ArticleBMC medical genomics2024
Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.
Article in BMC medical genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Epigenetic mechanisms in traumatic brain injury: a focus on astrocytes and therapeutic implications.Journal of neuroinflammation · 2026Review
- DNA methylation signatures associated with bipolar disorder in peripheral blood improve prediction models.EBioMedicine · 2026Article
- 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
- Post-traumatic stress disorder: evolving conceptualization and evidence, and future research directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning Among Breast Cancer Patients in Bangladesh.Cancer informatics · 2025Article
- Genetic Alterations in War-Related Post-Traumatic Stress Disorder: A Systematic Review.Bulletin of emergency and trauma · 2025Review
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Abstract
backgroundIncorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.
methodsElastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.
resultsThe eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.
conclusionThe inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.
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