Do Time-Dependent Repeated Measures of Anthropometric and Body Composition Indices Improve the Prediction of Incident Diabetes in the Cohort Study? Findings from a Community-Based Korean Genome and Epidemiology Study - Lee, Hye Ah; Park, Hyesook; Park, Bomi (Diabetes & metabolism journal, 2025)
Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone.Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models.Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men.Conclusion: Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.