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Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates

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 Syste...

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Autores principales: Siegel, Carole E., Laska, Eugene M., Lin, Ziqiang, Xu, Mu, Abu-Amara, Duna, Jeffers, Michelle K., Qian, Meng, Milton, Nicholas, Flory, Janine D., Hammamieh, Rasha, Daigle, Bernie J., Gautam, Aarti, Dean, Kelsey R., Reus, Victor I., Wolkowitz, Owen M., Mellon, Synthia H., Ressler, Kerry J., Yehuda, Rachel, Wang, Kai, Hood, Leroy, Doyle, Francis J., Jett, Marti, Marmar, Charles R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058082/
https://www.ncbi.nlm.nih.gov/pubmed/33879773
http://dx.doi.org/10.1038/s41398-021-01324-8
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author Siegel, Carole E.
Laska, Eugene M.
Lin, Ziqiang
Xu, Mu
Abu-Amara, Duna
Jeffers, Michelle K.
Qian, Meng
Milton, Nicholas
Flory, Janine D.
Hammamieh, Rasha
Daigle, Bernie J.
Gautam, Aarti
Dean, Kelsey R.
Reus, Victor I.
Wolkowitz, Owen M.
Mellon, Synthia H.
Ressler, Kerry J.
Yehuda, Rachel
Wang, Kai
Hood, Leroy
Doyle, Francis J.
Jett, Marti
Marmar, Charles R.
author_facet Siegel, Carole E.
Laska, Eugene M.
Lin, Ziqiang
Xu, Mu
Abu-Amara, Duna
Jeffers, Michelle K.
Qian, Meng
Milton, Nicholas
Flory, Janine D.
Hammamieh, Rasha
Daigle, Bernie J.
Gautam, Aarti
Dean, Kelsey R.
Reus, Victor I.
Wolkowitz, Owen M.
Mellon, Synthia H.
Ressler, Kerry J.
Yehuda, Rachel
Wang, Kai
Hood, Leroy
Doyle, Francis J.
Jett, Marti
Marmar, Charles R.
author_sort Siegel, Carole E.
collection PubMed
description 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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spelling pubmed-80580822021-05-05 Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates Siegel, Carole E. Laska, Eugene M. Lin, Ziqiang Xu, Mu Abu-Amara, Duna Jeffers, Michelle K. Qian, Meng Milton, Nicholas Flory, Janine D. Hammamieh, Rasha Daigle, Bernie J. Gautam, Aarti Dean, Kelsey R. Reus, Victor I. Wolkowitz, Owen M. Mellon, Synthia H. Ressler, Kerry J. Yehuda, Rachel Wang, Kai Hood, Leroy Doyle, Francis J. Jett, Marti Marmar, Charles R. Transl Psychiatry Article 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. Nature Publishing Group UK 2021-04-20 /pmc/articles/PMC8058082/ /pubmed/33879773 http://dx.doi.org/10.1038/s41398-021-01324-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Siegel, Carole E.
Laska, Eugene M.
Lin, Ziqiang
Xu, Mu
Abu-Amara, Duna
Jeffers, Michelle K.
Qian, Meng
Milton, Nicholas
Flory, Janine D.
Hammamieh, Rasha
Daigle, Bernie J.
Gautam, Aarti
Dean, Kelsey R.
Reus, Victor I.
Wolkowitz, Owen M.
Mellon, Synthia H.
Ressler, Kerry J.
Yehuda, Rachel
Wang, Kai
Hood, Leroy
Doyle, Francis J.
Jett, Marti
Marmar, Charles R.
Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title_full Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title_fullStr Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title_full_unstemmed Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title_short Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates
title_sort utilization of machine learning for identifying symptom severity military-related ptsd subtypes and their biological correlates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058082/
https://www.ncbi.nlm.nih.gov/pubmed/33879773
http://dx.doi.org/10.1038/s41398-021-01324-8
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