Association of oxidative stress, metacognition, and psychopathology in patients with schizophrenia: a case-control study

BackgroundMetacognitive deficits are common in schizophrenia (SZ) and may worsen symptoms and impair insight. Oxidative stress (OS) abnormalities have also been reported, but findings are inconsistent, and no study has examined their associations with metacognition and psychopathology.MethodsThis case-control study included 89 SZ patients and 90 healthy controls (HC). OS markers, including superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and glutathione peroxidase (GPX) were measured. The patient group and healthy control group underwent metacognition was assessed using the abbreviated Metacognitive Assessment Scale (MAS-A) and patients’ symptoms with the Positive and Negative Syndrome Scale (PANSS). Covariates included age, gender, education, BMI, and smoking, illness duration, onset age and medication.ResultsPatients showed significantly lower MAS-A total score and subscale scores (all p < 0.01) versus HC. Patients had lower SOD, CAT and GPX (130.69 vs 152.12 ng/L, 2.46 vs 6.62 ng/L, 158.09vs 197.75μmol/L) and higher MDA (9.22vs 7.34μmol/L) than controls (all p < 0.05). Partial correlation revealed that in patients: SOD was negatively correlated with positive/negative/PANSS total and MAS-A decentration scores; CAT was negatively correlated with general pathological/PANSS total scores, and positively correlated with MAS-A total score and its subscales (self-reflectivity, understanding the other’s mind, decentration, mastery), MDA was negatively correlated with negative symptom score and self-reflectivity score, and positively correlated with general pathological score; GPX was positively correlated with most clinical and metacognitive scores. Linear regression revealed SOD, CAT, and GPX significantly associated with the PANSS total score (β = -0.119, -6.169, -0.226; all p < 0.05), and with MAS-A total score (β = 0.021,2.879 0.049, all p < 0.001).ConclusionSchizophrenia patients exhibit OS abnormalities and metacognitive impairments. Greater OS severity correlates with worse metacognition and more severe psychopathology, suggesting OS as a key factor linking these domains.

Stigma and quality of life in hospitalized schizophrenia patient-family caregiver dyads in Northern China: an actor-partner interdependence model analysis

BackgroundSchizophrenia is a chronic and relapsing mental disorder that is consistently associated with a severely diminished quality of life (QoL) for patients. Existing research has predominantly focused on how the stigma experienced by patients with schizophrenia relates to their own QoL. However, stigma among family caregivers has received considerably less attention, and its potential association with patients’ QoL, in particular, remains underexplored. Therefore, this study aims to systematically analyze the dyadic associations of stigma—as experienced by both patients with schizophrenia and their family caregivers—with QoL, utilizing an actor-partner interdependence model (APIM). Through this framework, this study seeks to explore the interdependence of stigma between patients and their family caregivers and its correlational links to their quality of life.MethodsTwo hundred and sixty-four pairs of schizophrenic patients and their family caregivers were included, and the subjects’ stigma was measured using the Internalized Stigma of Mental Illness Scale and the Conjunctive Stigma Scale, respectively, and the quality of life was measured using the World Health Organization Quality of Life Measurement Short Form. The actor-partner effect of stigma on quality of life was explored by constructing an actor-partner reciprocity model.ResultsThe actor effect of stigma on quality of life was significant for people with schizophrenia and their family caregivers (β=-0.472, p < 0.001, β=-0.779, p < 0.001), and the partner effect of stigma on quality of life was significant for people with schizophrenia and their family caregivers (β=-0.128, p = 0.033, β=-0.419, p < 0.001).ConclusionIn future research and interventions aimed at improving the quality of life for people with schizophrenia and their caregivers, it is important to consider not only the individual’s own stigma, but also how the other person’s stigma is associated with one’s quality of life.

Association of MAP2 gene polymorphisms and altered expression with schizophrenia risk in a Chinese Han population

BackgroundSchizophrenia (SCZ) is a highly heritable primary psychotic disorder. The microtubule-associated protein 2 (MAP2) gene is essential for dendritic integrity and synaptic plasticity, positioning it as a key candidate for bridging genetic risk and neuropathology. Nevertheless, the role of common genetic variations within MAP2 in SCZ susceptibility remains to be elucidated.MethodsWe conducted a candidate gene association study of MAP2 in a Han Chinese cohort comprising 418 SCZ patients and 418 matched healthy controls. Targeted sequencing was used to genotype single nucleotide polymorphisms (SNPs). MAP2 mRNA levels were quantified by RT-qPCR and correlated with genotypes and clinical symptoms. Bioinformatic tools (such as GTEx, BrainSeq, 3DSNP, HaploReg, RegulomeDB and SNP2TFBS database) were employed for functional annotation of risk loci.ResultsWe identified multiple MAP2 SNPs associated with SCZ risk in a Han Chinese cohort. Specifically, the AA genotype of rs288057 and the GG genotype of rs288087 were significantly associated with increased disease risk (OR = 2.393 and 2.258, respectively). Expression analysis revealed a marked reduction in peripheral MAP2 mRNA levels in patients compared to controls. This downregulation was genotype-dependent: the risk AA at rs288057 and GG at rs288087 were correlated with lower mRNA levels, a finding supported by its significant eQTL effect in the GTEx and BrainSeq database. In silico annotation suggested rs288087 resides within a putative enhancer region, while rs288057 may affect a promoter-proximal regulatory site. Clinically, MAP2 expression showed a significant positive correlation with the severity of negative symptoms (SANS score). Furthermore, ROC analysis indicated that MAP2 expression levels distinguished patients from controls with an AUC of 0.728.ConclusionThis study identifies MAP2 as a schizophrenia risk gene, wherein non-coding variants likely reduce its expression via distinct regulatory mechanisms, linking this downregulation to core negative symptoms. These findings highlight MAP2’s pathophysiological and translational relevance.

Heat waves mess with your brain. Scientists are trying to figure out why.

It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western Europe. Yesterday, the UK recorded its highest ever June temperature at 36.1 °C (about 97 °F). But as the weather app on my phone confirmed, it felt like 39 °C.

It’s frightening that we are seeing such temperatures in the UK in June. According to the Met Office, the country’s national weather and climate service, June temperatures peaked at an average 19 °C (66 °F) in England between 1991 and 2020. Across Europe, the heat wave is likely to cause thousands of deaths. There will be other awful consequences for agriculture, infrastructure, and the health system.

But this week I want to look at what the heat does to our minds and brains. Personally, I’ve found it almost impossible to think straight. The heat is distracting and my mind is foggy. I dread to think about the conditions of people who work outdoors, in even hotter regions.

It’s not just exhaustion and confusion. The effects of heat on the brain can be deadly. And researchers are still trying to figure out why.

Studies have confirmed that as temperatures rise, people seem to get more irritable and more violent. Most of these studies are based on associations, though. It’s difficult to directly study how a heat wave might affect our thinking, says Catherine Thompson, a cognitive psychologist at Liverpool Hope University. 

She has been studying the effects of extreme heat on firefighters instead. It’s easier to measure people’s cognitive skills before and after they undergo scheduled training that involves entering a burning building.  

It’s early days, but the team found that firefighters found it harder to focus and control their attention immediately after heat exposure—something people in heat waves can empathize with, I’m sure. 

The firefighters’ skills returned to normal after 20 minutes or so of cooling down. But they’d experienced just 15 minutes of intense heat exposure. Thompson doesn’t know what the effects of living through a days-long heat wave might be—or how long they’ll last. Figuring that out might involve shipping cognitive test kits to thousands of people during the few days’ notice of an impending heat wave. “My guess [is] that no one’s done it because it’s just so difficult to do,” says Thompson. 

Still, researchers can learn about some of the impacts of heat waves through studies after the fact. And those studies suggest that the heat seems to have more disastrous outcomes for people with mental-health disorders. 

Those outcomes become apparent when temperatures rise above what is considered typical for a given region. “There seems to be a correlation where the hotter it gets, especially during the hottest times of the year, the worse the mental-health outcomes,” says Joshua Wortzel, who directs the Heat-Mind Lab at Hartford HealthCare in Connecticut.

In a study published in 2023, Emma Lawrence at the University of Oxford, who studies the effect of climate change on mental health, and her colleagues reviewed the evidence linking mental-health outcomes to ambient outdoor temperatures. They found that during heat waves, there was a 9.7% increase in the rate of hospital admissions for people with such conditions. 

“People who live with mental-health conditions are among the most susceptible to the physical impacts of heat,” says Lawrence. People with schizophrenia were found to have been three times more likely to die during the record-breaking heat wave that affected Canada in 2021, for example.

In order to protect people, we need a better understanding of the mechanisms underlying these effects. After all, a lot of things change when it’s very, very hot. Some people may end up stuck indoors, avoiding outdoor play and exercise, and it can be difficult to get a good night of sleep, for example. Sleep, socializing, and exercise are all really important for our mental health. 

But whether unusual heat does something specific to our brains is, as Wortzel puts it, “the million-dollar question.”

Research in lab animals suggests that excessive heat can alter the way chemical signals work in our brain. The levels of neurotransmitters like serotonin, for example, seem to increase when rats and mice are exposed to high temperatures, according to multiple studies. The heat may also interfere with the way networks in our brains communicate with each other. It might affect the way oxygen reaches our brain cells.

“There are so many biological reasons why brains may be negatively affected by heat,” says Wortzel.

Emerging research suggests that for whatever reason, children and young people are among the most vulnerable. In research published earlier this week, Wortzel and his colleagues saw a 2.97% increase in the suicide rate among people in the US aged 15 to 24 for every 1 °C increase in average monthly temperature. That’s more than double the increase seen in people over the age of 24 (which is concerning in its own right).

Other work hints that heat exposure might have long-term consequences for children’s brain development. Babies who were exposed to either extreme heat or cold appeared to have altered white matter by the time they were nine to 12 years old—although it’s not clear how these impacts might affect an individual child.

“It seems that extreme temperature exposure for very young children may affect their brain development,” says Lawrence, who spoke to me from Oxford. She was meant to be in London for Climate Action Week, but her event, which focused on extreme heat, ended up being canceled … owing to the extreme heat.

We are living through the effects of climate change. And that brings a new urgency to the question of how heat affects our brains. Children born in 2020 are predicted to experience around seven times the number of heat waves their grandparents did, says Lawrance. “[We] need to be serious about adapting to a warming world.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Mental Illness Shows Context-Specific Genetic Effects

Many DNA variants linked with neuropsychiatric disorders (NPD) that do not code for proteins depend on neuronal activation, a study suggests.

The findings, in Science, highlight the power of cell stimulation to reveal context-specific “hidden” genetic effects in conditions such as schizophrenia.

They suggest that genetic regulation is not fully revealed by measuring gene expression alone.

Instead, gene activity—at least in the brain—may depend on context and the physiological state of neurons.

“Liang et al. demonstrate that the genetic processes that underlie neuropsychiatric disease are heavily determined by a dynamic physiological environment rather than by fixed cellular conditions,” said Biao Zheng, PhD, and Panos Roussos, PhD, from Icahn School of Medicine at Mount Sinai in New York, in a Perspective article accompanying the study.

They added: “To understand disease genetics, we might need to study the genome in motion and not at rest.”

Genome-wide association studies have revealed hundreds of genetic loci associated with mental illness, with more than 280 identified for schizophrenia alone.

But many of these DNA regions do not encode proteins and their impact is often subtle and difficult to detect.

To investigate further, Lifan Liang, PhD, from the University of Chicago, and co-workers studied gene expression and chromatin accessibility in single neurons derived from induced pluripotent stem cells collected from a hundred human donors.

The single-cell multi-omics study involved assessing transcriptional and epigenomic profiles before and after neurons were activated through potassium-induced depolarization.

The team found that much of the activity in regulatory DNA regions only became apparent with neuronal stimulation.

Both the number of detectable expression quantitative trait loci (eQTLs)—genetic variants associated with differences in gene expression—and chromatin accessibility QTLs (caQTLs)—DNA variants associated with differences in chromatin accessibility—rose after neuronal stimulation.

Shared and cell type-specific transcription factors worked together, possibly through regulatory cascades, to drive cell type-specific neuronal responses to stimuli.

eQTLs after stimulation had substantially weaker overlap with brain eQTL catalogs derived from postmortem tissue compared with eQTLs before stimulation.

This suggested that many relationships between regulatory DNA activity and gene expression become detectable only during neuronal activation and could be missed by traditional tissue-based studies.

A higher number of caQTLs were associated with neuropsychiatric disease compared with eQTLs, suggesting that disease-associated genetic variants could have detectable effects on regulatory DNA even when downstream changes in gene expression were not obvious.

Supporting this, chromatin accessibility and transcriptional responses to neuronal activation often occurred at different times.

Regulatory regions associated with genes that respond rapidly to neuronal stimulation often remained accessible after transcription subsided. By contrast, some late response genes exhibited accessible chromatin before their expression was induced.

When taken together, these observations implied that chromatin accessibility can be an indication of both prior and future transcriptional potential.

“We identified thousands of cell type–specific and activity-dependent quantitative trait loci for gene expression (eQTLs) and chromatin accessibility (caQTLs), helping prioritize NPD risk variants and genes that manifested functional effects only upon neuronal stimulation,” the researchers asserted.

They added: “Our work provides mechanistic insights on neuron subtype–specific activity-dependent gene regulation, substantially expanding the repertoire of context-specific causal variants and genes for NPD and other brain traits.”

The post Mental Illness Shows Context-Specific Genetic Effects appeared first on Inside Precision Medicine.

Gender-specific symptom outcomes on cariprazine treatment: a 12-month naturalistic longitudinal follow-up study in schizophrenia

IntroductionA growing body of literature is focusing on third-generation antipsychotics and their unique characteristics, but few studies have examined gender as a crucial factor in response profiles. The present study aims to address this gap by analyzing the outcomes of 12-month naturalistic treatment with cariprazine to elucidate changes in specific psychopathological domains between men and women.MethodsThe present 12-month longitudinal naturalistic study involved a sample of individuals diagnosed with schizophrenia according to the DSM-5-TR treated with cariprazine at the outpatients’ psychiatric services of a major university and community hospitals in Italy. The assessments conducted included sociodemographic data, the Structured Clinical Interview for the DSM-5 (SCID-5), and the Positive and Negative Symptom Scale (PANSS) Total and Subscale scores, as well as the PANSS-derived Marder factors. The PANSS was administered at three time points: before starting the treatment with cariprazine (T0), after 6 months (T1), and after 12 months (T2).ResultsFifteen male and 17 female subjects were assessed at the three time points. The mean dose of cariprazine was 4.2 ± 1.3 mg for men and 4.0 ± 1.5 mg for women. Both genders exhibited improvements in all PANSS subscale symptoms after 6 and 12 months of cariprazine treatment compared to the baseline, with the only exception of the Uncontrolled hostility/excitement Marder factor among men. Progressive improvements through time points in symptom subscales were found in both sexes, reaching numerical differences in every PANSS subscale in both sexes at T2. Gender specifc response profiles emerged after 6 and 12 months of treatment in the PANSS subscales and items in men and women.DiscussionCariprazine exhibited significant efficacy in both sexes, with no significant differences between men and women despite a gender specific response profile emerged. Additional studies are needed to further investigate the efficacy profile and long-term outcome of cariprazine treatment by gender.

Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes

Schizophrenia’s genetic landscape just expanded dramatically. A new study in Nature Genetics identifies 641 previously unrecognized genes associated with schizophrenia, thanks to a modeling framework that captures how distant genetic variants regulate gene expression through co‑expression networks. The work reframes schizophrenia not as a collection of isolated genetic hits, but as a disorder shaped by long‑range regulatory relationships across the brain. The study is titled, “Co‑expression‑based models improve eQTL predictions for transcriptome‑wide association studies and highlight new schizophrenia‑associated genes.”

The research team, led by Giulio Pergola, PhD, at the Lieber Institute for Brain Development (LIBD), developed two trans‑aware predictive models—INGENE and MODULE—that quantify how variants far from a gene influence its expression through co‑regulated partners. Traditional transcriptome‑wide association studies (TWAS) focus almost exclusively on cis‑expression quantitative trait loci (cis–eQTLs), variants within ±1 Mb of a gene. But as the paper noted, “Most transcriptome‑wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained.” By contrast, the new models incorporate distal (trans) regulatory effects, capturing regulatory relationships that behave more like social networks than neighborhood blocks.

Using RNA‑seq data from six human post‑mortem brain regions and genetic data from more than 102,000 individuals, the team integrated cis‑based predictors (CIS, EpiXcan) with their new trans‑based frameworks. The combined approach improved gene‑expression prediction for 18,744 genes, and when applied to Psychiatric Genomics Consortium (PGC3) datasets, it identified 766 schizophrenia‑associated genes, including 641 not previously detected by TWAS.

Pergola said the field has been “looking for the light under the lamppost, focusing only on genes close to disease‑associated DNA variants.” By illuminating long‑range interactions, he explained, “we’ve essentially turned on lights across the entire neighborhood, revealing how distant genetic variants coordinate to build the genetic basis of schizophrenia.”

The findings converge on pathways involved in glutamate signaling, neuronal communication, immune processes, and neurodevelopment—biological systems repeatedly implicated in psychiatric risk. MODULE‑derived trans‑single nucleotide polymorphisms (SNPs) showed particularly strong enrichment for schizophrenia‑associated variants, and many overlapped with cis‑eQTLs for transcription factors such as GATAD2A, RERE, IRF3, and SP4, all previously prioritized in schizophrenia GWAS.

Daniel Weinberger, MD, CEO and director of LIBD, emphasized the shift in perspective: “Schizophrenia risk isn’t just about individual genes acting one after another—it’s about how networks of genes work together. Understanding these coordinated genetic programs brings us closer to precision psychiatry.”

By demonstrating that trans‑regulatory architecture is both detectable and biologically meaningful, the study provides a roadmap for expanding TWAS beyond local effects. It also underscores the importance of integrating multi‑region brain transcriptomics with large‑scale genetic cohorts to reveal disease‑relevant regulatory relationships.

The post Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes appeared first on GEN – Genetic Engineering and Biotechnology News.

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study

Background: Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting of preventive interventions. While prognostic models have primarily focused on psychosis, early recognition services require a transdiagnostic approach. Objective: This study aimed to predict global functioning within a 2-year follow-up using baseline clinical and structural magnetic resonance imaging (MRI) data in a population-based sample of young, help-seeking individuals presenting with affective and anxiety symptoms as well as attention-deficit hyperactivity disorder. Methods: We classified 357 help-seeking individuals aged 18‐35 years recruited from 9 sites as “impaired” (Global Assessment of Functioning [GAF] ≤60; n=228) or “nonimpaired” (GAF>60; n=129) at year 1 and/or year 2 follow-up. GAF classification group status at follow-up was predicted using linear support vector machine (SVM), decision tree, and large language model (LLM) Llama-3 using clinical assessments and/or structural MRI. Leave-one-site-out (SVM) or external sample (LLM) was used for validation. Results: SVM achieved balanced accuracy of 69.2% using clinical features only. Items related to baseline occupational functioning, interpersonal relationships, cognitive functioning, psychotic and affective symptoms, as well as the presence of anxiety disorder, were most predictive. The decision tree further reduced the feature set to 5 predictive items, achieving balanced accuracy of 76.6%. Although amygdala and hippocampal subregions achieved balanced accuracy of 57.1%, structural MRI did not improve the overall prediction. Llama-3 performed comparably well to SVM (balanced accuracy of 72.6%). Conclusions: Machine learning demonstrated good performance in predicting global functioning. Interestingly, the out-of-the-box LLM performed comparably well without being trained or fine-tuned, highlighting the potential of leveraging free-text data for mental health prognosis.