Abstract P347: Variability in Predictive Error of Waist Circumference Models: A Stratified Analysis Based on Body Mass Index, Waist Circumference, and Their Ratio

Abstract

Introduction: Waist circumference (WC) independently predicts cardiovascular risks, yet clinical measurements are sporadic. Some models have been developed for WC prediction, with body mass index (BMI) as a key predictor. While these models are generally efficient, variability in error across distinct subpopulations is possible. This study aimed to assess the prediction error (PE) variability of WC models across different strata of BMI, WC, and their ratio, and to evaluate the role of BMI for WC prediction. Hypothesis: 1) PE varies across BMI, WC, and their interrelated strata. 2) BMI is a key feature for WC prediction. Methods: Data from 30,298 individuals from the National Health and Nutrition Examination Survey (2007-2018) were analyzed. After outlier removal and kNN-imputation for missing values, machine learning predictive models for WC were developed. The base model included age, gender, ethnicity, height, weight, and BMI. A non-BMI model was also formulated, incorporating demographics and clinical variables selected via a feature-selection process. Root Mean Square Error (RMSE) across BMI, WC, and ratio of BMI-decile to WC-decile categories were assessed. Results: The base model's RMSE for the entire cohort was 5.70. Stratification by BMI revealed higher error rates for obesity classes II (5.88) and III (12.28), contrasting with lower RMSEs in participants with normal BMI (3.95), overweight (4.63), and obesity class I (5.41). Individuals with high WC had an increased RMSE (6.28) compared to those with low WC (5.04). Those with a BMI-decile/WC-decile below one had elevated error (6.98) compared to those with ratios equal to (4.60) or above one (5.52). Across all categories, the base model outperformed the non-BMI model (Figure 1). Conclusion: WC model PE demonstrated variability across BMI, WC, and their respective ratios, underscoring the predictive significance of BMI for WC. Considering population-specific variations in predictive WC modeling is important within clinical and research sets.