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Background: Twenty years after the first use of Deep Brain Stimulation (DBS) in obsessive-compulsive disorder (OCD), our knowledge of the long-term effects of this therapeutic option remains very limited. Objective: Our study aims to assess the long-term effectiveness and tolerability of DBS in OCD patients and to look for possible predictors of long-term response to this treatment. Methods: We studied the course of 25 patients with severe refractory OCD treated with DBS over an average follow-up period of 6.4 years (±3.2) and compared them with a control group of 25 patients with severe OCD who refused DBS and maintained their usual treatment. DBS was implanted at the ventral anterior limb of the internal capsule and nucleus accumbens (vALIC-Nacc) in the first six patients and later at the bed nucleus of stria terminalis (BNST) in the rest of patients. Main outcome was change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score between the two groups assessed using mixed models. Secondary effectiveness outcomes included Hamilton Depression Rating Scale (HDRS) and Global Assessment of Functioning (GAF) scores. Results: Obsessive symptoms fell by 42.5% (Y-BOCS score) in patients treated with DBS and by 4.8% in the control group. Fifty-six per cent of DBS-treated patients could be considered responders at the end of follow-up and 28% partial responders. Two patients among those who rejected DBS were partial responders (8%), but none of the non-DBS group achieved criteria for complete response. HDRS and GAF scores improved significantly in 39.2% and 43.6% among DBS-treated patients, while did not significantly change in those who rejected DBS (improvement limited to 6.2% in HDRS and 4.2% in GAF scores). No statistically significant predictors of response were found. Mixed models presented very large comparative effect sizes for DBS (4.29 for Y-BOCS, 1.15 for HDRS and 2.54 for GAF). Few patients experienced adverse effects and most of these effects were mild and transitory. Conclusions: The long-term comparative effectiveness and safety of DBS confirm it as a valid option for the treatment of severe refractory OCD.

Abstract Although the etiology of obsessive–compulsive disorder (OCD) is largely unknown, it is accepted that OCD is a complex disorder. There is a known bi-directional interaction between the gut microbiome and brain activity. Several authors have reported associations between changes in gut microbiota and neuropsychiatric disorders, including depression or autism. Furthermore, a pediatric-onset neuropsychiatric OCD-related syndrome occurs after streptococcal infection, which might indicate that exposure to certain microbes could be involved in OCD susceptibility. However, only one study has investigated the microbiome of OCD patients to date. We performed 16S ribosomal RNA gene-based metagenomic sequencing to analyze the stool and oropharyngeal microbiome composition of 32 OCD cases and 32 age and gender matched controls. We estimated different α- and β-diversity measures and performed LEfSe and Wilcoxon tests to assess differences in bacterial distribution. OCD stool samples showed a trend towards lower bacterial α-diversity, as well as an increase of the relative abundance of Rikenellaceae, particularly of the genus Alistipes, and lower relative abundance of Prevotellaceae, and two genera within the Lachnospiraceae: Agathobacer and Coprococcus. However, we did not observe a different Bacteroidetes to Firmicutes ratio between OCD cases and controls. Analysis of the oropharyngeal microbiome composition showed a lower Fusobacteria to Actinobacteria ratio in OCD cases. In conclusion, we observed an imbalance in the gut and oropharyngeal microbiomes of OCD cases, including, in stool, an increase of bacteria from the Rikenellaceae family, associated with gut inflammation, and a decrease of bacteria from the Coprococcus genus, associated with DOPAC synthesis.

Abstract Background Sleep disturbances have been reported in obsessive-compulsive disorder (OCD) patients, with heterogeneous results. The aim of our study was to assess sleep function in OCD and to investigate the relationship between sleep and the severity of obsessive-compulsive (OC) symptoms, depressive symptoms and trait anxiety. Methods Sleep quality was measured in 61 OCD patients and 100 healthy controls (HCs) using the Pittsburgh Sleep Quality Index (PSQI). Multiple linear regression was conducted to explore the association between sleep and psychopathological measures; a mediation analysis was also performed. Results OCD patients showed poor sleep quality and more sleep disturbances compared to HCs. The severity of depression, trait anxiety and OC symptomatology were correlated with poor sleep quality. Multiple linear regression analyses controlling for potential confounders revealed that the severity of depression and trait anxiety were independently related to poor sleep quality in OCD. A mediation analysis showed that both the severity of trait anxiety and depression mediate the relationship between the severity of OC symptoms and poor sleep quality among patients with OCD. Conclusions Our findings support the existence of sleep disturbances in OCD. Trait anxiety and depression play a key role in sleep quality among OCD patients.

BackgroundMachine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms’ worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.Methods127 OCD patients were assessed using the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient’s sociodemographic, clinical and contextual information.ResultsA Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR.ConclusionOur findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.