Association of Relative Fat Mass and Conicity Index with the Risk of Hyperuricemia in Obese Women with PCOS: A Cross-Sectional Study.

OBJECTIVE

Hyperuricemia (HUA) is a prevalent metabolic disorder closely linked to both obesity and polycystic ovary syndrome (PCOS). Traditional obesity indices, such as body mass index (BMI), may not fully capture the metabolic risks associated with fat distribution. This study aimed to investigate the association between relative fat mass (RFM), the conicity index (C-index), and the risk of HUA in obese women with PCOS to improve clinical metabolic risk stratification.

METHODS

This cross-sectional study included 487 obese women aged 18-45 years with PCOS diagnosed by the revised Rotterdam criteria. Anthropometric indices (RFM, C-index) were calculated and categorized into quartiles. Logistic regression, adjusted for age, diabetes, and hypertension, assessed associations with HUA. Restricted cubic spline (RCS) analyses evaluated nonlinear relationships, and subgroup analyses tested robustness across age and metabolic subgroups.

RESULTS

HUA was significantly more prevalent among obese women with PCOS (71.6%) compared to the rate in the non-PCOS counterparts (50.4%; p < 0.001). Elevated RFM was strongly associated with HUA, with adjusted ORs of 4.94 (95% CI: 1.52-16.11) and 3.41 (95% CI: 1.15-10.12) for the third and fourth quartiles, compared to the first (p < 0.05). Conversely, the C-index demonstrated a weaker association with the manifestation of HUA, with limited increases in risk across quartiles. The RCS analyses revealed a linear relationship between RFM and HUA after adjusting for potential confounders, while the C-index showed no significant dose-response trend. Finally, the subgroup analyses confirmed the stability of these associations across the age, hypertension, and hyperlipidemia subgroups.

CONCLUSION

RFM is significantly associated with HUA in obese women with PCOS and outperforms the C-index as a predictor of metabolic dysfunction. These findings underscore the potential clinical utility of RFM as a practical tool for early identification and metabolic risk stratification in this high-risk population.