Murphy JM, Laird NM, Monson RR, Sobol AM, Leighton AH. A 40-Year perspective on the prevalence of depression: the Stirling County study. Arch Gen Psychiatry. 2000;57(3):209–15. https://doi.org/10.1001/archpsyc.57.3.209.
World Health Organization. Depression and other common mental disorders: global health estimates. Geneva: World Health Organization; 2017.
Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T, Ferrari AJ, Hyman S, Laxminarayan R, Levin C, Lund C, Medina MM, Petersen I, Scott J, Shidhaye R, Vijayakumar L, Thornicroft G, Whiteford H. Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities, 3rd edition. Lancet. 2016;387(10028):1672–85. https://doi.org/10.1016/S0140-6736(15)00390-6.
Google Scholar
Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398(10312):1700–12.
Ferrari AJ, Santomauro DF, Mantilla Herrera AM, Shadid J, Ashbaugh C, Erskine HE, Charlson FJ, Degenhardt L, Scott JG, McGrath JJ, Allebeck P, Benjet C, Breitbrodt NJK, Brugha T, Dai X, Dandona L, Dandona R, Fischer F, Haagsma JA, Haro JM, Kieling C, Knudsen AKS, Kumar GA, Leung J, Majeed A, Mitchell PB, Moitra M, Mokdad AH, Molokhia M, Patten SB, Patton GC, Phillips MR, Soriano JB, Stein DJ, Stein MB, Szoeke CEI, Naghavi M, Hay SI, Murray CJL, Vos T, Whiteford HA. Global, regional, and National burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Psychiatry. 2022;9(2):137–50. https://doi.org/10.1016/S2215-0366(21)00395-3.
Qing W. The social determinants of depressive disorders in China. Lancet Psychiatry. 2021;8(11):939–40.
Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, Furukawa TA,Kessler RC, Kohrt BA, Maj M, McGorry P, Reynolds CF 3rd, Weissman MM, ChibandaD, Dowrick C, Howard LM, Hoven CW, Knapp M, Mayberg HS, Penninx BWJH, XiaoS, Trivedi M, Uher R, Vijayakumar L, Wolpert M. Time for united action on depression: A Lancet-World psychiatric association commission. Lancet. 2022;399(10328):957–1022. https://doi.org/10.1016/S0140-6736(21)02141-3.
Lu J, Xu X, Huang Y, Li T, Ma C, Xu G, Yin H, Xu X, Ma Y, Wang L, Huang Z, Yan Y, Wang B, Xiao S, Zhou L, Li L, Zhang Y, Chen H, Zhang T, Yan J, Ding H, Yu Y, Kou C, Shen Z, Jiang L, Wang Z, Sun X, Xu Y, He Y, Guo W, Jiang L, Li S, Pan W, Wu Y, Li G, Jia F, Shi J, Shen Z, Zhang N. Prevalence of depressive disorders and treatment in china: a cross-sectional epidemiological study. Lancet Psychiatry. 2021;8(11):981–90. https://doi.org/10.1016/S2215-0366(21)00251-0.
Google Scholar
Cui Y, Zhang H, Wang S, Lu J, He J, Liu L, Liu W. Stimulated Parotid saliva is a better method for depression prediction. Biomedicines. 2022;10(9):2220. https://doi.org/10.3390/biomedicines10092220.
Zhou JC, Cao Y, Xu XY, Xian ZP. Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model. World J Psychiatry. 2024;14(3):388–97. https://doi.org/10.5498/wjp.v14.i3.388.
Lee DY, Cho YH, Kim M, Jeong CW, Cha JM, Won GH, Noh JS, Son SJ, Park RW. Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach. Eur Psychiatry. 2023;66(1):e21. https://doi.org/10.1192/j.eurpsy.2023.10.
Shen L, Xu X, Yue S, Yin S. A predictive model for depression in Chinese middle-aged and elderly people with physical disabilities. BMC Psychiatry. 2024;24(1):305.
Baek JW, Chung K. Context deep neural network model for predicting depression risk using multiple regression. IEEE Access. 2020;8:18171–81. https://doi.org/10.1109/ACCESS.2020.2968393.
Kim K, Ryu JI, Lee BJ, Na E, Xiang YT, Kanba S, Kato TA, Chong MY, Lin SK, Avasthi A, Grover S, Kallivayalil RA, Pariwatcharakul P, Chee KY, Tanra AJ, Tan CH, Sim K, Sartorius N, Shinfuku N, Park YC, Park SC. A Machine-Learning-Algorithm-Based prediction model for psychotic symptoms in patients with depressive disorder. J Personalized Med. 2022;12(8):1218. https://doi.org/10.3390/jpm12081218.
Google Scholar
Kim KM, Kim JH, Rhee HS, Youn BY. Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest. Sci Rep. 2023;13(1):11473. https://doi.org/10.1038/s41598-023-38742-1.
Google Scholar
Zhang X, Fan H, Guo C, Li Y, Han X, Xu Y, Wang H, Zhang T. Establishment of a mild cognitive impairment risk model in middle-aged and older adults: a longitudinal study. Neurol Sci. 2024;45(9):4269–78. https://doi.org/10.1007/s10072-024-07536-2.
Google Scholar
Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. https://doi.org/10.1093/ije/dys203.
Google Scholar
Yu S. Uncovering the hidden impacts of inequality on mental health: a global study. Transl Psychiatry. 2018;8:98. https://doi.org/10.1038/s41398-018-0148-0.
Google Scholar
Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119–38. https://doi.org/10.1146/annurev-publhealth-031912-114409.
Google Scholar
Mansori K, Shiravand N, Shadmani FK, Moradi Y, Allahmoradi M, Ranjbaran M, Ahmadi S, Farahani A, Samii K, Valipour M. Association between depression with glycemic control and its complications in type 2 diabetes. Diabetes Metab Syndr. 2019;13(2):1555–60. https://doi.org/10.1016/j.dsx.2019.02.010.
Google Scholar
Yaroslavsky I, Pettit JW, Lewinsohn PM, Seeley JR, Roberts RE. Heterogeneous trajectories of depressive symptoms: adolescent predictors and adult outcomes. J Affect Disord. 2013;148(2–3):391–9. https://doi.org/10.1016/j.jad.2012.06.028.
Google Scholar
Butterworth P, Rodgers B. Mental health problems and marital disruption: is it the combination of husbands and wives’ mental health problems that predicts later divorce? Soc Psychiatry Psychiatr Epidemiol. 2008;43:758–63. https://doi.org/10.1007/s00127-008-0366-5.
Google Scholar
Patel V, Burns JK, Dhingra M, Tarver L, Kohrt BA, Lund C. Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry. 2018;17(1):76–89. https://doi.org/10.1002/wps.20492.
Google Scholar
Na KS, Cho SE, Geem ZW, Kim YK. Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm. Neurosci Lett. 2020;721:134804.
Ytterberg C, Cegrell L, von Koch L, Wiklander M. Depression symptoms 6 years after stroke are associated with higher perceived impact of stroke, limitations in ADL and restricted participation. Sci Rep. 2022;12(1):7816. https://doi.org/10.1038/s41598-022-11097-9.
Google Scholar
Hybels CF, Pieper CF, Blazer DG, Fillenbaum GG, Steffens DC. Trajectories of mobility and IADL function in older patients diagnosed with major depression. Int J Geriatr Psychiatry. 2010;25(1):74–81. https://doi.org/10.1002/gps.2300.
Google Scholar
Zhou L, Ma X, Wang W. Relationship between cognitive performance and depressive symptoms in Chinese older adults: the China health and retirement longitudinal study (CHARLS). J Affect Disord. 2021;281:454–8. https://doi.org/10.1016/j.jad.2020.12.059.
Google Scholar
Yan R, Li L, Duan X, Zhao J. Association of depressive symptoms with health service use and catastrophic health expenditure among Middle-Aged and older Chinese adults: analysis of Population-Based panel data. J Am Med Dir Assoc. 2023;24(5):664–71. https://doi.org/10.1016/j.jamda.2022.11.018.
Google Scholar
Sandberg M, Kristensson J, Midlöv P, Fagerström C, Jakobsson U. Prevalence and predictors of healthcare utilization among older people (60+): focusing on ADL dependency and risk of depression. Arch Gerontol Geriatr. 2012;54(3):e349–63. https://doi.org/10.1016/j.archger.2012.02.006.
Google Scholar
Bigatti SM, Hernandez AM, Cronan TA, Rand KL. Sleep disturbances in fibromyalgia syndrome: relationship to pain and depression. Arthritis Rheum. 2008;59(7):961–7. https://doi.org/10.1002/art.23828.
Google Scholar
Zhao Y, Atun R, Oldenburg B, McPake B, Tang S, Mercer SW, Cowling TE, Sum G, Qin VM, Lee JT. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in china: an analysis of population-based panel data. Lancet Glob Health. 2020;8(6):e840–9. https://doi.org/10.1016/S2214-109X(20)30127-3.
Gold SM, Köhler-Forsberg O, Moss-Morris R, Mehnert A, Miranda JJ, Bullinger M, Steptoe A, Whooley MA, Otte C. Comorbid depression in medical diseases. Nat Rev Dis Primers. 2020;6(1):69. https://doi.org/10.1038/s41572-020-0200-2.
Google Scholar
He Y, Jiang W, Hua Y, Zheng X, Huang C, Liu Q, Liu Y, Guo L. Dynamic associations between vision and hearing impairment and depressive symptoms among older Chinese adults. Arch Gerontol Geriatr. 2024;116:105217. https://doi.org/10.1016/j.archger.2023.105217.
Huang H, Meng F, Qi Y, et al. Association of hypertension and depression with mortality: an exploratory study with interaction and mediation models. BMC Public Health. 2024;24:1068. https://doi.org/10.1186/s12889-024-18548-0.
Google Scholar
Khalfan AF, Campisi SC, Lo RF, McCrindle BW, Korczak DJ. The association between adolescent depression and dyslipidemia. J Affect Disord. 2023;338:239–45. https://doi.org/10.1016/j.jad.2023.06.017.
Google Scholar
Cristea IA, Karyotaki E, Hollon SD, Cuijpers P, Gentili C. Biological markers evaluated in randomized trials of psychological treatments for depression: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2019;101:32–44. https://doi.org/10.1016/j.neubiorev.2019.03.022.
Google Scholar
Sharpe M, Walker J, Holm Hansen C, Martin P, Symeonides S, Gourley C, Wall L, Weller D, Murray G. SMaRT (Symptom management research Trials) Oncology-2 team. Integrated collaborative care for comorbid major depression in patients with cancer (SMaRT Oncology-2): a multicentre randomised controlled effectiveness trial. Lancet. 2014;384(9948):1099–108. https://doi.org/10.1016/S0140-6736(14)61231-9.
Jiang C, Zhu F, Qin TT. Relationships between chronic diseases and depression among Middle-aged and elderly people in china: A prospective study from CHARLS. Curr Med Sci. 2020;40(5):858–70. https://doi.org/10.1007/s11596-020-2270-5.
Google Scholar
Yan R, Hu Y, Yang J, Wang H, Wang Y, Song G. Depressive symptoms trajectories and cardiovascular disease in Chinese middle-aged and older adults: A longitudinal cohort study. J Affect Disord. 2025;380:456–65. https://doi.org/10.1016/j.jad.2025.03.154.
Google Scholar
Pizzi C, Rutjes AW, Costa GM, Fontana F, Mezzetti A, Manzoli L. Meta-analysis of selective serotonin reuptake inhibitors in patients with depression and coronary heart disease. Am J Cardiol. 2011;107(7):972–9. https://doi.org/10.1016/j.amjcard.2010.11.017.
Google Scholar
Gillen R, Tennen H, McKee TE, Gernert-Dott P, Affleck G. Depressive symptoms and history of depression predict rehabilitation efficiency in stroke patients. Arch Phys Med Rehabil. 2001;82(12):1645–9. https://doi.org/10.1053/apmr.2001.26249.
Google Scholar
D’Oro A, Patel DH, Wass S, Dolber T, Nasir K, Dobre M, Rahman M, Al-Kindi S. Depression and incident cardiovascular disease among patients with chronic kidney disease. Int J Cardiol Cardiovasc Risk Prev. 2023;18:200199. https://doi.org/10.1016/j.ijcrp.2023.200199.
Wang R, Wang J, Hu S. Study on the relationship of depression, anxiety, lifestyle and eating habits with the severity of reflux esophagitis. BMC Gastroenterol. 2021;21:1–10. https://doi.org/10.1186/s12876-021-01717-5.
Google Scholar
Chesney E, Goodwin GM, Fazel S. Risks of all-cause and suicide mortality in mental disorders: a meta-review. World Psychiatry. 2014;13(2):153–60. https://doi.org/10.1002/wps.20128.
Google Scholar
Saczynski JS, Beiser A, Seshadri S, Auerbach S, Wolf PA, Au R. Depressive symptoms and risk of dementia: the Framingham heart study. Neurology. 2010;75(1):35–41. https://doi.org/10.1212/WNL.0b013e3181e62138.
Google Scholar
Wang MY, Li J, Peng HY, Liu J, Huang KL, Li L, Yan ZF, Zhao ZH. Patients with different types of arthritis May be at risk for major depression: results from the National health and nutrition examination survey 2007–2018. Ann Palliat Med. 2021;10(5):5280–8. https://doi.org/10.21037/apm-21-279.
Mancuso CA, Rincon M, McCulloch CE, Charlson ME. Self-efficacy, depressive symptoms, and patients’ expectations predict outcomes in asthma. Med Care. 2001;39(12):1326–38. https://doi.org/10.1097/00005650-200112000-00008.
Google Scholar
Pai M, Muhammad T. Subjective social status and functional and mobility impairments among older adults: life satisfaction and depression as mediators and moderators. BMC Geriatr. 2023;23:685. https://doi.org/10.1186/s12877-023-04380-5.
Google Scholar
Li H, Jia J, Yang Z. Mini-Mental state examination in elderly chinese: A Population-Based normative study. J Alzheimers Dis. 2016;53(2):487–96. https://doi.org/10.3233/JAD-160119.
Google Scholar
Rasic D, Hajek T, Alda M, Uher R. Risk of mental illness in offspring of parents with schizophrenia, bipolar disorder, and major depressive disorder: a meta-analysis of family high-risk studies. Schizophr Bull. 2014;40(1):28–38. https://doi.org/10.1093/schbul/sbt114
Chen D, Duan S, Shi J, Li R, Huang X, Wu X, Xu Y, Zhao L. Association of social participation and patterns with depression: analysis of data from the China health and retirement longitudinal study. BMC Psychiatry. 2025;25:335. https://doi.org/10.1186/s12888-025-06692-9.
Google Scholar
Luger TM, Suls J, Vander Weg MW. How robust is the association between smoking and depression in adults? A meta-analysis using linear mixed-effects models. Addict Behav. 2014;39(10):1418–29. https://doi.org/10.1016/j.addbeh.2014.05.011
Farré A, Tirado J, Spataro N, Alías-Ferri M, Torrens M, Fonseca F. Alcohol induced depression: clinical, biological and genetic features. J Clin Med. 2020;9(8):2668. https://doi.org/10.3390/jcm9082668
Wang J, Wang N, Liu P, Liu Y. Social network site addiction, sleep quality, depression and adolescent difficulty describing feelings: a moderated mediation model. BMC Psychol. 2025;13:57. https://doi.org/10.1186/s40359-025-02372-1.
Google Scholar
Meyer JD, Murray TA, Brower CS, Cruz-Maldonado GA, Perez ML, Ellingson LD, Wade NG. Magnitude, timing and duration of mood state and cognitive effects of acute moderate exercise in major depressive disorder. Psychol Sport Exerc. 2022;61:102172. https://doi.org/10.1016/j.psychsport.2022.102172.
Yang D, Yang M, Bai J, Ma Y, Yu C. Association between physical activity intensity and the risk for depression among adults from the National health and nutrition examination survey 2007–2018. Front Aging Neurosci. 2022;14:844414. https://doi.org/10.3389/fnagi.2022.844414.
Rong H, Lai X, Jing R, Wang X, Fang H, Mahmoudi E. Association of sensory impairments with cognitive decline and depression among older adults in China. JAMA Netw Open. 2020;3(9):e2014186. https://doi.org/10.1001/jamanetworkopen.2020.14186.
Lu G, Liu Y, Wang J, Wu H, CNN-BiLSTM-Attention. A multi-label neural classifier for short texts with a small set of labels. Inf Process Manag. 2023;60(3):103320. https://doi.org/10.1016/j.ipm.2023.103320.
Uddin MA, Joolee JB, Lee YK. Depression level prediction using deep Spatiotemporal features and multilayer Bi-LTSM. IEEE Trans Affect Comput. 2022;13(2):864–70. https://doi.org/10.1109/TAFFC.2020.2970418.
Google Scholar
Chopannejad S, Roshanpoor A, Sadoughi F. Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals. Digit Health. 2024;10:1–20. https://doi.org/10.1177/20552076241234624.
Google Scholar
Dai W, Li X, Ji W, He S. Network Intrusion Detection Method Based on CNN-BiLSTM-Attention Model. IEEE Access. 2024;12:53099–111. https://doi.org/10.1109/ACCESS.2024.3384528.
Google Scholar
Hu J, Xu J, Li M, Jiang Z, Mao J, Feng L, Miao K, Li H, Chen J, Bai Z, Li X, Lu G, Li Y. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study. eClinicalMedicine. 2024;68:102409. https://doi.org/10.1016/j.eclinm.2023.102409.
Yin J, Liu H, Liu Z, Wang LE, Chen WV, Zhu D, Amos CI, Fang S, Lee JE, Wei Q. Genetic variants in Fanconi Anemia pathway genes BRCA2 and FANCA predict melanoma survival. J Invest Dermatol. 2015;135(2):542–50. https://doi.org/10.1038/jid.2014.416.
Google Scholar
Štrumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst. 2014;41:647–65. https://doi.org/10.1007/s10115-013-0679-x.
Google Scholar
Bi S, Guo D, Tan H, Chen Y, Li G. Inequalities in Mild Cognitive Impairment Risk Among Chinese Middle-Aged and Older Adults: Insights from an Integrated Learning Model. Risk Manag Healthc Policy. 2025;18:1793–808.
Google Scholar
Lin S, Wu Y, Fang Y. A hybrid machine learning model of depression Estimation in home-based older adults: a 7-year follow-up study. BMC Psychiatry. 2022;22:816. https://doi.org/10.1186/s12888-022-04439-4.
Su D, Zhang X, He K, Chen Y. Use of machine learning approach to predict depression in the elderly in china: A longitudinal study. J Affect Disord. 2021;282:289–98. https://doi.org/10.1016/j.jad.2020.12.160.
Google Scholar
Shao L, Zhu X, Li DL, Wu L, Lu X, Fan Y, Qiao Z, Hou L, Pan CW, Ke C. Quantifying depressive symptoms on incidence of common chronic diseases and Multimorbidity patterns in middle-aged and elderly Chinese adults. J Psychiatr Res. 2024;173:340–6. https://doi.org/10.1016/j.jpsychires.2024.03.032.
Google Scholar
Wang Z, Jia N. Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis. PLoS ONE. 2025;20(3):e0319232. https://doi.org/10.1371/journal.pone.0319232.
Google Scholar
Vu T, Dawadi R, Yamamoto M, Tay JT, Watanabe N, Kuriya Y, Oya A, Tran PNHT, Araki M. Prediction of depressive disorder using machine learning approaches: findings from the NHANES. BMC Med Inf Decis Mak. 2025;25:83. https://doi.org/10.1186/s12911-025-02903-1.
Google Scholar
Zhang Y, Xiong Y, Yu Q, Shen S, Chen L, Lei X. The activity of daily living (ADL) subgroups and health impairment among Chinese elderly: a latent profile analysis. BMC Geriatr. 2021;21(1):30. https://doi.org/10.1186/s12877-020-01986-x.
Google Scholar
Zhao L, Wang J, Deng H, Chen J, Ding D. Depressive symptoms and ADL/IADL disabilities among older adults from low-income families in dalian. Liaoning Clin Interv Aging. 2022;17:733–43. https://doi.org/10.2147/CIA.S354654.
Google Scholar
El-kenawy ESM, Khodadadi N, Mirjalili S, Abdelhamid AA, Eid MM, Ibrahim A. Greylag Goose Optimization: Nature-inspired optimization algorithm. Expert Syst Appl. 2024;238(Part E):122147. https://doi.org/10.1016/j.eswa.2023.122147.
Abdollahzadeh B, Khodadadi N, Barshandeh S, Trojovský P, Soleimanian Gharehchopogh F, El-kenawy EM, Abualigah L, Mirjalili S. Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning. Cluster Comput. 2024;27:5235–83. https://doi.org/10.1007/s10586-023-04221-5.
Google Scholar
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