Abstract
BACKGROUND: The link between body roundness index (BRI) and osteoarthritis (OA) has yet to be validated. Our aim was to explore this connection between BRI and OA risk. METHODS: This cross-sectional study utilized the 1999-2018 National Health and Nutrition Examination Survey retrieved data. To assess the association between BRI and OA risk, we performed weighted multivariable regression analysis (MVRA), with smooth curve fitting for potential nonlinear association and subgroup analysis and interaction tests for relationships in specific subgroups. A 7:3 ratio was adopted for the random division of the acquired data into training and validation sets. Subsequently, least absolute shrinkage and selection operator regression, along with MVRA, were conducted for the training set to isolate variables for a prediction model. This model was visualized using the nomogram and was followed by evaluation. Finally, the validation set was utilized to validate the model. RESULTS: This study enrolled 12,946 individuals. Following the adjustment for all covariables, OA risk increased by 18% with every unit rise in BRI (odd ratio [OR] = 1.18; 95% confidence interval [CI]: 1.13-1.23; P < 0.0001). Upon regarding BRI as a categorical variable, it was divided into quartiles for subsequent analysis. In comparison to quartile 1, the risk of OA was increased in quartile 2 (OR = 1.58; 95% CI: 1.22-2.03; P = 0.0006), quartile 3 (OR = 1.83; 95% CI: 1.40-2.40; P < 0.0001) and quartile 4 (OR = 2.70; 95% CI: 1.99-3.66; P < 0.0001). Smooth curve fitting revealed no non-linear relationships. None of the subgroups showed a statistically significant interaction (all P > 0.05). After selecting the variables, a prediction model was developed. The prediction model exhibited favorable discriminatory power, high accuracy, and potential clinical benefits in training and validation sets. CONCLUSIONS: The BRI was positively associated with OA risk. Our predictive model demonstrated that combining BRI with other easily accessible factors was helpful in assessing and managing high-risk OA groups.