TY - JOUR
T1 - Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder
AU - The PTSD Systems Biology Consortium
AU - Dean, Kelsey R.
AU - Hammamieh, Rasha
AU - Mellon, Synthia H.
AU - Abu-Amara, Duna
AU - Flory, Janine D.
AU - Guffanti, Guia
AU - Wang, Kai
AU - Daigle, Bernie J.
AU - Gautam, Aarti
AU - Lee, Inyoul
AU - Yang, Ruoting
AU - Almli, Lynn M.
AU - Bersani, F. Saverio
AU - Chakraborty, Nabarun
AU - Donohue, Duncan
AU - Kerley, Kimberly
AU - Kim, Taek Kyun
AU - Laska, Eugene
AU - Young Lee, Min
AU - Lindqvist, Daniel
AU - Lori, Adriana
AU - Lu, Liangqun
AU - Misganaw, Burook
AU - Muhie, Seid
AU - Newman, Jennifer
AU - Price, Nathan D.
AU - Qin, Shizhen
AU - Reus, Victor I.
AU - Siegel, Carole
AU - Somvanshi, Pramod R.
AU - Thakur, Gunjan S.
AU - Zhou, Yong
AU - Baxter, David
AU - Bierer, Linda
AU - Blessing, Esther
AU - Cho, Ji Hoon
AU - Coy, Michelle
AU - Desarnaud, Frank
AU - Fossati, Silvia
AU - Hoke, Allison
AU - Kumar, Raina
AU - Li, Meng
AU - Makotkine, Iouri
AU - Miller, Stacy Ann
AU - Petzold, Linda
AU - Price, Laura
AU - Qian, Meng
AU - Scherler, Kelsey
AU - Srinivasan, Seshamalini
AU - Suessbrick, Anna
N1 - Funding Information:
Acknowledgements We thank Rohini Bagrodia, Nikos Daskalakis, Charlotte Feld, Afia Genfi, Arsen Grigoryan, Roland Hart, Clare Henn-Haase, Kristin Holmes, Jenna Katz, Erin Koch, Sharon Lee, Amy Lehrner, Brie Loethen, Rebecca Lubin, Silas Mann, Nicholas Milton, Crystal Mora, Vince Passarelli, Emily Purchia, Amy Ran-sohoff, Alex Ropes, Ashik Siddique, Charu Sood, and Carly Walter for the assistance with data collection, management, and technical support. The High-Performance Computing Center at The University of Memphis also provided generous computing resources for this research. This work was supported by funding from the U.S. Army Research Office, through award numbers W911NF-13-1-0376, W911NF-17-2-0086, W911NF-18-2-0056, by the Army Research Laboratory under grant number W911NF-17-1-0069, and from the U. S. Department of Defense under W81XWH-10-1-0021, W81XWH-09-2-0044, and W81XWH-14-1-0043.
Publisher Copyright:
© 2019, The Author(s).
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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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U2 - 10.1038/s41380-019-0496-z
DO - 10.1038/s41380-019-0496-z
M3 - Article
C2 - 31501510
AN - SCOPUS:85073953764
VL - 25
SP - 3337
EP - 3349
JO - Molecular Psychiatry
JF - Molecular Psychiatry
SN - 1359-4184
IS - 12
ER -