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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., This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 2022

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.

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Facultad de Medicina, Departamento de Psiquiatría. Fecha de lectura: 24 de Mayo de 2012

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.