Association between erectile dysfunction and relative fat mass in adult men in the United States

Introduction

Erectile dysfunction (ED) is a prevalent and complex sexual health disorder among adult men, presenting significant epidemiological and public health implications. Global estimates indicate an annual incidence rate of 26 per 1,000 men, with approximately 322 million men affected worldwide, resulting in considerable detriment to quality of life and mental well-being (1). Epidemiological data reveal a marked increase in ED prevalence with advancing age (2), with incidence rates reaching 46 per 1,000 among men aged 60 to 69 years (3). Additionally, ED has been established as an independent risk factor for cardiovascular disease, correlating significantly with all-cause and cardiovascular mortality in adult men (4,5). Against the backdrop of global population aging, ED not only poses a physiological challenge but also emerges as a major public health concern. Consequently, investigating the etiology and underlying mechanisms of ED is of critical importance (6,7). Obesity and its associated lipid metabolism disorders are increasingly recognized as contributing factors in the pathogenesis and progression of ED. Although body mass index (BMI) remains the conventional metric for assessing obesity, its accuracy in reflecting body fat distribution and related metabolic risks is limited (8,9). Therefore, identifying more precise metrics for assessing body fat is imperative for advancing our understanding of the relationship between obesity and ED.

Relative fat mass (RFM) has recently gained attention as a promising body fat assessment metric. Initially developed by Woolcott et al. based on large-scale population data, RFM employs height and waist circumference (WC)—two easily accessible anthropometric measures—to predict body fat percentage (10). Unlike the traditional BMI, RFM estimates body fat distribution based on height and WC, providing a more accurate reflection of body fat content. It enables a more refined assessment of obesity-related health risks and offers a biologically more relevant measure than BMI or WC, particularly when investigating the relationship between fat distribution and metabolic disorders (7,11,12). A prospective study with a median follow-up of 19.8 years indicated that RFM outperforms BMI in predicting overall mortality and severe liver disease incidence (13). Moreover, a cross-sectional study identified a J-shaped relationship between RFM and depression, with a 3.3% increase in depression risk for each unit increase in RFM (14).

Despite growing evidence supporting RFM’s utility in assessing overall health, its association with ED remains inadequately explored. Additionally, the potential mediating roles of hypertension, cholesterol levels, and inflammatory markers in the relationship between RFM and ED remain unclear. Therefore, this study aims to evaluate the association between RFM and ED based on data from the National Health and Nutrition Examination Survey (NHANES) in the United States, examining potential threshold effects and mediation mechanisms. By adjusting for confounding factors and conducting subgroup analyses, this study sought to provide novel insights into the association between obesity and ED, potentially elucidating new pathways in ED pathophysiology. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-271/rc).

Methods

Study population and data source

All data in this study were obtained from the NHANES database, a long-standing, nationally representative survey conducted by the Centers for Disease Control and Prevention in the United States. Data collection in NHANES follows a rigorous protocol involving standardized data acquisition processes, equipment calibration, and staff training, ensuring high data reliability and stability. Through a complex, multistage sampling design, NHANES achieves representative results across demographic groups, encompassing a broad range of variables including demographic information, physical examinations, laboratory test results, and survey responses. The NHANES protocols were approved by the Research Ethics Review Board of the National Center for Health Statistics, U.S. Centers for Disease Control and Prevention, with all participants providing informed consent prior to inclusion. Further information on NHANES data is available at https://www.cdc.gov/nchs/nhanes/index.htm. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments

Our analysis utilized NHANES data from two consecutive cycles, 2001–2002 and 2003–2004. The initial dataset included 21,161 participants. To meet study requirements, the following exclusion criteria were applied: all female participants (n=10,860), males younger than 20 years of age (n=5,347), individuals lacking the anthropometric data necessary for calculating RFM (n=602), participants without ED data (n=394), and those missing relevant covariate information (n=509). The final analytical sample comprised 3,449 adult male participants for this cross-sectional study (Figure 1).

Figure 1 Flowchart of the participants selection from the NHANES 2001–2004. ED, erectile dysfunction; NHANES, National Health and Nutrition Examination Survey; RFM, relative fat mass.

Definitions of RFM and ED

RFM was calculated based on height, WC, and sex, following a validated formula developed to estimate body fat distribution (15). Measurements for RFM were obtained as follows:

RFM=64−(20×height÷WC)+(12×sex)[1]

Height was recorded in centimeters using a fixed stadiometer with participants standing barefoot on a hard surface, and hair accessories removed to ensure accuracy. WC was measured in centimeters using a non-elastic tape placed above the iliac crest and below the umbilicus, encircling the abdomen. These anthropometric data are stored in the BMX file. For male participants, sex was coded as 0.

The diagnosis of ED was based on self-reported data from questionnaires, a method validated in previous studies (16,17). Male participants aged 20 years or older were asked a standard question assessing erectile function. In response to the question, “How would you describe your ability to achieve and maintain an erection?”, participants who answered “sometimes able” or “never able” were classified as having ED. Individuals with cognitive impairments or language comprehension limitations were excluded from participation.

Adjusted covariates

To account for potential confounding factors influencing the primary outcomes, a set of covariates was included in the multivariate logistic regression analysis. Demographic and socioeconomic data were collected through structured questionnaires, where household income was stratified by poverty index ratios (PIRs) of 1.3 and 3.5 as thresholds (18,19). Alcohol consumption was defined as drinking at least 12 alcoholic beverages in the past year, and smoking status was classified as having smoked at least 100 cigarettes in a lifetime (20). Hypertension, diabetes, and heart disease were determined based on participant self-reports of previous physician diagnoses for each condition (21,22). Additionally, engagement in vigorous physical activity was defined as performing activities that significantly raised heart rate or induced sweating for at least 10 minutes in the past 30 days. Data for total cholesterol and inflammatory markers used in mediation analysis were obtained from laboratory analysis of participants’ blood samples.

Statistical analysis

Descriptive statistics were used to summarize the baseline characteristics of participants, with continuous variables presented as means ± standard deviations and categorical variables as frequencies and percentages. Normality of continuous variables was assessed, and the Mann-Whitney U test was applied to non-normally distributed variables. Categorical variables were compared using the chi-square test, and Fisher’s exact test was utilized when any expected cell count was below 10. Three models were constructed with adjusted covariates, and multivariate logistic regression was applied to evaluate the association between RFM and ED. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Subgroup analyses stratified by age, race, education level, marital status, PIR, and health behaviors (smoking and alcohol consumption) were conducted to explore the association between RFM and ED across different groups, and interaction P values were calculated. The log-likelihood ratio test was used to compare linear and segmented models to identify any inflection points at which RFM significantly affected ED risk, and a smoothing curve fit was applied to assess potential non-linear relationships between RFM and ED. Mediation analysis was conducted to investigate whether hypertension, total cholesterol, white blood cell (WBC) count, and C-reactive protein (CRP) mediated the association between RFM and ED. Indirect, direct, and total effects were estimated using the bootstrap method, with 95% CIs calculated based on 5,000 bootstrap samples. The proportion mediated was expressed as the ratio of the indirect effect to the total effect. Mediation analysis was performed using the mediation package in R software (version 4.4.2), while other statistical analyses were conducted using EmpowerStats (version 4.2). A two-sided P value <0.05 was considered statistically significant.

Results

Population characteristics

Table 1 presents the baseline characteristics of the study population, stratified by ED status. The total sample included 3,449 participants with a mean age of 49.51±18.13 years, of whom 947 were diagnosed with ED, yielding a prevalence rate of 27.46%. Non-Hispanic Whites constituted the largest racial group, accounting for 55.38% of the sample, and 48.25% of participants had completed at least a high school education. Additionally, 83.30% were married or cohabiting with a partner. Compared to non-ED participants, those with ED were significantly older (65.42±15.03 vs. 43.49±15.35 years, P<0.001), and had higher BMI (28.36±5.65 vs. 27.83±5.14 kg/m², P<0.001), WC (103.94±14.53 vs. 98.59±14.01 cm, P<0.001), smoking rates (70.01% vs. 55.56%, P<0.001), hypertension prevalence (52.59% vs. 22.62%, P<0.001), and diabetes prevalence (23.97% vs. 5.04%, P<0.001). ED patients also had higher incidences of cardiovascular conditions, including coronary heart disease, angina, congestive heart failure, heart attack, and stroke (P<0.001). Conversely, ED patients exhibited lower rates of high school or above education (39.18% vs. 51.68%, P<0.001), PIR (29.99% vs. 41.09%, P<0.001), physical activity (17.63% vs. 41.05%, P<0.001), and asthma prevalence (17.63% vs. 41.05%, P<0.001).

Association between RFM and ED

The relationship between RFM and ED was evaluated using multivariate logistic regression analysis (Table 2). In Model 1, unadjusted for covariates, each one-unit increase in RFM was associated with an 11% increase in ED risk (OR =1.110; 95% CI: 1.092, 1.128; P<0.001). After adjusting for age, race, education level, marital status, and PIR in model 2, RFM remained significantly associated with ED (OR =1.046; 95% CI: 1.024, 1.068; P<0.001). This association persisted in model 3, which further adjusted for alcohol consumption, smoking, diabetes, physical activity, asthma, and cardiovascular conditions (OR =1.046; 95% CI: 1.007, 1.087; P=0.02). Additionally, Table S1 showed the associations of BMI and WC with ED. In model 3, multivariate logistic regression analysis indicated that both BMI (OR =1.028) and WC (OR =1.019) were associated with ED, although their adjusted ORs were lower compared to that of RFM (OR =1.046). Furthermore, ROC curve analysis demonstrated that RFM had the highest predictive performance (AUC =0.642), followed by WC (AUC =0.611), while BMI showed the lowest AUC (0.523) (Figure S1).

Subgroup analysis

Subgroup analysis revealed variations in the association between RFM and ED across different population groups. Participants were stratified by age, race, education level, marital status, PIR, alcohol consumption, and smoking status. Adjusting for all confounding factors except the stratifying factor, we assessed whether the association between RFM and ED remained consistent across subgroups. As shown in Figure 2, significant interactions were observed in subgroups defined by age, education level, and PIR. The association between RFM and ED was more pronounced among participants aged <60 years (OR =1.109; P<0.001), those with at least a high school education (OR =1.096; P<0.001), and those with a PIR ≥3.5 (OR =1.126, P<0.001). By contrast, no significant interactions were found within subgroups based on race, marital status, smoking, or alcohol consumption (P for interaction >0.05).

Figure 2 Forest plot of subgroup analysis for the associations between RFM and ED. CI, confidence interval; ED, erectile dysfunction; GED, general educational development; OR, odds ratio; PIR, poverty index ratio; RFM, relative fat mass.

Smooth curve fitting and threshold effect analysis

After adjusting for multiple covariates, the risk of ED increased substantially when RFM exceeded a specific threshold. A U-shaped non-linear relationship between RFM and ED was observed through smooth curve fitting (Figure 3). Threshold effect analysis identified 29.01 as the inflection point; for RFM values >29.01, each one-unit increase in RFM was associated with a 12.7% increase in ED risk (OR =1.127; 95% CI: 1.092–1.128, P<0.001). This relationship was confirmed by log-likelihood ratio testing (P=0.001) (Table 3). Figure 3 illustrates the smooth curve fit for RFM and ED, with the shaded blue area representing the 95% CI and the red line depicting the U-shaped RFM-ED relationship.

Figure 3 Smooth curve fitting for RFM and ED. The shaded blue area representing the 95% CI and the red line depicting the U-shaped RFM-ED relationship. CI, confidence interval; ED, erectile dysfunction; RFM, relative fat mass.

Mediation analysis

Mediation analysis results are presented in Figure 4. Hypertension was found to partially mediate the relationship between RFM and ED, with an indirect effect of 0.005 (95% CI: 0.001–0.008, P=0.002), a direct effect of 0.033 (95% CI: 0.001–0.067, P=0.040), and a total effect of 0.038 (P=0.01), accounting for 12.46% of the association. Similarly, after fully adjusting for age, education level, race, marital status, household economic level, BMI, physical activity, asthma, and cardiovascular conditions, the mediating effect of total cholesterol was 14.63% (P=0.03). These findings confirm the mediating roles of hypertension and total cholesterol in the RFM-ED association. By contrast, the mediation effects of inflammatory markers, including WBC count and CRP, were not statistically significant (P>0.05).

Figure 4 Mediation analysis of the associations between RFM and ED. Adjusted for covariates according to model 3. (A) The mediating role of hypertension. (B) The mediating role of total cholesterol. (C) The mediating role of WBC count. (D) The mediating role of CRP. Model 3: further adjusted for BMI, drinking, smoking, diabetes, vigorous activity, moderate activity, asthma, coronary heart disease, angina, congestive heart failure, heart attack, stroke. BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; ED, erectile dysfunction; RFM, relative fat mass; WBC, white blood cell.

Discussion

In this cross-sectional study encompassing a nationally representative sample of 3,449 participants, a positive association between RFM and ED was observed. This association remained significant even after comprehensive adjustments for multiple covariates. Notably, when RFM exceeded 29.01, the risk of ED increased significantly, with each unit increase in RFM correlating to a 12.7% rise in ED prevalence. Furthermore, hypertension and total cholesterol were identified as mediators in this association, accounting for 12.46% and 14.63% of the effect, respectively.

As a novel indicator of visceral fat, RFM has shown substantial value in epidemiological studies and clinical research concerning obesity-related conditions. A study involving 31,008 adults demonstrated a correlation between RFM and all-cause mortality, proposing cutoff values of 40 for females and 30 for males to diagnose obesity and identify individuals at elevated risk of mortality (23). These findings align with our study, where threshold analysis identified an RFM cutoff of 29.01, beyond which ED prevalence increased significantly among adult males. Similarly, a prospective cohort study utilizing Cox proportional hazards models and weighted multivariable generalized linear models found that RFM >30 sharply increased the risk of all-cause and cardiovascular mortality in males (24). Furthermore, Cichosz et al. evaluated the predictive capacity of four body composition indicators for metabolic diseases, cardiovascular diseases, arthritis, cancer, and hospitalization, concluding that RFM outperformed traditional obesity measures such as BMI and WC in identifying risks for diabetes, hypertension, arthritis, and hospitalization (25). Additionally, research has highlighted a link between RFM and depression, with individuals exhibiting higher RFM being more prone to depression than those with lower RFM or WC (14). Despite these insights, the relationship between RFM and ED remains underexplored.

Riedner et al. identified central obesity as an independent risk factor for ED in elderly men (26). WC, a straightforward metric for assessing central obesity by reflecting visceral fat accumulation, has been widely utilized (27). In the European Male Aging Study, which recruited 3,369 men across eight centers, obesity defined by BMI ≥30 kg/m² or WC ≥102 cm increased the risk of ED by 45% compared to non-obese individuals. Notably, this association persisted in men with WC ≥102 cm but BMI <30 kg/m², indicating that WC alone may effectively predict ED risk (28). However, WC cutoffs may vary across populations, potentially leading to underestimation or overestimation of obesity in certain groups (29). To address WC’s limitations, novel indicators have been developed to reflect visceral fat more accurately in relation to ED risk. Xu et al. integrated body measurements and metabolic markers to create a visceral adiposity index, revealing higher levels in ED patients (30). Similarly, Lin et al. examined the relationship between multiple body shape indicators conicity index, WC-to-height ratio, body roundness index and ED, finding that each unit increase in body shape and conicity index corresponded to a 49% and 42% increase in ED risk, respectively (31). These results underscore the complex interplay of factors contributing to ED, highlighting the need for a comprehensive approach in understanding and managing this condition. Our study introduced RFM as an alternative obesity measure, and revealed its significant association with ED, it showed a stronger association with ED than BMI and WC, with a higher area under the ROC curve. Specifically, each unit increase in RFM was associated with a 4.6% increase in ED risk, with notable variation across subgroups. Racial background, age, and educational level appeared to moderate this association. For instance, in individuals aged ≥60 years, the RFM-ED association weakened, suggesting that multifactorial mechanisms may underlie ED in older adults. Moreover, the effect of RFM on ED was more pronounced among non-Hispanic Whites and those with higher educational attainment, indicating that these groups may be more vulnerable to the adverse effects of increased adiposity. In recent years, the management of ED has undergone substantial changes, including the widespread use of glucagon-like peptide-1 receptor agonists and phosphodiesterase type 5 inhibitors (32,33). These agents play a role in the treatment of functional hypogonadism associated with overweight and obesity, as well as vasculogenic ED caused by atherosclerosis, hypertension, and hyperlipidemia. These developments may have influenced both the prevalence and self-reporting patterns of ED in current populations. Therefore, while our findings provide meaningful insights into the relationship between adiposity and ED, future research using more recent datasets is warranted to validate these associations under contemporary clinical and behavioral contexts. In addition, the average age of participants in this study was 49 years, and the prevalence of ED was 27.46%, which is lower than that reported in epidemiological studies focusing on older populations. Subgroup analysis further revealed that the association between RFM and ED was more pronounced among individuals under 60 years of age (OR =1.109, P<0.001). Therefore, when interpreting the findings of this study, it is important to consider the influence of age distribution on the epidemiological characteristics of ED. Future research should conduct stratified analyses across broader age groups to more comprehensively evaluate the relationship between RFM and ED risk.

The underlying mechanisms by which central obesity may drive ED risk remain unclear. Studies have proposed that abnormal lipid metabolism, linked to vascular dysfunction, may increase the risk of ED (34). In an experiment involving rats fed a high-fat diet, La Favor et al. found that the diet impaired erectile function through endothelial damage, mediated by nitric oxide uncoupling (35). Furthermore, hypertension is commonly co-morbid in dyslipidemic patients and represents a major ED risk factor (36,37). Hypertensive patients chronically release vasoconstrictive factors such as angiotensin II and endothelin-1, activating a complex signal network impacting penile vascular health (38). Our mediation analysis showed that hypertension mediated 12.46% of the RFM-ED association, suggesting that hypertension might exacerbate ED risk by increasing vascular resistance and impairing endothelial function. Additionally, total cholesterol mediated 14.63% of the association, underscoring the importance of managing blood pressure and cholesterol levels in public health strategies targeting men with high RFM to reduce ED risk. In addition to vascular and lipid-related mechanisms, hormonal and glycometabolic pathways may also play a role. Central obesity is closely associated with insulin resistance and reduced testosterone levels, both of which are known to impair ED. Hypogonadism is common among obese men and has been identified as an independent risk factor for ED. Similarly, insulin resistance can lead to endothelial dysfunction and impaired nitric oxide signaling, linking metabolic health to erectile capacity. However, due to the lack of data on testosterone levels and insulin resistance in our dataset, future studies are warranted to further explore these biological pathways. Although we explored inflammatory mechanisms using available markers such as CRP and WBC count, our mediation analyses did not reveal significant effects. This may reflect the limited sensitivity of these markers in capturing subclinical inflammation relevant to ED. Future studies incorporating more specific inflammatory markers (e.g., interleukin-6, tumor necrosis factor-alpha, or oxidative stress biomarkers) are warranted to better understand the role of inflammation in the RFM-ED pathway.

Strengths and limitations

This study’s strengths include the use of a large, nationally representative dataset and adjustments for multiple confounding variables, allowing us to detect a robust association between RFM and ED. However, limitations exist. Firstly, the diagnosis of ED in this study was determined based on self-reported data, which may have been subject to recall or reporting bias, potentially resulting in an underestimation of the true prevalence. Furthermore, detailed clinical information on the severity of ED was not provided in the dataset, making it difficult for distinctions to be made between mild, moderate, and severe cases, thereby limiting the depth of the analysis. Moreover, the data were derived from the 2001–2004 NHANES cycles, which are the only cycles that include ED-related variables. Given the relative age of the dataset, the findings may not fully reflect current epidemiological trends. In addition, the lack of information on testosterone levels, detailed physical activity, partner status and peyronie’s disease or penile structural abnormalities also limited the ability to control for potential confounding factors. Additionally, our mediation analysis included only a limited set of variables, omitting important factors such as prior prostate cancer surgery and depression status, which may have constrained a more comprehensive investigation into the mechanisms linking RFM and ED. Lastly, due to the cross-sectional design of this study, the temporal relationship between RFM and ED cannot be established. As a result, causality cannot be inferred from the observed associations. Future research using prospective or longitudinal designs is warranted to confirm these findings and to better elucidate the potential causal and mechanistic pathways linking RFM with ED.

Conclusions

Higher RFM may increase ED risk, and this association remains significant after controlling for multiple confounding factors. As a valuable complementary indicator, RFM may play an important role in predicting the risk of ED. Incorporating RFM into routine assessments could aid in early risk stratification and lifestyle counseling. However, we emphasize that further validation in prospective and diverse clinical cohorts is necessary before RFM can be recommended for widespread use in ED risk screening.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-271/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-271/prf

Funding: This research was funded by the Bio & Medical Technology Development Program of the National Research Foundation (NRF), which was funded by the Korean government (MIST) (No. RS-2023-00236157), and Soonchunhyang University Research Fund.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-271/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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