What question did this study set out to answer?

This review aims to identify biomarkers that can predict treatment outcomes for antidepressant therapies in major depressive disorder.

June 19, 2026

Beyond Symptoms: Early Predictive Biomarkers of AntidepressantOutcomes in Major Depressive Disorder

Key Points

This review aims to identify biomarkers that can predict treatment outcomes for antidepressant therapies in major depressive disorder.
Summarized candidate biomarkers relevant to antidepressant efficacy stages: response, relapse, recovery.
Categorized biomarkers into functional types including neurotrophic, neuroendocrine, immune-inflammatory, neurotransmitter-related, and non-coding RNAs.
Evaluated the temporal utility and existing evidence regarding the association of these biomarkers with treatment outcomes.
Peripheral BDNF-related measures and inflammatory cytokines show early associations with treatment outcomes.
Weak associations observed for various biomarkers; many remain investigational and not ready for clinical use.
Need for harmonized laboratory methods and rigorous validation to translate findings into practice.

Abstract

The treatment efficacy of major depressive disorder is limited by its pronounced etiological and phenotypic heterogeneity and the partially understood neurobiology. Although pharmacotherapy is the first-line treatment, around 30% of depressive patients do not sufficiently benefit from selective serotonin reuptake inhibitors. This narrative review summarizes the candidate biomarkers that can reportedly predict antidepressant treatment outcomes across different care stages, including symptomatic response/remission, relapse/recurrence, and functional recovery and quality of life. It highlights the similarities and differences among antidepressants, constructs a functional division framework, and evaluates their temporal utility during treatment. Based on the pathophysiological assumptions of depression, we divide the biomarkers into eight functional types: neurotrophic factors (e.g., peripheral BDNF and BDNF DNA methylation), neuroendocrine markers (HPA-axis activity, cortisol), immune-inflammatory regulators (e.g., IL-6, IL-1β, TNF-α), neurotransmitter-related components (e.g., HTR2A variants), non-coding RNAs (e.g., miR-1202, miR-4707-3p, tiRNA-Gly-GCC-001), gut microbiota profiles, neuroimaging markers (e.g., hippocampal volume, ACC thickness, PET markers of serotonergic targets), and other molecular indices (e.g., CYP450 polymorphisms, S100B, CREB/pCREB). Evidence suggests that some biomarkers show early associations with treatment outcomes, particularly peripheral BDNF-related measures, inflammatory cytokines, hippocampal structure, selected miRNAs, CREB/pCREB, and S100B. However, the available evidence is mixed, the observed associations are generally weak, and most biomarkers remain investigational rather than suitable for routine clinical use. Thus, harmonized laboratory methods, large multi-ethnic cohorts, rigorous external validation, and integrative multimodal prediction models will be essential for achieving biomarker-guided precision psychiatry, so as to translate the existing findings into clinical practice.

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