Who Should We Target for Diabetes Prevention and Diabetes Risk Reduction?


Screening for individual diabetes risk is crucial to identify adult and pediatric high-risk target populations for referral into successful diabetes prevention programs. Determination of impaired glucose tolerance or elevated fasting glucose levels has been the “gold standard” to classify subjects at increased risk for and/or to diagnose type 2 diabetes (T2DM). However, this led to ignoring many individuals prone to develop the disease. Therefore, using a stepped strategy consisting of a preliminary assessment of risk factors, by using risk scores such as the Finnish Diabetes Risk Score (FINDRISC) adapted to the respective population, followed by a single blood test determining blood glucose or hemoglobin A1c, respectively, or an oral glucose tolerance test is a feasible and pragmatic method to more accurately detect individuals at risk for T2DM. Inclusion of further risk factors into the assessment such as physical inactivity, waist circumference, and prenatal factors needs to be thoroughly discussed to establish a valid and reliable stepped approach applicable to real world health care. This article provides an overview of the current literature and is intentionally focused on the identification of high-risk populations (both adult and pediatric) that will help to address the key issues around the prevention of T2DM in health care settings.

This is a preview of subscription content, log in to check access.


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

  1. 1.

    Schwarz PE, Albright AL. Prevention of type 2 diabetes: the strategic approach for implementation. Horm Metab Res. 2011;43(13):907–10.

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Schwarz PEH, et al. Diabetes prevention in practice, Vol. 1. In: Schwarz PEH, editor. Dresden: TUMAINI Institute for Prevention management; 2010. p. 268.

  3. 3.

    Lindstrom J, et al. Prevention of diabetes mellitus in subjects with impaired glucose tolerance in the finnish diabetes prevention study: results from a randomized clinical trial. J Am Soc Nephrol. 2003;14(7 Suppl 2):S108–13.

    PubMed  Article  Google Scholar 

  4. 4.

    Knowler WC, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    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.

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    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.

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Abdul-Ghani MA, et al. Risk of progression to type 2 diabetes based on relationship between postload plasma glucose and fasting plasma glucose. Diabetes Care. 2006;29(7):1613–8.

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med. 2002;136(8):575–81.

    PubMed  Google Scholar 

  9. 9.

    Aekplakorn W, et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care. 2006;29(8):1872–7.

    PubMed  Article  Google Scholar 

  10. 10.

    Wannamethee SG, et al. Metabolic syndrome vs Framingham Risk Score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus. Arch Intern Med. 2005;165(22):2644–50.

    PubMed  Article  Google Scholar 

  11. 11.

    Kanaya AM, et al. Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. Diabetes Care. 2005;28(2):404–8.

    PubMed  Article  Google Scholar 

  12. 12.

    McNeely MJ, et al. Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. Diabetes Care. 2003;26(3):758–63.

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Gray LJ, et al. The Leicester Risk Assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabet Med. 2010;27(8):887–95.

    PubMed  Article  CAS  Google Scholar 

  14. 14.

    Schwarz PE, et al. The Finnish Diabetes Risk Score is associated with insulin resistance and progression towards Type 2 diabetes. J Clin Endocrinol Metab. 2009;94(3):920–6.

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Schulze MB, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes: response to schwarz et Al. Diabetes Care. 2007;30(8):e88.

    Article  Google Scholar 

  16. 16.

    Glumer C, et al. A Danish diabetes risk score for targeted screening: the Inter99 study. Diabetes Care. 2004;27(3):727–33.

    PubMed  Article  Google Scholar 

  17. 17.

    • Lindstrom J, Tuomilehto J. The Diabetes Risk Score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725–31. This paper is the overall reference for the development and evaluation of the FINDRISC questionnaire, which today is the most commonly used risk detection tool.

    PubMed  Article  Google Scholar 

  18. 18.

    • Schwarz PE, et al. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res. 2009;41(2):86–97. This paper includes a comprehensive review about existing tools to predict the risk of T2DM in clinical and primary health care practice.

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    American Diabetes Association. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327–34.

    Article  Google Scholar 

  20. 20.

    National Institute for Health and Clinical Excellence. NICE public health guidance 35: Preventing type 2 diabetes: population and community-level interventions in high-risk groups and the general population, ed. London: National Institute for Health and Clinical Excellence; 2011.

  21. 21.

    Gillies CL, et al. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis. BMJ. 2008;336(7654):1180–5.

    PubMed  Article  Google Scholar 

  22. 22.

    American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33 Suppl 1:S62–9.

    Article  Google Scholar 

  23. 23.

    Ceriello A, Colagiuri S. International Diabetes Federation guideline for management of postmeal glucose: a review of recommendations. Diabet Med. 2008;25(10):1151–6.

    PubMed  Article  CAS  Google Scholar 

  24. 24.

    •• Paulweber B, et al. A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res. 2010;42 Suppl 1:S3–S36. This material consists of an evidence-based guideline for the prevention of T2DM. All available resources graded regarding their evidence were collected by more than 100 experts in the fields and were compiled into this comprehensive work.

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Gillies CL, et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ. 2007;334(7588):299.

    PubMed  Article  Google Scholar 

  26. 26.

    Abdul-Ghani MA, Tripathy D, DeFronzo RA. Contributions of beta-cell dysfunction and insulin resistance to the pathogenesis of impaired glucose tolerance and impaired fasting glucose. Diabetes Care. 2006;29(5):1130–9.

    PubMed  Article  CAS  Google Scholar 

  27. 27.

    Valensi P, et al. Pre-diabetes essential action: a European perspective. Diabetes Metab. 2005;31(6):606–20.

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Schwarz PE, Lindstrom J. From evidence to practice-the IMAGE project-new standards in the prevention of type 2 diabetes. Diabetes Res Clin Pract. 2011;91(2):138–40.

    PubMed  Article  Google Scholar 

  29. 29.

    •• Lindstrom J, et al. Take action to prevent diabetes–the IMAGE toolkit for the prevention of type 2 diabetes in Europe. Horm Metab Res. 2010;42 Suppl 1:S37–55. This paper represents the practice guideline for T2DM prevention and includes a toolkit on how to identify persons at diabetes risk by using different methods, how to plan and perform an adequate intervention to prevent T2DM, and how to manage the quality of the intervention. It is the corresponding material to Paulweber et al.’s [24••] cited evidence-based guideline.

    PubMed  Article  CAS  Google Scholar 

  30. 30.

    Khunti K, et al. A comparison of screening strategies for Type 2 diabetes and impaired glucose tolerance in a UK community setting: a cost per case analysis. Diabetic Med. 2010;27(Suppl1):SD2–SD28.

    Google Scholar 

  31. 31.

    Griffin SJ, et al. Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial. Lancet. 2011;378(9786):156–67.

    PubMed  Article  Google Scholar 

  32. 32.

    Franciosi M, et al. Use of the diabetes risk score for opportunistic screening of undiagnosed diabetes and impaired glucose tolerance: the IGLOO (Impaired Glucose Tolerance and Long-Term Outcomes Observational) study. Diabetes Care. 2005;28(5):1187–94.

    PubMed  Article  Google Scholar 

  33. 33.

    Rathmann W, et al. Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med. 2005;165(4):436–41.

    PubMed  Article  Google Scholar 

  34. 34.

    Glumer C, Borch-Johnsen K, Colagiuri S. Can a screening programme for diabetes be applied to another population? Diabet Med. 2005;22(9):1234–8.

    PubMed  Article  CAS  Google Scholar 

  35. 35.

    Mohan V, et al. A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians—the Chennai Urban Rural Epidemiology Study (CURES-38). Diabetes Obes Metab. 2007;9(3):337–43.

    PubMed  Article  CAS  Google Scholar 

  36. 36.

    Park PJ, et al. The performance of a risk score in predicting undiagnosed hyperglycemia. Diabetes Care. 2002;25(6):984–8.

    PubMed  Article  CAS  Google Scholar 

  37. 37.

    •• Hippisley-Cox J, et al. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ. 2009;338:b880. In this manuscript an alternative for identifying persons at increased diabetes risk is presented. By using computer-stored patient data persons at increased diabetes risk are identified on an routine database search alternative. This paper presents the development and validation of this attractive procedure.

    PubMed  Article  Google Scholar 

  38. 38.

    Taub NA, et al. Automated detection of high risk for impaired glucose regulation and type 2 diabetes mellitus, using primary care electronic data, in a multi-ethnic UK community setting. Diabetologia. 2009;52 Suppl 1:S325–6.

    Google Scholar 

  39. 39.

    • Abdul-Ghani MA, et al. Role of Glycated Hemoglobin in the Prediction of Future Risk of T2DM. J Clin Endocrinol Metab. 2011. In this paper a prospective cohort study was performed on more than 300 individuals to test the predictive value of HbA 1c and 1-hour glucose values for diabetes risk prediction.

  40. 40.

    Abdul-Ghani M, DeFronzo RA. Fasting hyperglycemia impairs glucose—but not insulin-mediated suppression of glucagon secretion. J Clin Endocrinol Metab. 2007;92(5):1778–84.

    PubMed  Article  CAS  Google Scholar 

  41. 41.

    Abdul-Ghani MA, et al. One-hour plasma glucose concentration and the metabolic syndrome identify subjects at high risk for future type 2 diabetes. Diabetes Care. 2008;31(8):1650–5.

    PubMed  Article  CAS  Google Scholar 

  42. 42.

    Abdul-Ghani MA, DeFronzo RA. Plasma glucose concentration and prediction of future risk of type 2 diabetes. Diabetes Care. 2009;32 Suppl 2:S194–8.

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Brodsky J, et al. Elevation of 1-hour plasma glucose during oral glucose tolerance testing is associated with worse pulmonary function in cystic fibrosis. Diabetes Care. 2011;34(2):292–5.

    PubMed  Article  CAS  Google Scholar 

  44. 44.

    Pugh SK, et al. Abnormal 1 hour glucose challenge test followed by a normal 3 hour glucose tolerance test: does it identify adverse pregnancy outcome? J Miss State Med Assoc. 2011;51(1):3–6.

    Google Scholar 

  45. 45.

    Skriver MV, et al. HbA1c as predictor of all-cause mortality in individuals at high risk of diabetes with normal glucose tolerance, identified by screening: a follow-up study of the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION), Denmark. Diabetologia. 2011;53(11):2328–33.

    Article  Google Scholar 

  46. 46.

    Zhang X, et al. A1C level and future risk of diabetes: a systematic review. Diabetes Care. 2011;33(7):1665–73.

    Article  Google Scholar 

  47. 47.

    Lorenzo C, et al. A1C between 5.7 and 6.4% as a marker for identifying pre-diabetes, insulin sensitivity and secretion, and cardiovascular risk factors: the Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Care. 2011;33(9):2104–9.

    Article  Google Scholar 

  48. 48.

    Hawley JA. Exercise as a therapeutic intervention for the prevention and treatment of insulin resistance. Diabetes Metab Res Rev. 2004;20(5):383–93.

    PubMed  Article  CAS  Google Scholar 

  49. 49.

    Bassuk SS, Manson JE. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. J Appl Physiol. 2005;99(3):1193–204.

    PubMed  Article  Google Scholar 

  50. 50.

    Telford RD. Low physical activity and obesity: causes of chronic disease or simply predictors? Med Sci Sports Exerc. 2007;39(8):1233–40.

    PubMed  Article  Google Scholar 

  51. 51.

    Yates T, et al. Effectiveness of a pragmatic education program designed to promote walking activity in individuals with impaired glucose tolerance: a randomized controlled trial. Diabetes Care. 2009;32(8):1404–10.

    PubMed  Article  Google Scholar 

  52. 52.

    Yates T, et al. The Pre-diabetes Risk Education and Physical Activity Recommendation and Encouragement (PREPARE) programme study: are improvements in glucose regulation sustained at 2 years? Diabet Med. 2011.

  53. 53.

    Laaksonen DE, et al. Physical activity in the prevention of type 2 diabetes: the Finnish diabetes prevention study. Diabetes. 2005;54(1):158–65.

    PubMed  Article  CAS  Google Scholar 

  54. 54.

    Sisson SB, Katzmarzyk PT. International prevalence of physical activity in youth and adults. Obes Rev. 2008;9(6):606–14.

    PubMed  Article  CAS  Google Scholar 

  55. 55.

    Carlson SA, et al. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans. Am J Prev Med. 2010;39(4):305–13.

    PubMed  Article  Google Scholar 

  56. 56.

    NHS Information Centre. Health Survey for England: Physical activity and fitness. 2009. www.ic.nhs.uk/statistics-and-data-collections/health-and-lifestyles-related-surveys/health-survey-for-england/health-survey-for-england--2008-physical-activity-and-fitness. 2009.

  57. 57.

    Troiano RP, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8.

    PubMed  Google Scholar 

  58. 58.

    Craig CL, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    PubMed  Article  Google Scholar 

  59. 59.

    Gill JM, et al. Sitting time and waist circumference are associated with glycemia in U.K. South Asians: data from 1,228 adults screened for the PODOSA trial. Diabetes Care. 2011;34(5):1214–8.

    PubMed  Article  Google Scholar 

  60. 60.

    Whitlock G, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373(9669):1083–96.

    PubMed  Article  Google Scholar 

  61. 61.

    Thamer C, et al. High visceral fat mass and high liver fat are associated with resistance to lifestyle intervention. Obesity (Silver Spring). 2007;15(2):531–8.

    Article  Google Scholar 

  62. 62.

    Salopuro TM, et al. Population-level effects of the national diabetes prevention programme (FIN-D2D) on the body weight, the waist circumference, and the prevalence of obesity. BMC Public Health. 2011;11:350.

    PubMed  Article  Google Scholar 

  63. 63.

    Janiszewski PM, Janssen I, Ross R. Does waist circumference predict diabetes and cardiovascular disease beyond commonly evaluated cardiometabolic risk factors? Diabetes Care. 2007;30(12):3105–9.

    PubMed  Article  CAS  Google Scholar 

  64. 64.

    •• Zimmet P, et al. The metabolic syndrome in children and adolescents. Lancet. 2007;369(9579):2059–61. This article provides an accessible diagnostic tool to identify the metabolic syndrome in children and adolescents globally with age- and gender-specific cutoffs and which is consistent with criteria for adults.

    PubMed  Article  Google Scholar 

  65. 65.

    • Ingelsson E, et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humans. Diabetes. 2010;59(5):1266–75. This study focused onto the identifying of specific physiologic effects in diabetes risk development and combined 14 different studies providing comprehensive physiologic information based on oral glucose tolerance testing and also euglycemic-hyperinsulinemic clamps.

    PubMed  Article  CAS  Google Scholar 

  66. 66.

    •• Dupuis J, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16. This landmark study identified a number of new genes for fasting glucose pathophysiology that are potentially relevant for targeting patients for diabetes risk reduction.

    PubMed  Article  CAS  Google Scholar 

  67. 67.

    Loos RJ, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40(6):768–75.

    PubMed  Article  CAS  Google Scholar 

  68. 68.

    Willer CJ, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40(2):161–9.

    PubMed  Article  CAS  Google Scholar 

  69. 69.

    • Speliotes EK, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48. This study provides new information about a number of genetic loci that are involved in diabetes risk modulation, especially anthropometric parameters. As part of this large meta-analysis, more than 200,000 individuals were genotyped and the information was associated with clinical traits.

    PubMed  Article  CAS  Google Scholar 

  70. 70.

    Prokopenko I, et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009;41(1):77–81.

    PubMed  Article  CAS  Google Scholar 

  71. 71.

    Lyssenko V, et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet. 2009;41(1):82–8.

    PubMed  Article  CAS  Google Scholar 

  72. 72.

    Chen WM, et al. Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J Clin Invest. 2008;118(7):2620–8.

    PubMed  CAS  Google Scholar 

  73. 73.

    Saxena R, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42(2):142–8.

    PubMed  Article  CAS  Google Scholar 

  74. 74.

    Zeggini E, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008.

  75. 75.

    Guan W, et al. Meta-analysis of 23 type 2 diabetes linkage studies from the International Type 2 Diabetes Linkage Analysis Consortium. Hum Hered. 2008;66(1):35–49.

    PubMed  Article  Google Scholar 

  76. 76.

    Gaulton KJ, et al. Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes. 2008;57(11):3136–44.

    PubMed  Article  CAS  Google Scholar 

  77. 77.

    Soranzo N, et al. Common variants at 10 genomic loci influence hemoglobin A(C) levels via glycemic and nonglycemic pathways. Diabetes. 2011;59(12):3229–39.

    Article  Google Scholar 

  78. 78.

    Kathiresan S, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009;41(1):56–65.

    PubMed  Article  CAS  Google Scholar 

  79. 79.

    Newton-Cheh C, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009.

  80. 80.

    Ehret GB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103–9.

    PubMed  Article  CAS  Google Scholar 

  81. 81.

    Fox ER, et al. Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study. Hum Mol Genet. 2011;20(11):2273–84.

    PubMed  Article  CAS  Google Scholar 

  82. 82.

    Stancakova A, et al. Association of 18 confirmed susceptibility loci for type 2 diabetes with indices of insulin release, proinsulin conversion, and insulin sensitivity in 5,327 nondiabetic Finnish men. Diabetes. 2009;58(9):2129–36.

    PubMed  Article  CAS  Google Scholar 

  83. 83.

    Yogev Y, Visser GH. Obesity, gestational diabetes and pregnancy outcome. Semin Fetal Neonatal Med. 2009;14(2):77–84.

    PubMed  Article  Google Scholar 

  84. 84.

    Gillman MW, et al. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics. 2003;111(3):e221–6.

    PubMed  Article  Google Scholar 

  85. 85.

    Hales CN, Barker DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia. 1992;35(7):595–601.

    PubMed  Article  CAS  Google Scholar 

  86. 86.

    Kanaka-Gantenbein C, Mastorakos G, Chrousos GP. Endocrine-related causes and consequences of intrauterine growth retardation. Ann N Y Acad Sci. 2003;997:150–7.

    PubMed  Article  Google Scholar 

  87. 87.

    Bush NC, et al. Higher maternal gestational glucose concentration is associated with lower offspring insulin sensitivity and altered beta-cell function. J Clin Endocrinol Metab. 2011;96(5):E803–9.

    PubMed  Article  CAS  Google Scholar 

  88. 88.

    • Orney A. Prenatal origin of obesity and their complications: gestational diabetes, maternal overweight and the paradoxical effects of fetal growth restriction and macrosimia. Reprod Toxicol. 2011;32:205–12. The impact of intrauterine growth disturbances, maternal overweight during pregnancy, or maternal gestational diabetes mellitus on metabolic, endocrine, hypothalamic, and epigenetic long-term effects on the offspring are discussed in this review article. Underlying mechanisms, postnatal consequences, and early identifiable markers for individuals at risk are also highlighted.

    Article  Google Scholar 

  89. 89.

    Coupe B, et al. Nutritional programming affects hypothalamic organization and early response to leptin. Endocrinology. 2010;151(2):702–13.

    PubMed  Article  CAS  Google Scholar 

  90. 90.

    Park JH, et al. Development of type 2 diabetes following intrauterine growth retardation in rats is associated with progressive epigenetic silencing of Pdx1. J Clin Invest. 2008;118(6):2316–24.

    PubMed  CAS  Google Scholar 

  91. 91.

    •• Han JC, Lawlor DA, Kimm SY. Childhood obesity. Lancet. 2010;375(9727):1737–48. This review provides recent data on the epidemiology, determinants, and risk factors for childhood obesity and raises the issue of prevention and nonpharmacologic, pharmacologic, and surgical treatment options. A table summarizes all known determinants and risk factors toward the development of childhood obesity.

    PubMed  Article  Google Scholar 

  92. 92.

    • Bluher S, et al. Age-specific stabilization in obesity prevalence in German children: a cross-sectional study from 1999 to 2008. Int J Pediatr Obes. 2011;6(2–2):e199–206. In this paper, data from 272,826 German children were analyzed for trends in overweight and obesity prevalence among German children 4 to 16 years of age between 1999 and 2008. Overweight/obesity prevalence increased between 1999 and 2003, but has been stabilizing or turning into a downward trend since 2004. This paper confirms the global trend of stabilizing prevalence rates of childhood obesity and adds important information for individual age groups.

    PubMed  Article  Google Scholar 

  93. 93.

    Wilmot EG, et al. Type 2 diabetes in younger adults: the emerging UK epidemic. Postgrad Med J. 2010;86(1022):711–8.

    PubMed  Article  Google Scholar 

  94. 94.

    American Diabetes Association. Type 2 diabetes in children and adolescents. American Diabetes Association. Pediatrics. 2000;105(3 Pt 1):671–80.

    Article  Google Scholar 

  95. 95.

    Hillier TA, Pedula KL. Complications in young adults with early-onset type 2 diabetes: losing the relative protection of youth. Diabetes Care. 2003;26(11):2999–3005.

    PubMed  Article  Google Scholar 

  96. 96.

    Baker JL, Olsen LW, Sorensen TI. Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med. 2007;357(23):2329–37.

    PubMed  Article  CAS  Google Scholar 

  97. 97.

    Wilson AJ, et al. Lifestyle modification and metformin as long-term treatment options for obese adolescents: study protocol. BMC Public Health. 2009;9:434.

    PubMed  Article  Google Scholar 

Download references


This work was supported by the European Commission AGREEMENT NUMBER–2006309, the Federal Ministry of Education and Research (BMBF), Germany (IFB AdiposityDiseases, FKZ: 01EO1001, to SB, JM, SH), by the Roland-Ernst-Stiftung für Gesundheitsforschung Dresden, Germany (SB), and by the Saxonian State Ministry of Social Affairs Dresden, Germany (to SB). We also would like to thank the Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig (Head: Prof. W. Kiess).


Conflicts of interest: S. Blüher: none; J. Markert: none; S. Herget: none; T. Yates: none; M. Davis: none; G. Müller: none; T. Waldow: none; P.E.H. Schwarz: none.

Author information



Corresponding author

Correspondence to Peter E. H. Schwarz.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Blüher, S., Markert, J., Herget, S. et al. Who Should We Target for Diabetes Prevention and Diabetes Risk Reduction?. Curr Diab Rep 12, 147–156 (2012). https://doi.org/10.1007/s11892-012-0255-x

Download citation


  • Type 2 diabetes mellitus
  • Risk score
  • Stepped risk strategy
  • Diabetes risk factors
  • Diabetes prevention