Correlation between obesity-related indices and hyperuricemia among the elderly population in China: A...

Hyperuricemia (HUA), a key risk factor for gout and cardiometabolic diseases, is underdiagnosed in elderly populations due to limited serum uric acid (SUA) testing in routine blood panels. Identifying noninvasive obesity-related indices for HUA could enhance early risk stratification, particularly in resource-constrained settings. This study evaluates the correlation and predictive performance of 6 obesity indices for HUA in Chinese elderly adults. A cross-sectional analysis of 8362 adults aged ≥ 65 years was conducted using 2021 health examination data from Guangdong, China. Multivariable logistic regression evaluated associations between obesity indices - Body Mass Index (BMI), Waist-to-Height Ratio (WHtR), Body Roundness Index (BRI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), and Cardiometabolic Index (CMI) - and HUA. Receiver Operating Characteristic (ROC) curves with DeLong's test compared differences in Area Under the Curve (AUC). HUA prevalence was 48.62%, higher in females (53.07%) than males (43.20%). Adjusted for confounders, all indices showed significant associations: BMI (OR = 1.106, 95% CI = 1.090-1.121), WHtR (OR = 1.056 per 0.01-unit, 95% CI = 1.047-1.064), BRI (OR = 1.204, 95% CI = 1.173-1.236), WWI (OR = 1.185, 95% CI = 1.120-1.253), ABSI (OR = 1.083 per 0.01-unit, 95% CI = 1.002-1.171), and CMI (OR = 1.606, 95% CI = 1.488-1.735). CMI demonstrated the highest AUC (0.645, 95% CI = 0.633-0.656), significantly outperforming BMI, WHtR, BRI, WWI, and ABSI (DeLong's P < .001). WHtR (AUC = 0.621, 95% CI = 0.609-0.633) and BRI (AUC = 0.619, 95% CI = 0.608-0.631) showed comparable performance (P = .386). While CMI had superior predictive accuracy, its reliance on blood lipids limits practicality for routine screening. WHtR and BRI - requiring only waist circumference and height - offer optimal balance between performance and clinical feasibility, especially in primary care settings lacking laboratory infrastructure. These noninvasive tools could complement SUA testing to improve HUA risk assessment in elderly populations.