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Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder
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 illnes...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714692/ https://www.ncbi.nlm.nih.gov/pubmed/31501510 http://dx.doi.org/10.1038/s41380-019-0496-z |
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author | Dean, Kelsey R. Hammamieh, Rasha Mellon, Synthia H. Abu-Amara, Duna Flory, Janine D. Guffanti, Guia Wang, Kai Daigle, Bernie J. Gautam, Aarti Lee, Inyoul Yang, Ruoting Almli, Lynn M. Bersani, F. Saverio Chakraborty, Nabarun Donohue, Duncan Kerley, Kimberly Kim, Taek-Kyun Laska, Eugene Young Lee, Min Lindqvist, Daniel Lori, Adriana Lu, Liangqun Misganaw, Burook Muhie, Seid Newman, Jennifer Price, Nathan D. Qin, Shizhen Reus, Victor I. Siegel, Carole Somvanshi, Pramod R. Thakur, Gunjan S. Zhou, Yong Hood, Leroy Ressler, Kerry J. Wolkowitz, Owen M. Yehuda, Rachel Jett, Marti Doyle, Francis J. Marmar, Charles |
author_facet | Dean, Kelsey R. Hammamieh, Rasha Mellon, Synthia H. Abu-Amara, Duna Flory, Janine D. Guffanti, Guia Wang, Kai Daigle, Bernie J. Gautam, Aarti Lee, Inyoul Yang, Ruoting Almli, Lynn M. Bersani, F. Saverio Chakraborty, Nabarun Donohue, Duncan Kerley, Kimberly Kim, Taek-Kyun Laska, Eugene Young Lee, Min Lindqvist, Daniel Lori, Adriana Lu, Liangqun Misganaw, Burook Muhie, Seid Newman, Jennifer Price, Nathan D. Qin, Shizhen Reus, Victor I. Siegel, Carole Somvanshi, Pramod R. Thakur, Gunjan S. Zhou, Yong Hood, Leroy Ressler, Kerry J. Wolkowitz, Owen M. Yehuda, Rachel Jett, Marti Doyle, Francis J. Marmar, Charles |
author_sort | Dean, Kelsey R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7714692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77146922020-12-11 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 Lee, Inyoul Yang, Ruoting Almli, Lynn M. Bersani, F. Saverio Chakraborty, Nabarun Donohue, Duncan Kerley, Kimberly Kim, Taek-Kyun Laska, Eugene Young Lee, Min Lindqvist, Daniel Lori, Adriana Lu, Liangqun Misganaw, Burook Muhie, Seid Newman, Jennifer Price, Nathan D. Qin, Shizhen Reus, Victor I. Siegel, Carole Somvanshi, Pramod R. Thakur, Gunjan S. Zhou, Yong Hood, Leroy Ressler, Kerry J. Wolkowitz, Owen M. Yehuda, Rachel Jett, Marti Doyle, Francis J. Marmar, Charles Mol Psychiatry Article 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. Nature Publishing Group UK 2019-09-10 2020 /pmc/articles/PMC7714692/ /pubmed/31501510 http://dx.doi.org/10.1038/s41380-019-0496-z Text en © The Author(s) 2019 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/. |
spellingShingle | Article Dean, Kelsey R. Hammamieh, Rasha Mellon, Synthia H. Abu-Amara, Duna Flory, Janine D. Guffanti, Guia Wang, Kai Daigle, Bernie J. Gautam, Aarti Lee, Inyoul Yang, Ruoting Almli, Lynn M. Bersani, F. Saverio Chakraborty, Nabarun Donohue, Duncan Kerley, Kimberly Kim, Taek-Kyun Laska, Eugene Young Lee, Min Lindqvist, Daniel Lori, Adriana Lu, Liangqun Misganaw, Burook Muhie, Seid Newman, Jennifer Price, Nathan D. Qin, Shizhen Reus, Victor I. Siegel, Carole Somvanshi, Pramod R. Thakur, Gunjan S. Zhou, Yong Hood, Leroy Ressler, Kerry J. Wolkowitz, Owen M. Yehuda, Rachel Jett, Marti Doyle, Francis J. Marmar, Charles Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title | Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title_full | Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title_fullStr | Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title_full_unstemmed | Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title_short | Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
title_sort | multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714692/ https://www.ncbi.nlm.nih.gov/pubmed/31501510 http://dx.doi.org/10.1038/s41380-019-0496-z |
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