Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders.

BACKGROUND: Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders. METHODS: The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration. RESULTS: Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006). CONCLUSIONS: Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Comorbid Patterns in the Homeless Population: A Theoretical Model to Enhance Patient Care.

INTRODUCTION: From the perspective of social determinants, homelessness perpetuates poor health and creates barriers to effective chronic disease management, necessitating frequent use of emergency department (ED) services. In this study we developed a screening algorithm (checklist) from common comorbidities observed in the homeless population in the United States. The result was a theoretical screening tool (checklist) to aid healthcare workers in the ED, including residents, medical students, and other trainees, to provide more efficacious treatment and referrals for discharge. METHODS: In this retrospective cohort study we used the Nationwide Emergency Department Sample (NEDS) to investigate comorbidities and ED utilization patterns relating to 23 injury-related, psychiatric, and frequent chronic medical conditions in the US adult (≥18 years of age) homeless population. Cases were identified from the NEDS database for 2014-2017 using International Classification of Diseases, 9th and 10 revisions, and Clinical Classification Software diagnosis codes. We performed a two-step cluster analysis including pathologies with ≥10% prevalence in the sample to identify shared comorbidities. We then compared the clusters by sociodemographic and ED-related characteristics, including age, gender, primary payer, and patient disposition from the ED. Chi-square analysis was used to evaluate categorical variables (ie, gender, primary payer, patient disposition from the ED), and analysis of variance for continuous variables (age). RESULTS: The study included 1,715,777 weighted cases. The two-step cluster analysis identified nine groups denominated by most prevalent disease: 1) healthy; 2) mixed psychiatric; 3) major depressive disorder (MDD); 4) psychosis; 5) addiction; 6) essential hypertension; 7) chronic obstructive pulmonary disease (COPD); 8) infectious disease; and (9) injury. The MDD, COPD, infectious disease, and Injury clusters demonstrated the highest prevalence of co-occurring disease, with the MDD cluster displaying the highest proportion of comorbidities. Although the addiction cluster existed independently, substance use was pervasive in all except the healthy cluster (prevalence 36-100%). We used the extracted screening algorithm to establish a screening tool (checklist) for ED healthcare workers, with physicians as the first point of contact for the initial use of the screening tool. CONCLUSION: Healthcare workers in the ED, including residents, medical students, and other trainees, provide services for homeless ED users. Screening tools (checklists) can help coordinate care to improve treatment, referrals, and follow-up care to reduce hospital readmissions. The screening tool may expedite targeted interventions for homeless patients with commonly occurring patterns of disease.

A Diagnosis and Biotype Comparison Across the Psychosis Spectrum: Investigating Volume and Shape Amygdala-Hippocampal Differences from the B-SNIP Study.

OBJECTIVE: Brain-based Biotypes for psychotic disorders have been developed as part of the B-SNIP consortium to create neurobiologically distinct subgroups within idiopathic psychosis, independent from traditional phenomenological diagnostic methods. In the current study, we aimed to validate the Biotype model by assessing differences in volume and shape of the amygdala and hippocampus contrasting traditional clinical diagnoses with Biotype classification. METHODS: A total of 811 participants from 6 sites were included: probands with schizophrenia (n = 199), schizoaffective disorder (n = 122), psychotic bipolar disorder with psychosis (n = 160), and healthy controls (n = 330). Biotype classification, previously developed using cognitive and electrophysiological data and K-means clustering, was used to categorize psychosis probands into 3 Biotypes, with Biotype-1 (B-1) showing reduced neural salience and severe cognitive impairment. MAGeT-Brain segmentation was used to determine amygdala and hippocampal volumetric data and shape deformations. RESULTS: When using Biotype classification, B-1 showed the strongest reductions in amygdala-hippocampal volume and the most widespread shape abnormalities. Using clinical diagnosis, probands with schizophrenia and schizoaffective disorder showed the most significant reductions of amygdala and hippocampal volumes and the most abnormal hippocampal shape compared with healthy controls. Biotype classification provided the strongest neuroanatomical differences compared with conventional DSM diagnoses, with the best discrimination seen using bilateral amygdala and right hippocampal volumes in B-1. CONCLUSION: These findings characterize amygdala and hippocampal volumetric and shape abnormalities across the psychosis spectrum. Grouping individuals by Biotype showed greater between-group discrimination, suggesting a promising approach and a favorable target for characterizing biological heterogeneity across the psychosis spectrum.

Association of white matter microstructure and extracellular free-water with cognitive performance in the early course of schizophrenia.

Schizophrenia (SZ) is proposed as a disorder of dysconnectivity underlying cognitive impairments and clinical manifestations. Although previous studies have shown extracellular changes in white matter of first-episode SZ, little is known about the transition period towards chronicity and its association with cognition. Free-water (FW) imaging was applied to 79 early course SZ participants and 29 controls to detect white matter axonal and extracellular differences during this phase of illness. Diffusion-weighted images were collected from two sites, harmonized, and processed using a pipeline separately modeling water diffusion in tissue (FAt) and extracellular space (FW). Tract-Based Spatial Statistics was performed using the ENIGMA-DTI protocols. SZ showed FAt reductions in the posterior thalamic radiation (PTR) and FW elevations in the cingulum compared to controls, suggesting FAt and FW changes in the early course of SZ. In SZ, greater FAt of the fornix & stria terminalis (FXST) was positively associated with Theory of Mind performance; average whole-brain FAt, FAt of the FXST and the PTR were positively associated with greater working memory performance; average whole-brain FAt was positively associated with visual learning. Further studies are necessary to better understand the neurobiological mechanisms of SZ for developing intervention strategies to preserve brain structure and function.

Social cognition in early course of schizophrenia: Exploratory factor analysis.

Social cognition is a central contributor to social functioning in schizophrenia. A better understanding of the underlying structure of social cognition in the early course schizophrenia could help us identify more precise targets for intervention in this population. In the present study, we performed an Exploratory Factor Analysis (EFA) on 90 patients within the early course of schizophrenia using 11 validated subtests assessing various domains of social cognitive skills. The factors derived from this analysis were then used to investigate relationships between these distinct domains of social cognition skills and neurocognitive performance, clinical symptoms, and social functioning satisfaction. The results revealed the presence of a 3-factor solution, representing the domains of Emotion Management, Emotion Recognition, and Theory of Mind, together accounting for 55.88% of the variance. Moreover, higher scores on the Theory of Mind factor were significantly related to higher social functioning satisfaction measures as well as with lower clinical symptoms severity. Our findings suggest that social cognitive skills are composed of three separate domains in the early course of schizophrenia and that theory of mind could be an important therapeutic target for early intervention.