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

Summary

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.

Conditions

• Major Depressive Disorder

Interventions

DRUG

SSRIs/SNRIs (Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors)

Patients will be treated with Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors.

DRUG

SSRIs/SNRIs+Mood Stabilizer

Patients will be treated with Mood Stabilizer combined with Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors.

DRUG

SSRIs/SNRIs+Quetiapine

Patients will be treated with Quetiapine combined with Selective Serotonin Reuptake Inhibitors/ Serotonin and Norepinephrine Reuptake Inhibitors.

DRUG

Usual Treatment

Patients' treatment will be decided by the clinical doctor.

Sponsors & Collaborators

• Air Force Military Medical University, China

  collaborator OTHER

• Guangzhou Psychiatric Hospital

  collaborator OTHER_GOV

• Dalian Seventh People's Hospital

  collaborator OTHER

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

  collaborator OTHER

• Shanghai Jiao Tong University School of Medicine

  collaborator OTHER

• Shanghai Mental Health Center

  lead OTHER

Principal Investigators

• Daihui Peng, MD. PhD. · Shanghai Mental Health Center

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

16 Years

Max Age

60 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2019-08-12

Primary Completion

2022-10-31

Completion

2022-10-31

Countries

• China

Study Locations