Discovery of depression-associated factors among childhood trauma victims from a large sample size: Using machine learning and network analysis.

Experiences of childhood trauma (CT) would lead to serious mental problems, especially depression. Therefore, it becomes crucial to identify influential factors related to depression and explore their associations. The objectives were to 1) identify critical depression-related factors using the extreme gradient boosting (XGBoost) method from a large-scale survey data; 2) explore associations between these factors for targeted interventions and treatments. A large-scale epidemiological study covering 63 universities was conducted in Jilin Province, China. The XGBoost model was trained and tested to classify young adults with CT experiences who had or did not have depression (N = 27,671). The essential factors were selected by SHapley Additive exPlanations (SHAP) value. Multiple logistic regression analyses were conducted for validation. The associations between these depression-related factors were further explored using network analysis. The XGBoost model selected the top 10 features associated with depression with satisfactory performance (AUC = 0.91; sensitivity = 0.88 and specificity = 0.76). These factors significantly differed between depression and non-depression groups (p < 0.001). There are strong positive associations between anxiety and obsessive-compulsive disorder (OCD), anxiety and post-traumatic stress disorder (PTSD), social anxiety disorder (SAD) and appearance anxiety, and negative associations between sleep quality and anxiety, sleep quality and PTSD among CT participants with depression. The cross-sectional design cannot draw causality, and biases in self-report measurements cannot be ignored. XGBoost model and network analysis were useful methods for discovering and understanding depression-related factors in this epidemiological study. Moreover, these essential factors could offer insights into future interventions and treatments for depressed young adults with CT experiences. • The XGBoost model identified the top 10 depression-related factors, including anxiety, sleep quality, PTSD, loneliness, and OCD, among others. • These selected factors exhibited variations between the depression and non-depression groups. • Specifically, anxiety emerged as the leading risk factor for depression. • Furthermore, among CT participants with depression, strong positive associations were observed between anxiety, OCD, PTSD, and SAD. [ABSTRACT FROM AUTHOR]