<![CDATA[Study ties higher schizophrenia rates in Black Americans to neighborhood vulnerability, spotlighting faster early-psychosis care and social supports.]]>
A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations
ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.
Family psychoeducation to support patients with psychotic illness: two-year outcomes from a pre–post longitudinal pilot study
BackgroundPsychoeducation for families of young adults with psychosis is an evidence-based intervention that alleviates carer burden. The implementation of programming is limited, leaving family carers shouldering a heavy burden without appropriate support.ObjectiveThis pre-post longitudinal pilot study evaluated the preliminary outcomes of a psychoeducational group intervention for family carers of young adults with psychosis, aimed at building skills and reducing carer burden to support recovery in their loved ones.MethodsThe intervention, co-developed and co-facilitated by healthcare professionals and individuals with family lived experience, was delivered in Edmonton, Canada. Participants (n= 13) completed the Family Burden Interview Schedule (FBIS) at pre-intervention, post-intervention, and at 6, 12, and 24-month follow-up. Linear mixed models assessed burden scores over time.ResultsThe overall model of total burden did not reach statistical significance. Exploratory post-hoc comparisons indicated a significant total burden reduction from pre-intervention to 6-months (p = 0.032), with no other significant changes. The overall family interaction burden subscale model showed no significant effect of time. Exploratory post-hoc analyses indicated a decrease in family interaction burden from pre- to post-intervention (p = 0.026) and to 6- months (p = 0.032), with no other significant changes.ConclusionThis pilot study provides preliminary and hypothesis-generating findings suggesting a co-produced, skills- and knowledge-based psychoeducational intervention may be associated with reductions in carer burden, particularly in the domain of family relations. Given the small sample size, further research with sufficient statistical power is warranted to evaluate the long-term impact and accessibility of the intervention and inform its integration into early psychosis care.
<![CDATA[Key schizophrenia facts: early warning signs, brain changes, treatment limits—and how AI could reveal biomarkers for more personalized care.]]>
Roles of NRXN1 in neuropsychiatric disorders: from genetic lesion to molecular mechanism
Numerous neuropsychiatric disorders frequently exhibit overlapping genetic risk factors, implying the molecular basis for their comorbidity. Nevertheless, the pathogenesis of these disorders remains elusive, particularly regarding how genetic variations impair the physiological function of risk genes and contribute to disease phenotypes. Neurexin 1 protein, encoded by NRXN1 gene, belongs to the neurexin family of presynaptic adhesion molecules. And neurexin 1 is involved in synaptogenesis and the maintenance of synaptic action. Genetic variations of NRXN1 have been demonstrated to be associated with a spectrum of neuropsychiatric disorders. Herein, this review focuses on the most recent and relevant literature concerning the genetic and molecular mechanisms through which NRXN1 variants contribute to the pathogenesis of neuropsychiatric disorders, particularly schizophrenia and autism spectrum disorder. Among them, we propose the isoform-dependent excitation-inhibition imbalance hypothesis of NRXN1 in autism spectrum disorder. And this hypothesis may account for both the elevated and decreased excitation-inhibition ratios observed in diverse individuals with autism spectrum disorder. Moreover, both schizophrenia and autism spectrum disorder involve deletions and alternative splicing of NRXN1, offering molecular evidence for their comorbidity. Then, we analyzed and summarized the current research status of NRXN1 in other neuropsychiatric disorders, including attention-deficit hyperactivity disorder, insomnia, epilepsy, suicide, and depression. Additionally, available limited researches on NRXN1-targeted therapeutic strategies and associated pharmacological studies are also incorporated. Finally, we discussed existing challenges in NRXN1 research within the context of neuropsychiatric disorders and proposed potential avenues to overcome these obstacles.
Romanian male patients with the dual diagnosis of schizophrenia and alcohol use disorder: a prospective study of clinical, social, and treatment-related factors affecting quality of life
BackgroundSchizophrenia frequently co-occurs with alcohol use disorder (AUD), resulting in a complex clinical profile associated with poor functional outcomes and reduced quality of life (QoL). Although both conditions independently impair psychosocial functioning, few studies have examined the combined effects of clinical, social, and treatment-related factors on QoL in patients with this dual diagnosis.MethodsThis prospective observational study included 88 male inpatients diagnosed with schizophrenia and comorbid AUD and who were followed over a 6-month period. Quality of life was assessed using the World Health Organization Quality of Life–BREF (WHOQoL–BREF). The clinical variables included severity of psychotic symptoms (Positive and Negative Syndrome Scale), alcohol use severity (Michigan Alcohol Screening Test), and treatment characteristics. Social and personal factors, such as self-care capacity, social support, education, and legal problems, were also evaluated. Multivariable regression analyses were conducted to identify predictors of QoL at baseline and follow-up.ResultsAt baseline, higher QoL was significantly associated with greater self-care capacity, social support, and higher positive symptom scores, while the need for antipsychotic treatment was associated with lower QoL. At the 6-month follow-up, better QoL was predicted by greater self-care capacity, higher educational level, and receipt of anti-craving medication. By contrast, negative and general psychopathology, medico-legal problems, and the need for antidepressant treatment were associated with poorer QoL. Alcohol use severity, as measured by the MAST, was not independently associated with QoL at either timepoint.ConclusionsIn patients with schizophrenia and comorbid AUD, QoL is shaped by a complex interaction of clinical severity, functional capacity, and treatment-related factors. Beyond symptom control, interventions targeting self-care, social functioning, and integrated addiction treatment appear essential to improve long-term outcomes. These findings support the implementation of a multidimensional, recovery-oriented approach for the management of patients with the dual diagnosis.
Assessing directional connections between symptoms, cognition, insight, and real-life functioning in schizophrenia: a partial ancestor graphs analysis
IntroductionSchizophrenia is a severe chronic mental disorder causing significant global disability. Understanding the intricate relationships between symptoms, cognitive functions, and real-life outcomes is essential for developing effective interventions. Prior research, while informative, could not often determine the direction of the association between these illness-related factors. This study aimed to investigate the possible causal connections within the interrelationships of these variables. MethodsThis cross-sectional study included 215 clinically stable patients diagnosed with schizophrenia. Comprehensive assessments covered psychopathology, neurocognition, social cognition, metacognition, clinical insight, and real-life functioning. Causal relationships were explored using Partial Ancestral Graphs, a causal discovery framework that accounts for mediators and confounders. The Greedy Fast Causal Inference algorithm was employed with 1,000 bootstrap replications to assign edge orientations.ResultsA central neurocognitive–metacognitive–functional system of directed connection emerged: visual learning was linked to attention/vigilance and working memory. Working memory showed a direct relationship with metacognition, which, in turn, was connected to real-life functioning. Two partly independent contributions to real-life functioning were also identified: conceptual disorganization and experiential negative symptoms, which were directly related to expressive deficits. Positive symptoms, depressive symptoms, and social cognition occupied peripheral positions, showing no significant connection with other variables. Unawareness and misattribution of symptoms showed an indeterminate association disconnected from the main network.DiscussionThe findings show a set of directed associations that start with neurocognitive abilities, pass through working memory and metacognition, and terminate in real-life functioning. Independently, conceptual disorganization and expressive negative symptoms also exert direct influences. These directed systems of connections provide operational guidance for clinical practice, highlighting critical targets for interventions such as cognitive remediation focused on working memory, metacognitive therapies, and strategies addressing disorganization and avolition, all aimed at improving real-life outcomes in schizophrenia.
Brain Histamine Map Links Genetic Factors to Mental Health and Psychiatric Disorders
A study headed by researchers at King’s College London and the University of Porto has mapped the histamine system in the brain. Histamine, a molecule more commonly associated with allergies, plays a separate but poorly understood role in brain function. The new study addresses this gap, building the first multiscale map of the histamine system which spans from genetics to behavior and related mental health conditions.
The findings provide a new framework for understanding how this often-overlooked chemical system contributes to brain function and could point towards new treatment strategies for histamine-related conditions such as depression, ADHD, and schizophrenia. The study was funded by the National institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre.
Daniel Martins, MD, PhD, visiting senior research fellow at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King’s College London, said, “This work provides a crucial foundation for future research. By integrating molecular biology, brain imaging, and computational analysis, it offers a new perspective on how neurotransmitter systems are organized across the human brain. As neuroscience moves toward more integrated and personalized models of mental health, understanding systems like histamine may prove essential for unlocking new approaches to diagnosis and treatment.”
Martins is first and corresponding author of the team’s published paper in Nature Mental Health, which is titled “Mapping histamine pathway networks in the human brain across cognition and psychiatric disorders.” In their paper the team concluded, “This study provides an integrated characterization of the histaminergic system in the human brain, leveraging transcriptomic, neuroimaging, and functional datasets to delineate its molecular organization and relevance to brain function underlying cognition and psychiatric disorders.”
Histamine is a neurotransmitter, a molecule crucial for neurons to communicate with one another, the authors explained. “Neuronal histamine plays a crucial role in the regulation of brain function, serving as a neuromodulator with widespread influence across multiple neurotransmitter systems.” However, neuroscience research has classically focused on understanding other neurotransmitter systems such as dopamine and serotonin.
As the investigators noted, the organization of histamine in the human brain remains incompletely characterized. However, they explained, dysregulation of the histaminergic system has been implicated in a number of neuropsychiatric conditions, including anxiety, depression, schizophrenia, and autism spectrum disorder (ASD), as well as neurodegenerative diseases including Alzheimer’s, Parkinson’s, and Huntington’s diseases. “Therefore, targeting the brain histamine system has garnered significant attention as a potential new therapeutic strategy for treating these disorders, with pharmacological interventions aimed at modulating histamine receptor activity showing promise in preclinical models.”
Histamine acts through four known histamine receptors, which are responsible for how the signal will influence receiver neurons. Each of these histamine receptors, (histamine receptor H1 (encoded by HRH1), H2 (HRH2), H3 (HRH3) and H4 (HRH4)), mediates distinct functions. For their newly reported study, Martins and colleagues carried out what they described as multimodal analysis, integrating transcriptomic, neuroimaging, developmental and functional datasets to map the architecture of the histaminergic system.
To build a comprehensive map of how histamine acts in the brain, researchers first combined genetic and molecular data with physical maps of the brain.
This revealed which brain regions receive more input from the brain’s histamine system, and which parts show greater capacity to respond to histamine. These molecular data were then linked with positron emission tomography imaging of histamine receptors in living individuals, as well as functional neuroimaging databases that map brain regions to specific cognitive processes and mental health conditions. This type of scan shows how different parts of the brain are working by tracking a tiny amount of radioactive tracer in real time.
Their results found that different histamine receptors were found on brain cells that either turn activity up (excitation) or turn it down (inhibition). “The findings reveal that histaminergic genes exhibit distinct cellular and regional expression profiles, closely aligning with known histaminergic neuroanatomy and function,” they wrote. “At the single-cell level, histamine receptor H1 and histamine receptor H2 were enriched in excitatory neurons, whereas histamine receptor H3 showed preferential expression in inhibitory populations.” This suggests histamine may be important in maintaining the balance between excitation and inhibition, a fundamental property of healthy brain function.
Brain regions with higher histamine-related gene expression were consistently associated with processes such as emotional regulation, stress and fear responses, decision-making, impulsivity, reward, sleep, and memory.
The parts of the brain where histamine-related genes were most active also overlapped significantly with brain regions known to be affected in several psychiatric conditions, including attention-deficit/hyperactivity disorder, major depressive disorder, schizophrenia, and anorexia nervosa. This is in keeping with previous hypotheses linking histamine to these disorders. “By linking histaminergic gene expression to brain-cell types, neurotransmitter systems, cognitive domains and psychiatric disorders, these correlational findings generate several hypotheses concerning histamine’s critical role in brain organization, neurodevelopment and mental health, which further experimental mechanistic work should prioritize and build onto investigate causal relationships,” the investigators concluded.
Martins said, “Current psychiatric treatments largely target neurotransmitters such as serotonin and dopamine, yet histamine interacts closely with these systems and influences their activity. By providing a detailed map of histamine-related pathways, this work suggests new opportunities for developing treatments that target this system more directly, particularly for symptoms such as cognitive dysfunction, fatigue, and impaired motivation.
While these findings do not establish a direct causal role, they suggest that histamine signalling may contribute to regional vulnerability in these disorders. This aligns with a growing view in psychiatry that mental health conditions arise from disruptions across interacting brain systems rather than a single chemical imbalance.”
This new map paints a neural picture of a previously lesser-studied molecule. It opens up future avenues of research into exactly what histamine is doing in various cell types and parts of the brain.
“We want to emphasise that these findings are hypothesis-generating and based on large-scale datasets that capture patterns rather than direct mechanisms,” commented senior author Steve Williams, PhD, professor of neuroimaging at IoPPN King’s College London. Future studies will focus on testing how histamine signaling changes in living individuals, for example through pharmacological interventions or longitudinal imaging approaches.
Co-author Daniel Van Wamelen, PhD, clinical senior lecturer in neuroscience at IoPPN, King’s College London and one of the authors on the paper said: “This kind of work is already taking place at King’s College London, for example in the iMarkHD project. In this project we use Positron Emission Tomography scans to study a specific histamine receptor (called H3) in people with Huntington’s disease, an inherited condition that affects the brain. The goal is to see how histamine activity changes in different parts of the brain over time, and how these changes relate to symptoms such as apathy, depression, and anxiety.”
The post Brain Histamine Map Links Genetic Factors to Mental Health and Psychiatric Disorders appeared first on GEN – Genetic Engineering and Biotechnology News.
<![CDATA[Anosognosia drives untreated schizophrenia into homelessness and jail. Here’s why civil care fails and how structured treatment can decrease the number of arrests.]]>
<![CDATA[Poor schizophrenia control drives relapse, homelessness and caregiver strain; data show $367B burden—why relapse prevention and LAIs matter.]]>