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Siegel CE, Laska EM, Lin Z, Xu M, Abu-Amara D, Jeffers MK, Qian M, Milton N, Flory JD, Hammamieh R, Daigle BJ, Gautam A, Dean KR, Reus VI, Wolkowitz OM, Mellon SH, Ressler KJ, Yehuda R, Wang K, Hood L, Doyle FJ, Jett M, Marmar CR. Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates. Transl Psychiatry 2021; 11:227. [PMID: 33879773 PMCID: PMC8058082 DOI: 10.1038/s41398-021-01324-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 02/23/2021] [Accepted: 03/16/2021] [Indexed: 12/14/2022] Open
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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Affiliation(s)
- Carole E Siegel
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
| | - Eugene M Laska
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Ziqiang Lin
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Mu Xu
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Duna Abu-Amara
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Michelle K Jeffers
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Meng Qian
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Nicholas Milton
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Janine D Flory
- Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rasha Hammamieh
- Military Readiness Systems Biology, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Bernie J Daigle
- Departments of Biological Sciences and Computer Science, The University of Memphis, Memphis, TN, USA
| | - Aarti Gautam
- Military Readiness Systems Biology, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Kelsey R Dean
- Department of Systems Biology, Harvard University, Cambridge, MA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Victor I Reus
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Owen M Wolkowitz
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Synthia H Mellon
- Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California, San Francisco, CA, USA
| | | | - Rachel Yehuda
- Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Wang
- Institute for Systems Biology, Seattle, WA, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Marti Jett
- Military Readiness Systems Biology, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Charles R Marmar
- Center for Alcohol Use Disorder and PTSD, Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
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