ArticleJournal of affective disorders2021
The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach.
Article in Journal of affective disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Combining DNA methylation features and clinical characteristics predicts ketamine treatment response for PTSD.iScience · 2026Article
- The relationship between social adversity, micro-RNA expression and post-traumatic stress in a prospective, community-based cohort.Nature. Mental health · 2026Article
- Epigenetic profiles of response to transcranial magnetic stimulation in treatment resistant depression.BMC medical genomics · 2025Article
- Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.bioRxiv : the preprint server for biology · 2025Article
- The protective role of self compassion in trauma recovery and its moderating impact on post traumatic symptoms and post traumatic growth.Scientific reports · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study.The Psychiatric quarterly · 2024Article
- Herbal Formula Extract Ameliorates Anxiety and Cognitive Impairment via Regulation of the Reelin/Dab-1 Pathway in a Murine Model of Post-Traumatic Stress Disorder.Pharmaceutics · 2024Article
- Does biological age mediate the relationship between childhood adversity and depression? Insights from the Detroit Neighborhood Health Study.Social science & medicine (1982) · 2024Article
- Developing Clinical Phenotype Data Collection Standards for Research in Africa.Global health, epidemiology and genomics · 2023Article
- Advances in the molecular neurobiology of posttraumatic stress disorder from global contexts: A systematic review of longitudinal studies.Global mental health (Cambridge, England) · 2023Review
- Hypermethylation at CREBBP Is Associated with Cognitive Impairment in a Mexican American Cohort.Journal of Alzheimer's disease : JAD · 2023Article
- DNA methylation of Nuclear Factor of Activated T Cells 1 mediates the prospective relation between exposure to different traumatic event types and post-traumatic stress disorder.Psychiatry research · 2022Article
- Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in a Predominantly African American Cohort.Journal of the American Statistical Association · 2022Article
- Leukocyte methylomic imprints of exposure to the genocide against the Tutsi in Rwanda: a pilot epigenome-wide analysis.Epigenomics · 2022Article
- Multiomic biological approaches to the study of child abuse and neglect.Pharmacology, biochemistry, and behavior · 2021Review
- Feasibility of predicting allele specific expression from DNA sequencing using machine learning.Scientific reports · 2021Article
- Machine Learning based Psychology: Advocating for A Data-Driven Approach.International journal of psychological researchArticle
- Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis.Digital healthArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundA range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD.
methodsWe used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS).
resultsML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R
conclusionMultiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention. LIMITATION: One of the limitations of this study is small sample size.
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