Post‐traumatic Psychopathology and Cardiovascular Risk: A Danish Case–Control Study of Sex‐Specific Associations
9 affiliations
- 1Department of Psychology University of California, Los Angeles Los Angeles CA USA
- 2Department of Epidemiology Boston University School of Public Health Boston MA USA
- 3Department of Clinical Epidemiology and Center for Population Medicine Aarhus University Hospital and Aarhus University Aarhus Denmark
- 4Department of Clinical Medicine Aarhus University Hospital and Aarhus University Aarhus Denmark
- 5Department of Epidemiology, Rollins School of Public Health Emory University Atlanta GA USA
- 6National Center for PTSD Executive Division White River Junction VT USA
- 7Department of Psychiatry Geisel School of Medicine at Dartmouth Hanover NH USA
- 8Washington University School of Public Health Saint Louis MO USA
- 9Department of Psychiatry Boston University School of Medicine Boston MA USA
Abstract
Background: There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post‐traumatic stress disorder—despite evidence that a range of psychiatric disorders may follow trauma. We applied machine learning to identify key post‐traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population‐based cohort.
Methods: We conducted a case–control study of adults aged ≥18 years, nested within a trauma‐exposed cohort of all Denmark residents from 1995 to 2018. Cases (n=43 994) experienced MACCE (non‐fatal myocardial infarction, non‐fatal stroke, coronary revascularization, cardiovascular death) between 1995 and 2019. Controls (n=175 872) were matched 4:1 on MACCE date, sex, year of trauma, and age at trauma. Machine learning model predictors included a range of psychiatric disorders diagnosed ≤3 years following trauma. Sex‐stratified machine learning models were trained using extreme gradient boosting (men: n=91 111; women: n=84 781) and tested in hold‐out datasets (men: n=22 777; women: n=21 197). We identified the 15 most important psychiatric disorder predictors of MACCE using Shapley Additive Explanations values.
Results: For men and women, the most important psychiatric predictor of MACCE was alcohol use disorders. Other important psychiatric predictors included depressive disorders, post‐traumatic stress disorder/stress‐related disorders, brain‐related/physiological conditions, and non‐substance‐induced delirium. Some sex‐specific findings also emerged (eg, depressive episodes more important for women than men). Overall, odds ratios in training and testing sets indicated that odds of MACCE were greater among those with (versus without) psychiatric disorders.
Conclusions: Results emphasize the importance of considering a range of psychiatric disorders when assessing cardiovascular risk following trauma.
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BibTeX
@article{sumner2026posttraumatic,
title = {{Post‐traumatic Psychopathology and Cardiovascular Risk: A Danish Case–Control Study of Sex‐Specific Associations}},
author = {Jennifer A. Sumner and Anthony J. Rosellini and Péter Szentkúti and Erzsébet Horváth‐Puhó and Hans Erik Bøtker and Timothy L. Lash and Paula P. Schnurr and Sandro Galea and Henrik Toft Sørensen and Jaimie L. Gradus},
journal = {Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease},
year = {2026},
month = sep,
doi = {10.1161/jaha.126.049492},
url = {https://doi.org/10.1161/jaha.126.049492},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Post‐traumatic Psychopathology and Cardiovascular Risk: A Danish Case–Control Study of Sex‐Specific Associations
AU - Jennifer A. Sumner
AU - Anthony J. Rosellini
AU - Péter Szentkúti
AU - Erzsébet Horváth‐Puhó
AU - Hans Erik Bøtker
AU - Timothy L. Lash
AU - Paula P. Schnurr
AU - Sandro Galea
AU - Henrik Toft Sørensen
AU - Jaimie L. Gradus
PY - 2026
DA - 2026/09/02
JO - Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
DO - 10.1161/jaha.126.049492
UR - https://doi.org/10.1161/jaha.126.049492
AB - Background: There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post‐traumatic stress disorder—despite evidence that a range of psychiatric disorders may follow trauma. We applied machine learning to identify key post‐traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population‐based cohort. Methods: We conducted a case–control study of adults aged ≥18 years, nested within a trauma‐exposed cohort of all Denmark residents from 1995 to 2018. Cases (n=43 994) experienced MACCE (non‐fatal myocardial infarction, non‐fatal stroke, coronary revascularization, cardiovascular death) between 1995 and 2019. Controls (n=175 872) were matched 4:1 on MACCE date, sex, year of trauma, and age at trauma. Machine learning model predictors included a range of psychiatric disorders diagnosed ≤3 years following trauma. Sex‐stratified machine learning models were trained using extreme gradient boosting (men: n=91 111; women: n=84 781) and tested in hold‐out datasets (men: n=22 777; women: n=21 197). We identified the 15 most important psychiatric disorder predictors of MACCE using Shapley Additive Explanations values. Results: For men and women, the most important psychiatric predictor of MACCE was alcohol use disorders. Other important psychiatric predictors included depressive disorders, post‐traumatic stress disorder/stress‐related disorders, brain‐related/physiological conditions, and non‐substance‐induced delirium. Some sex‐specific findings also emerged (eg, depressive episodes more important for women than men). Overall, odds ratios in training and testing sets indicated that odds of MACCE were greater among those with (versus without) psychiatric disorders. Conclusions: Results emphasize the importance of considering a range of psychiatric disorders when assessing cardiovascular risk following trauma.
SN - 2047-9980
ER -