Clustering longitudinal symptom trajectories is increasingly used to characterise clinical heterogeneity in psychiatric disorders. However, the relative performance of distance-based and model-based approaches remains insufficiently studied in psychiatric settings, where limited follow-up waves, measurement variability, and heterogeneous patient responses may affect clustering reliability. We aimed to systematically compare distance-based and model-based (Latent Class Mixed Models, LCMMs) methods, evaluating their performance in recovering the number of groups, classification accuracy, and clinical interpretability. We analysed longitudinal symptom data from 237 first-episode psychosis patients (PEPs cohort). We also performed simulations (500 replicates per scenario) with known group structures (3 or 4), varying group-size distributions (balanced/unbalanced) and levels of group separation (well-separated, partially overlapping, strongly overlapping). Recovery of the true or optimal number of groups was assessed using multiple validity indices. Classification accuracy was measured by weighted Cohen’s κ . Clinical interpretability was evaluated in terms of within-group homogeneity, stability across replications, and minimum group size. In the PEPs cohort, distance-based methods consistently identified two stable and clinically interpretable trajectory groups, whereas LCMMs frequently suggested more complex structures, often including very small classes. Concordance between methodological families was low, indicating that patient stratification was strongly method-dependent: distance-based methods grouped patients primarily by overall symptom severity, whereas LCMMs distinguished groups according to trajectory slope. In simulations, both approaches performed well when groups were clearly separated, but performance declined with increasing overlap and group-size imbalance. Distance-based methods generally produced more stable and homogeneous partitions, whereas LCMMs showed greater variability across replications. In psychiatric longitudinal studies with few assessment waves and moderate sample sizes, distance-based clustering methods may provide a robust strategy for trajectory-based partitioning. Model-based approaches such as LCMMs may offer advantages when complex nonlinear trajectories are expected, but their stability may be limited under noisy or weakly separated conditions. Combining multiple evaluation criteria and considering clinical interpretability are essential for reliable subgroup identification.

The role of genetic factors shaping liability for psychosis remains unclear. Youth with first-episode, early-onset psychosis and youth at increased familial risk for psychosis show higher rates of co-occurring psychiatric diagnoses and cognitive difficulties than the general population. This study assessed polygenic scores (PGS) for psychiatric diagnoses, cognition and educational attainment in 414 youth: N = 69 cases with non-affective, early-onset psychosis (schizophrenia, schizophreniform and schizoaffective disorders), N = 62 cases with affective, early-onset psychoses (bipolar or depressive disorders with psychotic symptoms), N = 52 offspring of patients with schizophrenia, N = 94 offspring of patients with bipolar disorder, and N = 117 healthy controls. After quality control, PGS were calculated using the PRS-CS tool. Differences in PGS were examined by fitting binary logistic models. Sensitivity analyses ruled out effects of potential confounders. PGS for schizophrenia and bipolar disorder were higher in youth with non-affective, early-onset psychoses and offspring of patients with schizophrenia, and higher PGS scores for attention deficit hyperactivity disorder (ADHD) were found in youth with non-affective, early-onset psychoses and offspring of patients with bipolar disorder, relative to controls. PGS for cognition and educational attainment were lower in youth with non-affective, early-onset psychoses, compared with youth with affective, early-onset psychoses, offspring of bipolar disorder patients, and controls (all PFDR<0.05). Conclusion: These findings indicate shared and distinct genetic liability profiles influenced by patient and parental diagnoses. In addition to liability for schizophrenia and bipolar disorder, polygenic profiles for ADHD, cognition, and educational attainment may determine the genetic architecture of psychosis.

BACKGROUND:Cognitive reserve (CR) is a protective factor in first-episode psychosis (FEP), influencing cognitive, clinical, and functional outcomes. CR is shaped by a combination of genetic, clinical, and environmental factors, yet the extent of their respective contributions remains unclear. This study investigates the influence of polygenic risk scores (PRS), clinical and environmental variables on CR in FEP. METHODS:A cohort of 174 individuals with non-affective FEP, aged 25.5 (SD=5.3), was analyzed. CR was assessed using a socio-behavioral proxy. PRS for educational attainment (PRSEA), intelligence (PRSIQ), cognitive performance (PRSCP), occupational attainment (PRSOA), physical activity (PRSPA), and schizophrenia (PRSSZ) were calculated. Age at onset, socioeconomic status, birth weight, and family history of psychosis were considered. Multiple regression models were employed to evaluate the impact of the different predictors on CR. RESULTS:PRSEA (p=0.002), age at onset (p=5.32x10-5), and family history of psychosis (p=0.001) emerged as the strongest contributors to CR. Higher PRSEA was associated with higher levels of CR, while earlier age at onset and positive family history were associated with lower CR. The model incorporating environmental, clinical, and genetic variables explained 17.7% of the variance in CR, and the one without PRS explained 13.5%. The inclusion of PRSEA in the model improved the explanatory power (Δadj.R2=0.042) and predictive accuracy (ΔRMSE=-0.288). CONCLUSIONS:These findings highlight the role of precision psychiatry in better understanding CR. Early identification of individuals with earlier onset, family history of psychosis, and lower genetic predisposition to educational attainment may help characterize those with lower CR.

BACKGROUND:Psychosis is characterized by both genetic and neurostructural abnormalities. However, the mechanisms linking genetic risk to brain structural alterations remain unclear. This study investigates associations between polygenic risk scores (PRS) and brain structural measures in individuals experiencing a first-episode psychosis (FEP) and healthy controls (HC). METHODS:A total of 241 participants (130 FEP, 111 HC, mean age = 24.8 years, 34.9% females) underwent structural magnetic resonance imaging (MRI) and genotyping. PRS for schizophrenia, educational attainment, brain cortical thickness, and surface area were computed using PRS continuous shrinkage (PRS-CS). MRI data provided measures of cortical thickness, surface area, subcortical volumes, and hippocampal subfields. Associations between PRS and brain measures were assessed using generalized linear models within each group. RESULTS:FEP participants had significantly higher PRS for schizophrenia (p.adj = 1.56e-6) and lower PRS for educational attainment (p.adj = 0.006) compared to HC, but groups did not differ in neurostructural PRS. Neuroimaging revealed trend-level reductions in left hippocampal volume (p = 0.040, p.adj = 0.280) and significant reductions in specific hippocampal subfields in FEP. In PRS-brain structure analyses, significant associations were observed only in HC, while in FEP, educational attainment PRS showed nominal associations with multiple hippocampal subfields. CONCLUSION:By incorporating polygenic scores for brain structural traits, our study shows that neurostructural genetic risk does not differ between FEP and HC, even as FEP participants exhibit significant reductions in specific hippocampal subfields. Genetic influences on brain structure in early psychosis appear subtle and region-specific, underscoring the complex interplay between distinct genetic domains and neurodevelopment in psychosis.

BACKGROUND:Identifying patients with first-episode psychosis (FEP) who are unlikely to achieve early clinical recovery (ECR) is critical for personalised intervention and resource allocation. ECR - defined as the concurrent achievement of symptomatic and functional remission - represents a clinically meaningful outcome that captures both illness control and functional reintegration. AIMS:To develop and externally validate prediction models for ECR using clinical, cognitive and genetic data. METHOD:We analysed two large, independent Spanish cohorts: the primeros episodios psicóticos cohort (N = 335), for model development and internal validation, and the Programa Asistencial a las Fases Iniciales de Psicosis cohort (N = 668), for external validation. Forty-seven baseline clinical and cognitive variables and 87 polygenic risk scores (PRSs) were examined. Predictors were selected using penalised logistic regression. Logistic regression and three machine learning algorithms were compared for discrimination, calibration and clinical utility. RESULTS:The best-performing model was a logistic regression using six routinely collected clinical and cognitive predictors (duration of untreated psychosis, days of treated psychosis, baseline functioning, insight, executive function and cognitive reserve), with an optimism-corrected area under the receiver operating characteristic curve of 0.73 in development and 0.63 in external validation. PRS models showed limited external generalisability and did not improve prediction. Machine learning algorithms offered no advantage over regression models. CONCLUSIONS:A simple, interpretable logistic regression model based on routine clinical and cognitive variables can predict early recovery in FEP with acceptable generalisability. These findings support the use of transparent, clinically grounded models in early psychosis care and highlight the current limitations of genetic predictors for individualised treatment.

Schizophrenia (SZ) is a deleterious brain disorder characterised by its heterogeneity and complex symptomatology consisting of positive, negative and cognitive deficits. Current antipsychotic drugs ameliorate the positive symptomatology, but are inefficient in treating the negative symptomatology and cognitive deficits. The neurodevelopmental glutamate hypothesis of SZ has opened new avenues in the development of drugs targeting the glutamatergic system. One of these new therapies involves the positive allosteric modulators (PAMs) of metabotropic glutamate receptors, mainly types 2/3 (mGluR2/3). mGluR2/3 PAMs are selective for the receptor, present high tolerability and can modulate the activity of the receptor for long periods. There is not much research in clinical trials regarding mGluR2/3 PAMs. However, several lines of evidence from animal models have indicated the efficiency of mGluR2/3 PAMs. In this review, focusing on in vivo animal studies, we will specifically discuss the utilization of SZ animal models and the various methods employed to assess animal behaviour before summarising the evidence obtained to date in the field of mGluR2/3 PAMs. By doing so, we aim to deepen our understanding of the underlying mechanisms and the potential efficiency of mGluR2/3 PAMs in treating SZ. Overall, mGluR2/3 PAMs have demonstrated efficiency in attenuating SZ-like behavioural and molecular deficits in animal models and could be useful for the early management of the disorder or to treat specific subsets of patients.

Gene expression profiling studies could be a valuable tool in identifying the specific genes and pathways involved in the mechanism of action of clozapine, leading to a better understanding of the molecular biology underlying treatment-resistant schizophrenia (TRS). We aimed to identify the co-expressed modules that reflect the genetic differences between clozapine-treated and non-clozapine-treated patients with schizophrenia as a proxy of TRS. Gene expression of DLPFC samples from 26 subjects with schizophrenia (13 clozapine treated and 13 non-clozapine treated) were analyzed using Clariom S Human Array. Weighted gene co-expression network analysis (WGCNA) was applied to identify modules of co-expressed genes and to test its association with clozapine treatment. As a result of our analysis of the gene co-expression architecture in the DLPFC, among the 13 modules identified, one module (green) was significantly associated with clozapine treatment (p = 3.7 × 10−2). This module was significantly enriched in astrocyte markers (5.7 × 10−29) and genes involved in the polygenic architecture of TRS (1.6 × 10−2). This finding provides cell type-specific associations that could help in the interpretation of the neurobiological basis of TRS. A better understanding of the specific DLPFC cell types involved in clozapine treatment will contribute to the study of potential pathways and ultimately help improve psychiatric classification tools in personalized medicine.

Schizophrenia (SZ) is a complex mental disorder influenced by genetic, environmental, and neurobiological factors, with current treatments ineffective for negative symptoms and cognitive deficits. The glutamatergic hypothesis of SZ highlights the N-methyl-D-aspartic acid (NMDA) receptor dysfunction as a key factor, causing excitatory-inhibitory imbalance and synaptic inefficiency. Positive allosteric modulators (PAMs) of the metabotropic glutamate receptor 2 (mGluR2), such as JNJ-46356479 (JNJ), could be useful in treating these symptoms, especially when used in early stages of the disease. Previous results have shown that JNJ can reverse certain SZ-related behavioral and neuropathological deficits. This study evaluates, for the first time, the effects of early treatment with JNJ or clozapine (CLZ) in reversing molecular deficits related to glutamatergic and GABAergic pathways in mice postnataly exposed to ketamine (KET) on postnatal days (PND) 7, 9, and 11. Animals received JNJ or CLZ daily in the adolescent period (PND 35-60). Specifically, we investigated alterations in brain protein levels of VGLUT1, as a marker of glutamatergic synapses, and GAD65/67, as markers of GABAergic synapses. Changes in brain expression of 240 selected genes involved in glutamate and GABA pathways were also evaluated. Results demonstrated that postnatal KET exposure increased hippocampal VGLUT1 levels which were partially normalized after both pharmacological treatments, especially with JNJ. Additionally, we identified some genes that showed altered brain expression after drug treatment in our mouse model. In conclusion, this study provides evidence of SZ-related glutamate signaling alterations in adult mice postnatally exposed to KET, as well as of the effectiveness of JNJ in improving these alterations when administered during the early stages of the disease.

Psychopathological manifestations and cognitive impairments are core features of psychotic disorders. Polygenic risk scores (PRS) offer insights into the relationships between genetic vulnerability, symptomatology, and cognitive impairments. This study used a network analysis to explore the connections between PRS, cognition, psychopathology, and overall functional outcomes in individuals experiencing a first episode of psychosis (FEP). The study sample comprised 132 patients with FEP. Genetic data were used to construct PRS for mental disorders and cognitive traits via PRS-continuous shrinkage. We conducted comprehensive clinical and neuropsychological assessments at 2 months post-diagnosis and again at a 2-year follow-up. A network analysis was performed to generate two distinct networks and their centrality indices, encompassing 19 variables across domains such as symptoms, cognition, functioning, and PRS. Variables were grouped within related domains, and stronger relationships were observed within domains than between them. PRS for schizophrenia showed weak negative associations with attention, working memory, and verbal memory, while PRS for cognitive performance showed weak positive associations with attention. Negative symptoms were negatively associated with functioning and verbal memory at both the 2-month and 2-year assessments, as well as with social cognition at 2 years. Poor functioning was moderately related to greater severity of Positive and Negative Syndrome Scale dimensions. This study identified pathways linking PRS, cognition, symptoms, and functioning, suggesting that genetic risk may serve as a marker of vulnerability and disorder progression. The findings also highlight the importance of considering genetic predispositions alongside clinical and cognitive factors to better understand the heterogeneity of psychotic disorders.

Importance:Bipolar disorder (BD) and major depressive disorder (MDD) aggregate within families, with risk often first manifesting as early psychopathology, including attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders. Objective:To determine whether polygenic scores (PGS) are associated with mood disorder onset independent of familial high risk for BD (FHR-BD) and early psychopathology. Design, Setting, and Participants:This cohort study used data from 7 prospective cohorts enriched in FHR-BD from Australia, Canada, the Netherlands, Spain, and the US. Participants with FHR-BD, defined as having at least 1 first-degree relative with BD, were compared with participants without FHR for any mood disorder. Participants were repeatedly assessed with variable follow-up intervals from July 1992 to July 2023. Data were analyzed from August 2023 to August 2024. Exposures:PGS indexed genetic liability for MDD, BD, anxiety, neuroticism, subjective well-being, ADHD, self-regulation, and addiction risk factor. Semistructured diagnostic interviews with relatives established FHR-BD. ADHD or anxiety disorder diagnoses before mood disorder onset constituted early psychopathology. Main Outcomes and Measures:The outcome of interest, mood disorder onset, was defined as a consensus-confirmed new diagnosis of MDD or BD. Cox regression examined associations of PGS, FHR-BD, ADHD, and anxiety with mood disorder onset. Kaplan-Meier curves and log-rank tests evaluated the probability of onset by PGS quartile and familial risk status. Results:A total of 1064 participants (546 [51.3%] female; mean [SD] age at last assessment, 21.7 [5.1] years), including 660 with FHR-BD and 404 without FHR for any mood disorder, were repeatedly assessed for mental disorders. A total of 399 mood disorder onsets occurred over a variable mean (SD) follow-up interval of 6.3 (5.7) years. Multiple PGS were associated with onset after correcting for FHR-BD and early psychopathology, including PGS for ADHD (hazard ratio [HR], 1.19; 95% CI, 1.06-1.34), self-regulation (HR, 1.19; 95% CI, 1.06-1.34), neuroticism (HR, 1.18; 95% CI, 1.06-1.32), MDD (HR, 1.17; 95% CI, 1.04-1.31), addiction risk factor (HR, 1.16; 95% CI, 1.04-1.30), anxiety (HR, 1.15; 95% CI, 1.02-1.28), BD (HR, 1.14; 95% CI, 1.02-1.28), and subjective well-being (HR, 0.89; 95% CI, 0.79-0.99). High PGS for addiction risk factor, anxiety, BD, and MDD were associated with increased probability of onset in the control group. High PGS for ADHD and self-regulation increased rates of onset among participants with FHR-BD. PGS for self-regulation, ADHD, and addiction risk factors showed stronger associations with onsets of BD than MDD. Conclusions and Relevance:In this cohort study, multiple PGS were associated with mood disorder onset independent of family history of BD and premorbid diagnoses of ADHD or anxiety. The association between PGS and mood disorder risk varied depending on family history status.

Gene expression studies in dorsolateral prefrontal cortex (DLPFC) of subjects with schizophrenia have repeatedly reported alterations in immune and inflammatory responses as well as neuronal and mitochondrial processes. Whether the findings are due to schizophrenia and/or to the effect of antipsychotic (AP) drug exposure is particularly intriguing.In the present study, we performed a transcriptomic study in DLPFC of 16 schizophrenia subjects with no detectable AP blood levels at the time of death. Each schizophrenia subject was matched to a control subject for sex, age, postmortem interval and storage time of the samples. Weighted co-expression network analysis (WGCNA) of transcriptomic data identified 33 modules of co-expressed genes. One of these modules was significantly associated with schizophrenia diagnosis. Protein-protein interaction networks were built within the genes in the module and submitted to gene set enrichment analysis on biological processes. Results revealed the implication of immune and inflammatory responses, cytoskeleton regulation, neurotrophic factors and protein post-translational modifications. Analysis of the promoter regions of the clustered genes predicted the enrichment of 6 transcription factors, including SP1, previously associated with schizophrenia.Present results in DLPFC of AP-free schizophrenia subjects indicate that the identified biological processes, especially immune and inflammatory response alterations are representative of schizophrenia disorder and not a mere effect of acute AP exposure.

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their response to treatment, considering both treatment efficacy and toxicity. However, several limitations have hindered its translation into clinical practice. Here, we describe the rationale, aims and methodology of Applied Pharmacogenetics to Predict Response to Treatment of First Psychotic Episode (the FarmaPRED-PEP project), which aims to develop and validate predictive algorithms to classify FEP patients according to their response to antipsychotics, thereby allowing the most appropriate treatment strategy to be selected. These predictors will integrate, through machine learning techniques, pharmacogenetic (measured as polygenic risk scores) and epigenetic data together with clinical, sociodemographic, environmental, and neuroanatomical data. To do this, the FarmaPRED-PEP project will use data from two already recruited cohorts: the PEPS cohort from the “Genotype-Phenotype Interaction and Environment. Application to a Predictive Model in First Psychotic Episodes” study (the PEPs study from the Spanish abbreviation) (N=335) and the PAFIP cohort from “Clinical Program on Early Phases of Psychosis” (PAFIP from the Spanish abbreviation) (N = 350). These cohorts will be used to create the predictor, which will then be validated in a new cohort, the FarmaPRED cohort (N = 300). The FarmaPRED-PEP project has been designed to overcome several of the limitations identified in pharmacogenetic studies in psychiatry: (1) the sample size; (2) the phenotype heterogeneity and its definition; (3) the complexity of the phenotype and (4) the gender perspective. The global reach of the FarmaPRED-PEP project is to facilitate the effective deployment of precision medicine in national health systems.

OBJECTIVE:First episode of psychosis (FEP) is associated with glucose homeostasis abnormalities even before pharmacological intervention. Given inconclusive GWAS results regarding a direct genetic link between schizophrenia and type 2 diabetes mellitus (T2DM), we hypothesized that FEP patients may exhibit altered genetic risk for glycemic traits. We compared polygenic risk scores (PRS) for T2DM (PRST2DM), fasting glucose (PRSFG), and glycated hemoglobin (HbA1c) (PRSHbA1c) between FEP patients and controls, examining their associations with glycemic measures over 24 months. METHODS:We analyzed data from 242 FEP patients and 119 controls, assessing fasting serum glucose and HbA1c at baseline and 24 months. We examined cross-sectional and longitudinal associations between PRS and glycemic measures within each group. RESULTS:FEP patients and controls did not differ significantly in PRS. Significant associations were observed for PRSFG with baseline serum glucose in controls (p = 0.008), PRSFG during follow-up (p = 0.034), PRSHbA1c at 24 months (p = 0.018), and HbA1c longitudinally (p = 0.025). After multiple testing corrections, only the association between PRSFG and baseline serum glucose in controls remained significant (p_adj = 0.023). No associations were found for PRST2DM. CONCLUSIONS: Despite the link between FEP and glycemic disturbances, PRST2DM did not differ between FEP patients and controls. However, PRS for glycemic traits showed associations with glycemic measures in both groups before multiple testing correction, suggesting that genetic predisposition may influence glucose homeostasis in early psychosis. The absence of a direct association between common genetic variants underlying T2DM and early glycemic dysregulation in FEP underscores the importance of considering environmental factors and epigenetic mechanisms.

Genome-wide association studies (GWAS) have revealed the polygenic nature of treatment-resistant schizophrenia TRS. Gene expression imputation allowed the translation of GWAS results into regulatory mechanisms and the construction of gene expression (GReX) risk scores (GReX-RS). In the present study we computed GReX-RS from the largest GWAS of TRS to assess its association with clinical features. We perform transcriptome imputation in the largest GWAS of TRS to find GReX associated with TRS using brain tissues. Then, for each tissue, we constructed a GReX-RS of the identified genes in a sample of 254 genotyped first episode of psychosis (FEP) patients to test its association with clinical phenotypes, including clinical symptomatology, global functioning and cognitive performance. Our analysis provides evidence that the polygenic basis of TRS includes genetic variants that modulate the expression of certain genes in certain brain areas (substantia nigra, hippocampus, amygdala and frontal cortex), which at the same time are related to clinical features in FEP patients, mainly persistence of negative symptoms and cognitive alterations in sustained attention, which have also been suggested as clinical predictors of TRS. Our results provide a clinical explanation of the polygenic architecture of TRS and give more insight into the biological mechanisms underlying TRS.

Background This study investigates the relationship between environmental risk factors and severe mental disorders using genome-wide methylation data. Methylation profile scores (MPS) and epigenetic clocks were utilized to analyze epigenetic alterations in a cohort comprising 211 individuals aged 6–17 years. Participants included offspring of schizophrenia (n = 30) and bipolar disorder (n = 82) patients, and a community control group (n = 99). The study aimed to assess differences in MPS indicative of intrauterine stress and epigenetic aging across familial risk groups, and their associations with cognition, prodromal psychotic symptoms, and global functioning through statistical models. Results Individuals at high familial risk demonstrated significant epigenetic alterations associated with pre-pregnancy maternal overweight/obesity, pre-eclampsia, early preterm birth and higher birth weight (p.adj ≤ 0.001) as well as decelerated epigenetic aging in the Horvath and Hannum epigenetic clocks (p.adj ≤ 0.005). Among offspring of schizophrenia patients, more severe positive and general prodromal psychotic symptoms correlated with MPS related to maternal pre-pregnancy BMI and overweight/obesity (p.adj ≤ 0.008) as well as with accelerated epigenetic aging across all examined epigenetic clocks (p.adj ≤ 0.012). Conclusions These findings underscore the potential of methylation analysis to quantify persistent effects of intrauterine events and their influence on the onset of psychotic symptoms, particularly in high-risk populations. Further research is essential to elucidate the underlying biological mechanisms during critical early stages of neurodevelopment.

Schizophrenia and bipolar disorder exhibit substantial clinical overlap, particularly in individuals at familial high risk, who frequently present sub-threshold symptoms before the onset of illness. Severe mental disorders are highly polygenic traits, but their impact on the stages preceding the manifestation of mental disorders remains relatively unexplored. Our study aimed to examine the influence of polygenic risk scores (PRS) on sub-clinical outcomes over a 2-year period in youth at familial high risk for schizophrenia and bipolar disorder and controls. The sample included 222 children and adolescents, comprising offspring of parents with schizophrenia (n = 38), bipolar disorder (n = 80), and community controls (n = 104). We calculated PRS for psychiatric disorders, neuroticism and cognition using the PRS-CS method. Linear mixed-effects models were employed to investigate the association between PRS and cognition, symptom severity and functioning. Mediation analyses were conducted to explore whether clinical features acted as intermediaries in the impact of PRS on functioning outcomes. SZoff exhibited elevated PRS for schizophrenia. In the entire sample, PRS for depression, neuroticism, and cognitive traits showed associations with sub-clinical features. The effect of PRS for neuroticism and general intelligence on functioning outcomes were mediated by cognition and symptoms severity, respectively. This study delves into the interplay among genetics, the emergence of sub-clinical symptoms and functioning outcomes, providing novel evidence on mechanisms underpinning the continuum from sub-threshold features to the onset of mental disorders. The findings underscore the interplay of genetics, cognition, and clinical features, providing insights for personalized early interventions.

BACKGROUND:A significant proportion of people with clozapine-treated schizophrenia develop 'checking' compulsions, a phenomenon yet to be understood. AIMS:To use habit formation models developed in cognitive neuroscience to investigate the dynamic interplay between psychosis, clozapine dose and obsessive-compulsive symptoms (OCS). METHOD:Using the anonymised electronic records of a cohort of clozapine-treated patients, including longitudinal assessments of OCS and psychosis, we performed longitudinal multi-level mediation and multi-level moderation analyses to explore associations of psychosis with obsessiveness and excessive checking. Classic bivariate correlation tests were used to assess clozapine load and checking compulsions. The influence of specific genetic variants was tested in a subsample. RESULTS:A total of 196 clozapine-treated individuals and 459 face-to-face assessments were included. We found significant OCS to be common (37.9%), with checking being the most prevalent symptom. In mediation models, psychosis severity mediated checking behaviour indirectly by inducing obsessions (r = 0.07, 95% CI 0.04-0.09; P < 0.001). No direct effect of psychosis on checking was identified (r = -0.28, 95% CI -0.09 to 0.03; P = 0.340). After psychosis remission (n = 65), checking compulsions correlated with both clozapine plasma levels (r = 0.35; P = 0.004) and dose (r = 0.38; P = 0.002). None of the glutamatergic and serotonergic genetic variants were found to moderate the effect of psychosis on obsession and compulsion (SLC6A4, SLC1A1 and HTR2C) survived the multiple comparisons correction. CONCLUSIONS:We elucidated different phases of the complex interplay of psychosis and compulsions, which may inform clinicians' therapeutic decisions.

BACKGROUND:Studies have shown associations between polygenic risk scores for educational attainment (PRSEA), cognitive reserve (CR), cognition, negative symptoms (NS), and psychosocial functioning in first-episode psychosis (FEP). However, their specific interactions remain unclear. This study aimed to investigate the mediating roles of CR, cognition, and NS in the relationship between PRSEA and psychosocial functioning one year after a FEP. Additionally, we sought to explore the impact of two NS subtypes on this relationship: diminished Expression (EXP-NS) and Motivation and Pleasure (MAP-NS). METHODS:A total of 138 FEP participants, predominantly male (70%), with a mean age of 24.77 years (SD = 5.29), underwent genetic, clinical, and cognitive assessments two months after study enrollment. Functioning evaluation followed at one-year follow-up. To investigate the mediating role of CR, cognition, and NS in the relationship between PRSEA and functioning, a serial mediation model was employed. Two further mediation models were tested to explore the differential impact of EXP-NS and MAP-NS. Mediation analysis was performed using the PROCESS macro version 4.1 within SPSS version 26. RESULTS:The serial mediation model revealed a causal chain for PRSEA > CR > cognition > NS > Functioning (β = -3.08, 95%CI [-5.73, -0.43], p = 0.023). When differentiating by type of NS, only EXP-NS were significantly associated in the casual chain (β = -0.17, 95% CI [-0.39, -0.01], p < 0.05). CONCLUSIONS:CR, cognition and NS -specifically EXP-NS- mediate the association between PRSEA and psychosocial functioning at one-year follow-up in FEP patients. These results highlight the potential for personalized interventions based on genetic predisposition.