AUTHOR=Yu Cong , Cao Jiamin , Chen Wenguang , Hong Ensi 

TITLE=Development and validation of a predictive model for depression risk in Chinese obese adults

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1574386

DOI=10.3389/fpubh.2025.1574386

ISSN=2296-2565

ABSTRACT=ObjectiveTo construct a prediction model for the risk of depression in the obese population, aiming to facilitate the early identification of high-risk individuals and guide personalized preventive interventions.MethodsThis study was based on the data from the China Health and Retirement Longitudinal Study (CHARLS 2015), the Center for Epidemiologic Studies Depression Scale-10 (CES-D10) to assess the depression of obese patients, Lasso regression and multivariable logistic regression were used to select predictors, the construction of a nomogram model, and the use of the random splitting method divided into a training set (n = 974) and a validation set (n = 418) by the 7:3 method, and the model was evaluated by the ROC curves and the AUC, the H-L goodness-of-fit test, the calibration graphs, and the clinical decision-making curve to assess the model.ResultsA total of 1,392 obese patients were finally included, with a prevalence of depression of 32.68%. Age, respiratory function, renal disease, digestive disease, grip strength, rheumatism and arthritis, and sleep duration were selected to construct the predictive nomogram model of depression risk in obese patients, and the AUCs of the training set and validation set were 0.715 (95% CI = 0.681–0.749) and 0.716 (95% CI = 0.665–0.767). This suggests that the model has moderate discriminatory power. Respectively, the H-L test was statistically insignificant (p > 0.05, H-L test; p > 0.05). Goodness of fit, calibration curves showed significant agreement between the model and actual observations, and clinical decision curves indicated good model calibration and net benefit.ConclusionThe model constructed in this study has good efficacy in predicting the occurrence of depression in the obese population and can be used for the early identification of high-risk groups and the adoption of targeted preventive measures to reduce the risk of depression.