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Childhood predictors of young-onset type 2 diabetes
Paul W Franks1, Robert L Hanson, William C Knowler
1Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E. Indian School Rd., Phoenix, AZ 85014, USA.
Insights
Identifying early risk factors for type 2 diabetes in American Indian youth is crucial. A combination of fasting glucose, HDL cholesterol, and BMI effectively predicts diabetes risk, similar to a complex score.
Area of Science:
- Pediatrics
- Endocrinology
- Public Health
Background:
- Young-onset type 2 diabetes poses a significant health challenge.
- Identifying early, modifiable risk factors is essential for effective prevention strategies.
Purpose of the Study:
- To identify early-life modifiable risk factors for young-onset type 2 diabetes.
- To evaluate the predictive power of various metabolic, anthropometric, and vascular traits.
Main Methods:
- Longitudinal data from 1,604 American Indian children (ages 5-19) were analyzed.
- A standardized multivariate score (zMS) was derived from multiple risk factors.
- Cox proportional hazard models were used to assess diabetes prediction.
Main Results:
- The zMS and its components (fasting glucose, 2-h glucose, HDL cholesterol, BMI) predicted type 2 diabetes.
- A parsimonious score of fasting glucose, HDL cholesterol, and BMI showed similar predictive power to the zMS.
- Familial risk factors did not alter the predictive magnitude of these scores.
Conclusions:
- Childhood metabolic syndrome features, particularly obesity, glucose dysregulation, and low HDL cholesterol, are key predictors of type 2 diabetes.
- Targeting these factors in childhood and adolescence is a promising prevention strategy.
- A simplified risk score can effectively identify high-risk individuals.
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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