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

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.

INTRODUCTION

Major depressive disorder (MDD) is a common debilitating mental disorder [1] that affects approximately 280 million people worldwide [2]. Per the criteria outlined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, MDD is characterised by at least two weeks of pervasive low mood, low self-esteem, and loss of interest [3]. Additionally, MDD is ranked by the World Health Organisation as the single leading cause of disability in the world [4]. Despite its prevalence, MDD is still underdiagnosed and undertreated in the clinical setting [5].

It has been suggested that the pathogenesis of MDD involves biological, psychological and genetic factors [6]. In practice, psychiatrists diagnose MDD based on patient-reported symptoms, clinical judgment, and other subjective psychometric or behavioural tests. Examples of psychometric tests include the Patient Health Questionnaire-9 [7], Hamilton Depression Rating Scale [8], and Beck Depression Inventory [9]. This heavy reliance on subjective measures may lead to discrepancies in the diagnosis of MDD. Furthermore, the clinical assessment cannot differentiate endogenous depression and reactive depression to life events. The inherently subjective nature of diagnosing MDD has, therefore, given rise to a need for biomarkers, which offer a conceivable target for assisting in the diagnosis of MDD [10].

To date, there has not been an overarching review that quantifies the wide variety of objective measures used to diagnose MDD in adults. Recent systematic reviews include studies on the efficacy of electroencephalogram (EEG) [11], inflammatory markers [12] and metabolomics-based biomarkers [13]. However, these existing systematic reviews restricted their literature search exclusively to studies that investigated only one modality. Therefore, this systematic review aims to provide an overview of the available objective measures (i.e., candidate biomarkers) across different modalities that may be used to diagnose MDD reported between 2013–2023. The various measures will be categorised based on their nature and specific biomarkers investigated. Its underlying theoretical concepts, strengths, weaknesses, and potential for further research will be explored.

METHODS

A systematic search was conducted using PubMed and PsycINFO from January 2013 to December 2023. A combination of search terms was used to ensure that all relevant papers referring to objective biomarkers available for diagnosing MDD were found (Supplementary Material). The protocol of this systematic review was accepted on PROSPERO on the 18th of December 2023: CRD42023488051.

Key inclusion criteria for our systematic review included articles that were published in a peer-reviewed journal in English, primary publications that introduced biomarkers used to diagnosed MDD in an adult population (above 18 years), and participants with a formal diagnosis of MDD. Papers were excluded if they were published in a non-peer-reviewed journal and/or not published in English; featured non-primary study designs (e.g., systematic review, metaanalysis, literature review); included children or adolescents in the study population; featured participants without a clinical diagnosis of MDD; featured participants with a diagnosis of other forms of MDD such as treatment resistant depression, vascular depression, remitted depression. The decision to exclude non-primary publications was made due to the nature of a systematic review. The decision to restrict the search to the last 10 years allowed his systematic review to focus on the most recent technology.

Following de-duplication, abstracts and titles of articles identified in our literature search were independently screened by two authors (A.S.H., R.H.) to determine which studies potentially met the inclusion criteria. Both authors then carried out full-text screening of potentially relevant studies. Data extraction of included articles was performed on Excel (Microsoft). Any discrepancies throughout the process were reviewed by both authors and resolved through discussion.

Data extraction consisted of study identification, study aims, country where the study was conducted, study design, sample size, participant demographics, and outcomes of interest. In this study, outcomes of interest included comparison method, biomarkers, medication-naive state, area under the curve (AUC) values, and sensitivity/specificity values.

RESULTS

The systematic search resulted in 4,972 articles after removing duplicates, of which 225 were accepted for full-text review. Of the 255 articles, 43 articles that reviewed 42 studies were deemed relevant and included in the review. One article reported on an updated version of a previous study (Figure 1).

Figure 1.

Flow diagram for identification of studies. MDD, major depressive disorder; AUC, area under the curve.

Of the 42 studies, 18 studies reported on the clinical utility of blood biomarkers to diagnose MDD; 13 studies on the clinical utility of neuroimaging/neurophysiology biomarkers; four studies on the clinical utility of urine biomarkers; one study each on the utility of auditory, vocal, dermatological and cerebrospinal fluid (CSF) biomarkers; three studies on the utility of combined biomarkers.

Most of the studies were conducted in China (18), Japan (4), and Brazil (3). Other countries include the USA, Germany, South Korea, Taiwan, Australia (2 each), Canada, Iran, Singapore, Croatia, Poland, Ukraine and Lithuania (1 each). All studies included were designed in the case-control format, with 15 studies incorporating a discovery and validation cohort within the same study.

Blood biomarkers

The 18 papers that reported on blood biomarkers for the diagnosis of MDD were broadly categorised into transcriptomics (4) [14-17], metabolomics (8) [16,18-24], genomics (4) [25-28], and combined (2) [29,30]. A summary of the following studies is shown in Table 1. For this review, relevant outcomes that were considered include sensitivity/specificity and AUC (Table 1).

Table 1.

A summary of blood biomarkers in diagnosing major depressive disorder in adults

Study	Category	Comparison method	Cohort (M/F), age (yr)	Biomarkers	Medicationnaive	AUC	Sensitivity/specificity (%)
Zhang et al. [17] (2022)	Transcriptomics	Mini-International Neuropsychiatric Interview, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale	HC=19 (6/13), Age: 22.74	CircularRNA–hsa_circ_0002473, hsa_circ_0079651, hsa_circ_0137187, hsa_circ_0006010, and hsa_circ_0113010	Y	Hsa_circ_0002473=0.8619	Hsa_circ_0002473=78.57/86.67
						Hsa_circ_0079651=0.7112	Hsa_circ_0079651=51.72/93.75
			MDD=29 (8/21), Age: 22.1			Hsa_circ_0137187=0.8190	Hsa_circ_0137187=89.29/66.67
						Hsa_circ_0006010=0.8367	Hsa_circ_0006010=85.71/78.57
						Hsa_circ_0113010=0.7091	Hsa_circ_0113010=51.72/93.75
Lin et al. [16] (2019)	Transcriptomics	Hamilton Depression Rating Scale	HC=125 (51/74), Age: 36.8	mRNA–Serine racemase (SSR), phosphoserine aminotransferase 1 (PSAT1), glycine C-acetyltransferase (GCAT), glutamate decarboxylase 1 (GAD1), and neuregulin 1 (NRG1)	Y	0.889	0.960/0.640
			MDD=25 (9/16), Age: 35.6
Gecys et al. [15] (2022)	Transcriptomics	Mini-International Neuropsychiatric Interview, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification	Discovery cohort:	mRNA - hsa-let-7e-5p, hsa-miR-125a-5p	N	hsa-let-7e-5p: 0.66	-
			HC=37 (8/29), Age: 21–65			hsa-miR-125a-5p: 0.600
			MDD=48 (12/36), Age: 19–75
			Validation cohort:
			HC=74 (20/54), Age: 21–68
			MDD=84 (16/68), Age: 19–75
Fan et al. [14] (2014)	Transcriptomics	DSM-IV, Hamilton Depression Rating Score	HC=46 (20/26), Age: 33.35	miRNA–miRNA-26b, miRNA-1972, miRNA-4485, miRNA-4498, miRNA-4743	N	Combined: 0.636	Combined: 54.4/79.0
			MDD=81 (35/46), Age: 33.28
Min et al. [20] (2023)	Metabolomics	DSM-IV, Hamilton Depression Rating Score	HC=41 (21/20), Age: 25–35	TNF-α, IL-6	Y	TNF-α=0.733	-
			MDD=113 (42/71), Age: 24–42			IL-6=0.783
Kim et al. [19] (2021)	Metabolomics	Beck Depression Index, Hamilton Depression Rating Score	HC=50 (24/26), Age: 24.8	IL-17, IL1β, CRP	Y	Male: IL-17=0.766	Male: IL-17=87.0/57.1
						Female: IL-1β=0.697	Female: IL-1β=85.0/53.4
			MDD=50 (23/27), Age: 24.18			IL-6 level=0.709	IL-6 level=51.9/84.6
						CRP=0.688	CRP=100/38.4
Zhang et al. [23] (2022)	Metabolomics	DSM-IV, Hamilton Depression Rating Score, Hamilton Anxiety Rating Scale	HC=25 (0/25), Age: 18–24	20 Lipids*	N	0.897	-
			MDD=28 (0/28), Age: 30–43
Levada et al. [21] (2020)	Metabolomics	DSM-5	HC=47 (20/27), Age: 37.8	IGF-1	N	0.820	83/71
			MDD=78 (30/480), Age: 38.2
Kageyama et al. [18] (2018)	Metabolomics	DSM-IV, Hamilton Depression Rating Score	Discovery:	Nervonic acid	N	-	Overall: 67.1/62.2
			HC=19 (10/9), Age: 36.1
			MDD=9 (3/6), Age: 39.1
			SZ=17 (8/9), Age: 33.6
			BD=6 (1/5), Age: 41.8
			Validation set:
			HC=100 (46/54), Age: 49.2
			MDD=45 (19/26), Age: 54.0
			SZ=115 (59/56), Age: 39
			BD=71 (37/34), Age: 45.2
Pan et al. [22] (2018)	Metabolomics	DSM-IV, Hamilton Depression Rating Score	Discovery set:	Dopamine, GABA, Tyramine, Kyneuramine	N	Discovery: 0.968	Discovery: 94.1/98.0
			HC=50 (25/25), Age: 34–36			Validation: 0.953	Validation: 93.9/87.5
			MDD=50 (24/26), Age: 37–39
			Validation set:
			HC=40 (22/18), Age: 37–39
			MDD=49 (23/26), Age: 36–39
			BD=30 (13/17), Age: 24–46
Zheng et al. [24] (2016)	Metabolomics	DSM-IV	Discovery set:	Peripheral Blood Mononuclear Cells (PBMC)–top 17 discriminants†	Y	Discovery: 0.926
			HC=50 (22/28), Age: 39.7			Validation: 0.870
			MDD=50 (20/30), Age: 38.9
			Validation set:
			HC=56 (24/32), Age: 31.4 (14.1)
			MDD=58 (19/39), Age: 39.5 (13.8)
			SZ=40 (9/31), Age: 27.5 (12.5)
Liu et al. [31] (2016)	Metabolomics	DSM-IV, Hamilton Depression Rating Scale	Discovery set:	17 Lipids‡	N	0.863	-
			HC=60 (30/30), Age: 43.98
			MDD=60 (30/30), Age: 42.42
			Validation set:
			HC=52 (26/26), Age: 33.67
			MDD=75 (40/35), Age: 36.04
Ghanbarirad et al. [26] (2021)	Genomics	DSM-5	HC=100 (68/32), Age: 31.9	Myelin Transcription Factor 1 (MYT1)	N	0.973	0.96/0.85
			MDD=100 (67/33), Age: 35.2
			BD=100 (68/32), Age: 27.1
Chiou and Huang [25] (2019)	Genomics	Hamilton Depression Rating Scale	HC=290 (143/297), Age: 31.3	BDNF	Y	Males: 0.652	Males: 81.1/48.5
			MDD=273 (66/207), Age: 39.4
Numata et al. [28] (2015)	Genomics	Hamilton Depression Rating Scale	Discovery set:	DNA Methylation of peripheral leukocytes–top 18 site discriminants	Y	-	100/100
			HC=19 (2/17), Age: 42.4
			MDD=20 (2/18), Age: 44.2
			Validation set:
			HC=12 (3/9), Age: 44.3 (10.8)
			MDD=12 (3/9), Age: 45.5
Karlović et al. [27] (2013)	Genomics	DSM-IV, Hamilton Depression Rating Scale	HC=142 (76/66), Age: 46.5	BDNF	N	0.892	83.9/93.0
			MDD=122 (56/66), Age: 45.8
Jiang et al. [30] (2017)	Combined	DSM-IV, Hamilton Depression Rating Scale	HC=35 (19/16), Age: 56.74	tPA, BDNF, Tropomyosin receptor kinase B, proBDNF, neurotrophin receptor p75NTR	Y	0.977	88.1/92.7
			MDD=35 (11/24), Age: 43.97
Bilello et al. [29] (2015)	Combined	DSM-IV, Hamilton Depression Rating Scale	HC=86 (46/40), Age: 20–72	Alpha1 antitrypsin, apolipoprotein CIII, BDNF, cortisol, epidermal growth factor, myeloperoxidase, prolactin, resistin, soluble tumor necrosis factor alpha receptor type II	N	0.963	92/93
			MDD=68 (34/34), Age: 19–68

Value of age are presented as mean, mean (standard deviation), or range.

^*Cer(d15:1/25:2), Cer(d18:1/16:0), Cer(d18:1/24:0), Cer(d40:1), Cer(d41:1 + O), Cer(m34:0 + O), Cer(m38:2 + O) and PI(16:1), Cer(d32:4), CerG2GNAc1(d32:1), CerG2GNAc1(d36:1), CerG2GNAc1(d38:4), GM2(d34:5), GM3(t39:6), PC(18:0/16:0), PC(20:1e/18:2), PC(26:2e), PC(40:10), TG(18:1/18:2/22:4), TG(20:0/18:1/18:1);

^†octanoic acid, hydroxylamine, benzoic acid, γ-aminobutyric acid, homoserine, malonic acid, isoleucine, lanosterol, valine, sorbitol, creatinine, ribulose 5-phosphate, ethanolamine, malic acid, fumaric acid, γ-tocopherol and dopamine;

^‡lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), 1-O-alkyl-2-acyl-PE (PE O), 1-O-alkyl-2-acyl-PC (PC O), sphingomyelin (SM), diacylglycerol (DG), and triacylglycerol (TG).

M, male; F, female; AUC, area under the curve; HC, healthy control; MDD, major depressive disorder; N, no; Y, yes; mRNA, messenger RNA; miRNA, microRNA; TNF, tumor necrosis factor; IL, interleukin; DSM, Diagnostic and Statistical Manual of Mental Disorders; CRP, C-reactive protein; IGF, insulin-like growth factor; SZ, schizophrenia; BD, bipolar disorder; GABA, gamma-aminobutyric acid

Four studies reported information on transcriptome-based blood biomarkers. Three studies reported on the utility of messenger ribonucleic acid (mRNA) [14-16], while one study reported on the utility of complementary RNA (cRNA) [17]. mRNA proved inconsistent in diagnosing MDD, with only one study [16] showing high AUC (0.889) and sensitivity (96.0%), while Gecys et al. [15] and Fan et al. [14] exhibited moderate performance under the receiver-operating characteristic (ROC) (0.60 and 0.631, respectively). Individual cRNA molecules showed promising AUC and sensitivity values in diagnosing MDD, with the best performing molecules exhibiting high AUC (0.862) and sensitivity/specificity (78.6%/86.7%) [17].

Eight studies reported information on metabolomics-based blood biomarkers [16,18-24]. Of the eight studies, two reported on the utility of lipid biomarkers [23,31] and two reported on the utility of inflammatory blood markers [19,20]. Zhang et al. [23] presented changes in 20 lipid profiles among the male population, while Liu et al. [31] studied 17 lipid profiles in both sexes. Both studies exhibited excellent performance of 0.897 and 0.863 under the ROC curve.

As for inflammatory markers, Min et al. [20] investigated the diagnostic value of interleukin (IL)-6 and tumor necrosis factor (TNF)-α, while Kim et al. [19] investigated the diagnostic value of IL-6, IL-17, IL-17B and C-reactive protein in people with MDD. Kim et al. [19] indicated that lowered IL-6 was a more accurate predictor of MDD than TNF-α. However, Min et al. [20] revealed that there was no significant difference in IL-6 between healthy controls and people with MDD.

Four other studies reported information on neurotransmitters, insulin-like growth factor (IGF)-1, nervonic acid, and Peripheral Blood Mononuclear Cells (PBMC) [18,21,22,24]. Pan et al. [22] reported excellent sensitivity for a combination panel of dopamine, GABA (gamma-aminobutyric acid), tyramine and kyneuramine in the diagnosis of MDD, while Levada et al. [21] indicated that increased IGF-1 in MDD exhibited good performance under the ROC curve (0.820). Zheng et al. [24] featured a combination panel of 17 discriminants found in PBMC, including neurotransmitters and metabolites in glucose metabolism, which exhibited a strong AUC value of 0.926.

Four studies reported information on genomics-based blood biomarkers. Two studies reported on the diagnostic value of the BDNF gene [25,27], while the other two studies reported on the MYT1 gene and DNA methylation of peripheral leukocytes [26,28]. Lowered BDNF levels in people with MDD on medications exhibited high ROC performance (0.892) [27], but this observation was not replicated in the non-medicated population (AUC=0.652) [25]. In people with MDD, downregulated MYT1, a gene that promotes myelin growth in neurogenesis, exhibited high AUC and sensitivity/specificity [26].

Two studies reported on blood biomarkers that spanned across different categories [29,30]. Bilello et al. [29] reported that a combination panel of genomics (e.g., BDNF) and metabolomics (e.g., apolipoprotein CIII, alpha-1 antitrypsin, cortisol, resistin, prolactin, myeloperoxidase, TNF-α) displayed excellent AUC and sensitivity/specificity in people with MDD who received antidepressants. For medication-naive populations, Jiang et al. [30] reported that a combination panel of genomics (e.g., BDNF, proBDNF), metabolomics (tPA) and neurogenic receptors (p75NTR, TrKB) also exhibited similar performance in AUC and sensitivity/specificity.

Neuroimaging/neurophysiology biomarkers

Thirteen studies reported on neuroimaging and neurophysiology biomarkers for the diagnosis of MDD. Most studies (10) used magnetic resonance imaging (MRI) as their imaging modality [32-41], with five studies reporting on functional MRI (fMRI) [32,34,37,40,41]. Other imaging modalities used include functional near-infrared spectroscopy (fNIRS) [42], EEG [43], and positron emission tomography (PET) [44] (Table 2).

Table 2.

A summary of neuroimaging/neurophysiology biomarkers in diagnosing major depressive disorder in adults

Study	Category	Comparison method	Cohort (M/F), age (yr)	Biomarkers	Medicationnaive	AUC	Sensitivity/specificity (%)
Guo et al. [32] (2018)	rs-fMRI	DSM-IV, Hamilton Depression Rating Scale	Discovery set:	VMHC - Voxel Mirrored Homotopic Connectivity	Y	Discovery set:	88.1/92.7
			HC=31 (14/17), Age: 29.7			PCC=0.922
			MDD=59 (20/39), Age: 30.3	1. Posterior Cingulate Cortex		Cuneus=0.911
			Validation set:	2. Cuneus		Validation set:
			HC=24 (13/11), Age: 24.1			PCC=0.769
			MDD=29 (15/14), Age: 27.0			Cuneus=0.858
Zhong et al. [41] (2017)	rs-fMRI	DSM-IV, Centre for Epidemiological Studies Depression Scale	Discovery set:	Whole brain functional connectivity:	Y	-	92/93
			HC=33 (16/17), Age: 20.8	1. Salience network
			MDD=29 (11/18), Age: 21	2. Default mode network
			Validation set:	3. Cerebellum
			HC=57 (26/31), Age: 21.5	4. Visual cortical areas
			MDD=46 (22/24), Age: 22.6	5. Affective network
Wei et al. [40] (2013).	rs-fMRI	Hamilton Depression Rating Scale	HC=20 (14/6), Age: 30.8	Hurst exponent	Y	-
				1. Salience network
			MDD=20 (10/10), Age: 34.3	2. Ventromedial prefrontal network
				3. Lateral prefrontal network
Liu et al. [34] (2013)	rs-fMRI	DMS-IV, Hamilton Depression Rating Scale	HC=20 (10/10), Age: 24.3	Fractional ALFF	Y	Right cerebellum posterior lobe=0.823	-
				1. Right cerebellum posterior lobe		Right middle frontal gyrus=0.854
			MDD=24 (12/12), Age: 28.1	2. Right middle frontal gyrus		Left superior occipital gyrus/cuneus=0.792
				3. Left superior occipital gyrus		Left parahippocampal gyrus=0.930
				4. Left parahippocampal gyrus
Oliveira et al. [37] (2013)	fMRI	DSM-IV, Hamilton Depression Rating Scale	Discovery set:	5. Overall brain activation in response to emotional facial expression	Y	Sad v.s. Neutral:	-
			HC=19 (8/11), Age: 42.8			HC=0.74, MDD=0.58
			MDD=19 (6/13), Age: 43.2			Happy v.s. Neutral:
			Validation set:			HC=0.70, MDD=0.53
			HC=18 (3/15), Age: 29.8
			MDD=18 (1/17), Age: 31.9
Niida et al. [36] (2019)	MRI	DSM-IV, Hamilton Depression Rating Scale	HC=43 (11/32), Age: 65.8	Gray Matter Volume	N	0.911	95.7/96.0
			MDD=92 (8/94), Age: 65.9	1. Subgenual Anterior Cingulate Cortex (SgACC)
			BD=32 (9/23), Age: 64.3
Hellewell et al. [33] (2019)	MRI	Mini-International Neuropsychiatric Interview, Hamilton Depression Rating Scale	Discovery set:	Gray Matter Volume	Y	Discovery: 0.750	-
			MDD=98 (53/45), Age: 33.3 (12.6)	1. Midline/Cingulate region		Validation: 0.840
			Validation set:	2. Medial temporal lobe region
			MDD=131 (54/77), Age: 33.2 (11.0)	3. Prefrontal Cortex
Liu et al. [35] (2016)	MRI	DSM-IV, Major Depression Inventory	HC=49 (25/24), Age: 39.0	Grey Matter Volume - Structural Asymmetry Index	Y	0.635	61.0/59.0
			MDD=39 (15/24), Age: 38.2	1. Dorsolateral Prefrontal Cortex
Ramasubbu et al. [38] (2019)	ASL-MRI	DSM-IV, Montgomery–Åsberg Depression Rating Scale	Discovery set:	Cerebral blood flow + Sex of participant	Y	-	80.0/75.0
			HC=19 (2/17), Age: 42.4
			MDD=20 (2/18), Age: 44.2
			Validation set:
			HC=12 (3/9), Age: 44.3
			MDD=12 (3/9), Age: 45.5
Schnyer et al. [39] (2017)	DTI-MRI	DSM-IV, Beck Depression Index, Inventory of Depression and Anxiety Symptoms	HC=25 (12/13), Age: 19–33	Fractional Anisotropy	N	-	Whole: 60.0/80.0
			MDD=25 (12/13), Age: 18–31	1. Right Corpus Callosum			Right hemisphere only: 56.0/84.0
				2. Right hemisphere only
Lubiński et al. [43] (2023)	EEG	Hamilton Depression Rating Scale	HC=20, Age: 47.3	Pattern Visual Evoked Potentials (PVEP)	Y	0.841	76.0/1.0
			MDD=29, Age: 46.8	1. AP100 (Amplitude)
				2. PTP 100 (Peak time)
Husain et al. [42] (2020)	fNIRS	DSM-V, Hamilton Depression Rating Scale	HC=105 (40/65), Age: 36.4	Regional oxy-hemoglobin	Y	Frontal=0.760	-
			MDD=105 (45/60), Age: 36.2	1. Frontal		Temporal=0.820
				2. Temporal
Pillai et al. [44] (2018)	PET	DSM-IV, Hamilton Depression Rating Scale, Beck Depression Index, Global Assessment Scale	HC=25 (25/0), Age: 40.2	5-HT1A Autoreceptor binding	Y	0.934	94.0/1.0
			MDD=16 (16/0), Age: 40.1	1. Dorsal Raphe Nuclei
				2. Median Raphe Nuclei

Value of age are presented as mean, mean (standard deviation), or range. M, male; F, female; AUC, area under the curve; HC, healthy control; MDD, major depressive disorder; N, no; Y, yes; fMRI, functional magnetic resonance imaging; rs-fMRI, resting state fMRI; MRI, magnetic resonance imaging; DSM, Diagnostic and Statistical Manual of Mental Disorders; PCC, posterior cerebral cortex; BD, bipolar disorder; ASL-MRI, arterial spin labelling-MRI; DTI-MRI, diffusion tensor imaging-MRI; EEG, electroencephalogram; PET, positron emission tomography

Four of the five studies utilising fMRI investigated the usage of resting state fMRI (rs-fMRI) [32,34,40,41]. Of the four rs-fMRI studies, two studies reported on the abnormal functional connectivity between brain hemispheres in people with MDD: Guo et al. [32] demonstrated that decreased voxel-mirrored homotopic connectivity in the posterior cingulate cortex (AUC=0.922) and cuneus (AUC=0.911) reliably identified MDD in participants, while Zhong et al. [41] identified that abnormal functional connectivity in the affective network (amygdala, pallidum, superior temporal lobe), salience network (anterior insula, dorsal anterior cingulate cortex), default mode network (precuneus/post cingulate cortex, inferior parietal gyrus, angular, thalamus), visual cortex and cerebellum was highly sensitive and specific for MDD (89.60%/93.90%).

The other two rs-fMRI studies reported on the diagnostic value of differences in spontaneous brain signal fluctuations [37,40]. Wei et al. [40] demonstrated that differences in Hurst exponent—a measure of regularity and persistence in brain signals—in the salience network, ventromedial prefrontal network, and lateral prefrontal network demonstrated high sensitivity and specificity for MDD (95.0%/85.0%). Liu et al. [34] demonstrated that lowered amplitude of low-frequency oscillations in the left parahippocampal gyrus (0.930) and right middle frontal gyrus (0.854) had excellent performance under the ROC curve.

Five other studies reported information using MRI-based biomarkers, with three studies utilising MRI [33,35,36] and two studies using arterial spin labelling-MRI (ASL-MRI) [38] and diffusion tensor imaging-MRI, respectively [39]. All three MRI studies reported the diagnostic value of changes in grey matter volume (GMV). Niida et al. [36] reported that increased GMV in the subgenual anterior cingulate cortex (SgACC) exhibited high AUC (0.911) and sensitivity/specificity (95.7%/96.0%), while Hellewell et al. [33] reported that decreased GMV in the midline, medial temporal lobe and prefrontal cortex exhibited moderate diagnostic value (AUC=0.750). Liu et al. [35] reported on the structural asymmetry of GMV and showed that reduced asymmetry of GMV in the dorsolateral prefrontal cortex of participants with MDD exhibited moderate sensitivity and specificity (61%/59%).

Ramasubbu et al. [38] utilised ASL-MRI to illustrate differences in cerebral blood flow in people with MDD. Following that, a combination panel of differences in cerebral blood flow and sex of participants revealed moderate sensitivity/specificity (80.0%/75.0%).

Three separate studies utilised EEG [43], fNIRS [42], and PET [44] to identify neuroimaging biomarkers in medication-naive cohorts. In Lubiński et al. [43], following a standardized visual stimulus, EEG was used to identify changes in pattern visual evoked potentials (PVEP) between people with MDD and healthy controls. A combination panel incorporating amplitude and peak time for PVEP exhibited excellent performance under the ROC curve (0.841). fNIRS was used to quantify variants in regional oxy-haemoglobin following a standardised verbal fluency test. Reduced oxy-haemoglobin in the frontal (0.760) and temporal (0.820) regions of people with MDD exhibited moderate AUC values [42]. Pillai et al. [44] utilised PET to investigate the diagnostic value of 5-HT1A autoreceptor binding in the dorsal and median raphe nuclei. Higher autoreceptor binding in the dorsal and median raphe nuclei demonstrated excellent AUC (0.934) and sensitivity/specificity (94.0%/84.0%) for MDD.

Urine biomarkers

Four papers reported on the diagnostic value of urinary biomarkers for the diagnosis of MDD [45-48]. Three studies investigated metabolomics-based biomarkers [46-48], while one study investigated proteomics-based biomarkers [45] (Table 3).

Table 3.

A summary of urinary biomarkers in diagnosing major depressive disorder in adults

Study	Category	Comparison method	Cohort (M/F), age (yr)	Biomarkers	Medicationnaive	AUC	Sensitivity/specificity (%)
Wu et al. [46] (2015)	Metabolomics	DSM-IV, Hamilton Depression Rating Scale	HC=30 (15/15), Age: 31.0	Argininosuccinate synthase	Y	0.784	83.3/76.7
			MDD=30 (15/15), Age: 34.0
Wang et al. [45] (2014)	Proteomics	Hamilton Depression Rating Scale	Discovery set:	Albumin, Alpha-1- microglobulin/bikunin precursor (AMBP), HSPG, apolipoprotein A-I (APOA1)	N	Discovery: 0.914	91.7/84.6
			HC=28 (15/13), Age: 31.6			Validation: 0.892
			MDD=42 (19/23), Age: 32.7
			Validation set:
			HC=13 (6/7), Age: 35.7
			MDD=24 (10/24), Age: 35.0
Zheng et al. [47] (2013)	Metabolomics	DSM-IV, Hamilton Depression Rating Scale	Discovery set:	Malonate, Formate, N-methylnicotinamide, M-hydroxyphenylacetate, Alanine	N	Discovery: 0.810	Not available
			HC=82 (53/29), Age: 34.2			Validation: 0.890
			MDD=82 (46/36), Age: 32.2
			Validation set:
			HC=52 (27/25), Age: 28.8
			MDD=44 (13/31), Age: 34.1
Zheng et al. [48] (2013)	Metabolomics	DMS-IV, Hamilton Depression Rating Scale	Discovery set:	Sorbitol, Uric acid, Azelaic acid, Quinolinic acid, Hippuric acid, Tyrosine	Y	0.905	Not available
			HC=82 (53/29), Age: 34.2
			MDD=82 (46/36), Age: 32.2
			Validation set:
			HC=52 (27/25), Age: 28.8
			MDD=42 (17/27), Age: 34.1

Value of age are presented as mean. M, male; F, female; AUC, area under the curve; HC, healthy control; MDD, major depressive disorder; N, no; Y, yes; DSM, Diagnostic and Statistical Manual of Mental Disorders

Of the three metabolomics studies, two employed a combination panel of metabolites for diagnosis [47,48], with one study focussed on a singular metabolite.46 Wu et al. [46] reported that the downregulation of argininosuccinate synthetase in plasma was moderately sensitive (83.7%) and specific (76.7%) in medication-naive MDD participants. In the first two separate studies, Zheng et al. [47] investigated a panel of urinary metabolites related to gut intestinal flora, glucose and tryptophan-nicotinic acid metabolism. Reduced malonate (glycolysis), m-hydroxyphenylacetate (gut intestinal flora) and increased Nmethylnicotinamide had good diagnostic values in a nonmedication naive population (AUC=0.810). The second study by Zheng et al. [48] involved a similar panel investigating the kynurenine, tyrosine-phenylalanine and oxidative stress pathways in addition to glucose and tryptophan-nicotinic acid metabolism. Increased sorbitol and decreased quinolinic acid, tyrosine, azelaic acid, hippuric acid, and uric acid showed strong performance under the ROC curve (0.905) in people with MDD who were medication-naive [48].

For the single proteomics study, Wang et al. [45] reported that a combination panel of upregulated peptides related to albumin, apolipoprotein, basement membrane peptides, and downregulated immune-related peptides exhibited excellent performance under the ROC curve (0.914) and high sensitivity/specificity (90.5%/92.9%).

Other biomarkers

Four separate studies reported on dermatological [49], voice [50], CSF [51] and auditory biomarkers [52]. Three studies utilised a combination panel of biomarkers that spanned different categories. None of the studies involved people with MDD who were medication-naïve (Table 4).

Table 4.

A summary of other biomarkers in diagnosing major depressive disorder in adults

Study	Category	Comparison method	Cohort (M/F), age (yr)	Biomarkers	Medicationnaive	AUC	Sensitivity/specificity (%)
Kim et al. [49] (2019)	Dermatological	Hamilton Depression Rating Scale-21	HC=31 (13/19), Age: 43.3	Skin conductance	N	-	70/71.0
			MDD=30 (12/18), Age: 42.5
Taguchi et al. [50] (2018)	Voice	DSM-5, Quick Inventory of Depressive Symptomatology-Self-Report, Japanese version	HC=36 (16/20), Age: 38	Mel-Frequency Cepstrum Coefficient (MFCC)	N	-	77.8/86.1
			MDD=36 (22/14), Age: 44.0
Schmidt et al. [51] (2015)	CSF	DSM-IV, Hamilton Depression Rating Scale	HC=32 (18/14), Age: 50.75	Neuro specific enolase (NSE), S100B	N	NSE=0.820	NSE=81.0/75.0
			MDD=31 (14/17), Age: 49.81
Lithgow et al. [52] (2015)	Hearing	DSM-IV, Mini-International Neuropsychiatric Interview, Montgomery–Åsberg Depression Rating Scale	HC=31 (12/19), Age: 33.07	Electrovestibulography	N	-	Accuracy 87.0% (Nil data on sensitivity/specificity)
			MDD=43 (18/25), Age: 44.09
Koo et al. [53] (2019)	Combined	Beck Depression Index, Hamilton Depression Rating Scale	HC=20 (7/13), Age: 47.15	1. Executive dysfunction - Trail Making Test A and B, Stroop Task	N	-	94.7/66.7
	1. Executive dysfunction		MDD=20 (9/11), Age: 51.05	2. Motor activity: Actigraphy
	2. Motor activity			3. Neuroimaging/ Neurophysiology: EEG, alpha power asymmetry
	3. Neuroimaging
Wan et al. [54] (2015)	Combined	Hamilton Depression Rating Scale	Discovery set:	miRNA of Peripheral Mononuclear Blood Cells (PMBC) - miR-221-3p, miR-34a-5p, Let-7d-3p, miR-451a	N	miR-221-3p=0.940	miR-221-3p=93.8/90.5
	1. Blood		HC=6 (2/4), Age: 23–41			miR-34a-5p=0.980	miR-34a-5p=96.9/95.2
	2. CSF		MDD=6 (3/3), Age: 23–41			Let-7d-3p=0.970	Let-7d-3p=90.6/90.5
			Validation set:			miR-451a=0.940	miR-451a=84.9/90.5
			HC=21 (10/11), Age: 26–41
			MDD=32 (15/17), Age: 24–44
Bortolasci et al. [55] (2014)	Combined	Hamilton Depression Rating Scale	HC=199 (82/117), Age: 46.4	1. Blood - PON1 enzyme, HDL	N	-	53.8/93.8
	1. Blood		MDD=91 (22/69), Age: 47.0	2. Combined - Smoking
	2. Lifestyle		BD=45 (12/33), Age: 44.5

Value of age are presented as mean or range. M, male; F, female; AUC, area under the curve; HC, healthy control; MDD, major depressive disorder; BD, bipolar disorder; N, no; Y, yes; DSM, Diagnostic and Statistical Manual of Mental Disorders; CSF, cerebrospinal fluid; NSE, neuron specific enolase; EEG, electroencephalogram; HDL, high density lipoprotein

Kim et al. [49] reported on the diagnostic value of skin conductance in people with MDD. Participants were subjected to a standardized mental arithmetic task and a relaxation task while their skin conductance was measured. Differences in the skin conductance responses (amplitude, slope, number of non-specific skin conductance responses, power spectral density) demonstrated moderate sensitivity/specificity (70.0%/71.0%) in differentiating people with MDD from healthy controls.

Taguchi et al. [50] reported on the diagnostic value of voice quality in people with MDD. Participants were subjected to a standardised vocal recording exercise, following which their fundamental frequency, root mean square of energy, zero crossing rate, harmonics to noise ratio and 12-dimensional mel frequency cepstral coefficient (MFCC-a set of coefficients representing the distribution of sound energy across different frequencies) was calculated. The study showed that the second order coefficient, i.e., MFCC-2, exhibited good sensitivity (77.8%) and specificity in distinguishing MDD from healthy controls.

Schmidt et al. [51] separately investigated the diagnostic value of neuron specific enolase (NSE) and glial cell protein S100beta (S100B)-markers of neuronal metabolism and glial cell activity, respectively. While S100B did not possess diagnostic value, decreased NSE in people with MDD demonstrated good performance under the ROC curve (AUC=0.820) and had good sensitivity (81.0%) and specificity (75.0%).

A panel of six sensory oto-acoustic features derived from electrovestibulography was used to distinguish between people with MDD and healthy controls [52]. Though AUC or sensitivity/specificity values were unavailable, the combination panel achieved 87.0% accuracy.

Koo et al. [53] utilised a combinatory panel of biomarkers pertaining to executive dysfunction (Trail Making Test A and B, Stroop Task), motor activity (actigraphy) and neurophysiology (EEG, alpha power asymmetry). Results revealed that reduced psychomotor speed, increased divided attention lowered motor activity and increased asymmetry in upper alpha power was sensitive (94.7%) but not specific (66.7%) for diagnosing MDD.

Two other panels investigated the diagnostic value of combinatory panels that involved blood biomarkers. Wan et al. [54] reported on the diagnostic value of changes in blood and CSF microRNA (miRNA) expression, with the best performing molecules exhibiting excellent ROC performance (0.98) and high sensitivity/specificity (96.9%/95.2%). Bortolasci et al. [55] reported on the collective diagnostic value of PON1 enzyme, high density lipoprotein (HDL) in blood and lifestyle smoking habit. Lower levels of PON1 enzyme, HDL and smoking habit were associated with high specificity (93.8%), but not sensitivity (53.8%) for diagnosing MDD.

DISCUSSION

In summary, 42 studies published between 2013 and 2023 were included in this systematic review. Most studies reported on the clinical utility of blood (18) and neuroimaging/neurophysiology (13) markers associated with the diagnosis of MDD. Four studies reported findings based on urine biomarkers, three studies on the utility of combined biomarkers, and one study each on auditory, vocal, dermatological and CSF biomarkers. mRNA (3), BDNF (2), and inflammatory markers (2) featured repeatedly in the analysis of blood biomarkers, while GMV (3) was similarly highlighted for neuroimaging/neurophysiology biomarkers.

Of all blood biomarkers, the MYT1 [26] and the combination panel of dopamine, GABA, tyramine, and kynuramine were the most sensitive in detecting MDD [22]. Increased 5-HT1A autoreceptor binding in the raphe nuclei of the brain exhibited the best sensitivity among all neuroimaging/neurophysiology biomarkers [44], while a combination panel of peptides that included albumin, AMBP, HSPG, and APOA1 exhibited the best sensitivity among all urinary biomarkers [45]. Other biomarkers with notable performance include NSE in CSF [51], and miRNAs of peripheral mononuclear blood cells in blood and CSF [54] (Table 5).

Table 5.

A summary of best performing biomarkers in diagnosing major depressive disorder in adults

Category	Biomarkers	Medicationnaive	AUC	Sensitivity/specificity (%)	Key findings
Blood (Genomics)	MYT1	N	0.973	0.96/0.85	Decreased levels indicate reduced neurogenesis
Blood (Metabolomics)	Dopamine, GABA, Tyramine, Kynuramine	N	0.968	94.1/98.0	Decreased levels indicate reduced dopamine circulation
Neuroimaging/Neurophysiology	5-HT1A autoreceptor binding in dorsal/median raphe	Y	0.934	94.0/1.0	Increased binding indicates decreased serotonin circulation
Urine	Albumin, AMBP, HSPG, and APOA1	N	0.914	90.5/92.9	Increased levels indicate increased MDD- associated dyslipidaemia
CSF	NSE	N	NSE=0.820	NSE=81.0/75.0	Increased levels indicate increased neuronal cell loss
Combined - Blood and CSF	miRNA of Peripheral Mononuclear Blood Cells (PMBC) - miR-221-3p, miR-34a-5p, Let-7d-3p, miR-451a	N	miR-221-3p=0.940	miR-221-3p=93.8/90.5	Decreased levels of BDNF-associated miRNA indicate reduced neurogenesis
			miR-34a-5p=0.980	miR-34a-5p=96.9/95.2
			Let-7d-3p=0.970	Let-7d-3p=90.6/90.5
			miR-451a=0.940	miR-451a=84.9/90.5

AUC, area under the curve; N, no; Y, yes; GABA, gamma-aminobutyric acid; CSF, cerebrospinal fluid; NSE, neuron specific enolase; MDD, major depressive disorder; miRNA, microRNA

The performance of these biomarkers can be attributed to the pathophysiology of MDD. The monoamine theory of depression attributes depressive symptoms to the reduced function of monoamine transmitters such as serotonin and norepinephrine. This explains why increased 5-HT1A autoreceptor expression by the dorsal and median raphe nuclei, which depletes the availability of serotonin in the body, is associated with MDD [56]. The neurotrophic model of depression also establishes the correlation between decreased BDNF (a neurogenesis-promoting protein), and higher frequency of depression, neuronal loss and cortical atrophy [57]. This explains why expression of MYT1-a neurogenesis-promoting genes-is decreased in people with MDD [26]. Similarly, increased expression of CSF NSE (which is released from neuronal cytoplasm during neuronal cell death) could reflect the increased levels of neuronal loss found in the MDD population [51].

As for the discriminant miRNAs-single-stranded noncoding RNAs that negatively regulate gene expression at the post-transcriptional level [14]-identified, a total of 734 genes related to neurotransmission, learning and memory were regulated by these 5 miRNAs [54]. These miRNA were found to be involved in BDNF down-regulation, thereby explaining its potential link to the depressive state. Additionally, the finding of increased levels of urinary APOA1 (a peptide involved in cholesterol transportation) in people with MDD aligns with the established relationship between dysregulated cholesterol metabolism and MDD symptoms, which does so via disrupting hypothalamic–pituitary–adrenal axis regulation and neurotransmitter synaptic formation [58].

However, discordance in sensitivity and AUC values surfaced between studies that analysed biomarkers of similar nature. For genetic biomarkers, mRNA proved inconsistent in diagnosing MDD in a non-medication naive population, with only Lin et al. [16] showing high AUC (0.889) and sensitivity (0.960), while Gecys et al. [15] and Fan et al. [14] exhibited moderate performance under the ROC (0.600 and 0.631, respectively). Similarly, Karlović et al. [27] revealed that serum BDNF had high AUC (0.892) in both male and female participants with MDD, whereas Chiou and Huang [25] revealed that serum BDNF had moderate AUC (0.652) in only males and was not diagnostic for females. For neuroimaging, all three studies that investigated reduction in GMV identified different regions as potential biomarkers, with Niida et al. [36] exhibiting excellent sensitivity, while Hellewell et al. [33] and Liu et al. [35] reflected only modest sensitivity.

For the mRNA studies, the mixed results could be attributed to differences in the specific type of biomarker investigated. Each mRNA study identified different molecules, which coded for different proteins and functions. Lin et al. [16] identified mRNA segments that code for the COMT gene, which degrades catecholamines [59]. This aligns with the dopamine theory of depression, which reflects the association between deficiency in dopaminergic transmission and the symptoms of depression [60]. On the other hand, Gecys et al. [15] identified mRNA segments relating to the toll-like receptor signaling pathway, while Fan et al. [14] identified mRNA segments relating to cancer signaling pathways. These pathways are not known to be associated with the pathophysiology of MDD, which could explain the reduced AUC performance.

Similarly for neuroimaging/neurophysiology biomarkers, the SgACC region—as investigated by Niida et al. [36]-possesses a higher density of serotonin transporters than other cortical areas [61]. It follows that a reduction in SgACC volume would be associated with depression. However, the midline, temporal lobe and prefrontal regions of the brain investigated by Hellewell et al. [33] and Liu et al. [35] have no known association with MDD, which could explain the relatively weaker AUC performance observed in these brain regions.

Differences in the medication status of the cohort investigated may have also affected the consistency of the results. For the studies investigating BDNF, Chiou and Huang [25] featured a medication-naive cohort, whereas Karlović et al. [27] did not. The heterogeneity in study design may have contributed to differences in the observed magnitude of diagnostic sensitivity of serum BDNF in persons with MDD.

Limitations

This inaugural review has comprehensively synthesised available literature reporting on biomarkers for the diagnosis of MDD. This includes both conventional biomarkers (e.g., blood, neuroimaging) and novel biomarkers, such as measures of auditory, vocal and skin characteristics. This review also delineates the accuracy and sensitivity of these measures and provides an update on the evidence supporting select biomarkers as candidates for diagnostic markers in MDD.

However, this study has several limitations. Given the small sample size of most of the included studies, the results may not be generalizable to a larger population. The casecontrol design of studies included in our review allows associations to be drawn between the biomarkers and MDD but is insufficient in establishing a causal relationship between both factors. Additionally, the decision to include both medication-naive and non-medication-naive cohorts was made due to the limited number of articles. Thirty-eight percent of the studies, and only one of 6 (17%) best performing biomarkers selected featured a medication-naïve population. This limits direct comparison between medication-naïve and non-medication-naïve cohorts. Geographical diversity of the studies was also skewed towards the Asian population, with a vast majority of studies originating from China (18/42). This over-representation may reduce the generalisability of the results. Due to the language capability of the researchers, the search was limited to only papers that were available in English and thus may have excluded other relevant papers reported in a different language. Lastly, the search strategy was restricted to the past 10 years (i.e., 2013–2023), owing to the rise in novel biomarkers used to detect MDD. This restriction may lead to the exclusion of certain biomarkers investigated more than 10 years ago.

Implications for future practice and research

This review may aid psychiatrists, psychologists, allied health professionals and service providers in implementing biological tests to assess MDD. While our review herein cannot confirm the causal association between a given biomarker and diagnosis of MDD, the available evidence supports that there are multiple biomarkers closely related to the hypothesised pathophysiology of MDD, which provides candidate markers for further investigation. Future research should focus on the best performing biomarkers and evaluate their combined sensitivity/specificity when utilised together. This can be conducted through machine learning methods, which have been increasingly incorporated into the classification of biomarkers for psychiatric conditions [62]. For example, machine learning methods extract common characteristics from combined data sets and incorporate them into supervised classifiers, allowing for the evaluation of similar data from different studies [63]. Recent examples of machine learning methods include the use of support vector machine and k-nearest neighbours in the evaluation of fNIRS signals as a neuroimaging marker for MDD [64].

Future research could also restrict study inclusion based on its medication-naive status to address this confounding effect. Future studies may also translate to a wider demographic by investigating the accuracy of diagnostic biomarkers in non-adult populations, such as adolescent or geriatric depression.

Conclusion

This systematic review identifies the variety of objective biomarkers used to diagnose MDD in the adult population in the past 10 years. The variety of biomarkers available range from blood, neuroimaging/neurophysiology, urine, dermatological, voice, CSF, hearing, executive dysfunction to motor activity, with varying levels of effectiveness. The most accurate biomarkers identified-MYT1 gene in blood [26]; 5-HT1A auto receptor binding in the dorsal and median raphe in PET neuroimaging/neurophysiology [22]; urinary albumin, AMBP, HSPB, APOA1 [45]; NSE in CSF [51]; microRNA in blood and CSF [54]-were intrinsically related to the pathophysiology of MDD.

It should be noted that clinical research pertaining to MDD biomarkers is in its infancy. Most of the leading hypotheses are based on results from cross-sectional research, treatment studies or symptomatology studies, which cannot determine causality [13]. To this date, there are also no biomarkers that have been approved for diagnostic purposes [65]. Therefore, the utility of this systematic review lies in highlighting biomarkers of significant performance and the theoretical models of depression that justifies its efficacy. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods, establishing the causal effect between the biomarkers and MDD, adjusting for the medication-naïve status of the investigated population, and widening the inclusion criteria to increase the generalisability of the results.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2024.0152.