Traumatic brain injury (TBI), mental health conditions (e.g., posttraumatic stress disorder [PTSD]), and vascular comorbidities (e.g., hypertension, diabetes) are highly prevalent in the Veteran population and may exacerbate age-related changes to cerebral white matter (WM). Our study examined (1) relationships between health conditions—TBI history, PTSD, and vascular risk—and cerebral WM micro- and macrostructure, and (2) associations between WM measures and cognition.

We analyzed diffusion tensor images from 183 older male Veterans (mean age = 69.18; SD = 3.61) with (n = 95) and without (n = 88) a history of TBI using tractography. Generalized linear models examined associations between health conditions and diffusion metrics. Total WM hyperintensity (WMH) volume was calculated from fluid-attenuated inversion recovery images. Robust regression examined associations between health conditions and WMH volume. Finally, elastic net regularized regression examined associations between WM measures and cognitive performance.

Veterans with and without TBI did not differ in severity of PTSD or vascular risk (p’s >0.05). TBI history, PTSD, and vascular risk were independently associated with poorer WM microstructural organization (p’s <0.5, corrected), however the effects of vascular risk were more numerous and widespread. Vascular risk was positively associated with WMH volume (p = 0.004, β=0.200, R2 = 0.034). Higher WMH volume predicted poorer processing speed (R2 = 0.052).

Relative to TBI history and PTSD, vascular risk may be more robustly associated with WM micro- and macrostructure. Furthermore, greater WMH burden is associated with poorer processing speed. Our study supports the importance of vascular health interventions in mitigating negative brain aging outcomes in Veterans.

Epidemiological studies show that despite the episodic nature, the long-term trajectory of depression can be variable. This study evaluated the heterogeneity of 10-year trajectory of major depressive disorder (MDD) related service utilization and associated clinical characteristics among US Veterans with a first diagnosis after 9/11.

Using a cohort design, electronic health record data for 293,265 Operation Enduring Freedom and Iraqi Freedom (OEF/OIF) Veterans were extracted to identify those with MDD between 2001 and 2021 with a full preceding year of clinical data and 10 years following the diagnosis. Latent class growth analysis compared clinical characteristics associated with four depression trajectories. Across all Veterans Affairs (VA)hospitals, 25,307 Veterans met our inclusion criteria. Demographic and clinical information from medical records was extracted and used as predictors of depression 10-year trajectories.

Among the study cohort (N = 25,307), 27.7% were characterized by brief contact, 41.7% were later re-entry, 17.6% were persistent contact and 12.9% were prolonged initial contact for depression related services. Compared to Veterans with trajectories showing brief contact, those with protracted treatment (persistent or prolonged initial contact) were more likely to be diagnosed with comorbid posttraumatic stress disorder (PTSD) and with MDD that was moderate to severe or recurrent.

Depression is associated with a range of treatment trajectories. The persistent and prolonged initial contact trajectories may have distinct characteristics and uniquely high resource utilization and disability income. We can anticipate that patients with comorbid PTSD may need longer-term care which has implications for brief models of care.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.

Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.

The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.

Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

Suicidal thoughts and behaviors are elevated among active-duty service members (ADSM) and veterans compared to the general population. Hence, it is a priority to examine maintenance factors underlying suicidal ideation among ADSM and veterans to develop effective, targeted interventions. In particular, interpersonal risk factors, hopelessness, and overarousal have been robustly connected to suicidal ideation and intent.

To identify the suicidal ideation risk factors that are most relevant, we employed network analysis to examine between-subjects (cross-sectional), contemporaneous (within seconds), and temporal (across four hours) group-level networks of suicidal ideation and related risk factors in a sample of ADSM and veterans (participant n = 92, observations n = 10 650). Participants completed ecological momentary assessment (EMA) surveys four times a day for 30 days, where they answered questions related to suicidal ideation, interpersonal risk factors, hopelessness, and overarousal.

The between-subjects and contemporaneous networks identified agitation, not feeling close to others, and ineffectiveness as the most central symptoms. The temporal network revealed that feeling ineffective was most likely to influence other symptoms in the network over time.

Our findings suggest that ineffectiveness, low belongingness, and agitation are important drivers of moment-to-moment and longitudinal relations between risk factors for suicidal ideation in ADSM and veterans. Targeting these symptoms may disrupt suicidal ideation.

Many combat veterans exhibit suicidal ideation and behaviour, but the relationships among experiences occurring during combat deployment and suicidality are still not fully understood. In this study, we tested the hypothesis that harassment during a combat deployment is associated with post-deployment suicidality and testosterone function.

Male combat veterans who made post-deployment suicide attempts and demographically matched veterans without a history of suicide attempts were enrolled in the study. Demographic and clinical parameters of study participants were assessed and recorded. Study participants were interviewed by a trained clinician using the Mini-International Neuropsychiatric Interview (MINI), the Deployment Risk and Resilience Inventory (DRRI) – Relationships within unit scale, the Scale for Suicidal Ideation (SSI), and the Brown–Goodwin Aggression Scale. Free testosterone levels were assessed in morning blood samples.

DRRI harassment scores were higher and free testosterone levels were lower among suicide attempters in comparison with non-attempters. In the whole sample, DRRI harassment scores positively correlated with SSI scores and negatively correlated with free testosterone levels. Free testosterone levels negatively correlated with SSI scores. Aggression scale scores positively correlated with DRRI harassment scores among non-attempters but not among attempters.

Our observations that harassment scores are associated with suicidality and testosterone levels, and suicidality is associated with testosterone levels may indicate that there is a link between deployment harassment, testosterone function and suicidality.

Psychiatric disorders may be a risk factor for long COVID, broadly defined as COVID-19 conditions continuing three months post-acute infection. In US Veterans with high psychiatric burden, we examined associations between psychiatric disorders and clinical diagnosis of long COVID.

We conducted a retrospective cohort study using health records from VA patients with a positive SARS-CoV-2 test from February 2020 to February 2023. Generalized linear models estimated associations between any psychiatric disorder and likelihood of subsequent diagnosis with long COVID (i.e. two or more long COVID clinical codes). Models were adjusted for socio-demographic, medical, and behavioral factors. Secondary models examined individual psychiatric disorders and age-stratified associations.

Among 660 217 VA patients with positive SARS-CoV-2 tests, 56.3% had at least one psychiatric disorder diagnosis and 1.4% were diagnosed with long COVID. Individuals with any psychiatric disorder had higher risk for long COVID diagnosis in models adjusted for socio-demographic factors, vaccination status, smoking, and medical comorbidities (relative risk, RR = 1.28, 95% CI 1.21–1.35), with the strongest associations in younger individuals. Considering specific disorders, depressive, anxiety, and stress-related disorders were associated with increased risk for long COVID diagnoses (RRs = 1.36–1.48), but associations were in the opposite direction for substance use and psychotic disorders (RRs = 0.78–0.88).

Psychiatric disorder diagnoses were associated with increased long COVID diagnosis risk in VA patients, with the strongest associations observed in younger individuals. Improved surveillance, treatment, and prevention for COVID-19 and its long-term sequelae should be considered for individuals with psychiatric conditions.

Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer’s disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD.

267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria.

Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria.

MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Although yoga shows some promise as an intervention for post-traumatic stress disorder (PTSD), little is known about how yoga reduces PTSD symptoms. The current study hypothesised that aspects of interoceptive awareness would mediate the effect of a yoga intervention on PTSD symptoms.

We used data from our recently completed randomised controlled trial of a 16-week holistic yoga programme for veterans and civilians diagnosed with PTSD (n = 141) that offered weekly 90-minute sessions. We conducted a mediation analysis using interoceptive awareness and other variables that were associated with PTSD symptom reduction at mid-treatment and treatment end.

Although measures of anxiety, interoceptive awareness, and spirituality were identified in individual mediator models, they were no longer found to be significant mediators when examined jointly in multiple mediator models. When examining the multiple mediator models, the strongest mediator of the yoga intervention on PTSD symptoms was mental well-being at mid-treatment and stigma at the treatment end. The total effect of yoga on CAPS and PCL at the treatment end mediated by stigma was 37.1% (–1.81/–4.88) and 33.6% (–1.91/–5.68), respectively.

Investigation of mental well-being and mental illness stigma as potential mediators is warranted in future studies of yoga as a treatment for PTSD as they may prove to be important foci for yoga interventions.

Little is known about environmental factors that may influence associations between genetic liability to suicidality and suicidal behavior.

This study examined whether a suicidality polygenic risk score (PRS) derived from a large genome-wide association study (N = 122,935) was associated with suicide attempts in a population-based sample of European-American US military veterans (N = 1664; 92.5% male), and whether cumulative lifetime trauma exposure moderated this association.

Eighty-five veterans (weighted 6.3%) reported a history of suicide attempt. After adjusting for sociodemographic and psychiatric characteristics, suicidality PRS was associated with lifetime suicide attempt (odds ratio 2.65; 95% CI 1.37–5.11). A significant suicidality PRS-by-trauma exposure interaction emerged, such that veterans with higher levels of suicidality PRS and greater trauma burden had the highest probability of lifetime suicide attempt (16.6%), whereas the probability of attempts was substantially lower among those with high suicidality PRS and low trauma exposure (1.4%). The PRS-by-trauma interaction effect was enriched for genes implicated in cellular and developmental processes, and nervous system development, with variants annotated to the DAB2 and SPNS2 genes, which are implicated in inflammatory processes. Drug repurposing analyses revealed upregulation of suicide gene-sets in the context of medrysone, a drug targeting chronic inflammation, and clofibrate, a triacylglyceride level lowering agent.

Results suggest that genetic liability to suicidality is associated with increased risk of suicide attempt among veterans, particularly in the presence of high levels of cumulative trauma exposure. Additional research is warranted to investigate whether incorporation of genomic information may improve suicide prediction models.

Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment.

Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108).

Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application.

Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.