Evaluation of the psychometric properties of the UBACC questionnaire in a multi-country psychiatric study in Africa.

BACKGROUND: The University of California, San Diego Brief Assessment of Capacity to Consent (UBACC) is a tool to assess the capacity of participants to consent in psychiatric research. However, little is known about the psychometric properties in low and middle-income countries. This study aimed to examine the psychometric properties of the UBACC. METHODS: We examined the reliability, latent factor structure, and item response of the first attempt of the UBACC items in a sample of 32,208 adults (16,467 individuals with psychosis and 15,741 controls) in Ethiopia, Kenya, South Africa, and Uganda; exploring these properties in the full sample and stratified by country, diagnostic status, sex, and ethnolinguistic language groups. RESULTS: Exploratory factor analysis (EFA) suggested a two-factor model for the overall sample. However, a three-factor model was more appropriate when examining the latent structure across country, language, and sex. Confirmatory factor analyses (CFA) revealed an adequately fitting three-factor model for the full sample and across country, sex, and language. A two-factor model, however, was more appropriate for English and Amharic languages. Across all groups, the internal consistency of the UBACC was low, indicating below-threshold reliability (Cronbach's α (95 % CI = 0.58 (0.57-0.59). Using a multidimensional item-response theory framework for the full sample revealed that UBACC item 8, measuring understanding of the benefits of study participation, was the most discriminating item. Many of the other items had below-threshold discriminating characteristics. CONCLUSION: EFA and CFA converged towards a two and three-dimensional structure for the UBACC, in line with the developers of the original scale. The differences in properties between populations and language groups, low internal consistency, and below-threshold item functioning suggest that investigations into the cultural and linguistic nuances are still warranted. Understanding the utility of consent tools, such as the UBACC, in underrepresented populations will be a part of the larger process which ensures that research participants are adequately protected.

Significant concordance of genetic variation that increases both the risk for obsessive-compulsive disorder and the volumes of the nucleus accumbens and putamen.

BACKGROUND: Many studies have identified changes in the brain associated with obsessive-compulsive disorder (OCD), but few have examined the relationship between genetic determinants of OCD and brain variation.AimsWe present the first genome-wide investigation of overlapping genetic risk for OCD and genetic influences on subcortical brain structures. METHOD: Using single nucleotide polymorphism effect concordance analysis, we measured genetic overlap between the first genome-wide association study (GWAS) of OCD (1465 participants with OCD, 5557 controls) and recent GWASs of eight subcortical brain volumes (13 171 participants). RESULTS: We found evidence of significant positive concordance between OCD risk variants and variants associated with greater nucleus accumbens and putamen volumes. When conditioning OCD risk variants on brain volume, variants influencing putamen, amygdala and thalamus volumes were associated with risk for OCD. CONCLUSIONS: These results are consistent with current OCD neurocircuitry models. Further evidence will clarify the relationship between putamen volume and OCD risk, and the roles of the detected variants in this disorder.Declaration of interestThe authors have declared that no competing interests exist.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

A review of systems biology research of anxiety disorders.

The development of "omic" technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders.

Amygdala Nuclei Volume and Shape in Military Veterans With Posttraumatic Stress Disorder.

BACKGROUND: The amygdala is a subcortical structure involved in socioemotional and associative fear learning processes relevant for understanding the mechanisms of posttraumatic stress disorder (PTSD). Research in animals indicates that the amygdala is a heterogeneous structure in which the basolateral and centromedial divisions are susceptible to stress. While the amygdala complex is implicated in the pathophysiology of PTSD, little is known about the specific contributions of the individual nuclei that constitute the amygdala complex. METHODS: Military veterans (n = 355), including military veterans with PTSD (n = 149) and trauma-exposed control subjects without PTSD (n = 206), underwent high-resolution T1-weighted anatomical scans. Automated FreeSurfer segmentation of the amygdala yielded 9 structures: basal, lateral, accessory basal, anterior amygdaloid, and central, medial, cortical, and paralaminar nuclei, along with the corticoamygdaloid transition zone. Subregional volumes were compared between groups using ordinary-least-squares regression with relevant demographic and clinical regressors followed by 3-dimensional shape analysis of whole amygdala. RESULTS: PTSD was associated with smaller left and right lateral and paralaminar nuclei, but with larger left and right central, medial, and cortical nuclei (p < .05, false discovery rate corrected). Shape analyses revealed lower radial distance in anterior bilateral amygdala and lower Jacobian determinant in posterior bilateral amygdala in PTSD compared with control subjects. CONCLUSIONS: Alterations in select amygdala subnuclear volumes and regional shape distortions are associated with PTSD in military veterans. Volume differences of the lateral nucleus and the centromedial complex associated with PTSD demonstrate a subregion-specific pattern that is consistent with their functional roles in fear learning and fear expression behaviors.

Neuroimaging genomics in psychiatry-a translational approach.

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene-gene epistasis, and gene-environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics-we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.