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Intergenerational symptom level transmission of comorbid anxiety and major depressive disorder: A dyadic network
Yanqiang Tao1, Sipu Guo2, Xiaoxu Lu3
1Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
Abstract:
Anxiety and major depressive disorder (MDD) frequently co-occur and exhibit familial aggregation, yet their symptom-level intergenerational transmission between parents and their children remains unclear. From a larger community sample (N = 5697), 332 parent-child dyads (with a comorbidity prevalence of 5.8%) met criteria for comorbid anxiety and MDD based on cut-off scores of 9 on the Patient Health Questionnaire-9 and 7 on the Generalized Anxiety Disorder 7-item. Symptom-level data from these dyads were analysed using network analysis combined with computer-simulated intervention modeling to identify bridge symptoms and targets for cross-generational prevention. The network analysis results revealed "feeling afraid" shared as a bridge symptom (Z scores = 1.68 and 1.38, respectively) in both parent and adolescent networks, and overall network structures were significantly similar across generations (M = 1.90, p > .05; S = 6.50, p > .05). In the combined parent-child network, "sleep" and "concentration" were identified as cross-generational bridge symptoms (Z scores = 1.42 and 1.38, respectively). Computer-simulated intervention analyses revealed that alleviating "sad mood" significantly reduced the overall severity of comorbid anxiety and MDD in both groups. Among aggravating symptoms, "suicidal ideation" had the most destabilising effect in parents, whereas "anhedonia" had the strongest impact in adolescents. Notably, "concentration" functioned as a bridge symptom and emerged as the most influential cross-generational intervention target. These findings provide evidence for symptom-level intergenerational transmission of internalising comorbidity, highlighting both shared and age-specific mechanisms. Computer-simulated intervention modelling offers a novel framework for identifying precise targets for early and family-informed interventions.
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