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Background: Studies have investigated how adults with severe depressive symptoms are more likely to attempt suicide, and these adults often have traumatic experiences and chaotic sleep/wake rhythms. Thus, this study using Latent class analysis aimed to investigate the relationship between childhood trauma class, chronotype, and suicide attempts among emerging adults with severe depressive symptoms. Methods: This study was conducted among emerging adults with severe depressive symptoms covering 63 Universities in Jilin Province, China. A total of 1,225 emerging adults (mean age = 19.6 ± 1.78) constructed the final sample. In addition to measuring socio-demographic characteristics, the Childhood Trauma Questionnaire-Short Form, the Single-Item Chronotyping, and a single item for suicide attempts were used to evaluate childhood trauma, chronotype, and suicide attempts, respectively. Latent class analysis was applied to identify the classes of childhood trauma within emerging adults who had severe depressive symptoms. Hierarchical logistic regression models were run to investigate the effects of socio-demographic characteristics, chronotype, and childhood trauma class on suicide attempts. Results: Three latent classes were identified: the Low-risk for childhood trauma class, the Neglect class, and the High-risk for childhood abuse class. Those who suffered sexual, emotional, and physical abuse at the same time were divided into the High-risk for childhood abuse class, and were significantly more likely to experience suicide attempts than those in the Neglect class (OR = 1.97, 95%CI = 1.34–2.89, p < 0.001) and the Low-risk for childhood trauma class (OR = 2.28, 95% CI = 1.50-3.46, p < 0.001). In terms of chronotype, the results showed that the chaotic type was a risk factor for suicide attempts when compared with the evening type (OR = 0.46, 95%CI = 0.27–0.78, p < 0.01), the moderately active type (OR = 0.53, 95%CI = 0.31–0.89, p < 0.05), and the daytime type (OR = 0.42, 95%CI = 0.21–0.86, p < 0.05). Overall, the significant risk factors for suicide attempts included being female, living in an urban area, having experienced sexual, emotional, and physical abuse simultaneously, and having a chaotic chronotype. Conclusion: Emerging adults suffering sexual, emotional, and physical abuse at the same time and identifying with chaotic chronotype showed a higher risk of attempting suicide. The findings provided a clinical reference to quickly identify those at high risk of suicide attempts among emerging adults with severe depressive symptoms. [ABSTRACT FROM AUTHOR]

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]