Osteoarthritis (OA) is a prevalent musculoskeletal disorder, characterized by alterations in articular cartilage, subchondral bone remodeling, and osteophyte formation (1). Additionally, OA progression is characterized by concomitant synovitis and degenerative changes in periarticular tissues, including meniscal degeneration, tendinopathy, and fibrotic transformation of the infrapatellar fat pad (IFP). Studies have demonstrated progressive IFP volume loss and structural fibrosis in end-stage OA, establishing IFP remodeling as a hallmark pathological feature of advanced disease (2). It is estimated that over 590 million people worldwide are affected by OA, with its prevalence progressively increasing with age (3). Moreover, OA is a direct cause of substantial joint pain, leading to functional limitations and emerging as a primary contributor to disability (4, 5). Its significant individual and societal impacts are particularly pronounced among older adults (6). However, the pathogenesis of OA remains inadequately understood. Recent findings have demonstrated OA was closely associated with sociodemographic factors such as age, sex, and socioeconomic status, (7) as well as case-related factors including genetic susceptibility and synovial inflammation (8). Given its high prevalence and consequential effects, the identification of risk factors for OA is of paramount importance.

Obesity is a metabolic disorder characterized by an imbalance between caloric intake and expenditure, leading to excessive body fat deposition. Although the intricacies of the relationship between OA and obesity have not been fully clarified, prior studies have documented a significant correlation between knee osteoarthritis (KOA) and obesity (9). Excessive weight gain may further exacerbate the stress on the knee joint, leading to further joint degradation (10). Meanwhile, the function of adipose tissue should not be overlooked. During metabolic regulation, adipose tissue converts surplus energy into fatty acids and glycerol and releases adipokines (11, 12), thereby contributing to systemic chronic inflammation. Recent studies have illustrated that obesity promotes the production of adipokines, which subsequently contribute to the development of osteoarthritis (13, 14).

The Body mass index (BMI) remains the predominant metric for evaluating obesity, with a threshold of BMI ≥ 30 denoting obesity. Nevertheless, BMI exhibits certain limitations in accurately reflecting obesity, as it fails to differentiate effectively between muscle and fat mass (15). Individuals with identical BMI values can possess varying distributions of body fat (16). Recently, researchers have explored more precise anthropometric indicators of obesity, including the Body Shape Index, the Body Roundness Index, and Relative Fat Mass (RFM). RFM has emerged as a promising measure that provides a more precise estimation of total body fat percentage in contrast to conventional metrics. It is calculated using a straightforward formula based on height and waist circumference, making it a practical tool for both clinical application and research settings (17). RFM has demonstrated significant associations with various health conditions, including stroke (18), depression (19) and type 2 diabetes (20).

However, limited research has investigated the potential link between RFM and OA. Considering the accuracy of RFM in predicting health outcomes and the established correlation between OA and obesity, we hypothesized that RFM might also be associated with OA. This study utilized information obtained from the National Health and Nutrition Examination Survey (NHANES) database to assess the potential relationship between RFM and OA.

This study primarily aimed to examine the association between RFM and OA in a nationally representative sample. In this cross-sectional study encompassing 28,535 participants, those with OA exhibited markedly elevated RFM levels compared to non-OA individuals, even after adjusting for confounding factors. Subgroup analyses and interaction tests consistently demonstrated a positive correlation between RFM and OA across all examined subgroups. The ROC analysis further indicated that RFM may serve as a more reliable predictor of OA relative to other anthropometric measures, including WWI, BMI, WT, and WTHR, exhibiting superior discriminative capability for OA evaluation. This study highlights the clinical utility of RFM as a predictive biomarker, highlighting that increased levels of RFM are linked to a higher prevalence of OA.

Given the widespread nature of both obesity and OA, which negatively impact individual health and quality of life while also presenting a considerable public health challenge, understanding this relationship is crucial. Notably, obesity is a well-established risk factor for OA, particularly concerning knee osteoarthritis and hip osteoarthritis. Our findings demonstrated that even after adjusting for the most comprehensive covariates in Model 3, there remained a significant positive association between RFM and the risk of OA, regardless of whether RFM was treated as a continuous or categorical variable. Notably, the OR for the highest RFM quartile (Q4) decreases across models—from 4.42 in Model 1, to 3.16 in Model 2, and to 2.51 in Model 3—suggesting that demographic and metabolic factors may partially mediate the observed association. Previous studies using logistic regression analysis have also confirmed that higher levels of RFM were significantly associated with an increased risk of OA (22). A case–control study involving 400 participants demonstrated a significant positive association between obesity and KOA incidence, with obese participants (BMI ≥ 25 kg/m²) exhibiting an elevated disease risk compared to normal-weight (23). Moreover, A population-based cohort study revealed that both overweight and obesity significantly increase the likelihood of developing osteoarthritis in the hands, hips, and knees (24). Notably, KOA exhibited a dose–response relationship with increasing BMI (24). In addition, a cross-sectional survey of Brazil’s obese population revealed a substantially elevated prevalence of self-reported joint pain, as well as hip and knee osteoarthritis (25).

Furthermore, various adiposity metrics beyond BMI including waist circumference (WC) and Weight-Adjusted Waist Index (WWI), demonstrate notable correlations with OA risk. A study conducted among the adult in the United States indicated that higher WWI values were significantly associated with greater OA risk (26). Another study also revealed a significant positive relationship between WWI and OA prevalence, suggesting that higher WWI values were associated with an elevated OA risk (21). A cross-sectional analysis identified a significant relationship between KOA and WC, revealing a particularly pronounced association in males with a WC > 102 cm and females with WC > 88 cm (27). In addition, a comprehensive study in South Korea validated that increased WC was associated with a increased risk of OA (28). RFM appears to represent a more reliable measure for evaluating obesity-OA associations in comparison to conventional metrics. Unlike BMI, RFM provides a more precise evaluation of body fat distribution and has shown a stronger correlation with DXA-measured adiposity indices (17, 29, 30). Moreover, our findings indicate that RFM demonstrates superior predictive performance compared to other obesity indicators, including WHTR, BMI, WT and WAIST. Consistent with our findings, a cross-sectional study involving 81 young Brazilian men also demonstrated that RFM has a stronger association with body fat compared to BMI (30). Similarly, another cross-sectional study showed that RFM demonstrated superior predictive performance for gallstones compared to WHTR, based on area under the ROC curve (AUC) analysis (0.696 VS 0.674) (31). In our study, individuals in Q4 (the highest RFM group) also exhibit a significantly greater risk of developing OA. Given the identified RFM threshold, regular monitoring of RFM may be beneficial from a public health perspective. Early identification of individuals approaching or exceeding this inflection point could facilitate timely interventions, thereby contributing to the prevention of obesity-related conditions such as osteoarthritis and promoting overall health. In addition, patients should be encouraged to engage in regular physical activity, maintain a healthy weight, and strive to keep their RFM within an optimal range, which may help reduce the incidence of OA.

Our study elucidated the complex interplay between obesity and OA. The analysis revealed that each 1-unit increase in RFM was associated with a 5% greater risk of OA. As evident from the RFM formula, waist circumference is the only modifiable variable. Based on the average height for males and females, we estimate that a 1-unit change in RFM corresponds approximately to a 3 cm change in waist circumference for individuals near the risk threshold (waist circumference >102 cm for males and >88 cm for females) (32). For individuals with higher waist circumferences, the same 1-unit change in RFM may correspond to approximately a 4 cm change in waist circumference. These findings suggest that monitoring and managing RFM—particularly through waist circumference control—may serve as an important preventive strategy for OA. Subgroup analyses revealed significant association between RFM and OA, with particularly pronounced variations across age and in participants with versus without diabetes. Age is a well-established risk factor for OA pathogenesis. With advancing age, articular cartilage undergoes progressive dehydration and chondrocyte depletion, contributing to structural degeneration (33). The age-associated chronic low-grade inflammatory state (inflammaging) also exacerbates OA progression through multiple pathways: enhanced antigenic exposure due to cartilage breakdown, accelerated cellular senescence in chondrocytes, and endocrine dysfunction affecting joint homeostasis (34). Moreover, age-related degeneration impacts all articular tissues, with the IFP exhibiting progressive fibrotic transformation and volume reduction (35). Accumulating evidence indicates that metabolic abnormalities induced by diabetes can alter the extracellular matrix components of cartilage cells (36). Moreover, it has been reported that nearly half of patients with DM suffer from various forms of arthritis (37).

Our research incorporates several important strengths. First, the large sample size (N = 28,535) significantly enhances the statistical power and reliability of the findings. Second, comprehensive subgroup analyses and adjustment for potential confounding factors improve the validity of the results. Third, RFM offers greater clinical applicability compared to alternative metrics due to its straightforward measurement process. Nevertheless, some limitations should be acknowledged. The cross-sectional design precludes causal inferences regarding the association between RFM and OA. Although we adjusted for multiple confounding variables, residual confounding from unmeasured factors remains possible. Additionally, while the NHANES provides nationally representative data for the United States, the external validity of findings may be constrained when considering populations with different demographic or clinical characteristics.

RFM was significantly associated with OA in the U. S. population. Compared to other obesity indicators, RFM offers superior practicality due to its ease of measurement, suggesting its potential utility in clinical OA assessment. However, prospective cohort studies are necessary to establish a more definitive causal relationship between RFM and OA.