Lifestyle Interventions and Prevention of Suicide

Over the past years, there has been a growing interest in the association between lifestyle psychosocial interventions, severe mental illness, and suicide risk. Patients with severe mental disorders have higher mortality rates, poor health states, and higher suicide risk compared to the general population. Lifestyle behaviors are amenable to change through the adoption of specific psychosocial interventions, and several approaches have been promoted. The current article provides a comprehensive review of the literature on lifestyle interventions, mental health, and suicide risk in the general population and in patients with psychiatric disorders. For this purpose, we investigated lifestyle behaviors and lifestyle interventions in three different age groups: adolescents, young adults, and the elderly. Several lifestyle behaviors including cigarette smoking, alcohol use, and sedentary lifestyle are associated with suicide risk in all age groups. In adolescents, growing attention has emerged on the association between suicide risk and internet addiction, cyberbullying and scholastic and family difficulties. In adults, psychiatric symptoms, substance and alcohol abuse, weight, and occupational difficulties seems to have a significant role in suicide risk. Finally, in the elderly, the presence of an organic disease and poor social support are associated with an increased risk of suicide attempt. Several factors may explain the association between lifestyle behaviors and suicide. First, many studies have reported that some lifestyle behaviors and its consequences (sedentary lifestyle, cigarette smoking underweight, obesity) are associated with cardiometabolic risk factors and with poor mental health. Second, several lifestyle behaviors may encourage social isolation, limiting the development of social networks, and remove individuals from social interactions; increasing their risk of mental health problems and suicide

Building a neurocognitive profile of suicidal risk in severe mental disorders

Background Research on the influence of neurocognitive factors on suicide risk, regardless of the diagnosis, is inconsistent. Recently, suicide risk studies propose applying a trans-diagnostic framework in line with the launch of the Research Domain Criteria Cognitive Systems model. In the present study, we highlight the extent of cognitive impairment using a standardized battery in a psychiatric sample stratified for different degrees of suicidal risk. We also differentiate in our sample various neurocognitive profiles associated with different levels of risk. Materials and methods We divided a sample of 106 subjects into three groups stratified by suicide risk level: Suicide Attempt (SA), Suicidal Ideation (SI), Patient Controls (PC) and Healthy Controls (HC). We conducted a multivariate Analysis of Variance (MANOVA) for each cognitive domain measured through the standardized battery MATRICS Consensus Cognitive Battery (MCCB). Results We found that the group of patients performed worse than the group of healthy controls on most domains; social cognition was impaired in the suicide risk groups compared both to HC and PC. Patients in the SA group performed worse than those in the SI group. Conclusion Social cognition impairment may play a crucial role in suicidality among individuals diagnosed with serious mental illness as it is involved in both SI and SA; noteworthy, it is more compromised in the SA group fitting as a marker of risk severity

A cast-mold approach to iron oxide and Pt/iron oxide nanocontainers and nanoparticles with a reactive concave surface.

We report the synthesis of various iron oxide nanocontainers and Pt−iron oxide nanoparticles based on a cast-mold approach, starting from nanoparticles having a metal core (either Au or AuPt) and an iron oxide shell. Upon annealing, the particles evolve to asymmetric core−shells and then to heterodimers. If iodine is used to leach Au out of these structures, asymmetric core−shells evolve into "nanocontainers", that is, iron oxide nanoparticles enclosing a cavity accessible through nanometer-sized pores, while heterodimers evolve into particles with a concave region. When starting from a metal domain made of AuPt, selective leaching of the Au atoms yields the same iron oxide nanoparticle morphologies but now encasing Pt domains (in their concave region or in their cavity). We found that the concave nanoparticles are capable of destabilizing Au nanocrystals of sizes matching that of the concave region. In addition, for the nanocontainers, we propose two different applications: (i) we demonstrate loading of ..

Mismatch Negativity and P3a Impairment through Different Phases of Schizophrenia and Their Association with Real-Life Functioning

Impairment in functioning since the onset of psychosis and further deterioration over time is a key aspect of subjects with schizophrenia (SCZ). Mismatch negativity (MMN) and P3a, indices of early attention processing that are often impaired in schizophrenia, might represent optimal electrophysiological candidate biomarkers of illness progression and poor outcome. However, contrasting findings are reported about the relationships between MMN-P3a and functioning. The study aimed to investigate in SCZ the influence of illness duration on MMN-P3a and the relationship of MMN-P3a with functioning. Pitch (p) and duration (d) MMN-P3a were investigated in 117 SCZ and 61 healthy controls (HCs). SCZ were divided into four illness duration groups: ≤ 5, 6 to 13, 14 to 18, and 19 to 32 years. p-MMN and d-MMN amplitude was reduced in SCZ compared to HCs, independently from illness duration, psychopathology, and neurocognitive deficits. p-MMN reduction was associated with lower “Work skills”. The p-P3a amplitude was reduced in the SCZ group with longest illness duration compared to HCs. No relationship between P3a and functioning was found. Our results suggested that MMN amplitude reduction might represent a biomarker of poor functioning in SCZ

The association between insight and depressive symptoms in schizophrenia: Undirected and Bayesian network analyses

Background. Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions. Methods. Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression. Results. After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms. Conclusions. In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem

The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients

Improving real-life functioning is the main goal of the most advanced integrated treatment programs in people with schizophrenia. The Italian Network for Research on Psychoses previously explored, by using network analysis, the interplay among illness-related variables, personal resources, context-related factors and real-life functioning in a large sample of patients with schizophrenia. The same research network has now completed a 4-year follow-up of the original sample. In the present study, we used network analysis to test whether the pattern of relationships among all variables investigated at baseline was similar at follow-up. In addition, we compared the network structure of patients who were classified as recovered at follow-up versus those who did not recover. Six hundred eighteen subjects recruited at baseline could be assessed in the follow-up study. The network structure did not change significantly from baseline to follow-up, and the overall strength of the connections among variables increased slightly, but not significantly. Functional capacity and everyday life skills had a high betweenness and closeness in the network at follow-up, as they had at baseline, while psychopathological variables remained more peripheral. The network structure and connectivity of non-recovered patients were similar to those observed in the whole sample, but very different from those in recovered subjects, in which we found few connections only. These data strongly suggest that tightly coupled symptoms/dysfunctions tend to maintain each other's activation, contributing to poor outcome in schizophrenia. Early and integrated treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia

The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients

Improving real-life functioning is the main goal of the most advanced integrated treatment programs in people with schizophrenia. The Italian Network for Research on Psychoses previously explored, by using network analysis, the interplay among illness-related variables, personal resources, context-related factors and real-life functioning in a large sample of patients with schizophrenia. The same research network has now completed a 4-year follow-up of the original sample. In the present study, we used network analysis to test whether the pattern of relationships among all variables investigated at baseline was similar at follow-up. In addition, we compared the network structure of patients who were classified as recovered at follow-up versus those who did not recover. Six hundred eighteen subjects recruited at baseline could be assessed in the follow-up study. The network structure did not change significantly from baseline to follow-up, and the overall strength of the connections among variables increased slightly, but not significantly. Functional capacity and everyday life skills had a high betweenness and closeness in the network at follow-up, as they had at baseline, while psychopathological variables remained more peripheral. The network structure and connectivity of non-recovered patients were similar to those observed in the whole sample, but very different from those in recovered subjects, in which we found few connections only. These data strongly suggest that tightly coupled symptoms/dysfunctions tend to maintain each other's activation, contributing to poor outcome in schizophrenia. Early and integrated treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia

Social cognition in people with schizophrenia: A cluster-analytic approach

Background The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses. Method A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer-Salovey-Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters. Results We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC. Conclusions If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person

Staging of Schizophrenia with the Use of PANSS: An International Multi-Center Study

Introduction: A specific clinically relevant staging model for schizophrenia has not yet been developed. The aim of the current study was to evaluate the factor structure of the PANSS and develop such a staging method.Methods: Twenty-nine centers from 25 countries contributed 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Analysis of covariance, Exploratory Factor Analysis, Discriminant Function Analysis, and inspection of resultant plots were performed.Results: Exploratory Factor Analysis returned 5 factors explaining 59% of the variance (positive, negative, excitement/hostility, depression/anxiety, and neurocognition). The staging model included 4 main stages with substages that were predominantly characterized by a single domain of symptoms (stage 1: positive; stages 2a and 2b: excitement/hostility; stage 3a and 3b: depression/anxiety; stage 4a and 4b: neurocognition). There were no differences between sexes. The Discriminant Function Analysis developed an algorithm that correctly classified >85% of patients.Discussion: This study elaborates a 5-factor solution and a clinical staging method for patients with schizophrenia. It is the largest study to address these issues among patients who are more likely to remain affiliated with mental health services for prolonged periods of time.<br /