Osteoarthritis (OA), a common chronic joint disease, is characterized by the progressive deterioration of articular cartilage, the formation of secondary osteophytes, and adaptive changes in subchondral bone (1, 2). Recently, a study by Hunter et al. in 2020 reported that OA affects approximately 3.8% of the global population, equating to approximately 250 million people (3). The incidence and prevalence of OA are estimated to increase significantly in the coming decades, primarily due to an aging population, increasing obesity rates, and a high incidence of traumatic knee injuries (4). OA represents a significant burden on global public health, causing widespread pain, severely limiting daily activities, and in some cases leading to disability, all of which significantly affect socioeconomic status and quality of life (5). Nevertheless, the etiology of OA remains unclear, and its risk factors are complex, including demographic-level factors such as age, sex, and obesity, as well as joint-level factors such as injury and malalignment (6). Therefore, determining modifiable risk factors may contribute to the development of strategies aimed at delaying OA progression and reducing its burden on public health.

Recently, an increasing number of scientific studies have indicated a strong association between obesity and the development of OA. Obesity may affect the development of OA by altering metabolism and inflammation in the body (7, 8). Obesity is a chronic and complex disease characterized by excessive fat deposition, which can lead to health damage. It can be diagnosed by measuring body mass index (BMI) or waist circumference (9, 10). However, the use of BMI and waist circumference to diagnose obesity has significant limitations. BMI does not account for the specifics of an individual’s fat distribution and is subject to significant racial and individual differences, which may lead to misclassification of obesity (11, 12). While the waist circumference index reflects abdominal fat accumulation to some extent, it also fails to reveal the full extent of body fat distribution (13). In contrast, the body roundness index (BRI), as introduced by Thomas et al., is an emerging assessment tool that offers a more comprehensive evaluation of body fat distribution (14). By considering the ratio of body width to body height, the BRI provides a more detailed assessment of an individual’s body shape, including the distribution of adiposity throughout the body, allowing it to more accurately reflect obesity status and its potential health risks (14).

To date, an increasing number of studies have indicated a correlation between the BRI and various health conditions, including cardiovascular disease, depression and diabetes (15–19). However, no studies have investigated the potential association between the BRI and OA. Therefore, the present study was conducted to explore the potential association between the BRI and OA among U.S. adults via data from the National Health and Nutrition Examination Survey (NHANES). In addition, our study elucidates the impact of obesity patterns on the risk of developing OA and facilitates the development of targeted prevention and treatment strategies.

Obesity has been considered the key modifiable risk factor for OA (34). Recently, a considerable number of clinical studies have focused on investigating the relationships between various indicators of obesity (such as BMI, waist circumference, and muscle mass) and the development and progression of OA (34–36). An observational study by Zhang et al. demonstrated that an increase in BMI, a traditional measure of obesity, is positively associated with the risk of OA, with the effect being particularly significant in patients with knee OA (35). In addition, a regional study in the U.S. revealed that an increase in waist circumference, a measure of abdominal obesity, is not only associated with an elevated risk of OA but also may predict a future decline in physical function (36). This highlights the importance of monitoring waist circumference alongside BMI in the clinical management of OA. Moreover, interest in the relationship between the loss of muscle mass, known as sarcopenia, and OA is increasing. Several studies have suggested that sarcopenia may increase the risk of OA by affecting the body’s metabolic and functional status (37–39). These findings demonstrate the close association between obesity and OA. However, these traditional obesity indicators have their limitations. For instance, BMI does not account for individual fat distribution, and waist circumference only reflects abdominal obesity without capturing overall body fat distribution. Given these limitations, our study therefore explores the relationship between BRI and OA to provide a more comprehensive understanding of how visceral fat may influence OA development. This not only reinforces the link between obesity and OA but also provides a new direction for future research, highlighting the importance of body fat distribution in OA risk assessment.

Previously, substantial evidence indicated a significant correlation between the BRI and various diseases (18, 40–43). A cross-sectional study revealed that the BRI was associated with an estimated low glomerular filtration rate in a Chinese population, providing evidence that central obesity may play a role in the progression of chronic kidney disease (40). A retrospective study conducted in Japan demonstrated a positive and non-linear correlation between the BRI and diabetes mellitus incidence, indicating a threshold effect: when BRI exceeds 4.137 in females and 3.146 in males, a significant positive correlation with the incidence of type 2 diabetes mellitus is observed (41, 44). Additionally, a cross-sectional study using NHANES data indicated a strong, nonlinear relationship between BRI and metabolic syndrome (MetS), suggesting that the BRI may effectively identify individuals at risk for MetS (42). Furthermore, meta-analyses suggest a bidirectional association between MetS and OA, implying that OA can predict an increased risk of MetS, while MetS may also promote the development of OA (45). Moreover, BRI is shown to outperform traditional anthropometric indices, such as BMI, body shape index, and body adiposity index, in predicting MetS (46). In addition, BRI has been a positive association with hypertension and cardiovascular disease (47). Consequently, these studies implies that BRI may be associated with the OA risk through its association with MetS, thereby supporting its use as an indicator for assessing OA risk.

On the basis of the above research findings, we hypothesize that there may be an intrinsic association between the BRI and OA. To the best of our knowledge, this is the first cross-sectional study investigating the association between the BRI and OA. By analyzing cross-sectional data from 20,232 participants, we identified a significant and nonlinear positive association between the BRI and OA, providing compelling evidence for understanding the relationship between these two variables. Furthermore, through rigorous multivariate logistic regression analyses, we excluded the interference of other potential confounders and confirmed that a high BRI is an independent and significant risk factor for the development of OA. Subgroup analyses revealed that the associations between the BRI and OA were consistent across various factors, including sex, education level, cigarette use, marital status, moderate recreational activities, PIR, hypertension, cancer, and diabetes. Additionally, racial differences were found to partially affect the association between BRI and OA. Racial differences may influence the onset and manifestation of OA, potentially through a combination of biological and social factors, including genetic susceptibility, environmental factors, and lifestyle (48). Moreover, the significant differences in radiological, symptomatic, and clinical manifestations of hand OA between races highlight the potential role of race-specific biological mechanisms and environmental risk factors in the pathogenesis of osteoarthritis (49). These findings offer new insight into clinical risk prediction, suggesting that early identification of individuals with high BRI and targeted interventions could slow the progression of OA and improve patient quality of life.

The BRI, a recently developed indicator for assessing the degree of obesity in humans, may be associated with the development of OA through a variety of mechanisms. A review by Sampath et al. emphasized the strong correlation between obesity, metabolic syndrome, and OA (50). Specifically, obesity induces a chronic low-grade inflammatory state, which can accelerate cartilage degradation. This process is mediated by elevated levels of different mediators, especially those with proinflammatory qualities, such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), thereby increasing the risk of developing OA. Moreover, individuals with obesity typically exhibit elevated levels of adipokines, which play pivotal roles in regulating energy metabolism, inflammation, and the immune response. Research by Xie and Chen investigated the potential of adipokines as novel therapeutic targets in osteoarthritis (OA) (51). Specific adipokines, such as leptin, adiponectin, and resistin, are expressed at abnormal levels in the joint fluid and serum of patients with OA, indicating that they may play critical roles in the pathological process of OA. For example, leptin and resistin have been shown to exert proinflammatory effects and enhance the expression of proinflammatory cytokines such as IL-1β and TNF-α. This, in turn, contributes to an increased risk of developing OA. In addition, Zhu and colleagues demonstrated that increasing Sirt3 expression in mice reduced OA lesions induced by a high-fat diet (52). These findings suggest that Sirt3 may play a role in protecting cartilage and slowing OA progression. Furthermore, the symptoms associated with decreased Sirt3 expression levels in individuals on high-fat diets could provide a potential mechanism for the observed positive correlation between BRI and OA. These studies suggest a potential mechanism by which the BRI may contribute to the development of OA.

This study has several strengths. First, this is an innovative study revealing the association between the BRI and OA. In the present study, the BRI, an emerging assessment tool, was used instead of traditional obesity measurement indicators such as BMI or waist circumference to evaluate individual body characteristics more comprehensively. Second, the reliability and representativeness of our study were significantly enhanced by the large sample size collected from the NHANES database and the precise covariate correction measures. Additionally, the sensitivity analysis enabled us to effectively minimize the probability of false-positive results. However, this study also has several limitations. Specifically, the cross-sectional study design limits our ability to explore causal relationships directly. Although self-reported OA data is commonly used in large epidemiological studies, it is important to acknowledge its limitations compared to clinically assessed OA. it is known that OA prevalence varies depending on the definition used, with radiographic OA being more frequent than symptomatic OA. However, within individual joint sites, the prevalence estimates for self-reported OA and symptomatic OA tend to be similar, providing a reasonable approximation for large-scale studies like ours (53, 54). Despite this, since the osteoarthritis data is self-reported, recall bias among participants may have affected the accuracy of the included variables. Future studies with large-scale prospective samples are needed to better elucidate the potential causal relationships.

The results of our study demonstrate a significantly positive correlation between BRI and OA. This finding highlights the necessity of early intervention in individuals with elevated BRI to prevent the onset of OA. However, the results of the current cross-sectional study are insufficient to establish a direct causal relationship. Further large-scale prospective studies are needed to validate our results.