Prolonged childhood and adolescent loneliness (CAL) is linked to various adverse mental health outcomes, yet its impact on schizophrenia spectrum disorders (SSD) has been understudied. While loneliness is associated with psychosis and worsens symptoms in SSD, few studies have explored the long-term effects of early loneliness on SSD risk. Understanding how CAL interacts with genetic liability to schizophrenia is essential for identification of high-risk individuals.

This study evaluated whether prolonged CAL is associated with increased SSD risk and examined the interaction between CAL and genetic liability for schizophrenia. Gender differences in these associations were also explored.

Data from the European Gene–Environment Interactions in Schizophrenia (EU-GEI) study were analysed, including 1261 individuals with SSD, 1282 unaffected siblings and 1525 healthy controls. CAL was retrospectively assessed for periods before age 12 years and age 12–16 years. Genetic risk was measured using polygenic risk scores for schizophrenia. Logistic regression models and the Relative Excess Risk due to Interaction (RERI) method were used to examine gene–environment interactions, with stratification by gender.

Prolonged CAL was associated with higher odds of SSD (odds ratio [95% CI] = 5.20 [3.85−7.01] for loneliness before age 12; odds ratio [95% CI] = 7.26 [5.63−9.38] for loneliness during adolescence). The interaction between CAL and genetic risk was strongest during adolescence (RERI [95% CI] = 23.46 [10.75−53.53]). Females showed a greater effect (odds ratio [95 %CI] = 10.04 [6.80−14.94]) than males (odds ratio [95% CI] = 5.50 [3.95−7.66]). Incorporating CAL and genetic interaction increased predictive values to 17% for SSD risk − rising to 22.5% in females − compared with 2.6 and 2.8%, respectively, for genetic risk alone.

Prolonged CAL significantly increases SSD risk, particularly in females. The inclusion of CAL alongside genetic risk substantially enhances predictive accuracy. Early identification of CAL could inform preventive strategies, especially in genetically vulnerable populations.

Emerging evidence suggests a potential association between “leaky gut syndrome” and low-grade systemic inflammation in individuals with psychiatric disorders, such as schizophrenia. Gut dysbiosis could increase intestinal permeability, allowing the passage of toxins and bacteria into the systemic circulation, subsequently triggering immune-reactive responses. This study delves into understanding the relationship between plasma markers of intestinal permeability and symptom severity in schizophrenia. Furthermore, the influence of lifestyle habits on these intestinal permeability markers was determined.

Biomarkers of intestinal permeability, namely lipopolysaccharide-binding protein (LBP), lipopolysaccharides (LPS), and intestinal fatty acid binding protein (I-FABP), were analyzed in 242 adult schizophrenia patients enrolled in an observational, cross-sectional, multicenter study from four centers in Spain (PI17/00246). Sociodemographic and clinical data were collected, including psychoactive drug use, lifestyle habits, the Positive and Negative Syndrome Scale to evaluate schizophrenia symptom severity, and the Screen for Cognitive Impairment in Psychiatry to assess cognitive performance.

Results revealed elevated levels of LBP and LPS in a significant proportion of patients with schizophrenia (62% and 25.6%, respectively). However, no statistically significant correlation was observed between these biomarkers and the overall clinical severity of psychotic symptoms or cognitive performance, once confounding variables were controlled for. Interestingly, adherence to a Mediterranean diet was negatively correlated with I-FABP levels (beta = −0.186, t = −2.325, p = 0.021), suggesting a potential positive influence on intestinal barrier function.

These findings underscore the importance of addressing dietary habits and promoting a healthy lifestyle in individuals with schizophrenia, with potential implications for both physical and psychopathological aspects of the disorder.

One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia.

Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model’s accuracy (% agreement between patient classification of our model and the CGI-S).

Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728).

We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.

The sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.

About 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15–20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.

The finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.

Network analysis has been used to explore the interplay between psychopathology and functioning in psychosis, but no study has used dedicated statistical techniques to focus on the bridge symptoms connecting these domains. The current study aims to estimate the network of depressive, negative, and positive symptoms, general psychopathology, and real-world functioning in people with first-episode schizophrenia or schizophreniform disorder, focusing on bridge nodes.

Baseline data from the OPTiMiSE trial were analyzed. The sample included 446 participants (age 40.0 ± 10.9 years, 70% males). The network was estimated with a Gaussian graphical model, using scores on individual items of the positive and negative syndrome scale (PANSS), the Calgary depression scale for schizophrenia, and the personal and social performance scale. Stability, strength centrality, expected influence (EI), predictability, and bridge centrality statistics were computed. The top 20% scoring nodes on bridge strength were selected as bridge nodes.

Nodes from different rating scales assessing similar psychopathological and functioning constructs tended to cluster together in the estimated network. The most central nodes (EI) were Delusions, Emotional Withdrawal, Depression, and Depressed Mood. Bridge nodes included Depression, Conceptual Disorganization, Active Social Avoidance, Delusions, Stereotyped Thinking, Poor Impulse Control, Guilty Feelings, Unusual Thought Content, and Hostility. Most of the bridge nodes belonged to the general psychopathology subscale of the PANSS. Depression (G6) was the bridge node with the highest value.

The current study provides novel insights for understanding the complex phenotype of psychotic disorders and the mechanisms underlying the development and maintenance of comorbidity and functional impairment after psychosis onset.

Deficits in emotional intelligence (EI) were detected in patients with bipolar disorder (BD), but little is known about whether these deficits are already present in patients after presenting a first episode mania (FEM). We sought (i) to compare EI in patients after a FEM, chronic BD and healthy controls (HC); (ii) to examine the effect exerted on EI by socio-demographic, clinical and neurocognitive variables in FEM patients.

The Emotional Intelligence Quotient (EIQ) was calculated with the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Performance on MSCEIT was compared among the three groups using generalized linear models. In patients after a FEM, the influence of socio-demographic, clinical and neurocognitive variables on the EIQ was examined using a linear regression model.

In total, 184 subjects were included (FEM n = 48, euthymic chronic BD type I n = 75, HC n = 61). BD patients performed significantly worse than HC on the EIQ [mean difference (MD) = 10.09, standard error (s.e.) = 3.14, p = 0.004] and on the understanding emotions branch (MD = 7.46, s.e. = 2.53, p = 0.010). FEM patients did not differ from HC and BD on other measures of MSCEIT. In patients after a FEM, EIQ was positively associated with female sex (β = −0.293, p = 0.034) and verbal memory performance (β = 0.374, p = 0.008). FEM patients performed worse than HC but better than BD on few neurocognitive domains.

Patients after a FEM showed preserved EI, while patients in later stages of BD presented lower EIQ, suggesting that impairments in EI might result from the burden of disease and neurocognitive decline, associated with the chronicity of the illness.

A cumulative environmental exposure score for schizophrenia (exposome score for schizophrenia [ES-SCZ]) may provide potential utility for risk stratification and outcome prediction. Here, we investigated whether ES-SCZ was associated with functioning in patients with schizophrenia spectrum disorder, unaffected siblings, and healthy controls.

This cross-sectional sample consisted of 1,261 patients, 1,282 unaffected siblings, and 1,525 healthy controls. The Global Assessment of Functioning (GAF) scale was used to assess functioning. ES-SCZ was calculated based on our previously validated method. The association between ES-SCZ and the GAF dimensions (symptom and disability) was analyzed by applying regression models in each group (patients, siblings, and controls). Additional models included polygenic risk score for schizophrenia (PRS-SCZ) as a covariate.

ES-SCZ was associated with the GAF dimensions in patients (symptom: B = −1.53, p-value = 0.001; disability: B = −1.44, p-value = 0.001), siblings (symptom: B = −3.07, p-value < 0.001; disability: B = −2.52, p-value < 0.001), and healthy controls (symptom: B = −1.50, p-value < 0.001; disability: B = −1.31, p-value < 0.001). The results remained the same after adjusting for PRS-SCZ. The degree of associations of ES-SCZ with both symptom and disability dimensions were higher in unaffected siblings than in patients and controls. By analyzing an independent dataset (the Genetic Risk and Outcome of Psychosis study), we replicated the results observed in the patient group.

Our findings suggest that ES-SCZ shows promise for enhancing risk prediction and stratification in research practice. From a clinical perspective, ES-SCZ may aid in efforts of clinical characterization, operationalizing transdiagnostic clinical staging models, and personalizing clinical management.

Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene–environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum.

The sample consisted of 1699 patients, 1753 unaffected siblings, and 1542 healthy comparison participants. The Structured Interview for Schizotypy-Revised (SIS-R) was administered to analyse scores of total, positive, and negative schizotypy in siblings and healthy comparison participants. The PRS-SCZ was trained using the Psychiatric Genomics Consortiums results and the ES-SCZ was calculated guided by the approach validated in a previous report in the current data set. Regression models were applied to test the independent and joint effects of PRS-SCZ and ES-SCZ (adjusted for age, sex, and ancestry using 10 principal components).

Both genetic and environmental vulnerability were associated with case-control status. Furthermore, there was evidence for additive interaction between binary modes of PRS-SCZ and ES-SCZ (above 75% of the control distribution) increasing the odds for schizophrenia spectrum diagnosis (relative excess risk due to interaction = 6.79, [95% confidential interval (CI) 3.32, 10.26], p < 0.001). Sensitivity analyses using continuous PRS-SCZ and ES-SCZ confirmed gene–environment interaction (relative excess risk due to interaction = 1.80 [95% CI 1.01, 3.32], p = 0.004). In siblings and healthy comparison participants, PRS-SCZ and ES-SCZ were associated with all SIS-R dimensions and evidence was found for an interaction between PRS-SCZ and ES-SCZ on the total (B = 0.006 [95% CI 0.003, 0.009], p < 0.001), positive (B = 0.006 [95% CI, 0.002, 0.009], p = 0.002), and negative (B = 0.006, [95% CI 0.004, 0.009], p < 0.001) schizotypy dimensions.

The interplay between exposome load and schizophrenia genetic liability contributing to psychosis across the spectrum of expression provide further empirical support to the notion of aetiological continuity underlying an extended psychosis phenotype.

There is evidence that environmental and genetic risk factors for schizophrenia spectrum disorders are transdiagnostic and mediated in part through a generic pathway of affective dysregulation.

We analysed to what degree the impact of schizophrenia polygenic risk (PRS-SZ) and childhood adversity (CA) on psychosis outcomes was contingent on co-presence of affective dysregulation, defined as significant depressive symptoms, in (i) NEMESIS-2 (n = 6646), a representative general population sample, interviewed four times over nine years and (ii) EUGEI (n = 4068) a sample of patients with schizophrenia spectrum disorder, the siblings of these patients and controls.

The impact of PRS-SZ on psychosis showed significant dependence on co-presence of affective dysregulation in NEMESIS-2 [relative excess risk due to interaction (RERI): 1.01, p = 0.037] and in EUGEI (RERI = 3.39, p = 0.048). This was particularly evident for delusional ideation (NEMESIS-2: RERI = 1.74, p = 0.003; EUGEI: RERI = 4.16, p = 0.019) and not for hallucinatory experiences (NEMESIS-2: RERI = 0.65, p = 0.284; EUGEI: −0.37, p = 0.547). A similar and stronger pattern of results was evident for CA (RERI delusions and hallucinations: NEMESIS-2: 3.02, p < 0.001; EUGEI: 6.44, p < 0.001; RERI delusional ideation: NEMESIS-2: 3.79, p < 0.001; EUGEI: 5.43, p = 0.001; RERI hallucinatory experiences: NEMESIS-2: 2.46, p < 0.001; EUGEI: 0.54, p = 0.465).

The results, and internal replication, suggest that the effects of known genetic and non-genetic risk factors for psychosis are mediated in part through an affective pathway, from which early states of delusional meaning may arise.

Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) have emerged as important peripheral inflammatory biomarkers. Recent data suggest a possible role of the immune system in the pathophysiology of suicidal behavior (SB). The aim of this study is to evaluate the association among NLR, MLR, and PLR and SB in patients with major depressive disorder (MDD), and to test its validity as a biomarker for suicidality.

We evaluated 538 patients with MDD (mean age [standard deviation] = 43.87 [14.36] years; females: 68.8%). A logistic regression model was estimated to determine the independent factors associated with suicide risk in patients with and without a history of suicide attempt (SA).

Three hundred ninety-three patients (74.7%) had a personal history of SA. Patients with a previous SA were more frequently female (71.9% vs. 59.6%; p = 0.007), significantly younger (41.20 vs. 51.77 years; p < 0.001), had lower depression severity at enrolment (15.58 vs. 18.42; p < 0.000), and significantly higher mean NLR and PLR ratios (2.27 vs. 1.68, p = 0.001; 127.90 vs. 109.97, p = 0.007, respectively). In the final logistic regression model, after controlling for age, sex, and depression severity, NLR was significantly associated with SB (β = 0.489, p = 0.000; odds ratio [95% confidence intervals] = 1.631 [1.266–2.102]). We propose a cut-off value of NLR = 1.30 (sensitivity = 75% and specificity = 35%).

Our data suggest that NLR may be a valuable, reproducible, easily accessible, and cost-effective strategy to determine suicide risk in MDD.

First-degree relatives of patients with psychotic disorder have higher levels of polygenic risk (PRS) for schizophrenia and higher levels of intermediate phenotypes.

We conducted, using two different samples for discovery (n = 336 controls and 649 siblings of patients with psychotic disorder) and replication (n = 1208 controls and 1106 siblings), an analysis of association between PRS on the one hand and psychopathological and cognitive intermediate phenotypes of schizophrenia on the other in a sample at average genetic risk (healthy controls) and a sample at higher than average risk (healthy siblings of patients). Two subthreshold psychosis phenotypes, as well as a standardised measure of cognitive ability, based on a short version of the WAIS-III short form, were used. In addition, a measure of jumping to conclusion bias (replication sample only) was tested for association with PRS.

In both discovery and replication sample, evidence for an association between PRS and subthreshold psychosis phenotypes was observed in the relatives of patients, whereas in the controls no association was observed. Jumping to conclusion bias was similarly only associated with PRS in the sibling group. Cognitive ability was weakly negatively and non-significantly associated with PRS in both the sibling and the control group.

The degree of endophenotypic expression of schizophrenia polygenic risk depends on having a sibling with psychotic disorder, suggestive of underlying gene–environment interaction. Cognitive biases may better index genetic risk of disorder than traditional measures of neurocognition, which instead may reflect the population distribution of cognitive ability impacting the prognosis of psychotic disorder.