Front. Neurosci. Frontiers in Neuroscience Front. Neurosci. 1662-453X Frontiers Media S.A. 10.3389/fnins.2025.1608722 Original Research RETRACTED: Correlation analysis between clinical effective emotional treatment and plasma N-methyl-D-aspartate receptor function-related indexes Feng Zuxing 1 Methodology Data curation Investigation Supervision Writing – review & editing Formal analysis Writing – original draft Li Yanli 2 † Writing – review & editing Writing – original draft Methodology Data curation Investigation Cai Ying 3 † Data curation Formal analysis Software Writing – original draft Yang Fan 4 5 Data curation Methodology Writing – review & editing Zhou Xiangfeng 1 Data curation Formal analysis Writing – original draft Feng Maoyang 4 Methodology Writing – original draft Wu Li 1 * Investigation Writing – review & editing Supervision Formal analysis Writing – original draft Department of Alcohol and Drug Dependence, Kunming Medical University Affiliated Mental Health Center, Kunming, China Department of Respiratory and Critical Care Medicine, Yan’an Hospital of Kunming City, Kunming, China Department of Anesthesiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming, China Department of Clinical Medicine, Baoshan College of Traditional Chinese Medicine, Baoshan, China Correspondence: Li Wu, 2589923041@qq.com

These authors have contributed equally to this work and share first authorship

 02 06 2025 2025 19 1608722 09 04 2025 12 05 2025 Copyright © 2025 Feng, Li, Cai, Yang, Zhou, Feng and Wu. 2025 Feng, Li, Cai, Yang, Zhou, Feng and Wu https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Background

With the use of ketamine, Glutamate (Glu) system has gradually become the focus of antidepressant effects. N-methyl-D-aspartate receptor (NMDAR), as one of the major ionic glutamate receptors, plays a dominant role in antidepressant effects, especially in synaptic plasticity. Few studies have been conducted on changes of NMDAR-related indicators and inflammatory cytokines in major depressive disorder (MDD) patient’s peripheral blood before and after effective clinical perceptual recovery. This study aims to investigate changes in plasma biomarker levels related to NMDAR function in MDD patients receiving effective antidepressant treatment and their relationship with clinical outcomes.

 Methods

A total of 70 subjects of MDD having been discharged with an improvement rated a 50% or greater reduction by the Hamilton Depression Scale (HAMD-17) were hereby recruited. Changes in scores on the Symptom Checklist-90 (SCL-90), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), HAMD, and Hamilton Anxiety Scale (HAMA) were compared before and after therapy. Plasma D-serine (DSR), Glycine (Gly), Threonine (Thr), Glu, serine hydroxymethyltransferase (SHMT), interleukin (IL)-6, IL-8, IL-1β, IL-17, and tumor necrosis factor-alpha (TNF-α) were detected using Enzyme-linked immunosorbent assays. Subsequently, ratios of Thr/Gly and DSR/Gly were calculated, and differences between each biomarker before and after therapy were determined.

 Results

(i) Plasma glutamate levels in MDD patients had increased at discharge and decreased levels of IL-17, IL-1β, IL-6, TNF-α, and IL-8. (ii) The change in total scores of HAMD and HAMA before and after hospitalization was weakly negatively correlated with the difference in IL-1β; the change in cognitive impairment was negatively correlated with the difference in Gly. (iii) The difference of IL-6 after effective antidepressant treatment was positively correlated with the difference in total HAMD score and the difference in despair. The difference in anxiety/somatization was positively correlated with that in IL-17, that in DSR and that in TNF-α, while being negatively correlated with that in IL-1β.

 Conclusion

Indicators related to NMDAR function, including Glu, IL-6, IL-8, IL-1β, IL-17, and TNF-α, were changed after effective antidepressants treatment in patients with MDD at an acute stage. For patients with acute MDD, after effective antidepressant treatment, the greater the improvement in cognitive impairment, the smaller the change in Gly. The improvement of depressive and anxiety symptoms, the reduction of feelings of despair, and the alleviation of somatic anxiety symptoms are associated with inflammatory cytokines.

 major depressive disorder (MDD) N-methyl-D-aspartate receptor (NMDAR) metabolism cytokines glutamate The author(s) declare that no financial support was received for the research and/or publication of this article. section-at-acceptance Perception Science Introduction

Major depressive disorder (MDD) is an affective disorder mainly manifested as obvious and lasting depression, and markedly diminished interest and pleasure. Depression affects more than 300 million people worldwide, accounting for about 4.4% of the global population (Liu et al., 2015; Tran et al., 2020). However, the pathogenesis of depression is complicated, and the therapeutic mechanisms of antidepressants have not been fully clarified.

Emerging evidence has unveiled the pivotal involvement of the glutamate (Glu) system in the pathophysiology of MDD. Synaptic plasticity plays a fundamental role in emotional, learning, and cognitive functions by regulating Glu signals and activities of various membrane receptors (Papakostas and Ionescu, 2015), making the number, distribution, and stability of receptors on the nerve cell membrane crucial for excitatory synaptic efficacy (Deutschenbaur et al., 2016). Indeed, plasma Glu levels in patients with MDD are positively correlated with the severity of depressive symptoms.

Glu receptors are classified into ionic and metabolic types. N-methyl-D-aspartate receptor (NMDAR), one of the major ionic glutamate receptors, plays a leading role in synaptic plasticity. The typical NMDAR signal is mediated by its ionic function and initiated by the binding of a co-agonist, D-serine (DSR) or glycine (Gly) with Glu. This binding induces conformational changes in the extracellular region of NMDAR, crucial for processes like long-term potentiation (LTP). Meanwhile, the moderate activation of NMDAR enables Ca2+ influx to promote the neuroprotective signaling pathway, activates the induction of survival genes mediated by phosphorylated cAMP response element-binding protein (CREB), and promotes the expression of brain-derived neurotrophic factor (BDNF) (Shinohara et al., 2021), thereby regulating the synaptic plasticity of learning, memory, and behavioral basis (Kasai et al., 2010).

Serine (SR) holds considerable significance as the precursor of Gly. DSR, an endogenous NMDAR co-agonist synthesized by L-serine through serine racemase, exists in glial cells (mainly astrocytes) and neurons, serving as not only a glial transmitte (Papouin et al., 2017) but also a neurotransmitter (Wolosker et al., 2016). Glial cells are the main source of DSR. They envelop nerve endings in NMDAR-rich brain regions, forming sheaths. This arrangement enables them to regulate key processes such as neurodevelopment, neurotransmission, neurotoxicity, cell migration, synaptic plasticity, and learning and memory, through modulating NMDAR activity. The application of glial cell-specific inhibitor sodium fluoroacetate significantly damages the LTP-like synaptic plasticity in the hippocampal CA1 region, which, however, can be reversed by exogenous DSR treatment (Han et al., 2015). DSR exhibits a higher affinity for the NMDA glycine site compared to Gly itself, with a threefold higher binding affinity (Matsui et al., 1995). Interestingly, Gly can inhibit the generation of SR, thus maintaining a trace DSR environment in the brain stem and spinal cord (Wolosker and Balu, 2020). This serves as a protective mechanism against NMDAR overactivation and excitotoxicity that may result from excessive synaptic DSR accumulation (Neame et al., 2019). While the existence of the blood–brain barrier (BBB) leads to the difference in the absolute levels of amino acids between plasma and brain (Hawkins and Egleton, 2006; Hosoya et al., 1999), the amino acid transporter system of BBB can transport plasma amino acids to the brain while transmitting the neurotransmitter amino acids in the brain to the blood in an inverse concentration gradient (Fu et al., 2012). There exists a correlation between the levels of amino acids in venous blood and cerebrospinal fluid, and the levels of plasma amino acids may indirectly reflect the levels of cerebral amino acids (Liu et al., 2023). For instance, Wei et al. (2017) have demonstrated that NMDAR enhancer DSR and NMDAR antagonist ketamine have potential cellular signaling pathways with common antidepressant-like properties. Meanwhile, previous clinical trials have suggested the improving effect of DSR on depression (Steffens et al., 2010), and a clinical follow-up study of patients receiving 4 weeks of DSR treatment (Fu et al., 2012) has also reported the efficacy of high-dose (60 mg/kg per day) DSR in improving the neurocognitive function.

Inflammation affects the release, transmission, and metabolism of Glu, resulting in the excessive extracellular concentration of Glu in the central nervous system (Haroon and Miller, 2017; Haroon et al., 2017), while proinflammatory cytokines and free radicals reduce the expression of Glu transporters on glial cells and reduce Glu uptake (Tilleux and Hermans, 2007). In addition, the inflammatory environment enhances the expression and release of Glu in astrocytes (Ida et al., 2008). The increase of Glu concentration in the extrasynaptic space inhibits the synthesis and release of BDNF. Moreover, proinflammatory cytokines, such as interleukin (IL)-6, IL-8, IL-17, tumor necrosis factor-alpha (TNF-α), can induce a significant increase in indoleamine-2,3-dioxygenase (IDO) activity. Meanwhile, the elevated glucocorticoids further stimulate tryptophan-2,3-dioxygenase (TDO), promote the development of tyrosine (Tyr) to kynurenine pathway (KP), and thus lead to the decrease of 5-hydroxytryptamine (5-HT) in the central and peripheral nervous system. Additionally, elevated proinflammatory cytokines can activate microglia and stimulate enzymes like IDO and kynurenine 3-monooxygenase (KMO), steering the KP towards the Trp-Kyn-3-HK-QA excitotoxic pathway. At the same time, 3-hydroxy-kynurenine (3-HK) and quinolinic acid (QA) are agonists of NMDAR that can significantly increase Ca2+ influx, inhibit Glu reabsorption by glial cells, and block the expression of glutamine synthase (Benaud et al., 2004). Consequently, this cascade leads to the downregulation of BDNF synthesis and other synaptic proteins, ultimately resulting in synaptic atrophy. Some depressive symptoms, such as fatigue, cognitive impairment, and sleep disorder, are associated with inflammatory factors (Kantrowitz et al., 2018). However, the current research results still lack consistency. Preclinical evidence suggests that central and peripheral inflammation activates mouse hippocampal microglia, thus possibly exerting negative effects on cognition and behavior (Haroon and Miller, 2017). In terms of clinical evidence, Rossi et al. (2017) have proposed that the level of IL-2 in cerebrospinal fluid is related to the anxiety state, while the levels of IL-1β and TNF-α in cerebrospinal fluid are correlated with the depression state. Weinberger et al. (2015) have found that the higher level of IL-17 in the male group and the higher levels of IL-1β, IL-6, and CRP in the female group may be related to the clinical symptoms of MDD, including depression, despair, suicidal ideation, low self-esteem, and reduced psychological elasticity (Figure 1).

Glutamate system in the pathophysiology of MDD. Glu: glutamate; Tyr: tyrosine; NMDAR: N-methyl-D-aspartate receptor; Gly: glycine; DSR: D-serine; 5-HT: 5-hydroxytryptamine; CREB: cAMP response element-binding protein; BDNF: brain-derived neurotrophic factor; BBB: blood–brain barrier; TNF-α: tumor necrosis factor-alpha; IDO: indoleamine-2,3-dioxygenase; TDO: tryptophan-2,3-dioxygenase; 3-HK: 3-hydroxy-kynurenine; quinolinic acid; QA: quinolinic acid; KP: kynurenine pathway; KMO: kynurenine 3-monooxygenase; IL: interleukin.

Animal experiments and clinical studies have also revealed the aberrant NMDAR gene expression in MDD patients (Wu et al., 2019). Specifically, Rădulescu et al. (2021) have suggested that some antidepressants mainly targeting monoamines may reduce the functions of glutamatergic NMDAR and AMPAR, but the detailed mechanism has not been elucidated. Carboni et al. (2019) have pointed out that the increase of TNF-α is significantly correlated with the HAMD scale in patients treated with paroxetine for 10 weeks (r = −0.22, p = 0.020), and a higher TNF-α baseline level is correlated with better treatment response. However, such a correlation is not observed in patients treated with venlafaxine (p > 0.05). Additionally, domestic studies have demonstrated the association of the decline of IL-6 and TNF-α with the improvement of depressive symptoms after antidepressant treatment with SSRIs (Al-Dujaili et al., 2019). Besides, the decline of TNF-α can predict a favorable antidepressant treatment effect. At present, some antidepressants (such as fluoxetine) have been confirmed to be capable of inducing adolescent-like plasticity in the adult brain through the mechanism of “iPlasticity” (Umemori et al., 2018). Meanwhile, there are many preclinical studies to explore the action mechanism of NMDAR on MDD. The clinical promotion of ketamine, a rapid antidepressant, has attracted considerable attention to the related research on the Glu system. However, several unresolved issues remain regarding the correlation between the efficacy of clinically used antidepressants and neural plasticity in patients with MDD. Questions persist regarding the consistency of neuroplasticity changes among patients treated with different antidepressants and the specific biomolecular mechanisms through which various antidepressants improve neuroplasticity. These aspects require further exploration in future research.

At present, as we all known that early improvement of depressive symptoms predicts better clinical response to treatment. Clinical remission is related to patient’s perception of recovery. Besides, depressive symptoms, objective and subjective attention, and subjective executive function were the significant explanatory variables for self-perception of remission (Vicent-Gil et al., 2023; Kan et al., 2020). Though some studies show that (McGlinchey et al., 2006) the patient’s perspective does not necessarily coincide with the clinician’s medical assessment, which means that there is a certain mismatch between the two perspectives, remission is still widely recognised as the most favourable out come of treatment for depression and primarily relies on changes in the amount and severity of depressive symptoms (Zimmerman et al., 2006). Changes in plasma Glu system-related indexes in patients with MDD before and after effective clinical perceptual recovery have been rarely reported. The mechanism of antidepressant treatment has not been explored from the perspective of the changes in NMDAR metabolites and inflammatory cytokines. Problems remain regarding the presence of any changes in plasma NMDAR function-related biomarkers before and after effective antidepressant treatment, and whether the improvement of symptoms related to plasma NMDAR function biomarkers in MDD patients receiving antidepressant treatment in the acute stage. To further clarify the above problems, the present study, taking MDD patients receiving effective antidepressant treatment as the object, was conducted to compare the changes of plasma NMDAR function-related biomarkers before and after treatment, and explore the related factors of effective antidepressant treatment from the perspective of NMDAR function.

 Research subjects and methods Research subjects

The inpatients in the Psychiatry Department of the Second Affiliated Hospital of Kunming Medical University from January 2019 to January 2022 were selected. The inclusion criteria included: (i) patients meeting the diagnostic criteria of depression in the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) (Liu, 2005); (ii) patients aged below 65 years old; (iii) patients with Hamilton Depression Scale-17 (HAMD-17) (Levine et al., 2000) ≥ 17 points at admission, symptom improvement at discharge after antidepressant treatment, and HAMD score reduction > 50%; and (iv) patients negative for Hypomania Check List-32 (HCL-32). Meanwhile, the exclusion criteria included: (i) patients unable to cooperate with the research; (ii) patients with previous or current comorbidity of cerebral organic diseases and other physical diseases; or (iii) pregnant or lactating female patients.

The enrolled patients were offered various drug therapy options, including selective serotonin reuptake inhibitors (SSRIs) or serotonin and norepinephrine reuptake inhibitors (SNRIs). Antidepressants were appropriately selected according to the patient’s condition, and the drug dose was adjusted with the change in the condition (Bradley et al., 2018). In addition, biofeedback therapy, low-frequency impulse electrotherapy, and group psychotherapy were employed as adjuvant therapies twice a week (60 min/time). The types and doses of SSRIs are as follows: fluoxetine 10 ~ 60 mg/d, paroxetine 10 ~ 50 mg/d, sertraline 40 ~ 100 mg/d, and escitalopram oxalate 15 ~ 20 mg/d. Meanwhile, the types and doses of SNRIs are as follows: venlafaxine 37.5 ~ 225 mg/d, and duloxetine 30 ~ 120 mg/d. In this study, drug dose statistics were calculated from data collected from patients in stable condition at discharge, following treatment plan adjustments by the doctor. Notably, some patients had prior exposure to antidepressant drugs, so not all treatment regimens started with the initial dose.

This study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Kunming Medical University (FEY-BG-39-2.0). All participants and their legal guardians signed the informed consent form.

 Tools

Firstly, the self-made general status questionnaire was used to record the sociodemographic data and disease characteristics of subjects, including gender, age, education level, etc.

Secondly, the Symptom Checklist 90 (SCL-90) was employed (Petzold et al., 2022) for accessing nine different symptom clusters of psychopathology, including somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism, involving 90 items. Each item on the SCL-90 was scored on a five-point scale from 1 to 5, and the psychological status of subjects was evaluated according to the total score of the scale.

The Self-rating Anxiety Scale (SAS) was adopted to evaluate the anxiety degree of subjects (Lai et al., 2010), which contained 20 items (15 positive scorings and 5 negative scorings) reflecting the subjective feelings of anxiety. The subjects were given on a 4-point scale ranging from 1 (none, or a little of the time) to 4 (most, or all of the time).

The Self-rating Depression Scale (SDS) was utilized to evaluate the depression degree of subjects (Lai et al., 2010), containing 20 items (10 positive scorings and 10 negative scorings) reflecting the subjective feelings of depression. Each item was scored 1–4 points according to the frequency of symptoms, 1 for none or a little of the time, 2 for sometimes, 3 for most of the time, and 4 for most or all of the time.

The Hamilton Anxiety Scale (HAMA) (Tang and Zhang, 1999) was used for evaluating the severity of anxiety symptoms, which contained 14 items. Each item was scored 0–4 points according to the degree of symptoms from mild to severe. Except for item 14 evaluated in combination with observation, the evaluation of all other items was based on the level of somatic anxiety and phychic anxiety according to the patient’s oral description. This scale was divided into two factorial structures, i.e., somatic anxiety (somatic muscular, sensory system, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, genitourinary symptoms, and autonomic nervous system symptoms) and phychic anxiety (anxious mood, tension, fear, insomnia, cognitive function, depressed mood, and behavior at interview).

The Hamilton Depression Scale-17 (HAMD-17) was adopted to evaluate the severity of depressive symptoms (Lai et al., 2010), with some items grading 0 (asymptomatic) to 4 (extremely severe) while some items grading 0 to 2. HAMD-17 involved 7 factorial structures: anxiety/somatization factor: psychic anxiety, somatic anxiety, gastrointestinal symptoms hypochondriasis, and insight; weight: weight loss; cognitive dysfunction: self-guilt, suicide, agitation, disintegration of personality or reality, paranoid symptoms, and obsessive-compulsive symptoms; diurnal mood variation; retardation: depression mood, work and interest, retardation, and sexual symptoms; sleep disorder: difficulty in falling asleep, lack of deep sleep, and early awakening; and despair: a sense of decreased ability, sense of despair, and sense of inferiority.

 Diagnosis and enrollment

All subjects were diagnosed by two psychiatrists with at least an intermediate-level professional title (or higher), and those with a consistent diagnosis from both psychiatrists were enrolled. The evaluation of scales was independently completed by a psychiatrist with an intermediate professional title on the day of enrollment or the morning of the next day. All subjects received blood biochemistry examination and brain magnetic resonance imaging (MRI) to exclude cerebral organic diseases and other physical diseases.

 Sample treatment Sample collection

Subjects showing effective therapeutic effects were sampled early in the morning on the day following admission, before receiving any medication or physical therapy, and again at 7 ~ 8 a.m. on the day of discharge. During each sampling, 3 mL of fasting venous blood from the elbow was collected into a dry anticoagulant tube. After standing at 4°C for 30 min, the samples were centrifuged at 3000 r/min and 4°C for 10 min. The resulting supernatant was then extracted and stored in a refrigerator at −80°C.

 Sample detection

The plasma levels of DSR (ng/mL), Gly (pg/mL), Glu (mg/L), Threonine (Thr) (ng/mL), serine hydroxymethyltransferase (SHMT) (ng/mL), p11 (pg/mL), IL-1β (pg/mL), IL-6 (pg/mL), TNF-α (pg/mL), IL-17 (pg/mL), and IL-8 (pg/mL) were detected using enzyme-linked immunosorbent assay. The assay kits were provided by Shanghai Yuanxin Biology Co., Ltd. The transportation and preservation of samples referred to the relevant provisions of the Guiding Principles for the Management of Phase I Clinical Trials of Drugs (Trial). Besides, the sampling, analysis, and testing all followed the laboratory standard operation process. The detection of each sample was repeated 3 times, and the specific operation was conducted in line with the instructions of the kit. The intra-assay and inter-assay coefficients of variation were 5 and 10%, respectively.

 Statistical methods

SPSS 25.0 was employed for statistical analysis. The measurement data in normal distribution are expressed as mean and standard deviation ( X ¯ ± S ), and the comparisons among multiple groups were performed using one-way analysis of variance (ANOVA), followed by the least significant difference (LSD) test for post hoc comparison. The measurement data of non-normal distribution were expressed as median and interquartile [M (P25, P75)], and multiple groups were compared using the Kruskal-Wallis H test. Subsequently, the Nemenyi test was conducted for post hoc comparison. The enumeration data are expressed as frequency and composition ratio [N (%)]. Furthermore, the difference analysis was carried out utilizing either the chi-square test or Fisher exact probability test. Pearson or Spearman correlation coefficients were employed to analyze the correlation among symptoms, severity, metabolites, and cytokines. Additionally, multivariate regression analysis was conducted based on these correlations. PASS 15.0 was used to estimate the sample size. The sample size estimation formula for multi-sample mean comparison was selected as: formula ① N = ψ 2 ( ∑ S i 2 / g ) / [ ∑ ( X ¯ i − X ¯ ) 2 / ( g − 1 ) ] and formula ② X ¯ = ∑ X i ¯ / g , where g is the number of research groups; Xi and −Si are the mean and standard deviation of each group, respectively; and Ψ2 denotes the non-centrality parameter of the noncentral F-distribution, which can be obtained by consulting the F-distribution critical value table in statistics books. According to previous research results, the number of cases in each group was calculated with the size of test α = 0.05 and the power of test 1-β = 0.9. Taking the mean and standard deviation of Glu levels (n = 10) in the pre-experiment as a reference and substituting them into the formula, it was calculated to be about 58 cases in each group. Considering the uncertainty during the experiment and early discharge, 70 cases were finally included as the Research subjects. The flow chart of the study is shown in Figure 2.

Flowchart of study. SCL-90: Symptom check list-90; SAS: the Self-rating Anxiety Scale; SDS: the Self-rating Depression Scale; HAMD: the Hamilton Depression Scale; HAMA: the Hamilton Anxiety Scale; DSR: D-serine; Gly: Glycine; Thr: Threonine; Glu: Glutamate; SHMT: serine hydroxymethyltransferase; IL: interleukin; TNF-α: tumor necrosis factor-alpha.

 Results Basic information

Herein, a total of 70 patients were included, aged between 11 and 62 years old, with an average age of (27.11 ± 15.47) years. The hospitalization duration ranged from 12 to 16 days, with an average stay of (14.07 ± 1.28) days. The types and doses of SSRIs were as follows: fluoxetine 37.38 ± 9.62 mg/d, paroxetine 20.00 ± 5.84 mg/d, sertraline 73.57 ± 24.89 mg/d, and escitalopram oxalate 16.67 ± 6.24 mg/d. Meanwhile, the types and doses of SNRIs included: venlafaxine 137.50 ± 77.06 mg/d and duloxetine 72.73 ± 23.00 mg/d. The specific information is shown in Table 1.

Basic demographic data [x ± s/n(%)].

Item Case (%)
Gender
Male 14 (20%)
Female 56 (80%)
Education level
Primary school and below 8 (11.4%)
Junior high school 31 (44.3%)
Senior school or secondary specialized school 15 (21.4%)
University or tertiary education institution 16 (22.9%)
Registered residence
Rural 22 (31.4%)
Urban 48 (68.6%)
Only-child or not
Only-child 29 (41.4%)
Non-only-child 41 (58.6%)
Comparison of scale scores

Changes in scale scores of MDD patients at admission and discharge were statistically significant (all p = 0.000) (Table 2). Taking [(score value at admission−score value at discharge) * 100%/score value at admission] as the reduction rate of each index (%), the reduction rate of SCL-90, SDS, SAS, HAMD, and HAMA was [28.85 (10.11,38.83)] (%), (21.23 ± 20.06) (%), [23.39 (10.81, 23.39)] (%), (67.54 ± 17.84) (%), and [60.66 (36.09, 75.00)] (%), respectively.

Comparison of scale scores at admission and discharge.

Scale At admission At discharge Z/t value p-value
Scale Factor
HAMD 27 (24,35) 8 (5,12.5) −9.156 0.000*
Anxiety/somatization 7 (6,9) 2 (1,3) −8.470 0.000*
Weight 1 (0,2) 0 (0,0) −4.794 0.000*
Cognitive dysfunction 5 (3,8.5) 1 (0,2) −7.637 0.000*
Diurnal mood variation 1 (0,2) 0 (0,0) −5.842 0.000*
Retardation 5 (4,7) 2 (1,3) −7.570 0.000*
Sleep disorder 4 (3,6) 1 (0,1) −8.408 0.000*
Despair 4 (3,6) 2 (1,2) −7.369 0.000*
HAMA 17 (13.5,23) 6 (4,10.75) −7.633 0.000*
Somatic 7 (3.5,9) 1 (0,3) −6.400 0.000*
Phychic 11.54 ± 4.19 5 (3,7) −7.008 0.000*
SCL-90 273.60 ± 69.68 204.24 ± 75.92 5.591 0.000*
SDS 63.51 ± 11.90 49.64 ± 14.09 6.248 0.000*
SAS 56.46 ± 12.52 43.47 ± 14.59 5.610 0.000*

HAMD: Hamilton Depression Scale; HAMA: Hamilton Anxiety Scale; SCL-90: Symptom Checklist 90; SDS: Self-rating Depression Scale; SAS: Self-rating Anxiety Scale; *P < 0.05.

 Changes in NMDAR function-related indexes

DSR/Gly, representing the ratio of DSR to Gly in peripheral circulation and indirectly reflecting the functional state of NMDAR, and Thr/Gly, indicating the transformation of Thr to Gly and the activity of SHMT, were both calculated.

As shown in Table 3, the plasma level of Glu in MDD patients at discharge was significantly higher than that at admission (t = −13.026, p < 0.005); the plasma levels of IL-17 (t1 = 9.252), IL-1β (t2 = 9.106), IL-6 (t3 = 14.321), TNF-α (t4 = 9.762), and IL-8 (t5 = 9.848) at discharge were lower compared to those at admission (all p < 0.005), while the plasma levels of p11(t6 = −9.656) and BDNF (t7 = −18.698) were higher at discharge than those at admission (all p < 0.005). Besides, no significant change in other metabolic indexes was observed either at admission or discharge.

Changes in NMDAR-related biomarkers of MDD patients at admission and discharge.

Item At admission At discharge Z/t value P-value
DSR (ng/mL) 88.08 (78.98, 94.12) 89.07 (79.35, 96.97) −0.260 0.795
Gly (pg/mL) 196.46 ± 38.03 200.11 ± 42.96 −0.528 0.598
Glu (mg/L) 20.16 ± 5.77 32.18 ± 5.04 −13.026 0.000*
Thr (ng/mL) 560.70 ± 142.49 579.97 ± 142.49 −0.839 0.403
SHMT (ng/mL) 611.72 ± 611.72 628.86 ± 165.00 −0.643 0.522
DSR/Gly 453.09 ± 118.47 447.23 ± 121.89 −0.287 0.775
Thr/Gly 2.95 ± 0.87 3.01 ± 0.92 0.407 0.684
IL-17 (pg/mL) 46.04 ± 7.27 34.36 ± 7.55 9.252 0.000*
IL-1β (pg/mL) 114.89 (106.11,126.75) 75.68 (57.98,86.76) 9.106 0.000*
IL-6 (pg/mL) 51.83 ± 6.70 35.43 ± 6.76 14.321 0.000*
TNF-α (pg/mL) 89.01 (79.46,100.44) 38.46 (31.05,50.89) 9.762 0.000*
IL-8 (pg/mL) 128.40 ± 24.12 88.39 ± 23.60 9.848 0.000*

DSR: d-serine; Gly: glycine; Glu: glutamate; Thr: threonine; SHMT: serine hydroxymethyltransferase; DSR/Gly: ratio of d-serine to glycine; Thr/Gly: ratio of threonine to glycine; IL-17: interleukin-17; IL-1β: interleukin-1β; IL-6: interleukin-6; TNF-α: tumor necrosis factor-α; IL-8: interleukin-8, *P < 0.05.

 Correlation analysis of differences of each index

Taking (baseline value—post-treatment value) as the difference of each index at admission and discharge, the difference of each factor of HAMD and HAMA and the difference of each measurement index were hereby analyzed by univariate linear correlation analysis, with p < 0.05 suggesting statistical significance. As shown in Table 4, the difference of the HAMD total score exhibited a weak negative correlation with that of IL-1β (r = −0.268, p = 0.027); the difference of cognitive dysfunction was negatively correlated with that of Gly (r = −0.332, p = 0.012); and the difference of the HAMA total score presented a weak negative correlation with that of IL-1β (r = −0.239, p = 0.050). The correlation between the difference of other indexes was not statistically significant.

Positive results of univariate correlation analysis between the score difference of the scale and the difference of each index.

Variable Variable Coefficient (r) P-value
HAMD total score difference IL-1β difference −0.268* 0.027*
Cognitive dysfunction difference Gly difference −0.332* 0.012*
HAMA total score difference IL-1β difference −0.239* 0.050*

HAMD: Hamilton Depression Scale; HAMA: Hamilton Anxiety Scale; IL-1β: interleukin-1β; Gly: glycine; *P < 0.05.

 Multivariate linear regression analysis

Multivariate linear regression analysis was performed by taking the difference of HMDA and HAMA total score and the difference of each factor score as dependent variables, taking IL-1β difference, IL-6 difference, TNF-α difference, IL-17 difference, IL-8 difference, DSR difference, Gly difference, Glu difference, Thr difference, as well as SHMT difference as independent variables, and taking demographic characteristics, SSRIs, and SNRIs as control variables. The results showed that the difference of IL-6 in MDD patients before and after effective antidepressant treatment was positively correlated with that of the HAMD total score (B = 0.328, p = 0.039) and that of despair (B = 0.080, p = 0.046); a positive correlation existed between the urban household registration and the score difference of anxiety/somatization factors (B = 2.275, p = 0.026); the only-child factor was negatively correlated with the score difference of body weight (B = −0.723, p = 0.035) and the difference of despair (B = −1.735, p = 0.019); there was a negative correlation between male MDD patients and sleep disorder (B = 1.354, p = 0.044); the high education level was negatively correlated with the difference of despair (B = −0.918, p = 0.022); the difference of somatic anxiety was positively correlated with that of IL-17 (B = 0.990, p = 0.030), DSR (B = 0.101, p = 0.044), or TNF-α (B = 0.071, p = 0.037), while being negatively correlated with that of IL-1β (B = −0.070, p = 0.029).

 Discussion Analysis of demographic data and antidepressant efficacy in patients with MDD

Multivariate linear regression analysis revealed a negative correlation between male MDD patients and the presence of sleep disorders. This suggested that among hospitalized MDD patients undergoing effective antidepressant treatment, males experienced better improvement in sleep compared to females. Consistently, Chen et al. (2005) and Tian et al. (2021) have also demonstrated that the gender difference in sleep quality may be related to physiological differences, that is, hormones and thinking patterns in females.

Multivariate linear regression analysis further indicated a negative correlation between MDD patients with higher education levels and the difference in sense of despair. This suggests that individuals with higher education levels experienced smaller changes in their sense of despair before and after treatment. Despair is a negative expectation or pessimism about the future (Wu et al., 2019). Beck’s cognitive therapy holds that the pathophysiology of depression consists of the depressive cognitive triad, logical errors, and potential cognitive assumptions. The depressive cognitive triad is an unrealistic negative concept about an individual, the surrounding world, and the future (Yu et al., 2023). Previous literature (Tomás et al., 2016) does not report a positive correlation between education and despair. This result may be caused by the sample deviation of subjects, or the relatively stable cognitive model of highly educated people.

Moreover, only-child MDD patients showed a negative correlation with the difference in despair, indicating minimal change in despair before and after treatment. This result indirectly supports the previous view with high acceptance: there are significant differences in the objective, subjective, and family support between the only-child population and the non-only-child population (Chen, 2008). Chen (2008) have proposed that the reason for such difference may be that the only-child population are accessible to more social resources than the non-only-child population. On the other hand, considering the stringent hospital control measures during the investigation, each inpatient was mandated to have only one immediate family member for 24-h escort. Before admission, the psychiatrist provided disease education to both the patient and their family members, instructing them to reinforce their support. Non-only-child patients had more time and opportunities to receive objective support from family than they typically would in their daily lives. In addition, it was also found that the only-child patients had a negative correlation with the difference of body weight factor, that is, the weight change of the only-child patients was smaller than that of the non-only-child patients. Previous studies have not directly linked the only-child factor with weight change in MDD patients. Depressive symptoms can definitely increase the risk of dietary disorders (Ouwens et al., 2009), and family function is often associated with dietary disorders (George et al., 2014). Unfortunately, there is still a lack of relevant longitudinal research. The role of the family model and social support on dietary disorders is worthy of further exploration. The present study results also indicated that changes in anxiety/somatization symptoms in urban MDD patients before and after treatment were greater than those in rural MDD patients, consistent with the results of previous studies. Some researchers (Liu, 2005) have demonstrated somatic pains as frequent in manual workers, and claimed that most rural patients have chronic pain since they are engaged in manual labor. Hence, the remission degree of rural MDD patients during hospitalization in the acute phase may be lower than that of urban patients with less manual labor.

While the demographic correlation analysis in this study yields intriguing conclusions, it should be noted that a more rigorous experimental design and a larger sample size are still needed for validation. The limitations, potential data offset, and regional constraints of this study underscore the necessity for further verification.

 Analysis of changes in NMDAR function-related indexes

In the index detection, the plasma Glu level was increased significantly after effective treatment, which is inconsistent with some research results indicating the reduction of Glu (Levine et al., 2000; Maes et al., 1998; Ogawa et al., 2018) while being consistent with the previous research results of cerebral functional magnetic resonance (Bradley et al., 2018; Portella et al., 2011). Meanwhile, it also supports the research results proposed by Nowak et al. (2003) that the binding efficacy of NMDAR in the frontal cortex of MDD patients is decreased sharply. While the change of the peripheral Glu level is still controversial, the important role of the Glu system in the MDD mechanism cannot be ignored. The increase of peripheral Glu level in the acute phase after treatment may be attributed to the following reasons: Firstly, previous relevant studies mostly focus on long-term chronic continuous antidepressant treatment, and the short-term antidepressant treatment after admission may not quickly change the increasing trend of peripheral Glu level, suggesting the constant existence of the excitotoxicity of Glu (Liao et al., 2020), which, however, may not be a key factor in changing depressive symptoms; Secondly, although patients are somewhat distanced from the original stressor after hospitalization, the relief of long-term stress stimuli, such as abnormal hormone secretion and emotional memory, requires time (Mahar et al., 2014). Besides, no significant difference in the changes in plasma DSR, Gly, and Thr levels was observed in MDD patients at admission and discharge. The present results are consistent with some research findings (Mauri et al., 1998; Sumiyoshi et al., 2004), while being inconsistent with the report of Altamura et al. (1995). Hashimoto et al. (2016) have demonstrated the abnormal synthesis and catabolism of SR counterparts in MDD patients. In a randomized double-blind controlled clinical trial, MDD patients taking high-dose DSR for 4 weeks present improved depressive symptoms (Kantrowitz et al., 2018). Partial agonists at the Gly site of NMDAR (e.g., D-cycloserine, GLYX-13) exhibit clinical efficacy in the treatment of major depression (Coyle and Balu, 2018). In this study, there was no significant change in the key enzyme (SHMT) before and after effective treatment. Similarly, no significant change in Thr, an important source of Gly was observed. In previous studies, it has been reported that SHMT activity in patients with non-psychotic MDD is significantly higher than that in patients with psychotic depression (Waziri et al., 1985), and that psychotic patients have lower SHMT activation than healthy controls. Since the results of previous studies lack consistency, the specific clinical application of the above biological indicators in MDD patients should be further explored. In addition to the number of metabolites in the peripheral circulation, more results may be obtained from the aspects of gene polymorphism, expression, and biological activity of metabolites. Herein, the cytokine detection results showed that the plasma levels of proinflammatory cytokines IL-17, IL-1β, IL-6, TNF-α, and IL-8 in MDD patients were significantly lower after effective antidepressant treatment than those at admission, which is consistent with the results of previous studies (Colasanto et al., 2020; Köhler et al., 2017; Liu et al., 2012), suggesting the critical role of inflammation in the pathogenesis of MDD.

 Correlation analysis between the changing trend of NMDAR function-related indexes and changes in symptoms Inflammatory cytokines

In this study, univariate analysis showed that the difference of IL-1β before and after effective antidepressant treatment was negatively correlated with that of HAMD and HAMA total score. This suggested that the changing trend of IL-1β was opposite to that of depression and anxiety symptoms, indicating that the change of IL-1β could reflect the curative effect to a certain extent in MDD patients with effective antidepressant treatment. In the study of Zou et al. (2018), the increase of peripheral IL-1β and TNF-α levels in MDD patients with ineffective antidepressant treatment has been found to be associated with the increase in HAMD score. In addition, the change of IL-6 before and after treatment was hereby also observed to be correlated with that in HAMD total score. This indicated that the changing trend of IL-6 level was consistent with the overall depressive symptoms. Menard et al. (2017) have labeled mouse recombinant IL-6 with biotin, and observed that peripheral IL-6 can enter the nucleus accumbens through the outside of blood vessels and act directly in the nucleus accumbens to induce depression-like behaviors. In the UK biological sample bank (Khandaker et al., 2020), IL-6R variation (decreased IL-6R activity) associated with a high serum IL-6 level is related to an increased risk of depression. The clinical research results of Fan et al. (2017) have also suggested that the level of IL-6 is positively correlated with the severity of depressive symptoms. Therefore, IL-6 is confirmed to hold considerable significance in the pathogenesis of depression. Moreover, in this study, the changing trend of IL-6 before and after effective antidepressant treatment was also consistent with that of despair. The findings align well with the research results of Kim et al. (2021). However, the exact drivers and mechanisms of elevated circulating inflammatory markers in depressive symptoms remain unclear. Further research is still necessarily important to analyze the interaction between metabolic conditions, inflammation, and individual depressive symptoms and to examine other sources. For example, the possible genetic susceptibility to systemic inflammation or physical stress-related response may be the basis of inflammation-depression symptoms. Notably, although IL-6 and IL-1β are both inflammatory cytokines, their changes present the opposite correlation with those in symptoms. However, currently, changes of peripheral inflammatory markers in patients with effective and ineffective treatment have been rarely reported (Köhler et al., 2018). Meanwhile, the redundancy, synergy, antagonism, and signal cascade of cytokine signal transduction (Himmerich et al., 2019) necessitate in-depth exploration into the field of cytokines.

 NMDAR function-related metabolic indexes

Emerging evidence has suggested that changes in inflammation and metabolism are more consistently mapped to “atypical” energy-related symptoms, especially sleep, appetite/weight gain, and fatigue (Lamers et al., 2020; Milaneschi et al., 2016; Milaneschi et al., 2017). In this study, univariate correlation analysis showed that the changing trend of Gly level was opposite to that of cognitive dysfunction score, suggesting that smaller changes in Gly levels before and after admission were associated with greater improvements in cognitive dysfunction among MDD patients hospitalized in the acute stage and receiving effective antidepressant treatment. While Peyrovian et al. (2019) have demonstrated the glycine locus of NMDARs as a promising target to alleviate the symptoms of depression and cognitive dysfunction, the change of Gly in this study is not positively correlated with cognitive dysfunction. The possible reasons may be as follows: On the one hand, SSRIs or SSNIs have little effect on the change of Gly level in peripheral blood, which is different from the mechanism of partial agonists at the Gly site of NMDAR in improving depressive symptoms; on the other hand, patients had a short hospitalization time, and those with acute MDD presented varying degrees of cognitive impairments. However, significant improvements in a range of depressive symptoms were observed following effective antidepressant treatment, leading to corresponding improvements in cognitive function.

 Metabolism-inflammation-symptom

Correlation analysis in this study showed that the difference of somatic anxiety was positively correlated with that of IL-17, DSR, or TNF-α, but negatively correlated with that of IL-1β. This suggested that the changing trend of somatic anxiety symptoms was consistent with that of IL-17, DSR, and TNF-α, while being contrary to that of IL-1β. This result integrates changes in inflammation and metabolism with anxiety symptoms in MDD patients, thereby offering clinical evidence for the impact of effective antidepressant treatment on NMDAR function. However, previous studies on the correlation between inflammatory cytokines and anxiety symptoms are inconsistent. For instance, Pallavi et al. (2015) have revealed that clinical anxiety score has a positive correlation with IL-1β and a negative correlation with IL-17 and TGF-β1. Oliveira Miranda et al. (2014) have evaluated the depression and anxiety of patients with colorectal cancer using the hospital anxiety and depression scale, and reported that the symptoms of anxiety and/or depression are positively correlated with the levels of cytokines IL-1β, IL-6, and TNF-α. Meanwhile, there are also many research results aligning with those of this study. For example, Duivis et al. (2013) have evaluated 2,861 participants in the depression and anxiety study from the Netherlands, and demonstrated the association of somatic symptoms with high levels of C-reactive protein (CRP), IL-6, and TNF-α, suggesting the crucial involvement of inflammation in physical symptoms of depression and anxiety. Additionally, the cytokine spectrum analysis in patients with generalized anxiety disorder by Vieira et al. (2010) has suggested that the production of Th17-derived cytokines is enhanced. Particularly, the TNF-α and IL-17 levels are significantly elevated in cell cultures containing activated T cells.

As a more effective synaptic NMDAR activator than Gly in function, DSR serves as the dynamic monitor of NMDAR function in the forebrain region (Wolosker and Balu, 2020). Accumulating evidence has confirmed the critical role of DSR in the limbic region of the brain as an NMDAR co-agonist. NMDAR in the amygdala and medial prefrontal cortex is of considerable importance for the acquisition and disappearance of fear memory and its related physiological symptoms (Tovote et al., 2015; Donello et al., 2023). Genetic evidence has suggested that DSR is associated with post-traumatic stress disorder (Van der Auwera et al., 2016; Zhang et al., 2023). Preclinical findings have also indicated enhanced short-term memory performance and exacerbated anxiety of D-amino acid oxidase knockout mice (Walker et al., 2020). DSR can normalize the defective social memory and social interaction in anxiety and depression models (Zoicas and Kornhuber, 2019). Previous clinical studies mostly focus on the correlation between DSR and the improvement of depressive symptoms. However, positive results were hereby obtained. Consequently, further studies are still warranted to explore different subtypes and treatment methods of depressive disorders.

 Limitations

The limitations of this study are as follows: Firstly, there lacks a healthy control group matched with the case group to further compare changes in peripheral circulation metabolic indexes in patients with MDD; Secondly, while the index changes of patients with effective antidepressant treatment have been considered, attention should also be paid to MDD patients with poor curative effects; Thirdly, comparisons with the norm of the above biomarkers should still be conducted, and the sample size is small, suggesting the necessity of more samples for validation; Fourthly, metabolic indexes are susceptible to numerous interference factors, while the wide age range of the sample and potential gender differences add complexity. And due to research constraints, we were unable to ensure uniform treatment duration for all subjects. Future studies should consider separately analyzing patients receiving antidepressant treatment for the first time and those with prior inadequate response to treatment. Further exploration of the MDD mechanism necessitates combining multiple MDD biomarkers to construct a comprehensive mathematical model.

 Conclusion

Herein, the following conclusions can finally be drawn out. Firstly, there are significant changes in NMDAR function-related indexes Glu, IL-6, IL-8, IL-1β, IL-17, and TNF-α in MDD patients in the acute phase before and after effective antidepressant treatment. The plasma Glu level at discharge is significantly higher than that at admission, and the plasma levels of IL-17, IL-1β, IL-6, TNF-α, and IL-8 are lower than those at admission; Secondly, before and after effective antidepressant treatment in patients with acute MDD, the greater the improvement of cognitive dysfunction, the smaller the change of Gly; the improvement degree of depression and anxiety symptoms is opposite to the changing trend of IL-1β level; the improvement of overall depressive symptoms and the decrease of despair are consistent with the decreasing trend of IL-6; and the improvement of somatic anxiety symptoms is consistent with the decreasing trend of IL-17, TNF-α, and DSR, while being opposite to the decreasing trend of IL-1β.

 Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

 Ethics statement

The studies involving humans were approved by the Ethics Committee of the Second Affiliated Hospital of Kunming Medical University (FEY-BG-39-2.0). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin. The manuscript presents research on animals that do not require ethical approval for their study.

 Author contributions

ZF: Methodology, Data curation, Investigation, Supervision, Writing – review & editing, Formal analysis, Writing – original draft. YL: Writing – review & editing, Writing – original draft, Methodology, Data curation, Investigation. YC: Data curation, Formal analysis, Software, Writing – original draft. FY: Data curation, Methodology, Writing – review & editing. XZ: Data curation, Formal analysis, Writing – original draft. MF: Methodology, Writing – original draft. LW: Investigation, Writing – review & editing, Supervision, Formal analysis, Writing – original draft.

 Acknowledgments

The authors thank all participants involved in this study. The clinical trial participants were fully informed about the objectives and procedures of the experiment and participated on a voluntary basis. Participants had the right to withdraw from the experiment at any time without facing any penalties.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 References Al-Dujaili A. H. Al-Hakeim H. K. Twayej A. J. Maes M. (2019). Total and ionized calcium and magnesium are significantly lowered in drug-naïve depressed patients: effects of antidepressants and associations with immune activation. Metab. Brain Dis. 34, 1493–1503. doi: 10.1007/s11011-019-00458-5, PMID: 31292851 Altamura C. Maes M. Dai J. Meltzer H. Y. (1995). Plasma concentrations of excitatory amino acids, serine, glycine, taurine and histidine in major depression. Eur. Neuropsychopharmacol. 5, 71–75. doi: 10.1016/0924-977X(95)00033-L Benaud C. Gentil B. J. Assard N. Court M. Garin J. Jerome D. . (2004). AHNAK interaction with the annexin 2/S100A10 complex regulates cell membrane cytoarchitecture. J. Cell Biol. 164, 133–144. doi: 10.1083/jcb.200307098, PMID: 14699089 Bradley K. A. Alonso C. M. Mehra L. M. Xu J. Gabbay V. (2018). Elevated striatal γ-aminobutyric acid in youth with major depressive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 86, 203–210. doi: 10.1016/j.pnpbp.2018.06.004, PMID: 29890194 Carboni L. McCarthy D. J. Delafont B. Filosi M. Ivanchenko E. Ratti E. . (2019). Biomarkers for response in major depression: comparing paroxetine and venlafaxine from two randomised placebo-controlled clinical studies. Transl. Psychiatry 9:182. doi: 10.1038/s41398-019-0521-7, PMID: 31375659 Chen S.-p. Z. J. (2008). Study on the relationship between social support and mental health of private college students. Modern. Prev. Med. 35, 3354–3356. Chen Y. Y. Kawachi I. Subramanian S. V. Acevedo-Garcia D. Lee Y. J. (2005). Can social factors explain sex differences in insomnia? Findings from a national survey in Taiwan. J. Epidemiol. Community Health 59, 488–494. doi: 10.1136/jech.2004.020511, PMID: 15911645 Colasanto M. Madigan S. Korczak D. J. (2020). Depression and inflammation among children and adolescents: a meta-analysis. J. Affect. Disord. 277, 940–948. doi: 10.1016/j.jad.2020.09.025, PMID: 33065836 Coyle J. T. Balu D. T. (2018). The role of serine racemase in the pathophysiology of brain disorders. Adv. Pharmacol. 82, 35–56. doi: 10.1016/bs.apha.2017.10.002, PMID: 29413527 Deutschenbaur L. Beck J. Kiyhankhadiv A. Mühlhauser M. Borgwardt S. Walter M. . (2016). Role of calcium, glutamate and NMDA in major depression and therapeutic application. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 64, 325–333. doi: 10.1016/j.pnpbp.2015.02.015, PMID: 25747801 Donello J. E. McIntyre R. S. Pickel D. B. Stahl S. M. (2023). Demystifying the antidepressant mechanism of action of Stinels, a novel class of Neuroplastogens: positive allosteric modulators of the NMDA receptor. Pharmaceuticals (Basel) 18:157. doi: 10.3390/ph18020157, PMID: 40005971 Duivis H. E. Vogelzangs N. Kupper N. de Jonge P. Penninx B. W. J. H. (2013). Differential association of somatic and cognitive symptoms of depression and anxiety with inflammation: findings from the Netherlands study of depression and anxiety (NESDA). Psychoneuroendocrinology 38, 1573–1585. doi: 10.1016/j.psyneuen.2013.01.002, PMID: 23399050 Fan N. Luo Y. Ou Y. He H. (2017). Altered serum levels of TNF-α, IL-6, and IL-18 in depressive disorder patients. Hum. Psychopharmacol. 32:e2588. doi: 10.1002/hup.2588, PMID: 28582802 Fu X. Y. Lu Y. R. Wu J. L. Wu X. Y. Bao A. M. (2012). Alterations of plasma aspartic acid, glycine and asparagine levels in patients with major depressive disorder. Zhejiang Da Xue Xue Bao Yi Xue Ban 41, 132–138. doi: 10.3785/j.issn.1008-9292.2012.02.002 George M. W. Fairchild A. J. Mark Cummings E. Davies P. T. (2014). Marital conflict in early childhood and adolescent disordered eating: emotional insecurity about the marital relationship as an explanatory mechanism. Eat. Behav. 15, 532–539. doi: 10.1016/j.eatbeh.2014.06.006, PMID: 25113902 Han H. Peng Y. Dong Z. (2015). D-serine rescues the deficits of hippocampal long-term potentiation and learning and memory induced by sodium fluoroacetate. Pharmacol. Biochem. Behav. 133, 51–56. doi: 10.1016/j.pbb.2015.03.017, PMID: 25843400 Haroon E. Miller A. H. (2017). Inflammation effects on brain glutamate in depression: mechanistic considerations and treatment implications. Curr. Top. Behav. Neurosci. 31, 173–198. doi: 10.1007/7854_2016_40 Haroon E. Miller A. H. Sanacora G. (2017). Inflammation, glutamate, and glia: a trio of trouble in mood disorders. Neuropsychopharmacology 42, 193–215. doi: 10.1038/npp.2016.199, PMID: 27629368 Hashimoto K. Yoshida T. Ishikawa M. Fujita Y. Niitsu T. Nakazato M. . (2016). Increased serum levels of serine enantiomers in patients with depression. Acta Neuropsychiatr 28, 173–178. doi: 10.1017/neu.2015.59 Hawkins B. T. Egleton R. D. (2006). Fluorescence imaging of blood-brain barrier disruption. J. Neurosci. Methods 151, 262–267. doi: 10.1016/j.jneumeth.2005.08.006, PMID: 16181683 Himmerich H. Patsalos O. Lichtblau N. Ibrahim M. A. A. Dalton B. (2019). Cytokine research in depression: principles, challenges, and open questions. Front. Psych. 10:30. doi: 10.3389/fpsyt.2019.00030, PMID: 30792669 Hosoya K. Sugawara M. Asaba H. Terasaki T. (1999). Blood-brain barrier produces significant efflux of L-aspartic acid but not D-aspartic acid: in vivo evidence using the brain efflux index method. J. Neurochem. 73, 1206–1211. doi: 10.1046/j.1471-4159.1999.0731206.x, PMID: 10461913 Ida T. Hara M. Nakamura Y. Kozaki S. Tsunoda S. Ihara H. (2008). Cytokine-induced enhancement of calcium-dependent glutamate release from astrocytes mediated by nitric oxide. Neurosci. Lett. 432, 232–236. doi: 10.1016/j.neulet.2007.12.047, PMID: 18255223 Kan K. Jörg F. Buskens E. Schoevers R. A. Alma M. A. (2020). Patients’ and clinicians’ perspectives on relevant treatment outcomes in depression: qualitative study. BJPsych Open 6, 1–7. doi: 10.1192/bjo.2020.27, PMID: 32364101 Kantrowitz J. T. Epstein M. L. Lee M. Lehrfeld N. Nolan K. A. Shope C. . (2018). Improvement in mismatch negativity generation during d-serine treatment in schizophrenia: correlation with symptoms. Schizophr. Res. 191, 70–79. doi: 10.1016/j.schres.2017.02.027, PMID: 28318835 Kasai H. Fukuda M. Watanabe S. Hayashi-Takagi A. Noguchi J. (2010). Structural dynamics of dendritic spines in memory and cognition. Trends Neurosci. 33, 121–129. doi: 10.1016/j.tins.2010.01.001, PMID: 20138375 Khandaker G. M. Zuber V. Rees J. Carvalho L. Mason A. M. Foley C. N. . (2020). Shared mechanisms between coronary heart disease and depression: findings from a large UK general population-based cohort. Mol. Psychiatry 25, 1477–1486. doi: 10.1038/s41380-019-0395-3, PMID: 30886334 Kim J. Kim J. H. Chang K. A. (2021). Sex difference in peripheral inflammatory biomarkers in drug-Naïve patients with major depression in young adulthood. Biomedicines 9:708. doi: 10.3390/biomedicines9070708, PMID: 34206551 Köhler C. A. Freitas T. H. Maes M. de Andrade N. Q. Liu C. S. Fernandes B. S. . (2017). Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr. Scand. 135, 373–387. doi: 10.1111/acps.12698, PMID: 28122130 Köhler C. A. Freitas T. H. Stubbs B. Maes M. Solmi M. Veronese N. . (2018). Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and Meta-analysis. Mol. Neurobiol. 55, 4195–4206. doi: 10.1007/s12035-017-0632-1 Lai B. P. Tang A. K. Lee D. T. Yip A. S. K. Chung T. K. H. (2010). Detecting postnatal depression in Chinese men: a comparison of three instruments. Psychiatry Res. 180, 80–85. doi: 10.1016/j.psychres.2009.07.015, PMID: 20493548 Lamers F. Milaneschi Y. Vinkers C. H. Schoevers R. A. Giltay E. J. Penninx B. W. J. H. (2020). Depression profilers and immuno-metabolic dysregulation: longitudinal results from the NESDA study. Brain Behav. Immun. 88, 174–183. doi: 10.1016/j.bbi.2020.04.002, PMID: 32272220 Levine J. Panchalingam K. Rapoport A. Gershon S. McClure R. J. Pettegrew J. W. (2000). Increased cerebrospinal fluid glutamine levels in depressed patients. Biol. Psychiatry 47, 586–593. doi: 10.1016/S0006-3223(99)00284-X, PMID: 10745050 Liao J. W. Yang H. Ma P. Wen J. Ma P. Li K. . (2020). Levels of serum glutamate and gamma-aminobutyricacid system in patients with depression. Chin. Ment. Health J. 34, 87–91. doi: 10.3969/j.issn.1000-6729.2020.2002 Liu J. C. (2005). Clinical control analysis of urban and rural patients with somatization disorder. J Clin Psy. 15:295. Liu Y. Ho R. C. Mak A. (2012). Interleukin (IL)-6, tumour necrosis factor alpha (TNF-α) and soluble interleukin-2 receptors (sIL-2R) are elevated in patients with major depressive disorder: a meta-analysis and meta-regression. J. Affect. Disord. 139, 230–239. doi: 10.1016/j.jad.2011.08.003, PMID: 21872339 Liu M. Ma W. He Y. Sun Z. Yang J. (2023). Recent Progress in mass spectrometry-based metabolomics in major depressive disorder research. Molecules 28:7430. doi: 10.3390/molecules28217430, PMID: 37959849 Liu C. C. Wu Y. F. Feng G. M. Gao X. X. Zhou Y. Z. Hou W. J. . (2015). Plasma-metabolite-biomarkers for the therapeutic response in depressed patients by the traditional Chinese medicine formula Xiaoyaosan: a (1)H NMR-based metabolomics approach. J. Affect. Disord. 185, 156–163. doi: 10.1016/j.jad.2015.05.005, PMID: 26186531 Maes M. Verkerk R. Vandoolaeghe E. Lin A. Scharpé S. (1998). Serum levels of excitatory amino acids, serine, glycine, histidine, threonine, taurine, alanine and arginine in treatment-resistant depression: modulation by treatment with antidepressants and prediction of clinical responsivity. Acta Psychiatr. Scand. 97, 302–308. doi: 10.1111/j.1600-0447.1998.tb10004.x, PMID: 9570492 Mahar I. Bambico F. R. Mechawar N. Nobrega J. N. (2014). Stress, serotonin, and hippocampal neurogenesis in relation to depression and antidepressant effects. Neurosci. Biobehav. Rev. 38, 173–192. doi: 10.1016/j.neubiorev.2013.11.009, PMID: 24300695 Matsui T. Sekiguchi M. Hashimoto A. Tomita U. Nishikawa T. Wada K. (1995). Functional comparison of D-serine and glycine in rodents: the effect on cloned NMDA receptors and the extracellular concentration. J. Neurochem. 65, 454–458. doi: 10.1046/j.1471-4159.1995.65010454.x, PMID: 7790891 Mauri M. C. Ferrara A. Boscati L. Bravin S. Zamberlan F. Alecci M. . (1998). Plasma and platelet amino acid concentrations in patients affected by major depression and under fluvoxamine treatment. Neuropsychobiology 37, 124–129. doi: 10.1159/000026491 McGlinchey J. B. Zimmerman M. Posternak A. Maes M. (2006). The impact of gender, age and depressed state on patients’ perspectives of remission. J. Affect. Disord. 95, 79–84. doi: 10.1016/j.jad.2006.04.021, PMID: 16753224 Menard C. Pfau M. L. Hodes G. E. Kana V. Wang V. X. Bouchard S. . (2017). Social stress induces neurovascular pathology promoting depression. Nat. Neurosci. 20, 1752–1760. doi: 10.1038/s41593-017-0010-3, PMID: 29184215 Milaneschi Y. Lamers F. Peyrot W. J. Abdellaoui A. Willemsen G. Hottenga J. J. . (2016). Polygenic dissection of major depression clinical heterogeneity. Mol. Psychiatry 21, 516–522. doi: 10.1038/mp.2015.86, PMID: 26122587 Milaneschi Y. Lamers F. Peyrot W. J. Baune B. T. Breen G. Dehghan A. . (2017). Genetic Association of Major Depression with Atypical Features and Obesity-Related Immunometabolic Dysregulations. JAMA Psychiatry 74, 1214–1225. doi: 10.1001/jamapsychiatry.2017.3016, PMID: 29049554 Neame S. Safory H. Radzishevsky I. Touitou A. Marchesani F. Marchetti M. . (2019). The NMDA receptor activation by d-serine and glycine is controlled by an astrocytic Phgdh-dependent serine shuttle. Proc. Natl. Acad. Sci. USA 116, 20736–20742. doi: 10.1073/pnas.1909458116, PMID: 31548413 Nowak G. Szewczyk B. Sadlik K. Piekoszewski W. Trela F. Florek E. . (2003). Reduced potency of zinc to interact with NMDA receptors in hippocampal tissue of suicide victims. Pol. J. Pharmacol. 55, 455–459., PMID: 14506326 Ogawa S. Koga N. Hattori K. Matsuo J. Ota M. Hori H. . (2018). Plasma amino acid profile in major depressive disorder: analyses in two independent case-control sample sets. J. Psychiatr. Res. 96, 23–32. doi: 10.1016/j.jpsychires.2017.09.014, PMID: 28950111 Oliveira Miranda D. Soares de Lima T. A. Ribeiro Azevedo L. Feres O. Ribeiro da Rocha J. J. Pereira-da-Silva G. (2014). Proinflammatory cytokines correlate with depression and anxiety in colorectal cancer patients. Biomed. Res. Int. 2014:739650, 1–5. doi: 10.1155/2014/739650, PMID: 25309921 Ouwens M. A. van Strien T. van Leeuwe J. F. (2009). Possible pathways between depression, emotional and external eating. A structural equation model. Appetite 53, 245–248. doi: 10.1016/j.appet.2009.06.001, PMID: 19505515 Pallavi P. Sagar R. Mehta M. Sharma S. Subramanium A. Shamshi F. . (2015). Serum cytokines and anxiety in adolescent depression patients: gender effect. Psychiatry Res. 229, 374–380. doi: 10.1016/j.psychres.2015.06.036, PMID: 26163725 Papakostas G. I. Ionescu D. F. (2015). Towards new mechanisms: an update on therapeutics for treatment-resistant major depressive disorder. Mol. Psychiatry 20, 1142–1150. doi: 10.1038/mp.2015.92, PMID: 26148812 Papouin T. Henneberger C. Rusakov D. A. Oliet S. H. R. (2017). Astroglial versus neuronal D-serine: fact checking. Trends Neurosci. 40, 517–520. doi: 10.1016/j.tins.2017.05.007 Petzold A. Fraser C. L. Abegg M. Alroughani R. Alshowaeir D. Alvarenga R. . (2022). Diagnosis and classification of optic neuritis. Lancet Neurol. 21, 1120–1134. doi: 10.1016/S1474-4422(22)00200-9, PMID: 36179757 Peyrovian B. Rosenblat J. D. Pan Z. Iacobucci M. Brietzke E. McIntyre R. S. (2019). The glycine site of NMDA receptors: a target for cognitive enhancement in psychiatric disorders. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 92, 387–404. doi: 10.1016/j.pnpbp.2019.02.001, PMID: 30738126 Portella M. J. de Diego-Adeliño J. Gómez-Ansón B. Morgan-Ferrando R. Vives Y. Puigdemont D. . (2011). Ventromedial prefrontal spectroscopic abnormalities over the course of depression: a comparison among first episode, remitted recurrent and chronic patients. J. Psychiatr. Res. 45, 427–434. doi: 10.1016/j.jpsychires.2010.08.010, PMID: 20875647 Rădulescu I. Drăgoi A. M. Trifu S. C. Cristea M. (2021). Neuroplasticity and depression: rewiring the brain's networks through pharmacological therapy (review). Exp. Ther. Med. 22:1131. doi: 10.3892/etm.2021.10565, PMID: 34504581 Rossi S. Studer V. Motta C. Polidoro S. Perugini J. Macchiarulo G. . (2017). Neuroinflammation drives anxiety and depression in relapsing-remitting multiple sclerosis. Neurology 89, 1338–1347. doi: 10.1212/WNL.0000000000004411, PMID: 28842450 Shinohara R. Aghajanian G. K. Abdallah C. G. (2021). Neurobiology of the rapid-acting antidepressant effects of ketamine: impact and opportunities. Biol. Psychiatry 90, 85–95. doi: 10.1016/j.biopsych.2020.12.006, PMID: 33568318 Steffens D. C. Jiang W. Krishnan K. R. Karoly E. D. Mitchell M. W. O’Connor C. M. . (2010). Metabolomic differences in heart failure patients with and without major depression. J. Geriatr. Psychiatry Neurol. 23, 138–146. doi: 10.1177/0891988709358592, PMID: 20101071 Sumiyoshi T. Anil A. E. Jin D. Jayathilake K. Lee M. Meltzer H. Y. (2004). Plasma glycine and serine levels in schizophrenia compared to normal controls and major depression: relation to negative symptoms. Int. J. Neuropsychopharmacol. 7, 1–8. doi: 10.1017/S1461145703003900, PMID: 14720317 Tang M. H. Zhang M. Y. (1999). Hamilton Anxiety Scale. Chinese Mental Health Journal. 8, 253–255. Tian Y. Zhang J. Liu J. Wang R. Wang Y. Chen X. . (2021). Sleep quality in patients with depressive disorder in Shandong province: a case-control study. J. Psychiatry 34, 534–538. doi: 10.3969/j.issn.2095-9346.2021.06.011 Tilleux S. Hermans E. (2007). Neuroinflammation and regulation of glial glutamate uptake in neurological disorders. J. Neurosci. Res. 85, 2059–2070. doi: 10.1002/jnr.21325, PMID: 17497670 Tomás C. C. Oliveira E. Sousa D. (2016). Proceedings of the 3rd IPLeiria's international health congress: Leiria, Portugal. 6-7 may 2016. BMC Health Serv. Res. 16:200. doi: 10.1186/s12913-016-1423-5 Tovote P. Fadok J. P. Lüthi A. (2015). Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331. doi: 10.1038/nrn3945, PMID: 25991441 Tran B. X. Ha G. H. Nguyen D. N. Nguyen T. P. do H. T. Latkin C. A. . (2020). Global mapping of interventions to improve quality of life of patients with depression during 1990-2018. Qual. Life Res. 29, 2333–2343. doi: 10.1007/s11136-020-02512-7, PMID: 32347440 Umemori J. Winkel F. Didio G. Llach Pou M. Castrén E. (2018). iPlasticity: induced juvenile-like plasticity in the adult brain as a mechanism of antidepressants. Psychiatry Clin. Neurosci. 72, 633–653. doi: 10.1111/pcn.12683, PMID: 29802758 Van der Auwera S. Teumer A. Hertel J. Homuth G. Völker U. Lucht M. J. . (2016). The inverse link between genetic risk for schizophrenia and migraine through NMDA (N-methyl-D-aspartate) receptor activation via D-serine. Eur. Neuropsychopharmacol. 26, 1507–1515. doi: 10.1016/j.euroneuro.2016.03.019, PMID: 27394076 Vicent-Gil M. Serra-Blasco M. Navarra-Ventura G. (2023). In pursuit of full recovery in major depressive disorder. Eur Arch Psy Clin 273, 1095–1104. doi: 10.1007/s00406-022-01487-5 Vieira M. M. Ferreira T. B. Pacheco P. A. Barros P. O. Almeida C. R. M. Araújo-Lima C. F. . (2010). Enhanced Th17 phenotype in individuals with generalized anxiety disorder. J. Neuroimmunol. 229, 212–218. doi: 10.1016/j.jneuroim.2010.07.018, PMID: 20709414 Walker W. H. Walton J. C. DeVries A. C. Nelson R. J. (2020). Circadian rhythm disruption and mental health. Transl. Psychiatry 10:28. doi: 10.1038/s41398-020-0694-0, PMID: 32066704 Waziri R. Mott J. Wilcox J. (1985). Differentiation of psychotic from nonpsychotic depression by a biological marker. J. Affect. Disord. 9, 175–180. doi: 10.1016/0165-0327(85)90098-9, PMID: 2932492 Wei I. H. Chen K. T. Tsai M. H. Wu C. H. Lane H. Y. Huang C. C. (2017). Acute amino acid d-serine administration, similar to ketamine, produces antidepressant-like effects through identical mechanisms. J. Agric. Food Chem. 65, 10792–10803. doi: 10.1021/acs.jafc.7b04217, PMID: 29161812 Weinberger J. F. Raison C. L. Rye D. B. Montague A. R. Woolwine B. J. Felger J. C. . (2015). Inhibition of tumor necrosis factor improves sleep continuity in patients with treatment resistant depression and high inflammation. Brain Behav. Immun. 47, 193–200. doi: 10.1016/j.bbi.2014.12.016, PMID: 25529904 Wolosker H. Balu D. T. (2020). D-serine as the gatekeeper of NMDA receptor activity: implications for the pharmacologic management of anxiety disorders. Transl. Psychiatry 10:184. doi: 10.1038/s41398-020-00870-x, PMID: 32518273 Wolosker H. Balu D. T. Coyle J. T. (2016). The rise and fall of the d-serine-mediated Gliotransmission hypothesis. Trends Neurosci. 39, 712–721. doi: 10.1016/j.tins.2016.09.007, PMID: 27742076 Wu J. Lu A. D. Zhang L. P. Zuo Y. X. Jia Y. P. (2019). Study of clinical outcome and prognosis in pediatric core binding factor-acute myeloid leukemia. Zhonghua Xue Ye Xue Za Zhi 40, 52–57. doi: 10.3760/cma.j.issn.0253-2727.2019.01.010, PMID: 30704229 Yu X. Wang S. Wu W. Chang H. Shan P. Yang L. . (2023). Exploring new mechanism of depression from the effects of virus on nerve cells. Cells 12:1767. doi: 10.3390/cells12131767 Zhang W. Wang W. Zhou Y. Wang J. (2023). A comparative study of blood and hippocampal D-serine change patterns in drug-naïve patients and animal models of depression. Psychiatry Res. 348:116453. doi: 10.1016/j.psychres.2025.116453 Zimmerman M. McGlinchey J. B. Posternak M. A. Friedman M. Attiullah N. Boerescu D. (2006). How should remission from depression be defined? The depressed patient’s perspective. Am. J. Psychiatry 163, 148–150. doi: 10.1176/appi.ajp.163.1.148, PMID: 16390903 Zoicas I. Kornhuber J. (2019). The role of the N-methyl-D-aspartate receptors in social behavior in rodents. Int. J. Mol. Sci. 20:5599. doi: 10.3390/ijms20225599, PMID: 31717513 Zou W. Feng R. Yang Y. (2018). Changes in the serum levels of inflammatory cytokines in antidepressant drug-naïve patients with major depression. PLoS One 13:e0197267. doi: 10.1371/journal.pone.0197267, PMID: 29856741

Edited by: Luwei Xiao, Fudan University, China

Reviewed by: Maryam Khalili, Kerman University of Medical Sciences, Iran

Harsha Halahalli, Nitte University, India