. Precision medicine in diabetes: an opportunity for clinical translation. Ann N Y Acad Sci. 2018;1411(1):140–52. https://doi.org/10.1111/nyas.13588.

    Article  PubMed  Google Scholar 

  11. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53. https://doi.org/10.1038/nature08494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41–7. https://doi.org/10.1038/nature18642.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Consortium STD, Estrada K, Aukrust I, Bjorkhaug L, Burtt NP, Mercader JM, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311(22):2305–14. https://doi.org/10.1001/jama.2014.6511.

    Article  CAS  Google Scholar 

  14. Moltke I, Grarup N, Jorgensen ME, Bjerregaard P, Treebak JT, Fumagalli M, et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature. 2014;512(7513):190–3. https://doi.org/10.1038/nature13425.

    Article  CAS  PubMed  Google Scholar 

  15. Floyd JS, Psaty BM. The application of genomics in diabetes: barriers to discovery and implementation. Diabetes Care. 2016;39(11):1858–69. https://doi.org/10.2337/dc16-0738.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 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(9):1219–24. https://doi.org/10.1038/s41588-018-0183-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pallares-Mendez R, Aguilar-Salinas CA, Cruz-Bautista I, Del Bosque-Plata L. Metabolomics in diabetes, a review. Ann Med. 2016;48(1–2):89–102. https://doi.org/10.3109/07853890.2015.1137630.

    Article  CAS  PubMed  Google Scholar 

  18. Piening BD, Zhou W, Contrepois K, Rost H, Gu Urban GJ, Mishra T, et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 2018;6(2):157–70.e8. https://doi.org/10.1016/j.cels.2017.12.013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rhee EP, Yang Q, Yu B, Liu X, Cheng S, Deik A, et al. An exome array study of the plasma metabolome. Nat Commun. 2016;7:12360. https://doi.org/10.1038/ncomms12360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. O’Connell TM. The complex role of branched chain amino acids in diabetes and cancer. Meta. 2013;3(4):931–45. https://doi.org/10.3390/metabo3040931.

    Article  CAS  Google Scholar 

  21. Wang TJ, Ngo D, Psychogios N, Dejam A, Larson MG, Vasan RS, et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest. 2013;123(10):4309–17. https://doi.org/10.1172/JCI64801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lopez-Villar E, Martos-Moreno GA, Chowen JA, Okada S, Kopchick JJ, Argente J. A proteomic approach to obesity and type 2 diabetes. J Cell Mol Med. 2015;19(7):1455–70. https://doi.org/10.1111/jcmm.12600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. O’Connell TM, Markunas CA. DNA methylation and microRNA-based biomarkers for risk of type 2 diabetes. Curr Diabetes Rev. 2016;12(1):20–9.

    Article  Google Scholar 

  24. Hartstra AV, Bouter KE, Backhed F, Nieuwdorp M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care. 2015;38(1):159–65. https://doi.org/10.2337/dc14-0769.

    Article  CAS  PubMed  Google Scholar 

  25. Grarup N, Sandholt CH, Hansen T, Pedersen O. Genetic susceptibility to type 2 diabetes and obesity: from genome-wide association studies to rare variants and beyond. Diabetologia. 2014;57(8):1528–41. https://doi.org/10.1007/s00125-014-3270-4.

    Article  CAS  PubMed  Google Scholar 

  26. Pleis JR, Lucas JW, Ward BW. Summary health statistics for U.S. adults: National Health Interview Survey, 2008. Vital Health Stat 10. 2009;242:1–157.

    Google Scholar 

  27. Hannon TS, Bacha F, Lin Y, Arslanian SA. Hyperinsulinemia in African-American adolescents compared with their American white peers despite similar insulin sensitivity: a reflection of upregulated beta-cell function? Diabetes Care. 2008;31(7):1445–7. https://doi.org/10.2337/dc08-0116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vistisen D, Witte DR, Tabak AG, Herder C, Brunner EJ, Kivimaki M, et al. Patterns of obesity development before the diagnosis of type 2 diabetes: the Whitehall II cohort study. PLoS Med. 2014;11(2):e1001602. https://doi.org/10.1371/journal.pmed.1001602.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Unwin N, Shaw J, Zimmet P, Alberti KG. Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention. Diabet Med. 2002;19(9):708–23.

    Article  CAS  Google Scholar 

  30. Choi KM, Lee J, Kim DR, Kim SK, Shin DH, Kim NH, et al. Comparison of ADA and WHO criteria for the diagnosis of diabetes in elderly Koreans. Diabet Med. 2002;19(10):853–7.

    Article  CAS  Google Scholar 

  31. Sadikot SM, Nigam A, Das S, Bajaj S, Zargar AH, Prasannakumar KM, et al. Comparing the ADA 1997 and the WHO 1999 criteria: prevalence of diabetes in India study. Diabetes Res Clin Pract. 2004;66(3):309–15. https://doi.org/10.1016/j.diabres.2004.04.009.

    Article  CAS  PubMed  Google Scholar 

  32. Botas P, Delgado E, Castano G, Diaz de Grenu C, Prieto J, Diaz-Cadorniga FJ. Comparison of the diagnostic criteria for diabetes mellitus, WHO-1985, ADA-1997 and WHO-1999 in the adult population of Asturias (Spain). Diabet Med. 2003;20(11):904–8.

    Article  CAS  Google Scholar 

  33. Boj SF, van Es JH, Huch M, Li VS, Jose A, Hatzis P, et al. Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell. 2012;151(7):1595–607. https://doi.org/10.1016/j.cell.2012.10.053.

    Article  CAS  PubMed  Google Scholar 

  34. Nicolson TJ, Bellomo EA, Wijesekara N, Loder MK, Baldwin JM, Gyulkhandanyan AV, et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes. 2009;58(9):2070–83. https://doi.org/10.2337/db09-0551.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kido Y, Burks DJ, Withers D, Bruning JC, Kahn CR, White MF, et al. Tissue-specific insulin resistance in mice with mutations in the insulin receptor, IRS-1, and IRS-2. J Clin Invest. 2000;105(2):199–205. https://doi.org/10.1172/JCI7917.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hashimoto N, Kido Y, Uchida T, Asahara S, Shigeyama Y, Matsuda T, et al. Ablation of PDK1 in pancreatic beta cells induces diabetes as a result of loss of beta cell mass. Nat Genet. 2006;38(5):589–93. https://doi.org/10.1038/ng1774.

    Article  CAS  PubMed  Google Scholar 

  37. Lyssenko V, Eliasson L, Kotova O, Pilgaard K, Wierup N, Salehi A, et al. Pleiotropic effects of GIP on islet function involve osteopontin. Diabetes. 2011;60(9):2424–33. https://doi.org/10.2337/db10-1532.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mussig K, Staiger H, Machicao F, Haring HU, Fritsche A. Genetic variants affecting incretin sensitivity and incretin secretion. Diabetologia. 2010;53(11):2289–97. https://doi.org/10.1007/s00125-010-1876-8.

    Article  CAS  PubMed  Google Scholar 

  39. Andersson SA, Olsson AH, Esguerra JL, Heimann E, Ladenvall C, Edlund A, et al. Reduced insulin secretion correlates with decreased expression of exocytotic genes in pancreatic islets from patients with type 2 diabetes. Mol Cell Endocrinol. 2012;364(1–2):36–45. https://doi.org/10.1016/j.mce.2012.08.009.

    Article  CAS  PubMed  Google Scholar 

  40. Gutierrez-Aguilar R, Kim DH, Casimir M, Dai XQ, Pfluger PT, Park J, et al. The role of the transcription factor ETV5 in insulin exocytosis. Diabetologia. 2014;57(2):383–91. https://doi.org/10.1007/s00125-013-3096-5.

    Article  CAS  PubMed  Google Scholar 

  41. Dimas AS, Lagou V, Barker A, Knowles JW, Magi R, Hivert MF, et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes. 2014;63(6):2158–71. https://doi.org/10.2337/db13-0949.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Florez JC, Jablonski KA, Bayley N, Pollin TI, de Bakker PI, Shuldiner AR, et al. TCF7L2 polymorphisms and progression to diabetes in the diabetes prevention program. N Engl J Med. 2006;355(3):241–50. https://doi.org/10.1056/NEJMoa062418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Delahanty LM, Pan Q, Jablonski KA, Watson KE, McCaffery JM, Shuldiner A, et al. Genetic predictors of weight loss and weight regain after intensive lifestyle modification, metformin treatment, or standard care in the Diabetes Prevention Program. Diabetes Care. 2012;35(2):363–6. https://doi.org/10.2337/dc11-1328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pollin TI, Jablonski KA, McAteer JB, Saxena R, Kathiresan S, Kahn SE, et al. Triglyceride response to an intensive lifestyle intervention is enhanced in carriers of the GCKR Pro446Leu polymorphism. J Clin Endocrinol Metab. 2011;96(7):E1142–7. https://doi.org/10.1210/jc.2010-2324.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 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(4):1340–8. https://doi.org/10.2337/db10-1119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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(6):481–7. https://doi.org/10.1016/S2213-8587(14)70050-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gatza ML, Silva GO, Parker JS, Fan C, Perou CM. An integrated genomics approach identifies drivers of proliferation in luminal-subtype human breast cancer. Nat Genet. 2014;46(10):1051–9. https://doi.org/10.1038/ng.3073.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R, Chen R, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311):311ra174. https://doi.org/10.1126/scitranslmed.aaa9364.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wessel J, Chu AY. Integrative genomic approach identifies molecular subtypes of type 2 diabetes. Diabetes. 65(Supplement 1). https://doi.org/10.2337/db16-1-381.

  50. • Ahlqvist E, Storm P, Karajamaki 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(5):361–9. https://doi.org/10.1016/S2213-8587(18)30051-2. References 51 and 52 represent the two recent approaches to identify T2D subtypes.

    Article  PubMed  Google Scholar 

  51. • Udler MS, Kim J, von Grotthuss M, Bonas-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(9):e1002654. https://doi.org/10.1371/journal.pmed.1002654. References 51 and 52 represent the two recent approaches to identify T2D subtypes.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Reitman ML, Schadt EE. Pharmacogenetics of metformin response: a step in the path toward personalized medicine. J Clin Invest. 2007;117(5):1226–9. https://doi.org/10.1172/JCI32133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005;37(7):710–7. https://doi.org/10.1038/ng1589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16(2):85–97. https://doi.org/10.1038/nrg3868.

    Article  CAS  PubMed  Google Scholar 

  55. Multhaup ML, Seldin MM, Jaffe AE, Lei X, Kirchner H, Mondal P, et al. Mouse-human experimental epigenetic analysis unmasks dietary targets and genetic liability for diabetic phenotypes. Cell Metab. 2015;21(1):138–49. https://doi.org/10.1016/j.cmet.2014.12.014.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70. https://doi.org/10.1038/nature11412.

    Article  CAS  Google Scholar 

  57. Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372(26):2481–98. https://doi.org/10.1056/NEJMoa1402121.

    Article  CAS  Google Scholar 

  58. Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74. https://doi.org/10.1038/nature11247.

    Article  CAS  Google Scholar 

  59. GTEx Consortium. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60. https://doi.org/10.1126/science.1262110.

    Article  CAS  Google Scholar 

  60. Mele M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015;348(6235):660–5. https://doi.org/10.1126/science.aaa0355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rivas MA, Pirinen M, Conrad DF, Lek M, Tsang EK, Karczewski KJ, et al. Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science. 2015;348(6235):666–9. https://doi.org/10.1126/science.1261877.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gaulton KJ, Nammo T, Pasquali L, Simon JM, Giresi PG, Fogarty MP, et al. A map of open chromatin in human pancreatic islets. Nat Genet. 2010;42(3):255–9. https://doi.org/10.1038/ng.530.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Pasquali L, Gaulton KJ, Rodriguez-Segui SA, Mularoni L, Miguel-Escalada I, Akerman I, et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet. 2014;46(2):136–43. https://doi.org/10.1038/ng.2870.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Varshney A, Scott LJ, Welch RP, Erdos MR, Chines PS, Narisu N, et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A. 2017;114(9):2301–6. https://doi.org/10.1073/pnas.1621192114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Type 2 Diabetes Knowledge Portal. www.type2diabetesgenetics.org/. Accessed October 2018.

  66. Fitipaldi H, McCarthy MI, Florez JC, Franks PW. A global overview of precision medicine in type 2 diabetes. Diabetes. 2018;67(10):1911–22. https://doi.org/10.2337/dbi17-0045.

    Article  CAS  PubMed  Google Scholar 

  67. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5. https://doi.org/10.1056/NEJMp1500523.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Indiana University Grand Challenge. https://grandchallenges.iu.edu/precision-health/index.html. Accessed Nov 2018.

  69. •• Trans-Omics for Precision Medicine (TOPMed) Program. https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program. Accessed Nov 2018. Example of one of the latest large-scale efforts generating multi-omic measures for advancing our understanding of T2D and cardiovascular genomics.

  70. Brody JA, Morrison AC, Bis JC, O’Connell JR, Brown MR, Huffman JE, et al. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nat Genet. 2017;49(11):1560–3. https://doi.org/10.1038/ng.3968.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403. https://doi.org/10.1056/NEJMoa012512.

    Article  CAS  Google Scholar 

  72. Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(4):537–44.

    Article  CAS  Google Scholar 

  73. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50. https://doi.org/10.1056/NEJM200105033441801.

    Article  CAS  PubMed  Google Scholar 

  74. Hivert MF, Christophi CA, Franks PW, Jablonski KA, Ehrmann DA, Kahn SE, et al. Lifestyle and metformin ameliorate insulin sensitivity independently of the genetic burden of established insulin resistance variants in diabetes prevention program participants. Diabetes. 2016;65(2):520–6. https://doi.org/10.2337/db15-0950.

    Article  CAS  PubMed  Google Scholar 

  75. Wessel J, Marrero DG. Genetic testing for type 2 diabetes in high-risk children: the case for primordial prevention. Res Ideas Outcomes. 2017;3(e20695).

  76. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079–94. https://doi.org/10.1016/j.cell.2015.11.001.

    Article  CAS  PubMed  Google Scholar 

  77. Mutie PM, Giordano GN, Franks PW. Lifestyle precision medicine: the next generation in type 2 diabetes prevention? BMC Med. 2017;15(1):171. https://doi.org/10.1186/s12916-017-0938-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Anderson SL, Trujillo JM, McDermott M, Saseen JJ. Determining predictors of response to exenatide in type 2 diabetes. J Am Pharm Assoc. 2003;52(4):466–71. https://doi.org/10.1331/JAPhA.2012.10217.

    Article  Google Scholar 

  79. 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(9):1864–7. https://doi.org/10.2337/dc11-2301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Florez JC. The pharmacogenetics of metformin. Diabetologia. 2017;60(9):1648–55. https://doi.org/10.1007/s00125-017-4335-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group, Wellcome Trust Case Control Consortium, Zhou K, Bellenguez C, Spencer CC, Bennett AJ, et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet. 2011;43(2):117–20. https://doi.org/10.1038/ng.735.

    Article  CAS  Google Scholar 

  82. 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(9):1055–9. https://doi.org/10.1038/ng.3632.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 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(10):1939–44. https://doi.org/10.2337/dc07-2248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Fu H, Cao D, Boye KS, Curtis B, Schuster DL, Kendall DM, et al. Early glycemic response predicts achievement of subsequent treatment targets in the treatment of type 2 diabetes: a post hoc analysis. Diabetes Ther. 2015;6(3):317–28. https://doi.org/10.1007/s13300-015-0119-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Martono DP, Lub R, Lambers Heerspink HJ, Hak E, Wilffert B, Denig P. Predictors of response in initial users of metformin and sulphonylurea derivatives: a systematic review. Diabet Med. 2015;32(7):853–64. https://doi.org/10.1111/dme.12688.

    Article  CAS  PubMed  Google Scholar 

  86. Shah M, Varghese RT, Miles JM, Piccinini F, Dalla Man C, Cobelli C, et al. TCF7L2 genotype and alpha-cell function in humans without diabetes. Diabetes. 2016;65(2):371–80. https://doi.org/10.2337/db15-1233.

    Article  CAS  PubMed  Google Scholar 

  87. Franks PW, McCarthy MI. Exposing the exposures responsible for type 2 diabetes and obesity. Science. 2016;354(6308):69–73. https://doi.org/10.1126/science.aaf5094.

    Article  CAS  PubMed  Google Scholar 

  88. Dabelea D, Bell RA, D’Agostino RB Jr, Imperatore G, Johansen JM, Linder B, et al. Incidence of diabetes in youth in the United States. JAMA. 2007;297(24):2716–24. https://doi.org/10.1001/jama.297.24.2716.

    Article  PubMed  Google Scholar 

  89. Liu LL, Yi JP, Beyer J, Mayer-Davis EJ, Dolan LM, Dabelea DM, et al. Type 1 and type 2 diabetes in Asian and Pacific Islander U.S. youth: the SEARCH for diabetes in youth study. Diabetes Care. 2009;32(Suppl 2):S133–40. https://doi.org/10.2337/dc09-S205.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Mayer-Davis EJ, Beyer J, Bell RA, Dabelea D, D’Agostino R Jr, Imperatore G, et al. Diabetes in African American youth: prevalence, incidence, and clinical characteristics: the SEARCH for diabetes in youth study. Diabetes Care. 2009;32(Suppl 2):S112–22. https://doi.org/10.2337/dc09-S203.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Gill-Carey O, Hattersley AT. Genetics and type 2 diabetes in youth. Pediatr Diabetes. 2007;8(Suppl 9):42–7. https://doi.org/10.1111/j.1399-5448.2007.00331.x.

    Article  PubMed  Google Scholar 

  92. Hannon TS, Rao G, Arslanian SA. Childhood obesity and type 2 diabetes mellitus. Pediatrics. 2005;116(2):473–80. https://doi.org/10.1542/peds.2004-2536.

    Article  PubMed  Google Scholar 

  93. Buse JB, D’Alessio DA, Riddle MC. Can we RISE to the challenge of youth-onset type 2 diabetes? Diabetes Care. 2018;41(8):1560–2. https://doi.org/10.2337/dci18-0025.

    Article  PubMed  Google Scholar 

  94. Rise Consortium. Metabolic contrasts between youth and adults with impaired glucose tolerance or recently diagnosed type 2 diabetes: II. Observations using the oral glucose tolerance test. Diabetes Care. 2018;41(8):1707–16. https://doi.org/10.2337/dc18-0243.

    Article  CAS  Google Scholar 

  95. Rise Consortium. Metabolic contrasts between youth and adults with impaired glucose tolerance or recently diagnosed type 2 diabetes: I. Observations using the hyperglycemic clamp. Diabetes care. 2018;41(8):1696–706. https://doi.org/10.2337/dc18-0244.

    Article  CAS  Google Scholar 

  96. Rise Consortium. Impact of insulin and metformin versus metformin alone on beta-cell function in youth with impaired glucose tolerance or recently diagnosed type 2 diabetes. Diabetes Care. 2018;41(8):1717–25. https://doi.org/10.2337/dc18-0787.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA

    Yuan Lin & Jennifer Wessel

  2. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

    Jennifer Wessel

  3. Diabetes Translational Research Center, Indiana University School of Medicine, Indianapolis, IN, USA

    Jennifer Wessel

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  1. Yuan Lin
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  2. Jennifer Wessel
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Lin, Y., Wessel, J. The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges. Curr Diab Rep 19, 16 (2019). https://doi.org/10.1007/s11892-019-1137-2

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