Identifying risk for type 2 diabetes in different age cohorts: does one size fit all?
Open Access
- 27 October 2017
- journal article
- Published by BMJ in BMJ Open Diabetes Research & Care
- Vol. 5 (1) , e000447
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.Keywords
Funding Information
- Task Order 40 from the Centers for Disease Control and Prevention (CDC) and by RTI International.
This publication has 11 references indexed in Scilit:
- Risk scores based on self-reported or available clinical data to detect undiagnosed Type 2 Diabetes: A systematic reviewDiabetes Research and Clinical Practice, 2012
- A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA studyDiabetologia, 2012
- Risk models and scores for type 2 diabetes: systematic reviewBMJ, 2011
- Two Risk-Scoring Systems for Predicting Incident Diabetes Mellitus in U.S. Adults Age 45 to 64 YearsAnnals of Internal Medicine, 2009
- Diabetes Risk CalculatorDiabetes Care, 2008
- A simple clinical score for type 2 diabetes mellitus screening in the Canary IslandsDiabetes Research and Clinical Practice, 2008
- Identifying Individuals at High Risk for DiabetesDiabetes Care, 2005
- The Diabetes Risk ScoreDiabetes Care, 2003
- Applied Logistic Regression2000
- Cardia: study design, recruitment, and some characteristics of the examined subjectsJournal of Clinical Epidemiology, 1988