Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2024 Feb 15:rs.3.rs-3952163.
doi: 10.21203/rs.3.rs-3952163/v1.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Agaz Wani  1 , Seyma Katrinli  2 , Xiang Zhao  3 , Nikolaos Daskalakis  4 , Anthony Zannas  5 , Allison Aiello  6 , Dewleen Baker  7 , Marco Boks  8 , Leslie Brick  9 , Chia-Yen Chen  10 , Shareefa Dalvie  11 , Catherine Fortier  12 , Elbert Geuze  13 , Jasmeet Hayes  14 , Ronald Kessler  15 , Anthony King  16 , Nastassja Koen  17 , Israel Liberzon  18 , Adriana Lori  19 , Jurjen Luykx  20 , Adam Maihofer  21 , William Milberg  22 , Mark Miller  23 , Mary Mufford  24 , Nicole Nugent  25 , Sheila Rauch  26 , Kerry Ressler  12 , Victoria Risbrough  7 , Bart Rutten  27 , Dan Stein  17 , Murrary Stein  7 , Robert Ursano  28 , Mieke Verfaellie  23 , Erin Ware  29 , Derek Wildman  1 , Erika Wolf  30 , Caroline Nievergelt  7 , Mark Logue  3 , Alicia Smith  2 , Monica Uddin  1 , Eric Vermetten  31 , Christiaan Vinkers  32
Affiliations

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Agaz Wani et al. Res Sq. .

Update in

  • Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.
    Wani AH, Katrinli S, Zhao X, Daskalakis NP, Zannas AS, Aiello AE, Baker DG, Boks MP, Brick LA, Chen CY, Dalvie S, Fortier C, Geuze E, Hayes JP, Kessler RC, King AP, Koen N, Liberzon I, Lori A, Luykx JJ, Maihofer AX, Milberg W, Miller MW, Mufford MS, Nugent NR, Rauch S, Ressler KJ, Risbrough VB, Rutten BPF, Stein DJ, Stein MB, Ursano RJ, Verfaellie MH, Vermetten E, Vinkers CH, Ware EB, Wildman DE, Wolf EJ, Nievergelt CM, Logue MW, Smith AK, Uddin M. Wani AH, et al. BMC Med Genomics. 2024 Sep 27;17(1):235. doi: 10.1186/s12920-024-02002-6. BMC Med Genomics. 2024. PMID: 39334086 Free PMC article.

Abstract

Background: Incorporating genomic data into risk prediction has become an increasingly useful 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: 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: 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-0.003), 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: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Keywords: DNA methylation; Machine learning; PTSD; Risk scores.

PubMed Disclaimer

Figures

Figure 1
Figure 1
The confusion matrix for Model 1 displays an accuracy of 92% on test data (N = 307), while the ROC curve indicates an AUC of 96% during the 10-fold cross-validation using all data (N = 1226).
Figure 2
Figure 2
Distribution and variation of risk scores between cases and controls in test data (N =307) in figure legend, 0 is No PTSD and 1 is PTSD. A) The distribution of risk scores for Models 1, 2, and 3 is shown for both cases and controls. B) The difference in risk scores between cases and controls is displayed. Model 1 calculates exposure and methylation risk scores (eMRS), while Model 2 calculates risk scores based only on methylation variables (MoRS). Model 3 calculates risk scores based on methylation variables adjusted for exposure variables (MoRSAE). The risk scores are higher in PTSD cases compared to controls. The Wilcox test confirms a significant difference in risk scores between cases and controls with p < 0.001 for all models (1, 2, and 3).
Figure 3
Figure 3
The confusion matrix for Model 2 displays an accuracy of 89% on test data (N = 307), while the ROC curve indicates an AUC of 95% during the 10-fold cross-validation using all data (N = 1226).
Figure 4
Figure 4
The confusion matrix for Model 3 displays an accuracy of 84% on test data (N = 307), while the ROC curve exhibits an AUC of 89% during the 10-fold cross-validation process using all data (N = 1226).
Figure 5
Figure 5
Distribution and difference in risk scores (eMRS) between PTSD cases and controls pre- and post-deployment (N = 262) — in figure legend, 0 is No PTSD and 1 is PTSD. A) The distribution of risk scores revealed that individuals who developed PTSD post-deployment had higher scores compared to those who did not, both before and after deployment. B) The difference in risk scores showed there was a significant (p < 0.001) difference in risk scores in those with PTSD post-deployment using Wilcox test.
Figure 6
Figure 6
Distribution and difference in risk scores (MoRS) between cases and controls pre- and post-deployment (N = 262) — in figure legend, 0 is No PTSD and 1 is PTSD. A) Distribution of risk scores between cases and controls. Risk scores are higher in those who developed PTSD post-deployment than who didn’t in both pre and post deployment. B) Difference in risk scores between cases and controls. Wilcox test showed a significant difference (p < 0.001) in risk scores between cases and controls.
Figure 7
Figure 7
Distribution and difference in risk scores (MoRSAE) between cases and controls pre- and post-deployment (N = 262) — in figure legend, 0 is No PTSD and 1 is PTSD. A) The distribution of MoRSAE is higher in those who developed PTSD post-deployment B) The difference in risk scores showed there was a significant (p < 0.001) difference in risk scores in those with PTSD post-deployment using Wilcox test.
See this image and copyright information in PMC

References

    1. Yehuda R, Hoge CW, McFarlane AC, Vermetten E, Lanius RA, Nievergelt CM, et al. Post-traumatic stress disorder. Nature Reviews Disease Primers. 2015;1(1):15057. - PubMed
    1. Kessler RC, Sonnega A, Bromet E, Hughes M, Nelson CB. Posttraumatic stress disorder in the National Comorbidity Survey. Archives of general psychiatry. 1995;52(12):1048–60. - PubMed
    1. Kulka RA, Schlenger WE, Fairbank JA, Hough RL, Jordan BK, Marmar CR, et al. Trauma and the Vietnam war generation: Report of findings from the National Vietnam Veterans Readjustment Study. Philadelphia, PA, US: Brunner/Mazel; 1990. xxix, 322–xxix, p.
    1. Brady KT, Killeen TK, Brewerton T, Lucerini S. Comorbidity of psychiatric disorders and posttraumatic stress disorder. The Journal of clinical psychiatry. 2000;61 Suppl 7:22–32. - PubMed
    1. Kessler RC, Wang PS. The Descriptive Epidemiology of Commonly Occurring Mental Disorders in the United States. 2008;29(1):115–29. - PubMed

Publication types