Decoding the Inflammatory Signature of the Major Depressive Episode: Insights from Peripheral Immunophenotyping in Active and Remitted Condition

Decoding the Inflammatory Signature of the Major Depressive Episode: Insights from Peripheral Immunophenotyping in Active and Remitted Condition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decoding the Inflammatory Signature of the Major Depressive Episode: Insights from Peripheral Immunophenotyping in Active and Remitted Condition Federico Daray, Leandro Grendas, Ángeles Arena, Vera Tifner, Romina Álvarez Casiani, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3346140/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although the immune system's role in the pathogenesis and persistence of depression is increasingly recognized, there is a lack of comprehensive understanding regarding the involvement of innate and adaptive immune cells. This study aims to bridge this knowledge gap by providing a deepening assessment of immunological profiles integrated into clinical and biochemical parameters in individuals with Major Depressive Episode (MDE). This multicenter case-control sex and age-matched study recruiting 121 participants divided into patients with active and remitted MDE and healthy controls (HC). Biochemical parameters, humoral responses (pro- and anti-inflammatory), and specific innate and adaptive immune cell populations were measured. Patients with MDE showed monocytosis, increased high-sensitivity C-reactive protein and Erythrocyte Sedimentation Rate levels, and an altered proportion of specific monocyte subsets. CD4 lymphocytes exhibited increased activation and exhaustion and a higher frequency of CD4 + CD25 + FOXP3 + regulatory T cells. Additionally, patients with MDE showed increased plasma levels of sTREM2, IL-17 and IL-6. This profile denoted an immune dysregulation and inflammation in MDE. Boruta analyses identified markers with significant discriminative potential for distinguishing between patients with MDE and HC. Cluster analysis revealed that patients with MDE exhibited at least three different patterns of immune system activation, suggesting a different stage of inflammation or possible differences in the underlying mechanism involved. Our findings give a deeper understanding of the role of inflammation and its mediators in MDE, illuminating the way for novel therapeutic strategies tailored to specific subgroups of patients. Biological sciences/Neuroscience Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Depression is one of the most frequent mental disorders globally 1 ; in many cases, it is recurrent and highly disabling, which makes it one of the leading causes of disability worldwide 2 . In addition to being associated with high levels of morbidity, it is estimated that 10% of depressed patients make suicide attempts throughout the disease, which also increases mortality 3 . Despite its substantial impact on morbidity and mortality, the underlying causes and mechanisms of depression remain poorly elucidated, hampering the development of more tailored therapeutic interventions to modify the disease state or progression. The term “depression” typically refers to a Major Depressive Episode (MDE), according to the main international classifications. This category includes various psychiatric disorders, as the Major Depressive Disorder (MDD), Persistent Depressive Disorder (PDD), Bipolar Disorders (BD), Adjustment Disorders, or Depressive Disorder Due to Substance Use or to Another Medical Condition 4 . Consequently, the clinical heterogeneity of depression is substantial, requiring at least five characteristics from a list of nine, with at least one of which must be low mood or anhedonia, to make the diagnosis 4 . This approach theoretically results in 227 potential combinations of criteria that qualify for an MDE diagnosis, even allowing for the possibility that two patients may receive the same diagnosis without sharing any symptoms. In a study involving 2154 depressed individuals, researchers observed 137 unique symptom profiles 5 . Such clinical heterogeneity is reflected in the modest response exhibited by current treatments, which leaves a substantial subset of patients with treatment non-response. Unfortunately, we also lack biomarkers to distinguish among these subgroups or provide insights into their long-term evolution or treatment response. Immunology can significantly reduce diagnostic heterogeneity in patients with depression by providing additional insights into the underlying mechanisms of the disease. Although the relationship between the immune system and depression is not new, in recent years, growing evidence has emphasized the crucial role of the immune system in developing and maintaining depression 6 . It has been proposed that inflammation contributes to the clinical scenario and sickness context that lead to chronic maladaptive behavior 7–9 . In this sense, most studies have focused on humoral proinflammatory biomarkers, such as interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α) and C-reactive protein (CRP) 10–13 . Moreover, the most extensive meta-analysis up to date, analyzing a total of 107 studies that reported measurements from 5,166 patients with depression and 5,083 controls, found increases in the mean levels of CRP, IL-3, IL-6, IL-12, IL-18, sIL-2R and TNFα in patients with depression 14 . Despite the extensive research on humoral biomarkers, less exploration has been made regarding the involvement of innate and adaptive immune cells in depression 15, 16 . Of note, our research group has been at the forefront of this area, demonstrating significant alterations in the proportion and activation of the three subtypes of circulating monocytes in patients with severe Major Depressive Disorder 17 . These observations have been replicated by others 18 . Furthermore, Lynall et al. 19 , in a case-control study, proposed the existence of a peripheral cell-stratified subgroup termed “Inflamed depression”. This subgroup is differentiated by distinct myeloid- versus lymphoid-biased immune cell profiles, providing additional insights into the complex interplay between immune cells and depression 19 . A recently meta-analysis confirms widespread alterations in circulating myeloid and lymphoid cells, consistent with dysfunction in both innate and adaptive immunity 20 . Introducing a biologically characterized phenotype of Major Depressive Episode (MDE) into classification systems will hold substantial clinical relevance. Inflammation is a biological phenomenon that can be thought as a response, process, or system state of any perturbations, including physiological and behavioral defenses to promote adaptation to environmental stressors 21, 22 . A deeper understanding of inflammation and its mediators should lead to advances in the therapeutics of mood disorders. In the current research landscape in immunology and depression, it's crucial to address a common limitation: many studies concentrate solely on soluble factors or cellular components of the proinflammatory response, potentially neglecting the broader picture. To overcome this, our study aimed to comprehensively characterize and integrate various biochemical parameters (including white blood cells (WBC), CRP, erythrocyte sedimentation rate (ESR)), the pro- and antiinflammatory humoral response (including cytokines, chemokines, and neurotrophic factors) together with the cellular compartment of the innate (classical, nonclassical, and intermediate monocytes), and adaptive immune response (T cells proportions, activation and exhausted state of CD4 T cells as well as regulatory T cells) in addition to clinical characteristics of patients with Mood Disorders who were experiencing an active MDE, compared with those with a remitted MDE, and healthy controls (HC). By encompassing this dual perspective, we aim to understand better the immunological intricacies associated with depression. This inclusive examination is essential for a holistic grasp of these complex conditions. 2. Material and methods 2.1 Study design This multicenter case-control sex and age-matched study started recruiting participants in March 2019 and finished in December 2022. The patients were recruited from the Hospital General de Agudos “Dr. Teodoro Álvarez”, Hospital General de Agudos “Dr. Enrique Tornú”, Hospital General de Agudos “Dr. Cosme Argerich”, Hospital General de Agudos “José María Ramos Mejía” , and Hospital Neuropsiquiátrico “Dr. Braulio A. Moyano” in Buenos Aires. All these Hospitals serve a sizable urban catchment area in Buenos Aires and treat mainly low-income patients without insurance. The Institutional Review Board of each Hospital approved the study. 2.2 Sample Patients meeting the following criteria were included: (a) age between 18 and 65 years, (b) diagnosed with DSM 5 MDD or BPD in a current MDE (c) willing and able to sign a consent form to participate. Exclusion criteria were: (a) have a comorbid diagnosis of obsessive-compulsive disorder (OCD), psychotic disorders, or Posttraumatic stress disorder (PTSD), (c) have a diagnosis of borderline personality disorder (BPD), or (d) have a diagnosis of substance use disorder in the last 30 days,. Sex and age-matched healthy controls between 18 and 65 were recruited from the same community, ensuring a comparable sampel. Exclusions for HC were: (a) having the diagnosis of any mental disorder, (b) having a diagnosis of substance use disorder in the last 30 days, (c) having a first-degree relative diagnosed with a mood disorder, d) not having the capacity to sign a consent form. Exclusions for all participants (MDE patients and HC) are (a) the presence of a chronic or acute physical illness with an inflammatory component, (b) receiving medication with antiinflammatory or immunomodulatory properties, (c) getting infected with SARS-CoV-2 in the 30 days previous to the evaluation, (d) having received the vaccine for SARS-CoV-2 or any other vaccine in the 30 days previous to the evaluation, (e) being pregnant, breastfeeding, having had an abortion or miscarriage during the previous 30 days to the evaluation. 2.3 Measures A trained interviewer gathered information regarding participant characteristics, including questions regarding clinical and demographic variables. The International Neuropsychiatric Interview, version 7.0.2 23 was used for diagnostic purposes, and the 17-item Hamilton Depression Rating Scale (HDRS-17) 24 to establish the severity of the MDE. Then, three groups of participants were defined: Group 1, “Patients with an active MDE”. The diagnosis of MDE as well as the type of mood disorders (major depressive disorder (MDD) or bipolar disorder (BD)), was determined by the MINI interview. Depression severity was established with the Hamilton Depression Rating Scale 17 (HDRS-17), and a score of > 7 was used to define an active MDE. Group 2, “Patients with remitted MDE.” The diagnosis of a history of MDE and type of mood disorders was determined by MINI. Depression severity with HDRS-17 and a score of ≤ 7 was used to define a remitted MDE. Group 3, “Healthy Controls” (HC) participants didn’t meet any diagnostic criteria by the MINI and scored ≤7 on the HDRS-17. The cutoff score of 7 on the HDRS-17 aligns with the recommendation by the NICE guidelines for depression 25 . Moreover, other questionnaires were used to control for other potential sources of variations in the inflammatory level beyond depression, the Columbia-Suicide Severity Rating Scale (C-SSRS) 26 to define if the patients had suicidal ideation or behavior, the Adverse Childhood Experiences (ACEs) questionnaire, the Brugha Stressful Life Events Scale 27 , and the International Physical Activity Questionnaire (IPAQ) 28 . Weight and height were measured. 2.4 Blood sample collection, processing, and biochemical analysis Blood samples were drawn by venipuncture and collected into EDTA-coated tubes (BD, Vacutainer) in the morning on the same day of completing the psychiatric evaluations. A total of 20 mL of blood was obtained on the day of the clinical assessment. From these, 10 mL was used for routine biochemical laboratory tests, including the Hemogram Analysis, Erythrocyte Sedimentation Rate (ESR), and high-sensitivity C-reactive protein (hs-CRP) measurements. The remaining blood sample was used for the direct Immunophenotyping staining, plasma separation, and peripheral blood mononuclear cells (PBMC) isolation as previously described 29 . 2.5 Immunophenotyping by direct blood staining Three different antibody cocktails were used to determine the circulating monocyte subsets proportion, the activation markers on T cells, and the frequency of Tregs employing the appropriate combination of the following anti-human antibodies (BioLegend) ( 1 ) Monocytes cocktail : CD11b-Brilliant Violet 421™ (Cat # 101251, RRID: AB_2562904), HLA-DR-PE (Cat # 307606, RRID: AB_314684), CD86-biotin (Cat # 305404, RRID: AB_314524) plus DyLight™ 649-conjugated Streptavidin (Cat # 405224), CD14-PE/Cyanine7 (Cat # 325618, RRID: AB_830691), and CD16-fluorescein isothiocyanate (FITC) (Cat # 302005, RRID: AB_314205); ( 2 ) T cell cocktail : CD3-PE/Cyanine7 (Cat # 300316, RRID: AB_314052), CD4-APC/Cyanine7(Cat # 317418, RRID: AB_571947), CD8-PE (Cat # 317418, RRID: AB_571947), CD69-PerCP/Cyanine5.5 (Cat # 310926, RRID: AB_2074956), CD44-BV421(Cat # 103040, RRID: AB_2616903), PD1-APC (Cat # 621610, RRID: AB_2832830) and LAG3-Alexa Fluor 488 (Cat # 369326, RRID: AB_2721362) and, ( 3 ) Tregs : CD3-PECy7(Cat # 317418, RRID: AB_571947), CD4-APCCy7 (Cat # 317418, RRID: AB_571947), CD25- Alexa Fluor 647 (Cat # 302618, RRID: AB_493045) (surface) and FOXP3-PE (Cat # 320108, RRID: AB_492986) (intracellular). The direct staining in 100 µL of fresh anti-coagulated blood sample was standardized in our lab 30 . Briefly, for cell surface antigen staining, samples were incubated with the appropriate antibody cocktail on ice for 30 minutes in the dark and then fixed with 100µL of Citofix Buffer (BD Bioscience) for additional 20 minutes on ice. Then, cells were washed with PBS and centrifuged at 800 x g for 5 minutes. Next, to eliminate erythrocytes, the bottom of blood cells was incubated with 1 mL ACK Lysing Buffer (Thermofisher Scientific) for 10 minutes at 25ºC. Only for Tregs, intracellular staining was performed after surface staining, and a specific kit (True-Nuclear™ Transcription Factor Buffer Set, Biolegend) was used. Briefly, 300 uL of 1X True Nuclear Fixation Buffer was added and incubated for 60 min at room temperature and in the dark. After that, cell permeabilization was performed by centrifuging the cells at 800G for 5 minutes with 200 uL of the True Nuclear 1X Perm Buffer, repeated twice. The FOXP3 antibody, diluted in True Nuclear 1x Perm Buffer, was added and incubated for 30 min in the dark at room temperature. Cells were maintained with 1X Perm Buffer, and finally, all the three cocktails were washed with 1 mL PBS and analyzed by flow cytometry (BD Canto I) employing the FlowJo software. The three monocyte subsets were defined by the expression of CD16 vs. CD14 as classical (CD16 neg CD14 ++ ), nonclassical (CD16 ++ CD14 neg ), and intermediate (CD16 + CD14 + ) as previously reported by our group 17 . In addition, the activation status of CD3 + CD4 + lymphocytes were measured by the expression levels of the activation markers CD69 and CD44 and exhaustion markers PD1 and LAG3. Finally, the frequency of Tregs was determined by CD3 + CD4 + CD25 ++ FOXP3 + cells. 2.6 Plasma level of cytokines, chemokines and neurotrophic factors determined by bead-based immunoassay Plasma levels of cytokines, chemokines and neurotrophic factors were measured using two LEGENDplex Panels (Biolegend) that allow the simultaneous quantification of several molecules in 50 uL of the plasma sample. LEGENDplex customized Human Inflammation Panel 1 was employed to measure (IL-1β, IFNγ, IL-17, IL-33, IL-8, IL-10, IL-12p70 and IL-23) and the LEGENDplex Human Neuroinflammation Panel 1 to measure (TGF-β, β-NGF, CX3CL1, BDNF, sTREM-2, IL-18, IL-6, TNFα and MCP-1). All experiments were performed following the manufacturer’s instructions. The system is a bead-based multiplex assay panel using fluorescence-encoded beads, which can be read by flow cytometry and provides a standard curve to obtain concentrations of each cytokine based on the mean fluorescence intensity of the PE channel. Additionally, IL-6 was determined by high-sensitivity ELISA kit (Enzo Life Sciences) following the manufacturer’s instructions. 2.7 Analysis by flow cytometry The samples were run in an external FACS core facility from the National System of Flow Cytometry, Argentina (FACS Canto I, Becton Dickinson). Data were analyzed by Flowjo software (Tree Star Inc). Bivariate dot plots with appropriate parameters were selected to define the gating strategy. The threshold for positivity was set using fluorescence minus one (FMO) for each marker. 2.8 Data analysis All statistical analyzes were performed using RStudio 2022.02.1 + 461 3131 . Descriptive statistics were used to summarize participants’ characteristics. Categorical variables were reported as absolute and relative frequencies (%), while quantitative variables were reported as means and standard deviations (SD) for normally distributed variables or as the median and interquartile range (IQR) for non-normally distributed variables. The Shapiro-Wilk test was used to assess the normality of each quantitative variable. The graphics and statistical comparison of Figs. 1, 2, and 3 were performed using GraphPad Prism software. Comparisons among participants in the three groups were conducted based on the variable type. Specifically, categorical variables were compared using Pearson's chi-squared or Fisher's exact test, normally distributed quantitative variables were compared using ANOVA, and non-normally distributed quantitative variables were compared using the Kruskal-Wallis rank sum test. In variables with significant between-group differences among groups, pairwise comparisons were performed. The Specific post-hoc test and multiple comparison adjustments employed are indicated in the figure legends. A significance level of p < 0.05 was considered statistically significant in all cases. A level of statistical significance was set at p < 0.05 was considered in all analyses cases. For subsequent analyses, missing data were imputed using the k-nearest neighbors method with a value of k = 5, using the kNN option in the VIM package 32 . The sample size of the three groups was calculated considering as objective the identification of significant differences in the quantitative variables by means of the ANOVA test. For a significance level of 5%, a power of 80%, and an effect size f = 0.30 (a medium effect), a total of 111 individuals was required, 37 patients per group. We have computed a needed sample size for one-way ANOVA using G*Power 3.1.9.4 software. The correlation between variables was evaluated using the Spearman correlation coefficient. To select the most important variables for classifying individuals, Random Forest, in combination with the Boruta algorithm, were employed 33 . The dataset was split into a training set comprising 70% of the observations and a test set comprising the remaining 30% while maintaining the proportionality of groups in the original data. Boruta was applied to the training data, and then a Random Forest model with the selected variables was fitted to the test data to evaluate the classification performance of the obtained models. A clustering analysis was performed considering both active and remitted cases. Initially, a principal component analysis was applied to reduce the dimensionality of the data. The permutation-based test was used to determine the number of components to retain, employing the factoextra 34 and PCAtest packages. Subsequently, a hierarchical clustering analysis consolidated by k-means clustering was conducted based on the retained factors from the previous analysis. The suggested partition was determined based on the relative gain of inertia using the HCPC option in the FactoMineR package 35 . 3. Results 3.1 Sociodemographic and clinical characteristics of the participants. The sociodemographic and clinical characteristics of the participants are summarized in Table 1 . We recruited 121 participants: 39 patients with an active MDE (32.2%), 40 with a remitted MDE (33.1%), and 42 HC subjects (34.7%). The three groups did not differ in sex and age, showing an appropriate matching. As anticipated, patients showed higher levels of unemployment and lower educational level compared to HC. On the other hand, as expected, the patients presented higher scores on the HAMD-17, higher levels of suicidal risk, and more than 85% were undergoing psychopharmacological treatment. Also, patients showed higher levels of adverse childhood events and stressful events. Additional clinical characteristics of the patients are described in Supplementary Table S1 . Table 1 Sociodemographic and clinical characteristics of participants Variable Active MDE Remitted MDE Healthy Control p-value n 39 40 42 Age (median [IQR]) 43.00 [34.50, 50.50] 41.00 [29.50, 52.25] 39.00 [30.25, 49.00] 0.654 Gender = Male (%) 10 (25.6) 14 ( 35.0) 14 ( 33.3) 0.633 Civil status (%) 0.189 Married/Living with a partner 11 (28.2) 5 ( 12.5) 13 ( 31.0) Separated/Divorced/Widower 8 (20.5) 11 ( 27.5) 5 ( 11.9) Single 20 (51.3) 24 ( 60.0) 24 ( 57.1) Scholarship (%) NA None 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) Incomplete primary 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) Complete primary 3 ( 7.7) 2 ( 5.0) 0 ( 0.0) Incomplete high school 11 (28.2) 4 ( 10.0) 1 ( 2.4) Complete high school 6 (15.4) 5 ( 12.5) 2 ( 4.8) Incomplete college 10 (25.6) 18 ( 45.0) 16 ( 38.1) Complete college 9 (23.1) 11 ( 27.5) 23 ( 54.8) Employment status = Unemployed/Retired (%) 20 (51.3) 16 ( 40.0) 5 ( 11.9) 0.001 Diagnostic summary (%) NA Bipolar disorder I 6 (15.4) 21 ( 52.5) 0 ( 0.0) Bipolar disorder II 10 (25.6) 2 ( 5.0) 0 ( 0.0) Major depressive disorder 23 (59.0) 16 ( 40.0) 0 ( 0.0) Non-specific Bipolar disorder 0 ( 0.0) 1 ( 2.5) 0 ( 0.0) None depressive disorder 0 ( 0.0) 0 ( 0.0) 42 (100.0) HAM-D Total score (median [IQR]) 14.00 [11.00, 18.00] 3.50 [1.00, 5.00] 0.00 [0.00, 1.00] < 0.001 On pshycopharmacological treatment = Yes (%) 31 (81.6) 38 ( 95.0) 0 ( 0.0) < 0.001 Total number of ACEs (median [IQR]) 4.00 [2.00, 6.00] 3.50 [2.00, 6.00] 0.00 [0.00, 1.75] < 0.001 IPAQ (%) 0.609 Low 23 (59.0) 18 ( 45.0) 19 ( 45.2) Median 8 (20.5) 9 ( 22.5) 12 ( 28.6) High 8 (20.5) 13 ( 32.5) 11 ( 26.2) Suicide ideation month = Yes (%) 19 (55.9) 2 ( 8.7) 0 ( 0.0) < 0.001 Suicide Behaviour month = Yes (%) 1 ( 2.6) 1 ( 2.5) 0 ( 0.0) 0.543 Suicide ideation life = Yes (%) 30 (85.7) 30 (100.0) 0 ( 0.0) < 0.001 Suicide Behaviour life = Yes (%) 13 (33.3) 16 ( 40.0) 0 ( 0.0) < 0.001 Number of Stressful Life Events (median [IQR]) 10.00 [7.00, 14.00] 10.00 [7.75, 14.00] 8.50 [6.00, 12.00] 0.156 Stressful Life Events Total Score (median [IQR]) 307.00 [206.50, 444.00] 298.00 [197.50, 426.25] 187.50 [139.25, 316.50] 0.001 Weight (median [IQR]) 70.00 [60.05, 79.00] 66.80 [60.00, 84.25] 70.00 [60.75, 78.50] 0.999 Height (mean (SD)) 165.38 (8.92) 164.93 (8.59) 167.76 (8.73) 0.291 BMI (median [IQR]) 24.40 [22.55, 30.80] 25.70 [22.75, 30.10] 24.25 [22.00, 27.67] 0.422 Amount of cigarettes per day (median [IQR]) 0.00 [0.00, 19.00] 0.00 [0.00, 6.50] 0.00 [0.00, 0.00] 0.001 Alcohol use = Yes (%) 17 (43.6) 11 ( 27.5) 31 ( 75.6) < 0.001 3.2 The inflammatory status of MDE patients is characterized by monocytosis and an altered proportion of monocyte subsets. The routine biochemical laboratory tests, using an automated cell counter and analyzer, are summarized in Table 2 . Significant differences were observed when comparing HC and MDE patients with active disease. These differences were observed in the median percentage of monocytes (p = 0.011) and their absolute count (p = 0.001) and for the mean percentage of lymphocytes (p = 0.004), even though its absolute number did not change, among groups. Table 2 Comparison of the biochemical parameters among participants Variable Active MDE Remitted MDE Healthy Control p-value Hematocrit (median [IQR]) 41.20 [39.18, 43.55] 41.65 [39.42, 44.95] 42.50 [40.90, 44.30] 0.631 Hemoglobin (median [IQR]) 13.30 [12.80, 14.40] 13.80 [12.93, 14.78] 14.10 [13.40, 14.70] 0.269 Erythrocytes (mean (SD)) 4.73 (0.52) 4.71 (0.53) 4.85 (0.40) 0.318 MCV (median [IQR]) 88.73 [85.20, 91.21] 89.00 [85.74, 91.40] 86.47 [84.06, 89.54] 0.047 MCH (median [IQR]) 29.00 [28.08, 30.02] 29.00 [27.88, 30.00] 28.79 [27.86, 29.71] 0.653 MCHC (median [IQR]) 32.80 [32.50, 33.45] 32.90 [32.38, 33.83] 33.33 [32.51, 33.96] 0.137 Leukocytes (median [IQR]) 7.40 [6.10, 9.80] 6.56 [5.20, 8.00] 6.60 [5.70, 7.50] 0.138 Segmented neutrophils Percentage (median [IQR]) 58.00 [52.00, 66.00] 60.00 [50.00, 64.60] 55.00 [50.00, 60.00] 0.154 Segmented neutrophils absolute count (median [IQR]) 4.02 [3.35, 5.68] 3.69 [2.70, 4.96] 3.59 [3.02, 4.42] 0.134 Lymphocytes Percentage (mean (SD)) 32.38 (7.64) 35.62 (11.36) 38.16 (6.83) 0.004 Lymphocytes absolute count (median [IQR]) 2.44 [1.86, 2.98] 2.09 [1.87, 2.42] 2.52 [2.15, 2.77] 0.059 Monocytes Percentage (median [IQR]) 5.50 [3.00, 8.00] 4.50 [3.00, 7.75] 3.00 [2.00, 5.00] 0.011 Monocytes absolute count (median [IQR]) 0.39 [0.27, 0.63] 0.30 [0.19, 0.52] 0.22 [0.14, 0.30] 0.001 Eosinophils Percentage (median [IQR]) 2.00 [2.00, 2.00] 2.00 [1.15, 2.00] 2.00 [1.00, 3.00] 0.756 Eosinophils absolute count (median [IQR]) 0.15 [0.11, 0.20] 0.13 [0.09, 0.20] 0.13 [0.10, 0.20] 0.448 Basophils Percentage (median [IQR]) 0.00 [0.00, 0.30] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.137 Basophils absolute count (median [IQR]) 0.00 [0.00, 0.02] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.137 ESR (median [IQR]) 15.00 [10.00, 21.00] 10.00 [7.00, 13.25] 10.00 [6.25, 12.00] 0.003 Urea (median [IQR]) 29.00 [22.75, 34.25] 29.50 [25.25, 36.00] 29.00 [23.00, 36.00] 0.846 Creatinine (median [IQR]) 7.38 [0.94, 9.30] 7.70 [0.95, 10.10] 8.10 [7.22, 10.00] 0.166 GOT (median [IQR]) 23.50 [15.25, 30.75] 23.00 [19.00, 31.00] 25.00 [18.00, 30.00] 0.903 GPT (median [IQR]) 20.50 [13.25, 33.00] 29.00 [20.00, 34.00] 24.00 [18.00, 31.00] 0.276 Alkaline phosphatase (median [IQR]) 125.00 [81.00, 189.00] 159.50 [121.75, 202.00] 155.00 [118.00, 179.00] 0.365 Total Bilirubin (median [IQR]) 0.48 [0.38, 0.70] 0.48 [0.34, 0.64] 0.66 [0.50, 0.86] 0.013 Sodium (mean (SD)) 137.85 (2.88) 138.11 (4.16) 138.37 (3.21) 0.757 Potassium (median [IQR]) 4.30 [4.00, 4.60] 4.30 [4.00, 4.62] 4.30 [4.00, 4.50] 0.729 Chloride (median [IQR]) 99.00 [90.00, 100.00] 99.00 [90.00, 101.55] 93.00 [89.00, 99.00] 0.113 hs-CRP (median [IQR]) 2.81 [0.52, 9.15] 1.19 [0.56, 3.59] 0.55 [0.20, 1.92] 0.002 Moreover, the median percentage of monocytes (p = 0.016) and their absolute number (p < 0.001) were increased in patients with active MDE compared to HC. These results indicate that the main hematopoietic response is coming from monocytosis promotion. Additionally, a reduced percentage of lymphocytes (p = 0.0024) was also found in patients with active MDE compared to HC ( Supplementary Figure S1 A) . No significant changes were observed in other cellular compartments (neutrophils, eosinophils, basophils, including erythrocytes). Another two peripheral blood inflammatory indicators are the hs-CRP and the ESR, and a chronic low-grade systemic inflammation could be reflected by a small but significant increment of their values. In this sense, we observed a significant difference in the concentration of hs-CRP (p = 0.002) and ESR value (p = 0.003), among groups, see Table 2 . Furthermore, we detected an increase in the concentration of hs-CRP in active MDE and remitted MDE compared to HC (p = 0.004 and 0.021, respectively) and ERS value in active MDE compared to remitted MDE and HC (p = 0.028 and 0.003, respectively), see Supplementary Figure S1 A . Considering the monocytosis and the increment in the systemic proinflammatory parameters hs-CRP and ESR in patients with MDE, we also evaluated changes in the proportion of the three subtypes of circulating monocytes, classical (CD14 ++ CD16 − ), intermediate (CD14 + CD16 + ), and nonclassical (CD14 − CD16 ++ ) by flow cytometry, as another proinflammatory hallmark. As we previously reported using peripheral blood mononuclear cells (PBMCs) 17 , here we also found, by direct staining on fresh peripheral blood, a higher proportion of nonclassical and intermediate monocytes in concordance with a reduced percentage of classical monocytes in both active and remitted patients with MDE vs. HC, see Fig. 1A-D . 3.3 Increased activation and exhausted phenotype in CD4 lymphocytes of patients with MDE is associated with a higher frequency of FOXP3 regulatory T cells. Even though we observed a reduced percentage of total lymphocytes in the hemogram, no difference was found in the absolute number (Table 2 ). Neither the percentage of CD4 T cells, nor the ratio of CD4/CD8 T cells measured by flow cytometry showed significant differences between patients with MDE and HC ( Supplementary Table S2 ). Nonetheless, we observed a significant increment in the median percentages of activation markers CD69 + (p = 0.007), and exhaustion markers PD1 + (p = 0.013) and LAG3 + (p = 0.014), on CD4 + T lymphocytes in patients with active MDE compared to HC, see Fig. 2A-D . We did not observe significant changes in the CD44 activation marker ( Supplementary Table S2). Even though there is no consensus regarding the reduction or increase number of regulatory T cells in depression, we found here a significant increase in the frequency of CD4 + CD25 + FOXP3 + Tregs in patients with active (p = 0.003) as well as remitted MDE (p = 0.015) compared with HC, see Fig. 2E . No differences were observed between active vs. remitted MDE. 3.4 Inflammatory and Neuroinflammation panels showed increased levels of sTREM2, IL-17, and IL-6 in MDE patients. The LEGENDPlex system was used to assess sixteen molecules, including cytokines, chemokines, and neurotrophic factors, in the plasma of patients with MDE. The level of each molecule was quantified based on the standard curve ( Supplementary Fig. S1 A, S1B) . This assessment utilized a customized human inflammatory panel (Fig. 3A ) along with a human neuroinflammation panel (Fig. 3B ) . Interestingly, we have found a robust and significantly higher level of sTREM2, a biomarker of microglia activation, in patients with active MDE compared with HC (p = 0.0089), see Fig. 3B . We have also observed a significant higher level of IL-17 in patients with remitted MDE compared with HC (p = 0.0147), see Fig. 3A , and a trend in the median value comparing active MDE patients vs HC (p = 0.068). Although with no statistical differences, some classical cytokines from the innate and adaptive immune response (IL-1β, IL-12, IFN-γ, and IL-10), showed an increased concentration trend, see Fig. 3A . A strong positive correlation was observed between IL-10 and IL-12 (r = 0.781), IL-33 and IL-10 (r = 0.726), as well as IL-1β and IFN-γ (r = 0.704), based on a correlation analysis using the Spearman test. For detailed correlation results, please refer to Supplementary Fig. S2 and Supplementary Table S3 . Considering that the IL-6 measurement was under the low range of detection of the legendPlex system, we have measured this cytokine in the plasma of HC and patients with MDE employing a highly sensitive ELISA kit (Enzo Life Sciences). We found that patients with active MDE show higher levels than the remitted MDE group (p = 0.0278) Fig. 3C . 3.5 Boruta and Random Forest validate discriminatory power of biological markers in patients with MDE. The Boruta selection algorithm was used to find the most important markers to discriminate individuals between MDE patients and HC, as well as active MDE, remitted MDE and HC. First, the model was applied to discriminate between patients with MDE vs HC. From the training set (n = 84), the variables lymphocytes percentage, monocytes absolute count, classical monocytes, nonclassical monocytes, Intermediate monocytes, hs-CRP, MCP1, CD4 + PD1 + , CD4 + LAG3 + and CD4 + CD25 + FOXP3 + Tregs were selected (Fig. 4A). These markers were used in a Random Forest model to classify a separate test dataset, achieving an overall classification accuracy of 83.8%. Twenty-one over twenty-four (21/24) patients with MDE were correctly classified (87.5%) and 10/13 HC (76.9%). Afterwards, the model was applied to be able to discriminate among patients with active MDE, remitted MDE and HC. Similarly, the Boruta selection algorithm identified markers that were important for discrimination: classical monocytes, nonclassical monocytes, intermediate monocytes, monocytes absolute count, ESR, hs-CRP, CD4 + CD69 + , CD4 + LAG3 + and CD4 + CD25 + FOXP3 + Tregs, (Fig. 4B). Random Forest model was then trained using these selected variables and applied to classify the test dataset, achieving an overall classification accuracy of 70%. Here, 7/12 patients with active MDE (58.3%), 8/12 remitted MDE (66.7%), and 10/13 HC (84.6%) were correctly classified. 3.6 Clustering analysis indicates different inflammatory segregation among patients with MDE Principal component analysis (PCA) was applied to reduce the dimensionality of the data, yielding nine principal components, which together accounted for 62.5% of the total variance-covariance. The first principal component (PC1, 14.2% total variance-covariance) was most strongly weighted on IL-17A, IL-23, IL-10, IL-12p70, IL-33, TNF-alpha, bNGF, the absolute count and the percentage of Basophils and the percentage of Monocytes. The second principal component (PC2, 10.4% total variance-covariance) was most weighted on the absolute count and the percentage of segmented neutrophils, the absolute count of Monocytes and Leukocytes, the percentage of Lymphocytes, HS CRP and IL-8. The factor loadings of the variables in the nine principal components are represented in Fig. 5A . The Clustering analysis was used to detect different inflammatory segregation among MDE patients. Three distinct clusters were identified, as revealed by the dendrogram graphic (Fig. 5B). Comparing the clinical and sociodemographic characteristics among the three clusters we observed similar distribution of the active or remitted condition of MDE with no significant statistical differences (p = 0.623), neither on the severity of depressive symptoms considering HAMD-17 scale (p = 0.398). Nonetheless cluster 1 and 3 showed median score higher than 7 (HAMD-17 = 9.5 and 8, respectively) compared with cluster 2 (HAMD-17 = 6), see Supplementary Table S4 . A significant difference was observed in the percentage of subjects receiving pharmacological treatment among clusters. In Cluster 1, 95.3% of the cases were under psychopharmacological treatment, as well as 95.0% in Cluster 2, in contrast, only 60% of individuals in Cluster 3 were receiving treatment (p = 0.001). The biochemical parameters analysis across these three clusters show again that clusters 1 and 3 displayed higher median values of absolute leukocyte number. In concordance, cluster 1 showed the highest value of lymphocyte number, and cluster 3 the highest percentage and absolute number of monocytes ( Table 3 ). Furthermore, cluster 3 also exhibited the highest medians for basophil percentage and absolute count. Table 3 Comparison of biochemical parameters among clusters Variable 1 2 3 p-value Erythrocytes (mean (SD)) 4.73 (0.55) 4.72 (0.49) 4.69 (0.51) 0.981 MCV (median [IQR]) 89.86 [87.00, 93.00] 88.44 [86.23, 90.01] 87.00 [84.50, 90.15] 0.177 Leukocytes (median [IQR]) 7.68 [6.18, 9.83] 5.40 [5.00, 7.45] 6.56 [5.88, 7.30] 0.021 Segmented neutrophils Percentage (mean (SD)) 58.91 (11.16) 56.21 (9.70) 57.86 (6.94) 0.642 Segmented neutrophils absolute count (median [IQR]) 4.46 [3.29, 6.84] 3.16 [2.50, 4.73] 3.84 [3.28, 4.72] 0.074 Lymphocytes Percentage (median [IQR]) 32.65 [25.00, 41.25] 38.00 [31.50, 43.00] 29.80 [25.50, 33.90] 0.073 Lymphocytes absolute count (median [IQR]) 2.58 [2.07, 2.97] 2.09 [1.84, 2.27] 1.86 [1.68, 2.13] 0.004 Monocytes Percentage (median [IQR]) 4.00 [3.00, 8.00] 4.00 [3.00, 5.00] 8.60 [7.10, 9.95] < 0.001 Monocytes absolute count (median [IQR]) 0.38 [0.21, 0.58] 0.21 [0.19, 0.33] 0.52 [0.46, 0.67] < 0.001 Eosinophils Percentage (median [IQR]) 2.00 [1.00, 2.00] 2.00 [2.00, 2.00] 2.00 [1.65, 4.45] 0.228 Eosinophils absolute count (median [IQR]) 0.15 [0.09, 0.20] 0.11 [0.10, 0.18] 0.14 [0.11, 0.28] 0.726 Basophils Percentage (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 1.00 [0.55, 1.00] < 0.001 Basophils absolute count (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.06 [0.03, 0.07] < 0.001 ESR (median [IQR]) 10.00 [7.00, 15.00] 14.00 [9.00, 18.00] 16.00 [5.00, 26.25] 0.376 hs-CRP (median [IQR]) 2.10 [0.51, 7.95] 1.49 [0.91, 3.67] 2.03 [0.59, 3.62] 0.952 The proportion of the three monocyte subtypes is altered in patients with MDE, but the segregation in these three clusters did not show significant differences ( Supplementary Table S5 ). Interestingly, the lowest percentage of CD3 + CD4 + T cells, determined by flow cytometry, was observed in cluster 3, in concordance with the biochemical laboratory analysis. In the same sense, clusters 1 and 3 showed an increased median value of exhaustion CD4 + LAG3 marker compared to cluster 2 ( Supplementary Table S5 ). Considering cytokines, chemokines, and neurotrophic factors, the cluster segregation denoted that cluster 3 is characterized by a clear proinflammatory profile with high levels of TNFα, CX3CL-1, IL-12p70, IL-17A, IL-23, and IL-33, associated with high level of IL-8 and IL-10, as well as increased medians for b-NGF and the lowest level for BDNF, ( Table 4 ) . Cluster 2 is mainly characterized by the highest level of MCP1, IL-1β, IFN-γ, IL-23, and IL-8. Cluster 1 displayed the lowest median level of most cytokines ( Table 4 ) . Table 4 Inflammatory and Neuroinflammation Panel of Cytokines among clusters Variable 1 2 3 p-value MCP1 (median [IQR]) 52.53 [33.01, 78.63] 64.17 [46.09, 111.27] 29.73 [26.18, 46.45] 0.002 sTREM2 (median [IQR]) 976.33 [779.43, 1300.05] 1044.76 [748.32, 1263.49] 931.84 [736.20, 1170.49] 0.847 BDNF (median [IQR]) 5120.64 [2920.72, 7086.43] 4854.92 [2078.03, 6313.16] 2786.38 [1462.40, 4050.00] 0.085 IL-6 (median [IQR]) 1.09 [0.87, 1.74] 1.53 [0.97, 1.92] 1.62 [0.81, 5.27] 0.122 bNGF (median [IQR]) 10.24 [4.39, 18.35] 14.79 [12.78, 19.79] 24.31 [12.95, 71.84] 0.011 IL-18 (median [IQR]) 115.80 [41.46, 236.91] 105.73 [15.55, 173.78] 165.89 [93.46, 218.94] 0.403 TNF-alpha (median [IQR]) 0.00 [0.00, 38.91] 92.98 [56.81, 135.10] 127.93 [61.75, 179.53] < 0.001 CX3CL-1 (median [IQR]) 437.32 [420.50, 993.47] 517.64 [461.84, 587.34] 1361.44 [907.78, 1717.06] 0.001 IL-1beta (median [IQR]) 16.19 [6.46, 44.57] 58.89 [38.62, 104.55] 29.01 [13.92, 40.03] < 0.001 IFN-gama (median [IQR]) 9.69 [3.30, 12.57] 27.12 [17.31, 29.10] 9.35 [7.27, 23.93] < 0.001 IL-8 (median [IQR]) 4.49 [0.00, 10.55] 96.72 [29.88, 129.69] 27.33 [16.18, 45.05] < 0.001 IL-10 (median [IQR]) 0.00 [0.00, 7.07] 17.05 [9.09, 28.68] 27.48 [12.85, 37.87] < 0.001 IL-12p70 (median [IQR]) 5.44 [3.24, 6.77] 10.52 [8.78, 13.86] 15.91 [6.86, 18.10] < 0.001 IL-17A (median [IQR]) 0.73 [0.00, 1.75] 3.16 [2.63, 4.44] 8.15 [2.58, 11.83] < 0.001 IL-23 (median [IQR]) 4.25 [3.09, 10.03] 22.06 [12.10, 24.63] 19.88 [14.93, 27.84] < 0.001 IL-33 (median [IQR]) 26.30 [10.21, 50.01] 83.69 [67.52, 124.72] 216.85 [107.88, 303.04] < 0.001 The cluster analysis suggests that patients with MDE have inflammatory signs, identifiable by cellular and plasma molecules characterization. Each cluster could represent a different stage of the same process or different inflammatory pathways reaching the same phenotype. 4. Discussion The present study provides a comprehensive understanding of the immune system in patients with MDE across different stages of the disease, comparing it with HC. The most novel findings include increased monocytosis with an increment of intermediate and nonclassical monocyte subsets at the expense of classical monocytes, indicating a transitional activation of the monocytic population. We also observed a notable augmentation in the activation of CD4 T lymphocytes and elevated exhaustion markers in patients with active MDE compared to HC. Furthermore, there was a significant increase in the frequency of CD4 + CD25 + FOXP3 + Tregs in both active and remitted MDE patients compared to HC, which could be reflecting a compensatory anti-inflammatory immune system response. Finally, we observed increased levels of soluble markers of neuroinflammation, such as sSTREM2 and IL-17. Machine learning techniques identified a panel of biomarkers that can discriminate between patients with MDE and HC with an overall classification accuracy of 83.8%. Most of these biomarkers are related to immune cell activation. Finally, cluster analysis suggests three distinct clusters unrelated to the clinical expression of the disease. Since the 1990s, it has been established that depression is associated with increased white blood cell count and monocytes 36 , which led to the formulation of the monocytes and lymphocytes hypothesis in MDD 37 . This hypothesis suggests that alterations in these immune cells play a role in the pathophysiology of depression. A recently published meta-analysis also supported this by demonstrating an overall increase in the total number of monocytes in depressed individuals (seven studies; SMD = 0.60; 95% CI, 0.19–1.01; P < 0.01; I 2 = 66%) 20 . Consistent with these findings, our study observed a significant monocytosis in patients experiencing an active major depressive episode (MDE). These patients exhibited a clear elevation in the median percentage and absolute number of monocytes compared to HC. These results suggest an abnormal hematopoietic response in individuals with depression, specifically an enhanced production of monocytes. We further investigated the proportion of different subtypes of circulating monocytes (classical, intermediate, and nonclassical) using flow cytometry. The results indicate an expansion for the nonclassical and intermediate monocytes and a reduced percentage of classical monocytes in patients with MDE compared to HC. Even though we did not find statistical differences comparing active vs. remitted MDE, more pronounced changes were observed in active conditions. These results indicate that patients with MDE are characterized by a proinflammatory status and enhanced transition to intermediate and nonclassical subsets, as predicted by the monocyte transitional model of Patel et al. 38 . We have been one of the first groups to describe changes in the percentage and activation status of the three circulating monocyte subtypes in patients with severe MDD 17 . Herein, we reinforce and expand the aforementioned findings by utilizing a distinct sample of patients, ensuring sex and age matching, and employing a simplified approach of directly measuring immune parameters in a small blood sample. This technical approach holds promise for potential translation into clinical practice, as it offers a convenient and feasible screening method. Dysregulation of the adaptive immune system in MDE patients has been suggested, with decreased numbers of circulating T cells, an increase in the ratio of CD4 + relative to CD8 + T cells, and some immunosuppression features 15 . Our finding also suggests a dysregulation of the T cell compartment in patients with mood disorders during MDE. While there were no significant differences in the absolute numbers of total lymphocytes, CD4 T cells, or the ratio of CD4 to CD8 T cells between MDE patients and HC, a notable increment in the activation status of CD4 + CD69 + , and exhausted CD4 + PD1 + and CD4 + LAG3 + T lymphocytes were observed in patients with active MDE compared to healthy controls. These results indicate that a CD4 lymphocyte activation process is ongoing in patients with MDE, associated with potential exhaustion of this compartment in the pathogenesis of depression. In this sense, it has been demonstrated that memory CD4 + T cells are abundant in adult humans, and its activation does not necessary depend on the encounter with the antigen 39, 40 . The increased markers of cellular activation and exhaustion in CD4 T cells among patients with MDE is a novel concept that may explain the high comorbidity of these patients with non-psychiatric medical conditions 41 , particularly autoimmune diseases 42 . Furthermore, this concept aligns with recent studies that have demonstrated a higher degree of premature T cell aging 43 . Furthermore, there was a significant increase in the frequency of CD4 + CD25 + FOXP3 + Tregs in both active and remitted MDE patients compared to healthy controls. Tregs play a crucial role in maintaining immune homeostasis and suppressing excessive immune responses. The observed increase in Tregs suggests a compensatory mechanism by the immune system to counterbalance potential proinflammatory processes and regulate immune activity in MDE, as was suggested by the compensatory immune-regulatory reflex system (CIRS) concept 44 . CIRS is involved in MDD and BD by regulating the primary immune-inflammatory response, thereby contributing to spontaneous and antidepressant-promoted recovery from the acute phase of illness 44 . The simultaneously increased levels of both the pro-and antiinflammatory cytokines are reported in the brain of MDD patients; this indicates activity of both the IRS and CIRS in MDD. Speculation is rife that the disrupted IRS-CIRS elements might determine the onset, episodes, neuroprogressive processes, treatment response, and recovery of patients with MDD 45 . Patients with MDE exhibit the typical features of an ongoing inflammatory response, including increased expression of proinflammatory cytokines and their receptors, elevated acute phase reactive proteins levels, and adhesion molecules in peripheral blood, cerebrospinal fluid, and brain 14, 46 . Nonetheless, soluble proinflammatory biomarkers can be increased in depression as well as numerous inflammatory pathologies; there are clear differences in the magnitude or concentration levels of these factors comparing acute inflammation in response to infections compared to the low-grade systemic inflammation as reflected by chronic conditions. High sensitivity CRP (hs-CRP) is a marker of acute phase response, but it has been used extensively as a measure of low-grade inflammation in psychiatric 47 and physical conditions 48, 49 . Meta-analyses of cross-sectional studies confirm that mean concentrations of circulating hs-CRP and inflammatory cytokines such as interleukin 6 (IL-6) are higher in patients with acute depression than controls 10, 12, 50, 51 . Our findings also show increased hs-CRP levels in patients with active MDE compared to HC, but interestingly, we found that individuals with MDE in remission still exhibited elevated hs-CRP levels compared to HC, suggesting that residual inflammation may persist even after symptom improvement. Similarly, our study revealed elevated levels of IL-6 in individuals with active MDE compared to those with MDE in remission. This indicates that IL-6 may serve as a marker of ongoing inflammation during the active phase of the disease. Altogether, these findings underscore the role of inflammation in MDE. In addition to the two well-described nonspecific markers of inflammation, our study found a potential novel, more specific biomarker of neuroinflammation for MDE, the soluble triggering receptor expressed on myeloid cell 2 (sTREM2). This is a protein receptor largely expressed in microglial cells in the brain. It plays a crucial role in regulating microglial function and modulating the immune response in the central nervous system (CNS). sTREM2 refers to the soluble form of this receptor, which can be measured in the cerebrospinal fluid (CSF) or peripheral blood. Changes in sTREM2 levels have been associated with the activation of microglia in neurodegenerative and neuroinflammatory diseases. A recent metanalysis of 22 observational studies, which included 5716 participants, comparing individuals with Alzheimer’s vs. controls, showed a significant increase in CSF of sTREM2 level (standardized mean difference [SMD]: 0.41, 95% confidence intervals [CI]: 0.24, 0.58, p < 0.001) 52 . This marker also increased in conditions of neuroinflammation, such as angiitis of the CNS 53 and amyotrophic lateral sclerosis (ALS) 54 . The role of sTREM2 in MDE has not been studied, and the present study is the first one reporting increased sTREM2 levels in the plasma of patients with active MDE compared with HC. Further research is needed to fully understand the role of sTREM2 in MDE and its role as a diagnostic or therapeutic target. We also identified an increase in IL-17 levels, constituting an exciting soluble marker due to its association with autoimmune pathologies 55 . IL-17 is considered a signature cytokine of CD4 + T helper 17 (Th17) cells; however, it can also be produced by different cell types, including CD8 + T cells, natural Th17 cells, innate lymphoid cells (ILCs), γδ T cells, natural killer (NKT) cells, and neutrophils. Animal studies have indicated that inflammatory Th17 cells contribute to depression-like behavior 56 . Interestingly, studies have demonstrated that the administration of anti-interleukin-17A (IL-17A) antibodies can lead to a reduction in depressive symptoms in mice 57 . In humans, there have been few studies that have examined the role of IL-17 in depression. One of the most recent studies found an increase in IL-6 and IL-17 levels in patients with a first depressive episode compared to controls 58 . In the same study, treatment with antidepressants decreased plasma levels of IL-6 and IL-17, although the latter remained elevated compared to controls 58 . Furthermore, this study also revealed that the HAMD score exhibited a moderate correlation with IL-6 and a strong correlation with IL-17 58 . This study suggests that autoimmunity may play a role in the etiology or pathogenesis of depression. Our results support this idea, as we have observed significant elevated levels of IL-17 in patients with remitted MDE and a trend (p = 0.06) in active MDE compared to HC. The fact that this cytokine is elevated in remitted patients may indicate that inflammation can have a chronic role in depression beyond periods of active illness, which could be highly relevant for those cases of mood disorders characterized by neuroprogression. The two Boruta analyses revealed different markers for discriminating between MDE patients and HC. First, comparing between MDE (active and remitted) and HC, the selected markers, including lymphocytes percentage, monocytes absolute count, classical monocytes, non-classical monocytes, intermediate monocytes, hs-CRP, MCP1, CD4 + PD1 + , CD4 + LAG3 + , and CD4 + CD25 + FOXP3 + Tregs, demonstrated a significant discriminative potential. When the Random Forest model was applied to an independent test dataset, an impressive overall classification accuracy of 83.8% was achieved. Secondly, when considering the classification of active MDE, remitted MDE, and healthy controls, the Boruta algorithm identified important markers, including classical monocytes, non-classical monocytes, intermediate monocytes, monocytes absolute count, ESR, hs-CRP, CD4 + CD69 + , CD4 + LAG3 + , and CD4 + CD25 + FOXP3 + Tregs. Subsequently, the Random Forest model, trained using these selected variables, demonstrated an overall classification accuracy of 70%. Altogether, these findings highlight the potential of utilizing immune cell biomarkers to differentiate MDE patients from HC, as well as distinguish between different states of the disorder. The discriminatory power exhibited by the selected markers in the Random Forest model suggests their relevance in understanding the underlying mechanisms and aiding in the diagnostic process of MDE. Further research and validation studies are warranted to explore these markers' clinical utility and generalizability in larger and more diverse patient populations. The clustering analysis revealed the presence of three distinct clusters based on immunological profiles among patients with MDE. First, these results suggest that patients with MDE, regardless of whether they are experiencing or have remitted from a MDE, exhibit signs of an inflammatory state. Cluster 1 is characterized by the highest number of leukocytes, mainly given by the increment in lymphocyte count. Nonetheless, this cluster showed the lowest proinflammatory cytokines levels, probably due to a different state of the inflammation process. Cluster 3 displayed the most robust inflammatory pattern, with high levels of TNFα, CX3CL-1, IL-12p70, IL-17A, IL-23, and IL-33, associated with the highest level of IL-10, as well as increased medians for b-NGF and the lowest level for BDNF. This profile is also associated with the highest absolute number and percentage of circulating monocytes as well as the lowest absolute number and percentage of circulating lymphocytes, denoting an active inflammatory process. Noteworthy, a lower percentage of individuals in Cluster 3 were receiving pharmacological treatment, indicating a potential association between the immunological profile and treatment status. Cluster 2 has some cardinal signs of more acute inflammation as the elevated levels of MCP1, which precede the monocytosis, but also increased levels of some proinflammatory cytokines such as IL-1β, IFN-γ, and IL-8. Similarly, the absolute number of monocytes is closer to a HC value, as well as the percentage of lymphocytes, suggesting as possible initiation of the inflammatory process. Based on these results, the following questions emerge: Do the observed clusters represent distinct stages of the same underlying process, or do they indicate different inflammatory pathways that converge to produce a common phenotype? The lack of significant differences in the distribution of active or remitted MDE and the severity of depressive symptoms across the clusters suggests that they may not signify distinct stages of the same illness. However, it cannot be ruled out that these clusters represent different trajectories of the same disease, considering the limitations of our cross-sectional study design. Definitive answers to these questions will require future studies with a longitudinal design, which will provide further insights into this matter. Our study demonstrates several noteworthy strengths. Firstly, we assessed changes in cellular levels of the monocyte compartment and T cells, considering a specific plasma cytokine milieu. This allowed us to establish a distinct profile for MDE patients, defining subtypes of the condition. This approach fills a critical gap in the literature, as this area has received inadequate attention thus far. Secondly, our standardized methodology employed three cocktails of antibodies with a minimal blood sample volume of only 100 µl each. This approach allowed us to accurately measure the proportion and activation of monocytes, the proportion of CD4 to CD8 lymphocytes, and Tregs, as well as the activation and exhaustion of T cells, utilizing direct blood staining. Such an approach holds promise for rapid translation into clinical practice. Thirdly, our study is a multicenter investigation that carefully matched participants based on age and sex, two variables known to significantly influence the immune system. By controlling for these factors, we strengthened the validity and generalizability of our results. Finally, our rigorous patient selection process excluded individuals with known causes of inflammation or immune system activation, ensuring the focus remained on the specific MDE condition. Additionally, we included patients at various stages of the disease, a novel aspect not previously explored in MDE immunotyping studies. Some limitations of the study need to be acknowledged. Firstly, we measured the immune cell profile in peripheral blood, which may not fully reflect the immune activity in the central nervous system (CNS). However, evidence suggests that inflammatory factors originating in the blood can reach the CNS through various pathways, including passive or active transport across the blood-brain barrier, immune cell transmigration, and vagal nerve signaling. While peripheral blood analysis provides valuable insights, it is important to recognize the potential disparities between peripheral and CNS immune responses. Another significant limitation is that most participants received psychopharmacological treatment at the time of inclusion. It is well-known that many psychotropic medications can impact the immune system, potentially confounding the interpretation of immunological findings. Furthermore, our study focused exclusively on individuals with MDE, which could be a limitation as MDD and BP may exhibit distinct immune profiles. However, in clinical practice, MDE is the most commonly encountered presentation, and there are currently no precise indicators that reliably classify between these two groups. Therefore, we included MDD and BP patients to explore whether immunological markers could provide insights into their shared pathophysiology. Despite these limitations, our study provides valuable insights into the immunological aspects of MDE providing a global view of the phenomenon, analyzing both the humoral and the innate and adaptive cellular components. Further research is needed to fully understand the implications of these immune alterations in a longitudinal process to pave the way for potential advancements in clinical practice. Declarations AUTHORSHIP CONTRIBUTION A.R.A. and V.T.: Investigation, Methodology and samples processing, Formal analysis, Validation, Visualization, review and edit the manuscript. L.N.G., R.I.A.C, A.O., M.B.P., F.H., and C.R.P.: Investigation, Recruitment and follow up of patients, sample collection, data curation, review and edit the manuscript. L.C.C: Data curation, Methodology, Software analysis, Validation, Visualization, review and edit the manuscript. G.V.: Conceptualization, analysis and discussion, review & editing manuscript. F.M.D., E.A.C.S. and A.E.E.: Conceived and designed the study, conceptualization, Funding acquisition, Methodology, Supervision, Project administration, Resources, Writing-original draft, Writing-review & editing. ACKNOWLEDGEMENTS The authors want to acknowledge the participants gratefully, and professionals for technical assistance. E.A.C.S. and A.E.E. want to thank Dr. Florencia Quiroga for the availability of the BD FACSCanto I and also thank the advice of Dr. Virginia Polo, and Dr. Tomás Langer from INBIRS CONICET-UBA and the National System of Flow Cytometry, Argentina. We would like to express our sincere gratitude to Dr. Oscar Bottasso for taking the time to read and provide valuable insights on our study’s results and to Sudan Neupane for their invaluable feedback and insightful suggestions that significantly improved the quality of this manuscript. DATA AVAILABILITY The data supporting this study's findings are available from the corresponding authors. Disclosure of Interest: The authors have declared no potential conflicts of interest. Funding sources The study was supported by grants from: Brain & Behavior Research Foundation. 2019 NARSAD Young Investigator Grant ID 27855 Ministerio de Ciencia, Tecnología e Innovación, Argentina through PID-2018-0054 to F.M.D. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina, trough PIP 2015-0567 to A.E.E, Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Argentina, through PICT 2017-2431 to A.E.E and PICT 2018-03070 to E.A.C.S. A.R.A., and L.C.C are recipients of fellowships from CONICET and, V.T., A.O. and R.I.A.C are recipients of fellowships from the Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Argentina. F.M.D., E.A.C.S and A.E.E. are career investigators at CONICET, Argentina. The funding sources had no involvement in the study design, in the collection, analysis, and interpretation of data, in the writing of the manuscript, and in the decision to submit the paper for publication. References WHO. 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Translational evidence for the Inflammatory Response System (IRS)/Compensatory Immune Response System (CIRS) and neuroprogression theory of major depression. Progress in neuro-psychopharmacology & biological psychiatry 2021; 111: 110343. Enache D, Pariante CM, Mondelli V. Markers of central inflammation in major depressive disorder: A systematic review and meta-analysis of studies examining cerebrospinal fluid, positron emission tomography and post-mortem brain tissue. Brain, behavior, and immunity 2019; 81: 24-40. von Känel R, Hepp U, Kraemer B, Traber R, Keel M, Mica L et al. Evidence for low-grade systemic proinflammatory activity in patients with posttraumatic stress disorder. J Psychiatr Res 2007; 41 (9) : 744-752. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. Jama 1999; 282 (22) : 2131-2135. Danesh J, Whincup P, Walker M, Lennon L, Thomson A, Appleby P et al. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. BMJ (Clinical research ed) 2000; 321 (7255) : 199-204. Goldsmith DR, Bekhbat M, Le N-A, Chen X, Woolwine BJ, Li Z et al. Protein and gene markers of metabolic dysfunction and inflammation together associate with functional connectivity in reward and motor circuits in depression. Brain, behavior, and immunity 2020; 88: 193-202. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK et al. A meta-analysis of cytokines in major depression. Biological psychiatry 2010; 67 (5) : 446-457. Zhou W, Zhou Y. Association between Cerebrospinal Fluid Soluble TREM2, Alzheimer's Disease and Other Neurodegenerative Diseases. 2023; 12 (10). Guo T, Ma J, Sun J, Xu W, Cong H, Wei Y et al. Soluble TREM2 is a potential biomarker for the severity of primary angiitis of the CNS. Frontiers in immunology 2022; 13: 963373. Jericó I, Vicuña-Urriza J, Blanco-Luquin I, Macias M, Martinez-Merino L, Roldán M et al. Profiling TREM2 expression in amyotrophic lateral sclerosis. Brain, behavior, and immunity 2023; 109: 117-126. Astry B, Venkatesha SH, Moudgil KD. Involvement of the IL-23/IL-17 axis and the Th17/Treg balance in the pathogenesis and control of autoimmune arthritis. Cytokine 2015; 74 (1) : 54-61. Beurel E, Harrington LE, Jope RS. Inflammatory T helper 17 cells promote depression-like behavior in mice. Biological psychiatry 2013; 73 (7) : 622-630. Kim J, Suh YH, Chang KA. Interleukin-17 induced by cumulative mild stress promoted depression-like behaviors in young adult mice. 2021; 14 (1) : 11. Mao L, Ren X, Wang X, Tian F. Associations between Autoimmunity and Depression: Serum IL-6 and IL-17 Have Directly Impact on the HAMD Scores in Patients with First-Episode Depressive Disorder. Journal of immunology research 2022; 2022: 6724881. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Introduction","content":"\u003cp\u003eDepression is one of the most frequent mental disorders globally \u003csup\u003e1\u003c/sup\u003e; in many cases, it is recurrent and highly disabling, which makes it one of the leading causes of disability worldwide \u003csup\u003e2\u003c/sup\u003e. In addition to being associated with high levels of morbidity, it is estimated that 10% of depressed patients make suicide attempts throughout the disease, which also increases mortality \u003csup\u003e3\u003c/sup\u003e. Despite its substantial impact on morbidity and mortality, the underlying causes and mechanisms of depression remain poorly elucidated, hampering the development of more tailored therapeutic interventions to modify the disease state or progression.\u003c/p\u003e \u003cp\u003eThe term \u0026ldquo;depression\u0026rdquo; typically refers to a Major Depressive Episode (MDE), according to the main international classifications. This category includes various psychiatric disorders, as the Major Depressive Disorder (MDD), Persistent Depressive Disorder (PDD), Bipolar Disorders (BD), Adjustment Disorders, or Depressive Disorder Due to Substance Use or to Another Medical Condition \u003csup\u003e4\u003c/sup\u003e. Consequently, the clinical heterogeneity of depression is substantial, requiring at least five characteristics from a list of nine, with at least one of which must be low mood or anhedonia, to make the diagnosis \u003csup\u003e4\u003c/sup\u003e. This approach theoretically results in 227 potential combinations of criteria that qualify for an MDE diagnosis, even allowing for the possibility that two patients may receive the same diagnosis without sharing any symptoms. In a study involving 2154 depressed individuals, researchers observed 137 unique symptom profiles \u003csup\u003e5\u003c/sup\u003e. Such clinical heterogeneity is reflected in the modest response exhibited by current treatments, which leaves a substantial subset of patients with treatment non-response. Unfortunately, we also lack biomarkers to distinguish among these subgroups or provide insights into their long-term evolution or treatment response. Immunology can significantly reduce diagnostic heterogeneity in patients with depression by providing additional insights into the underlying mechanisms of the disease.\u003c/p\u003e \u003cp\u003eAlthough the relationship between the immune system and depression is not new, in recent years, growing evidence has emphasized the crucial role of the immune system in developing and maintaining depression \u003csup\u003e6\u003c/sup\u003e. It has been proposed that inflammation contributes to the clinical scenario and sickness context that lead to chronic maladaptive behavior \u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. In this sense, most studies have focused on humoral proinflammatory biomarkers, such as interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α) and C-reactive protein (CRP) \u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. Moreover, the most extensive meta-analysis up to date, analyzing a total of 107 studies that reported measurements from 5,166 patients with depression and 5,083 controls, found increases in the mean levels of CRP, IL-3, IL-6, IL-12, IL-18, sIL-2R and TNFα in patients with depression \u003csup\u003e14\u003c/sup\u003e. Despite the extensive research on humoral biomarkers, less exploration has been made regarding the involvement of innate and adaptive immune cells in depression \u003csup\u003e15, 16\u003c/sup\u003e. Of note, our research group has been at the forefront of this area, demonstrating significant alterations in the proportion and activation of the three subtypes of circulating monocytes in patients with severe Major Depressive Disorder \u003csup\u003e17\u003c/sup\u003e. These observations have been replicated by others \u003csup\u003e18\u003c/sup\u003e. Furthermore, Lynall et al.\u003csup\u003e19\u003c/sup\u003e, in a case-control study, proposed the existence of a peripheral cell-stratified subgroup termed \u0026ldquo;Inflamed depression\u0026rdquo;. This subgroup is differentiated by distinct myeloid- versus lymphoid-biased immune cell profiles, providing additional insights into the complex interplay between immune cells and depression \u003csup\u003e19\u003c/sup\u003e. A recently meta-analysis confirms widespread alterations in circulating myeloid and lymphoid cells, consistent with dysfunction in both innate and adaptive immunity \u003csup\u003e20\u003c/sup\u003e. Introducing a biologically characterized phenotype of Major Depressive Episode (MDE) into classification systems will hold substantial clinical relevance.\u003c/p\u003e \u003cp\u003eInflammation is a biological phenomenon that can be thought as a response, process, or system state of any perturbations, including physiological and behavioral defenses to promote adaptation to environmental stressors \u003csup\u003e21, 22\u003c/sup\u003e. A deeper understanding of inflammation and its mediators should lead to advances in the therapeutics of mood disorders.\u003c/p\u003e \u003cp\u003eIn the current research landscape in immunology and depression, it's crucial to address a common limitation: many studies concentrate solely on soluble factors or cellular components of the proinflammatory response, potentially neglecting the broader picture. To overcome this, our study aimed to comprehensively characterize and integrate various biochemical parameters (including white blood cells (WBC), CRP, erythrocyte sedimentation rate (ESR)), the pro- and antiinflammatory humoral response (including cytokines, chemokines, and neurotrophic factors) together with the cellular compartment of the innate (classical, nonclassical, and intermediate monocytes), and adaptive immune response (T cells proportions, activation and exhausted state of CD4 T cells as well as regulatory T cells) in addition to clinical characteristics of patients with Mood Disorders who were experiencing an active MDE, compared with those with a remitted MDE, and healthy controls (HC). By encompassing this dual perspective, we aim to understand better the immunological intricacies associated with depression. This inclusive examination is essential for a holistic grasp of these complex conditions.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis multicenter case-control sex and age-matched study started recruiting participants in March 2019 and finished in December 2022. The patients were recruited from the \u003cem\u003eHospital General de Agudos \u0026ldquo;Dr. Teodoro \u0026Aacute;lvarez\u0026rdquo;, Hospital General de Agudos \u0026ldquo;Dr. Enrique Torn\u0026uacute;\u0026rdquo;, Hospital General de Agudos \u0026ldquo;Dr. Cosme Argerich\u0026rdquo;, Hospital General de Agudos \u0026ldquo;Jos\u0026eacute; Mar\u0026iacute;a Ramos Mej\u0026iacute;a\u0026rdquo;\u003c/em\u003e, and \u003cem\u003eHospital Neuropsiqui\u0026aacute;trico \u0026ldquo;Dr. Braulio A. Moyano\u0026rdquo;\u003c/em\u003e in Buenos Aires. All these Hospitals serve a sizable urban catchment area in Buenos Aires and treat mainly low-income patients without insurance. The Institutional Review Board of each Hospital approved the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample\u003c/h2\u003e \u003cp\u003ePatients meeting the following criteria were included: (a) age between 18 and 65 years, (b) diagnosed with DSM 5 MDD or BPD in a current MDE (c) willing and able to sign a consent form to participate. Exclusion criteria were: (a) have a comorbid diagnosis of obsessive-compulsive disorder (OCD), psychotic disorders, or Posttraumatic stress disorder (PTSD), (c) have a diagnosis of borderline personality disorder (BPD), or (d) have a diagnosis of substance use disorder in the last 30 days,.\u003c/p\u003e \u003cp\u003eSex and age-matched healthy controls between 18 and 65 were recruited from the same community, ensuring a comparable sampel. Exclusions for HC were: (a) having the diagnosis of any mental disorder, (b) having a diagnosis of substance use disorder in the last 30 days, (c) having a first-degree relative diagnosed with a mood disorder, d) not having the capacity to sign a consent form.\u003c/p\u003e \u003cp\u003eExclusions for all participants (MDE patients and HC) are (a) the presence of a chronic or acute physical illness with an inflammatory component, (b) receiving medication with antiinflammatory or immunomodulatory properties, (c) getting infected with SARS-CoV-2 in the 30 days previous to the evaluation, (d) having received the vaccine for SARS-CoV-2 or any other vaccine in the 30 days previous to the evaluation, (e) being pregnant, breastfeeding, having had an abortion or miscarriage during the previous 30 days to the evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measures\u003c/h2\u003e \u003cp\u003e A trained interviewer gathered information regarding participant characteristics, including questions regarding clinical and demographic variables. The International Neuropsychiatric Interview, version 7.0.2 \u003csup\u003e23\u003c/sup\u003e was used for diagnostic purposes, and the 17-item Hamilton Depression Rating Scale (HDRS-17) \u003csup\u003e24\u003c/sup\u003e to establish the severity of the MDE.\u003c/p\u003e \u003cp\u003eThen, three groups of participants were defined: Group 1, \u0026ldquo;Patients with an active MDE\u0026rdquo;. The diagnosis of MDE as well as the type of mood disorders (major depressive disorder (MDD) or bipolar disorder (BD)), was determined by the MINI interview. Depression severity was established with the Hamilton Depression Rating Scale 17 (HDRS-17), and a score of \u0026gt;\u0026thinsp;7 was used to define an active MDE. Group 2, \u0026ldquo;Patients with remitted MDE.\u0026rdquo; The diagnosis of a history of MDE and type of mood disorders was determined by MINI. Depression severity with HDRS-17 and a score of \u0026le; 7 was used to define a remitted MDE. Group 3, \u0026ldquo;Healthy Controls\u0026rdquo; (HC) participants didn\u0026rsquo;t meet any diagnostic criteria by the MINI and scored \u0026le;7 on the HDRS-17. The cutoff score of 7 on the HDRS-17 aligns with the recommendation by the NICE guidelines for depression \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, other questionnaires were used to control for other potential sources of variations in the inflammatory level beyond depression, the Columbia-Suicide Severity Rating Scale (C-SSRS) \u003csup\u003e26\u003c/sup\u003e to define if the patients had suicidal ideation or behavior, the Adverse Childhood Experiences (ACEs) questionnaire, the Brugha Stressful Life Events Scale \u003csup\u003e27\u003c/sup\u003e, and the International Physical Activity Questionnaire (IPAQ) \u003csup\u003e28\u003c/sup\u003e. Weight and height were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Blood sample collection, processing, and biochemical analysis\u003c/h2\u003e \u003cp\u003eBlood samples were drawn by venipuncture and collected into EDTA-coated tubes (BD, Vacutainer) in the morning on the same day of completing the psychiatric evaluations. A total of 20 mL of blood was obtained on the day of the clinical assessment. From these, 10 mL was used for routine biochemical laboratory tests, including the Hemogram Analysis, Erythrocyte Sedimentation Rate (ESR), and high-sensitivity C-reactive protein (hs-CRP) measurements. The remaining blood sample was used for the direct Immunophenotyping staining, plasma separation, and peripheral blood mononuclear cells (PBMC) isolation as previously described \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immunophenotyping by direct blood staining\u003c/h2\u003e \u003cp\u003eThree different antibody cocktails were used to determine the circulating monocyte subsets proportion, the activation markers on T cells, and the frequency of Tregs employing the appropriate combination of the following anti-human antibodies (BioLegend) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eMonocytes cocktail\u003c/b\u003e: CD11b-Brilliant Violet 421\u0026trade; (Cat # 101251, RRID: AB_2562904), HLA-DR-PE (Cat # 307606, RRID: AB_314684), CD86-biotin (Cat # 305404, RRID: AB_314524) plus DyLight\u0026trade; 649-conjugated Streptavidin (Cat # 405224), CD14-PE/Cyanine7 (Cat # 325618, RRID: AB_830691), and CD16-fluorescein isothiocyanate (FITC) (Cat # 302005, RRID: AB_314205); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cb\u003eT cell cocktail\u003c/b\u003e: CD3-PE/Cyanine7 (Cat # 300316, RRID: AB_314052), CD4-APC/Cyanine7(Cat # 317418, RRID: AB_571947), CD8-PE (Cat # 317418, RRID: AB_571947), CD69-PerCP/Cyanine5.5 (Cat # 310926, RRID: AB_2074956), CD44-BV421(Cat # 103040, RRID: AB_2616903), PD1-APC (Cat # 621610, RRID: AB_2832830) and LAG3-Alexa Fluor 488 (Cat # 369326, RRID: AB_2721362) and, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cb\u003eTregs\u003c/b\u003e: CD3-PECy7(Cat # 317418, RRID: AB_571947), CD4-APCCy7 (Cat # 317418, RRID: AB_571947), CD25- Alexa Fluor 647 (Cat # 302618, RRID: AB_493045) (surface) and FOXP3-PE (Cat # 320108, RRID: AB_492986) (intracellular).\u003c/p\u003e \u003cp\u003eThe direct staining in 100 \u0026micro;L of fresh anti-coagulated blood sample was standardized in our lab \u003csup\u003e30\u003c/sup\u003e. Briefly, for cell surface antigen staining, samples were incubated with the appropriate antibody cocktail on ice for 30 minutes in the dark and then fixed with 100\u0026micro;L of Citofix Buffer (BD Bioscience) for additional 20 minutes on ice. Then, cells were washed with PBS and centrifuged at 800 x g for 5 minutes. Next, to eliminate erythrocytes, the bottom of blood cells was incubated with 1 mL ACK Lysing Buffer (Thermofisher Scientific) for 10 minutes at 25\u0026ordm;C.\u003c/p\u003e \u003cp\u003eOnly for Tregs, intracellular staining was performed after surface staining, and a specific kit (True-Nuclear\u0026trade; Transcription Factor Buffer Set, Biolegend) was used. Briefly, 300 uL of 1X True Nuclear Fixation Buffer was added and incubated for 60 min at room temperature and in the dark. After that, cell permeabilization was performed by centrifuging the cells at 800G for 5 minutes with 200 uL of the True Nuclear 1X Perm Buffer, repeated twice. The FOXP3 antibody, diluted in True Nuclear 1x Perm Buffer, was added and incubated for 30 min in the dark at room temperature. Cells were maintained with 1X Perm Buffer, and finally, all the three cocktails were washed with 1 mL PBS and analyzed by flow cytometry (BD Canto I) employing the FlowJo software.\u003c/p\u003e \u003cp\u003eThe three monocyte subsets were defined by the expression of CD16 vs. CD14 as classical (CD16\u003csup\u003eneg\u003c/sup\u003eCD14\u003csup\u003e++\u003c/sup\u003e), nonclassical (CD16\u003csup\u003e++\u003c/sup\u003eCD14\u003csup\u003eneg\u003c/sup\u003e), and intermediate (CD16\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003e+\u003c/sup\u003e) as previously reported by our group \u003csup\u003e17\u003c/sup\u003e. In addition, the activation status of CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e lymphocytes were measured by the expression levels of the activation markers CD69 and CD44 and exhaustion markers PD1 and LAG3. Finally, the frequency of Tregs was determined by CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e++\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Plasma level of cytokines, chemokines and neurotrophic factors determined by bead-based immunoassay\u003c/h2\u003e \u003cp\u003ePlasma levels of cytokines, chemokines and neurotrophic factors were measured using two LEGENDplex Panels (Biolegend) that allow the simultaneous quantification of several molecules in 50 uL of the plasma sample. LEGENDplex customized Human Inflammation Panel 1 was employed to measure (IL-1β, IFNγ, IL-17, IL-33, IL-8, IL-10, IL-12p70 and IL-23) and the LEGENDplex Human Neuroinflammation Panel 1 to measure (TGF-β, β-NGF, CX3CL1, BDNF, sTREM-2, IL-18, IL-6, TNFα and MCP-1).\u003c/p\u003e \u003cp\u003eAll experiments were performed following the manufacturer\u0026rsquo;s instructions. The system is a bead-based multiplex assay panel using fluorescence-encoded beads, which can be read by flow cytometry and provides a standard curve to obtain concentrations of each cytokine based on the mean fluorescence intensity of the PE channel.\u003c/p\u003e \u003cp\u003eAdditionally, IL-6 was determined by high-sensitivity ELISA kit (Enzo Life Sciences) following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analysis by flow cytometry\u003c/h2\u003e \u003cp\u003eThe samples were run in an external FACS core facility from the National System of Flow Cytometry, Argentina (FACS Canto I, Becton Dickinson). Data were analyzed by Flowjo software (Tree Star Inc). Bivariate dot plots with appropriate parameters were selected to define the gating strategy. The threshold for positivity was set using fluorescence minus one (FMO) for each marker.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Data analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyzes were performed using RStudio 2022.02.1\u0026thinsp;+\u0026thinsp;461\u003csup\u003e3131\u003c/sup\u003e. Descriptive statistics were used to summarize participants\u0026rsquo; characteristics. Categorical variables were reported as absolute and relative frequencies (%), while quantitative variables were reported as means and standard deviations (SD) for normally distributed variables or as the median and interquartile range (IQR) for non-normally distributed variables. The Shapiro-Wilk test was used to assess the normality of each quantitative variable. The graphics and statistical comparison of Figs.\u0026nbsp;1, 2, and 3 were performed using GraphPad Prism software.\u003c/p\u003e \u003cp\u003eComparisons among participants in the three groups were conducted based on the variable type. Specifically, categorical variables were compared using Pearson's chi-squared or Fisher's exact test, normally distributed quantitative variables were compared using ANOVA, and non-normally distributed quantitative variables were compared using the Kruskal-Wallis rank sum test. In variables with significant between-group differences among groups, pairwise comparisons were performed. The Specific post-hoc test and multiple comparison adjustments employed are indicated in the figure legends. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in all cases. A level of statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered in all analyses cases.\u003c/p\u003e \u003cp\u003eFor subsequent analyses, missing data were imputed using the k-nearest neighbors method with a value of k\u0026thinsp;=\u0026thinsp;5, using the kNN option in the VIM package \u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe sample size of the three groups was calculated considering as objective the identification of significant differences in the quantitative variables by means of the ANOVA test. For a significance level of 5%, a power of 80%, and an effect size f\u0026thinsp;=\u0026thinsp;0.30 (a medium effect), a total of 111 individuals was required, 37 patients per group. We have computed a needed sample size for one-way ANOVA using G*Power 3.1.9.4 software.\u003c/p\u003e \u003cp\u003eThe correlation between variables was evaluated using the Spearman correlation coefficient.\u003c/p\u003e \u003cp\u003eTo select the most important variables for classifying individuals, Random Forest, in combination with the Boruta algorithm, were employed \u003csup\u003e33\u003c/sup\u003e. The dataset was split into a training set comprising 70% of the observations and a test set comprising the remaining 30% while maintaining the proportionality of groups in the original data. Boruta was applied to the training data, and then a Random Forest model with the selected variables was fitted to the test data to evaluate the classification performance of the obtained models.\u003c/p\u003e \u003cp\u003eA clustering analysis was performed considering both active and remitted cases. Initially, a principal component analysis was applied to reduce the dimensionality of the data. The permutation-based test was used to determine the number of components to retain, employing the factoextra \u003csup\u003e34\u003c/sup\u003e and PCAtest packages. Subsequently, a hierarchical clustering analysis consolidated by k-means clustering was conducted based on the retained factors from the previous analysis. The suggested partition was determined based on the relative gain of inertia using the HCPC option in the FactoMineR package \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sociodemographic and clinical characteristics of the participants.\u003c/h2\u003e \u003cp\u003eThe sociodemographic and clinical characteristics of the participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We recruited 121 participants: 39 patients with an active MDE (32.2%), 40 with a remitted MDE (33.1%), and 42 HC subjects (34.7%). The three groups did not differ in sex and age, showing an appropriate matching. As anticipated, patients showed higher levels of unemployment and lower educational level compared to HC. On the other hand, as expected, the patients presented higher scores on the HAMD-17, higher levels of suicidal risk, and more than 85% were undergoing psychopharmacological treatment. Also, patients showed higher levels of adverse childhood events and stressful events. Additional clinical characteristics of the patients are described in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and clinical characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive MDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemitted MDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.00 [34.50, 50.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00 [29.50, 52.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.00 [30.25, 49.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 ( 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 ( 33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCivil status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 ( 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 ( 31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced/Widower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 ( 27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 ( 11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 ( 60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 ( 57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScholarship (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 ( 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 ( 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 ( 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 ( 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 ( 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 ( 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 ( 45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 ( 38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 ( 27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 ( 54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u0026thinsp;=\u0026thinsp;Unemployed/Retired (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 ( 40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 ( 11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic summary (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 ( 52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 ( 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor depressive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 ( 40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-specific Bipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 ( 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone depressive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAM-D Total score (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.00 [11.00, 18.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50 [1.00, 5.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn pshycopharmacological treatment\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 ( 95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of ACEs (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 [2.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50 [2.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 1.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPAQ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 ( 45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 ( 45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 ( 22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 ( 28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 ( 32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 ( 26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide ideation month\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 ( 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide Behaviour month\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 ( 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 ( 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide ideation life\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide Behaviour life\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 ( 40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Stressful Life Events (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00 [7.00, 14.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00 [7.75, 14.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.50 [6.00, 12.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStressful Life Events Total Score (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307.00 [206.50, 444.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298.00 [197.50, 426.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.50 [139.25, 316.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.00 [60.05, 79.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.80 [60.00, 84.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.00 [60.75, 78.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.38 (8.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.93 (8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167.76 (8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.40 [22.55, 30.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.70 [22.75, 30.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.25 [22.00, 27.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmount of cigarettes per day (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 6.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 ( 27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 ( 75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 The inflammatory status of MDE patients is characterized by monocytosis and an altered proportion of monocyte subsets.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe routine biochemical laboratory tests, using an automated cell counter and analyzer, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant differences were observed when comparing HC and MDE patients with active disease. These differences were observed in the median percentage of monocytes (p\u0026thinsp;=\u0026thinsp;0.011) and their absolute count (p\u0026thinsp;=\u0026thinsp;0.001) and for the mean percentage of lymphocytes (p\u0026thinsp;=\u0026thinsp;0.004), even though its absolute number did not change, among groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the biochemical parameters among participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive MDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemitted MDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.20 [39.18, 43.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.65 [39.42, 44.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.50 [40.90, 44.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.30 [12.80, 14.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.80 [12.93, 14.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.10 [13.40, 14.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocytes (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.73 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.71 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.85 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.73 [85.20, 91.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.00 [85.74, 91.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.47 [84.06, 89.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.00 [28.08, 30.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.00 [27.88, 30.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.79 [27.86, 29.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.80 [32.50, 33.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.90 [32.38, 33.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.33 [32.51, 33.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocytes (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.40 [6.10, 9.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.56 [5.20, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.60 [5.70, 7.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophils Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.00 [52.00, 66.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.00 [50.00, 64.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.00 [50.00, 60.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02 [3.35, 5.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.69 [2.70, 4.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.59 [3.02, 4.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes Percentage (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.38 (7.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.62 (11.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.16 (6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.44 [1.86, 2.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09 [1.87, 2.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52 [2.15, 2.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.50 [3.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.50 [3.00, 7.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 [2.00, 5.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 [0.27, 0.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30 [0.19, 0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22 [0.14, 0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00 [2.00, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00 [1.15, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00 [1.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15 [0.11, 0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13 [0.09, 0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13 [0.10, 0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00 [10.00, 21.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 [7.00, 13.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.00 [6.25, 12.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.00 [22.75, 34.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.50 [25.25, 36.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.00 [23.00, 36.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.38 [0.94, 9.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.70 [0.95, 10.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.10 [7.22, 10.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.50 [15.25, 30.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.00 [19.00, 31.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.00 [18.00, 30.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.50 [13.25, 33.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.00 [20.00, 34.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.00 [18.00, 31.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125.00 [81.00, 189.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.50 [121.75, 202.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155.00 [118.00, 179.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Bilirubin (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 [0.38, 0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48 [0.34, 0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66 [0.50, 0.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137.85 (2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.11 (4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138.37 (3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30 [4.00, 4.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.30 [4.00, 4.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.30 [4.00, 4.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.00 [90.00, 100.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.00 [90.00, 101.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.00 [89.00, 99.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.81 [0.52, 9.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 [0.56, 3.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55 [0.20, 1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, the median percentage of monocytes (p\u0026thinsp;=\u0026thinsp;0.016) and their absolute number (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were increased in patients with active MDE compared to HC. These results indicate that the main hematopoietic response is coming from monocytosis promotion. Additionally, a reduced percentage of lymphocytes (p\u0026thinsp;=\u0026thinsp;0.0024) was also found in patients with active MDE compared to HC (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA)\u003c/b\u003e. No significant changes were observed in other cellular compartments (neutrophils, eosinophils, basophils, including erythrocytes).\u003c/p\u003e \u003cp\u003eAnother two peripheral blood inflammatory indicators are the hs-CRP and the ESR, and a chronic low-grade systemic inflammation could be reflected by a small but significant increment of their values. In this sense, we observed a significant difference in the concentration of hs-CRP (p\u0026thinsp;=\u0026thinsp;0.002) and ESR value (p\u0026thinsp;=\u0026thinsp;0.003), among groups, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Furthermore, we detected an increase in the concentration of hs-CRP in active MDE and remitted MDE compared to HC (p\u0026thinsp;=\u0026thinsp;0.004 and 0.021, respectively) and ERS value in active MDE compared to remitted MDE and HC (p\u0026thinsp;=\u0026thinsp;0.028 and 0.003, respectively), see \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eConsidering the monocytosis and the increment in the systemic proinflammatory parameters hs-CRP and ESR in patients with MDE, we also evaluated changes in the proportion of the three subtypes of circulating monocytes, classical (CD14\u003csup\u003e++\u003c/sup\u003eCD16\u003csup\u003e\u0026minus;\u003c/sup\u003e), intermediate (CD14\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e), and nonclassical (CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCD16\u003csup\u003e++\u003c/sup\u003e) by flow cytometry, as another proinflammatory hallmark. As we previously reported using peripheral blood mononuclear cells (PBMCs) \u003csup\u003e17\u003c/sup\u003e, here we also found, by direct staining on fresh peripheral blood, a higher proportion of nonclassical and intermediate monocytes in concordance with a reduced percentage of classical monocytes in both active and remitted patients with MDE vs. HC, see \u003cb\u003eFig.\u0026nbsp;1A-D\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Increased activation and exhausted phenotype in CD4 lymphocytes of patients with MDE is associated with a higher frequency of FOXP3 regulatory T cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEven though we observed a reduced percentage of total lymphocytes in the hemogram, no difference was found in the absolute number (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Neither the percentage of CD4 T cells, nor the ratio of CD4/CD8 T cells measured by flow cytometry showed significant differences between patients with MDE and HC (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). Nonetheless, we observed a significant increment in the median percentages of activation markers CD69\u003csup\u003e+\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.007), and exhaustion markers PD1\u003csup\u003e+\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.013) and LAG3\u003csup\u003e+\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.014), on CD4\u003csup\u003e+\u003c/sup\u003e T lymphocytes in patients with active MDE compared to HC, see \u003cb\u003eFig.\u0026nbsp;2A-D\u003c/b\u003e. We did not observe significant changes in the CD44 activation marker (\u003cb\u003eSupplementary Table S2).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEven though there is no consensus regarding the reduction or increase number of regulatory T cells in depression, we found here a significant increase in the frequency of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Tregs in patients with active (p\u0026thinsp;=\u0026thinsp;0.003) as well as remitted MDE (p\u0026thinsp;=\u0026thinsp;0.015) compared with HC, see \u003cb\u003eFig.\u0026nbsp;2E\u003c/b\u003e. No differences were observed between active vs. remitted MDE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Inflammatory and Neuroinflammation panels showed increased levels of sTREM2, IL-17, and IL-6 in MDE patients.\u003c/h2\u003e \u003cp\u003eThe LEGENDPlex system was used to assess sixteen molecules, including cytokines, chemokines, and neurotrophic factors, in the plasma of patients with MDE. The level of each molecule was quantified based on the standard curve (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, S1B)\u003c/b\u003e. This assessment utilized a customized human inflammatory panel (Fig.\u0026nbsp;3A\u003cb\u003e)\u003c/b\u003e along with a human neuroinflammation panel (Fig.\u0026nbsp;3B\u003cb\u003e)\u003c/b\u003e. Interestingly, we have found a robust and significantly higher level of sTREM2, a biomarker of microglia activation, in patients with active MDE compared with HC (p\u0026thinsp;=\u0026thinsp;0.0089), see \u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e. We have also observed a significant higher level of IL-17 in patients with remitted MDE compared with HC (p\u0026thinsp;=\u0026thinsp;0.0147), see \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e, and a trend in the median value comparing active MDE patients vs HC (p\u0026thinsp;=\u0026thinsp;0.068). Although with no statistical differences, some classical cytokines from the innate and adaptive immune response (IL-1β, IL-12, IFN-γ, and IL-10), showed an increased concentration trend, see \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA strong positive correlation was observed between IL-10 and IL-12 (r\u0026thinsp;=\u0026thinsp;0.781), IL-33 and IL-10 (r\u0026thinsp;=\u0026thinsp;0.726), as well as IL-1β and IFN-γ (r\u0026thinsp;=\u0026thinsp;0.704), based on a correlation analysis using the Spearman test. For detailed correlation results, please refer to \u003cb\u003eSupplementary Fig. S2 and Supplementary Table S3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eConsidering that the IL-6 measurement was under the low range of detection of the legendPlex system, we have measured this cytokine in the plasma of HC and patients with MDE employing a highly sensitive ELISA kit (Enzo Life Sciences). We found that patients with active MDE show higher levels than the remitted MDE group (p\u0026thinsp;=\u0026thinsp;0.0278) \u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Boruta and Random Forest validate discriminatory power of biological markers in patients with MDE.\u003c/h2\u003e \u003cp\u003eThe Boruta selection algorithm was used to find the most important markers to discriminate individuals between MDE patients and HC, as well as active MDE, remitted MDE and HC.\u003c/p\u003e \u003cp\u003eFirst, the model was applied to discriminate between patients with MDE vs HC. From the training set (n\u0026thinsp;=\u0026thinsp;84), the variables lymphocytes percentage, monocytes absolute count, classical monocytes, nonclassical monocytes, Intermediate monocytes, hs-CRP, MCP1, CD4\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eLAG3\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e FOXP3\u0026thinsp;+\u0026thinsp;Tregs were selected (Fig.\u0026nbsp;4A). These markers were used in a Random Forest model to classify a separate test dataset, achieving an overall classification accuracy of 83.8%. Twenty-one over twenty-four (21/24) patients with MDE were correctly classified (87.5%) and 10/13 HC (76.9%).\u003c/p\u003e \u003cp\u003eAfterwards, the model was applied to be able to discriminate among patients with active MDE, remitted MDE and HC. Similarly, the Boruta selection algorithm identified markers that were important for discrimination: classical monocytes, nonclassical monocytes, intermediate monocytes, monocytes absolute count, ESR, hs-CRP, CD4\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eLAG3\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Tregs, (Fig.\u0026nbsp;4B). Random Forest model was then trained using these selected variables and applied to classify the test dataset, achieving an overall classification accuracy of 70%. Here, 7/12 patients with active MDE (58.3%), 8/12 remitted MDE (66.7%), and 10/13 HC (84.6%) were correctly classified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Clustering analysis indicates different inflammatory segregation among patients with MDE\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was applied to reduce the dimensionality of the data, yielding nine principal components, which together accounted for 62.5% of the total variance-covariance. The first principal component (PC1, 14.2% total variance-covariance) was most strongly weighted on IL-17A, IL-23, IL-10, IL-12p70, IL-33, TNF-alpha, bNGF, the absolute count and the percentage of Basophils and the percentage of Monocytes. The second principal component (PC2, 10.4% total variance-covariance) was most weighted on the absolute count and the percentage of segmented neutrophils, the absolute count of Monocytes and Leukocytes, the percentage of Lymphocytes, HS CRP and IL-8. The factor loadings of the variables in the nine principal components are represented in \u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe Clustering analysis was used to detect different inflammatory segregation among MDE patients. Three distinct clusters were identified, as revealed by the dendrogram graphic (Fig.\u0026nbsp;5B).\u003c/p\u003e \u003cp\u003eComparing the clinical and sociodemographic characteristics among the three clusters we observed similar distribution of the active or remitted condition of MDE with no significant statistical differences (p\u0026thinsp;=\u0026thinsp;0.623), neither on the severity of depressive symptoms considering HAMD-17 scale (p\u0026thinsp;=\u0026thinsp;0.398). Nonetheless cluster 1 and 3 showed median score higher than 7 (HAMD-17\u0026thinsp;=\u0026thinsp;9.5 and 8, respectively) compared with cluster 2 (HAMD-17\u0026thinsp;=\u0026thinsp;6), see \u003cb\u003eSupplementary Table S4\u003c/b\u003e. A significant difference was observed in the percentage of subjects receiving pharmacological treatment among clusters. In Cluster 1, 95.3% of the cases were under psychopharmacological treatment, as well as 95.0% in Cluster 2, in contrast, only 60% of individuals in Cluster 3 were receiving treatment (p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe biochemical parameters analysis across these three clusters show again that clusters 1 and 3 displayed higher median values of absolute leukocyte number. In concordance, cluster 1 showed the highest value of lymphocyte number, and cluster 3 the highest percentage and absolute number of monocytes \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Furthermore, cluster 3 also exhibited the highest medians for basophil percentage and absolute count.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of biochemical parameters among clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocytes (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.73 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.72 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.69 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.86 [87.00, 93.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.44 [86.23, 90.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.00 [84.50, 90.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocytes (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.68 [6.18, 9.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.40 [5.00, 7.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.56 [5.88, 7.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophils Percentage (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.91 (11.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.21 (9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.86 (6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.46 [3.29, 6.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16 [2.50, 4.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.84 [3.28, 4.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.65 [25.00, 41.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.00 [31.50, 43.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.80 [25.50, 33.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.58 [2.07, 2.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09 [1.84, 2.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.86 [1.68, 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.00 [3.00, 8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00 [3.00, 5.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.60 [7.10, 9.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 [0.21, 0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21 [0.19, 0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52 [0.46, 0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00 [1.00, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00 [2.00, 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00 [1.65, 4.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15 [0.09, 0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11 [0.10, 0.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14 [0.11, 0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils Percentage (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 [0.55, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils absolute count (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06 [0.03, 0.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00 [7.00, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.00 [9.00, 18.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00 [5.00, 26.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.10 [0.51, 7.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49 [0.91, 3.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03 [0.59, 3.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe proportion of the three monocyte subtypes is altered in patients with MDE, but the segregation in these three clusters did not show significant differences (\u003cb\u003eSupplementary Table S5\u003c/b\u003e). Interestingly, the lowest percentage of CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cells, determined by flow cytometry, was observed in cluster 3, in concordance with the biochemical laboratory analysis. In the same sense, clusters 1 and 3 showed an increased median value of exhaustion CD4\u003csup\u003e+\u003c/sup\u003eLAG3 marker compared to cluster 2 (\u003cb\u003eSupplementary Table S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eConsidering cytokines, chemokines, and neurotrophic factors, the cluster segregation denoted that cluster 3 is characterized by a clear proinflammatory profile with high levels of TNFα, CX3CL-1, IL-12p70, IL-17A, IL-23, and IL-33, associated with high level of IL-8 and IL-10, as well as increased medians for b-NGF and the lowest level for BDNF, \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Cluster 2 is mainly characterized by the highest level of MCP1, IL-1β, IFN-γ, IL-23, and IL-8. Cluster 1 displayed the lowest median level of most cytokines \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInflammatory and Neuroinflammation Panel of Cytokines among clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCP1 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.53 [33.01, 78.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.17 [46.09, 111.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.73 [26.18, 46.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esTREM2 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e976.33 [779.43, 1300.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1044.76 [748.32, 1263.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e931.84 [736.20, 1170.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDNF (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5120.64 [2920.72, 7086.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4854.92 [2078.03, 6313.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2786.38 [1462.40, 4050.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 [0.87, 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.53 [0.97, 1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62 [0.81, 5.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebNGF (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.24 [4.39, 18.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.79 [12.78, 19.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.31 [12.95, 71.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-18 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.80 [41.46, 236.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105.73 [15.55, 173.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.89 [93.46, 218.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-alpha (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 38.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.98 [56.81, 135.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.93 [61.75, 179.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCX3CL-1 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437.32 [420.50, 993.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e517.64 [461.84, 587.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1361.44 [907.78, 1717.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1beta (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.19 [6.46, 44.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.89 [38.62, 104.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.01 [13.92, 40.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFN-gama (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.69 [3.30, 12.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.12 [17.31, 29.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.35 [7.27, 23.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-8 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.49 [0.00, 10.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.72 [29.88, 129.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.33 [16.18, 45.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-10 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 7.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.05 [9.09, 28.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.48 [12.85, 37.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-12p70 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.44 [3.24, 6.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.52 [8.78, 13.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.91 [6.86, 18.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-17A (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 [0.00, 1.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16 [2.63, 4.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.15 [2.58, 11.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-23 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.25 [3.09, 10.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.06 [12.10, 24.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.88 [14.93, 27.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-33 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.30 [10.21, 50.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.69 [67.52, 124.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216.85 [107.88, 303.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe cluster analysis suggests that patients with MDE have inflammatory signs, identifiable by cellular and plasma molecules characterization. Each cluster could represent a different stage of the same process or different inflammatory pathways reaching the same phenotype.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study provides a comprehensive understanding of the immune system in patients with MDE across different stages of the disease, comparing it with HC. The most novel findings include increased monocytosis with an increment of intermediate and nonclassical monocyte subsets at the expense of classical monocytes, indicating a transitional activation of the monocytic population. We also observed a notable augmentation in the activation of CD4 T lymphocytes and elevated exhaustion markers in patients with active MDE compared to HC. Furthermore, there was a significant increase in the frequency of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Tregs in both active and remitted MDE patients compared to HC, which could be reflecting a compensatory anti-inflammatory immune system response. Finally, we observed increased levels of soluble markers of neuroinflammation, such as sSTREM2 and IL-17. Machine learning techniques identified a panel of biomarkers that can discriminate between patients with MDE and HC with an overall classification accuracy of 83.8%. Most of these biomarkers are related to immune cell activation. Finally, cluster analysis suggests three distinct clusters unrelated to the clinical expression of the disease.\u003c/p\u003e \u003cp\u003eSince the 1990s, it has been established that depression is associated with increased white blood cell count and monocytes \u003csup\u003e36\u003c/sup\u003e, which led to the formulation of the monocytes and lymphocytes hypothesis in MDD \u003csup\u003e37\u003c/sup\u003e. This hypothesis suggests that alterations in these immune cells play a role in the pathophysiology of depression. A recently published meta-analysis also supported this by demonstrating an overall increase in the total number of monocytes in depressed individuals (seven studies; SMD\u0026thinsp;=\u0026thinsp;0.60; 95% CI, 0.19\u0026ndash;1.01; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;66%)\u003csup\u003e20\u003c/sup\u003e. Consistent with these findings, our study observed a significant monocytosis in patients experiencing an active major depressive episode (MDE). These patients exhibited a clear elevation in the median percentage and absolute number of monocytes compared to HC. These results suggest an abnormal hematopoietic response in individuals with depression, specifically an enhanced production of monocytes. We further investigated the proportion of different subtypes of circulating monocytes (classical, intermediate, and nonclassical) using flow cytometry. The results indicate an expansion for the nonclassical and intermediate monocytes and a reduced percentage of classical monocytes in patients with MDE compared to HC. Even though we did not find statistical differences comparing active vs. remitted MDE, more pronounced changes were observed in active conditions. These results indicate that patients with MDE are characterized by a proinflammatory status and enhanced transition to intermediate and nonclassical subsets, as predicted by the monocyte transitional model of Patel et al. \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe have been one of the first groups to describe changes in the percentage and activation status of the three circulating monocyte subtypes in patients with severe MDD \u003csup\u003e17\u003c/sup\u003e. Herein, we reinforce and expand the aforementioned findings by utilizing a distinct sample of patients, ensuring sex and age matching, and employing a simplified approach of directly measuring immune parameters in a small blood sample. This technical approach holds promise for potential translation into clinical practice, as it offers a convenient and feasible screening method.\u003c/p\u003e \u003cp\u003eDysregulation of the adaptive immune system in MDE patients has been suggested, with decreased numbers of circulating T cells, an increase in the ratio of CD4\u003csup\u003e+\u003c/sup\u003e relative to CD8\u003csup\u003e+\u003c/sup\u003e T cells, and some immunosuppression features \u003csup\u003e15\u003c/sup\u003e. Our finding also suggests a dysregulation of the T cell compartment in patients with mood disorders during MDE. While there were no significant differences in the absolute numbers of total lymphocytes, CD4 T cells, or the ratio of CD4 to CD8 T cells between MDE patients and HC, a notable increment in the activation status of CD4\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003e, and exhausted CD4\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eLAG3\u003csup\u003e+\u003c/sup\u003e T lymphocytes were observed in patients with active MDE compared to healthy controls. These results indicate that a CD4 lymphocyte activation process is ongoing in patients with MDE, associated with potential exhaustion of this compartment in the pathogenesis of depression. In this sense, it has been demonstrated that memory CD4\u003csup\u003e+\u003c/sup\u003e T cells are abundant in adult humans, and its activation does not necessary depend on the encounter with the antigen \u003csup\u003e39, 40\u003c/sup\u003e. The increased markers of cellular activation and exhaustion in CD4 T cells among patients with MDE is a novel concept that may explain the high comorbidity of these patients with non-psychiatric medical conditions \u003csup\u003e41\u003c/sup\u003e, particularly autoimmune diseases \u003csup\u003e42\u003c/sup\u003e. Furthermore, this concept aligns with recent studies that have demonstrated a higher degree of premature T cell aging \u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, there was a significant increase in the frequency of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Tregs in both active and remitted MDE patients compared to healthy controls. Tregs play a crucial role in maintaining immune homeostasis and suppressing excessive immune responses. The observed increase in Tregs suggests a compensatory mechanism by the immune system to counterbalance potential proinflammatory processes and regulate immune activity in MDE, as was suggested by the compensatory immune-regulatory reflex system (CIRS) concept \u003csup\u003e44\u003c/sup\u003e. CIRS is involved in MDD and BD by regulating the primary immune-inflammatory response, thereby contributing to spontaneous and antidepressant-promoted recovery from the acute phase of illness \u003csup\u003e44\u003c/sup\u003e. The simultaneously increased levels of both the pro-and antiinflammatory cytokines are reported in the brain of MDD patients; this indicates activity of both the IRS and CIRS in MDD. Speculation is rife that the disrupted IRS-CIRS elements might determine the onset, episodes, neuroprogressive processes, treatment response, and recovery of patients with MDD \u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with MDE exhibit the typical features of an ongoing inflammatory response, including increased expression of proinflammatory cytokines and their receptors, elevated acute phase reactive proteins levels, and adhesion molecules in peripheral blood, cerebrospinal fluid, and brain \u003csup\u003e14, 46\u003c/sup\u003e. Nonetheless, soluble proinflammatory biomarkers can be increased in depression as well as numerous inflammatory pathologies; there are clear differences in the magnitude or concentration levels of these factors comparing acute inflammation in response to infections compared to the low-grade systemic inflammation as reflected by chronic conditions. High sensitivity CRP (hs-CRP) is a marker of acute phase response, but it has been used extensively as a measure of low-grade inflammation in psychiatric \u003csup\u003e47\u003c/sup\u003e and physical conditions \u003csup\u003e48, 49\u003c/sup\u003e. Meta-analyses of cross-sectional studies confirm that mean concentrations of circulating hs-CRP and inflammatory cytokines such as interleukin 6 (IL-6) are higher in patients with acute depression than controls \u003csup\u003e10, 12, 50, 51\u003c/sup\u003e. Our findings also show increased hs-CRP levels in patients with active MDE compared to HC, but interestingly, we found that individuals with MDE in remission still exhibited elevated hs-CRP levels compared to HC, suggesting that residual inflammation may persist even after symptom improvement. Similarly, our study revealed elevated levels of IL-6 in individuals with active MDE compared to those with MDE in remission. This indicates that IL-6 may serve as a marker of ongoing inflammation during the active phase of the disease. Altogether, these findings underscore the role of inflammation in MDE.\u003c/p\u003e \u003cp\u003eIn addition to the two well-described nonspecific markers of inflammation, our study found a potential novel, more specific biomarker of neuroinflammation for MDE, the soluble triggering receptor expressed on myeloid cell 2 (sTREM2). This is a protein receptor largely expressed in microglial cells in the brain. It plays a crucial role in regulating microglial function and modulating the immune response in the central nervous system (CNS). sTREM2 refers to the soluble form of this receptor, which can be measured in the cerebrospinal fluid (CSF) or peripheral blood. Changes in sTREM2 levels have been associated with the activation of microglia in neurodegenerative and neuroinflammatory diseases. A recent metanalysis of 22 observational studies, which included 5716 participants, comparing individuals with Alzheimer\u0026rsquo;s vs. controls, showed a significant increase in CSF of sTREM2 level (standardized mean difference [SMD]: 0.41, 95% confidence intervals [CI]: 0.24, 0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003csup\u003e52\u003c/sup\u003e. This marker also increased in conditions of neuroinflammation, such as angiitis of the CNS \u003csup\u003e53\u003c/sup\u003e and amyotrophic lateral sclerosis (ALS) \u003csup\u003e54\u003c/sup\u003e. The role of sTREM2 in MDE has not been studied, and the present study is the first one reporting increased sTREM2 levels in the plasma of patients with active MDE compared with HC. Further research is needed to fully understand the role of sTREM2 in MDE and its role as a diagnostic or therapeutic target.\u003c/p\u003e \u003cp\u003eWe also identified an increase in IL-17 levels, constituting an exciting soluble marker due to its association with autoimmune pathologies \u003csup\u003e55\u003c/sup\u003e. IL-17 is considered a signature cytokine of CD4\u003csup\u003e+\u003c/sup\u003e T helper 17 (Th17) cells; however, it can also be produced by different cell types, including CD8\u003csup\u003e+\u003c/sup\u003e T cells, natural Th17 cells, innate lymphoid cells (ILCs), γδ T cells, natural killer (NKT) cells, and neutrophils. Animal studies have indicated that inflammatory Th17 cells contribute to depression-like behavior \u003csup\u003e56\u003c/sup\u003e. Interestingly, studies have demonstrated that the administration of anti-interleukin-17A (IL-17A) antibodies can lead to a reduction in depressive symptoms in mice \u003csup\u003e57\u003c/sup\u003e. In humans, there have been few studies that have examined the role of IL-17 in depression. One of the most recent studies found an increase in IL-6 and IL-17 levels in patients with a first depressive episode compared to controls \u003csup\u003e58\u003c/sup\u003e. In the same study, treatment with antidepressants decreased plasma levels of IL-6 and IL-17, although the latter remained elevated compared to controls \u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, this study also revealed that the HAMD score exhibited a moderate correlation with IL-6 and a strong correlation with IL-17 \u003csup\u003e58\u003c/sup\u003e. This study suggests that autoimmunity may play a role in the etiology or pathogenesis of depression. Our results support this idea, as we have observed significant elevated levels of IL-17 in patients with remitted MDE and a trend (p\u0026thinsp;=\u0026thinsp;0.06) in active MDE compared to HC. The fact that this cytokine is elevated in remitted patients may indicate that inflammation can have a chronic role in depression beyond periods of active illness, which could be highly relevant for those cases of mood disorders characterized by neuroprogression.\u003c/p\u003e \u003cp\u003eThe two Boruta analyses revealed different markers for discriminating between MDE patients and HC. First, comparing between MDE (active and remitted) and HC, the selected markers, including lymphocytes percentage, monocytes absolute count, classical monocytes, non-classical monocytes, intermediate monocytes, hs-CRP, MCP1, CD4\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eLAG3\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003eTregs, demonstrated a significant discriminative potential. When the Random Forest model was applied to an independent test dataset, an impressive overall classification accuracy of 83.8% was achieved. Secondly, when considering the classification of active MDE, remitted MDE, and healthy controls, the Boruta algorithm identified important markers, including classical monocytes, non-classical monocytes, intermediate monocytes, monocytes absolute count, ESR, hs-CRP, CD4\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eLAG3\u003csup\u003e+\u003c/sup\u003e, and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e Tregs. Subsequently, the Random Forest model, trained using these selected variables, demonstrated an overall classification accuracy of 70%. Altogether, these findings highlight the potential of utilizing immune cell biomarkers to differentiate MDE patients from HC, as well as distinguish between different states of the disorder. The discriminatory power exhibited by the selected markers in the Random Forest model suggests their relevance in understanding the underlying mechanisms and aiding in the diagnostic process of MDE. Further research and validation studies are warranted to explore these markers' clinical utility and generalizability in larger and more diverse patient populations.\u003c/p\u003e \u003cp\u003eThe clustering analysis revealed the presence of three distinct clusters based on immunological profiles among patients with MDE. First, these results suggest that patients with MDE, regardless of whether they are experiencing or have remitted from a MDE, exhibit signs of an inflammatory state. Cluster 1 is characterized by the highest number of leukocytes, mainly given by the increment in lymphocyte count. Nonetheless, this cluster showed the lowest proinflammatory cytokines levels, probably due to a different state of the inflammation process. Cluster 3 displayed the most robust inflammatory pattern, with high levels of TNFα, CX3CL-1, IL-12p70, IL-17A, IL-23, and IL-33, associated with the highest level of IL-10, as well as increased medians for b-NGF and the lowest level for BDNF. This profile is also associated with the highest absolute number and percentage of circulating monocytes as well as the lowest absolute number and percentage of circulating lymphocytes, denoting an active inflammatory process. Noteworthy, a lower percentage of individuals in Cluster 3 were receiving pharmacological treatment, indicating a potential association between the immunological profile and treatment status. Cluster 2 has some cardinal signs of more acute inflammation as the elevated levels of MCP1, which precede the monocytosis, but also increased levels of some proinflammatory cytokines such as IL-1β, IFN-γ, and IL-8. Similarly, the absolute number of monocytes is closer to a HC value, as well as the percentage of lymphocytes, suggesting as possible initiation of the inflammatory process.\u003c/p\u003e \u003cp\u003eBased on these results, the following questions emerge: Do the observed clusters represent distinct stages of the same underlying process, or do they indicate different inflammatory pathways that converge to produce a common phenotype? The lack of significant differences in the distribution of active or remitted MDE and the severity of depressive symptoms across the clusters suggests that they may not signify distinct stages of the same illness. However, it cannot be ruled out that these clusters represent different trajectories of the same disease, considering the limitations of our cross-sectional study design. Definitive answers to these questions will require future studies with a longitudinal design, which will provide further insights into this matter.\u003c/p\u003e \u003cp\u003eOur study demonstrates several noteworthy strengths. Firstly, we assessed changes in cellular levels of the monocyte compartment and T cells, considering a specific plasma cytokine milieu. This allowed us to establish a distinct profile for MDE patients, defining subtypes of the condition. This approach fills a critical gap in the literature, as this area has received inadequate attention thus far. Secondly, our standardized methodology employed three cocktails of antibodies with a minimal blood sample volume of only 100 \u0026micro;l each. This approach allowed us to accurately measure the proportion and activation of monocytes, the proportion of CD4 to CD8 lymphocytes, and Tregs, as well as the activation and exhaustion of T cells, utilizing direct blood staining. Such an approach holds promise for rapid translation into clinical practice. Thirdly, our study is a multicenter investigation that carefully matched participants based on age and sex, two variables known to significantly influence the immune system. By controlling for these factors, we strengthened the validity and generalizability of our results. Finally, our rigorous patient selection process excluded individuals with known causes of inflammation or immune system activation, ensuring the focus remained on the specific MDE condition. Additionally, we included patients at various stages of the disease, a novel aspect not previously explored in MDE immunotyping studies.\u003c/p\u003e \u003cp\u003eSome limitations of the study need to be acknowledged. Firstly, we measured the immune cell profile in peripheral blood, which may not fully reflect the immune activity in the central nervous system (CNS). However, evidence suggests that inflammatory factors originating in the blood can reach the CNS through various pathways, including passive or active transport across the blood-brain barrier, immune cell transmigration, and vagal nerve signaling. While peripheral blood analysis provides valuable insights, it is important to recognize the potential disparities between peripheral and CNS immune responses. Another significant limitation is that most participants received psychopharmacological treatment at the time of inclusion. It is well-known that many psychotropic medications can impact the immune system, potentially confounding the interpretation of immunological findings. Furthermore, our study focused exclusively on individuals with MDE, which could be a limitation as MDD and BP may exhibit distinct immune profiles. However, in clinical practice, MDE is the most commonly encountered presentation, and there are currently no precise indicators that reliably classify between these two groups. Therefore, we included MDD and BP patients to explore whether immunological markers could provide insights into their shared pathophysiology.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study provides valuable insights into the immunological aspects of MDE providing a global view of the phenomenon, analyzing both the humoral and the innate and adaptive cellular components. Further research is needed to fully understand the implications of these immune alterations in a longitudinal process to pave the way for potential advancements in clinical practice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAUTHORSHIP CONTRIBUTION\u003c/p\u003e\n\u003cp\u003eA.R.A. and V.T.: Investigation, Methodology and samples processing, Formal analysis, Validation, Visualization, review and edit the manuscript.\u003c/p\u003e\n\u003cp\u003eL.N.G., R.I.A.C, A.O., M.B.P., F.H., and C.R.P.: Investigation, Recruitment and follow up of patients, sample collection, data curation, review and edit the manuscript.\u003c/p\u003e\n\u003cp\u003eL.C.C: Data curation, Methodology, Software analysis, Validation, Visualization, review and edit the manuscript.\u003c/p\u003e\n\u003cp\u003eG.V.: Conceptualization, analysis and discussion, review \u0026amp; editing manuscript.\u003c/p\u003e\n\u003cp\u003eF.M.D., E.A.C.S. and A.E.E.: Conceived and designed the study, conceptualization, Funding acquisition, Methodology, Supervision, Project administration, Resources, Writing-original draft, Writing-review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGEMENTS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors want to acknowledge the participants gratefully, and professionals for technical assistance. E.A.C.S. and A.E.E. want to thank Dr. Florencia Quiroga for the availability of the BD FACSCanto I and also thank the advice of Dr. Virginia Polo, and Dr. Tom\u0026aacute;s Langer from INBIRS CONICET-UBA and the National System of Flow Cytometry, Argentina. We would like to express our sincere gratitude to Dr. Oscar Bottasso for taking the time to read and provide valuable insights on our study\u0026rsquo;s results and to Sudan Neupane for their invaluable feedback and insightful suggestions that significantly improved the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are available from the corresponding authors.\u003c/p\u003e\n\u003cp\u003eDisclosure of Interest: The authors have declared no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding sources\u003c/p\u003e\n\u003cp\u003eThe study was supported by grants from:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eBrain \u0026amp; Behavior Research Foundation. 2019 NARSAD Young Investigator Grant ID 27855\u003c/li\u003e\n \u003cli\u003eMinisterio de Ciencia, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n, Argentina through PID-2018-0054 to F.M.D.\u003c/li\u003e\n \u003cli\u003eConsejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas (CONICET), Argentina, trough PIP 2015-0567 to A.E.E,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAgencia Nacional de Promoci\u0026oacute;n Cient\u0026iacute;fica y Tecnol\u0026oacute;gica (ANPCyT), Argentina, through PICT 2017-2431 to A.E.E and PICT 2018-03070 to E.A.C.S.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA.R.A., and L.C.C are recipients of fellowships from CONICET and, V.T., A.O. and R.I.A.C are recipients of fellowships from the Agencia Nacional de Promoci\u0026oacute;n Científica y Tecnol\u0026oacute;gica (ANPCyT), Argentina.\u003c/p\u003e\n\u003cp\u003eF.M.D., E.A.C.S and A.E.E. are career investigators at CONICET, Argentina.\u003c/p\u003e\n\u003cp\u003eThe funding sources had no involvement in the study design, in the collection, analysis, and interpretation of data, in the writing of the manuscript, and in the decision to submit the paper for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. 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This study aims to bridge this knowledge gap by providing a deepening assessment of immunological profiles integrated into clinical and biochemical parameters in individuals with Major Depressive Episode (MDE). This multicenter case-control sex and age-matched study recruiting 121 participants divided into patients with active and remitted MDE and healthy controls (HC). Biochemical parameters, humoral responses (pro- and anti-inflammatory), and specific innate and adaptive immune cell populations were measured. Patients with MDE showed monocytosis, increased high-sensitivity C-reactive protein and Erythrocyte Sedimentation Rate levels, and an altered proportion of specific monocyte subsets. CD4 lymphocytes exhibited increased activation and exhaustion and a higher frequency of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFOXP3\u003csup\u003e+\u003c/sup\u003e regulatory T cells. Additionally, patients with MDE showed increased plasma levels of sTREM2, IL-17 and IL-6. This profile denoted an immune dysregulation and inflammation in MDE. Boruta analyses identified markers with significant discriminative potential for distinguishing between patients with MDE and HC. Cluster analysis revealed that patients with MDE exhibited at least three different patterns of immune system activation, suggesting a different stage of inflammation or possible differences in the underlying mechanism involved. Our findings give a deeper understanding of the role of inflammation and its mediators in MDE, illuminating the way for novel therapeutic strategies tailored to specific subgroups of patients.\u003c/p\u003e","manuscriptTitle":"Decoding the Inflammatory Signature of the Major Depressive Episode: Insights from Peripheral Immunophenotyping in Active and Remitted Condition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-13 16:19:41","doi":"10.21203/rs.3.rs-3346140/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ad5be5e-133e-42d0-ba45-7e3dd39100cc","owner":[],"postedDate":"September 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":24559299,"name":"Biological sciences/Neuroscience"},{"id":24559300,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2023-09-22T13:12:27+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-13 16:19:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3346140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3346140","identity":"rs-3346140","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}