Association of relative fat mass with prevalence of erectile dysfunction in US men: an analysis of NHANES 2001-2004.

Abstract

The high prevalence of erectile dysfunction (ED) underscores the critical importance of interventions and preventive measures targeting potential risk factors, among which obesity stands out. Relative fat mass (RFM) emerges as a superior indicator for quantifying body fat compared to traditional metrics like body mass index (BMI) or waist circumference (WC). However, research on the relationship between RFM and ED is extremely limited. A total of 3627 participants from the National Health and Nutrition Examination Survey 2001-2004 were eligible for analysis. The RFM is calculated using the following formula: RFM = 64-(20×height/WC). Weighted multivariable logistic regression models were utilized to assess the correlation between RFM and ED, supplemented by smooth curve fitting to further explore the linear association. When all potential covariates adjusted, continuous RFM demonstrated a positive association with ED prevalence (odds ratio (OR): 1.11, 95% confidence interval (CI): 1.05-1.18, P = 0.002). When RFM was categorized into tertiles (T1-T3), participants in T3 group exhibited a significantly higher likelihood of ED (OR: 2.19, 95% CI: 1.19, 4.05, P = 0.020) compared to those in T1. Subgroup analyses revealed a stronger correlation among participants aged over 60 years, obese individuals, and those with hypertension, while weaker correlations were observed among those with diabetes and cardiovascular disease (CVD). After sensitivity analysis for severe ED, the aforementioned regression analysis results remained statistically significant. The final ROC analysis demonstrated that the predictive ability of RFM was superior to that of BMI and WC, with an AUC (95% CI) of 0.639 (0.619-0.659). Elevated RFM demonstrated a linear correlation with increased incidence of ED and exhibited strong predictive capability for ED, underscoring the importance of obesity intervention for ED. Future studies with larger clinical samples are necessary to confirm our findings and expand the application value of RFM in assessing ED risk.