Association Between Novel Anthropometric Indices and Overactive Bladder: a Population-based Study

Overview

Journal Front Nutr

Date 2025 Feb 6

PMID 39911808

Authors

Heng Liu

Huqiang Dong

Mingchu Jin

Yu Zhou

Haidong Hao

Yutang Yuan

Hongtao Jia

Min He

Affiliations

Soon will be listed here.

Abstract

Background: Abdominal obesity is recognized as a key risk factor for developing OAB. However, traditional measures of obesity, such as the waist-to-height ratio (WHtR), waist circumference, and body mass index (BMI), may not sufficiently capture fat distribution in the body. This study aims to evaluate the relationship between novel anthropometric indices and OAB, providing a more accurate assessment of obesity-related risk factors.

Methods: The National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2018 were utilized, comprising 27,560 participants. To assess the association and discriminative ability of novel anthropometric indices, including the Body Roundness Index (BRI), A Body Shape Index (ABSI), Waist-to-Weight Index (WWI), and Relative Fat Mass (RFM), with OAB, we employed multivariable logistic regression, restricted cubic spline (RCS) analysis, subgroup analysis, and receiver operating characteristic (ROC) curve methods.

Results: Multivariable logistic regression analysis indicated that higher levels of novel anthropometric indices were positively associated with OAB prevalence. One z-score increase in WWI, BRI, RFM, and ABSI was associated with a 16, 31, 57, and 5% higher likelihood of OAB, respectively. RCS analysis revealed a non-linear relationship between RFM and OAB. ROC analysis indicated that WWI (AUC = 0.680) and RFM (AUC = 0.661) provided better diagnostic accuracy than traditional measures such as BMI (AUC = 0.599). Subgroup analyses supported the robustness of these findings.

Conclusion: Novel anthropometric indices were positively associated with OAB prevalence. WWI and RFM demonstrated significantly better diagnostic value for OAB than BMI and WHtR. Future studies should investigate the potential of combining multiple anthropometric indices to improve predictive accuracy and conduct prospective studies to determine causality.

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