• Journal Article

Identifying risk for type 2 diabetes in different age cohorts: Does one size fit all?

Citation

Alva, M., Hoerger, T., Zhang, P., & Gregg, E. (2017). Identifying risk for type 2 diabetes in different age cohorts: Does one size fit all? BMJ Open Diabetes Research and Care, 5(1), [e000447]. DOI: 10.1136/bmjdrc-2017-000447

Abstract

Objective To estimate age-specific risk equations for type 2 diabetes onset in young, middle-aged, and older US adults, and to compare the performance of simple equations based on readily available demographic information alone, against enhanced equations that require both demographic and clinical information (fasting plasma glucose, high-density lipoprotein, and triglyceride levels).

Research design and methods We estimated the probability of developing diabetes by age group using data from the Coronary Artery Risk Development in Young Adults (for ages 18–40 years), Atherosclerosis Risk in Communities (for ages 45–64 years), and the Cardiovascular Health Study (for ages 65 years and older). Simple and enhanced equations were estimated using logistic regression models, and performance was compared by age group. Thresholds based on these risk equations were evaluated using split-sample bootstraps and calibrating the constant of one age cohort to others.

Results Simple risk equations had an area under the receiver-operating curve (AUROC) of 0.72, 0.79, 0.75, and 0.69 for age groups 18–30, 28–40, 45–64, and 65 and older, respectively. The corresponding AUROCs for enhanced equations were 0.75, 0.85, 0.85, and 0.81. Risk equations based on younger populations, when applied to older cohorts, underpredict diabetes incidence and risk. Conversely, risk equations based on older populations overpredict the likelihood of diabetes in younger cohorts.

Conclusions In general, risk equations are more successful in middle-aged adults than in young and old populations. The results demonstrate the importance of applying age-specific risk equations to identify target populations for intervention. While the predictive capacity of equations that include biomarkers is better than of those based solely on self-reported variables, biomarkers are more important in older populations than in younger ones.

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