Serum proteomic changes related to residual impairment in remittent depression are associated with immune and inflammatory processes | Scientific Reports

Scientific Reports volume 14, Article number: 24482 (2024) Cite this article

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In patients with major depressive disorder, various functional areas are impaired, negatively impacting the quality of life. Remission can restore pre-depression functions; however, some patients may still have residual impairments. Distinguishing between near-normal recovery and residual impairment helps identify those at a high risk of relapse risk and helps tailor treatment. Accordingly, we aimed to discover and validate biomarkers that distinguish between near-normal recovery and residual impairment in remission states through serum proteome analysis. Pooled serum and individual serum samples from three groups (depression status, remission status with residual impairment, and remission status with normal recovery) were analyzed using liquid chromatography-tandem mass spectrometry. The combination of four proteins—antithrombin-III, serum amyloid A4 protein, C1q subcomponent subunit B, and serum amyloid P-component—was selected as a candidate biomarker. The trend of protein changes suggests complement C1q subcomponent subunit B and serum amyloid P-component as potential biomarkers for distinguishing remission from residual impairment. Changes in complement C1q subcomponent subunit B and serum amyloid P-component suggest that the complement system and inflammation-related immune mechanisms are associated with residual impairment in remittent major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric condition characterized by symptoms such as depressed mood, anhedonia, appetite changes, and suicidal thoughts. These symptoms impair psychosocial, academic, and occupational functioning1. MDD is widespread globally and ranks among the leading causes of disability 2,3,4. Several hypotheses have been proposed to explain its etiology, including hypothalamic–pituitary–adrenal axis dysfunction, inflammation, and genetic and epigenetic abnormalities. However, the pathophysiological mechanisms of MDD remain to be elucidated 2,5. This incomplete understanding, along with the absence of definitive biomarkers, hinders the development of effective diagnostic and therapeutic strategies for MDD.

The primary treatment strategy for MDD is antidepressant therapy6, with complete remission as the therapeutic goal7,8. Clinically, complete remission is defined by a Hamilton Depression Rating Scale-17 (HAMD-17) score ≤ 7, indicating the absence or minimal presence of depressive symptoms8,9. MDD impairs various functional areas, including daily activities, interpersonal relationships, and work abilities. Patients who achieve remission can expect to recover pre-depression functions. Patients who achieve remission are less likely to relapse and have better psychological and occupational functioning than those in non-remission. However, even with significant improvement in depressive symptoms, some patients may still experience persistent functional impairment10,11. A study examining cognitive impairment in remitted and non-remitted patients with MDD12 discovered that patients who achieved remission after antidepressant treatment showed no significant differences from controls in overall cognitive function. However, 24% of these remitted patients exhibited at least one type of cognitive impairment, particularly in executive function and attention.

Residual mental and physical symptoms, including lack of energy, sleep disorders, and cognitive deficits, are associated with functional impairments 13,14. Monitoring normal functional recovery in remission is crucial, as residual functional impairments in remitted patients increase the risk of relapse 8. Therefore, differentiating between remitted patients with near-normal recovery and those with residual impairments can help identify high-risk individuals. This identification enables the development of customized treatment strategies to rapidly reduce relapse risk based on individual treatment prognosis.

Therefore, this study aimed to identify and verify biomarkers that can distinguish between a near-normal recovery remission state and a remission state with residual impairment through serum proteome analysis. These biomarkers can help identify remission patients with residual impairment and those at a high risk of relapse, thereby aiding in the development of rapid and precise treatment strategies.

The Institutional Review Board of Eulji University approved this study (approval number: EU23-46, November 6, 2023), which was conducted in accordance with the latest version of the Declaration of Helsinki. Written informed consent was obtained from all participants. The study participants were a naturalistic cohort of outpatients with MDD aged 19 years or older with no history of head injury. Depression severity was assessed using the HAMD-17. Patients with depressive status were diagnosed based on a HAMD-17 score > 7. Patients in remission were classified using a HAMD-17 score of ≤ 7. Residual impairments in patients in remission referred to social, physical, and occupational functions that were less functional than before, including social relationships, work life, and learning ability, rather than sequelae such as cognitive function and sleep disturbances. These impairments were clinically diagnosed based on the comprehensive assessment of clinicians. The discovery and validation sets comprised 25 and 55 patients with MDD, respectively, and were divided into three groups: depression status (D), remission status with residual impairment (R1), and remission status with normal recovery (R0). Some patients in group D and all patients in the R1 and R0 groups were receiving medication, including antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), and serotonin modulators, either alone or in combination. Some patients were also taking additional antipsychotic medications. Table 1 presents the patients’ demographic information. To obtain serum from each participant, blood was collected in a vacuum collection tube without added anticoagulant and centrifuged at 4000 × g for 5 min to separate serum from the coagulated blood. Isolated serum was stored at − 80 ℃. Samples pooled from equal amounts of serum from all participants were used to construct the ion library (Fig. 1).

Experiment flowchart. After obtaining individual sera from experimental participants, pooled sera were prepared, and serum proteins were digested into peptides for mass spectrometry. Protein identification and biomarker discovery were performed using DDA data from pooled serum and DIA data from individual serum. MRM validation was subsequently performed on the selected marker candidates.

To deplete highly abundant proteins, a sample mixed with serum and multiple-affinity removal system buffer A at a ratio of 1:3 was injected into a multiple-affinity removal system liquid chromatography (LC) column-human 6 (human 6-HC, 4.6 × 50 mm; Agilent Technologies, Santa Clara, CA, USA). The low-abundance protein fraction was concentrated using a Nanosep Centrifugal Device with an Omega Membrane 3 K (Pall Corporation, Port Washington, NY, USA) and subsequently vacuum dried using a vacuum concentrator (Scan Vac, LaboGene, Lynge, Denmark). The vacuum-dried samples were re-suspended in buffer, and the protein concentration of each sample was determined using a bicinchoninic acid assay (Thermo Fisher Scientific, Cleveland, OH, USA). Each sample and the pooled sample contained 100 μg and 1 mg of serum protein, respectively. For reduction, samples were treated with 5 mM Tris (2-carboxyethyl) phosphine (Pierce, Rockford, IL, USA) at 37 ℃ and 400 rpm for 30 min. Subsequently, 15 mM iodoacetamide (Sigma-Aldrich, St. Louis, MO, USA) was added for alkylation, and the mixture was incubated at 400 rpm at 25 ℃ for 1 h in the dark. For digestion into peptides, mass spectrometry (MS)-grade trypsin (Promega, Madison, WI, USA) was treated at 37 ℃ and 800 rpm for 15 h. Subsequently, cleaning and desalting were conducted using a C18 cartridge (Waters, Milford, MA, USA). The digested pooled sample was fractionated into 12 fractions using an OFFGEL Fractionator (Agilent Technologies) and Immobiline DryStrip (pH 3–10; GE Healthcare, Madison, WI, USA).

Each sample and 12 fractions of pooling samples were analyzed using the Nano-LC system Ekspert nLC415 (Eksigent Technologies, Dublin, CA, USA) coupled to the AB SCIEX 5600 triple TOF mass spectrometer (AB SCIEX, Concord, Canada). Each sample was scanned using the sequential window acquisition of all theoretical mass spectra acquisition, which is based on the data-independent acquisition method, and 12 fractions of the pooled sample were scanned using the data-dependent acquisition method. Mobile phases A and B were 0.1% formic acid in water and acetonitrile, respectively. The NanoLC trap column (0.5 mm × 350 μm; 3 μm; Eksigent Technologies) and Chrom XP NanoLC column (150 mm × 75 μm; 3 μm; Eksigent Technologies) were used. The data were analyzed in the positive ion mode, and the flow rate was 300 nL/min. The gradient program employed included the following steps: the mobile phase solution B transitioned from 5 to 40% over the initial 105 min, shifted from 40 to 90% in just 0.5 min, remained at 90% for 6 min, rapidly decreased from 90 to 5% in 0.5 min, and finally maintained at 5% for the last 8 min.

The inclusion criteria of the peptides used for quantification were as follows: peptides without (1) a mis-cleaved site, (2) modifications, and (3) M amino acids due to oxidation; and (4) peptides with 7–15 sequence lengths. Synthetic peptides for the selected peptide sequence were purchased (Peptron Inc., Daejeon, Korea). The transitions of peptides for quantification were extracted using Skyline software (https://proteome.gs.washington.edu/software/skyline.). The declustering potential and collision energy were optimized for each peptide transition, and the entrance potential and collision exit potential were set to fixed values of 10 and 12, respectively. MRM was performed using Sciex Exion LC and a QTRAP 5500 mass spectrometer (AB Sciex). The sample injection volume was 5 μL. An ACQUITY UPLC BEH C18 VanGuard pre-column (130 Å, 1.7 μm, 2.1 mm × 5 mm, Waters) combined with an ACQUITY UPLC BEH C18 column (130 Å, 1.7 μm, 2.1 mm × 150 mm, Waters) was used as an analytical column. Mobile phases A and B were the same as those in Sect. 2.2. The flow rate was 250 μL/min, and the final chromatogram was generated for 30 min with the following gradient: 1 min, 5% B; 20 min, 40% B; 25 min, 90% B; and 30 min, 5% B. The source parameters of the acquisition method included curtain gas = 30 psi, low collision gas, ion spray = 5500 V, 400℃, ion source gas 1 = 40 psi, and ion source 2 = 60 psi.

The raw data from 12 fraction profiling pooled samples were imported into ProteinPilot (AB SCIEX) for protein identification and ion library construction. Subsequently, library matching was performed with the raw data of each sample using PeakView (v.2.2, AB SCIEX), and MarkerView (v.1.3.1, AB SCIEX) was used for data normalization and t-test analysis. For the t-test conducted to explore proteins with differences between groups, the criteria of p < 0.05 and fold-change ≥ 1.2 were applied. GraphPad Prism software (v.8.4.2, La Jolla, CA, USA) was used for outlier confirmation, data visualization, normality testing, difference testing, and receiver operating characteristic (ROC) curve analysis. The Shapiro–Wilk test was used to assess the normality of the data, and parametric or nonparametric statistics were selected accordingly based on the normality results. To compare the quantitative results of MRM for biomarker validation, analysis of variance (ANOVA) with post-hoc tests (Tukey’s and Dunn’s multiple comparisons tests) was used. Additionally, multiple logistic regression was applied for ROC analysis.

We compared serum protein profiles among the D, R1, and R0 groups to discover biomarkers that can distinguish between remission states close to normal recovery and those with residual impairment. Eighty-nine proteins were identified through ion library matching, and the group average expression levels were confirmed as a heat map (Fig. 2A). Through t-tests between groups, the differentially expressed proteins (DEPs) that had a p-value < 0.05 and fold-change ≥ 1.2 were as follows: four for D vs. R1, ten for D vs. R0, and three for R0 vs. R1. No protein was common across all three comparative combinations, but intersections were confirmed between D vs. R1 and D vs. R0 and between D vs. R0 and R0 vs. R1. Among DEPs, antithrombin-III (ANT3) and serum amyloid A4 protein (SAA4) correspond to the intersection of D vs. R1 and D vs. R0. These two proteins showed a significant decreasing trend in the remission state (whether impairment remained) group compared with the MDD group (Figs. 2B–C). The complement C1q subcomponent subunit B (C1QB) and serum amyloid P-component (SAP) correspond to the intersection of D vs. R0 and R0 vs. R1 (Figs. 2D–E). These proteins exhibited a significant increase in the remission state, which is closer to normal recovery, compared with the depressive state and remission state with residual impairment. The combination of these four proteins was selected as a candidate biomarker because it can distinguish between depressive and remission states, as well as between the complete remission state, which is closer to normal, and the remission state with residual impairment (Table 2).

Differentially expressed proteins (DEPs) between groups. (A) Heatmap showing the average expression levels of the identified proteins in the depression status (D), remission status with residual impairment (R1), and remission status with normal recovery (R0) groups. DEPs had a p-value < 0.05 and fold-change < 1.2. (B) Antithrombin-III and (C) serum amyloid A4 protein are proteins that belong to the intersection between D vs. R0 and D vs. R1. (D) Complement C1q subcomponent subunit B and (E) serum amyloid P-component are proteins that belong to the intersection between D vs. R0 and R0 vs. R1. The scatter plot represents mean ± SD. *p < 0.05, **p < 0.01.

MRM validation was performed for four candidate biomarker proteins, and Supplementary Table 1 presents the transitions for each peptide sequence. Figures 3A–D show the quantitative results of four proteins. No significant differences in ANT3 and SAA4 proteins were observed between groups. C1QB and SAP tended to increase in concentration, being lowest in the D group, followed by the R1 and R0 groups, and the order of increase in C1QB concentration was consistent with the discovery results. Additionally, both proteins showed significant differences between the D and R0 groups (C1QB: p = 0.002, SAP: p = 0.047). For C1QB, a significant difference was also observed between the D and R1 groups (p = 0.003). Furthermore, the discrimination ability of the combination of C1QB and SAP among the three groups was evaluated using ROC analysis (Fig. 3E and Supplementary Table 2). The area under the curve demonstrated the best discrimination ability for D vs. R0 to 0.8338, followed by D vs. R1 to 0.7812 and R0 vs. R1 to 0.6561.

MRM quantification of protein candidates and discrimination ability evaluation. (A–D) Scatter plot showing MRM quantification of four protein candidates: antithrombin-III, serum amyloid A4 protein, complement C 1q subcomponent subunit B (C1QB), and serum amyloid P-component. The scatter plot represents mean ± SD. *p < 0.05, **p < 0.01. (E) Discrimination performance evaluated by the combination of C1QB and serum amyloid P-component.

This study aimed to identify and validate biomarkers that can distinguish between remission states close to normal recovery and those with residual impairments through serum protein analysis. Therefore, we confirmed DEPs in the comparison between the D, R1, and R0 groups, as well as common DEP between-group comparisons. We discovered that the combination of ANT3, SAA4, C1QB, and SAP can contribute to the distinction between disease and remission states and between remission with residual impairment and remission state close to normal. MRM validation revealed that C1QB and SAP proteins showed significant differences between patients with depression and patients who achieved normal remission. The R0 group had the highest levels, followed by the R1 and D groups, respectively. This pattern is consistent with the discovery results, indicating the potential of C1QB and SAP as biomarkers for distinguishing between a remission state close to normal recovery and those with residual impairments. If further validation confirms their effectiveness based on statistical significance, these protein markers will be useful for identifying remission patients with residual impairments at high risk of relapse and for developing faster, more precise treatment strategies.

Previous studies have reported that the four identified proteins are associated with depression. ANT3 is a glycoprotein synthesized in the liver and acts as a major natural anticoagulant by inhibiting the coagulation cascade15. Several studies have reported an increase in plasma ANT3 in MDD16,17. Following occipital repetitive transcranial magnetic stimulation (rTMS) therapy in patients with MDD, blood ANT3 levels significantly decreased, demonstrating good predictive performance for treatment response after 5 days of rTMS and a positive correlation with severity as assessed using HAMD18. Observations of hypercoagulability in patients with depression and reports of chronic stressors inducing low-grade coagulation suggest that ANT3-related anti-inflammatory and anticoagulant processes may influence the pathophysiology of depression17. Serum amyloid A (SAA) is a family of apolipoproteins related to high-density lipoproteins and is mainly synthesized in the liver19. SAA4 is another subtype of SAA, and SAA subtypes are expressed at different levels or in response to inflammation19. SAA is a multifunctional acute-phase protein that is induced by interleukin-1β and interleukin-6 and is involved in cholesterol transport and metabolism; it regulates inflammatory responses through anti-inflammatory and pro-inflammatory activities20. Serum amyloid A1 (SAA1), a subtype of SAA, has shown increased levels in the pooled serum of the depression group compared with the control group21. A cross-sectional study of inflammatory markers in psychiatric outpatients showed that higher HAMD-17 scores were significantly associated with increased SAA levels22. Another study observed elevated SAA in the plasma of remitted, untreated women with MDD, implying that inflammatory conditions may persist, even in clinical remission23. These findings suggest that persistent low-grade inflammation, even in clinical remission, may hinder the recovery of damaged functions and contribute to residual impairment. ANT3 and SAA4 did not show significant results in the validation; however, their potential as depression markers is supported by existing evidence, highlighting the need for further validation.

In this study, C1QB and SAMP proteins, which show potential for distinguishing between depressive and remission states, as well as between remission with residual impairment and near-normal remission, are also related to inflammation and immune response. C1QB is a subunit that, along with C1QA and C1QC, constitutes C1q, a component of C124. C1QB is a marker of drug treatment efficacy for MDD, showing the highest levels in the control group and decreasing significantly in patients with depression treated with drugs and untreated patients, respectively25. C1q is an innate immune pattern recognition receptor protein that activates the classical complement pathway and plays a crucial role in preventing autoimmune diseases through complement opsonization and maintaining normal tissue homeostasis26. Furthermore, C1q can bind to receptors on macrophages, regulating their phenotype and inflammatory responses by, for example, inducing cytokine production and removing oxidized low-density lipoproteins26,27. C1q deficiency is associated with autoimmune diseases and problems with overall complement system activation are associated with inflammation and inflammation-related diseases26. Elevation of C1q in the central nervous system is an early response to damage. The interaction between C1q and microglial cells can effectively suppress neuroinflammation by reducing pro-inflammatory cytokine secretion, indicating that complement system-mediated neuroinflammation may involve C1q mediation28. In addition to the finding that C1QB, a subunit of C1q, increases as patients approach normal recovery, a previous study29 has shown that C1q knockout mice exhibited electric foot shock-induced learned helplessness behavior, with significantly reduced C1q messenger ribonucleic acid levels in the frontal cortex. These results suggest that complement pathway components and their activity may influence the pathophysiological mechanisms of depression associated with inflammation. Furthermore, the production of C1 complexes in neurons in the central nervous system in response to inflammatory pathways and the activation of microglia and astrocytes through C3 can increase neurotoxicity, leading to neuronal damage and death, which may contribute to synaptic loss and cognitive impairment28. Inflammation-related structural and functional changes can occur in the hippocampus and prefrontal cortex, with chronic immune dysregulation or inflammation and excessive release of pro-inflammatory cytokines being key pathological features of relapsed MDD30. Therefore, residual social, occupational, physical, and functional impairments in remitted depression may be related to extensive neurological system damage due to persistent inflammation. These pathophysiological effects could be associated with a higher risk of depression relapse.

SAP, also known as pentraxin-2, is a glycoprotein belonging to the pentraxin protein family and is synthesized and secreted by hepatocytes under normal conditions31. As a pattern recognition protein, SAP is involved in innate immunity, similar to C-reactive protein and pentraxin-3, and can activate the classical complement pathway through interaction with C1q32. SAP is also a ubiquitous component of human amyloid deposits, contributing to the persistence and formation of amyloid, and is associated with Alzheimer’s disease33. A study comparing plasma levels of SAP at baseline, 2 weeks, and 12 weeks in patients with MDD receiving escitalopram monotherapy (an SSRI treatment) for 8 to 12 weeks34 discovered a significant increase in SAP levels in the depression group compared with the control group at baseline. Additionally, SAP levels significantly decreased over the course of treatment. The relationship between changes in SAP levels and the alleviation of depression symptoms remains unclear. The proposed biological mechanism for increased SAP in depression is as follows: 1) Aβ increases through direct interaction with Aβ peptide at the carbohydrate-binding site of SAP; 2) central SAP, Aβ, and complement C1q attract and activate microglia; and 3) peripheral SAP regulates macrophage activation through Fcγ receptors34. This mechanism may enhance the release of inflammatory cytokines, promoting macrophage activation and neuronal cell death34. In this study, SAP levels tended to increase as remission approached normal; this is potentially related to inflammation. Decreased SAP in the depressed state may indicate abnormal complement activity and immune processes involving C1q. Symptom remission might result from immune responses such as anti-inflammatory activities, leading to increased SAP levels for further immune response activity. Furthermore, the neurobiological mechanisms that preserve cognitive function in MDD remain to be fully elucidated. However, the degree and severity of cognitive dysfunction are mediated by the location of inflammation, neurotoxicity, and apoptosis11. This finding suggests that residual social, physical, and occupational impairment in remitted depressed patients may result from cognitive dysfunction associated with immune and inflammatory response mechanisms.

The four proteins discovered in this study are related to the complement system, inflammation, and coagulation processes. The pathophysiology of inflammation activated by various stress factors is closely linked to depression35,36,37. Among the immune processes associated with inflammation, evidence of altered levels of immune-related proteins suggests that residual social, occupational, and physical dysfunction in remitted MDD may stem from cognitive and functional impairments driven by inflammatory and immune response mechanisms (Fig. 4). The interaction between the complement and coagulation systems can contribute to various diseases38. Targeted research on these interactions can help explore the relationships and molecular mechanisms between depression and comorbidities that have a bidirectional relationship with depression. SAA is a well-known indicator of cardiovascular disease and interacts with high-density lipoprotein function to alter the biological effects under certain clinical conditions39. Additionally, studies have discovered a higher prevalence of MDD in patients with hypercholesterolemia40 and higher levels of total cholesterol, apolipoprotein B, and high-density lipoprotein-cholesterol in the MDD group compared with the control group41. These findings highlight the complex and bidirectional association between depression and cardiovascular disease. As inflammatory cells, platelets can express several complement receptors, and the binding of these receptors to C1q causes platelet activation and coagulation38. SAP is involved in C1q-mediated complement pathway activity and plays an important role in cardiovascular processes such as fibrosis, coagulation, and inflammation31. ANT3 has anticoagulant and inflammatory response control functions, and the observation of high procoagulant index and fibrinogen expression in patients with MDD in remission suggests a relationship between MDD, improved coagulation promotion, and cardiovascular disease risk18.

Putative schematic diagram of residual impairment in major depressive disorder remission. Inflammation can be activated in response to stressors. The four proteins discovered in this study––antithrombin-III (ANT3), serum amyloid A4 protein (SAA4), C1QB, and serum amyloid P-component––are related to inflammation, complement system, and anticoagulation, and these pathways interact with each other. Potentially, inflammation may contribute to the pathogenesis of depression, comprehensively affecting the peripheral-blood–brain barrier (BBB)-brain zone. The periphery and brain communicate bidirectionally. Inflammation that persists even in remission of major depressive disorder can impede recovery, resulting in residual symptoms (such as cognitive dysfunction) and, consequently, occupational, physical, and social impairments. Changes in inflammation, the complement system, and coagulation pathways may be associated with depression and bidirectional comorbidities.

This preliminary study identified potential biomarker candidates for screening patients with residual impairments in depression; however, there are limitations in the interpretation and discussion. The study participants consisted of a small number of psychiatric outpatients. As a naturalistic cohort with less strict control over medication use and lifestyle, the study offers the advantage of producing results that are closer to clinical applicability. Patients used antidepressants either alone or in combination with other classes of antidepressants, and some were prescribed additional antipsychotics. Various medication-related factors may affect the identified immune and inflammatory pathways. In addition to medication variables, age and the presence of comorbidities should be considered in studies involving a depressed population, as the prevalence of depression increases with age. Age differences between study participant groups may indicate different bioenvironments. In this study, group D in the validation set included more younger patients than groups R1 and R0, suggesting that age differences may have influenced the differences in biomarker levels in the validation data. Physical conditions such as cardiovascular disease, diabetes, hypertension, and chronic pain, which are common in older patients, may have a bidirectional relationship with depression42 and may share mechanisms related to the inflammatory hypothesis, one of the proposed pathophysiological mechanisms of depression. Future studies with larger sample sizes, controlled medication use (to confirm the effects of a single drug), and consideration of demographic variables (to control for confounding factors) may provide further evidence on the pathophysiological mechanisms associated with residual impairments and the treatment of depression.

The trend of protein changes between depressed states, remission states with residual impairment, and near-normal remission states suggests that C1QB and SAP could serve as biomarkers for distinguishing remission with residual impairment. Inflammatory responses can trigger depression and hinder recovery. Alterations in proteins involved in coagulation (anticoagulation), the complement system, and inflammatory response provide insight into the pathophysiological mechanisms behind the persistence of impairments in patients with MDD who have achieved remission. These findings also shed light on the bidirectional comorbidity between depression and cardiovascular disease.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

de Zwart, P. L., Jeronimus, B. F. & de Jonge, P. Empirical evidence for definitions of episode, remission, recovery, relapse and recurrence in depression: a systematic review. Epidemiol. Psychiatr. Sci. 28, 544–562 (2019).

Article Google Scholar

Cui, L. et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct. Target. Ther. 9, 1–32 (2024).

Google Scholar

Alon, N. et al. Social Determinants of Mental Health in Major Depressive Disorder: Umbrella Review of 26 Meta-analyses and Systematic Reviews. Psychiatry Res. 335, 115854 (2024).

Ramos-da-Silva, L., Carlson, P. T., Silva-Costa, L. C., Martins-de-Souza, D. & de Almeida, V. Molecular mechanisms associated with antidepressant treatment on Major Depression. Complex psychiatry 7, 49–59 (2021).

Article CAS PubMed PubMed Central Google Scholar

Milaneschi, Y., Lamers, F., Berk, M. & Penninx, B. W. Depression heterogeneity and its biological underpinnings: toward immunometabolic depression. Biol. Psychiatry 88, 369–380 (2020).

Article CAS PubMed Google Scholar

Holck, A. et al. Plasma serotonin levels are associated with antidepressant response to SSRIs. J. Affect. Disord. 250, 65–70 (2019).

Article CAS PubMed PubMed Central Google Scholar

Hung, C.-I., Lin, G., Chiang, M.-H. & Chiu, C.-Y. Metabolomics-based discrimination of patients with remitted depression from healthy controls using 1H-NMR spectroscopy. Sci. Rep. 11, 15608 (2021).

Article CAS PubMed PubMed Central Google Scholar

Parish, A. L., Gillis, B. & Anthamatten, A. Pharmacotherapy for depression and anxiety in the primary care setting. JNP-J. Nurse Pract. 19, 104556 (2023).

Article PubMed PubMed Central Google Scholar

Mendlewicz, J. Towards achieving remission in the treatment of depression. Dialogues Clin. Neurosci. 10, 371–375 (2008).

Article PubMed PubMed Central Google Scholar

Hammer-Helmich, L. et al. Functional impairment in patients with major depressive disorder: the 2-year PERFORM study. Neuropsychiatr. Dis. Treat. 14, 239–249 (2018).

Pan, Z. et al. Cognitive impairment in major depressive disorder. CNS Spectr. 24, 22–29 (2019).

Article PubMed Google Scholar

Liu, J. et al. The percentage of cognitive impairment in patients with major depressive disorder over the course of the depression: a longitudinal study. J. Affect. Disord. 329, 511–518 (2023).

Article PubMed Google Scholar

Xiao, L. et al. Comparison of residual depressive symptoms and functional impairment between fully and partially remitted patients with major depressive disorder: a multicenter study. Psychiatry Res. 261, 547–553 (2018).

Article PubMed Google Scholar

Nil, R., Luetolf, S. & Seifritz, E. Residual symptoms and functionality in depressed outpatients: a one-year observational study in Switzerland with escitalopram. J. Affect. Disord. 197, 245–250 (2016).

Article PubMed Google Scholar

Quinsey, N. S., Greedy, A. L., Bottomley, S. P., Whisstock, J. C. & Pike, R. N. Antithrombin: in control of coagulation. Int. J. Biochem. Cell Biol. 36, 386–389 (2004).

Article CAS PubMed Google Scholar

Choi, H. et al. Discovery of screening biomarkers for major depressive disorder in remission by proteomic approach. Diagnostics 11, 539 (2021).

Article CAS PubMed PubMed Central Google Scholar

Shi, Y. et al. Identifying Plasma Biomarkers with high specificity for major depressive disorder: a multi-level proteomics study. J. Affect. Disord. 277, 620–630 (2020).

Article CAS PubMed Google Scholar

Song, R. et al. Potential of antithrombin III as a biomarker of antidepressive effect in major depressive disorder. Front. Psychiatry 12, 678384 (2021).

Article PubMed PubMed Central Google Scholar

De Beer, M. et al. Characterization of constitutive human serum amyloid A protein (SAA4) as an apolipoprotein. J. Lipid Res. 36, 526–534 (1995).

Article PubMed Google Scholar

Eklund, K. K., Niemi, K. & Kovanen, P. Immune functions of serum amyloid A. Critical Reviews™ in Immunology 32, (2012).

Wang, Q. et al. iTRAQ technology-based identification of human peripheral serum proteins associated with depression. Neuroscience 330, 291–325 (2016).

Article CAS PubMed Google Scholar

Bryleva, E. et al. The acute-phase mediator serum amyloid A is associated with symptoms of depression and fatigue. Acta psychiatr. Scand. 135, 409–418 (2017).

Article CAS PubMed Google Scholar

Kling, M. A. et al. Sustained low-grade pro-inflammatory state in unmedicated, remitted women with major depressive disorder as evidenced by elevated serum levels of the acute phase proteins C-reactive protein and serum amyloid A. Biol. psychiatry 62, 309–313 (2007).

Article ADS CAS PubMed Google Scholar

Szefler, S. J., Bonilla, F. A., Akdis, C. A. & Sampson, H. Pediatric allergy: principles and practice (Third edition). 90-100 (Elsevier Health Sciences, 2016).

Lee, S., Mun, S., Lee, J. & Kang, H.-G. Discovery and validation of protein biomarkers for monitoring the effectiveness of drug treatment for major depressive disorder. J. Psychiatr. Res. 169, 7–13 (2024).

Article PubMed Google Scholar

Bohlson, S. S., O’Conner, S. D., Hulsebus, H. J., Ho, M.-M. & Fraser, D. A. Complement, c1q, and c1q-related molecules regulate macrophage polarization. Front. Immunol. 5, 107530 (2014).

Article Google Scholar

Kouser, L. et al. Emerging and novel functions of complement protein C1q. Front. Immunol. 6, 146246 (2015).

Article Google Scholar

Tang, S. et al. The complement system: a potential target for the comorbidity of chronic pain and depression. Korean J. Pain 37, 91 (2024).

Article CAS PubMed PubMed Central Google Scholar

Madeshiya, A. K., Whitehead, C., Tripathi, A. & Pillai, A. C1q deletion exacerbates stress-induced learned helplessness behavior and induces neuroinflammation in mice. Transl. Psychiatr. 12, 50 (2022).

Article CAS PubMed PubMed Central Google Scholar

Liu, C.-H. et al. Role of inflammation in depression relapse. J. neuroinflamm. 16, 1–11 (2019).

Article ADS PubMed PubMed Central Google Scholar

Xi, D. et al. SAP: structure, function, and its roles in immune-related diseases. Int. J. Cardiol. 187, 20–26 (2015).

Article PubMed Google Scholar

Ma, Y. J. et al. Heterocomplexes of mannose-binding lectin and the pentraxins PTX3 or serum amyloid P component trigger cross-activation of the complement system. J. Biol. Chem. 286, 3405–3417 (2011).

Article CAS PubMed Google Scholar

Pepys, M. The pentraxins 1975–2018: serendipity, diagnostics and drugs. Front. Immunol. 9, 422781 (2018).

Article Google Scholar

Yang, J. et al. Serum amyloid P component level is associated with clinical response to escitalopram treatment in patients with major depressive disorder. J. Psychiatr. Res. 146, 172–178 (2022).

Article PubMed Google Scholar

Krishnadas, R. & Cavanagh, J. Depression: an inflammatory illness?. J. Neurol. Neurosurg. Psychiatry 83, 495–502 (2012).

Article Google Scholar

Miller, A. H. & Raison, C. L. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat. Rev. Immunol. 16, 22–34 (2016).

Article CAS PubMed PubMed Central Google Scholar

Marx, W. et al. Major depressive disorder. Nat. Rev. Dis. Primers 9, 44 (2023).

Article PubMed Google Scholar

Rawish, E., Sauter, M., Sauter, R., Nording, H. & Langer, H. F. Complement, inflammation and thrombosis. Br. J. Pharmacol. 178, 2892–2904 (2021).

Article CAS PubMed Google Scholar

Zewinger, S. et al. Serum amyloid A: high-density lipoproteins interaction and cardiovascular risk. Eur. Heart J. 36, 3007–3016 (2015).

CAS PubMed Google Scholar

Nakao, M. & Yano, E. Relationship between major depression and high serum cholesterol in Japanese men. Tohoku J. Exp. Med. 204, 273–287 (2004).

Article PubMed Google Scholar

Sarandol, A. et al. Oxidation of apolipoprotein B-containing lipoproteins and serum paraoxonase/arylesterase activities in major depressive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 30, 1103–1108 (2006).

Article CAS PubMed Google Scholar

Latas, M. et al. Diagnostic psychiatric and somatic comorbidity in patients with depression in the Western Balkan countries. PLoS One 19, e0295754 (2024).

Article CAS PubMed PubMed Central Google Scholar

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This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1009196).

Department of Senior Healthcare, Graduate School, Eulji University, Uijeongbu, 11759, Republic of Korea

Seungyeon Lee & Hee-Gyoo Kang

Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Seongnam, 13135, Republic of Korea

Sora Mun & Hee-Gyoo Kang

Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, 35233, Republic of Korea

Eun-Jeong Joo

Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, 11759, Republic of Korea

Eun-Jeong Joo

Department of Biomedical Laboratory Science, Graduate School, Eulji University, Uijeongbu, 11759, Republic of Korea

Yeeun Yun, Hee-Gyoo Kang & Jiyeong Lee

Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Uijeongbu, 11759, Republic of Korea

Jiyeong Lee

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Seungyeon Lee: Conceptualization, Formal analysis, Methodology, Writing—original draft, review and editing, Visualization. Sora Mun, Eun-Jeong Joo and Yeeun Yun: Formal analysis, Methodology. Hee-Gyoo Kang and Jiyeong Lee: Conceptualization, Investigation, Supervision, Writing—review & editing.

Correspondence to Hee-Gyoo Kang or Jiyeong Lee.

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Lee, S., Mun, S., Joo, EJ. et al. Serum proteomic changes related to residual impairment in remittent depression are associated with immune and inflammatory processes. Sci Rep 14, 24482 (2024). https://doi.org/10.1038/s41598-024-75983-0

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Received: 14 June 2024

Accepted: 09 October 2024

Published: 18 October 2024

DOI: https://doi.org/10.1038/s41598-024-75983-0

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