ArticleTranslational psychiatry2021
Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Towards scalable biomarker discovery in posttraumatic stress disorder: triangulating genomic and phenotypic evidence from a health system biobank.Molecular psychiatry · 2026Article
- Combining DNA methylation features and clinical characteristics predicts ketamine treatment response for PTSD.iScience · 2026Article
- The Clinical Significance of the Manchester Colour Wheel in a Sample of People Treated for Insured Injuries.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Prefrontal metabolite alterations in individuals with posttraumatic stress disorder: a 7T magnetic resonance spectroscopy study.bioRxiv : the preprint server for biology · 2024Article
- Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention.Journal of affective disorders · 2024Observational
- A treeless absolutely random forest with closed-form estimators of expected proximities.Statistical analysis and data mining · 2024Article
- Resolving Heterogeneity in Posttraumatic Stress Disorder Using Individualized Structural Covariance Network Analysis.Depression and anxiety · 2024Article
- Using machine learning algorithm to predict the risk of post-traumatic stress disorder among firefighters in Changsha.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Deriving psychiatric symptom-based biomarkers from multivariate relationships between psychophysiological and biochemical measures.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- A General Iterative Clustering Algorithm.Statistical analysis and data mining · 2022Article
- Post-traumatic stress disorder: clinical and translational neuroscience from cells to circuits.Nature reviews. Neurology · 2022Review
- Hypothalamic-pituitary-adrenal axis and renin-angiotensin-aldosterone system in adulthood PTSD and childhood maltreatment history.Frontiers in psychiatry · 2022Article
- Identifying subtypes of PTSD to promote precision medicine.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- Prefrontal Metabolite Alterations in Individuals with Posttraumatic Stress Disorder: A 7T Magnetic Resonance Spectroscopy Study.Chronic stress (Thousand Oaks, Calif.)Article
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Authors and funding
23 authors.
Funding
Abstract
We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.
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