OBJECTIVE - Optimal prevention of young-onset type 2 diabetes requires identification of the early-life modifiable risk factors. We aimed to do this using longitudinal data in 1,604 5- to 19-year-old initially nondiabetic American Indians. RESEARCH DESIGN AND METHODS - For type 2 diabetes prediction, we derived an optimally weighted, continuously distributed, standardized multivariate score (zMS) comprising commonly measured metabolic, anthropometric, and vascular traits (i.e., fasting and 2-h glucose, A1C, BMI, waist circumference, fasting insulin, HDL cholesterol, triglycerides, and blood pressures) and compared the predictive power for each feature against zMS. RESULTS - In separate Cox proportional hazard models, adjusted for age, sex, and ethnicity, zMS and each of its component risk factors were associated with incident type 2 diabetes. Stepwise proportional hazards models selected fasting glucose, 2-h glucose, HDL cholesterol, and BMI as independent diabetes predictors; individually, these were weaker predictors than zMS (P < 0.01). However, a parsimonious summary score combining only these variables had predictive power similar to that of zMS (P = 0.33). Although intrauterine diabetes exposure or parental history of young-onset diabetes increased a child's absolute risk of developing diabetes, the magnitude of the diabetes-risk relationships for zMS and the parsimonious score were similar irrespective of familial risk factors. CONCLUSIONS - We have determined the relative value of the features of the metabolic syndrome in childhood for the prediction of subsequent type 2 diabetes. Our findings suggest that strategies targeting obesity, dysregulated glucose homeostasis, and low HDL cholesterol during childhood and adolescence may have the most success in preventing diabetes. © 2007 by the American Diabetes Association.
CITATION STYLE
Franks, P. W., Hanson, R. L., Knowler, W. C., Moffett, C., Enos, G., Infante, A. M., … Looker, H. C. (2007). Childhood predictors of young-onset type 2 diabetes. Diabetes, 56(12), 2964–2972. https://doi.org/10.2337/db06-1639
Mendeley helps you to discover research relevant for your work.