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

Dean, Kelsey R. ; Hammamieh, Rasha ; Mellon, Synthia H. ; Abu-Amara, Duna ; Flory, Janine D. ; Guffanti, Guia ; Wang, Kai ; Daigle, Bernie J. ; Gautam, Aarti and Lee, Inyoul , et al. (2019) In Molecular Psychiatry
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... (More)

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.

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@article{5210c355-346a-4c20-a476-06d4a77137d5,
  abstract     = {<p>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.</p>},
  author       = {Dean, Kelsey R. and Hammamieh, Rasha and Mellon, Synthia H. and Abu-Amara, Duna and Flory, Janine D. and Guffanti, Guia and Wang, Kai and Daigle, Bernie J. and Gautam, Aarti and Lee, Inyoul and Yang, Ruoting and Almli, Lynn M. and Bersani, F. Saverio and Chakraborty, Nabarun and Donohue, Duncan and Kerley, Kimberly and Kim, Taek Kyun and Laska, Eugene and Young Lee, Min and Lindqvist, Daniel and Lori, Adriana and Lu, Liangqun and Misganaw, Burook and Muhie, Seid and Newman, Jennifer and Price, Nathan D. and Qin, Shizhen and Reus, Victor I. and Siegel, Carole and Somvanshi, Pramod R. and Thakur, Gunjan S. and Zhou, Yong and Hood, Leroy and Ressler, Kerry J. and Wolkowitz, Owen M. and Yehuda, Rachel and Jett, Marti and Doyle III, Francis J. and Marmar, Charles},
  issn         = {1359-4184},
  language     = {eng},
  month        = {09},
  publisher    = {Nature Publishing Group},
  series       = {Molecular Psychiatry},
  title        = {Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder},
  url          = {http://dx.doi.org/10.1038/s41380-019-0496-z},
  doi          = {10.1038/s41380-019-0496-z},
  year         = {2019},
}