Patterns of adverse childhood experiences and depressive symptom trajectories in young adults: A longitudinal study of college students in China

Association between negative life events through mental health and non-suicidal self-injury with young adults: evidence for sex moderate correlation

BACKGROUND

Non-suicidal self-injury (NSSI) has exhibited an increasing trend in recent years and is now globally recognized as a major public health problem among adolescents and young adults. Negative life events (NLEs) are positively associated with NSSI. We sought to explore (1) whether sex plays a role in the risk of NLEs leading to NSSI and (2) the role played by mental health (MH).

METHODS

We adopted a multi-stage cluster sampling method to select college students across four grades from May to June 2022. Generalized linear models were used to evaluate the relationships between NLEs, sex, MH and NSSI, presented as incidence-rate ratios (RRs) with 95% confidence intervals (CIs). We examined the complex relationship between these variables using the PROCESS method for moderation analysis.

RESULTS

Following the exclusion of data that did not meet the study requirements, data from 3,578 students (mean age: 20.53 [± 1.65] years) were included. Poisson regression results indicate that high-level NLEs (RR = 0.110, 95%CI: 0.047-0.173) are associated with increased NSSI. Furthermore, interaction effects were observed among sex, NLEs and NSSI. MH and sex moderated the relationship between NLEs and NSSI.

CONCLUSION

Identifying risk factors for NSSI is also important when exploring the interaction between NLEs and MH given the potential for NSSI to significantly increase the risk of later psychopathological symptoms and substance abuse problems. In addition, the significance of sex differences in risk factors for NSSI should be determined. This study evaluated how the impact of NLEs on NSSI can be reduced among adolescents from multiple perspectives.

Unraveling the core symptoms of mental health in senior grade three students- a network analysis

Background

Adolescence is not only an important transitional period of many developmental challenges, but also a high risk period for mental health problems. Psychotherapy is recommended for mental health problems in adolescents, but its effectiveness is not always satisfactory. One possible contributing factor may be the lack of clarity surrounding core symptoms.

Methods

In this study, we investigated the mental health status of senior grade three students, a group of adolescents facing college entrance exams, by the Middle School Student Mental Health Test (MHT) and analyzed the core symptoms by network analysis. This study was conducted through an online survey platform (www.xiaodongai.com) from 15 February 2023 to 28 March 2024. The subjects scanned a QR code with their mobile phone to receive the questionnaire.

Results

The mean age of these 625 students were 18.11 ± 2.90 years. There are 238 male participants and 387 female participants. 107 individuals scored above 56 (107/461, 23.2%), with individual scale scores over 8 up to over 60% of participating students. Notably, the top three prominent symptoms were "academic anxiety", "allergic tendency" and "somatic symptoms". However, upon conducting network analysis, it became evident that three strongest edges in this network were "somatic symptoms" and "impulsive tendency", "academic anxiety" and "social anxiety" as well as "social anxiety" and "Loneliness tendency". "somatic symptoms", "social anxiety" and "self-blame tendency" exerted the highest expected influence. This suggests that, statistically speaking, these three symptoms exhibited the strongest interconnections within the network.

Limitation

Cross-sectional analysis; Bias in self-reported variables.

Conclusion

These findings can deepen the knowledge of mental health among senior grade three students and provide some implications (i.e., targeting symptoms having highest expected influence) for clinical prevention and intervention to address the mental health needs of this particular group.

A Scoping Review on Adverse Childhood Experiences Studies Using Latent Class Analysis: Strengths and Challenges

Adverse childhood experiences (ACEs) studies reveal the profound impacts of experiencing trauma and hardships in childhood. However, the cumulative risk approach of treating ACEs obscures the heterogeneity of ACEs and their consequences, making actionable interventions impossible. latent class analysis (LCA) has increasingly been used to address these concerns by identifying underlying subgroups of people who experience distinctive patterns of co-occurring ACEs. Though LCA has its strengths, the existing research produces few comparable findings because LCA results are dependent on ACEs measures and indicators, which vary widely by study. Therefore, a scoping review of ACEs studies using LCA that focuses on ACEs measures, indicators, and findings is needed to inform the field. Following Arksey and O'Malley's five-stage scoping review methodological framework, we first identified 211 articles from databases of EBSCOhost, PubMed, and Scopus using "adverse childhood experiences" for title search and "latent class analysis" for abstract search. Based on the inclusion criteria of peer-reviewed articles written in English published from 2012 to 2022 and the exclusion criteria of nonempirical studies and the LCA not analyzing ACEs, we finally selected 58 articles in this scoping review. Results showed LCA has been increasingly endorsed in the ACEs research community to examine the associations between ACEs and human health and well-being across culturally diverse populations. LCA overcame the limitations of the traditional methods by revealing specific ACEs clusters that exert potent effects on certain outcomes. However, the arbitrary nature of selecting ACEs indicators, measures, and the limited use of theory impedes the field from moving forward.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Role of polygenic risk scores in the association between chronotype and health risk behaviors

BACKGROUND

This study explores the association between chronotypes and adolescent health risk behaviors (HRBs) by testing how genetic background moderates these associations and clarifies the influence of chronotypes and polygenic risk score (PRS) on adolescent HRBs.

METHODS

Using VOS-viewer software to select the corresponding data, this study used knowledge domain mapping to identify and develop the research direction with respect to adolescent risk factor type. Next, DNA samples from 264 students were collected for low-depth whole-genome sequencing. The sequencing detected HRB risk loci, 49 single nucleotide polymorphisms based to significant SNP. Subsequently, PRSs were assessed and divided into low, moderate, and high genetic risk according to the tertiles and chronotypes and interaction models were constructed to evaluate the association of interaction effect and clustering of adolescent HRBs. The chronotypes and the association between CLOCK-PRS and HRBs were examined to explore the association between chronotypes and mental health and circadian CLOCK-PRS and HRBs.

RESULTS

Four prominent areas were displayed by clustering information fields in network and density visualization modes in VOS-viewer. The total score of evening chronotypes correlated with high-level clustering of HRBs in adolescents, co-occurrence, and mental health, and the difference was statistically significant. After controlling covariates, the results remained consistent. Three-way interactions between chronotype, age, and mental health were observed, and the differences were statistically significant. CLOCK-PRS was constructed to identify genetic susceptibility to the clustering of HRBs. The interaction of evening chronotypes and high genetic risk CLOCK-PRS was positively correlated with high-level clustering of HRBs and HRB co-occurrence in adolescents, and the difference was statistically significant. The interaction between the sub-dimensions of evening chronotypes and the high genetic CLOCK-PRS risk correlated with the outcome of the clustering of HRBs and HRB co-occurrence.

CONCLUSIONS

The interaction of PRS and chronotype and the HRBs in adolescents appear to have an association, and the three-way interaction between the CLOCK-PRS, chronotype, and mental health plays important roles for HRBs in adolescents.

Role of body mass index in the relationship between adverse childhood experiences, resilience, and mental health: a multivariate analysis

OBJECTIVES

Depression among adolescents is a global concern. Adverse childhood experiences (ACEs) have been correlated with negative physical and mental health such as obesity and depression; however, increasing evidence has suggested that their correlation might be moderated by BMI and resilience. In this study, we aim to explore (1) whether resilience moderate the risk of mental health by ACEs; (2) whether BMI is a moderator of this relationship.

STUDY DESIGN

Adolescents were obtained from 4 grade college students by a multi-stage convenience sampling method in the period of May to Jun, 2022.

METHODS

We use the Connor-Davidson Resilience scale, Depression, Anxiety and Stress Scale-21 Item (DASS-21) questionnaires to measure the ACEs, BMI, resilience and mental health. The primary exposure was ACEs and the primary outcome was mental health; while resilience and BMI were moderators. Multivariable linear regression model was used to establish the relationship of ACEs, resilience and BMI against mental health status. Moderate analysis was employed by PROCESS method to explore the relationship between these variables.

RESULTS

A total of 3600 individuals were initially enrolled, after excluding 22 with invalid questionnaires, 3578 adolescents were finally included. The mean age was (20.53 ± 1.65) years old. After adjusted for covariates, multivariable linear regression suggest that the high level ACEs (, β =0.58, , 95%CI:0.54,0.62, P < 0.01), resilience (, β=-0.27, 95%CI: , 95%CI: -0.28,-0.26, P < 0.01) were associated with higher depression symptoms, and BMI (, β =0.073, 95%CI: 0.002-0.15, P < 0.05) was associated with higher depression symptoms. There is also the interaction between resilience, ACEs and mental health (depression, anxiety and stress symptoms). In the relationship between ACEs and mental health, resilience and BMI played a moderator role.

CONCLUSIONS

The moderate analysis also provided further evidence of a link between resilience, ACEs, BMI and mental health. The findings shed new light on potential mechanisms between ACEs and mental health, including the effects of the co-interaction of resilience and BMI, adding to previous literature. ACEs may be a profound variable to measure adolescents' psychosocial environment to influence mental health, and resilience moderate this effect and is also moderated by BMI.