Premature aging in co‐occurring Alcohol Use Disorder and Post‐Traumatic Stress Disorder from a functional connectome perspective. (20th December 2022) - Joseph, Jane E; Bustos, Nicholas; Nowling, Duncan; Warner, Graham; Barlis, Katie L; Mintzer, Jacobo E; Dean, Brian; Lawson, Andrew; Jensen, Jens H.; Benitez, Andreana; Peterson, Alan; Fox, Peter T; et al.
Abstract: Background: Post‐Traumatic Stress Disorder (PTSD) and Alcohol Use Disorder (AUD) may be associated with premature brain aging which increases the risk for Alzheimer's Disease and Related Dementias (ADRD). While each disorder alone has been studied as a risk factor for ADRD, few studies have examined premature brain aging in co‐occurring PTSD/AUD. It is important to know whether co‐occurring PTSD/AUD may amplify ADRD risk. Consequently, the present study tested the hypothesis that, relative to healthy controls (HC), PTSD/AUD would show more pronounced premature brain aging than AUD alone. Method: A functional connectome approach was used to develop a model of healthy brain aging. The graph‐theory measure eigenvector centrality was calculated from resting state fMRI scans in 60 mid‐life HC (HC‐mid; mean age=56.9), 69 older adult HC (HC‐older; mean age = 74.1), 36 AUD (mean age=54.4), and 30 PTSD/AUD participants (mean age=55.7). AUD and PTSD/AUD diagnoses were determined by a structured clinical interview. Connectome node features that were most predictive of age in HC‐mid and HC‐older were used in predictive modeling with bootstrap aggregation. These features were from multiple cortical areas and resting state networks. The healthy aging model was transferred to the AUD and PTSD/AUD groups to estimate brain age. Estimated ages in AUD and PTSD/AUD were compared to the predicted age in HC‐mid and HC‐older using generalized linear mixed modeling (GLMM). Result: Predictive modeling in HC yielded an aggregate model with 49.5% accuracy in predicting age (p < .0001), with some network features showing increases in eigenvector centrality with age and others showing decreases. After model transfer and correcting for age estimation bias, the GLMM yielded a main effect of diagnostic group (Wald c 2 3 =141.6, p < .0001). Predicted age for PTSD/AUD was significantly higher (M=61.0) than HC‐mid (M=57.2; p=.048) but predicted age for AUD (M=59.9) was not different from HC‐mid (p=.133). HC‐older predicted age (M=74.1) was different from all other groups (p < .0001). Conclusion: In this preliminary analysis, premature brain aging in PTSD/AUD was more pronounced than in AUD alone which may suggest greater vulnerability to AD‐like neurodegenerative processes that are manifest at the level of large‐scale network connectivity.