Sex: a key consideration in understanding the etiology of psychiatric disorders and improving treatment

Neurodegenerative pathways and metabolic changes in the hippocampus and cortex of mice exposed to urban particulate matter: Insights from an integrated interactome analysis

Recently, urban particulate matter (UPM) exposure has been associated with the development of brain disorders. This study uses bioinformatic analyses to elucidate the molecular unexplored mechanisms underlying the effects of UPM exposure on the brain. Mice are exposed to UPM (from 3 days to 20 weeks), and their behavioral patterns measured. We measure pathology and gene expression in the hippocampus and cortical regions of the brain. An integrated interactome of genes is established, which enriches information on metabolic processes. Using this network, we isolate the core genes that are differentially expressed in the samples. We observe cognitive loss and pathological changes in the brains of mice at 16 or 20 weeks of exposure. Through network analysis of core-differential genes and measurement of pathway activity, we identify differences in the response to UPM exposure between the hippocampus and cortex. However, neurodegenerative disease pathways are implicated in both tissues following short-term exposure to UPM. There were also significant changes in metabolic function in both tissues depending on UPM exposure time. Additionally, the cortex of UPM-exposed mice shows more similarities with psychiatric disorders than with neurodegenerative diseases. The connectivity map database is used to isolate genes contributing to changes in expression due to UPM exposure. New approaches for inhibiting or preventing the brain damage caused by UPM exposure can be developed by targeting the functions and selected genes identified in this study.

Gender differences in transdiagnostic domains and function of adults measured by DSM-5 assessment scales at the first clinical visit: a cohort study

BACKGROUND

Measurement-based care has been called for as best practice in psychiatric care and learning health systems and use of transdiagnostic measures was suggested as part of the DSM-5. Our objective is to examine gender differences in first visit socioeconomic, transdiagnostic, and functional characteristics of a dynamic, real-world measurement-based care cohort.

METHODS

Transdiagnostic, functional, and clinical measures were collected from 3,556 patients at first visit in an ambulatory psychiatric clinic. All patients were evaluated at the first visit by board-certified psychiatrists or licensed clinical psychologists. Demographic variables and clinical diagnoses were collected from the Electronic Medical Record. Self-report measures were collected that assessed transdiagnostic symptoms (DSM-5 Level 1 Cross-cutting Measure and Level 2 symptom scales), disability, alcohol use, attention deficit hyperactivity disorder (ADHD) symptoms, depression, anxiety, mania, suicidal thoughts and behaviors, and trauma exposure.

RESULTS

Men and women did not differ in age, BMI, household income, high school graduation rate, race, or ethnicity, but women were more likely to be formerly married and less likely to have commercial insurance. Compared to men, women reported significantly higher overall psychopathology on the transdiagnostic Level 1 Cross-cutting measure and had higher depression, anxiety, sleep, anger, ADHD combined presentation, and suicidality severity. Women also had higher disability scores than men. However, men reported higher alcohol, tobacco and substance use, and more risky behavior than women. Trauma exposure differed significantly by gender; men reported more exposure to accidents, war-related trauma, serious accidents, and major disasters and women reported more unwanted sexual contact.

CONCLUSIONS

This cross-sectional study of a transdiagnostic, ecologically-valid real-word measurement-based care cohort demonstrates gender differences in socioeconomic factors, trauma exposure, transdiagnostic symptoms, and functioning.

Deriving psychiatric symptom-based biomarkers from multivariate relationships between psychophysiological and biochemical measures

Identification of biomarkers for psychiatric disorders remains very challenging due to substantial symptom heterogeneity and diagnostic comorbidity, limiting the ability to map symptoms to underlying neurobiology. Dimensional symptom clusters, such as anhedonia, hyperarousal, etc., are complex and arise due to interactions of a multitude of complex biological relationships. The primary aim of the current investigation was to use multi-set canonical correlation analysis (mCCA) to derive biomarkers (biochemical, physiological) linked to dimensional symptoms across the anxiety and depressive spectrum. Active-duty service members (N = 2,592) completed standardized depression, anxiety and posttraumatic stress questionnaires and several psychophysiological and biochemical assays. Using this approach, we identified two phenotype associations between distinct physiological and biological phenotypes. One was characterized by symptoms of dysphoric arousal (anhedonia, anxiety, hypervigilance) which was associated with low blood pressure and startle reactivity. This finding is in line with previous studies suggesting blunted physiological reactivity is associated with subpopulations endorsing anxiety with comorbid depressive features. A second phenotype of anxious fatigue (high anxiety and reexperiencing/avoidance symptoms coupled with fatigue) was associated with elevated blood levels of norepinephrine and the inflammatory marker C-reactive protein in conjunction with high blood pressure. This second phenotype may describe populations in which inflammation and high sympathetic outflow might contribute to anxious fatigue. Overall, these findings support the growing consensus that distinct neuropsychiatric symptom patterns are associated with differential physiological and blood-based biological profiles and highlight the potential of mCCA to reveal important psychiatric symptom biomarkers from several psychophysiological and biochemical measures.