University of Groningen External validation of the KORA S4/F4 prediction models for the risk of developing type 2 diabetes in older adults

Recently, prediction models for type 2 diabetes mellitus (T2DM) in older adults (aged C55 year) were developed in the KORA S4/F4 study, Augsburg, Germany. We aimed to externally validate the KORA models in a Dutch population. We used data on both older adults (n = 2,050; aged C55 year) and total non-diabetic population (n = 6,317; aged 28–75 year) for this validation. We assessed performance of base model (model 1: age, sex, BMI, smoking, parental diabetes and hypertension) and two clinical models: model 1 plus fasting glucose (model 2); and model 2 plus uric acid (model 3). For 7-year risk of T2DM, we calculated C-statistic, Hosmer–Lemeshow v-statistic, and integrated discrimination improvement (IDI) as measures of discrimination, calibration and reclassification, respectively. After a median follow-up of 7.7 years, 199 (9.7%) and 374 (5.9%) incident cases of T2DM were ascertained in the older and total population, respectively. In the older adults, C-statistic was 0.66 for model 1. This was improved for model 2 and model 3 (C-statistic = 0.81) with significant IDI. In the total population, these respective C-statistics were 0.77, 0.85 and 0.85. All models showed poor calibration (P \ 0.001). After adjustment for the intercept and slope of each model, we observed good calibration for most models in both older and total populations. We validated the KORA clinical models for prediction of T2DM in an older Dutch population, with discrimination similar to the development cohort. However, the models need to be corrected for intercept and slope to acquire good calibration for application in a different setting.

[1]  Trisha Greenhalgh,et al.  Risk models and scores for type 2 diabetes: systematic review , 2011, BMJ : British Medical Journal.

[2]  R. Gans,et al.  Plasma procalcitonin and risk of type 2 diabetes in the general population , 2011, Diabetologia.

[3]  Simon J. Griffin,et al.  Risk Assessment Tools for Identifying Individuals at Risk of Developing Type 2 Diabetes , 2011, Epidemiologic reviews.

[4]  Yvonne Vergouwe,et al.  External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients. , 2010, American journal of epidemiology.

[5]  R Holle,et al.  Prediction models for incident Type 2 diabetes mellitus
in the older population: KORA S4/F4 cohort study , 2010, Diabetic medicine : a journal of the British Diabetic Association.

[6]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[7]  David M Nathan,et al.  10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. , 2009, Lancet.

[8]  Ronald P. Stolk,et al.  Population ageing research: a family of disciplines , 2009, European Journal of Epidemiology.

[9]  Henry Kahn,et al.  Two Risk-Scoring Systems for Predicting Incident Diabetes Mellitus in U.S. Adults Age 45 to 64 Years , 2009, Annals of Internal Medicine.

[10]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: validating a prognostic model , 2009, BMJ : British Medical Journal.

[11]  P. Ducimetiere,et al.  Cardiometabolic determinants of mortality in a geriatric population: is there a "reverse metabolic syndrome"? , 2009, Diabetes & metabolism.

[12]  M. Boaz,et al.  Body mass index and risk of all‐cause and cardiovascular mortality in hospitalized elderly patients with diabetes mellitus , 2009, Diabetic medicine : a journal of the British Diabetic Association.

[13]  Saskia le Cessie,et al.  Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study , 2009, BMJ : British Medical Journal.

[14]  D. de Zeeuw,et al.  Albuminuria assessed from first-morning-void urine samples versus 24-hour urine collections as a predictor of cardiovascular morbidity and mortality. , 2008, American journal of epidemiology.

[15]  Jean Tichet,et al.  Predicting Diabetes: Clinical, Biological, and Genetic Approaches , 2008, Diabetes Care.

[16]  Y Vergouwe,et al.  Updating methods improved the performance of a clinical prediction model in new patients. , 2008, Journal of clinical epidemiology.

[17]  Ralph B D'Agostino,et al.  Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. , 2007, Archives of internal medicine.

[18]  Heejung Bang,et al.  Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study. , 2005, Diabetes care.

[19]  R. Holle,et al.  Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. , 2005, Archives of internal medicine.

[20]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[21]  Jaakko Tuomilehto,et al.  The diabetes risk score: a practical tool to predict type 2 diabetes risk. , 2003, Diabetes care.

[22]  H. Löwel,et al.  High prevalence of undiagnosed diabetes mellitus in Southern Germany: Target populations for efficient screening. The KORA survey 2000 , 2003, Diabetologia.

[23]  N. Wareham,et al.  Diabetes risk score: towards earlier detection of Type 2 diabetes in general practice , 2000, Diabetes/metabolism research and reviews.