The Clinical Utility of Biomarkers in Diagnosing Major Depressive Disorder in Adults: A Systematic Review of Literature From 2013 to 2023

Shi Han Ang, Roger C. Ho, Roger S. McIntyre, Zhisong Zhang*, Soon Kiat Chang, Kayla M. Teopiz, Cyrus S.H. Ho

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)

Abstract

Objective The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimaging/neurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimaging/neurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Original languageEnglish
Pages (from-to)341-356
Number of pages16
JournalPsychiatry Investigation
Volume22
Issue number4
DOIs
Publication statusPublished - Apr 2025

Bibliographical note

Publisher Copyright:
© 2025 Korean Neuropsychiatric Association.

Keywords

  • Biomarker
  • Blood
  • Depression
  • Machine learning
  • Neuroimaging
  • Neurophysiology

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