Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder

Research output: Contribution to journalArticle


title = "Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder",
abstract = "Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.",
author = "Dean, {Kelsey R.} and Rasha Hammamieh and Mellon, {Synthia H.} and Duna Abu-Amara and Flory, {Janine D.} and Guia Guffanti and Kai Wang and Daigle, {Bernie J.} and Aarti Gautam and Inyoul Lee and Ruoting Yang and Almli, {Lynn M.} and Bersani, {F. Saverio} and Nabarun Chakraborty and Duncan Donohue and Kimberly Kerley and Kim, {Taek Kyun} and Eugene Laska and {Young Lee}, Min and Daniel Lindqvist and Adriana Lori and Liangqun Lu and Burook Misganaw and Seid Muhie and Jennifer Newman and Price, {Nathan D.} and Shizhen Qin and Reus, {Victor I.} and Carole Siegel and Somvanshi, {Pramod R.} and Thakur, {Gunjan S.} and Yong Zhou and {The PTSD Systems Biology Consortium} and Leroy Hood and Ressler, {Kerry J.} and Wolkowitz, {Owen M.} and Rachel Yehuda and Marti Jett and {Doyle III}, {Francis J.} and Charles Marmar",
year = "2020",
month = dec,
doi = "10.1038/s41380-019-0496-z",
language = "English",
volume = "25",
pages = "3337--3349",
journal = "Molecular Psychiatry",
issn = "1359-4184",
publisher = "Nature Publishing Group",
number = "12",