|
|
|
| Prediction model for depressive symptoms among overweight and obese adults in Qinghai Province |
| LI Tong, HAN Shukui, REN Yitao, FU Lulu, FAN Pengcheng, CHEN Hongru
|
| Department of Public Health, Qinghai University Medical College, Xining, Qinghai 810008, China |
|
|
|
|
Abstract Objective To construct a prediction model for depressive symptoms among overweight and obese adults based on machine-learning algorithms. Methods Residents aged ≥18 years with body mass index ≥24.0 kg/m2 were recruited from three autonomous counties (districts) in Qinghai Province using the convenience sampling method from July 2024 to August 2025. Data on age, gender and the number of common chronic diseases were collected through questionnaire surveys. The Pittsburgh Sleep Quality Index, International Physical Activity Questionnaire and Patient Health Questionnaire-9 were used to assess sleep disorders, physical activity level and depressive symptoms, respectively. The participants were randomly divided into a training set and a validation set at a ratio of 7∶3. LASSO regression, Boruta algorithm and random forest model were applied to screen predictive factors. Five prediction models including random forest, logistic regression, decision tree, extreme gradient boosting and support vector machine were established. The prediction performance of the models was compared using the area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis. SHAP values were adopted to evaluate the contribution of each variable to the model. Results A total of 1 065 overweight and obese adults were investigated, with a median age of 55.00 (interquartile range, 15.00) years. There were 498 males (46.76%) and 567 females (53.24%). Depressive symptoms were detected in 448 overweight and obese adults, with a detection rate of 42.07%. Annual household income, ethnicity, number of common chronic diseases, sleep disorders and physical activity level were identified as predictive factors for depressive symptoms among overweight and obese adults. Among the five established prediction models, the random forest model showed relatively optimal predictive performance. In the training set, the AUC value, accuracy, sensitivity, specificity, F1-score and Brier score were 0.839, 0.769, 0.682, 0.833, 0.713 and 0.168, respectively; the corresponding values in the validation set were 0.725, 0.646, 0.560, 0.708, 0.570 and 0.208, respectively. The maximum clinical net benefit of 0.277 was obtained at a threshold probability of 25%. Variable importance ranking indicated that sleep disorders contributed the most to the random forest model. Conclusion The random forest model constructed in this study has good discrimination and clinical utility for predicting the risk of depressive symptoms among overweight and obese adults.
|
|
Received: 09 June 2026
Revised: 15 August 2026
Published: 22 September 2026
|
|
|
|
|
|
[1] World Health Organization.Depression[EB/OL].[2026-08-15].https://www.who.int/news-room/fact-sheets/detail/depression.
[2] Wang R Z,He Y,Deng Y T,et al.Body weight in neurological and psychiatric disorders:a large prospective cohort study[J].Nat Ment Heal,2024,2(1):41-51.
[3] Karageorgiou V,Casanova F,O'Loughlin J,et al.Body mass index and inflammation in depression and treatment-resistant depression:a mendelian randomisation study[J/OL].BMC Med,2023,21(1)[2026-08-15].https://doi.org/10.1186/s12916-023-03001-7.
[4] Yu C,Cao J M,Chen W G,et al.Development and validation of a predictive model for depression risk in Chinese obese adults[J/OL].Front Public Health,2025,13[2026-08-15].https://doi.org/10.3389/fpubh.2025.1574386.
[5] 刘书莲,张玉晶,赵培文,等.我国老年人抑郁风险预测模型的构建[J].护理研究,2025,39(4):591-597.
[6] 徐勤,邹圣强,陶秀彬,等.安徽省老年人抑郁症状的危险因素分析及风险预测模型的构建[J].牡丹江医学院学报,2024,45(6):45-50.
[7] U.S.Centers for Disease Control and Prevention.NHIS-adult alcohol use:glossary[EB/OL].[2026-08-15].https://archive.cdc.gov/www_cdc_gov/nchs/nhis/alcohol/alcohol_glossary.htm.
[8] WS/T 428—2013 成人体重判定[S].
[9] World Health Qrganization.Waist circumference and waist-hip ratio:report of a WHO expert consultation[R].Geneva:WHO,2008.
[10] 刘贤臣,唐茂芹,胡蕾,等.匹兹堡睡眠质量指数的信度和效度研究[J].中华精神科杂志,1996,29(2):103-107.
[11] 樊萌语,吕筠,何平平.国际体力活动问卷中体力活动水平的计算方法[J].中华流行病学杂志,2014,35(8):961-964.
[12] Sun Y J,Kong Z F,Song Y Q,et al.The validity and reliability of the PHQ-9 on screening of depression in neurology:a cross sectional study[J/OL].BMC Psychiatry,2022,22(1)[2026-08-15].https://doi.org/10.1186/s12888-021-03661-w.
[13] Ren J J,Zhou L,Li Y Z,et al.The independent and combined associations between the intake of ultra-processed foods,sedentary behavior,and depressive symptoms in young adults[J/OL].Front Nutr,2025,12[2026-08-15].https://doi.org/10.3389/fnut.2025.1675892.
[14] 青海省人民政府.地理和自然状况[EB/OL].[2026-08-15].http://www.qinghai.gov.cn/dmqh/system/2020/10/10/010368324.shtml.
[15] Wu Z Y,Wang F,Wang D W,et al.Depression above the plateau:the relationship between altitude and depression risk[J].Acta Psychologica Sin,2024,56(12):1773-1787.
[16] Cappelli F,Castronuovo G,Grimaldi S,et al.Random forest and feature importance measures for discriminating the most influential environmental factors in predicting cardiovascular and respiratory diseases[J/OL].Int J Environ Res Public Health,2024,21(7)[2026-08-15].https://doi.org/10.3390/ijerph21070867.
[17] 梁茵茵,袁瑞,刘光涛,等.湖州市中学生抑郁症状现况调查[J].预防医学,2025,37(6):622-627,631.
[18] Irwin M R.Insomnia and inflammation conspire to heighten depression risk:implications for treatment and prevention of mood disorders[J].Biol Psychiatry,2025,98(11):819-829.
[19] Huang Z T,Luo Y,Han L,et al.Patterns of cardiometabolic multimorbidity and the risk of depressive symptoms in a longitudinal cohort of middle-aged and older Chinese[J].J Affect Disord,2022,301:1-7.
[20] Jespersen A,Madden R A,Whalley H C,et al.Socioeconomic status and depression-a systematic review[J/OL].Epidemiol Rev,2025,47[2026-08-15].https://doi.org/10.1093/epirev/mxaf011.
[21] 王尊晖,张丛笑,沈利明,等.西湖区中老年人群抑郁症状调查[J].预防医学,2025,37(3):296-299.
[22] Pearce M,Garcia L,Abbas A,et al.Association between physical activity and risk of depression:a systematic review and meta-analysis[J].JAMA Psychiatry,2022,79(6):550-559.
[23] 中华医学会,中华医学会杂志社,中华医学会全科医学分会,等.抑郁症基层诊疗指南(实践版·2021)[J].中华全科医师杂志,2021,20(12):1261-1268. |
|
|
|
