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Childhood Predictors of Young-Onset Type 2 Diabetes

¹ Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
² Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University Hospital, Umeå, Sweden

Address correspondence to Dr. Robert L. Hanson, Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, 1550 E. Indian School Rd., Phoenix, AZ 85014. E-mail: rhanson{at}phx.niddk.nih.gov

Abbreviations: DBP, diastolic blood pressure; IED, intrauterine exposure to diabetes; ODP, offspring of a diabetic parent; OGTT, oral glucose tolerance test; ONDP, offspring of nondiabetic parents; ROC, receiver operating characteristic; ROC AUC, area under the ROC curve; SBP, systolic blood pressure; zMS, standardized multivariate score

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.

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