Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.
DNA methylation
Machine learning
PTSD
Risk scores
Journal
BMC medical genomics
ISSN: 1755-8794
Titre abrégé: BMC Med Genomics
Pays: England
ID NLM: 101319628
Informations de publication
Date de publication:
27 Sep 2024
27 Sep 2024
Historique:
received:
12
02
2024
accepted:
29
08
2024
medline:
28
9
2024
pubmed:
28
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
BACKGROUND
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.
METHODS
METHODS
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.
RESULTS
RESULTS
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.
CONCLUSION
CONCLUSIONS
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.
Identifiants
pubmed: 39334086
doi: 10.1186/s12920-024-02002-6
pii: 10.1186/s12920-024-02002-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
235Subventions
Organisme : National Institutes of Health, United States
ID : K12HD085850
Organisme : National Institutes for Minority Health and Health Disparities
ID : R01MD011728
Organisme : National Institutes for Minority Health and Health Disparities
ID : R01MD011728
Organisme : National Institutes for Minority Health and Health Disparities
ID : R01MD011728
Organisme : VA Rehabilitation Research and Development Traumatic Brain Injury National Research Center
ID : B3001-C
Organisme : NIMH NIH HHS
ID : K23MH112852
Pays : United States
Organisme : NIMH NIH HHS
ID : R01MH105379
Pays : United States
Organisme : NIMH NIH HHS
ID : U01MH087981
Pays : United States
Organisme : NIMH NIH HHS
ID : U01MH087981
Pays : United States
Organisme : U.S. Department of Defense
ID : #W81XWH-11-1-0073
Organisme : NCATS NIH HHS
ID : UL1TR000433
Pays : United States
Organisme : NIH HHS
ID : R01MH106595
Pays : United States
Organisme : NIH HHS
ID : R01MH106595
Pays : United States
Organisme : NIH HHS
ID : R01MH106595
Pays : United States
Organisme : U.S. Department of Veterans Affairs
ID : BX005872
Organisme : U.S. Department of Veterans Affairs
ID : I01 CX-001276-01
Organisme : The Dutch Research Council
ID : 917.18.336
Organisme : Bill and Melinda Gates Foundation
ID : OPP 1017641
Organisme : The National Institute of Aging, United States
ID : RF1AG068121
Organisme : The National Institute of Mental Health
ID : R01MH108826
Organisme : The National Institute of Mental Health
ID : R01MH108826
Organisme : The National Institute of Mental Health
ID : R01MH108826
Organisme : The National Institute of Mental Health
ID : R01MH108826
Informations de copyright
© 2024. The Author(s).
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