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

  • Yuan Lin
  • Jennifer WesselEmail author
Genetics (AP Morris, Section Editor)
  • 74 Downloads
Part of the following topical collections:
  1. Topical Collection on Genetics

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.

Keywords

Type 2 diabetes Precision diabetes Review Genetics Genomics Heterogeneity 

Notes

Compliance with Ethical Standards

Conflict of Interest

Jennifer Wessel and Yuan Lin declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

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

  1. 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.CrossRefPubMedGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefPubMedGoogle Scholar
  4. 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. CrossRefPubMedGoogle Scholar
  5. 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.CrossRefPubMedGoogle Scholar
  6. 6.
    International HapMap Consortium. The international HapMap project. Nature. 2003;426(6968):789–96.  https://doi.org/10.1038/nature02168.CrossRefGoogle Scholar
  7. 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. CrossRefGoogle Scholar
  8. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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.CrossRefPubMedGoogle Scholar
  11. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefPubMedGoogle Scholar
  15. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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.CrossRefPubMedGoogle Scholar
  18. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefPubMedGoogle Scholar
  25. 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.CrossRefPubMedGoogle Scholar
  26. 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. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefPubMedGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefPubMedGoogle Scholar
  34. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 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.CrossRefPubMedGoogle Scholar
  37. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 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.CrossRefPubMedGoogle Scholar
  39. 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.CrossRefPubMedGoogle Scholar
  40. 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.CrossRefPubMedGoogle Scholar
  41. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 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. 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. CrossRefPubMedGoogle Scholar
  51. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  52. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 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.CrossRefPubMedGoogle Scholar
  55. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.  https://doi.org/10.1038/nature11412.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Type 2 Diabetes Knowledge Portal. www.type2diabetesgenetics.org/. Accessed October 2018.
  66. 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.CrossRefPubMedGoogle Scholar
  67. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Indiana University Grand Challenge. https://grandchallenges.iu.edu/precision-health/index.html. Accessed Nov 2018.
  69. 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. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 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.CrossRefGoogle Scholar
  72. 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.CrossRefGoogle Scholar
  73. 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.CrossRefPubMedGoogle Scholar
  74. 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.CrossRefPubMedGoogle Scholar
  75. 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).Google Scholar
  76. 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.CrossRefPubMedGoogle Scholar
  77. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 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.CrossRefGoogle Scholar
  79. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Florez JC. The pharmacogenetics of metformin. Diabetologia. 2017;60(9):1648–55.  https://doi.org/10.1007/s00125-017-4335-y.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 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.CrossRefGoogle Scholar
  82. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 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.CrossRefPubMedGoogle Scholar
  86. 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.CrossRefPubMedGoogle Scholar
  87. 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.CrossRefPubMedGoogle Scholar
  88. 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.CrossRefPubMedGoogle Scholar
  89. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  91. 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.CrossRefPubMedGoogle Scholar
  92. 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.CrossRefPubMedGoogle Scholar
  93. 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.CrossRefPubMedGoogle Scholar
  94. 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.CrossRefGoogle Scholar
  95. 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.CrossRefGoogle Scholar
  96. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Epidemiology, Richard M. Fairbanks School of Public HealthIndiana UniversityIndianapolisUSA
  2. 2.Department of MedicineIndiana University School of MedicineIndianapolisUSA
  3. 3.Diabetes Translational Research CenterIndiana University School of MedicineIndianapolisUSA

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