Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

It is just that people treat you like a human being: The meaning of dignity for patients with substance use disorders

Introduction Patients who suffer from substance use disorder (SUD) might receive services from different service providers in an opioid maintenance treatment programme (OMT) and have a widespread and complex need for nursing. Background Literature reveals that prejudices against people with SUD exist. There is a lack of studies exploring patients with SUD experiences of preserving their dignity in the encounter with healthcare staff. The aim of the study was to gain insight into the meaning of dignity for patients with SUD. Methods The research design was descriptive and interpretative. In the interpretation of qualitative in‐depth interviews with six patients, a hermeneutical approach based on Gadamer (Truth and method, Sheed & Ward, London, UK, 1989) was used. Results Analysis resulted in three mains themes about the meaning of dignity: (a) The material dimension. (b) To be respected by others. (c) The inner experience. Factors enhancing dignity in the encounters were as follows: (a) Being respected and acknowledged. (b) Being cared for. (c) Knowledge and persistent relation. Factors depriving dignity were as follows: (a) Stigma and prejudice. (b) Insufficient relations and lack of confirmation. (c) Experiencing disrespectful/patronising attitudes and lack of knowledge. Conclusions The material dimension of dignity containing an aesthetically aspect was important for these patients. Dignity was also experienced as strongly connected to respect. Dignity can be enhanced by treating patients with SUD with understanding and respect, and dignity can be inhibited through stigmatization of patients with SUD, as well as by caregivers’ lack of knowledge. Relevance to clinical practice The study clarifies a need for more knowledge about SUD among healthcare staff, as well as promotes ethical awareness in encounters with patients regardless of their background

Mapping cortical and subcortical asymmetry in obsessive-compulsive disorder: findings from the ENIGMA consortium

Accepted ManuscriptBACKGROUND: Lateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.METHODS: We studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.RESULTS: In the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -20.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.CONCLUSIONS: The results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.This research was funded by the Max Planck Society (Germany). Additional funding was from the Japan Society for the Promotion of Science (KAKENHI Grant No. 18K15523 [to YA], KAKENHI Grant No. 16K04344 [to YH], KAKENHI Grant Nos. 16K19778 and 18K07608 [to TNakam], and KAKENHI Grant No. 26461762 [to AN]); the Carlos III Health Institute (Grant No. PI14/00419 [to PA], Grant No. PI040829 cofunded by European Regional Development Fund [to LL], Grant No. FI17/00294 [to IM-Z], Grant No. PI16/00950 [to JMM], and Grant Nos. CPII16/00048, PI13/01958, and PI16/00889 cofunded by European Regional Development Funds [to CS-M]); the Ontario Mental Health Foundation (Research Training Fellowship [to SHA]); Alberta Innovates Translational Health Chair in Child and Youth Mental Health (to PDA), the Ontario Brain Institute (to PDA); the National Institute of Mental Health (Grant No. K23MH104515 [to JTB], Grant No. K23-MH092397 [to BPB], Grant No. K23MH082176 [to KDF), Grant No. R21MH101441 [to RM], Grant No. R01MH081864 [to JO and JP], and Grant No. R01MH085900 [to JO and JF], Grant No. R21MH093889 [to HBS]); Fundação de Amparo à Pesquisa do Estado de São Paulo (Grant No. 2011/21357–9 [to MCB], Grant No. 2011/21357–9 [to GFB], Grant No. 2011/21357–9 [to MQH], and Grant No. 2011/21357–9 [to ECM]); the Swiss National Science Foundation (Grant No. 320030_130237 [to SB; principal investigator, Susanne Walitza]); the Hartmann Müller Foundation (Grant No. 1460 [to SB]); the David Judah Fund at the Massachusetts General Hospital (to BPB); EU FP7 Project TACTICS (Grant No. 278948 [to JB]); the National Natural Science Foundation of China (Grant No. 81560233 [to YC] and Grant No. 81371340 [to ZW]); the International OCD Foundation (Grant No. K23 MH115206 [to PG]); the Wellcome Sir Henry Dale Fellowship (Grant No. 211155/Z/18/Z [to TUH]); the Jacobs Foundation (to TUH); the Brain and Behavior Research Foundation (2018 NARSAD Young Investigator Grant No. 27023 [to TUH]); the Agency for Medical Research and Development (Grant No. JP18dm0307002 [to YH]); the Michael Smith Foundation for Health Research (to FJ-F); the Federal Ministry of Education and Research of Germany (Grant No. BMBF-01GW0724 [to NK]); the Deutsche Forschungsgemeinschaft (Grant No. KO 3744/7–1 [to KK]); the Helse Vest Health Authority (Grant Nos. 911754 and 911880 [to GK]); the Norwegian Research Council (Grant No. HELSEFORSK 243675 [to GK]); the Marató TV3 Foundation (Grant Nos. 01/2010 and 091710 [to LL]); the Agency for Management of University and Research Grants (Grant No. 2017 SGR 881 [to LL] and 2017 SGR 1247 from the Generalitat de Catalunya [to JMM]); Fundação para a Ciência e a Tecnologia (Grant No. PDE/BDE/113604/2015 from the PhD-iHES Program [to RM], Grant No. PDE/BDE/113601/2015 from the PhD-iHES Program [to PSM]); the Japanese Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Scientific Research (Grant Nos. 22591262, 25461732, and 16K10253 [to TNakao]); the Government of India Department of Science and Technology (DST INSPIRE Faculty Grant No. -IFA12-LSBM-26 [to JCN] and Grant No. SR/S0/HS/0016/2011 [to YCJR]); the Government of India Department of Biotechnology (Grant No. BT/06/IYBA/2012 [to JCN] and Grant No. BT/PR13334/Med/30/259/2009 [to YCJR]); the New York State Office of Mental Health (to HBS); the Italian Ministry of Health (Grant No. RC13-14-15-16A [to GS]); the National Center for Advancing Translational Sciences (Grant No. UL1TR000067/KL2TR00069 [to ERS]); the Canadian Institutes of Health Research (to SES); the Michael Smith Foundation for Health Research (to SES); the British Columbia Provincial Health Services Authority (to SES); the Netherlands Organization for Scientific Research (Grant No. NWO/ZonMW Vidi 917.15.318 [to GAvW]); the Wellcome-DBT India Alliance (Grant No. 500236/Z/11/Z [to GV]); the Shanghai Key Laboratory of Psychotic Disorders (Grant No. 13dz2260500 [to ZW])

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: Medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker