Feng Yue, Chen Li, Yuan Qi, Ma Lin, Zhao Wen, Bai Lu, Chen Jing
Department of Gynecological Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Depress Anxiety. 2025 Jul 23;2025:8078557. doi: 10.1155/da/8078557. eCollection 2025.
Depressive symptoms in older adults are associated with adverse psychosocial outcomes. Understanding how depressive symptoms interrelate can enhance intervention strategies. While network analysis has advanced our comprehension of depressive symptom structure, few studies have explored dynamic interactions in older populations. This study examined both cross-sectional and longitudinal networks of depressive symptoms in older adults to identify core symptoms and symptom interactions over time. Participants aged 60 and older with complete two-wave data (baseline: 2018; follow-up: 2020) from the China Health and Retirement Longitudinal Study (CHARLS) were included ( = 6621). Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), administered face-to-face by trained interviewers. Cross-sectional networks were estimated using the Ising model for each time point, and a cross-lagged panel network (CLPN) model was applied to examine longitudinal symptom interactions over time. Network accuracy and stability were assessed through bootstrap procedures. Participants had a mean age of 67.34 years, 52% male, and 93.7% Han ethnicity. "Felt depressed" ( = 1.244 at Wave 1, = 1.251 at Wave 2) demonstrated the highest strength centrality in both cross-sectional networks. Node strength exhibited strong stability (correlation stability [CS]-coefficient = 0.75 for both waves). The presence of edges ( = 0.802; < 0.001) and edge weights ( = 0.921, < 0.001) across two cross-sectional networks showed high reproducibility. In the longitudinal network, "lack of happiness" showed the highest out-expected influence (out-EI; = 1.404), followed by "felt depressed" ( = 0.994). Both in-expected influence (in-EI) and out-EI showed acceptable stability (CS-coefficient = 0.594). Targeting core symptoms, such as "felt depressed" and "lack of happiness" may disrupt depressive symptom networks and reduce overall depression severity, informing precision interventions in older adults. Clinicians could prioritize these symptoms in screening and treatment. Future research should explore whether symptom-targeted interventions can reshape network structures over time.
老年人的抑郁症状与不良的心理社会后果相关。了解抑郁症状之间的相互关系可以增强干预策略。虽然网络分析增进了我们对抑郁症状结构的理解，但很少有研究探讨老年人群中的动态相互作用。本研究考察了老年人抑郁症状的横断面网络和纵向网络，以确定核心症状及症状随时间的相互作用。纳入了来自中国健康与养老追踪调查（CHARLS）的60岁及以上且有完整两波数据（基线：2018年；随访：2020年）的参与者（n = 6621）。抑郁症状采用10项流行病学研究中心抑郁量表（CESD - 10）进行评估，由经过培训的访谈员面对面施测。使用伊辛模型对每个时间点的横断面网络进行估计，并应用交叉滞后面板网络（CLPN）模型来考察症状随时间的纵向相互作用。通过自助程序评估网络的准确性和稳定性。参与者的平均年龄为67.34岁，男性占52%，汉族占93.7%。“感到沮丧”（第1波时β = 1.244，第2波时β = 1.251）在两个横断面网络中均表现出最高的强度中心性。节点强度表现出很强的稳定性（两波的相关稳定性[CS]系数均为0.75）。两个横断面网络之间边的存在（β = 0.802；p < 0.001）和边权重（β = 0.921，p < 0.001）显示出高再现性。在纵向网络中，“缺乏幸福感”表现出最高的外向预期影响（外向EI；β = 1.404），其次是“感到沮丧”（β = 0.994）。内向预期影响（内向EI）和外向EI均表现出可接受的稳定性（CS系数 = 0.594）。针对核心症状，如“感到沮丧”和“缺乏幸福感”，可能会破坏抑郁症状网络并降低总体抑郁严重程度，为老年人的精准干预提供依据。临床医生在筛查和治疗中可以优先考虑这些症状。未来的研究应探索针对症状的干预措施是否能随着时间重塑网络结构。
