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Screening Strategies for Type 2 Diabetes and Risk Stratification in Minorities

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Ethnic Diversities, Hypertension and Global Cardiovascular Risk

Part of the book series: Updates in Hypertension and Cardiovascular Protection ((UHCP))

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Abstract

Type 2 diabetes mellitus (T2DM) is one of the fast-growing diseases of the modern and postmodern era, affecting people across the diversity of countries and ethnic groups worldwide. Undiagnosed and/or inappropriately managed, T2DM is associated with major complications including cardiovascular disease (CVD) and chronic kidney disease (CKD), which are driving the cost and morbimortality related to diabetes. The onset of T2DM, related complications, and progression can be prevented and delayed through timely diagnosis and implementation of effective interventions. For this to be cost-effective, appropriate strategies are needed to identify those who are more likely to benefit from further testing and interventions. Diabetes risk screening and risk stratification have developed in the last four decades, paralleling the improvement in the understanding of the natural history of the diseases and strategies for modifying it. While the initial focus was on biochemical tests, multivariable absolute risk prediction models which have flourished in the last two decades are gaining popularities in risk stratifications for diabetes and related major complications. While the principle of screening are the same across populations and settings, the performance of risk screening tools can vary across ethnic groups, reflecting differences in natural history of the diseases and other interfering factors. It is therefore important to assess the performance of existing tools and make necessary adaptations prior to their introduction in new populations.

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References

  1. International Diabetes Federation. IDF Diabetes Atlas, 7th ed. Brussels: International Diabetes Federation; 2015.

    Google Scholar 

  2. Walker RJ, et al. Racial differences in spatial patterns for poor glycemic control in the Southeastern United States. Ann Epidemiol. 2018;28(3):153–9.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Selvin E, et al. Identifying trends in undiagnosed diabetes in U.S. adults by using a confirmatory definition: a cross-sectional study. Ann Intern Med. 2017;167(11):769–76.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Jang M, et al. Participation of racial and ethnic minorities in technology-based interventions to self-manage type 2 diabetes: a scoping review. J Transcult Nurs. 2018. https://doi.org/10.1177/1043659617723074.

    Article  PubMed  Google Scholar 

  5. Sheehy A, et al. Minority status and diabetes screening in an ambulatory population. Diabetes Care. 2011;34(6):1289–94.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tuomilehto J, 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.

    Article  CAS  PubMed  Google Scholar 

  7. Meigs JB, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Talmud PJ, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Park KS. The search for genetic risk factors of type 2 diabetes mellitus. Diabetes Metab J. 2011;35(1):12–22.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Asia Pacific Cohort Studies Collaboration. The effects of diabetes on the risks of major cardiovascular diseases and death in the Asia-Pacific region. Diabetes Care. 2003;26(2):360–6.

    Article  Google Scholar 

  11. World Health Organization. Screening for type 2 diabetes: report of a World Health Organization and International Diabetes Federation meeting. 2003.

    Google Scholar 

  12. Pan X-R, 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  PubMed  Google Scholar 

  13. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393.

    Article  PubMed Central  Google Scholar 

  14. Diabetes Prevention Program Research Group. Prevention of type 2 diabetes with troglitazone in the Diabetes Prevention Program. Diabetes. 2005;54(4):1150.

    Article  Google Scholar 

  15. Unwin N, et al. Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention. Diabet Med. 2002;19(9):708–23.

    Article  CAS  PubMed  Google Scholar 

  16. Edelstein SL, et al. Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies. Diabetes. 1997;46(4):701–10.

    Article  CAS  PubMed  Google Scholar 

  17. Ramachandran A, et al. Significance of impaired glucose tolerance in an Asian Indian population: a follow-up study. Diabetes Res Clin Pract. 1986;2(3):173–8.

    Article  CAS  PubMed  Google Scholar 

  18. Bertram MY, Vos T. Quantifying the duration of pre-diabetes. Aust N Z J Public Health. 2010;34(3):311–4.

    Article  PubMed  Google Scholar 

  19. Harris MI, et al. Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care. 1992;15(7):815–9.

    Article  CAS  PubMed  Google Scholar 

  20. Gerstein HC, et al. Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies. Diabetes Res Clin Pract. 2007;78(3):305–12.

    Article  PubMed  Google Scholar 

  21. Ramachandran A, et al. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006;49(2):289–97.

    Article  CAS  PubMed  Google Scholar 

  22. Echouffo-Tcheugui JB, et al. Screening for type 2 diabetes and dysglycemia. Epidemiol Rev. 2011;33(1):63–87.

    Article  PubMed  Google Scholar 

  23. Herron CA. Screening in diabetes mellitus: report of the Atlanta workshop. Diabetes Care. 1979;2(4):357–62.

    Article  CAS  PubMed  Google Scholar 

  24. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. National Diabetes Data Group Diabetes. 1979;28(12):1039–57.

    Google Scholar 

  25. American Diabetes Association. Screening for diabetes. Diabetes Care. 1989;12(8):588–90.

    Article  Google Scholar 

  26. World Health Organisation and International Diabetes Federation, Definition and diagnosis of diabetes and intermediate hyperglycemia: report of a WHO/IDF consultation. Geneva; 2006.

    Google Scholar 

  27. World Health Organisation Expert Consultation, use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: abbreviated report of a WHO Consultation. WHO Press: Geneva; 2011. p. 25.

    Google Scholar 

  28. Kengne AP, et al. Alternative indices of glucose homeostasis as biochemical diagnostic tests for abnormal glucose tolerance in an African setting. Prim Care Diabetes. 2017;11(2):119–31.

    Article  PubMed  Google Scholar 

  29. Cavagnolli G, et al. Effect of ethnicity on HbA1c levels in individuals without diabetes: systematic review and meta-analysis. PLoS One. 2017;12(2):e0171315.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. NCD Risk Factor Collaboration (NCD-RisC). Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331,288 participants. Lancet Diabetes Endocrinol. 2015;3(8):624–37.

    Article  Google Scholar 

  31. Selvin E. Are there clinical implications of racial differences in HbA1c? A difference, to be a difference, must make a difference. Diabetes Care. 2016;39(8):1462–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Herman WH. Are there clinical implications of racial differences in HbA1c? Yes, to not consider can do great harm! Diabetes Care. 2016;39(8):1458–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Herman WH, Cohen RM. Racial and ethnic differences in the relationship between HbA1c and blood glucose: implications for the diagnosis of diabetes. J Clin Endocrinol Metab. 2012;97(4):1067–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sacks DB. Hemoglobin A1c and race: should therapeutic targets and diagnostic cutoffs differ among racial groups? Clin Chem. 2016;62(9):1199–201.

    Article  CAS  PubMed  Google Scholar 

  35. Engelgau MM, Narayan KM, Herman WH. Screening for type 2 diabetes. Diabetes Care. 2000;23(10):1563–80.

    Article  CAS  PubMed  Google Scholar 

  36. Ziemer DC, et al. Random plasma glucose in serendipitous screening for glucose intolerance: screening for impaired glucose tolerance study 2. J Gen Intern Med. 2008;23(5):528–35.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Saudek CD, et al. A new look at screening and diagnosing diabetes mellitus. J Clin Endocrinol Metab. 2008;93(7):2447–53.

    Article  CAS  PubMed  Google Scholar 

  38. Borch-Johnsen K, et al. Screening for Type 2 diabetes--should it be now? Diabet Med. 2003;20(3):175–81.

    Article  CAS  PubMed  Google Scholar 

  39. Kim KS, et al. Diagnostic value of glycated haemoglobin HbA(1c) for the early detection of diabetes in high-risk subjects. Diabet Med. 2008;25(8):997–1000.

    Article  CAS  PubMed  Google Scholar 

  40. Cheng C, Kushner H, Falkner BE. The utility of fasting glucose for detection of prediabetes. Metabolism. 2006;55(4):434–8.

    Article  CAS  PubMed  Google Scholar 

  41. Standards of medical care in diabetes--2010. Diabetes Care. 2010;33 Suppl 1:S11–61.

    Google Scholar 

  42. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Geneva: World Health Organization; 2011.

    Google Scholar 

  43. Gomez-Perez FJ, et al. HbA1c for the diagnosis of diabetes mellitus in a developing country. A position article. Arch Med Res. 2010;41(4):302–8.

    Article  PubMed  Google Scholar 

  44. Sacks DB, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem. 2002;48(3):436–72.

    CAS  PubMed  Google Scholar 

  45. Ko GT, et al. The reproducibility and usefulness of the oral glucose tolerance test in screening for diabetes and other cardiovascular risk factors. Ann Clin Biochem. 1998;35(Pt 1):62–7.

    Article  PubMed  Google Scholar 

  46. Priya M, et al. Comparison of capillary whole blood versus venous plasma glucose estimations in screening for diabetes mellitus in epidemiological studies in developing countries. Diabetes Technol Ther. 2011;13(5):586–91.

    Article  PubMed  Google Scholar 

  47. Rush E, Crook N, Simmons D. Point-of-care testing as a tool for screening for diabetes and pre-diabetes. Diabet Med. 2008;25(9):1070–5.

    Article  CAS  PubMed  Google Scholar 

  48. Ritchie GE, et al. Comparison of near-patient capillary glucose measurement and a risk assessment questionnaire in screening for type 2 diabetes in a high-risk population in rural India. Diabetes Care. 2011;34(1):44–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. American Diabetes Association. American diabetes alert. Diabetes Forecast. 1993;46(3):54–5.

    Google Scholar 

  50. Stern MP, et al. Predicting diabetes: moving beyond impaired glucose tolerance. Diabetes. 1993;42(5):706–14.

    Article  CAS  PubMed  Google Scholar 

  51. Herman WH, et al. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes Care. 1995;18(3):382–7.

    Article  CAS  PubMed  Google Scholar 

  52. Lloyd-Jones DM. Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation. 2010;121(15):1768–77.

    Article  PubMed  Google Scholar 

  53. Moons K, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012. https://doi.org/10.1136/heartjnl-2011-301246.

    Article  PubMed  Google Scholar 

  54. Moons K, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012. https://doi.org/10.1136/heartjnl-2011-301247.

    Article  PubMed  Google Scholar 

  55. Buijsse B, et al. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33(1):46–62.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Noble D, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Thoopputra T, et al. Survey of diabetes risk assessment tools: concepts, structure and performance. Diabetes Metab Res Rev. 2012;28(6):485–98.

    Article  PubMed  Google Scholar 

  58. Brown N, et al. Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review. Diabetes Res Clin Pract. 2012;98(3):369–85.

    Article  PubMed  Google Scholar 

  59. Collins GS, et al. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9(1):103.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Glümer C, et al. Risk scores for type 2 diabetes can be applied in some populations but not all. Diabetes Care. 2006;29(2):410–4.

    Article  PubMed  Google Scholar 

  61. de Sousa AGP, et al. Derivation and external validation of a simple prediction model for the diagnosis of type 2 diabetes mellitus in the Brazilian urban population. Eur J Epidemiol. 2009;24(2):101–9.

    Article  Google Scholar 

  62. Hanif M, et al. Detection of impaired glucose tolerance and undiagnosed type 2 diabetes in UK South Asians: an effective screening strategy. Diabetes Obes Metab. 2008;10(9):755–62.

    Article  CAS  PubMed  Google Scholar 

  63. Bindraban NR, et al. Prevalence of diabetes mellitus and the performance of a risk score among Hindustani Surinamese, African Surinamese and ethnic Dutch: a cross-sectional population-based study. BMC Public Health. 2008;8(1):271.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Spijkerman AM, et al. The performance of a risk score as a screening test for undiagnosed hyperglycemia in ethnic minority groups: data from the 1999 health survey for England. Diabetes Care. 2004;27(1):116–22.

    Article  PubMed  Google Scholar 

  65. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2008;31(Supplement 1):S55–60.

    Article  CAS  Google Scholar 

  66. Gao W, et al. A simple Chinese risk score for undiagnosed diabetes. Diabet Med. 2010;27(3):274–81.

    Article  CAS  PubMed  Google Scholar 

  67. Balkau B, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008;31(10):2056–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Masconi KL, et al. Predictive modeling for incident and prevalent diabetes risk evaluation. Exp Rev Endocrinol Metab. 2015;10(3):277–84.

    Article  CAS  Google Scholar 

  69. Dhippayom T, Chaiyakunapruk N, Krass I. How diabetes risk assessment tools are implemented in practice: a systematic review. Diabetes Res Clin Pract. 2014;

    Google Scholar 

  70. Steyerberg EW, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Nucci LB, et al. A nationwide population screening program for diabetes in Brazil. Rev Panam Salud Publica. 2004;16(5):320–7.

    Article  PubMed  Google Scholar 

  72. Sargeant LA, et al. Who attends a UK diabetes screening programme? Findings from the ADDITION-Cambridge study. Diabet Med. 2010;27(9):995–1003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Whiting DR, et al. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94(3):311–21.

    Article  PubMed  Google Scholar 

  74. Haffner SM, et al. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med. 1998;339(4):229–34.

    Article  CAS  PubMed  Google Scholar 

  75. Colhoun HM, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364(9435):685–96.

    Article  CAS  PubMed  Google Scholar 

  76. Collins R, et al. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial. Lancet. 2003;361(9374):2005–16.

    Article  PubMed  CAS  Google Scholar 

  77. Bulugahapitiya U, et al. Is diabetes a coronary risk equivalent? Systematic review and meta-analysis. Diabet Med. 2009;26(2):142–8.

    Article  CAS  PubMed  Google Scholar 

  78. Gaede P, et al. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008;358(6):580–91.

    Article  CAS  PubMed  Google Scholar 

  79. Gaede P, et al. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N Engl J Med. 2003;348(5):383–93.

    Article  PubMed  Google Scholar 

  80. Echouffo-Tcheugui JB, Ogunniyi MO, Kengne AP, Estimation of absolute cardiovascular risk in individuals with diabetes mellitus: rationale and approaches. ISRN Cardiol. 2011. 2011: 242656.

    Article  Google Scholar 

  81. Stevens RJ, et al. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clin Sci (Lond). 2001;101(6):671–9.

    Article  CAS  Google Scholar 

  82. Kothari V, et al. UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke. 2002;33(7):1776–81.

    Article  PubMed  Google Scholar 

  83. Asia Pacific Cohort Studies Collaboration. Systolic blood pressure, diabetes and the risk of cardiovascular diseases in the Asia-Pacific region. J Hypertens. 2007;25(6):1205–13.

    Article  CAS  Google Scholar 

  84. Asia Pacific Cohort Studies Collaboration. Cholesterol, diabetes and major cardiovascular diseases in the Asia-Pacific region. Diabetologia. 2007;50(11):2289–97.

    Article  Google Scholar 

  85. Asia Pacific Cohort Studies Collaboration. Smoking, diabetes and cardiovascular diseases in men in the Asia-Pacific Region. J Diabetes. 2009;1:173–81.

    Article  Google Scholar 

  86. Kengne AP, et al. Association of C-reactive protein with cardiovascular disease mortality according to diabetes status: pooled analyses of 25,979 participants from four U.K. prospective cohort studies. Diabetes Care. 2012;35(2):396–403.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Coutinho M, et al. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care. 1999;22(2):233–40.

    Article  CAS  PubMed  Google Scholar 

  88. Selvin E, et al. Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus. Ann Intern Med. 2004;141(6):421–31.

    Article  CAS  PubMed  Google Scholar 

  89. Miettinen H, et al. Retinopathy predicts coronary heart disease events in NIDDM patients. Diabetes Care. 1996;19(12):1445–8.

    Article  CAS  PubMed  Google Scholar 

  90. van Hecke MV, et al. Diabetic retinopathy is associated with mortality and cardiovascular disease incidence: the EURODIAB prospective complications study. Diabetes Care. 2005;28(6):1383–9.

    Article  PubMed  Google Scholar 

  91. Targher G, et al. Retinopathy predicts future cardiovascular events among type 2 diabetic patients: the Valpolicella Heart Diabetes Study. Diabetes Care. 2006;29(5):1178.

    Article  PubMed  Google Scholar 

  92. Juutilainen A, et al. Retinopathy predicts cardiovascular mortality in type 2 diabetic men and women. Diabetes Care. 2007;30(2):292–9.

    Article  PubMed  Google Scholar 

  93. Chamnan P, et al. Cardiovascular risk assessment scores for people with diabetes: a systematic review. Diabetologia. 2009;52(10):2001–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. van Dieren S, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart. 2012;98(5):360–9.

    Article  PubMed  Google Scholar 

  95. Levey AS, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130(6):461–70.

    Article  CAS  PubMed  Google Scholar 

  96. Levey AS, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007;53(4):766–72.

    Article  CAS  PubMed  Google Scholar 

  97. Levey AS, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med. 2012;9(11):e1001344.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Chien KL, et al. A prediction model for the risk of incident chronic kidney disease. Am J Med. 2010;123(9):836–846 e2.

    Article  PubMed  Google Scholar 

  100. Thakkinstian A, et al. A simplified clinical prediction score of chronic kidney disease: a cross-sectional-survey study. BMC Nephrol. 2011;12(1):45.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Ando M, et al. A simple model for predicting incidence of chronic kidney disease in HIV-infected patients. Clin Exp Nephrol. 2011;15(2):242–7.

    Article  CAS  PubMed  Google Scholar 

  102. Kwon KS, et al. A simple prediction score for kidney disease in the Korean population. Nephrology (Carlton). 2012;17(3):278–84.

    Article  Google Scholar 

  103. Rigatto C, Sood MM, Tangri N. Risk prediction in chronic kidney disease: pitfalls and caveats. Curr Opin Nephrol Hypertens. 2012;21(6):612–8.

    Article  PubMed  Google Scholar 

  104. Lin CC, et al. Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes. Sci Rep. 2017;7(1):10177.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Dunkler D, et al. Risk prediction for early CKD in type 2 diabetes. Clin J Am Soc Nephrol. 2015;10(8):1371–9.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Elley CR, et al. Derivation and validation of a renal risk score for people with type 2 diabetes. Diabetes Care. 2013;36(10):3113–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Wan EYF, et al. Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study. BMC Nephrol. 2017;18(1):257.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Woodward M, et al. Prediction of 10-year vascular risk in patients with diabetes: the AD-ON risk score. Diabetes Obes Metab. 2016;18(3):289–94.

    Article  CAS  PubMed  Google Scholar 

  109. Fraccaro P, et al. An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK. BMC Med. 2016;14:104.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Mogueo A, et al. Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans. BMC Nephrol. 2015;16:94.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Jardine MJ, et al. Prediction of kidney-related outcomes in patients with type 2 diabetes. Am J Kidney Dis. 2012. https://doi.org/10.1053/j.ajkd.2012.04.025.

    Article  CAS  PubMed  Google Scholar 

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Kengne, A.P. (2018). Screening Strategies for Type 2 Diabetes and Risk Stratification in Minorities. In: Modesti, P., Cappuccio, F., Parati, G. (eds) Ethnic Diversities, Hypertension and Global Cardiovascular Risk. Updates in Hypertension and Cardiovascular Protection. Springer, Cham. https://doi.org/10.1007/978-3-319-93148-7_18

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