Abstract
BACKGROUND: Most studies examining the association between adverse childhood experiences (ACEs) and depressive symptoms using traditional regression analyses fail to capture how specific types of ACEs relate to certain depressive symptoms, thereby impeding deeper insight into their associations. A simulation-based network analysis provides a novel approach to explore these complex interrelationships. OBJECTIVES: This study aims to explore the complex relationship between ACEs and depressive symptoms, and to identify the types of ACEs that most strongly influence depressive symptoms among middle-aged and older. METHODS: We conducted a secondary analysis of data from the China Health and Retirement Longitudinal Study. Depressive symptoms were assessed using 10-item Center for Epidemiologic Studies Depression Scale. 8 ACEs indicators were obtained through self-reported questionnaires. The Ising model was used to estimate the network structure and analyze the bridge indices of each node. The NodeIdentifyR algorithm was applied to simulate the effects of hypothetically decreasing or increasing the activation of individual ACE nodes on the summed scores of the depressive symptom network. An age-stratified analysis (45–60 vs. ≥60 years) was further conducted to test the robustness of the results. RESULTS: The final analysis included 13,771 participants aged ≥ 45 years. The Ising model indicated that household mental illness (HMI) showed the highest bridge centrality. Under hypothetical scenarios of increased node activation, HMI was associated with the largest increase in overall depressive symptom network activation, suggesting a critical risk factor for symptom worsening. In contrast, under hypothetical scenarios of reduced node activation, parental death (PD) showed the greatest reduction in projected depressive symptom sum scores, indicating that PD could have a propelling effect on depressive symptom levels. An age-stratified analysis reached consistent results. DISCUSSION: Our findings indicate that HMI and PD represent critical markers for early detection of depression risk in middle-aged and older adults. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40359-026-04082-8.