Who Should We Target for Diabetes Prevention and Diabetes Risk Reduction?
- 414 Downloads
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.
KeywordsType 2 diabetes mellitus Risk score Stepped risk strategy Diabetes risk factors Diabetes prevention
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.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 2.Schwarz PEH, et al. Diabetes prevention in practice, Vol. 1. In: Schwarz PEH, editor. Dresden: TUMAINI Institute for Prevention management; 2010. p. 268.Google Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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.Google Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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
- 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. PubMedCrossRefGoogle Scholar
- 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.• 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.Google Scholar
- 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.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.CrossRefGoogle Scholar
- 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.Google Scholar
- 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.
- 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. PubMedCrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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.Google Scholar
- 79.Newton-Cheh C, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009.Google Scholar
- 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. CrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar
- 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. PubMedCrossRefGoogle Scholar