Subtype as a key to reduce heterogeneity of treatment effects in major depressive disorder

Introduction

Major depressive disorder (MDD) is a leading contributor to disability and suicide. It is the most costly brain disorder in Europe. Although multiple treatments are of proven efficacy, individual responses to treatments vary considerably and MDD recurrence is common. There is considerable motivation to improve treatment regimens for individuals with MDD.

Challenges in Treatment

However, it has been challenging because of the fundamental lack of understanding about the causes of variable treatment outcomes. MDD is widely accepted as a heterogeneous disorder; yet, most research strategies effectively consider MDD as a single disorder. Progress in understanding the variable treatment response will depend on “patient stratification,” i.e., identifying and accounting for patient heterogeneity when evaluating treatment efficacy.

Project Overview

SUBTREAT proposes a unique direction which considers subtype as the key to link aetiological and treatment effect heterogeneity. Our approach is to break down the heterogeneous treatment outcomes of MDD into more narrowly defined subtypes with divergent aetiologies.

Work Packages

Specifically, I propose three work packages:

1. Dissect treatment heterogeneity across subtypes: A particularly innovative aspect of SUBTREAT is that we will use advanced data science approaches to identify novel subtypes which correlate with differential treatment outcomes.

2. Determine divergent causes underlying MDD subtypes: We will comprehensively investigate causes at three levels including genetic and causal epidemiological risk factors, and brain cell types.

3. Develop a novel prediction algorithm for treatment outcomes stratified by patient subgroups: SUBTREAT will illuminate the causes of MDD subtypes and the principal patterns of how subtypes contribute to differential long-term treatment outcomes.

Conclusion

SUBTREAT findings will promote targeted drug development and treatment optimization for patient subgroups to achieve precision psychiatry.