Sociodemographic Risk Factors for Serious Psychological Distress among U.S. Veterans: Findings from the 2016 National Health Interview Survey.

Serious Psychological Distress (SPD) is a measure of mental health associated with poor functioning. This study identified sociodemographic risk factors for SPD, among veterans using Veterans Health Administration (VHA), TRICARE or the Civilian Health and Medical Programs for Uniformed Services (CHAMP) (all referred herein as VA coverage) and compared risk factors for SPD to non-veterans. VA coverage offers preventative care and treatment for illnesses and injuries to veterans with the aim of improving their quality of life. Veterans with and with no SPD, using VA coverage aged 18 to 64 years were sampled from the 2016 National Health Interview Survey (NHIS) (n = 525 total, n = 48 veterans with serious psychological distress) were compared to each other and to non-veterans sampled from the NHIS (n = 24,121 total and n = 1055 with serious psychological distress), by sex, age group, race/ethnicity, education level, living arrangements, education level, number of chronic health conditions, and region of residence. The greatest proportion of veterans with SPD were female, middle aged (45-64 years), white, had less than a high school education, and lived alone or with other adults (compared to those living with a spouse/partner). The greatest proportion of veterans with SPD lived in the Southern and Western U.S. regions, and the smallest proportion lived in the Northeastern U.S. region. Hispanic and white veterans were at increased risk for SPD relative to black veterans, and relative to their same race/ethnic counterparts in the non-veteran civilian population. Additional analyses suggest that veterans with SPD experience greater barriers to care compared to veterans without SPD. Further research is warranted to examine access to mental and physical health care providers in U.S. regions with the greatest proportions of veterans with SPD. Particular attention is needed for female veterans due to their high rates of SPD relative to male veterans.

Study protocol: Identifying transcriptional regulatory alterations of chronic effects of blast and disturbed sleep in United States Veterans.

Injury related to blast exposure dramatically rose during post-911 era military conflicts in Iraq and Afghanistan. Mild traumatic brain injury (mTBI) is among the most common injuries following blast, an exposure that may not result in a definitive physiologic marker (e.g., loss of consciousness). Recent research suggests that exposure to low level blasts and, more specifically repetitive blast exposure (RBE), which may be subconcussive in nature, may also impact long term physiologic and psychological outcomes, though findings have been mixed. For military personnel, blast-related injuries often occur in chaotic settings (e.g., combat), which create challenges in the immediate assessment of related-injuries, as well as acute and post-acute sequelae. As such, alternate means of identifying blast-related injuries are needed. Results from previous work suggest that epigenetic markers, such as DNA methylation, may provide a potential stable biomarker of cumulative blast exposure that can persist over time. However, more research regarding blast exposure and associations with short- and long-term sequelae is needed. Here we present the protocol for an observational study that will be completed in two phases: Phase 1 will address blast exposure among Active Duty Personnel and Phase 2 will focus on long term sequelae and biological signatures among Veterans who served in the recent conflicts and were exposed to repeated blast events as part of their military occupation. Phase 2 will be the focus of this paper. We hypothesize that Veterans will exhibit similar differentially methylated regions (DMRs) associated with changes in sleep and other psychological and physical metrics, as observed with Active Duty Personnel. Additional analyses will be conducted to compare DMRs between Phase 1 and 2 cohorts, as well as self-reported psychological and physical symptoms. This comparison between Service Members and Veterans will allow for exploration regarding the natural history of blast exposure in a quasi-longitudinal manner. Findings from this study are expected to provide additional evidence for repetitive blast-related physiologic changes associated with long-term neurobehavioral symptoms. It is expected that findings will provide foundational data for the development of effective interventions following RBE that could lead to improved long-term physical and psychological health.

Candidate endophenotypes for genetic studies of suicidal behavior.

Twin, adoption, and family studies have established the heritability of suicide attempts and suicide. Identifying specific suicide diathesis-related genes has proven more difficult. As with psychiatric disorders in general, methodological difficulties include complexity of the phenotype for suicidal behavior and distinguishing suicide diathesis-related genes from genes associated with mood disorders and other suicide-associated psychiatric illness. Adopting an endophenotype approach involving identification of genes associated with heritable intermediate phenotypes, including biological and/or behavioral markers more proximal to genes, is an approach being used for other psychiatric disorders. Therefore, a workshop convened by the American Foundation for Suicide Prevention, the Department of Psychiatry at Columbia University, and the National Institute of Mental Health sought to identify potential target endophenotypes for genetic studies of suicidal behavior. The most promising endophenotypes were trait aggression/impulsivity, early-onset major depression, neurocognitive function, and cortisol social stress response. Other candidate endophenotypes requiring further investigation include serotonergic neurotransmission, second messenger systems, and borderline personality disorder traits.

Computational prediction of methylation status in human genomic sequences.

Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called hdfinder) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using hdfinder, we have depicted the entire genomic methylation patterns for all 22 human autosomes.