Purpose: The study examined the associations of multiple psychiatric and chronic conditions with the self-reported history of major depressive disorder (MDD) among patients with opioid use disorder (OUD) and tested whether the associations differed by gender. Methods: We conducted a secondary data analysis of baseline data from a clinical trial including 1,646 participants with OUD, of which 465 had MDD. A variable cluster analysis was used to classify chronic medical and psychiatric conditions. Multivariable logistic regression analyses were used to estimate their associations with MDD in subjects with OUD. Results: Nine variables were divided into three clusters: cluster 1 included heart condition, hypertension, and liver problems; cluster 2 included gastrointestinal (GI) problems and head injury, and cluster 3 included anxiety disorder, bipolar disorder, and schizophrenia. The overall prevalence of MDD in participants with OUD was 28.3% (22.8% for males and 39.5% for females). Gender, anxiety disorder, schizophrenia, liver problems, heart condition, GI problems, and head injury were significantly associated with MDD. Gender-stratified analyses showed that bipolar disorder, liver problems and individuals with one chronic condition were associated with MDD only in males, whereas heart condition, hypertension, and GI problems were associated with MDD only in females. In addition, anxiety disorder, head injury, individuals with one or more than two psychiatric conditions, and individuals with more than two chronic conditions were associated with MDD regardless of gender. Conclusions: Treatment plans in patients with OUD should not only address MDD but also co-morbid psychiatric and chronic medical conditions that occur with MDD.

Introduction: The homeostatic role of the endocannabinoid system (ECS) is mediated through the actions of endocannabinoids. Intake of exogenous cannabinoids found in Cannabis sativa alter the function of the ECS which may then impact other organ systems. Use of cannabis has been inconsistently linked to adverse cardiometabolic outcomes. Rates of cannabis use are high among patients with psychiatric conditions who are already at higher risk of cardiometabolic diseases when compared to the general population. Cannabis use patterns and cardiometabolic risk variables in this population need further study to clarify the links between use and outcomes. Methods: Patients with psychiatric conditions from the St. Joseph’s Healthcare Hamilton Hospital were enrolled into the Cannabis and Physical Health study. Sociodemographic data, medical history, cigarette use, and cannabis use patterns were collected. In addition, cardiometabolic profile data were collected including body mass index, blood pressure, lipids, and HbA1c. Multivariable regression analyses were conducted, and a Bonferroni correction applied. Results: This cross-sectional study enrolled 200 patients (female: n=86, 43.0%), 18 years of age and older. Among 79 cannabis users (female: n=34, 43.0%), the majority (n=53, 67.1%) consumed cannabis daily and had a diagnosis of a moderate cannabis use disorder (CUD; n=57, 72.2%, CUD score = 4.3 ± 3.4). Use of cannabis was initiated on average at 15.2 ± 3.5 years of age and used for an average of 13.5 ± 11.0 years. There was no association between cannabis use and cardiometabolic risk factors when adjusted for age, sex, psychiatric diagnosis, antipsychotic medication use, and cigarette smoking (P>0.006 for all outcomes). Conclusions: Our findings indicate that in this sample of patients with psychiatric diagnoses, patients who use cannabis had a similar cardiometabolic profile to non-users. Patterns of cannabis use highlight the importance of reducing cannabis consumption and preventing or slowing the progression of CUD in this population, as well as limiting adolescent exposure to cannabis. / Thesis / Master of Science (MSc) / The endocannabinoid system regulates several processes in the body via endocannabinoid signaling, and cannabinoids found in cannabis can change endocannabinoid system function. Cardiovascular events and changes in appetite have been noted with cannabis use, and this is especially important in some vulnerable populations at risk of increased cannabis use; one of these groups include patients with psychiatric conditions who tend to use cannabis but also already have an increased cardiometabolic risk. In this thesis, the relationship between cannabis use and cardiometabolic risk was examined in 200 patients, and patterns and determinants of cannabis use explored. Our results demonstrated that of the 79 cannabis users, most consumed cannabis daily, and had a moderate cannabis use disorder. On average, users began cannabis consumption at 15 years of age and for an average duration of 14 years. Moderate cannabis use was not related to cardiometabolic risk in these patients. Our data demonstrate the need to prevent or slow the progression of cannabis use disorder in these patients and the importance of reducing early exposure of adolescents to cannabis.

Syftet med studien är att undersöka hur behandlingspersonal på SiS-institutioner uppfattarvillkoren för att behandla tvångsomhändertagna ungdomar med psykiatriska tillstånd. Enskilda intervjuer med behandlingspersonal från SiS-institutioner har genomförts. Begreppen habitualisering och typifiering användes för vidare analys av resultatet tillsammans med olika förhållningssätt gentemot ungdomarna utifrån personaltyperna informella supporters, administratörer och vårdgivare. Resultatet påvisade att en majoritet av ungdomarna på SiS-institution har psykiatriska tillstånd. Behandlingspersonalen ska behandla psykosociala problem, men det framkommer att de även behöver hantera ungdomarnas psykiatriska vårdbehov trots bristande kompetens. De möjligheter och hinder som framkommit är ofta sammanvävdamed varandra men de aspekterna av vikt är behandlingsmetoder, formell och personlig kompetens samt personalens förhållningssätt gentemot ungdomarna. Även organisatoriska faktorer har framkommit ha en påverkan på behandlingsarbetet. Studien visar att felaktig behandling kan förlänga ungdomarnas sjukdomsförlopp. Vi anser därför att behandlingssituationen för placerade ungdomar med psykiatriska tillstånd behöver förbättras. / The study aims to explore how treatment staff at SiS-institutions perceive the conditions for treating youths with psychiatric conditions in compulsory care. Interviews were conducted with staff from SiS institutions. The concepts habitualization and typification are used for analysis of the results along with different approaches towards the youth based on the staff types informal supporters, administrators and caregivers. The results indicate that the majority of youths at SiS-institutions have psychiatric conditions. While the staff's primary focus is on treating psychosocial issues, it became evident that they handle the youths' psychiatric needs despite lacking competence. Identified possibilities and challenges are intertwined, emphasizing the importance of treatment methods, formal and personal competence, and the staff's approach. Organizational factors are found to influence treatment outcomes. The study shows that incorrect treatment can prolong the youths' illness. Therefore, we believe that the treatment situation for youths with psychiatric conditions needs improvement.

L'imagerie par résonance magnétique fonctionnelle à l'état de repos (IRMf-R) s'est imposée comme une technologie diagnostique prometteuse. Toutefois, l'application dans la pratique clinique des biomarqueurs de l'IRMf-R visant à capturer les mécanismes biologiques sous-jacents aux troubles psychiatriques a été entravée par le manque de généralisation. Le diagnostic de ces troubles repose entièrement sur des évaluations comportementales et les taux élevés de comorbidités et de chevauchement génétique et symptomatique confirment l'existence de facteurs latents communs à toutes les pathologies. De grandes mutations génétiques rares, appelées variants du nombre de copies (CNV), ont été associées à une série de troubles psychiatriques et ont des effets beaucoup plus importants sur la structure et la fonction du cerveau, ce qui en fait une voie prometteuse pour démêler la génétique des catégories diagnostiques actuelles. L'apprentissage multitâche est une approche prometteuse pour extraire des représentations communes à des tâches connexes, qui permet de mieux utiliser les données en tirant parti des informations partagées et en améliorant la généralisabilité. Nous avons recueilli un ensemble de données sans précédent composé de 19 CNV et de troubles psychiatriques et nous avons cherché à évaluer systématiquement les avantages potentiels de l'apprentissage multitâche pour la précision de la prédiction, afin d'effectuer un diagnostic conjoint de ces conditions interdépendantes. Nous avons estimé les tailles d'effet pour chaque condition, comparé la précision du diagnostic en utilisant des méthodes courantes d'apprentissage automatique, puis en utilisant l'apprentissage multitâches. Nous avons tenté de contrôler les multiples facteurs confondants tout au long des analyses et discutons des différentes approches permettant de le faire dans le contexte de la modélisation prédictive. L'hypothèse selon laquelle les facteurs latents partagés entre les CNV et les troubles psychiatriques les rendraient suffisamment liés en tant que tâches de prédiction pour bénéficier d'un apprentissage conjoint n'a pas été confirmée. Cependant, nous avons également appliqué l'apprentissage multitâche entre les sites pour prédire une cible commune et nous avons montré que la prédiction peut être améliorée lorsque les tâches sont très étroitement liées. Nous avons mis en œuvre un modèle léger de partage des paramètres durs, mais nos résultats et la littérature montrent que ce cadre n'est pas bien adapté aux tâches hétérogènes ou, de manière contre-intuitive, aux échantillons de petite taille. Nous pensons qu'il est possible d'exploiter les similitudes entre les CNV et les troubles psychiatriques en utilisant des méthodes qui modélisent les relations entre les tâches, mais la petite taille des échantillons pour les CNV rares constitue une limitation majeure pour l'application de l'apprentissage multitâche. / Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising diagnostic technology, however translation into clinical practice of rs-fMRI biomarkers that aim to capture the biological mechanisms underlying psychiatric disorders has been hindered by lack of generalizability. The diagnosis of these disorders is completely based on behavioural assessments and high rates of comorbidities and genetic and symptom overlap supports the existence of latent factors shared across conditions. Rare large genetic mutations, called copy number variants (CNVs), have been associated with a range of psychiatric conditions and have much larger effect sizes on brain structure and function, which makes them a promising avenue for untangling the genetics of the current diagnostic categories. Multi-task learning is a promising approach to extract common representations across related tasks that makes better use of data by leveraging shared information and improves generalizability. We collected an unprecedented dataset consisting of 19 CNVs and psychiatric disorders and aimed to systematically assess the potential benefits for prediction accuracy of using multi-task learning to perform joint diagnosis of these interlinked conditions. We estimated effect sizes for each condition, benchmarked diagnostic accuracy using common machine learning methods, and then using multi-task learning. We attempted to control for multiple confounding factors throughout the analyses, and discuss different approaches to do so in the predictive modelling context. The hypothesis that latent factors shared between CNVs and psychiatric conditions would make them sufficiently related as prediction tasks to benefit from being learned jointly was not supported. However, we also applied multi-task learning across sites to predict a common target and showed that prediction can be improved when tasks are very tightly related. We implemented a lightweight hard parameter sharing model, but evidence from our results and the literature shows this framework is not well suited to heterogeneous tasks or, counterintuitively, to small sample sizes. While we believe there is potential to exploit the similarities between CNVs and psychiatric conditions using methods that model relationships between tasks, small sample sizes for rare CNVs are a major limitation for the application of multi-task learning.