Fang Jia, Wu Wenwen, Yang Chen, Li Huiyuan, Cheng Wencan, Zhang Ni, Zhang Baoyi, Zhang Ye, Zhang Meifen
School of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
School of Nursing, Fudan University, Shanghai, China.
BMC Psychiatry. 2025 Jul 15;25(1):700. doi: 10.1186/s12888-025-07144-0.
Depressive symptoms among middle-aged and older adults are a significant public health concern, with varying symptom trajectories over time. Understanding these trajectories and their predictors can inform targeted interventions.
To identify subgroups of depressive symptom trajectories, determine predictors of these subgroups, and explore the core symptoms and their predictive relationships.
This study analyzed 7,166 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study across four waves (2011, 2013, 2015, 2018). Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale. Group-based trajectory modeling (GBTM) identified depressive symptom trajectories. Multivariate logistic regression explored influencing factors, while Cross-lagged panel network models (CLPN) were used to identify core symptoms.
Three distinct trajectory groups were identified: "stable low" (66.4%), "decline followed by an increase" (27.8%), and "continuously rising" (5.8%). Females, those with lower education, poor self-reported health, unmarried status and rural residents were associated with worsening symptoms. CLPN analysis revealed "depressive mood" as the core symptom, with "feeling lonely" and "could not get going" predicting "depressive mood."
This study identifies distinct trajectories of depressive symptoms in older adults and pinpoints "depressive mood" as a core symptom, which is dynamically predicted by loneliness and a lack of behavioral activation. Therefore, an effective public health strategy should involve not only identifying at-risk individuals based on their trajectory profiles but also targeting these specific precursor symptoms to prevent escalation.
中老年人群的抑郁症状是一个重大的公共卫生问题,其症状轨迹会随时间变化。了解这些轨迹及其预测因素可为有针对性的干预措施提供依据。
识别抑郁症状轨迹的亚组,确定这些亚组的预测因素,并探索核心症状及其预测关系。
本研究分析了来自中国健康与养老追踪调查的7166名年龄≥45岁的参与者在四个调查周期(2011年、2013年、2015年、2018年)的数据。使用10项流行病学研究中心抑郁量表评估抑郁症状。基于群组的轨迹模型(GBTM)识别抑郁症状轨迹。多因素逻辑回归探索影响因素,同时使用交叉滞后面板网络模型(CLPN)识别核心症状。
识别出三个不同的轨迹组:“持续低水平”(66.4%)、“先下降后上升”(27.8%)和“持续上升”(5.8%)。女性、受教育程度较低者、自我报告健康状况较差者、未婚者和农村居民的症状更易恶化。CLPN分析显示“抑郁情绪”是核心症状,并发现“感到孤独”和“提不起劲”可预测“抑郁情绪”。
本研究识别出老年人抑郁症状的不同轨迹,并确定“抑郁情绪”为核心症状,且孤独感和行为缺乏激活可动态预测该症状。因此,有效的公共卫生策略不仅应基于轨迹特征识别高危个体,还应针对这些特定前驱症状以预防症状升级。