Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.
Journal: BMC medical genomics
Volume: 17
Issue: 1
Year of Publication: 2024
Affiliated Institutions:
Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA.
Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA.
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA.
University of North Carolina at Chapel Hill, Carolina Stress Initiative, Chapel Hill, NC, USA.
Robert N Butler Columbia Aging Center, Department of Epidemiology, Columbia University, New York, NY, USA.
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, UT, Netherlands.
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Biogen Inc., Translational Sciences, Cambridge, MA, USA.
Department of Pathology, University of Cape Town, Cape Town, Western Province, South Africa.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Brain Research and Innovation Centre, Netherlands Ministry of Defence, Utrecht, UT, Netherlands.
Department of Psychology, The Ohio State University, Columbus, OH, USA.
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA.
Department of Psychiatry & Mental Health, University of Cape Town, Cape Town, Western Province, South Africa.
Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA.
Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, UT, Netherlands.
VA Boston Healthcare System, GRECC/TRACTS, Boston, MA, USA.
Boston University School of Medicine, Psychiatry, Boston, MA, USA.
University of Cape Town, Neuroscience Institute, Cape Town, Western Province, South Africa.
Department of Emergency Medicine, Warren Alpert Brown Medical School, Providence, RI, USA.
Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA.
School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, Maastricht, Limburg, Netherlands.
Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA.
Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA.
Department of Psychiatry, Leiden University Medical Center, Leiden, ZH, Netherlands.
Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, Holland, Netherlands.
Survey Research Center, University of Michigan, Institute for Social Research, Ann Arbor, MI, USA.
VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA.
Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA. monica@usf.edu.
Abstract summary
Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.
Authors & Co-authors:
Wani Agaz H AH
Katrinli Seyma S
Zhao Xiang X
Daskalakis Nikolaos P NP
Zannas Anthony S AS
Aiello Allison E AE
Baker Dewleen G DG
Boks Marco P MP
Brick Leslie A LA
Chen Chia-Yen CY
Dalvie Shareefa S
Fortier Catherine C
Geuze Elbert E
Hayes Jasmeet P JP
Kessler Ronald C RC
King Anthony P AP
Koen Nastassja N
Liberzon Israel I
Lori Adriana A
Luykx Jurjen J JJ
Maihofer Adam X AX
Milberg William W
Miller Mark W MW
Mufford Mary S MS
Nugent Nicole R NR
Rauch Sheila S
Ressler Kerry J KJ
Risbrough Victoria B VB
Rutten Bart P F BPF
Stein Dan J DJ
Stein Murray B MB
Ursano Robert J RJ
Verfaellie Mieke H MH
Vermetten Eric E
Vinkers Christiaan H CH
Ware Erin B EB
Wildman Derek E DE
Wolf Erika J EJ
Nievergelt Caroline M CM
Logue Mark W MW
Smith Alicia K AK
Uddin Monica M
Study Outcome
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