Understanding Early-Life Stress Impact on Mental-Physical Comorbidity Using Machine Learning

Understanding the influence of early-life stress (ELS) on the development of chronic diseases can inform preventive healthcare strategies. This study examines the role of ELS in predicting mental-physical comorbidities, specifically cardiovas-cular disease (CVD) and diabetes, using machine learning models on UK Biobank data. The analysis focuses on individuals with a history of depression, highlighting the increased significance of ELS factors such as maternal smoking and childhood abuse in this cohort. Additionally, sex-specific differences in the impact of ELS on these health outcomes are explored, revealing that females are more susceptible to certain ELS factors associated with the development of CVD, while males are more susceptible to other ELS factors in developing diabetes. Results suggest that targeted, sex-specific interventions addressing ELS could mitigate long-term health impacts, particularly in depressed individuals.