萧条(经济学)
抑郁症状
多项式logistic回归
流行病学研究中心抑郁量表
广义估计方程
纵向研究
晚年抑郁症
人口
医学
逻辑回归
临床心理学
心理健康
公共卫生
人口学
心理学
精神科
环境卫生
内科学
认知
统计
护理部
病理
机器学习
社会学
计算机科学
经济
宏观经济学
数学
作者
Chao Li,Jin Liu,Yumeng Ju,Bangshan Liu,Yan Zhang
标识
DOI:10.1177/00207640231164020
摘要
Depressive symptoms, which are continuously changing, are an essential manifestation of depression and can increase the risk of mental disorders and other diseases. Because the causes and cures for depression have not yet been identified, finding the characteristics, and risk factors of depressive symptom trajectories can help us identify at-risk populations early and reduce the related public disease burden.Herein we aimed to figure out the specific manifestations of depressive symptom trajectories among Chinese adults, explore the risk profiles of trajectory groups with higher depression burdens, and test the longitudinal associations between blood biomarkers with depressive symptoms.Trajectories of participants' depressive symptoms measured by the Center for Epidemiologic Studies Depression scores were modeled with growth mixture models from 2011 to 2018. Multinomial logistic models tested associations of baseline covariates with trajectories. Generalized estimating equations were used to explore the longitudinal associations between blood data and depressive symptoms in two waves from 2011 to 2015.Among the sample of 5,641 individuals aged 40 or over, four heterogeneous depressive symptom trajectories were defined: stable-low, high-decrease, stable-high, and low-increase. At baseline, demographic factors and health statuses such as gender, education, income, and self-reported health status were associated with trajectories. A significant association was found between high-density lipoprotein and depressive symptoms.These findings provide clues for predicting and identifying adults with elevated depression burdens in middle and late life and may facilitate the development of targeted preventive strategies for this population.
科研通智能强力驱动
Strongly Powered by AbleSci AI