PTSD, Comorbidities, Gender, and Increased Risk of Cardiovascular Disease in a Large Military Cohort.

Importance: Posttraumatic stress disorder (PTSD) is a prevalent mental health problem that increases risk of cardiovascular disease (CVD). It is not known whether gender or comorbidities modify associations between PTSD and CVD. Objective: To assess risk of hypertension and atherosclerotic CVD (ASCVD) associated with PTSD in a predominantly young military population, and determine if gender or PTSD comorbidities modify these associations. Design setting and participants: Using administrative medical records, this longitudinal, retrospective cohort study assessed relationships of PTSD, gender, comorbidities (metabolic risk factors [MRF], behavioral risk factors [BRF], depression, and sleep disorders) to subsequent hypertension and ASCVD among 863,993 active-duty U.S. Army enlisted soldiers (86.2% male; 93.7%

Types of COVID-19 Disaster Work and Psychological Responses in National Guard Service Members.

INTRODUCTION: The National Guard (NG) served as a critical component of the U.S. response to the coronavirus disease 2019 pandemic. Understanding the impact of types of pandemic-related disaster work on mental health responses can aid in sustaining NG service members' health and preparation for subsequent activations and future pandemics. MATERIALS AND METHODS: We surveyed 1,363 NG unit (NGU) service members (88% Army; 80% enlisted; 32% 30 to 39 years old; 84% male) following activation in response to the pandemic. Surveys were administered between August and December 2020, which was approximately 2 to 3 months post-activation. Surveys assessed overall activation stress, participation in different types of disaster work, probable post-traumatic stress disorder (PTSD), anxiety and depression, and anger. A disaster work stress scale assessed different types of disaster work during activation and associated stress levels. For each individual, we calculated an overall work task stress (WTS) scaled score, with a maximum score of 100. Logistic regression analyses were conducted to examine the relationship of high-stress disaster work tasks to post-activation PTSD, anxiety and depression, and anger, adjusting for socio-demographic and service-related variables. The study was approved by the Institutional Review Board of the Uniformed Services University (USU) in Bethesda, MD. RESULTS: Among NGU service members, 12.7% (n = 172) described their activation as very/extremely stressful. The work tasks with the highest scaled scores were as follows: (1) Patient transportation (WTS scaled score = 100); (2) working with the dead (WTS = 82.2); and (3) working with families of coronavirus disease 2019 patients (WTS = 72.7). For each individual's work tasks, we identified the work task associated with the highest WTS score. The top one-third of WTS scores were classified as the high-stress group. Approximately 9% of participants (n = 111) had probable PTSD, 6.7% (n = 85) had clinically significant anxiety and depression, and 12.3% (n = 156) had high anger. Multivariable logistic regression analyses, adjusting for covariates, found that NGU service members exposed to the highest level of disaster WTS were more likely to report PTSD (odds ratio [OR] = 1.48 [95% confidence interval [CI] = 1.13-1.94], χ2 = 7.98), anxiety and depression (OR = 1.91 [95% CI = 1.17-3.13]; χ2 = 6.67), and anger (OR = 1.63 [95% CI = 1.13-2.37]; χ2 = 6.66) post-activation. CONCLUSIONS: Identifying work tasks associated with high levels of stress can help detect individuals at risk for adverse mental health responses post-exposure. Distinguishing features of high-stress work conditions can be generalized to other types of work conditions and disaster response and are important targets for planning and preventive efforts.

Association of 5-HTTLPR With Post-Traumatic Stress Disorder in US Service Members.

Objective: Post-traumatic stress disorder (PTSD) is a mental disorder that manifests after exposure to a stressful traumatic event, such as combat experience. Accumulated evidence indicates an important genetic influence in the development of PTSD. The serotonin transporter (5-HTT) gene has been identified as a candidate for PTSD and a polymorphism of the serotonin transporter-linked promoter region (5-HTTLPR) is associated with the disorder in the general population. However, whether it is associated with PTSD in active military service members has not been investigated. This study aimed to investigate the relationship between 5-HTTLPR and PTSD in service members. Methods: Leucocyte genomic DNA was extracted from service members, including those with PTSD (n = 134) or without PTSD (n = 639). The 5-HTTLPR polymorphism was detected by means of 2 stages of TaqMan fluorescent PCR assay. PTSD symptoms and symptom severity were assessed using the PTSD Checklist (PCL), a 17-item, DSM-based, self-report questionnaire with well-established validity and reliability. PTSD was determined based on endorsement of DSM-IV criteria and a PCL total score ≥ 44. Results: Significant differences in biallele distribution were observed between PTSD and controls (χ2 = 7.497, P = .024). The frequency of SS, SL, and LL genotypes in the PTSD group was 0.17, 0.56, and 0.27 respectively, compared to the frequencies of 0.27, 0.43, and 0.29 in non-PTSD controls. Carriers of the L allele had higher scores for reexperiencing and arousal symptoms on the PCL, compared to SS homozygote carriers (P < .05). The triallele genotypes showed no significant differences in distribution between the PTSD and control groups (P > .05) and no relationship with PTSD symptom severity. The interaction of triallelic genotypes of 5-HTTLPR and traumatic life events was associated with re-experiencing, avoidance, and arousal (P < .05 for all). Multiple regression analysis revealed significant correlations between both biallelic and triallelic genotypes of 5-HTTLPR, the interaction of the number of stressful lifetime events, and 5-HTTLPR genotypes with PCL total score (P < .001). Conclusion: Our findings suggested that 5-HTT might play a minor role in PTSD, and the interaction between 5-HTTLPR and the environment had effects on PCL score, complementing and emphasizing 5-HTT for PTSD, especially in the military population.

Perspectives of suicide loss survivors: Qualitative analysis of data from a psychological autopsy study of U.S. Army soldiers.

INTRODUCTION: Suicide loss survivors can provide information not otherwise available about the circumstances preceding a suicide. In this study, we analyzed interview data from suicide loss survivors collected as part of a psychological autopsy study of U.S. Army soldiers. METHODS: Next-of-kin (NOK) (n = 61) and Army supervisors (SUP) (n = 107) of suicide decedents (n = 135) who had died in the last 2-3 months answered open-ended questions about suicide risk factors, ideas for improving suicide prevention, and the impact of the suicide. Responses were coded using conventional content analysis methods to identify common themes. RESULTS: Many NOK (30%) and SUP (50%) did not observe any signs of risk preceding the soldier's suicide. The most common idea regarding suicide prevention from SUP was that the suicide was inevitable, whereas NOK were more likely to emphasize the importance of increasing mental health treatment and reducing stigma. Both NOK and SUP reported negative effects of the suicide, but SUP reported some positive effects (e.g., increased unit connectedness). CONCLUSIONS: Results underscore the challenges of using informants to identify soldiers at high risk of suicide, given many respondents did not observe any warning signs. Findings also highlight attitudinal barriers present in the military that, if targeted, may increase soldiers' help-seeking and willingness to disclose their risk.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. METHODS: Administrative data were examined for n=1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed one or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least two standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. RESULTS: 19.8% of patients had clinically significant ISI improvement. Model AU-ROC (SE) was 0.60 (0.03). The 20% of test sample patients with highest probabilities of improvement were twice as likely to have clinically significant improvement as the remaining 80% (36.5% versus 15.7%; χ21=9.2, p=.002). Nearly 85% of prediction accuracy was due to ten variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. CONCLUSIONS: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value.

Overview and Design of the Child Maltreatment in Military Families Life Course Study.

OBJECTIVE: This report presents an overview of the objectives, design, and analytic strategy of the Child Maltreatment in Military Families Life Course Study, an investigation of factors associated with child maltreatment in active duty military families. METHOD: The study uses a case-control retrospective research design and discrete-time survival methodology to examine service member demographic characteristics, family characteristics, military-related characteristics, and military family life events associated with child maltreatment incidents that meet the Department of Defense definition of child abuse or neglect. The sample includes all active duty families with a first occurrence of child maltreatment anytime between Fiscal Year (FY) 2009 and FY 2018 (n = 28,684), and a representative sample of control families with children under age of 18 during the same period (n = 589,417). Analyses include child maltreatment and domestic abuse data from the Family Advocacy Program Central Registry; sponsor socio-demographic, military-related, and family data from the Active Duty Military Personnel Master and Defense Enrollment Eligibility Reporting System data files; deployment data from the Contingency Tracking System; and mental health data from the Medical Data Repository. RESULTS AND CONCLUSIONS: Study results identify risk and protective factors associated with child maltreatment in military families, subgroups at elevated risk of child maltreatment, and periods of heightened risk during the military family life course. These results are expected to improve the ability to identify families most at-risk for particular types of child maltreatment and inform prevention strategies that promote the health and safety of military families.

Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk.

BACKGROUND: Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI). METHODS: We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA. RESULTS: In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI. CONCLUSIONS: PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA. LIMITATIONS: At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention. METHODS: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service. RESULTS: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs). CONCLUSIONS: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions.

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference.

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

National Guard Deployment in Support of COVID-19: Psychological and Behavioral Health.

INTRODUCTION: The National Guard (NG) served as a critical component of the USA's response to the Coronavirus Disease 2019 (COVID-19) pandemic, while concurrently managing their personal responses to the pandemic. Determining whether the activation of NG service members in response to the COVID-19 pandemic was associated with a greater psychological strain can identify NG's needs for mental health support. MATERIALS AND METHODS: We surveyed 3993 NG unit (NGU) service members (75% Army NG, 79% enlisted, 52% 30-49 years old, and 81% males) during the COVID-19 pandemic, with surveys administered between August and November 2020. Almost half (46%) of NGU service members reported being activated in response to COVID-19 (mean activation length = 18.6 weeks). Activated service members completed the survey approximately 2 to 3 months post-activation. Surveys assessed demographics, service-related characteristics, unit cohesion and positive leadership skills (leadership), and COVID-19 activation, and outcomes including probable post-traumatic stress disorder (PTSD), clinically significant anxiety and depression, and anger. Descriptive and logistic regression analyses were conducted. The study was approved by the Institutional Review Board of the Uniformed Services University of the Health Sciences in Bethesda, MD. RESULTS: In all, 9.7% met the criteria for probable PTSD, 7.6% reported clinically significant anxiety and depression, and 13.2% reported feeling angry/anger outbursts. Multivariate logistic regression analyses, adjusting for demographic and service-related characteristics, indicated that COVID-19 activation was not associated with a greater risk of PTSD, anxiety and depression, or anger. Regardless of activation status, NGU service members with low levels of unit cohesion and leadership were more likely to report PTSD and anger, and low levels of unit cohesion were associated with clinically significant anxiety and depression. CONCLUSIONS: COVID-19 activation did not increase the risk of mental health difficulties among NGU service members. However, low levels of unit cohesion were associated with the risk of PTSD, anxiety and depression, and anger, and low levels of leadership were associated with the risk of PTSD and anger. The results suggest a resilient psychological response to COVID-19 activation and the potential for strengthening all NG service members through enhancing unit cohesion and leadership support. Future research on specific activation exposures, including the type of work tasks in which service members are engaged, particularly those associated with high-stress work conditions, is needed to help better understand their activation experience and how it may influence post-activation responses.

Prospective association of attachment style with suicide attempts among US Army soldiers.

BACKGROUND: Insecure attachment styles are associated with retrospectively reported suicide attempts (SAs). It is not known if attachment styles are prospectively associated with medically documented SAs. METHODS: A representative sample of US Army soldiers entering service (n = 21 772) was surveyed and followed via administrative records for their first 48 months of service. Attachment style (secure, preoccupied, fearful, dismissing) was assessed at baseline. Administrative medical records identified SAs. Discrete-time survival analysis examined associations of attachment style with future SA during service, adjusting for time in service, socio-demographics, service-related variables, and mental health diagnosis (MH-Dx). We examined whether associations of attachment style with SA differed based on sex and MH-Dx. RESULTS: In total, 253 respondents attempted suicide. Endorsed attachment styles included secure (46.8%), preoccupied (9.1%), fearful (15.7%), and dismissing (19.2%). Examined separately, insecure attachment styles were associated with increased odds of SA: preoccupied [OR 2.5 (95% CI 1.7-3.4)], fearful [OR 1.6 (95% CI 1.1-2.3)], dismissing [OR 1.8 (95% CI 1.3-2.6)]. Examining attachment styles simultaneously along with other covariates, preoccupied [OR 1.9 (95% CI 1.4-2.7)] and dismissing [OR 1.7 (95% CI 1.2-2.4)] remained significant. The dismissing attachment and MH-Dx interaction was significant. In stratified analyses, dismissing attachment was associated with SA only among soldiers without MH-Dx. Other interactions were non-significant. Soldiers endorsing any insecure attachment style had elevated SA risk across the first 48 months in service, particularly during the first 12 months. CONCLUSIONS: Insecure attachment styles, particularly preoccupied and dismissing, are associated with increased future SA risk among soldiers. Elevated risk is most substantial during first year of service but persists through the first 48 months. Dismissing attachment may indicate risk specifically among soldiers not identified by the mental healthcare system.

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers.

BACKGROUND: Suicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation exome-wide in a large, ancestrally diverse sample. METHODS: We sequenced whole genomes of 13,584 soldiers from the Army STARRS (Army Study to Assess Risk and Resilience in Servicemembers), including 979 individuals with a history of suicide attempt. Uncommon, nonsilent protein-coding variants were analyzed exome-wide for association with suicide attempt using gene-collapsed and single-variant analyses. RESULTS: We identified 19 genes with variants enriched in individuals with history of suicide attempt, either through gene-collapsed or single-variant analysis (Bonferroni padjusted < .05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS2, and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with 9 genes expressed at the highest levels in the brain versus other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (padjusted = .035) in addition to nominally significant genes including YWHAE and ACTR6, all of which have reported associations with other mental disorders. CONCLUSIONS: These results advance the molecular characterization of suicide attempt behavior and support the utility of whole-genome sequencing for complementing the findings of genome-wide association studies in suicide research.

Optimism, Sociability, and the Risk of Future Suicide Attempt among U.S. Army Soldiers.

INTRODUCTION: Most research on suicide attempts among U.S. service members has been focused on risk factors that occur during service. There is an important gap in our understanding of premilitary factors, such as personality characteristics, that may be associated with future suicide attempt risk during service. Of particular importance is identifying risk factors for the 1/3 of suicide attempters who never receive a mental health diagnosis (MH-Dx)-and therefore are not identified as having a mental health problem in the military healthcare system-prior to their suicide attempt. MATERIALS AND METHODS: Using two components of the Army Study to Assess Risk and Resilience in Servicemembers, we examined the association of personality facets from the Tailored Adaptive Personality Assessment System, a computerized instrument administered prior to entering service, with medically documented suicide attempts during service. A 2010-2016 sample of historical administrative records from U.S. Regular Army enlisted soldiers with complete data on 11 commonly administered Tailored Adaptive Personality Assessment System facets was examined using a series of logistic regression analyses to identify the facets associated with future suicide attempt. Significant facets were then applied to data from a longitudinal cohort study of 11,288 soldiers surveyed upon entering basic combat training and followed via administrative records for their first 48 months of service. This research was approved by the Institutional Review Boards at the collaborating institutions. RESULTS: Analysis of the historical administrative data (87.0% male, 61.6% White non-Hispanic), found that low Optimism (odds ratio (OR) = 1.2 [95% CI = 1.0-1.4]) and high/low (vs. moderate) Sociability (OR = 1.3 [95%CI = 1.1-1.6]) were associated with suicide attempt after adjusting for other univariable-significant facets and socio-demographic and service-related variables. When examined in the longitudinal survey cohort, low Optimism (OR = 1.7 [95% CI = 1.1-2.4]) and high/low (vs. moderate) Sociability (OR = 1.7 [95% CI = 1.1-2.5]) were still associated with increased odds of documented suicide attempt during service, even after adjusting for each other, socio-demographic and service-related variables, and medically documented MH-Dx. Mental health diagnosis had a significant two-way interaction with Optimism (F = 5.27, p = 0.0236) but not Sociability. Stratified analyses indicated that low Optimism was associated with suicide attempt among soldiers without, but not among those with, a MH-Dx. Interactions of Optimism and Sociability with gender were nonsignificant. In the full model, population attributable risk proportions for Optimism and Sociability were 15.0% and 18.9%, respectively. Optimism and Sociability were differentially associated with suicide attempt risk across time in service. CONCLUSIONS: Optimism and Sociability, assessed prior to entering U.S. Army service, are consistently associated with future suicide attempt during service, even after adjusting for other important risk factors. While Sociability is equally associated with suicide attempt among those with and without a MH-Dx, Optimism is specifically associated with suicide attempt among soldiers not identified in the mental healthcare system. Risk differences across time in service suggest that Optimism and Sociability interact with stressors and contextual factors in particular developmental and Army career phases.

A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Associations of active-duty mental health trajectories with post-military adjustment: Results from the STARRS Longitudinal Study.

BACKGROUND: Many servicemembers experience difficulties transitioning from military to civilian life. We examined whether changes in mental health observed during active duty were associated with indices of post-military adjustment. METHODS: Survey data from the multi-wave Army STARRS Pre/Post Deployment Study (PPDS; conducted 2012-2014) were linked to follow-up data from wave 1 of the STARRS Longitudinal Study (STARRS-LS1; conducted 2016-2018). Empirical Bayes estimates of intercepts and slopes of posttraumatic stress, problematic anger, and depressive symptoms during the PPDS were extracted from mixed-effects growth models and evaluated as predictors of life stress among 1080 participants who had separated or retired from the Army at STARRS-LS1; and of job satisfaction among 586 veterans who were employed at STARRS-LS1. RESULTS: Higher average levels and larger increases in posttraumatic stress, anger, and depression over the deployment period were each associated with increased stress and (in the case of anger and depression) reduced job satisfaction. Posttraumatic stress and anger slopes were associated with overall stress (b = 5.60, p < 0.01 and b = 15.64, p = 0.04, respectively) and relationship stress (b = 5.50, p = 0.01 and b = 22.86, p = 0.01, respectively) beyond the average levels of those symptoms. LIMITATIONS: Some transition-related difficulties may have resolved before outcome assessment; some measures were not previously validated. CONCLUSIONS: Larger increases in posttraumatic stress and anger over a deployment period were associated with increased stress after leaving the Army, even after controlling for average symptom levels during the same period. Monitoring changes in mental health during active duty may help identify personnel who need additional support to facilitate the military-to-civilian transition.

Predictors of suicide attempt within 30 days of first medically documented major depression diagnosis in U.S. army soldiers with no prior suicidal ideation.

BACKGROUND: Understanding mental health predictors of imminent suicide attempt (SA; within 30 days) among soldiers with depression and no prior suicide ideation (SI) can inform prevention and treatment. The current study aimed to identify sociodemographic and service-related characteristics and mental disorder predictors associated with imminent SA among U.S. Army soldiers following first documented major depression diagnosis (MDD) with no history of SI. METHODS: In this case-control study using Army Study to Assess Risk and Resilience in Servicemembers (STARRS) administrative data, we identified 101,046 active-duty Regular Army enlisted soldiers (2010-2016) with medically-documented MDD and no prior SI (MDD/No-SI). We examined risk factors for SA within 30 days of first MDD/No-SI using logistic regression analyses, including socio-demographic/service-related characteristics and psychiatric diagnoses. RESULTS: The 101,046 soldiers with documented MDD/No-SI were primarily male (78.0%), < 29 years old (63.9%), White (58.1%), high school-educated (74.5%), currently married (62.0%) and < 21 when first entering the Army (56.9%). Among soldiers with MDD/No-SI, 2,600 (2.6%) subsequently attempted suicide, 16.2% (n = 421) within 30 days (rate: 416.6/100,000). Our final multivariable model identified: Soldiers with less than high school education (χ23 = 11.21, OR = 1.5[95%CI = 1.2-1.9]); combat medics (χ22 = 8.95, OR = 1.5[95%CI = 1.1-2.2]); bipolar disorder (OR = 3.1[95%CI = 1.5-6.3]), traumatic stress (i.e., acute reaction to stress/not PTSD; OR = 2.6[95%CI = 1.4-4.8]), and "other" diagnosis (e.g., unspecified mental disorder: OR = 5.5[95%CI = 3.8-8.0]) diagnosed same day as MDD; and those with alcohol use disorder (OR = 1.4[95%CI = 1.0-1.8]) and somatoform/dissociative disorders (OR = 1.7[95%CI = 1.0-2.8]) diagnosed before MDD were more likely to attempt suicide within 30 days. Currently married soldiers (χ22 = 6.68, OR = 0.7[95%CI = 0.6-0.9]), those in service 10 + years (χ23 = 10.06, OR = 0.4[95%CI = 0.2-0.7]), and a sleep disorder diagnosed same day as MDD (OR = 0.3[95%CI = 0.1-0.9]) were less likely. CONCLUSIONS: SA risk within 30 days following first MDD is more likely among soldiers with less education, combat medics, and bipolar disorder, traumatic stress, and "other" disorder the same day as MDD, and alcohol use disorder and somatoform/dissociative disorders before MDD. These factors identify imminent SA risk and can be indicators for early intervention.

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment.

Importance: Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective: To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants: This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures: Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results: This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance: In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.