The dimensional structure of the Positive and Negative Syndrome Scale in first-episode schizophrenia spectrum disorders: an Exploratory Graph Analysis from the OPTiMiSE trial.

The Positive and Negative Syndrome Scale (PANSS) is the most widely used rating scale to assess psychotic symptoms in patients with schizophrenia and other primary psychoses. However, a definitive consensus regarding its dimensional structure remains elusive. The present work aims to determine the number of dimensions of the scale through a network analysis approach in a sample of individuals experiencing first-episode schizophrenia spectrum disorder (FE-SSD) with minimal or no prior exposure to antipsychotic treatment. Baseline data of 446 participants (age 25.96 ± 5.99 years, 70% males) enrolled in the OPTiMiSE trial were analysed. Exploratory Graph Analysis (EGA) was conducted to evaluate the dimensionality of the PANSS, and a bootstrap approach (bootEGA) was employed to assess model stability. The analysis was replicated, excluding unstable items with stability values below 0.75, until a stable model was achieved. The analysis of the 30 items of the PANSS revealed inadequate structural consistency, resulting in the exclusion of 9 unstable items. The final model comprised 21 symptoms distributed across four communities (Positive, Cognitive/Disorganised, Excited/Aggressive and Negative) but lacked a depressive domain. In conclusion, we propose a concise version of the PANSS, incorporating 21 items, to better assess the core symptoms of the first episode of SSD. This revised version provides clinicians with a robust psychometric tool with reduced administration time, but the complementary administration of a dedicated instrument for evaluating affective symptoms is advisable.

Anticholinergic load: A commonly neglected and preventable risk to cognition during schizophrenia treatment?

Background: Cognitive impairment is a widespread feature of schizophrenia, affecting nearly 80 % of patients. Prior research has linked the anticholinergic burden of psychiatric medications to these cognitive deficits. However, the impact of the anticholinergic burden from medications for physical morbidity remains underexplored. This study aimed to evaluate the anticholinergic burden of psychiatric and physical medications in patients with schizophrenia and assess its impact on cognitive function. Methods: A total of 178 patients with schizophrenia were recruited. The assessments included an ad hoc questionnaire for collecting demographic and clinical data. Anticholinergic burden was evaluated using the cumulative Drug Burden Index (cDBI) for each participant, and cognitive function was assessed using MATRICS. Psychopathology was measured using the PANSS, CDSS, CAINS, and the CGI-S. Statistical analysis included Student's t-tests, ANOVA, Pearson correlations, and multiple linear regressions. Results: The average cDBI was 1.3 (SD = 0.9). The model developed explained 40.80 % of the variance. The variable with the greatest weight was the cDBI (B = -11.148, p = 0.010). Negative-expression (B = -2.740, p = 0.011) and negative-experiential (B = -1.175, p = 0.030) symptoms were also associated with lower global cognitive score. However, more years of education (B = 5.140, p < 0.001) and cigarettes per day (B = 1.331, p < 0.001) predicted a better global cognitive score. Conclusion: This study identified specific predictors of global cognition in schizophrenia, with anticholinergic burden emerging as the strongest factor. Our findings underscore the importance of considering the anticholinergic burden of treatments, in addition to negative symptoms, when designing interventions to optimize or maintain cognitive function in patients with schizophrenia.

Lithium response in bipolar disorder: Epigenome-wide DNA methylation signatures and epigenetic aging.

Lithium (Li) is the first-line treatment for bipolar disorder (BD) even though only 30 % of BD patients are considered excellent responders. The mechanisms by which Li exerts its action are not clearly understood, but it has been suggested that specific epigenetic mechanisms, such as methylation processes, may play a role. In this regard, DNA methylation patterns can be used to estimate epigenetic age (EpiAge), which is accelerated in BD patients and reversed by Li treatment. Our first aim was to compare the DNA methylation profile in peripheral blood between BD patients categorized as excellent responders to Li (Ex-Rp) and non-responders (N-Rp). Secondly, EpiAge was estimated to detect differential age acceleration between the two groups. A total of 130 differentially methylated positions (DMPs) and 16 differentially methylated regions (DMRs) between Ex-Rp (n = 26) and N-Rp (n = 37) were identified (FDR adjusted p-value < 0.05). We found 122 genes mapping the DMPs and DMRs, nine of which (HOXB6, HOXB3, HOXB-AS3, TENM2, CACNA1B, ANK3, EEF2K, CYP1A1, and SORCS2) had previously been linked to Li response. We found genes related to the GSK3ß pathway to be highly represented. Using FUMA, we found enrichment in Gene Ontology Cell Component for the synapse. Gene network analysis highlighted functions related to the cell cycle, nervous system development and function, and gene expression. No significant differences in age acceleration were found between Ex-Rp and N-Rp for any of the epigenetic clocks analysed. Our findings indicate that a specific methylation pattern could determine the response to Li in BD patients. We also found that a significant portion of the differentially methylated genes are closely associated with the GSK3ß pathway, reinforcing the role of this system in Li response. Future longitudinal studies with larger samples will help to elucidate the epigenetic mechanisms underlying Li response.

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia.

BACKGROUND: 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. METHODS: 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). RESULTS: 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). DISCUSSION: 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.

Impact of cognitive reserve in clinical, neurocognitive and lifestyle factors in chronic schizophrenia and early stages of schizophrenia.

INTRODUCTION: Although there is evidence that higher cognitive reserve (CR) is a protective factor and it has been related to better prognosis, there have been no studies to date that have explored the CR level and its impact in clinical, neurocognitive and lifestyle outcomes according to the stage of the disease: early stage of psychosis (ESP) or chronic schizophrenia (SCZ). MATERIAL AND METHODS: A total of 60 patients in the ESP and 225 patients with SCZ were enrolled in the study. To test the predictive capacity of CR for each diagnostic group, a logistic regression analysis was conducted. Hierarchical linear regression analyses were performed to explore the associations between CR and different outcomes. The mediation analyses were performed according to the principles of Baron and Kenny. RESULTS: Patients with SCZ showed lower CR than those in the ESP (p<0.001). CR correctly classified 79.6% of the cases (p<0.001; Exp(B)=1.062). In ESP group, CR was related to working memory (p=0.030) and negative symptoms (p=0.027). CR (t=3.925, p<0.001) and cannabis use (t=2.023, p=0.048) explained 26.7% of the variance on functioning (p=0.003). In patients with SCZ, CR predicted all cognitive domains, negative symptoms (R2=0.091, p=0.001) and functioning (R2=0.074, p=0.005). In both ESP and SCZ groups, higher CR was associated with lower body mass index and circumference. In ESP group, the effect of adherence to Mediterranean diet on functioning (p=0.037) was mediated by CR level (p=0.003). CONCLUSIONS: The implications of CR depend on the stage of the disease (ESP vs. SCZ), with a greater effect on neurocognition and negative symptoms in patients with chronic SCZ.

From gut to brain: A network model of intestinal permeability, inflammation, and psychotic symptoms in schizophrenia.

Impaired intestinal permeability has recently been suggested as a possible source of chronic inflammation in schizophrenia, but its association with specific psychopathological features remains uncertain. This study aimed to explore the interaction between intestinal permeability, inflammation, and positive and negative symptoms in schizophrenia using a network analysis approach. The study sample comprised 281 adults with schizophrenia (age 40.29 ± 13.65 years, 63.0 % males), enrolled in a cross-sectional observational study assessing intestinal permeability. We estimated the network with a Gaussian graphical model, incorporating scores from 14 individual items of the Positive and Negative Syndrome Scale (PANSS), along with body mass index (BMI), and plasma C-reactive protein (CRP) and lipopolysaccharide-binding protein (LBP) levels. We calculated strength centrality and expected influence and used bridge centrality statistics to identify the bridge nodes. Distinct but highly interconnected clusters emerged for positive and negative symptoms. The biological variables were closely associated with each other. LBP was positively linked with CRP and BMI, but only indirectly connected to psychopathology. CRP exhibited direct positive relationships with various PANSS items and bridged LBP and BMI with psychopathology. Bridge nodes included Conceptual Disorganisation (P2), Active Social Avoidance (G16), Suspiciousness/Persecution (P6), and CRP. These findings support the role of gut-derived inflammation as a mechanism underlying greater symptom severity in schizophrenia and emphasise the importance of addressing dietary habits not only to enhance physical health but also to contribute to improving psychotic symptoms.

What factors should we modify to promote high functioning and prevent functional decline in people with schizophrenia?

Background: Since research in schizophrenia mainly focuses on deficits and risk factors, we need studies searching for high-functioning protective factors. Thus, our objective was to identify protective (PFs) and risk factors (RFs) separately associated with high (HF) and low functioning (LF) in patients with schizophrenia. Methods: We collected information (sociodemographic, clinical, psychopathological, cognitive, and functional) from 212 outpatients with schizophrenia. Patients were classified according to their functional level (PSP) as HF (PSP > 70, n = 30) and LF (PSP ≤ 50, n = 95). Statistical analysis consisted of Chi-square test, Student's t-test, and logistic regression. Results: HF model: variance explained: 38.4-68.8%; PF: years of education (OR = 1.227). RFs: receiving a mental disability benefit (OR = 0.062) and scores on positive (OR = 0.719), negative-expression (OR = 0.711), and negative-experiential symptoms (OR = 0.822), and verbal learning (OR = 0.866). LF model: variance explained: 42.0-56.2%; PF: none; RFs: not working (OR = 6.900), number of antipsychotics (OR = 1.910), and scores on depressive (OR = 1.212) and negative-experiential symptoms (OR = 1.167). Conclusion: We identified specific protective and risk factors for high and low functioning in patients with schizophrenia and confirmed that high functioning factors are not necessarily the opposite of those associated with low functioning. Only negative experiential symptoms are a shared and inverse factor for high and low functioning. Mental health teams must be aware of protective and risk factors and try to enhance or reduce them, respectively, to help their patients improve or maintain their level of functioning.

Psychological impact of the COVID-19 pandemic and lockdown in a Spanish sample with anxiety disorder: sex differences.

The early psychological impact of the COVID-19 pandemic and lockdown is greater in people with mental disorders. This study explored the differences in the psychological impact on people with an anxiety disorder by sex in Spain.