NCT04209166

A Study of Individualized Diagnosis and Treatment for Major Depressive Disorder With Atypical Features

Status unknown NA Last updated 23 December 2019

What this trial tests

NA trial testing SSRIs/SNRIs (Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors) in Major Depressive Disorder in 780 participants. Status unknown.

Timeline

12 August 2019

Primary endpoint
31 October 2022

31 October 2022

Quick facts

Lead sponsor	Shanghai Mental Health Center
Phase	NA
Status	Status unknown
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	single
Primary purpose	treatment
Enrollment	780
Start date	12 August 2019
Primary completion	31 October 2022
Estimated completion	31 October 2022
Sites	5 locations across China

Drugs / interventions tested

• SSRIs/SNRIs (Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors)
• SSRIs/SNRIs+Mood Stabilizer — full drug profile →
• SSRIs/SNRIs+Quetiapine — full drug profile →
• Usual Treatment — full drug profile →

Conditions studied

• Major Depressive Disorder — all drugs for Major Depressive Disorder →

Sponsor

Shanghai Mental Health Center — full company profile →

Who can join

Adults 16 to 60, any sex, with Major Depressive Disorder. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The lifetime prevalence of major depressive disorder (MDD) is 10%\~20%. Worldwide, nearly 340 million individuals have suffered the torture of depression. World Health Organization has reported that MDD would become the most serious global burden of disease and eventually turn into a public health problem in 2030. Varied clinical symptoms, inappropriate treatment, unclear pathogenesis, and lack of recurrent risk early-warning predictors cause a series of clinical problems, such as low diagnostic rate, low effective treatment rate, and high recurrent rate. Hence, this study aims to search multidimensional markers for early diagnosis of MDD, to establish optimized personalized therapy, and to explore sensitive recurrence predictors.

Based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), MDD is subdivided into eight different clinical specifiers, one of which the incident rate of MDD with atypical features reaches 30%\~38%. However, there is still a lack of meta-evidence for the clinical treatment strategy in MDD with atypical features. And 45.4 percentage of MDD with atypical features convert to bipolar disorder. Therefore, this study will focus on three issues about what's the objective endophenotype in MDD with atypical features, how to select appropriate personalized treatment for MDD with atypical features, what's the predictive biomarker of conversion to bipolar disorder.

Based on the investigators' previous findings, this study will investigate adult depression at a cross-sectional study and a prospective cohort study. Multivariate informatics analysis was performed from three research dimensions (cognitive neuropsychology, metabonomics, and multimodal neuroimaging), including atypical features, "cold/hot" cognition assessment, KP (kynurenine pathway) metabolomics and inflammatory factors, multimodal MRI robust property. Referring guidelines for the diagnosis and treatment of depression and evidence-based medicine evidence, MDD with atypical features are divided into f groups (antidepressants, antidepressants+mood stabilizers, mood stabilizers, treat as usual). Then, the investigators perform follow-up to verify optimized treatment strategies and to explore risk factors of conversion from MDD with atypical features to bipolar disorder. Furthermore, this study performs correlation analysis to analyze cross-omics data, weight coefficient analysis to analyze multidimensional indexes, clustering analysis to analyze multivariate bio-information data, and artificial intelligence technologies (such as pattern recognition, and machine learning) to realize the transformation from medical data to practical transformation. Eventually, this study builds three specific models (the multidimensional early diagnosis models for MDD with atypical features, the optimized personalized therapy model, and the recurrence and conversion risk early-warning model), which form the integrated intelligent platform for multidimensional diagnosis, personalized treatment, recovery management of MDD with atypical features.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

1. A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial and prognosis study.
   Zhou R, Zhang H, He S, Li Y, et al · · 2023 · cited 1× · PMID 37143128 · DOI 10.1186/s13063-023-07317-w
2. A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial
   Zhou R, Zhang H, He S, Li Y, et al · · 2022 · DOI 10.21203/rs.3.rs-1800349/v1