Ang Shi-Han, Ho Roger C, McIntyre Roger S, Zhang Zhisong, Chang Soon-Kiat, Teopiz Kayla M, Ho Cyrus Sh
Department of Psychological Medicine, National University of Singapore, Singapore.
Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore.
Psychiatry Investig. 2025 Apr;22(4):341-356. doi: 10.30773/pi.2024.0152. Epub 2025 Apr 11.
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.
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.
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.
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.
目前可用于客观诊断成人重度抑郁症(MDD)的生物标志物的种类和功效尚不清楚。本系统评价旨在识别和评估用于诊断成人MDD的各种客观标志物。
在过去10年(2013 - 2023年)通过PubMed和PsycINFO应用检索策略,以获取支持使用生物标志物诊断MDD的最新现有证据。数据通过叙述性综合报告。
该评价纳入了42项研究。基于以下指标进行结果综合:血液、神经影像学/神经生理学、尿液、皮肤病学、听觉、声音、脑脊液以及组合指标,并根据其敏感性/特异性和曲线下面积值进行评估。发现血液(MYT1基因)、神经影像学/神经生理学(背侧和中缝核中的5 - HT1A自身受体结合)、尿液(联合白蛋白、AMBP、HSPB、APOA1)、脑脊液(神经元特异性烯醇化酶、微小RNA)生物标志物的最佳预测指标与MDD的病理生理学密切相关。
有多种生物标志物可用于诊断MDD,表现最佳的生物标志物与MDD的病理生理学内在相关。未来研究的潜力在于通过机器学习方法研究已识别的表现最佳的生物标志物的联合敏感性,并确定这些生物标志物与MDD之间的因果关系。