A public early intervention approach to first-episode psychosis: Treated incidence over 7 years in the Emilia-Romagna region.

AIM: To estimate the treated incidence of individuals with first-episode psychosis (FEP) who contacted the Emilia-Romagna public mental healthcare system (Italy); to examine the variability of incidence and user characteristics across centres and years. METHODS: We computed the raw treated incidence in 2013-2019, based on FEP users aged 18-35, seen within or outside the regional program for FEP. We modelled FEP incidence across 10 catchment areas and 7 years using Bayesian Poisson and Negative Binomial Generalized Linear Models of varying complexity. We explored associations between user characteristics, study centre and year comparing variables and socioclinical clusters of subjects. RESULTS: Thousand three hundred and eighteen individuals were treated for FEP (raw incidence: 25.3 / 100.000 inhabitant year, IQR: 15.3). A Negative Binomial location-scale model with area, population density and year as predictors found that incidence and its variability changed across centres (Bologna: 36.55; 95% CrI: 30.39-43.86; Imola: 3.07; 95% CrI: 1.61-4.99) but did not follow linear temporal trends or density. Centers were associated with different user age, gender, migrant status, occupation, living conditions and cluster distribution. Year was associated negatively with HoNOS score (R = -0.09, p < .001), duration of untreated psychosis (R = -0.12, p < .001) and referral type. CONCLUSIONS: The Emilia-Romagna region presents a relatively high but variable incidence of FEP across areas, but not in time. More granular information on social, ethnic and cultural factors may increase the level of explanation and prediction of FEP incidence and characteristics, shedding light on social and healthcare factors influencing FEP.

Linguistic profile automated characterisation in pluripotential clinical high-risk mental state (CHARMS) conditions: methodology of a multicentre observational study.

INTRODUCTION: Language is usually considered the social vehicle of thought in intersubjective communications. However, the relationship between language and high-order cognition seems to evade this canonical and unidirectional description (ie, the notion of language as a simple means of thought communication). In recent years, clinical high at-risk mental state (CHARMS) criteria (evolved from the Ultra-High-Risk paradigm) and the introduction of the Clinical Staging system have been proposed to address the dynamicity of early psychopathology. At the same time, natural language processing (NLP) techniques have greatly evolved and have been successfully applied to investigate different neuropsychiatric conditions. The combination of at-risk mental state paradigm, clinical staging system and automated NLP methods, the latter applied on spoken language transcripts, could represent a useful and convenient approach to the problem of early psychopathological distress within a transdiagnostic risk paradigm. METHODS AND ANALYSIS: Help-seeking young people presenting psychological distress (CHARMS+/- and Clinical Stage 1a or 1b; target sample size for both groups n=90) will be assessed through several psychometric tools and multiple speech analyses during an observational period of 1-year, in the context of an Italian multicentric study. Subjects will be enrolled in different contexts: Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Section of Psychiatry, University of Genoa-IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Mental Health Department-territorial mental services (ASL 3-Genoa), Genoa, Italy; and Mental Health Department-territorial mental services (AUSL-Piacenza), Piacenza, Italy. The conversion rate to full-blown psychopathology (CS 2) will be evaluated over 2 years of clinical observation, to further confirm the predictive and discriminative value of CHARMS criteria and to verify the possibility of enriching them with several linguistic features, derived from a fine-grained automated linguistic analysis of speech. ETHICS AND DISSEMINATION: The methodology described in this study adheres to ethical principles as formulated in the Declaration of Helsinki and is compatible with International Conference on Harmonization (ICH)-good clinical practice. The research protocol was reviewed and approved by two different ethics committees (CER Liguria approval code: 591/2020-id.10993; Comitato Etico dell'Area Vasta Emilia Nord approval code: 2022/0071963). Participants will provide their written informed consent prior to study enrolment and parental consent will be needed in the case of participants aged less than 18 years old. Experimental results will be carefully shared through publication in peer-reviewed journals, to ensure proper data reproducibility. TRIAL REGISTRATION NUMBER: DOI:10.17605/OSF.IO/BQZTN.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup.

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Incidental findings on brain MRI in patients with first-episode and chronic psychosis.

Brain incidental findings (IFs) are unexpected brain abnormalities detected by a structural magnetic resonance (MRI) examination. We conducted a study to assess whether brain IFs are associated with first-episode psychosis (FEP) and chronic psychosis (affective vs. non-affective) compared to healthy controls (HC). Chi-squared analyses were run to compare the frequency of several IFs across groups. Logistic regression analyses were run to explore the association between group and IFs, accounting for sex, age, MRI field strength. We observed a higher frequency of most IFs in both FEP and chronic psychosis groups compared to HC, however most of the chi-squared tests did not reach significance. Patients with FEP and chronic psychosis were 3-4 times more likely to show deep white matter hyperintensities (WMH) than HC. Patients with FEP and affective chronic psychosis were 3-4 times more likely to show ventricular asymmetries than HC. All chronic patients were more likely to show periventricular WMH, liquoral spaces enlargements and ventricular system enlargements respectively. Our results suggest that deep WMH and ventricular asymmetries are associated with both the early and the chronic stages of psychosis, thus representing potential vulnerability factors already present before the onset of the symptoms, possibly due to neurodevelopmental insults.

Classification of Psychoses Based on Immunological Features: A Machine Learning Study in a Large Cohort of First-Episode and Chronic Patients.

For several years, the role of immune system in the pathophysiology of psychosis has been well-recognized, showing differences from the onset to chronic phases. Our study aims to implement a biomarker-based classification model suitable for the clinical management of psychotic patients. A machine learning algorithm was used to classify a cohort of 362 subjects, including 160 first-episode psychosis patients (FEP), 70 patients affected by chronic psychiatric disorders (schizophrenia, bipolar disorder, and major depressive disorder) with psychosis (CRO) and 132 health controls (HC), based on mRNA transcript levels of 56 immune genes. Models distinguished between FEP, CRO, and HC and between the subgroup of drug-free FEP and HC with a mean accuracy of 80.8% and 90.4%, respectively. Interestingly, by using the feature importance method, we identified some immune gene transcripts that contribute most to the classification accuracy, possibly giving new insights on the immunopathogenesis of psychosis. Therefore, our results suggest that our classification model has a high translational potential, which may pave the way for a personalized management of psychosis.

Childhood trauma and glucose metabolism in patients with first-episode psychosis.

Although the associations between first-episode psychosis (FEP) and metabolic abnormalities on one side, and childhood trauma (CT) and risk of developing psychosis on the other are both well established, evidence on the relationship between CT and metabolic dysregulation in terms of abnormal glucose metabolism is very limited. We tested whether, already at illness onset, FEP patients with a history of CT show dysregulation of a broad range of glucose metabolism markers. In particular, in 148 FEP patients we evaluated serum concentrations of c-peptide, insulin, plasminogen-activator-inhibitor-1 (PAI-1), resistin, visfatin, glucagon, glucagon-like peptide-1 (GLP-1), gastric-inhibitor-peptide (GIP), leptin, and ghrelin. We also assessed CT with the Childhood Experience of Care and Abuse Questionnaire, and stressful life events (SLEs) with a semi-structured interview. Psychopathology, cannabis and tobacco habits, Body Mass Index (BMI) were recorded. Serum concentrations of markers were analyzed from peripheral blood. Ninety-five patients (56 % males, mean age 29.5) reported CT. Multivariate models showed that CT is associated only with the concentrations of c-peptide and insulin after adjusting for age, sex, BMI and SLEs. FEP patients who had experienced CT showed higher c-peptide and insulin serum concentrations. Our study reports that CT might be associated with the metabolic abnormalities in the first stage of psychosis, suggesting that a thorough anamnestic evaluation at psychosis onset that would include the history of CT could be helpful for clinicians in order to implement early programmes of healthy lifestyle education and to guide choice of therapeutic interventions for trauma.

Altered syntactic abilities in first episode patients: An inner phenomenon characterizing psychosis.

BACKGROUND: Research has consistently shown that language abilities represent a core dimension of psychosis; however, to date, very little is known about syntactic comprehension performance in the early stages of psychosis. This study aims to compare the linguistic abilities involved in syntactic comprehension in a large group of First Episode Psychosis (FEP) patients and healthy controls (HCs). METHODS: A multiple choice test of comprehension of syntax was administered to 218 FEP patients (166 non-affective FEP patients [FEP-NA] and 52 affective FEP patients [FEP-A]) and 106 HCs. All participants were asked to match a sentence they listen with one out of four vignettes on a pc screen. Only one vignette represents the stimulus target, while the others are grammatical or non-grammatical (visual) distractors. Both grammatical and non-grammatical errors and performance in different syntactic constructions were considered. RESULTS: FEP committed greater number of errors in the majority of TCGB language domains compared to HCs. Moreover, FEP-NA patients committed significantly more non-grammatical (z = -3.2, p = 0.007), locative (z = -4.7, p < 0.001), passive-negative (z = -3.2, p = 0.02), and relative (z = -4.6, p < 0.001) errors compared to HCs as well as more passive-affirmative errors compared to both HCs (z = -4.3, p < 0.001) and FEP-A (z = 3.1, p = 0.04). Finally, we also found that both FEP-NA and FEP-A committed more grammatical (FEP-NA: z = -9.2, p < 0.001 and FEP-A: z = -4.4, p < 0.001), total (FEP-NA: z = -8.2, p < 0.001 and FEP-A: z = 3.9, p =  0.002), and active-negative (FEP-NA: z = -5.8, p < 0.001 and FEP-A: z = -3.5, p = 0.01) errors compared to HCs. CONCLUSIONS: This study shows that the access to syntactic structures is already impaired in FEP patients, especially in those with FEP-NA, ultimately suggesting that language impairments represent a core and inner feature of psychosis even at early stages.

Non literal language comprehension in a large sample of first episode psychosis patients in adulthood.

To date no data still exist on the comprehension of figurative language in the early phases of psychosis. The aim of this study is to investigate for the first time the comprehension of metaphors and idioms at the onset of the illness. Two-hundred-twenty eight (228) first episode psychosis (FEP) patients (168 NAP, non-affective psychosis; 60 AP, affective psychosis) and 70 healthy controls (HC) were assessed. Groups were contrasted on: a) type of stimulus (metaphors vs idioms) and b) type of response (OPEN = spontaneous explanations vs CLOSED = multiple choice answer). Moreover, a machine learning (ML) approach was adopted to classifying participants. Both NAP and AP had a poorer performance on OPEN metaphors and idioms compared to HC, with worse results on spontaneous interpretation of idioms than metaphors. No differences were observed between NAP and AP in CLOSED tasks. The ML approach points at CLOSED idioms as the best discriminating variable, more relevant than the set of pre-frontal and IQ scores. Deficits in non-figurative language may represent a core feature of psychosis. The possibility to identify linguistic features discriminating FEP may support the early recognition of patients at risk to develop psychosis, guiding provision of personalized and timely interventions.