Metabolic Management Model in Psychiatric Outpatients: a Real-World Experience.

Obesity and weight gain represent a challenging issue in people suffering from schizophrenia and other severe mental illnesses (SMI). Most of clinical guidelines for the management of overweight and obesity in people with SMI share a stepwise approach starting with more conservative interventions, with diet, physical activity, lifestyle coaching and behavioral modifications. The aim of this study was to evaluate the effectiveness of a group weight management program in a real-world outpatient Italian setting. Data from 100 patients diagnosed with schizophrenia or bipolar disorder, undergone to a group metabolic management program, were analyzed through a 12 months follow-up. The main body weight (kgs) decreased from 98.01±18.30 at baseline to 93.29±17.36 (p>0.001) at 6 months to 90.35±17.90 at 12 months. Parallel statistically significant decreases were found for BMI, waist circumference, glycaemia and systolic blood pressure. After patients' segmentation into normal-weight, overweight and obese at baseline, the significant of the decrease emerged only between baseline and the 6-month endpoint, thus suggesting that the program was successful in the short-term. Notwithstanding the limitations of the study, the 12-month intervention evaluated demonstrated feasibility and a high retention rate. This allowed a relevant weight reduction during the first six months, followed by durable maintenance until the end of the study. Current NICE recommendation guidance indicates that people with SMI, particularly those on antipsychotic treatment, should be provided with integrated nutrition and exercise programmes by their healthcare professional. Future research should focus on the effectiveness and cost-effectiveness of this kind of interventions and their reliability in the different real-world healthcare settings.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods: This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results: Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion: This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders.

Introduction: Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints. Methods: The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Results: Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. Discussion: This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.

Optimizing and Predicting Antidepressant Efficacy in Patients with Major Depressive Disorder Using Multi-Omics Analysis and the Opade AI Prediction Tools.

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from "canonical treatment" to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.

A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.

Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients' state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression's stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.

Persistent negative symptoms in recent-onset psychosis: Relationship to treatment response and psychosocial functioning.

Negative symptoms are associated with poor clinical and psychosocial outcome in schizophrenia. Their prevalence and identification in first-episode patients remains controversial. In a large cohort of patients in the early stage of schizophrenia, schizophreniform or schizoaffective disorder, we investigated, over the different phases of the OPTiMiSE trial (baseline, 4, 10 and 22 weeks of treatment), the prevalence of negative symptoms of moderate severity, unconfounded by depression and extrapyramidal symptoms at baseline. Moreover, we assessed symptomatic remission, attrition rate and psychosocial functioning in subjects with short-term (4 weeks) persistent unconfounded negative symptoms (PNS) and in those with negative symptoms that did not persist at follow-up and/or were confounded at baseline (N-PNS). Negative symptoms of moderate severity were observed in 59% of subjects at baseline. They were associated with worse psychosocial functioning and longer duration of psychosis at intake in the study. Eleven percent of subjects had PNS unconfounded at baseline and 7.9% had PNS unconfounded at both baseline and end of 4-week treatment. Psychosocial functioning was comparable in PNS and N-PNS subjects at baseline but it was significantly worse in the former group after 4-weeks. PNS subjects showed lower remission and higher attrition rates at the end of all treatment phases. Fifty-six percent of subjects completing phase 3 (clozapine treatment) had PNS, and 60% of them were non-remitters at the end of this phase. The presence of short-term PNS during the first phases of psychosis was associated with poor clinical outcome and resistance to antipsychotic treatment, including clozapine.

Disorganization and cognitive impairment in schizophrenia: New insights from electrophysiological findings.

In subjects with schizophrenia (SCZ), the disorganization dimension is a strong predictor of real-life functioning. "Conceptual disorganization" (P2), "Difficulty in abstract thinking" (N5) and "Poor attention" (G11) are core features of the disorganization factor, evaluated using the Positive and Negative Syndrome Scale. The heterogeneity of this dimension and its overlap with neurocognitive deficits are still debated. Within the multicenter study of the Italian Network for Research on Psychoses, we investigated electrophysiological and neurocognitive correlates of disorganization and its component items to assess the heterogeneity of this dimension and its possible overlap with neurocognitive deficits. Resting state EEG was recorded in 145 stabilized SCZ and 69 matched healthy controls (HC). Spectral amplitude was averaged in ten frequency bands. Neurocognitive domains were assessed by MATRICS Consensus Cognitive Battery (MCCB). RAndomization Graphical User software explored band spectral amplitude differences between groups and correlations with disorganization and MCCB scores in SCZ. Correlations between disorganization and MCCB scores were also investigated. Compared to HC, SCZ showed increased delta, theta, and beta 1 and decreased alpha 2 activity. A negative correlation between alpha 1 and disorganization was observed in SCZ. At the item level, only "N5" showed the same correlation. MCCB neurocognitive composite score was associated with disorganization, "P2" and "N5". Our findings suggest only a partial overlap between disorganization and neurocognitive impairment. The association of alpha 1 with the "N5" item suggests that some aspects of disorganization could be underpinned by the impairment of basic neurobiological functions that are only partially evaluated using MCCB.

A large European, multicenter, multinational validation study of the Brief Negative Symptom Scale.

Negative symptoms represent an unmet need of treatment in schizophrenia. Although a consensus exists on negative symptom construct, and second generation assessment instruments reflecting the consensus are available, studies still rely upon old assessment instruments, that do not reflect recent conceptualizations and might limit progress in the search for effective treatments. This is often the case in the European context, where one of the challenges encountered in designing large studies is the availability of validated instruments in the many languages of the continent. To address this challenge and promote sound research on negative symptoms in Europe, the ECNP Schizophrenia Network coordinated a large multicenter, multinational validation study of the Brief Negative Symptom Scale (BNSS). Clinically-stable subjects with schizophrenia (SCZ, N = 249) were recruited from 10 European Countries. Apart from BNSS, subjects were administered the Positive and Negative Syndrome Scale (PANSS) and standardized instruments for depression, extrapyramidal symptoms and psychosocial functioning. Results showed an excellent internal consistency, convergent and discriminant validity of BNSS and replicated a 5 factor-model. A larger number of subjects with predominant negative symptoms, i.e. the target population for clinical trials, was identified by using the BNSS compared to the PANSS. Regression analysis showed that BNSS-avolition, a key negative symptom poorly assessed by PANSS, explained 23.9% of psychosocial functioning, while no combination of the PANSS core negative symptoms showed the same impact on functioning. The study demonstrated that BNSS has substantial advantages with respect to PANSS for the identification of the avolition domain and subjects with predominant negative symptoms.

Impact of Reward and Loss Anticipation on Cognitive Control: An Event-Related Potential Study in Subjects With Schizophrenia and Healthy Controls.

INTRODUCTION: Deficits of cognitive functions and motivation are core aspects of schizophrenia. The interaction of these deficits might contribute to impair the ability to flexibly adjust behavior in accordance with one's intentions and goals. Many studies have focused on the anterior N2 as a correlate of cognitive control based on motivational value. AIMS: Given the key role of motivation impairment in schizophrenia as a predictor of functional outcome, we aimed to study the impact of reward- and avoidance-based motivation on cognitive control using N2. METHOD: Event-related potentials were recorded during the execution of the "Monetary Incentive Delay (MID)" task in 34 patients with schizophrenia (SCZ) stabilized on second-generation antipsychotics and 22 healthy controls (HC). Cognitive domains were assessed using the MATRICS Consensus Cognitive Battery. Negative symptom domains (Avolition/apathy and Expressive deficit), as well as positive and disorganization dimensions were also assessed in SCZ. RESULTS: We did not observe any group difference in N2 amplitude or latency. In HC, N2 amplitude was significantly larger for anticipation of large loss with regard to all reward conditions and for all incentive versus neutral conditions. In SCZ, N2 amplitude did not discriminate between large loss and reward or between incentive and neutral conditions. N2 amplitude was not correlated with psychopathological dimensions or MCCB-assessed cognitive deficits in SCZ. CONCLUSION: Our data in HC are in line with the hypothesis that N2 amplitude reflects the impact of motivational salience on cognitive control. Our results in SCZ indicate a deficit in the discrimination of motivational salience to the service of cognitive control, independently of psychopathology and other cognitive deficits.

Neurophysiological correlates of Avolition-apathy in schizophrenia: A resting-EEG microstates study.

Background: The "Avolition-apathy" domain of the negative symptoms was found to include different symptoms by factor analytic studies on ratings derived by different scales. In particular, the relationship of anhedonia with this domain is controversial. Recently introduced negative symptom rating scales provide a better assessment of anhedonia, allowing the distinction of anticipatory and consummatory aspects, which might be related to different psychopathological dimensions. The study of associations with external validators, such as electrophysiological, brain imaging or cognitive indices, might shed further light on the status of anhedonia within the Avolition-apathy domain. Objectives: We used brain electrical microstates (MSs), which represent subsecond periods of quasi-stable scalp electrical field, associated with resting-state neural networks (and thus with global patterns of functional connectivity), to test whether the component symptoms of Avolition-apathy share the same correlates. Method: We analyzed multichannel resting EEGs in 142 individuals with schizophrenia (SCZ) and in 64 healthy controls (HC), recruited within the add-on EEG study of the Italian Network for Research on Psychoses. Relative time contribution, duration and occurrence of four MS classes (MS-A/-B/-C/-D) were calculated. Group differences on MS parameters (contribution and duration) and their associations with negative symptom domains (assessed using the Brief Negative Symptoms Scale) were investigated. Results: SCZ, in comparison to HC, showed increased contribution and duration of MS-C. The contribution of MS-A positively correlated with Avolition-apathy, but not with Expressive deficit. Within the Avolition-apathy domain, anticipatory anhedonia, avolition and asociality, but not consummatory anhedonia, showed the same correlations with MS-A contribution. Conclusion: Our findings support the existence of distinct electrophysiological correlates of Avolition-apathy with respect to Expressive deficit, and lend support to the hypothesis that only the anticipatory component of anhedonia shares the same pathophysiological underpinnings of the Avolition-apathy domain.

Avolition-Apathy and White Matter Connectivity in Schizophrenia: Reduced Fractional Anisotropy Between Amygdala and Insular Cortex.

The avolition/apathy domain of negative symptoms includes motivation- and pleasure-related impairments. In people with schizophrenia, structural and functional abnormalities were reported in key regions within the motivational reward system, including ventral-tegmental area (VTA), striatum (especially at the level of the nucleus accumbens, NAcc), orbitofrontal cortex (OFC), as well as amygdala (Amy) and insular cortex (IC). However, the association of the reported abnormalities with avoliton-apathy is still controversial. In the present study, we investigated white matter connectivity patterns within these regions, using a probabilistic analysis of diffusion tensor imaging (DTI) data, in male subjects with schizophrenia. Thirty-five male subjects with schizophrenia (SCZ) and 17 male healthy controls (HC) matched for age, underwent DTI. SCZ were evaluated using the Schedule for Deficit Syndrome (SDS), the Positive and Negative Syndrome Scale (PANSS), and the MATRICS Consensus Cognitive Battery (MCCB). Probabilistic tractography was applied to investigate pathways connecting the Amy and the NAcc with the OFC and IC. Reduced fractional anisotropy (FA) was observed in left Amy-ventral anterior IC connections, in SCZ compared with controls. This abnormality was negatively correlated with avolition/apathy but not with expressive deficit scores. SCZ showed also a reduced connectivity index between right NAcc and medial OFC, as compared with controls. Finally, the left NAcc-dorsal anterior IC connectivity index was negatively correlated with working memory scores. Our results indicate that only the avolition/apathy domain of negative symptoms is related to abnormal connectivity in the motivation-related circuits. The findings also demonstrate that distinct alterations underlie cognitive impairment and avolition/apathy.

Interplay Among Psychopathologic Variables, Personal Resources, Context-Related Factors, and Real-life Functioning in Individuals With Schizophrenia: A Network Analysis.

Importance: Enhanced understanding of factors associated with symptomatic and functional recovery is instrumental to designing personalized treatment plans for people with schizophrenia. To date, this is the first study using network analysis to investigate the associations among cognitive, psychopathologic, and psychosocial variables in a large sample of community-dwelling individuals with schizophrenia. Objective: To assess the interplay among psychopathologic variables, cognitive dysfunctions, functional capacity, personal resources, perceived stigma, and real-life functioning in individuals with schizophrenia, using a data-driven approach. Design, Setting, and Participants: This multicenter, cross-sectional study involved 26 university psychiatric clinics and/or mental health departments. A total of 921 community-dwelling individuals with a DSM-IV diagnosis of schizophrenia who were stabilized on antipsychotic treatment were recruited from those consecutively presenting to the outpatient units of the sites between March 1, 2012, and September 30, 2013. Statistical analysis was conducted between July 1 and September 30, 2017. Main Outcomes and Measures: Measures covered psychopathologic variables, neurocognition, social cognition, functional capacity, real-life functioning, resilience, perceived stigma, incentives, and service engagement. Results: Of 740 patients (221 women and 519 men; mean [SD] age, 40.0 [10.9] years) with complete data on the 27 study measures, 163 (22.0%) were remitted (with a score of mild or better on 8 core symptoms). The network analysis showed that functional capacity and everyday life skills were the most central and highly interconnected nodes in the network. Psychopathologic variables split in 2 domains, with positive symptoms being one of the most peripheral and least connected nodes. Functional capacity bridged cognition with everyday life skills; the everyday life skills node was connected to disorganization and expressive deficits. Interpersonal relationships and work skills were connected to avolition; the interpersonal relationships node was also linked to social competence, and the work skills node was linked to social incentives and engagement with mental health services. A case-dropping bootstrap procedure showed centrality indices correlations of 0.75 or greater between the original and randomly defined samples up to 481 of 740 case-dropping (65.0%). No difference in the network structure was found between men and women. Conclusions and Relevance: The high centrality of functional capacity and everyday life skills in the network suggests that improving the ability to perform tasks relevant to everyday life is critical for any therapeutic intervention in schizophrenia. The pattern of network node connections supports the implementation of personalized interventions.

The complex relationship between self-reported 'personal recovery' and clinical recovery in schizophrenia.

Self-reported 'personal recovery' and clinical recovery in schizophrenia (SRPR and CR, respectively) reflect different perspectives in schizophrenia outcome, not necessarily concordant with each other and usually representing the consumer's or the therapist's point of view. By means of a cluster analysis on SRPR-related variables, we identified three clusters. The first and third cluster included subjects with the best and the poorest clinical outcome respectively. The second cluster was characterized by better insight, higher levels of depression and stigma, lowest self-esteem and personal strength, and highest emotional coping. The first cluster showed positive features of recovery, while the third cluster showed negative features. The second cluster, with the most positive insight, showed a more complex pattern, a somewhat 'paradoxical' mixture of positive and negative personal and clinical features of recovery. The present results suggest the need for a characterization of persons with schizophrenia along SRPR and CR dimensions to design individualized and integrated treatment programs aimed to improve insight and coping strategies, reduce stigma, and shape recovery styles.

Add-on oral olanzapine worsens hallucinations in schizoaffective disorder.

Anecdotal evidence tends to favour olanzapine in the treatment of hallucinations in patients with schizophrenia spectrum disorders; however, no conclusive evidence is available on this topic. We report here a clinical case in which a 46-year-old man, suffering from a schizoaffective disorder (depressed type), underwent olanzapine treatment (20 mg/day). After inducing an initial amelioration, the patient had a re-exacerbation of auditory hallucinations and a clinical and psychosocial worsening, which subsided after olanzapine discontinuation. Olanzapine may induce a worsening of hallucinations in a psychotic disorder with substantial affective component and therefore its use should be carefully evaluated in such cases.

Physiological correlates of positive symptoms in schizophrenia.

Patients with schizophrenia have been hypothesized to have a functional impairment in filtering irrelevant sensory information, which may result in positive symptoms such as hallucinations or delusions. Many evidences suggest that abnormalities in the event-related brain potentials (ERPs), resting state electroencephalography (EEG) and synchronized oscillatory activity of neurons may reflect core pathophysiological mechanisms of schizophrenia. Abnormalities in amplitude and latency of the ERPs reflecting aberrations in gating and difficulties in the detection of changes in auditory stimuli, as well as defects in stimuli evaluation and integration of information are common in patients with schizophrenia. This chapter highlights the findings of electrophysiological studies in schizophrenia dealing with early sensory perception and attention, automatic sensory detection of stimuli changes and cognitive evaluation and integration of information, relevant to the pathophysiological mechanisms underpinning hallucinations and delusions. Results of electrophysiological studies investigating the neural correlates of positive symptoms suggest aberrant intrinsic organization of functional brain networks.

Electrophysiological aberrations associated with negative symptoms in schizophrenia.

Clinical heterogeneity is a confound common to all of schizophrenia research. Deficit schizophrenia has been proposed as a homogeneous disease entity within the schizophrenia syndrome. The use of the Schedule for the Deficit Syndrome (SDS) has allowed the definition of a subgroup dominated by persistent and primary negative symptoms. While a number of studies have appeared over the years examining the electrophysiological correlates of the cluster of negative symptoms in schizophrenia, only a few studies have actually focused on the Deficit Syndrome (DS). In this chapter, electrophysiological investigations utilizing EEG, Evoked Potentials (EPs), polysomnography (PSG), or magnetoencephalography (MEG) to probe "negative symptoms," or "Deficit Syndrome" are reviewed. While this line of research is evidently in its infancy, two significant trends emerge. First, spectral EEG studies link increased slow wave activity during wakefulness to the prevalence of negative symptoms. Second, sleep studies point to an association between decrease in slow wave sleep and prevalence of negative symptoms. Several studies also indicate a relationship of negative symptoms with reduced alpha activity. A host of other abnormalities including sensory gating and P300 attenuation are less consistently reported. Three studies specifically addressed electrophysiology of the DS. Two of the three studies provided evidence suggesting that the DS may be a separate disease entity and not simply a severe form of schizophrenia.