| The Clinical Utility of Biomarkers in Diagnosing Major Depressive Disorder in Adults: A Systematic Review of Literature From 2013 to 2023 |
Shi-han Ang1 
, Roger C. Ho1,2,3
, Roger S. McIntyre4,5,6
, Zhisong Zhang7,8
, Soon-kiat Chang1,2, Kayla M. Teopiz4
, Cyrus SH Ho1
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1Department of Psychological Medicine, National University of Singapore, Singapore
2Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
3Division of Life Science (LIFS), Hong Kong University of Science and Technology, Hong Kong
4Brain and Cognition Discovery Foundation, Toronto, ON, Canada
5Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
6Department of Psychiatry, University of Toronto, Toronto, ON, Canada
7Faculty of Education, Huaibei Normal University, Huaibei, China
8Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), Huaibei, China |
Correspondence: Zhisong Zhang ,Tel: +86-13856105376, Email: rsczzs@chnu.edu.cn
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Received: May 17, 2024 Revised: October 23, 2024 Accepted: December 2, 2024 Published online: April 11, 2025 |
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| 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. |
| Key words
Depression; Biomarker; Blood; Neuroimaging; Neurophysiology; Machine learning |
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