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There is growing interest in new factors contributing to the genesis of eating disorders (EDs). Research recently focused on the study of microbiota. Dysbiosis, associated with a specific genetic susceptibility, may contribute to the development of anorexia nervosa (AN), bulimia nervosa, or binge eating disorder, and several putative mechanisms have already been identified. Diet seems to have an impact not only on modification of the gut microbiota, facilitating dysbiosis, but also on its recovery in patients with EDs. This systematic review based on the PICO strategy searching into PubMed, EMBASE, PsychINFO, and Cochrane Library examined the literature on the role of altered microbiota in the pathogenesis and treatment of EDs. Sixteen studies were included, mostly regarding AN. Alpha diversity and short-chain fatty acid (SCFA) levels were lower in patients with AN, and affective symptoms and ED psychopathology seem related to changes in gut microbiota. Microbiota-derived proteins stimulated the autoimmune system, altering neuroendocrine control of mood and satiety in EDs. Microbial richness increased in AN after weight regain on fecal microbiota transplantation. Microbiota homeostasis seems essential for a healthy communication network between gut and brain. Dysbiosis may promote intestinal inflammation, alter gut permeability, and trigger immune reactions in the hunger/satiety regulation center contributing to the pathophysiological development of EDs. A restored microbial balance may be a possible treatment target for EDs. A better and more in-depth characterization of gut microbiota and gut-brain crosstalk is required. Future studies may deepen the therapeutic and preventive role of microbiota in EDs.

Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.

Childhood trauma and adversities (CTA) and aberrant salience (AS) have a pivotal role in schizophrenia development, but their interplay with psychotic symptoms remains vague. We explored the mediation performed by AS between CTA and psychotic symptomatology in schizophrenia. We approached 241 adults suffering from schizophrenia spectrum disorders (SSDs), who have been in the unit for at least 12 consecutive months, excluding the diagnosis of dementia, and recent substance abuse disorder, and cross-sectional evaluated through the Aberrant Salience Inventory (ASI), Childhood Trauma Questionnaire Short-Form (CTQ-SF), and Positive and Negative Symptom Scale (PANSS). We tested a path-diagram where AS mediated the relationship between CTA and psychosis, after verifying each measure one-dimensionality through confirmatory factor analysis. The final sample comprised 222 patients (36.9% female), with a mean age of 42.4 (± 13.3) years and an average antipsychotic dose of 453.6 (± 184.2) mg/day (chlorpromazine equivalents). The mean duration of untreated psychosis was 1.8 (± 2.0) years while the mean onset age was 23.9 (± 8.2) years. Significant paths were found from emotional abuse to ASI total score (β = 0.39; p < .001) and from ASI total score to PANSS positive (β = 0.17; p = .019). Finally, a statistically significant indirect association was found from emotional abuse to PANSS positive mediated by ASI total score (β = 0.06; p = .041; CI 95% [0.01, 0.13]). Emotional abuse has an AS-mediated effect on positive psychotic symptomatology. AS evaluation could allow a better characterization of psychosis as well as explain the presence of positive symptoms in adults with SSDs who experienced CTA. •Aberrant salience (AS) is associated with psychotic symptoms in schizophrenia.•Childhood trauma and adversities (CTA) are particularly relevant in schizophrenia.•The relationship between AS, CTA and the development of psychosis is unclear.•AS could act as a mediator between CTA and positive symptoms in adulthood.•CTA early recognition may improve long-term prognosis in patients with higher AS.

Background Aberrant salience is a well-known construct associated with the development and maintenance of psychotic symptoms in schizophrenia. However, only a few studies have investigated aberrance salience as a trait, with no study investigating the association between the five aberrant salience domains and psychotic symptoms. We aimed to explore the role of aberrant salience and its domains on psychotic dimensions in both clinically remitted and non-remitted patients. Methods A sample of 102 patients diagnosed with schizophrenia spectrum disorders was divided according to the Positive and Negative Syndrome Scale (PANSS) remission criteria into two groups: remitted and non-remitted. Differences regarding psychotic symptomatology assessed by the PANSS and aberrant salience measured by the Aberrant Salience Inventory (ASI) were explored. Finally, a correlation analysis between the PANSS and the ASI was run. Results Significantly higher ASI scores were evident among non-remitted patients. Positive symptoms (i.e. delusions, conceptual disorganization, and hallucinatory behaviour) and general psychopathology (i.e. postural mannerisms, unusual thought content) were correlated to the aberrant salience subscales ‘sharpening of senses’, ‘heightened emotionality’ and ‘heightened cognition’ and with the ASI total score. Significant correlations emerged between negative symptoms (blunted affect and social withdrawal) and ‘heightened cognition'. Finally, lack of spontaneity of conversation was related to the subscales ‘heightened emotionality’ and ‘heightened cognition’, as well as to the ASI total score. Conclusions These preliminary results support the hypothesis of an association between aberrant salience and psychotic symptoms in schizophrenia. Further research is needed, especially into the mechanisms underlying salience processing, in addition to social and environmental factors and cognitive variables.

Post-Traumatic Stress Disorder (PTSD) is a complex disorder involving dysregulation of stress-related hormones and neurotransmitter systems. Research focused on the endocannabinoid system (eCBS) for anxiety and stress regulation, cognitive and emotional responses modulation and aversive memories extinction, leading to the hypothesis that it could represent a possible alternative treatment target for PTSD. In this systematic review, we summarize evidence about the efficacy and safety of medicinal cannabidiol (CBD), Δ9-tetrahydrocannabinol (Δ9-THC), and nabilone in PTSD treatment. The PRISMA statement guidelines were followed. A systematic literature search was conducted in MEDLINE/PubMed, Scopus and Web of Science by two independent researchers, who also performed data extraction and quality assessment. Among the initial 495 papers, 234 were screened for eligibility and 10 were included. Studies suggested that different medicinal cannabinoids at distinct doses and formulations could represent promising treatment strategies for the improvement of overall PTSD symptomatology as well as specific symptom domains (e.g., sleep disorders, arousal disturbances, suicidal thoughts), also influencing quality of life, pain and social impact. Although there is a robust rationale for treatment with drugs that target the eCBS and the results are promising, further studies are needed to investigate the safety and efficacy profile of their prolonged use.

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated metabolic complications and the overall outcome. In this study, machine learning techniques were applied to static and dynamic glucose-derived measures to detect BED among 281 individuals with high weight. Data from the classic (2 h) and the extended (5 h) glucose load were computed by multiple algorithms and two models with the most relevant features were trained to detect BED within the sample. The models were then tested on an independent cohort ( N  = 21). The model based on the 5 h-long glucose load exhibited the best performance (sensitivity = 0.75, specificity = 0.67, F score = 0.71) diagnosing BED in 7 out of 10 cases. Sex, HOMA-IR, HbA1c and plasma glucose in different times, and hypoglycemia events were the most sensitive features for BED diagnosis. This study is the first to use metabolic hallmarks to train ML algorithms for detecting BED in individuals at high risk for metabolic complications. ML techniques applied to objective and reliable glycemic features might prompt the identification of BED among individuals at high risk for metabolic complications, enabling timely and tailored multidisciplinary treatment.

Background Apathy is a significant yet frequently overlooked aspect of Major Depressive Disorder (MDD), often confused with anhedonia, and with the potential to influence clinical outcomes and quality of life regardless of the severity of depressive symptoms. Despite its importance, the relationship between apathy and depression remains underrepresented in current research. This study aims to assess the prevalence of apathy in a sample of outpatients affected by MDD and to explore its clinical, cognitive, and functional correlates. Methods Outpatients with MDD ( n  = 154) during a clinical stability phase have been assessed using the Apathy Evaluation Scale-Clinician version (AES-C), Dimensional Apathy Scale (I-DAS), MADRS, BDI-II, MMSE, PANSS, EQ-5D-3 L, and CGI-S. Patients were categorized into two groups according to the presence/absence of apathy (AES-C score ≥ 42). Groups comparison and a stepwise logistic regression were run to identify clinical differences between groups and the predictors of apathy. Results Apathy was detected in 48% of the sample. Patients with apathy scored higher on depression scales, had lower cognitive performance, more negative and general psychopathology, greater clinical severity, and reduced quality of life. Significant differences emerged in initiation and executive apathy. Logistic regression identified MADRS item 8 “inability to feel” and BDI-II item 12 “loss of interest” as the factors showing the strongest association with apathy. Conclusion Apathy is a prevalent and clinically relevant feature in MDD, associated with greater psychopathology and functional impairment. Findings support the need to routinely assess apathy in clinical setting, even during symptomatic stability phases. Specific symptoms may serve as key markers for its identification. Highlights Apathy is a prevalent and clinically relevant feature in major depressive disorder (MDD). Patients with apathy reported greater clinical severity and poorer quality of life. “Inability to feel” and “loss of interest” are the strongest predictors of apathy in MDD. Apathy in MDD might be a bridging dimension between anhedonia and cognitive symptoms.

Background Research has shown that a history of childhood adversities is common in patients with psychiatric disorders but few studies have investigated links between specific types of adversity and specific psychiatric disorders. Methods We investigated the frequency of early childhood adversities in a sample consisting of 91 patients with diagnosis of schizophrenic spectrum disorders (SSD), 74 patients with bipolar disorder (BD), 83 patients with major depressive disorder (MDD) and 85 healthy controls and sought to identify adverse early childhood life events that predict the development of major psychiatric disorders. The Childhood Experiences of Care and Abuse questionnaire was used to collect data on traumatic experiences occurring before the age of 17 years and comprehensive demographic data were also collected. The data were analyzed with chi-squared tests, t-tests, post-hoc and logistic regression. Results Maternal absence/loss and economic difficulties in the early life were more prevalent in the BD group than other groups. Escape from home, cannabis abuse, psychological abuse, physical abuse and loneliness were more frequent in the SSD group than in other groups. Paternal absence, neglect of core needs, serious familial tension and absence of adult and peer confidants were all less common in the HC group than in the other groups. The regression model confirmed that different types of adversities play a crucial role in the development of the three investigated disorders. Conclusions Our results support that SSD, BD and MDD are associated to different childhood adversities. This suggests that psychosocial interventions that reduce the incidence of these early life adversities might reduce the incidence of severe and disabling psychiatric disorders.

Background This study aims to examine the underlying associations between eating, affective and metacognitive symptoms in patients with binge eating disorder (BED) through network analysis (NA) in order to identify key variables that may be considered the target for psychotherapeutic interventions. Methods A total of 155 patients with BED completed measures of eating psychopathology, affective symptoms, emotion regulation and metacognition. A cross-sectional network was inferred by means of Gaussian Markov random field estimation using graphical LASSO and the extended Bayesian information criterion (EBIC-LASSO), and central symptoms of BED were identified by means of the strength centrality index. Results Impaired self-monitoring metacognition and difficulties in impulse control emerged as the symptoms with the highest centrality. Conversely, eating and affective features were less central. The centrality stability coefficient of strength was above the recommended cut-off, thus indicating the stability of the network. Conclusions According to the present NA findings, impaired self-monitoring metacognition and difficulties in impulse control are the central nodes in the psychopathological network of BED whereas eating symptoms appear marginal. If further studies with larger samples replicate these results, metacognition and impulse control could represent new targets of psychotherapeutic interventions in the treatment of BED. In light of this, metacognitive interpersonal therapy could be a promising aid in clinical practice to develop an effective treatment for BED.

Background Impaired metabolic response such as blood glucose fast fluctuations may be hypothesized in binge eating disorder (BED) and food addiction (FA) by virtue of the repetitive consumption of highly processed food. Conversely, rapid changes in plasma glucose (i.e., hypoglycemia) may trigger craving for the same food products. The investigation of early glycemic disturbances in BED and FA could enhance the understanding of the metabolic mechanisms involved in the maintenance of the disorders. Present study investigated hypoglycemia events during a 5-h-long oral glucose tolerance test (OGTT) in people with BED, FA, and the comorbid phenotype. Further, the association between the severity of eating psychopathology and the variability in hypoglycaemia events was explored. Methods Two-hundred participants with high weight and no diabetes completed the extended OGTT and were screened for BED, FA, BED-FA, or no-BED/FA. The four groups were compared in hypoglycemia events, OGTT-derived measures, and eating psychopathology. The association between predictors (eating psychopathology), confounders (demographics, metabolic features), and the outcomes (hypoglycemia, early/late hypoglycemia, severe hypoglycemia, reactive hypoglycemia) was examined through logistic regression. Results Hypoglycemia in general, and reactive hypoglycemia were highly frequent (79% and 28% of the sample, respectively). Hypoglycemia events (< 70 mg/dL) were equally experienced among groups, whilst severe hypoglycemia (< 54 mg/dL) was more frequent in BED at the late stage of OGTT (5 h; χ 2  = 1.120, p  = .011). The FA and BED groups exhibited significantly higher number of reactive hypoglycemia (χ 2  = 13.898, p  = .003), in different times by diagnosis (FA: 210′–240′; BED: at the 270′). FA severity was the only predictor of early and reactive hypoglycemia. Conclusions People with BED or FA are prone to experiencing reactive hypoglycemia; FA severity may predict early and symptomatic hypoglycemia events. This can further reinforce disordered eating behaviours by promoting addictive responses, both biologically and behaviourally. These results inform professionals dealing with eating disorders about the need to refer patients for metabolic evaluation. On the other hand, clinicians dealing with obesity should screen for and address BED and FA in patients seeking care for weight loss. Plain English summary Impairment in blood glucose control may be attended in binge eating disorder (BED) and food addiction (FA), two distinct eating disorders which are characterized by the recurrent consumption of highly palatable food rich in high-glucose index carbohydrates. Conversely, rapid changes in blood glucose, such as hypoglycemia, may intensify craving for high-calorie products, thus reinforcing pathological eating behaviours. This study investigated the presence of hypoglycemia events in people suffering from BED, FA, both, or no eating disorder, and explored whether the severity of eating behaviours correlated with a higher probability of having hypoglycemia. Results showed that people with BED and FA experienced more episodes of symptomatic hypoglycemia than those with obesity but no eating disorder. The severity of binge eating was associated with more severe hypoglycemia events, indicated by lower plasma glucose values. Lastly, people with severe FA were more prone to experiencing early post-meal hypoglycemia accompanied by symptoms. These results inform professionals dealing with eating disorders about the need to refer patients for metabolic evaluation. On the other hand, clinicians dealing with obesity should screen for and address BED and FA in patients seeking care for weight loss.