Adverse childhood experiences are associated with vascular changes in adolescents that are risk factors for future cardiovascular disease

Allostasis, health, and development in Latin America

The lifespan is influenced by adverse childhood experiences that create predispositions to poor health outcomes. Here we propose an allostatic framework of childhood experiences and their impact on health across the lifespan, focusing on Latin American and Caribbean countries. This region is marked by significant social and health inequalities nested in environmental and social stressors, such as exposure to pollution, violence, and nutritional deficiencies, which critically influence current and later-life health outcomes. We review several manifestations across cognition, behavior, and the body, observed at the psychological (e.g., cognitive, socioemotional, and behavioral dysfunctions), brain (e.g., alteration of the development, structure, and function of the brain), and physiological levels (e.g., dysregulation of the body systems and damage to organs). To address the complexity of the interactions between environmental and health-related factors, we present an allostatic framework regarding the cumulative burden of environmental stressors on physiological systems (e.g., cardiovascular, metabolic, immune, and neuroendocrine) related to health across the life course. Lastly, we explore the relevance of this allostatic integrative approach in informing regional interventions and public policy recommendations. We also propose a research agenda, potentially providing detailed profiling and personalized care by assessing the social and environmental conditions. This framework could facilitate the delivery of evidence-based interventions and informed childhood-centered policy-making.

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.

Adverse Childhood Experiences and Electronic Cigarette Use among U.S. Young Adults

(1) Background: Adverse childhood experiences (ACEs), which are potentially traumatic childhood events, have been associated with increased tobacco product use. Less is known about electronic cigarette (e-cigarette) use during young adulthood. This study explored the associations between ACEs and current e-cigarette use among U.S. young adults. (2) Methods: This study was a secondary analysis of 2021 Behavioral Risk Factor Surveillance System data including 2537 young adults aged 18-24 years. Unadjusted and adjusted logistic regression analyses were conducted. (3) Results: Of the participants, 19.2% currently used e-cigarettes, and 22.1% reported 1 ACE, 13.0% reported 2 ACEs, 10.7% reported 3 ACEs, and 30.6% reported ≥4 ACEs. Unadjusted results indicated that participants who experienced 1 ACE (odds ratio (OR) = 1.76, 95% confidence interval (CI) = 1.01-3.07), 2 ACEs (OR = 2.18, 95%CI = 1.24-3.83), 3 ACEs (OR = 2.63, 95%CI = 1.41-4.90), and ≥4 ACEs (OR = 3.69, 95%CI = 2.23-6.09) were at increased odds of reporting current e-cigarette use than participants who experienced 0 ACEs. Adjusted results indicated that participants who experienced 3 ACEs were at 2.20 times higher odds (95%CI = 1.15-4.23) and participants who experienced ≥4 ACEs were at 2.73 times higher odds (95%CI = 1.58-4.71) of reporting current e-cigarette use than participants who experienced 0 ACEs. (4) Conclusions: Young adults exposed to ACEs are at risk of using e-cigarettes.