Active clinical trials for "Depressive Disorder, Major"

Cancer and depression commonly occur together, and each worsens the other. We conducted a large psychotherapy study treating depression in breast cancer patients, showing that psychotherapy lowers symptoms. Surprisingly, no studies have compared depression-focused psychotherapy to antidepressant medication for patients with breast cancer and depression. We applied to the National Cancer Institute for a large, cross-national grant. Reviewers asked us to first demonstrate that patients would accept either psychotherapy or medication as treatment. Thanks to funding from the Columbia Herbert Irving Cancer Center, we will test this study approach. We will randomly assign 20 patients with both non-metastatic breast cancer and major depression to 12 weeks of tele-therapy (by Zoom) with either interpersonal psychotherapy or a serotonin reuptake inhibitor. We expect patients in both treatments to report improvement in depression symptoms. We will also measure C-reactive protein, a blood test of inflammation elevated in both cancer and depression, which may predict medication response.

70% of Europeans will be exposed to a potentially traumatic event (PTE). Following this experience, people are likely to develop various psychiatric disorders such as post-traumatic stress disorder (PTSD) or a major depressive episode (MDE). However, not all subjects have the same risk to develop a pathology, and resilience capacities, which depend on multiple factors are difficult to predict. Currently, there are no objective tools to stratify exposed subjects according to their risk of developing pathological responses to stress, which leads to difficulties in allocating means of prevention and treatment. Recently, new biological hypotheses explaining vulnerability/resilience to stress and depression, implicating the GPR56 and ELK1 genes, have been described. Previous studies have shown that evaluation of the vulnerability risk can be obtained from clinical, cognitive, biological or brain imaging variables, but no study has integrated these different approaches. Therefore, the project presented here aims at integrating behavioral, biological and neuroimaging data to predict the development of psychiatric disease. In this study, a prospective cohort of 255 violent trauma victims will be set up in 3 French cities for a period of 2 years. Eligible subjects will be included in the month following PTE and will be followed longitudinally for 12 months. Evaluations at 1, 3, 6 and 12 months will be performed, during which the subject will complete various clinical and cognitive tests. A blood sample will be collected at each visit to study biological processes including the regulation of genetic and epigenetic expression, in particular the expression of the GPR56 and ELK1 genes in the blood. For eligible subjects a brain MRI will be proposed at the first visit. We hypothesize that the genetic expression of ELK1 and GPR56 is predictive of the development of psychiatric pathologies at 6 and 12 months post-PTE. The ambition of this project is also to highlight the importance of a multimodal approach integrating a triad of markers (behavioral, biological and neuroimaging) to test this hypothesis.

Despite international efforts to identify biomarkers of depression, none has been transferred to clinical practice, neither for diagnosis, evolution, nor therapeutic response. This led us to build a French national cohort (through the clinical and research network named SoPsy within the French biological psychiatry society (AFPBN) and sleep society (SFRMS)), to better identify markers of sleep and biological rhythms and validate more homogeneous subgroups of patients, but also to specify the manifestations and pathogeneses of depressive disorders.

The purpose of this study is to identify the variations in gut microbiota compositions between two subtypes of major depressive disorder.

Differentiation between major depressive disorder (MDD) and bipolar disorder (BD) as soon as possible in the patient journey represents a major clinical issue. When the patient is in a depressive phase, the symptoms are similar between the two pathologies and the current clinical scales fail in distinguishing them. Physicians often report this difficulty and as a consequence, the mean time from onset to bipolar disorder diagnosis is currently 7.5 years. These diagnostic delays and misdiagnosis lead to damaging consequences for patients and their loved ones: worsening of symptoms, comorbidities, suicide risk and inadequate care resulting in severe impairment in social and occupational functioning. Faced with these high expectations for accurate diagnostic methods for an earlier management of psychiatric patients, the combination of relevant clinical features and biomarkers could stand for a solution, leading to a personalised approach in patients with mood disorders. In a first clinical discovery study, a panel of RNA biomarkers in the blood of patients with a major depressive episode (MDE) has been identified, allowing to differentiate bipolar disorder from MDD (unipolar depression). These biomarkers are based on RNA modifications, namely RNA editing, that could be identified using molecular biology, NGS and artificial intelligence. This panel constitutes EDIT-B test, which is based on Alcediag's proprietary and patented biomarkers and algorithms. The present study aims to validate the biomarker signatures proposed by Alcediag by measuring the association between the modifications of the RNA editing and major depressive disorder/ bipolar disorder diagnosis, in patients with a MDE in real-life setting pilot centres.

Major depressive disorder (MDD) is a chronic disease with a prevalence around 8-12% and is considered one of the most debilitating disease worldwide. A recent orientation is the analysis of language in relation to the description of images with a high and varied semantic and emotional content. It can be studied that changes in the description of an image check if these changes are associated with the evolution of a person with probable impairment both in memory and cognitive as well as emotional, psychiatric, behavioral and even in their interaction with environmental factors especially those associated with socialization and loneliness. The present study has the objective of validating Accexible as a tool for Screening and Monitoring Depression.

The goal of this observational study focuses on understanding and addressing a subset of persistent neuropsychiatric symptoms occurring within 3 months after mild to moderate COVID-19 infection (COVID-DNP). COVID-DNP encompasses major depressive episodes (MDE) with or without additional neuropsychiatric symptoms.

Objective: Wearable technology holds promising potential for mental health monitoring and detection. Samsung has developed an algorithm that they believe can detect signs of depression and anxiety in smartwatch users. They have used this algorithm to create a "Mindfulness Index," which is an easily understood visual index of mental health. The primary aim of the study is to evaluate the performance of Samsung's Mindfulness Index in identifying those who have received a diagnosis of Major Depressive Disorder (MDD) from a clinician-administered semi-structured diagnostic interview. Research Procedures: The target sample size is 75 individuals diagnosed with current Major Depressive Disorder and 75 healthy controls. To meet this target, the recruitment target is set at 215 participants. Participants will be assigned to the MDD condition, or the healthy control condition based on their score on the Beck Depression Inventory. Each subject will be followed for 3 months. Participants will be provided with a Samsung smartphone and Samsung smartwatch. Participants will be asked to wear the smartwatch 24 hours per day, except while charging. This smartwatch will collect data on heartrate, sleep time, and step count. During the study, each day participants will receive texts prompting a link to a "daily diary." These surveys will ask about depression and anxiety symptoms. Additionally, during the first 3 weeks of the study, participants will participate in ecological momentary assessment; texts will be sent 5 times per day prompting participants to fill out a survey about how they currently feel in that moment. These extra surveys will stop after the first 3 weeks of the study, but the daily diary surveys will continue throughout the study. Furthermore, virtual clinician visits will occur at weeks 4, 8, and 12.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. The aim of this project is the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. In this phase of the project a naturalistic cohort of 300 MDD patients will be recruited. The data collected will be used to assess, in real-world conditions, the capability of an innovative algorithm (integrating clinical, omics and gender data of other 300 patients con MDD) to predict the treatment outcomes. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

This study looks at the role of the Nociceptin/Orphanin FQ receptor system in the brain of individuals with current or past major depressive disorder (MDD). It also examines how individuals with a history of depression make certain decisions and which brain regions are involved in such decisions. Information collected through MRI, PET, biospecimens (i.e., blood, saliva) and behavioral tasks will be used to predict depressive symptoms in the future.