Yanan Qiao1, Siyuan Liu1, Yuxia Zhang1, Ying Wu2, Yueping Shen1, Chaofu Ke1. 1. Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China. 2. Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.
Abstract
BACKGROUND: Few studies have investigated the bidirectional association between depression and multimorbidity from a longitudinal perspective. We aimed to explore the bidirectional relationship between depression and multimorbidity in a middle-aged and elderly Chinese population. METHODS: Participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) were included. Depression was measured with a 10-item version of the Center for Epidemiological Studies Depression Scale (CESD-10). In stage I, we assessed the association of baseline depression with follow-up multimorbidity. In stage II, we examined whether multimorbidity increases the risk of depression. Logistic regression models were used to estimate the odds ratios (ORs) and confidence intervals (CIs). The ORs were then converted to risk ratios (RRs) using a proposed formula. RESULTS: A total of 7056 subjects without multimorbidity and 7587 subjects without depression at baseline were included in stage I and stage II. In stage I, the adjusted RRs (95% CIs) of depressed participants developing one disease, two diseases, three diseases, and ≥4 diseases were 1.15 (0.96-1.35), 1.64 (1.36-1.99), 1.84 (1.44-2.35) and 2.42 (1.75-3.34), respectively. In stage II, compared with individuals without any disease, the adjusted RRs (95% CIs) of developing depression for individuals carrying one disease, two diseases, three diseases, and ≥4 diseases were 1.08 (0.96-1.22), 1.39 (1.22-1.57), 1.46 (1.23-1.70) and 1.62 (1.34-1.93), respectively. CONCLUSIONS: Baseline depression increases the risk of future multimorbidity, and multimorbidity also contributes to an increased risk of incident depression in middle-aged and elderly Chinese adults.
BACKGROUND: Few studies have investigated the bidirectional association between depression and multimorbidity from a longitudinal perspective. We aimed to explore the bidirectional relationship between depression and multimorbidity in a middle-aged and elderly Chinese population. METHODS: Participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) were included. Depression was measured with a 10-item version of the Center for Epidemiological Studies Depression Scale (CESD-10). In stage I, we assessed the association of baseline depression with follow-up multimorbidity. In stage II, we examined whether multimorbidity increases the risk of depression. Logistic regression models were used to estimate the odds ratios (ORs) and confidence intervals (CIs). The ORs were then converted to risk ratios (RRs) using a proposed formula. RESULTS: A total of 7056 subjects without multimorbidity and 7587 subjects without depression at baseline were included in stage I and stage II. In stage I, the adjusted RRs (95% CIs) of depressed participants developing one disease, two diseases, three diseases, and ≥4 diseases were 1.15 (0.96-1.35), 1.64 (1.36-1.99), 1.84 (1.44-2.35) and 2.42 (1.75-3.34), respectively. In stage II, compared with individuals without any disease, the adjusted RRs (95% CIs) of developing depression for individuals carrying one disease, two diseases, three diseases, and ≥4 diseases were 1.08 (0.96-1.22), 1.39 (1.22-1.57), 1.46 (1.23-1.70) and 1.62 (1.34-1.93), respectively. CONCLUSIONS: Baseline depression increases the risk of future multimorbidity, and multimorbidity also contributes to an increased risk of incident depression in middle-aged and elderly Chinese adults.
Authors: Ana Isabel González-González; Robin Brünn; Julia Nothacker; Christine Schwarz; Edris Nury; Truc Sophia Dinh; Maria-Sophie Brueckle; Mirjam Dieckelmann; Beate Sigrid Müller; Marjan van den Akker Journal: Int J Environ Res Public Health Date: 2021-12-21 Impact factor: 3.390
Authors: Greta Jianjia Cheng; Abram L Wagner; Brendan Q O'Shea; Carly A Joseph; Jessica M Finlay; Lindsay C Kobayashi Journal: Innov Aging Date: 2022-07-30