Childhood emotional dysregulation paths for suicide-related behaviour engagement in adolescence.

This study aimed at identifying the heterogeneous trajectories of emotional dysregulation across childhood and to study the relationship between specific trajectories and adolescent suicide-related behaviour (SRB). Data from the Millennium Cohort Study (N = 13,853 children; 49.07% female, M = 3.13 years at baseline, SD = 0.2) were used to identify the emotional dysregulation trajectories from 3 to 8 years old, using growth mixture modelling. Moreover, 1992 participants (52.86% female) from the initial sample were used to study the relationship between childhood emotional dysregulation trajectory and engagement in both self-harm and suicide attempt at age 17, using logistic regression. Some other time-invariant and proximal (adolescent) risk factors were incorporated into this analysis. Six emotional dysregulation trajectories were identified. Self-harm at age 17 was significantly associated with the history of self-harm and other proximal factors, but not with emotional dysregulation trajectory membership. Childhood trajectories featured by earlier emotional dysregulation were associated with higher risk of lifetime suicide attempt, as well as other proximal factors (concurrent self-harm). This study found differential risk profiles involved in both SRB forms. A relationship between early emotional dysregulation and suicide attempt engagement in adolescence was identified. Early interventions should be developed to deal with SRB risk factors from childhood.

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning.

BACKGROUND: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. METHODS: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. RESULTS: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. CONCLUSIONS: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.

Relationship between immunometabolic status and cognitive performance among major depression disorder patients.

BACKGROUND: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) METHODS: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. RESULTS: Significant differences were obtained in the inflammatory/oxidative index between both groups (F(1157)= 12.93; p < .001), also in cognitive performance (F(1157)= 56.75; p < .001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F(7157)= 11.24; p < .01) and explained 31% of variance. The condition, being either MDD or HD, (B=-0.97; p < .001), age (B=-0.28; p < .001) and the interaction between inflammatory/oxidative index and MetS (B=-0.38; p = .02) were factors associated to cognitive performance. LIMITATIONS: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. CONCLUSIONS: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD.

Age at illness onset and physical activity are associated with cognitive impairment in patients with current diagnosis of major depressive disorder.

BACKGROUND: Cognitive impairment has been reported in patients with Major Depressive Disorder (MDD). This study aims to explore the association between lifestyle habits and health-related factors and the presence of cognitive symptoms in MDD patients. METHODS: Demographic, clinical, health-related variables and cognitive scores measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB) were compared between 74 patients with current MDD and 68 healthy controls (HC). To test the hypothesis of associated factors to cognitive symptoms, multivariate backward stepwise linear regression models were run. RESULTS: Significant neuropsychological deficits were evident in MDD compared with HC in the global cognitive index (F=8.29; df=1, 140; p=0.005). In the regression analysis performed on MDD and HC, years of schooling (ß=-0.11; p=<0.001), job status (ß=-0.50; p=0.016), physical activity (ß=-0.25; p=0.04) and age at illness onset (ß=0.17; p=0.017) were statistically significant factors associated to cognitive impairment. The regression model ran in HC showed that only years of schooling were significant (ß=-0.07; p=<0.001) in this group. LIMITATIONS: Sample size was relatively small. Everyday cognitive skills were not evaluated. CONCLUSIONS: MDD patients have cognitive deficits. These deficits are linked with the years of education, job status, age of onset of the disease and the performance of physical activity. These results support the importance of the implementation of interventions targeting the cognitive reserve and lifestyle habits of MDD patients, in addition to the conventional therapeutic approach focused on symptoms control.

The ICD-11 developmental field study of reliability of diagnoses of high-burden mental disorders: results among adult patients in mental health settings of 13 countries.

Reliable, clinically useful, and globally applicable diagnostic classification of mental disorders is an essential foundation for global mental health. The World Health Organization (WHO) is nearing completion of the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11). The present study assessed inter-diagnostician reliability of mental disorders accounting for the greatest proportion of global disease burden and the highest levels of service utilization - schizophrenia and other primary psychotic disorders, mood disorders, anxiety and fear-related disorders, and disorders specifically associated with stress - among adult patients presenting for treatment at 28 participating centers in 13 countries. A concurrent joint-rater design was used, focusing specifically on whether two clinicians, relying on the same clinical information, agreed on the diagnosis when separately applying the ICD-11 diagnostic guidelines. A total of 1,806 patients were assessed by 339 clinicians in the local language. Intraclass kappa coefficients for diagnoses weighted by site and study prevalence ranged from 0.45 (dysthymic disorder) to 0.88 (social anxiety disorder) and would be considered moderate to almost perfect for all diagnoses. Overall, the reliability of the ICD-11 diagnostic guidelines was superior to that previously reported for equivalent ICD-10 guidelines. These data provide support for the suitability of the ICD-11 diagnostic guidelines for implementation at a global level. The findings will inform further revision of the ICD-11 diagnostic guidelines prior to their publication and the development of programs to support professional training and implementation of the ICD-11 by WHO member states.

Clinical utility of ICD-11 diagnostic guidelines for high-burden mental disorders: results from mental health settings in 13 countries.

In this paper we report the clinical utility of the diagnostic guidelines for ICD-11 mental, behavioural and neurodevelopmental disorders as assessed by 339 clinicians in 1,806 patients in 28 mental health settings in 13 countries. Clinician raters applied the guidelines for schizophrenia and other primary psychotic disorders, mood disorders (depressive and bipolar disorders), anxiety and fear-related disorders, and disorders specifically associated with stress. Clinician ratings of the clinical utility of the proposed ICD-11 diagnostic guidelines were very positive overall. The guidelines were perceived as easy to use, corresponding accurately to patients' presentations (i.e., goodness of fit), clear and understandable, providing an appropriate level of detail, taking about the same or less time than clinicians' usual practice, and providing useful guidance about distinguishing disorder from normality and from other disorders. Clinicians evaluated the guidelines as less useful for treatment selection and assessing prognosis than for communicating with other health professionals, though the former ratings were still positive overall. Field studies that assess perceived clinical utility of the proposed ICD-11 diagnostic guidelines among their intended users have very important implications. Classification is the interface between health encounters and health information; if clinicians do not find that a new diagnostic system provides clinically useful information, they are unlikely to apply it consistently and faithfully. This would have a major impact on the validity of aggregated health encounter data used for health policy and decision making. Overall, the results of this study provide considerable reason to be optimistic about the perceived clinical utility of the ICD-11 among global clinicians.

Las urgencias psiquiátricas: oportunidades para la adherencia terapéutica. El programa E.M.S.E. Barcelona / Multidisciplinary Team of Specialized support (EMSE): A mobile outreach Project for psychiatric emergency home visits in the city of Barcelona

Introducción: se presenta la implantación de un nuevo dispositivo asistencias, el Equipo Multidisciplinar de Soporte Especializado (EMSE). Este es resultado de un programa de soporte y atención a las urgencias psiquiátricas extrahospitalarias en la ciudad de Barcelona (061). Se describen las características del programa, las fases de desarrollo y se realiza una evaluación. Métodos: se analiza la fase 2, se estudian los pacientes valorados en el domicilio en el periodo del 1 de enero del 2004 al 31 de diciembre de 2004. Las alertas son 196, de las cuales se generan 109 casos que serán tributarios de intervención domiciliaria. Resultados: el 55,5% son trastornos mentales graves desvinculados de la red, y el 22,6% son trastornos mentales vinculados pero que han sido atendidos hace más de cuatro semanas. La historia de trastorno psiquiátrico se caracteriza por tener un tiempo medio sin recibir tratamiento de 40 meses (DS ± 59,2 meses). El delirio, conductas de aislamiento y la agitación-agresividad son las razones de consulta principales. Los diagnósticos se agrupan en el espectro del trastorno esquizofrénico. Las recomendaciones-disposiciones de la consulta, el 48,4% se resuelven en ingresos involuntarios y el 22,6% finalizan en el Centro de Salud Mental. En la evaluación a los cuatro meses, se obtiene un 80% de vinculación en los casos gestionados por EMSE. Conclusiones: la utilidad del programa se constata al comprobar la adherencia al tratamiento. Además este dispositivo puede ser predictor y observatorio de detección de necesidades(AU)

Introduction: our group has set up a program for psychiatric emergency home interventions. It is composed of a multidisciplinary mobile outreach team (EMSE) that attends to spychicatic emergency units called for on the local emergency number (061). Objective: to describe the program, the phses of development and a preliminary evaluation. Methods: a descriptive analysis of home interventions from January 2004 to December 2004. During 2004 teher were 196 calls, of which 109 required home intervention. Results: 55,5% were severe mental disorders that were not being treated at the time. The most frequent motives for consultation were delusions, behavior associated with isolation and agitation-aggression. The diagnoses were classified in the spectrum of schizophrenic disorders. The results from intervention indicate that 48,4% are admitted involuntarily and 22,6% are referred to mental health community centres. The evaluation is positive and results in a 80% follow-up and adherence to treatment by patients. Conclusions: This program is proving both satisfactory and necessary. The adherence to treatment reflects the success of the interventions. The program will also be useful in predicting and detecting further needs(AU)