Altered NK Cell Receptor Profiles and Immune-Inflammatory Markers in Adolescent Major Depressive Disorder: Associations with Cognitive Impairment

Altered NK Cell Receptor Profiles and Immune-Inflammatory Markers in Adolescent Major Depressive Disorder: Associations with Cognitive Impairment | 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 Research Article Altered NK Cell Receptor Profiles and Immune-Inflammatory Markers in Adolescent Major Depressive Disorder: Associations with Cognitive Impairment Jiahui Wang, Lingzhi Hou, Cai Li, Yitong Liu, Yan Xu, Yang He, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7186161/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Immune dysregulation and cognitive deficits are increasingly recognized in adolescent major depressive disorder (MDD), yet their interrelationship remains unclear. This study aimed to investigate peripheral immune-inflammatory alterations and natural killer (NK) cell phenotypes, and explore their association with cognitive function in adolescent MDD. Methods Fifty-four first-episode, drug-naïve adolescents with MDD and 33 matched healthy controls (HCs) were enrolled. Group differences in peripheral blood immune-inflammatory indices (NLR, PLR, MLR, SII, SIRI), NK cell surface receptors (HLA-DR, NKp46, NKp30, NKG2A, NKG2C, KIR2DL1, ILT2, CD57), and cognitive function were analyzed, along with their intercorrelations. Results Compared with HCs, patients with MDD showed lower NEU, NLR, PLR, and SII levels, alongside elevated LYM counts. NK cells exhibited reduced overall proportions but increased expression of HLA-DR, NKp46, NKG2A, and ILT2, with decreased CD57 expression in the MDD group. Significant cognitive impairments were observed in speed of processing, reasoning and problem solving, and social cognition. Furthermore, several immune-inflammatory markers (MLR, SII, SIRI) and NK cell receptors (HLA-DR, NKG2C, NKp30, CD57) were significantly correlated with performance across multiple cognitive domains. Conclusion Our findings reveal significant associations between NK cell phenotypes, systemic immune-inflammatory markers, and cognitive function in adolescent MDD. These results suggest a potential regulatory role of NK cells within the immune–cognitive axis, possibly reflecting both intermediary functions and inflammation-independent neuroimmune mechanisms. This study provides novel insight into potential biomarkers and immunomodulatory targets for early intervention in adolescent MDD. major depressive disorder natural killer cells immune-inflammation cognitive impairment NK cell surface receptors boimarker Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Major depressive disorder (MDD) constitutes a collection of chronic conditions characterized by a loss of interest, persistent depressive mood, and lack of pleasure as underlying symptoms. In recent years, a notable trend has emerged towards an earlier age of onset in patients ( 1 ). Epidemiological data indicate that over 40% of MDD cases begin during adolescence, with increasing incidence in this age group ( 2 ). Compared to adults, adolescent MDD is associated with greater symptom severity, elevated suicide risk, higher rates of bipolar conversion, and nearly threefold risk of recurrence in adulthood ( 3 – 5 ). Considering the relevant risk factors, neural connections, and temporal continuity, adolescent MDD can be interpreted as an early manifestation of adult MDD ( 6 ). Learning challenges, poor interpersonal interactions, and a reduced physical health status are frequently associated with adolescent MDD ( 7 , 8 ). It is estimated that approximately 20–30% of patients with MDD exhibit cognitive impairment in attention, information comprehension and processing, problem solving, and decision making, which often persist even after emotional symptoms have resolved ( 9 ). Additionally, persistent cognitive impairment has been linked to increased relapse risk and reduced treatment efficacy ( 10 ). Current clinical diagnosis remains primarily symptom-based, lacking objective biological indicators. This diagnostic limitation may partly account for the suboptimal treatment outcomes, as fewer than one-third of adolescents achieve remission after first-line treatment, and up to 40% show no response to initial medication or psychotherapy ( 11 , 12 ). Therefore, exploring the underlying pathogenesis may offer valuable insights into more effective diagnosis and therapeutic strategies. Mounting evidence suggests that immune and inflammatory disorders may influence the susceptibility, pathogenesis, and prognosis of depression ( 13 , 14 ). Antidepressant treatment has also been shown to reduce proinflammatory cytokine levels in patients with depression ( 15 ). Composite inflammatory indicators, such as the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR), the systemic immune-inflammatory index (SII), and the systemic inflammatory response index (SIRI), are easily accessible yet clinically valuable. In comparison to individual biochemical indicators, they are more sensitive and objective in assessing the state of immune-inflammatory alterations. Although widely used in non-psychiatric conditions for prognostic and severity assessment ( 16 – 18 ), their application in psychiatric research remains limited. This study aims to explore their potential utility as cost-effective biomarkers in adolescent MDD. Immune dysregulation in MDD specifically involves both an activated inflammatory response system and alterations in the quantity and function of T cells and natural killer (NK) cells ( 19 – 21 ). While most studies have focused on adaptive immune cells such as T cells and monocytes, emerging evidence suggests a significant role of innate immunity in the pathophysiology of depression ( 22 – 24 ). As key players in innate immune response, NK cells rapidly respond to inflammatory signals and express a repertoire of activating and inhibitory receptors that dynamically reflect immune status ( 25 ). Preclinical studies have shown that NK cells may alleviate depressive-like symptoms by modulating peripheral and central inflammation ( 26 , 27 ). However, existing human studies on the percentage and activity of NK cells in MDD are limited, with inconsistent results ( 28 – 30 ). Numerous studies have demonstrated that depressed individuals frequently exhibit reduced NK cell activity (NKCA) ( 31 ), and a higher percentage of NK cells following antidepressant treatment has been associated with better clinical outcomes ( 32 ). These features make NK cells a promising and tractable target for immunophenotypic characterization in MDD. To date, few studies have investigated the immunophenotypic profile of peripheral NK cells in patients with depression, particularly in the adolescent population. Although limited, studies in related psychiatric conditions such as autism spectrum disorders (ASD), bipolar disorder (BP) and schizophrenia (SZ) have reported alterations in NK cell surface receptors, including human leukocyte antigen-DR isotype (HLA-DR), the natural killer group 2 member A (NKG2A), the natural killer group 2 member C (NKG2C), natural cytotoxicity triggering receptor 3 (NKp30), natural cytotoxicity triggering receptor 1 (NKp46), and the immunoglobulin-like transcript 2 (ILT2) ( 33 ). Notably, certain receptor expressions have also been associated with cognitive performance, suggesting that NK cell phenotypes may reflect neuroimmune interactions relevant to psychiatric symptoms ( 34 ). These findings underscore the theoretical and clinical importance of exploring NK cell receptor abnormalities in adolescent MDD. Given accumulating evidence implicating NK cells in the pathophysiology of psychiatric disorders, we hypothesize the existence of an immune–cognitive regulatory axis in MDD, wherein NK cells of distinct phenotypic profiles may act as critical intermediaries linking peripheral immune alterations to cognitive dysfunction. To minimize the confounding effects of medication on the immune function, we recruited adolescents with first-episode, drug-naïve MDD. This population enables a more accurate characterization of immune phenotypes associated with the early stage of illness. Through this investigation, we aim to explore the potential of developing more objective, immune-based tools for auxiliary diagnosis and treatment of MDD. 2. Materials and Methods 2.1 Participants A total of 54 first-episode, unmedicated adolescent inpatients with MDD were recruited from the Department of Psychiatry at the First Affiliated Hospital of Zhengzhou University. Diagnoses were made by board-certified psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2023-KY-0770-002) and conducted following the Declaration of Helsinki. Written informed consent was obtained from the guardians of all participants. All participants were interviewed with the Structured Clinical Interview for DSM-5 (SCID), and assessed for depressive symptoms using the Hamilton Depression Scale (HAMD) ( 35 ). The MDD group included patients aged 13–18 years who met the following criteria: (a) first-episode, drug-naïve MDD diagnosed per DSM-5, with a HAMD score ≥ 20; (b) no history of smoking, alcohol use, allergies, infectious or chronic diseases, surgery, or vaccination in the past month; (c) no acute infections within 14 days. A total of 33 matched healthy controls (HCs) were recruited via public advertisements. Inclusion criteria for the HC group were: (a) no personal history of psychiatric disorders; (b) no medication use (prescription, over-the-counter, or supplements) in the past month; (c) no history of smoking, alcohol use, allergies, infections, chronic diseases, surgery, or vaccination in the past month; (d) no infections in the past 14 days. Exclusion criteria for both groups included: (a) current or past diagnosis of any other psychiatric or neurological disorders; (b) intellectual disability; (c) major systemic or organic brain diseases (e.g., tumors, heart disease, diabetes, or head trauma). 2.2 Peripheral blood processing and flow cytometry analysis of NK cell phenotypes Peripheral venous blood (4 mL) was collected from each participant. One milliliter was analyzed using an automatic hematology analyzer to detect platelet (PLT), neutrophil (NEU), monocyte (MONO), and lymphocyte (LYM, ×10⁹/L) count, and to calculate NLR, MLR, PLR, SII, and SIRI. The remaining 3 mL was anticoagulated with ethylenediaminetetraacetic acid (EDTA), and peripheral blood mononuclear cells (PBMC) were extracted within 3 hours. Whole blood was diluted 1:1 with Phosphate-Buffered Saline (PBS) and layered over 6 mL of lymphocyte separator medium. After centrifugation at 2000 rpm for 30 minutes at 20°C (acceleration rate: 1), the PBMC layer was collected, washed with PBS, and centrifuged again at 4000 rpm for 10 minutes (acceleration rate: 5). The PBMC pellet was resuspended in cryopreservation solution and stored at -80°C. For flow cytometric phenotyping, frozen PBMCs were rapidly thawed in a 37°C water bath, washed with PBS, and resuspended to a final concentration of 0.2-1×10 6 cells/100 µL. Fc blocking was performed with Human TruStain FcX at room temperature for 10 minutes, followed by staining with fluorochrome-conjugated antibodies in the dark for 20 minutes. A four-tube antibody panel was used. Each tube contained lineage markers (PerCP-CD45, FITC-CD3, BV421-CD56), with additional tube-specific antibodies as follows: (a) Tube 1: APC-HLA-DR, PE-NKG2C; (b) Tube 2: PE-NKp30, APC-NKp46; (c) Tube 3: PE-KIR2DL1, APC-NKG2A; (d) Tube 4: APC-CD57, PE-ILT2. After staining, cells were washed with PBS, filtered through a 70 µm mesh, and analyzed using an ACEA NovoCyte instrument. Data were exported as FCS files and processed using FlowJo (v10.8). Reagents and equipment details are summarized in Supplementary Table 1. 2.3 Cognitive function assessment The MATRICS Consensus Cognitive Battery (MCCB) was initially developed by the National Institutes of Mental Health (NIMH) to accurately assess cognition in SZ ( 36 , 37 ), and is now widely used in MDD research ( 38 ). Trained clinicians administered the MCCB to both the MDD and HC groups. T-scores were computed for all seven cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition. 2.4 Statistical analysis Statistical analyses and data visualization were performed using SPSS version 25.0 and GraphPad Prism version 8.0. The Shapiro–Wilk test was used to assess the normality of continuous variables. Normally distributed variables were expressed as mean ± standard deviation ( x̄ ± s ), and group comparisons for these variables were performed using the independent samples t -test or Welch’s t -test, depending on the equality of variances assessed by Levene’s test. Non-normally distributed data were expressed as median (Q1–Q3), and between-group differences were assessed using the Mann–Whitney U test. Spearman’s ρ was used for correlation analysis due to the non-normal distribution of most variables. Between-group differences in correlation coefficients were evaluated using Fisher’s Z test. Raw values were converted to z-scores to allow for cross-variable comparisons on a common scale. Multiple linear regression analyses were performed to examine the association between immune-inflammatory indicators and cognitive outcomes, adjusting for demographic variables. A two-sided p value < 0.05 was considered statistically significant. All statistical tests were two-tailed. 3. Results 3.1 Group differences 3.1.1 Demographic and clinical characteristics In our study, two patients and five healthy individuals were excluded due to inadequate blood sample quality. The MDD group comprised 20 males and 32 females, with a median age of 15.0 (13.0–16.0) years, while the HC group included 9 males and 19 females, with a median age of 15.0 (13.5–16.5) years. There were no significant differences between groups in age ( p = 0.371), sex ( p = 0.577), body mass index (BMI) ( p = 0.820), years of education ( p = 0.397), years of father’s education ( p = 0.979), and years of mother’s education ( p = 0.416). The median disease duration in the MDD group was 12 ( 3 – 21 ) months. Physical or psychotic symptoms were present in 80% of patients. The detailed results are presented in Table 1 . Table 1 Demographics of the MDD and HC group. Variables MDD group (n = 52) HC group (n = 28) t/z value p value Age, med (IQR) 15.0(14.0–16.0) 15.0(13.5–16.5) -0.894 0.371 Sex, Male/Female 20/32 9/19 -0.557 0.577 BMI (kg/m 2 ), med (IQR) 20.80(18.33–23.26) 20.17(18.41–21.93) -0.227 0.820 Years of education, med (IQR) 9(7.63–10.36) 9(7.13–10.88) -0.847 0.397 Years of father’s education, med (IQR) 9(6.75–11.25) 9(7.50–10.50) -0.026 0.979 Years of mother’s education, med (IQR) 9(6.38–11.63) 9(7.50–10.50) -0.813 0.416 Disease duration, med (IQR) (months) 12( 3 – 21 ) - Accompanying symptoms 42 - Abbreviations: MDD, Major Depressive Disorder; HC, healthy control; IQR, interquartile range; BMI, body mass index. 3.1.2 Peripheral immune-inflammatory markers Peripheral blood analysis revealed significantly reduced NEU ( p = 0.032) and elevated LYM ( p = 0.003) in the MDD group compared to the HC group. No significant differences were observed in PLT ( p = 0.353) or MONO ( p = 0.062). Analysis of derived inflammatory indices showed that NLR ( p < 0.001), PLR ( p = 0.001), and SII ( p < 0.001) were significantly lower in the MDD group, while no significant differences in MLR ( p = 0.535) or SIRI ( p = 0.073) (Fig. 1 ; Supplementary Table 2). 3.1.3 NK cell surface receptor expression The proportion of CD3⁻CD56⁺ NK cells was significantly lower in the MDD group compared to the HC group ( p = 0.005). Phenotypic analysis revealed significantly elevated expression of HLA-DR ( p < 0.001), NKp46 ( p < 0.001), NKG2A ( p = 0.028), and ILT2 ( p = 0.009), while CD57 expression was significantly reduced in the MDD group ( p = 0.004). No significant group differences were observed in the expression of NKG2C ( p = 0.141), NKp30 ( p = 0.591), and KIR2DL1 ( p = 0.452). Consistently, the z-score plot showed an upward trend for most receptors in the MDD group, except for NKG2C, NKp30, and CD57, which exhibited a downward trend (-0.67 ≤ z ≤ 1.96) (Fig. 2 ; Supplementary Table 3). 3.1.4 Cognitive performance across domains The MDD group exhibited significantly lower scores in the following cognitive domains: speed of processing ( p < 0.001), reasoning and problem solving ( p < 0.001), and social cognition ( p = 0.019). The overall composite score, reflecting global cognitive performance, was also significantly reduced in the MDD group ( p < 0.001). No significant differences were found in attention/vigilance ( p = 0.080), working memory ( p = 0.073), verbal learning ( p = 0.143), and visual learning ( p = 0.146). The z-score plot demonstrated a widespread decline across cognitive domains in the MDD group (-0.99 ≤ z ≤ -0.35) (Fig. 3 ; Supplementary Table 4). 3.2 Correlation analysis An overview of all correlation analyses is presented in Fig. 4 . Detailed group-specific statistics are reported in Supplementary Tables 5–7 (sub-tables: a = MDD group, b = HC group). However, none of the regression models reached statistical significance after covariate adjustment (all p > 0.05) (Table 8). 3.2.1 Associations between immune-inflammatory markers and cognitive function In the MDD group, MLR was negatively correlated with reasoning and problem solving ( ρ = -0.282, p = 0.043), while both SII and SIRI were negatively associated with working memory ( ρ = -0.318, p = 0.021; ρ = -0.292, p = 0.036). These associations were not observed in the HC group, and Fisher’s Z test confirmed significant between-group differences ( Z = -2.629 to -2.131, all p < 0.05). In the HC group, NEU was positively correlated with speed of processing and attention/vigilance ( ρ = 0.407, p = 0.032; ρ = 0.437, p = 0.020), while both MONO and SIRI showed positive correlations with speed of processing ( ρ = 0.433, p = 0.021; ρ = 0.469, p = 0.012) and reasoning and problem solving ( ρ = 0.417, p = 0.027; ρ = 0.384, p = 0.044). These correlations were absent in the MDD group, with Fisher’s Z test confirming significant group differences ( Z = -2.478 to -1.83, all p < 0.05). 3.2.2 Associations between NK cell surface receptors and cognitive function In the MDD group, HLA-DR expression was negatively correlated with both speed of processing and overall composite ( ρ = -0.346, p = 0.012; ρ = -0.285, p = 0.040). NKG2C was positively associated with working memory ( ρ = 0.312, p = 0.024), and NKp30 showed positive correlations with attention/vigilance and visual learning ( ρ = 0.383, p = 0.005; ρ = 0.285, p = 0.041). CD57 expression was also positively correlated with visual learning ( ρ = 0.305, p = 0.028). These associations were absent in the HC group, and Fisher’s Z test revealed significant group differences for HLA-DR with speed of processing, NKG2C with working memory, and CD57 with visual learning ( Z = -1.664 to 2.777, all p < 0.05). In the HC group, ILT2 expression was negatively associated with speed of processing and attention/vigilance ( ρ = -0.460, p = 0.014; ρ = -0.398, p = 0.036), while positively correlated with social cognition ( ρ = 0.430, p = 0.022). These associations were not significant in the MDD group. Fisher’s Z test confirmed significant differences in the correlations of ILT2 with social cognition ( Z = -2.557, p < 0.05). 3.2.3 Associations between NK cell surface receptors and immune-inflammatory markers In the MDD group, NKp46 expression was negatively correlated with LYM ( ρ = -0.289, p = 0.038) and positively correlated with PLR ( ρ = 0.304, p = 0.028). NKG2A showed a negative correlation with LYM ( ρ = -0.275, p = 0.048). These correlations were not observed in the HC group, and Fisher’s Z test confirmed no significant between-group differences ( Z = -1.59 to -0.266, all p > 0.05). In the HC group, ILT2 expression was negatively associated with NEU, SII, and SIRI ( ρ = -0.402, p = 0.034; ρ = -0.381, p = 0.045; ρ = -0.459, p = 0.014), while these associations were absent in the MDD group. Fisher’s Z test confirmed no significant differences between groups ( Z = 0.757 to 1.416, all p > 0.05). 4. Discussion In summary, our study revealed significant alterations in peripheral immune-inflammatory markers and NK cell surface receptor expression in adolescents with MDD. Patients exhibited distinct immune profiles, including reduced NEU, NLR, PLR, and SII levels, and increased expression of HLA-DR, NKp46, NKG2A, and ILT2, alongside decreased CD57. Cognitive performance, particularly in speed of processing, reasoning and problem solving, and social cognition, was significantly impaired in the MDD group. Correlation analyses suggested potential links between immune dysregulation and cognitive deficits, highlighting possible mechanisms underlying adolescent MDD. 4.1 Abnormal immune-inflammatory markers Currently studied inflammatory biomarkers in psychiatry commonly include cytokines, acute-phase proteins, and brain-derived neurotrophic factor (BDNF). Accumulating evidence has suggested their involvement in various psychiatric disorders, although the underlying mechanisms remain incompletely understood. Our study investigated several immune-inflammatory indicators and their relationships with MDD status and symptoms, aiming to identify potential diagnostic or prognostic biomarkers. NEU, a primary innate immune cell, eliminates pathogens through phagocytosis, degranulation, and cytokine release ( 39 ). LYM, a key effector of the adaptive immune system, performs regulatory and protective functions ( 24 ). NLR represents the balance between innate and adaptive immunity, widely used as a marker of systemic inflammation. Our findings indicated reduced NEU and elevated LYM in MDD patients, resulting in a lower NLR. However, previous studies have reported mixed results. Some studies suggest a nonlinear association, with both high and low NLR linked to depressive symptoms such as anhedonia and sleep disturbance ( 40 ). Elevated NEU or NLR has been observed in unmedicated MDD patients or those with suicide risk ( 41 , 42 ), while some studies found no significant difference ( 43 ). Besides, a research reported higher NLR in bipolar mania patients but lower NLR in unipolar depression, suggesting distinct inflammation across different episodes of depression ( 44 ). These observations suggest that NLR could serve as a potential biomarker of early-stage immune dysregulation in adolescent depression, though larger studies are warranted. Although PLT has traditionally been recognized for its role in hemostasis, it is increasingly acknowledged as a key player in immune regulation ( 45 ). PLT can interact with various immune cells, release exosomes, and influence both innate and adaptive immunity ( 46 – 48 ). PLR reflects the balance between platelet activation and lymphocyte-mediated immune regulation. Our study found reduced PLR in the MDD group despite no significant change in PLT. It may primarily reflect increased LYM levels, consistent with an immune shift toward adaptive regulation in adolescent depression ( 45 ). In contrast to our results, a large-scale study reported elevated PLR in depression, although its predictive value appeared lower than that of NLR ( 49 ). Interestingly, the association between elevated PLR and depression was limited to studies conducted in China, indicating potential geographic or demographic influences on immune-inflammatory responses ( 50 ). SII and SIRI, which integrate multiple immune cell types, are considered more comprehensive indices than single ratios such as NLR or PLR ( 51 ). Ryan et al. found that SII was negatively correlated with baseline HAMD scores ( 52 ), while another study found a significant decrease in SII following electroconvulsive therapy (ECT) ( 53 ). Elevated SII and SIRI in MDD have also been associated with depression severity ( 54 ), and Li et al. linked their increase to higher depression risk ( 55 ). Contrary to these findings, our study observed a significant reduction in SII, while no significant difference in SIRI, suggesting potential stage-specific or age-related immune variations in MDD. Our findings may reflect chronic inflammation-induced neutrophil exhaustion or glucocorticoid-mediated suppression of neutrophil mobilization, which has been described in early-stage or adolescent MDD as part of immune deviation or depletion ( 56 , 57 ). The inconsistencies of results emphasize the complexity of immune involvement in MDD and highlight the importance of interpreting inflammatory markers within a systems-level framework. 4.2 Abnormal NK cell surface receptor expression NK cells are a major subset of lymphocytes in the innate immune system. Their function is regulated by a dynamic balance between inhibitory and activating surface receptors, many of which recognize HLA class I molecules or stress-induced ligands ( 58 ). Under homeostatic conditions, NK cells express inhibitory receptors such as KIRs, ILT-2, and NKG2A. Upon immune activation, stimulatory receptors including NKG2C, NKp46, and NKp30 are upregulated to initiate immune responses ( 59 ). According to the “discontinuity theory,” the immune system is more responsive to abrupt changes than gradual or continuous stimulation, which may underlie the heterogeneous development of NK cell subsets across disease states ( 60 ). In our study, several receptor expression patterns showed significant differences between MDD patients and HCs. We observed a significantly higher proportion of HLA-DR⁺ NK cells in the MDD group. Consistent with our result, Tarantino et al. found that HLA-DR was overexpressed in 50–83% of patients with first-episode psychiatric disorders, and that other phenotypic alterations in NK cells were primarily present in individuals with high HLA-DR expression ( 33 ). Interestingly, they also found that HLA-DR expression was negatively correlated with IFN-γ levels, suggesting a state of NK cell exhaustion in psychiatric populations. Mechanistically, HLA-DR + NK cells can be expanded by various cytokines, including IL-2 and IL-15 ( 61 , 62 ), and the sustained activation may lead to functional decline or reduced cytokine production over time. The upregulation of HLA-DR in our MDD cohort may reflect a compensatory immune activation in response to subclinical inflammation, eventually progressing toward an exhausted phenotype. Our findings indicated that the expression of NKp46 was significantly increased in the MDD group, while NKp30 showed no difference between groups. NKp46 has been shown to regulate cytokine production and the expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), a TNF superfamily member that induces apoptosis in target cells ( 63 , 64 ). Murine models further support a role for NKp46 in regulating TRAIL levels ( 65 ), and TRAIL receptors are overexpressed in MDD patients with a history of childhood trauma ( 66 ), suggesting a potential link between NK cell activation, apoptotic signaling, and stress-related immunopathology. Given that NKp46 is the only natural cytotoxicity receptor with a known murine homologue, animal models may offer a valuable insight into its mechanistic role in depressive pathophysiology. NKG2A is an inhibitory receptor that maintains NK cell quiescence under steady-state conditions, while NKG2C is an activating receptor typically upregulated in response to viral infections ( 67 ). Recent studies have linked increased NKG2C + NK cells with ASD, BP, and SZ ( 68 , 69 ), whereas evidence regarding shifts in inhibitory receptors such as NKG2A in MDD remains limited. However, our study demonstrated elevated NKG2A expression in the MDD group, while no significant differences in NKG2C were observed. This pattern supports a hypoactive or suppressed NK cell phenotype, potentially contributing to the immune dysfunction observed in depression ( 70 ). Given that NKG2A engagement transmits strong inhibitory signals, its upregulation may also reflect functional exhaustion or immune deviation in depression. CD57 + NK cell is a mature subpopulation of NK cells with strong cytotoxic potential and high IFN-γ production ( 71 ). Our findings revealed a significant reduction in CD57 expression in the MDD group, suggesting impaired NK cell maturation. Although no systematic studies have explored CD57 alterations in MDD, prior evidence indicates that CD57⁺ NK cells regulate excessive inflammation and may play a homeostatic role in immune balance ( 72 ). They also tend to express lower levels of activating receptors, such as NKp30 ( 73 ), consistent with our observation of reduced CD57 and NKp30 expression. This parallel downregulation may reflect a broader suppression or dysregulation of terminal NK cell differentiation in MDD. ILT2 is another inhibitory receptor that suppresses NK cell effector functions, including granule release, cytoskeletal rearrangement, and IFN-γ signaling ( 74 ). In our study, we observed elevated ILT2 expression in MDD patients. It aligns with reports of increased ILT2 + NK cells in BP and SZ populations ( 33 ), hinting at a possible convergent upregulation of immunoinhibitory checkpoints in psychiatric conditions. However, unlike HLA-DR or NKp46, ILT2 showed notable interindividual variability, underscoring that its dysregulation may not be uniform across all MDD patients. Taken together, these findings point to an important—but selectively engaged—inhibitory axis in NK cell regulation in adolescent MDD. Further studies should clarify whether this pattern reflects a distinct immune endophenotype, compensatory adaptation, or vulnerability marker for depression. 4.3 Abnormal cognitive function Cognitive impairments are highly prevalent in adolescents with MDD, with rates reaching up to 83%, particularly affecting executive function, verbal and spatial memory, and attention ( 75 ). Cognitive dysfunction in depression has been linked to long-term psychosocial development. Evidence suggests that distinct cognition contributes to the educational gradient observed between MDD and non-MDD populations ( 76 ), and higher academic ability during adolescence is associated with fewer depressive symptoms in early adulthood ( 77 ). Deficits in executive function, such as speed of processing and reasoning and problem solving, may reflect reduced prefrontal efficiency, contributing to impaired decision-making and diminished productivity ( 78 ). Impairment in verbal and visual learning, on the other hand, may exacerbate social withdrawal and negatively affect quality of life ( 79 ). In our study, MDD patients showed lower z-scores across all seven MCCB domains compared to HCs, with significant impairments in speed of processing, reasoning and problem solving, and social cognition. These findings suggest a broad cognitive dysfunction, consistent with previous literature ( 38 , 80 ). Clinical factors such as episode frequency, disease duration, and symptom severity have been shown to modulate the degree of cognitive impairment in MDD ( 81 ). Notably, patients with recurrent depression tend to experience more persistent and severe cognitive deficits, particularly in executive domains, compared to those with first-episode MDD ( 82 , 83 ). Our study, which focused on a first-episode, drug-naïve adolescent population, further highlights the presence of early cognitive disturbances in MDD, underscoring the need for early identification and intervention strategies. 4.4 Association between immune-inflammatory markers and cognitive function Existing studies have demonstrated that immune-inflammatory dysregulation is closely associated with cognitive impairments across various psychiatric disorders ( 84 ). While preliminary studies have linked composite markers such as NLR, PLR, and SII with depressive severity, their direct relationship with cognitive performance in MDD remains limited. In light of these gaps, our study explores potential associations between immune-inflammatory indices and cognitive function in adolescents with first-episode, drug-naïve MDD. Recent research from SZ populations has shown that higher NLR, PLR, and SII levels are significantly associated with poorer cognitive deficits, as measured by the Mini-Mental State Examination (MMSE) ( 85 ). These findings align with a broader trend across clinical populations. For instance, elevated NLR, MLR, and SII were linked to cognitive decline in surgical patients ( 86 ), whereas lower levels of NLR and MLR served as protective factors for post-stroke cognitive impairment ( 87 ). These findings show that systemic immune-inflammatory responses may play a generalized role in cognitive dysfunction. Our findings build on this emerging literature by demonstrating that SII and SIRI levels were negatively correlated with working memory, while MLR was negatively associated with reasoning and problem solving in MDD. These results suggest that peripheral immune markers could serve as useful indicators for early detection and individualized treatment strategies targeting cognitive dysfunction in depression, possibly reflecting shared immunopathological mechanisms across neuropsychiatric and somatic conditions. 4.5 Association between NK cell surface receptors and cognitive function Recently, evidence shows that phenotypically distinct NK cells can infiltrate the central nervous system (CNS) and influence cognition and behavior ( 88 , 89 ). However, few studies have examined the link between NK cell surface receptor profiles and cognitive function in psychiatric disorders ( 90 ). Consistent with our initial hypothesis of an immune–cognitive regulatory axis in MDD, our findings suggest that altered NK cell phenotypes may contribute to cognitive impairment. Specifically, MDD patients showed a general pattern of upregulated NK cell surface receptors (except for NKG2C, NKp30, and CD57), paralleled by a global decline in cognitive performance. As shown in the z-score plot (Fig. 3 ), cognitive decline followed a uniform downward trend across domains, whereas NK receptor expression varied in direction and magnitude, highlighting the complexity of peripheral immune responses in MDD. Notably, HLA-DR expression was significantly higher in the MDD group and negatively correlated with speed of processing. Similarly, decreased NKG2C expression was paralleled by poor performance in working memory, and reduced CD57 was linked to deficits in visual learning. These inverse correlations may reflect an immune dysregulation state that contributes to neurocognitive dysfunction. Interestingly, NKp30 expression was positively correlated with attention/vigilance, though without significant between-group differences in either expression or correlation strength. It suggests that the regulatory role of NKp30 in cognition may not be disease-specific, but rather a general feature present across populations. Mechanistically, animal studies demonstrate that NK cell deficiency impairs both short- and long-term memory in mice ( 91 ), supporting a possible link between NK activity and cognition. In humans, HLA-DR expression correlates with structural language and social awareness in ASD patients ( 68 ), suggesting a broader neuroimmune influence. NKG2C + NK cells produce higher levels of IFN-γ and IL-17, and have been linked to neurotoxicity in neurodegenerative diseases ( 92 ). Systemic NK cell depletion significantly reduces IFN-γ levels in the prefrontal cortex, leading to impaired gamma-aminobutyric acid (GABA) signaling and poor performance on working memory ( 89 ). Collectively, these findings underscore the dual role of NK cells in peripheral immune regulation and central cognitive processes. Our study further contributes to the hypothesis, suggesting that NK cell surface phenotypes may serve as peripheral indicators of cognitive dysfunction in MDD. 4.6 Association between NK cell surface receptors and immune-inflammatory markers To our knowledge, no prior studies have directly examined the relationship between NK cell surface receptor expression and composite immune-inflammatory markers in MDD. To address this gap, we examined their correlations in both the MDD and HC groups. In our study, NKp46 expression was negatively associated with LYM and positively associated with PLR, and NKG2A also showed a negative correlation with LYM in the MDD group. Existing literature has suggested that NK receptor expression may influence systemic inflammation. For example, elevated NLR is linked to reduced IFN‑γ release from NK cells in healthy individuals ( 93 ), and the receptor such as HLA-DR, NKG2C, or CD57, regulates IFN-γ levels during activation ( 71 , 89 ), suggesting a potential pathway through which NK receptors may regulate systemic immune responses via cytokine signaling. These findings provide a theoretical basis for exploring the interplay between NK phenotypes and peripheral inflammation. Our correlation analysis revealed a notable divergence across immune and cognitive domains. Specifically, all inflammation–cognition correlations showed significant group differences, while no such differences were observed in NK receptor–inflammation associations. Only partial NK receptor–cognition correlations differed between groups. This pattern suggests that inflammation–cognition links are more sensitive to MDD status, supporting a state-dependent role of systemic inflammation in cognitive outcomes. Meanwhile, NK cells may contribute to cognitive dysfunction through inflammation-independent pathways, such as direct CNS infiltration or neuroimmune signaling. Together, these findings refine the conceptual model of the immune–cognitive axis in adolescent depression. Rather than serving solely as intermediaries between peripheral inflammation and cognitive dysfunction, NK cells may play a more direct neuroimmune role. Despite several immune-inflammatory markers and NK receptors exhibiting significant bivariate associations with cognitive domains, none remained significant in multivariate models. This discrepancy may reflect limited statistical power due to the modest sample size, shared variance among predictors, or the influence of unmeasured intermediate mechanisms, highlighting the need for further mechanistic investigation. 5. Conclusion In essence, our study supports a multi-level integrative model in which NK cells serve as critical immunological intermediaries within the immune–cognitive axis of adolescent MDD. The convergence of findings across molecular (NK cell surface receptor), cellular (blood-based immune markers), and functional (cognitive domains) levels strengthens the plausibility of this network. Our findings suggest that NK cell phenotypes may not only bridge systemic inflammation and cognitive impairment but also influence cognition through inflammation-independent pathways such as neuroimmune signaling. This is the first study to implicate NK receptor dysregulation in adolescent MDD, suggesting a potential age-specific immune mechanism distinct from adults. These findings not only provide support for the biopsychosocial model of depression but also identify NK cell receptors as promising targets for future neuroimmune and immunomodulatory therapies. Limitation Several limitations of this study should be noted. First, the relatively small sample size may have limited the power to detect subtle group differences and correlations. Second, the cross-sectional design precludes a causal interpretation of the association between immune alterations and cognitive impairment. Third, immune-inflammatory and NK cell phenotypic analysis were obtained from peripheral blood, which may not accurately represent the immune status of the CNS. Lastly, potential confounding factors such as lifestyle, circadian rhythm, or perceived stress were not systematically controlled and should be addressed in future longitudinal studies to enhance the robustness and generalizability of the findings. Abbreviations ASD autism spectrum disorders BDNF brain-derived neurotrophic factor BMI body mass index BP bipolar disorder CNS central nervous system DSM-5 the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition ECT electroconvulsive therapy EDTA ethylenediaminetetraacetic acid GABA gamma-aminobutyric acid HAMD the Hamilton Depression Scale HLA-DR human leukocyte antigen-DR isotype NK cells natural killer cell NKCA NK cell activity HC healthy control HLA-DR human leukocyte antigen-DR isotype ILT2 immunoglobulin-like transcript 2 LYM lymphocytes MCCB MATRICS Consensus Cognitive Battery MDD major depressive disorder MLR monocyte-to-lymphocyte ratio MMSE Mini Mental State Examination MONO monocytes NEU neutrophils NIMH the National Institutes of Mental Health NKG2A/NKG2C natural killer group 2 member A/C NK cell natural killer cells NKp30 natural cytotoxicity triggering receptor 3 NKp46 natural cytotoxicity triggering receptor 1 NLR neutrophil-to-lymphocyte ratio PBMC peripheral blood mononuclear cells PBS Phosphate Buffered Saline PLR platelet-to-lymphocyte ratio PLT platelets SCID the Structured Clinical Interview for DSM-5 SII systemic immune-inflammation index SIRI systemic inflammation response index SZ schizophrenia TRAIL tumor necrosis factor related apoptosis-inducing ligand. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2023-KY-0770-002). Clinical trial number: not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality, but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant NO. 81801325), Henan Provincial Fund for the Cultivation of Outstanding Young Talents in Health Science and Technology Innovation (Grant NO. YXKC2020035), Henan Scientific and Technological Development Program (Grant NO. 252102311004), Key Research and Development Program of Henan Province (Grant NO. 251111313200), National Natural Science Foundation of China (Grant NO. 81801335), Henan Scientific and Technological Development Program (Grant NO. 232102311051), and Henan Scientific and Technological Development Program (Grant NO. 242102311050). Authors' contributions Jiahui Wang: Data curation, Formal analysis, Investigation, Software, Visualization, Writing- original draft. Lingzhi Hou: Data curation, Investigation, Validation, Writing- original draft; Cai Li: Funding acquisition; Project administration, Resources; Supervision. Yitong Liu: Investigation, Software, Validation; Yan Xu: Data curation; Investigation; Yang He: Resources; Software; Lei Yang: Funding acquisition; Resources; Li Wang: Resources; Qidong Liu: Resources; Jun Cheng: Resources; Yanyan Zhang: Resources; Yunmiao Ma: Investigation; Haiwei Xu: Funding acquisition; Methodology, Project administration; Resources; Hong Li: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review and editing. Acknowledgements We are deeply grateful to all participants for their involvement in this study. We also appreciate the language editing assistance provided by ChatGPT (OpenAI), under the full supervision of the authors. References Thapar A, Eyre O, Patel V, Brent D. Depression in young people. Lancet. 2022 Aug 20;400(10352):617–31. Zhang Y, Li Z, Feng Q, Xu Y, Yu R, Chen J, et al. 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Garofalo S, Cocozza G, Mormino A, Bernardini G, Russo E, Ielpo D, et al. Natural killer cells and innate lymphoid cells 1 tune anxiety-like behavior and memory in mice via interferon-γ and acetylcholine. Nature Communications. 2023;14(1):3103. Terrén I, Orrantia A, Vitallé J, Astarloa-Pando G, Zenarruzabeitia O, Borrego F. Modulating NK cell metabolism for cancer immunotherapy. Seminars in Hematology. 2020 Oct 1;57(4):213–24. Kemeny ME. Psychobiological responses to social threat: Evolution of a psychological model in psychoneuroimmunology. Brain, behavior, and immunity. 2009;23(1):1–9. Goldeck D, Schulte C, Teixeira dos Santos MC, Scheller D, Öttinger L, Pawelec G, et al. Higher Frequencies of T-Cells Expressing NK-Cell Markers and Chemokine Receptors in Parkinson’s Disease. Journal of Ageing and Longevity. 2023 Mar;3(1):1–10. Kim BR, Chun S, Cho D, Kim KH. Association of neutrophil-to-lymphocyte ratio and natural killer cell activity revealed by measurement of interferon-gamma levels in a healthy population. J Clin Lab Anal. 2019 Jan 1;33(1):e22640. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Editor invited by journal 06 Aug, 2025 Submission checks completed at journal 05 Aug, 2025 First submitted to journal 05 Aug, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7186161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503574469,"identity":"325289fe-dcec-4ca8-9f68-95837b74e7f9","order_by":0,"name":"Jiahui Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Wang","suffix":""},{"id":503574470,"identity":"b0e3ba15-fd22-48f8-a7bf-9a24bc065f8a","order_by":1,"name":"Lingzhi Hou","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lingzhi","middleName":"","lastName":"Hou","suffix":""},{"id":503574471,"identity":"b55af687-fd86-479e-9b6c-ed33a2198bc1","order_by":2,"name":"Cai Li","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Cai","middleName":"","lastName":"Li","suffix":""},{"id":503574472,"identity":"779bc958-f06d-43ba-b6b1-73e2c4a6855a","order_by":3,"name":"Yitong Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Liu","suffix":""},{"id":503574473,"identity":"7dc4e9b0-57aa-4cd6-8996-f7037e1618ed","order_by":4,"name":"Yan Xu","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Xu","suffix":""},{"id":503574474,"identity":"6b1a38b0-9602-4061-8f4f-f85ffcfcf9cd","order_by":5,"name":"Yang He","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"He","suffix":""},{"id":503574475,"identity":"cc8459e6-76b8-4c4f-b3f8-dae0ce3185cc","order_by":6,"name":"Lei Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yang","suffix":""},{"id":503574476,"identity":"70f41fd0-a490-4e77-8d8f-c0fc71ff9789","order_by":7,"name":"Li Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":503574479,"identity":"41ecc852-7a34-4d63-8f78-598a27bc35c3","order_by":8,"name":"Qidong Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qidong","middleName":"","lastName":"Liu","suffix":""},{"id":503574482,"identity":"b7a3aae1-d2f6-4eaf-8fc2-05e63e38e29e","order_by":9,"name":"Jun Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Cheng","suffix":""},{"id":503574484,"identity":"ee6f09ff-ec4a-4e4e-bacb-0eb119b66d87","order_by":10,"name":"Yanyan Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Zhang","suffix":""},{"id":503574485,"identity":"896afa80-45af-464f-9e12-22b439b9345c","order_by":11,"name":"Yunmiao Ma","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yunmiao","middleName":"","lastName":"Ma","suffix":""},{"id":503574486,"identity":"e26f8674-28ff-4efe-80e3-67d74c15d14b","order_by":12,"name":"Haiwei Xu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Haiwei","middleName":"","lastName":"Xu","suffix":""},{"id":503574487,"identity":"f2241405-ba0a-493e-81c6-1a3801baa0a0","order_by":13,"name":"Hong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIie3RMQrCQBBA0Q2B1WJ02w2bQywsKELQq2wQYpPCIwzkEpuLpJ4gWOoBUuUGyQVESxth0lnsq+fDDCNEFP2hrV4Rza8ClEJmIjWUY5BVngViJ8K5tbwVFj03MVgZAU+wgpJprjlJ3t/NVQ+wTzHN2o61mK9MsAMckGS6YSY7A/4Bljw/cQ6IFiV1ObZ4hiz0De8WpS+fV+LxpFTTTzMn+ZLgsvkoiqLotzfOSDKjoBzKyQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-22 10:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7186161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7186161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89985232,"identity":"ca8b4fde-c53b-4f9a-ab83-b3c1a8fbff91","added_by":"auto","created_at":"2025-08-27 06:41:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2582295,"visible":true,"origin":"","legend":"\u003cp\u003eBetween-group differences of immune-inflammatory markers.\u003c/p\u003e\n\u003cp\u003eNote: MDD, Major Depressive Disorder; HC, healthy control; NEU, neutrophil count; LYM, lymphocyte count; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/a6f1a1d149497d4f66677c33.jpg"},{"id":89983321,"identity":"e032b6eb-69c2-4014-bdc0-476b66083a4e","added_by":"auto","created_at":"2025-08-27 06:33:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1008673,"visible":true,"origin":"","legend":"\u003cp\u003eGating strategy for flow cytometry and between-group differences of NK cell surface receptor expression.\u003c/p\u003e\n\u003cp\u003eRepresentative flow cytometry plots for each group are shown on the left (panel a, left), illustrating gating strategies for CD3⁻CD56⁺ NK cells and subsequent analysis of surface markers including HLA-DR, NKG2C, NKp30, NKG2A, KIR2DL1, CD57, and ILT2. The Z-score plot (panel b, top right) summarizes group-wise standardized expression of these receptors, enabling a comparative overview. Box plots (panel c, bottom right) show statistical comparisons of the proportions of NK cells and expression levels of surface receptors between the two groups. Y-axis scales differ between panels to highlight variations within groups. Note: MDD, Major Depressive Disorder; HC, healthy control; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/51aeaaff1990121d17515f01.jpg"},{"id":89985230,"identity":"b8d16987-ac08-4a29-a47a-3bec87608c7b","added_by":"auto","created_at":"2025-08-27 06:41:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":696852,"visible":true,"origin":"","legend":"\u003cp\u003eZ-score of MCCB scores in the MDD and HC groups.\u003c/p\u003e\n\u003cp\u003eNote: MDD, Major Depressive Disorder; HC, healthy control; MCCB, MATRICS consensus cognitive battery; SoP, speed of processing; AV, attention/vigilance; WM, working memory; Vrbl, verbal learning; Vis, visual learning; RPS, reasoning and problem solving; SC, social cognition.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/d7d8e61cbb159a7cf2beb2e3.jpg"},{"id":89985554,"identity":"a186f347-01a6-4658-87ba-7d2465852da6","added_by":"auto","created_at":"2025-08-27 06:49:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4446563,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among immune-inflammatory markers, NK cell surface receptors, and cognitive function.\u003c/p\u003e\n\u003cp\u003eNote: MDD, Major Depressive Disorder; HC, healthy control; PLT, platelet count; NEU, neutrophil count; LYM, lymphocyte count; MONO, monocyte count; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; SoP, speed of processing; AV, attention/vigilance; WM, working memory; Vrbl, verbal learning; Vis, visual learning; RPS, reasoning and problem solving; SC, social cognition; Comp, overall composite; *, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; \u0026nbsp;**, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/f27e3d68c21d6d43edea1a5e.jpg"},{"id":89987113,"identity":"05e09251-648e-4e9b-a437-5c6f7f1b6e00","added_by":"auto","created_at":"2025-08-27 06:57:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9861842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/8d06dcfc-c2da-41c6-9120-18da61a2c590.pdf"},{"id":89983306,"identity":"0ac2485e-1f2e-4317-85e2-c19219843420","added_by":"auto","created_at":"2025-08-27 06:33:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":77225,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7186161/v1/841ec4162848fd4e599dd07a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered NK Cell Receptor Profiles and Immune-Inflammatory Markers in Adolescent Major Depressive Disorder: Associations with Cognitive Impairment","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMajor depressive disorder (MDD) constitutes a collection of chronic conditions characterized by a loss of interest, persistent depressive mood, and lack of pleasure as underlying symptoms. In recent years, a notable trend has emerged towards an earlier age of onset in patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Epidemiological data indicate that over 40% of MDD cases begin during adolescence, with increasing incidence in this age group (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Compared to adults, adolescent MDD is associated with greater symptom severity, elevated suicide risk, higher rates of bipolar conversion, and nearly threefold risk of recurrence in adulthood (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Considering the relevant risk factors, neural connections, and temporal continuity, adolescent MDD can be interpreted as an early manifestation of adult MDD (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Learning challenges, poor interpersonal interactions, and a reduced physical health status are frequently associated with adolescent MDD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It is estimated that approximately 20\u0026ndash;30% of patients with MDD exhibit cognitive impairment in attention, information comprehension and processing, problem solving, and decision making, which often persist even after emotional symptoms have resolved (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Additionally, persistent cognitive impairment has been linked to increased relapse risk and reduced treatment efficacy (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Current clinical diagnosis remains primarily symptom-based, lacking objective biological indicators. This diagnostic limitation may partly account for the suboptimal treatment outcomes, as fewer than one-third of adolescents achieve remission after first-line treatment, and up to 40% show no response to initial medication or psychotherapy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, exploring the underlying pathogenesis may offer valuable insights into more effective diagnosis and therapeutic strategies.\u003c/p\u003e\u003cp\u003eMounting evidence suggests that immune and inflammatory disorders may influence the susceptibility, pathogenesis, and prognosis of depression (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Antidepressant treatment has also been shown to reduce proinflammatory cytokine levels in patients with depression (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Composite inflammatory indicators, such as the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR), the systemic immune-inflammatory index (SII), and the systemic inflammatory response index (SIRI), are easily accessible yet clinically valuable. In comparison to individual biochemical indicators, they are more sensitive and objective in assessing the state of immune-inflammatory alterations. Although widely used in non-psychiatric conditions for prognostic and severity assessment (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), their application in psychiatric research remains limited. This study aims to explore their potential utility as cost-effective biomarkers in adolescent MDD.\u003c/p\u003e\u003cp\u003eImmune dysregulation in MDD specifically involves both an activated inflammatory response system and alterations in the quantity and function of T cells and natural killer (NK) cells (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). While most studies have focused on adaptive immune cells such as T cells and monocytes, emerging evidence suggests a significant role of innate immunity in the pathophysiology of depression (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). As key players in innate immune response, NK cells rapidly respond to inflammatory signals and express a repertoire of activating and inhibitory receptors that dynamically reflect immune status (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Preclinical studies have shown that NK cells may alleviate depressive-like symptoms by modulating peripheral and central inflammation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, existing human studies on the percentage and activity of NK cells in MDD are limited, with inconsistent results (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Numerous studies have demonstrated that depressed individuals frequently exhibit reduced NK cell activity (NKCA) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and a higher percentage of NK cells following antidepressant treatment has been associated with better clinical outcomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). These features make NK cells a promising and tractable target for immunophenotypic characterization in MDD.\u003c/p\u003e\u003cp\u003eTo date, few studies have investigated the immunophenotypic profile of peripheral NK cells in patients with depression, particularly in the adolescent population. Although limited, studies in related psychiatric conditions such as autism spectrum disorders (ASD), bipolar disorder (BP) and schizophrenia (SZ) have reported alterations in NK cell surface receptors, including human leukocyte antigen-DR isotype (HLA-DR), the natural killer group 2 member A (NKG2A), the natural killer group 2 member C (NKG2C), natural cytotoxicity triggering receptor 3 (NKp30), natural cytotoxicity triggering receptor 1 (NKp46), and the immunoglobulin-like transcript 2 (ILT2) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Notably, certain receptor expressions have also been associated with cognitive performance, suggesting that NK cell phenotypes may reflect neuroimmune interactions relevant to psychiatric symptoms (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These findings underscore the theoretical and clinical importance of exploring NK cell receptor abnormalities in adolescent MDD.\u003c/p\u003e\u003cp\u003eGiven accumulating evidence implicating NK cells in the pathophysiology of psychiatric disorders, we hypothesize the existence of an immune\u0026ndash;cognitive regulatory axis in MDD, wherein NK cells of distinct phenotypic profiles may act as critical intermediaries linking peripheral immune alterations to cognitive dysfunction. To minimize the confounding effects of medication on the immune function, we recruited adolescents with first-episode, drug-na\u0026iuml;ve MDD. This population enables a more accurate characterization of immune phenotypes associated with the early stage of illness. Through this investigation, we aim to explore the potential of developing more objective, immune-based tools for auxiliary diagnosis and treatment of MDD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eA total of 54 first-episode, unmedicated adolescent inpatients with MDD were recruited from the Department of Psychiatry at the First Affiliated Hospital of Zhengzhou University. Diagnoses were made by board-certified psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2023-KY-0770-002) and conducted following the Declaration of Helsinki. Written informed consent was obtained from the guardians of all participants. All participants were interviewed with the Structured Clinical Interview for DSM-5 (SCID), and assessed for depressive symptoms using the Hamilton Depression Scale (HAMD) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe MDD group included patients aged 13\u0026ndash;18 years who met the following criteria: (a) first-episode, drug-na\u0026iuml;ve MDD diagnosed per DSM-5, with a HAMD score\u0026thinsp;\u0026ge;\u0026thinsp;20; (b) no history of smoking, alcohol use, allergies, infectious or chronic diseases, surgery, or vaccination in the past month; (c) no acute infections within 14 days. A total of 33 matched healthy controls (HCs) were recruited via public advertisements. Inclusion criteria for the HC group were: (a) no personal history of psychiatric disorders; (b) no medication use (prescription, over-the-counter, or supplements) in the past month; (c) no history of smoking, alcohol use, allergies, infections, chronic diseases, surgery, or vaccination in the past month; (d) no infections in the past 14 days. Exclusion criteria for both groups included: (a) current or past diagnosis of any other psychiatric or neurological disorders; (b) intellectual disability; (c) major systemic or organic brain diseases (e.g., tumors, heart disease, diabetes, or head trauma).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Peripheral blood processing and flow cytometry analysis of NK cell phenotypes\u003c/h2\u003e\u003cp\u003ePeripheral venous blood (4 mL) was collected from each participant. One milliliter was analyzed using an automatic hematology analyzer to detect platelet (PLT), neutrophil (NEU), monocyte (MONO), and lymphocyte (LYM, \u0026times;10⁹/L) count, and to calculate NLR, MLR, PLR, SII, and SIRI. The remaining 3 mL was anticoagulated with ethylenediaminetetraacetic acid (EDTA), and peripheral blood mononuclear cells (PBMC) were extracted within 3 hours. Whole blood was diluted 1:1 with Phosphate-Buffered Saline (PBS) and layered over 6 mL of lymphocyte separator medium. After centrifugation at 2000 rpm for 30 minutes at 20\u0026deg;C (acceleration rate: 1), the PBMC layer was collected, washed with PBS, and centrifuged again at 4000 rpm for 10 minutes (acceleration rate: 5). The PBMC pellet was resuspended in cryopreservation solution and stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor flow cytometric phenotyping, frozen PBMCs were rapidly thawed in a 37\u0026deg;C water bath, washed with PBS, and resuspended to a final concentration of 0.2-1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/100 \u0026micro;L. Fc blocking was performed with Human TruStain FcX at room temperature for 10 minutes, followed by staining with fluorochrome-conjugated antibodies in the dark for 20 minutes. A four-tube antibody panel was used. Each tube contained lineage markers (PerCP-CD45, FITC-CD3, BV421-CD56), with additional tube-specific antibodies as follows: (a) Tube 1: APC-HLA-DR, PE-NKG2C; (b) Tube 2: PE-NKp30, APC-NKp46; (c) Tube 3: PE-KIR2DL1, APC-NKG2A; (d) Tube 4: APC-CD57, PE-ILT2. After staining, cells were washed with PBS, filtered through a 70 \u0026micro;m mesh, and analyzed using an ACEA NovoCyte instrument. Data were exported as FCS files and processed using FlowJo (v10.8). Reagents and equipment details are summarized in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Cognitive function assessment\u003c/h2\u003e\u003cp\u003eThe MATRICS Consensus Cognitive Battery (MCCB) was initially developed by the National Institutes of Mental Health (NIMH) to accurately assess cognition in SZ (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and is now widely used in MDD research (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Trained clinicians administered the MCCB to both the MDD and HC groups. T-scores were computed for all seven cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses and data visualization were performed using SPSS version 25.0 and GraphPad Prism version 8.0. The Shapiro\u0026ndash;Wilk test was used to assess the normality of continuous variables. Normally distributed variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cem\u003ex̄\u003c/em\u003e \u0026plusmn; \u003cem\u003es\u003c/em\u003e), and group comparisons for these variables were performed using the independent samples \u003cem\u003et\u003c/em\u003e-test or Welch\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, depending on the equality of variances assessed by Levene\u0026rsquo;s test. Non-normally distributed data were expressed as median (Q1\u0026ndash;Q3), and between-group differences were assessed using the Mann\u0026ndash;Whitney U test. Spearman\u0026rsquo;s \u003cem\u003eρ\u003c/em\u003e was used for correlation analysis due to the non-normal distribution of most variables. Between-group differences in correlation coefficients were evaluated using Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test. Raw values were converted to z-scores to allow for cross-variable comparisons on a common scale. Multiple linear regression analyses were performed to examine the association between immune-inflammatory indicators and cognitive outcomes, adjusting for demographic variables. A two-sided \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical tests were two-tailed.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Group differences\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Demographic and clinical characteristics\u003c/h2\u003e\u003cp\u003eIn our study, two patients and five healthy individuals were excluded due to inadequate blood sample quality. The MDD group comprised 20 males and 32 females, with a median age of 15.0 (13.0\u0026ndash;16.0) years, while the HC group included 9 males and 19 females, with a median age of 15.0 (13.5\u0026ndash;16.5) years. There were no significant differences between groups in age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.371), sex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.577), body mass index (BMI) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.820), years of education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.397), years of father\u0026rsquo;s education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.979), and years of mother\u0026rsquo;s education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.416). The median disease duration in the MDD group was 12 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) months. Physical or psychotic symptoms were present in 80% of patients. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDemographics of the MDD and HC group.\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=\"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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMDD group (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC group (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et/z\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, med (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0(14.0\u0026ndash;16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0(13.5\u0026ndash;16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, Male/Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20/32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), med (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.80(18.33\u0026ndash;23.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.17(18.41\u0026ndash;21.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education, med (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(7.63\u0026ndash;10.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(7.13\u0026ndash;10.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of father\u0026rsquo;s education, med (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(6.75\u0026ndash;11.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(7.50\u0026ndash;10.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of mother\u0026rsquo;s education, med (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(6.38\u0026ndash;11.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(7.50\u0026ndash;10.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration, med (IQR) (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eAccompanying symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: MDD, Major Depressive Disorder; HC, healthy control; IQR, interquartile range; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Peripheral immune-inflammatory markers\u003c/h2\u003e\u003cp\u003ePeripheral blood analysis revealed significantly reduced NEU (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and elevated LYM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) in the MDD group compared to the HC group. No significant differences were observed in PLT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.353) or MONO (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062). Analysis of derived inflammatory indices showed that NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and SII (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly lower in the MDD group, while no significant differences in MLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.535) or SIRI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.073) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 NK cell surface receptor expression\u003c/h2\u003e\u003cp\u003eThe proportion of CD3⁻CD56⁺ NK cells was significantly lower in the MDD group compared to the HC group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Phenotypic analysis revealed significantly elevated expression of HLA-DR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NKp46 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NKG2A (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), and ILT2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), while CD57 expression was significantly reduced in the MDD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). No significant group differences were observed in the expression of NKG2C (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.141), NKp30 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.591), and KIR2DL1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.452). Consistently, the z-score plot showed an upward trend for most receptors in the MDD group, except for NKG2C, NKp30, and CD57, which exhibited a downward trend (-0.67\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003ez\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1.96) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Cognitive performance across domains\u003c/h2\u003e\u003cp\u003eThe MDD group exhibited significantly lower scores in the following cognitive domains: speed of processing (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reasoning and problem solving (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social cognition (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). The overall composite score, reflecting global cognitive performance, was also significantly reduced in the MDD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found in attention/vigilance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080), working memory (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.073), verbal learning (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.143), and visual learning (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.146). The z-score plot demonstrated a widespread decline across cognitive domains in the MDD group (-0.99\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003ez\u003c/em\u003e \u0026le; -0.35) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Correlation analysis\u003c/h2\u003e\u003cp\u003eAn overview of all correlation analyses is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Detailed group-specific statistics are reported in Supplementary Tables\u0026nbsp;5\u0026ndash;7 (sub-tables: a\u0026thinsp;=\u0026thinsp;MDD group, b\u0026thinsp;=\u0026thinsp;HC group). However, none of the regression models reached statistical significance after covariate adjustment (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;8).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Associations between immune-inflammatory markers and cognitive function\u003c/h2\u003e\u003cp\u003eIn the MDD group, MLR was negatively correlated with reasoning and problem solving (\u003cem\u003eρ\u003c/em\u003e = -0.282, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), while both SII and SIRI were negatively associated with working memory (\u003cem\u003eρ\u003c/em\u003e = -0.318, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; \u003cem\u003eρ\u003c/em\u003e = -0.292, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036). These associations were not observed in the HC group, and Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test confirmed significant between-group differences (\u003cem\u003eZ\u003c/em\u003e = -2.629 to -2.131, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn the HC group, NEU was positively correlated with speed of processing and attention/vigilance (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.407, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032; \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.437, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), while both MONO and SIRI showed positive correlations with speed of processing (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.433, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and reasoning and problem solving (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.417, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027; \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.384, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044). These correlations were absent in the MDD group, with Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test confirming significant group differences (\u003cem\u003eZ\u003c/em\u003e = -2.478 to -1.83, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Associations between NK cell surface receptors and cognitive function\u003c/h2\u003e\u003cp\u003eIn the MDD group, HLA-DR expression was negatively correlated with both speed of processing and overall composite (\u003cem\u003eρ\u003c/em\u003e = -0.346, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012; \u003cem\u003eρ\u003c/em\u003e = -0.285, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040). NKG2C was positively associated with working memory (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.312, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), and NKp30 showed positive correlations with attention/vigilance and visual learning (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.383, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.285, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). CD57 expression was also positively correlated with visual learning (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.305, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). These associations were absent in the HC group, and Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test revealed significant group differences for HLA-DR with speed of processing, NKG2C with working memory, and CD57 with visual learning (\u003cem\u003eZ\u003c/em\u003e = -1.664 to 2.777, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn the HC group, ILT2 expression was negatively associated with speed of processing and attention/vigilance (\u003cem\u003eρ\u003c/em\u003e = -0.460, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; \u003cem\u003eρ\u003c/em\u003e = -0.398, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), while positively correlated with social cognition (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.430, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). These associations were not significant in the MDD group. Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test confirmed significant differences in the correlations of ILT2 with social cognition (\u003cem\u003eZ\u003c/em\u003e = -2.557, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Associations between NK cell surface receptors and immune-inflammatory markers\u003c/h2\u003e\u003cp\u003eIn the MDD group, NKp46 expression was negatively correlated with LYM (\u003cem\u003eρ\u003c/em\u003e = -0.289, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and positively correlated with PLR (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.304, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). NKG2A showed a negative correlation with LYM (\u003cem\u003eρ\u003c/em\u003e = -0.275, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). These correlations were not observed in the HC group, and Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test confirmed no significant between-group differences (\u003cem\u003eZ\u003c/em\u003e = -1.59 to -0.266, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn the HC group, ILT2 expression was negatively associated with NEU, SII, and SIRI (\u003cem\u003eρ\u003c/em\u003e = -0.402, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034; \u003cem\u003eρ\u003c/em\u003e = -0.381, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045; \u003cem\u003eρ\u003c/em\u003e = -0.459, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), while these associations were absent in the MDD group. Fisher\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e test confirmed no significant differences between groups (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.757 to 1.416, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn summary, our study revealed significant alterations in peripheral immune-inflammatory markers and NK cell surface receptor expression in adolescents with MDD. Patients exhibited distinct immune profiles, including reduced NEU, NLR, PLR, and SII levels, and increased expression of HLA-DR, NKp46, NKG2A, and ILT2, alongside decreased CD57. Cognitive performance, particularly in speed of processing, reasoning and problem solving, and social cognition, was significantly impaired in the MDD group. Correlation analyses suggested potential links between immune dysregulation and cognitive deficits, highlighting possible mechanisms underlying adolescent MDD.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Abnormal immune-inflammatory markers\u003c/h2\u003e\u003cp\u003eCurrently studied inflammatory biomarkers in psychiatry commonly include cytokines, acute-phase proteins, and brain-derived neurotrophic factor (BDNF). Accumulating evidence has suggested their involvement in various psychiatric disorders, although the underlying mechanisms remain incompletely understood. Our study investigated several immune-inflammatory indicators and their relationships with MDD status and symptoms, aiming to identify potential diagnostic or prognostic biomarkers.\u003c/p\u003e\u003cp\u003eNEU, a primary innate immune cell, eliminates pathogens through phagocytosis, degranulation, and cytokine release (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). LYM, a key effector of the adaptive immune system, performs regulatory and protective functions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). NLR represents the balance between innate and adaptive immunity, widely used as a marker of systemic inflammation. Our findings indicated reduced NEU and elevated LYM in MDD patients, resulting in a lower NLR. However, previous studies have reported mixed results. Some studies suggest a nonlinear association, with both high and low NLR linked to depressive symptoms such as anhedonia and sleep disturbance (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Elevated NEU or NLR has been observed in unmedicated MDD patients or those with suicide risk (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), while some studies found no significant difference (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Besides, a research reported higher NLR in bipolar mania patients but lower NLR in unipolar depression, suggesting distinct inflammation across different episodes of depression (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These observations suggest that NLR could serve as a potential biomarker of early-stage immune dysregulation in adolescent depression, though larger studies are warranted.\u003c/p\u003e\u003cp\u003eAlthough PLT has traditionally been recognized for its role in hemostasis, it is increasingly acknowledged as a key player in immune regulation (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). PLT can interact with various immune cells, release exosomes, and influence both innate and adaptive immunity (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). PLR reflects the balance between platelet activation and lymphocyte-mediated immune regulation. Our study found reduced PLR in the MDD group despite no significant change in PLT. It may primarily reflect increased LYM levels, consistent with an immune shift toward adaptive regulation in adolescent depression (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In contrast to our results, a large-scale study reported elevated PLR in depression, although its predictive value appeared lower than that of NLR (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Interestingly, the association between elevated PLR and depression was limited to studies conducted in China, indicating potential geographic or demographic influences on immune-inflammatory responses (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSII and SIRI, which integrate multiple immune cell types, are considered more comprehensive indices than single ratios such as NLR or PLR (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Ryan et al. found that SII was negatively correlated with baseline HAMD scores (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), while another study found a significant decrease in SII following electroconvulsive therapy (ECT) (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Elevated SII and SIRI in MDD have also been associated with depression severity (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), and Li et al. linked their increase to higher depression risk (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Contrary to these findings, our study observed a significant reduction in SII, while no significant difference in SIRI, suggesting potential stage-specific or age-related immune variations in MDD.\u003c/p\u003e\u003cp\u003eOur findings may reflect chronic inflammation-induced neutrophil exhaustion or glucocorticoid-mediated suppression of neutrophil mobilization, which has been described in early-stage or adolescent MDD as part of immune deviation or depletion (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The inconsistencies of results emphasize the complexity of immune involvement in MDD and highlight the importance of interpreting inflammatory markers within a systems-level framework.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Abnormal NK cell surface receptor expression\u003c/h2\u003e\u003cp\u003eNK cells are a major subset of lymphocytes in the innate immune system. Their function is regulated by a dynamic balance between inhibitory and activating surface receptors, many of which recognize HLA class I molecules or stress-induced ligands (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Under homeostatic conditions, NK cells express inhibitory receptors such as KIRs, ILT-2, and NKG2A. Upon immune activation, stimulatory receptors including NKG2C, NKp46, and NKp30 are upregulated to initiate immune responses (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). According to the \u0026ldquo;discontinuity theory,\u0026rdquo; the immune system is more responsive to abrupt changes than gradual or continuous stimulation, which may underlie the heterogeneous development of NK cell subsets across disease states (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, several receptor expression patterns showed significant differences between MDD patients and HCs. We observed a significantly higher proportion of HLA-DR⁺ NK cells in the MDD group. Consistent with our result, Tarantino et al. found that HLA-DR was overexpressed in 50\u0026ndash;83% of patients with first-episode psychiatric disorders, and that other phenotypic alterations in NK cells were primarily present in individuals with high HLA-DR expression (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Interestingly, they also found that HLA-DR expression was negatively correlated with IFN-γ levels, suggesting a state of NK cell exhaustion in psychiatric populations. Mechanistically, HLA-DR\u003csup\u003e+\u003c/sup\u003e NK cells can be expanded by various cytokines, including IL-2 and IL-15 (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), and the sustained activation may lead to functional decline or reduced cytokine production over time. The upregulation of HLA-DR in our MDD cohort may reflect a compensatory immune activation in response to subclinical inflammation, eventually progressing toward an exhausted phenotype.\u003c/p\u003e\u003cp\u003eOur findings indicated that the expression of NKp46 was significantly increased in the MDD group, while NKp30 showed no difference between groups. NKp46 has been shown to regulate cytokine production and the expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), a TNF superfamily member that induces apoptosis in target cells (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Murine models further support a role for NKp46 in regulating TRAIL levels (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), and TRAIL receptors are overexpressed in MDD patients with a history of childhood trauma (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), suggesting a potential link between NK cell activation, apoptotic signaling, and stress-related immunopathology. Given that NKp46 is the only natural cytotoxicity receptor with a known murine homologue, animal models may offer a valuable insight into its mechanistic role in depressive pathophysiology.\u003c/p\u003e\u003cp\u003eNKG2A is an inhibitory receptor that maintains NK cell quiescence under steady-state conditions, while NKG2C is an activating receptor typically upregulated in response to viral infections (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Recent studies have linked increased NKG2C\u003csup\u003e+\u003c/sup\u003e NK cells with ASD, BP, and SZ (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), whereas evidence regarding shifts in inhibitory receptors such as NKG2A in MDD remains limited. However, our study demonstrated elevated NKG2A expression in the MDD group, while no significant differences in NKG2C were observed. This pattern supports a hypoactive or suppressed NK cell phenotype, potentially contributing to the immune dysfunction observed in depression (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Given that NKG2A engagement transmits strong inhibitory signals, its upregulation may also reflect functional exhaustion or immune deviation in depression.\u003c/p\u003e\u003cp\u003eCD57\u003csup\u003e+\u003c/sup\u003e NK cell is a mature subpopulation of NK cells with strong cytotoxic potential and high IFN-γ production (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Our findings revealed a significant reduction in CD57 expression in the MDD group, suggesting impaired NK cell maturation. Although no systematic studies have explored CD57 alterations in MDD, prior evidence indicates that CD57⁺ NK cells regulate excessive inflammation and may play a homeostatic role in immune balance (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). They also tend to express lower levels of activating receptors, such as NKp30 (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), consistent with our observation of reduced CD57 and NKp30 expression. This parallel downregulation may reflect a broader suppression or dysregulation of terminal NK cell differentiation in MDD.\u003c/p\u003e\u003cp\u003eILT2 is another inhibitory receptor that suppresses NK cell effector functions, including granule release, cytoskeletal rearrangement, and IFN-γ signaling (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). In our study, we observed elevated ILT2 expression in MDD patients. It aligns with reports of increased ILT2\u003csup\u003e+\u003c/sup\u003e NK cells in BP and SZ populations (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), hinting at a possible convergent upregulation of immunoinhibitory checkpoints in psychiatric conditions. However, unlike HLA-DR or NKp46, ILT2 showed notable interindividual variability, underscoring that its dysregulation may not be uniform across all MDD patients. Taken together, these findings point to an important\u0026mdash;but selectively engaged\u0026mdash;inhibitory axis in NK cell regulation in adolescent MDD. Further studies should clarify whether this pattern reflects a distinct immune endophenotype, compensatory adaptation, or vulnerability marker for depression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Abnormal cognitive function\u003c/h2\u003e\u003cp\u003eCognitive impairments are highly prevalent in adolescents with MDD, with rates reaching up to 83%, particularly affecting executive function, verbal and spatial memory, and attention (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Cognitive dysfunction in depression has been linked to long-term psychosocial development. Evidence suggests that distinct cognition contributes to the educational gradient observed between MDD and non-MDD populations (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), and higher academic ability during adolescence is associated with fewer depressive symptoms in early adulthood (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Deficits in executive function, such as speed of processing and reasoning and problem solving, may reflect reduced prefrontal efficiency, contributing to impaired decision-making and diminished productivity (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Impairment in verbal and visual learning, on the other hand, may exacerbate social withdrawal and negatively affect quality of life (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). In our study, MDD patients showed lower z-scores across all seven MCCB domains compared to HCs, with significant impairments in speed of processing, reasoning and problem solving, and social cognition. These findings suggest a broad cognitive dysfunction, consistent with previous literature (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClinical factors such as episode frequency, disease duration, and symptom severity have been shown to modulate the degree of cognitive impairment in MDD (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Notably, patients with recurrent depression tend to experience more persistent and severe cognitive deficits, particularly in executive domains, compared to those with first-episode MDD (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Our study, which focused on a first-episode, drug-na\u0026iuml;ve adolescent population, further highlights the presence of early cognitive disturbances in MDD, underscoring the need for early identification and intervention strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Association between immune-inflammatory markers and cognitive function\u003c/h2\u003e\u003cp\u003eExisting studies have demonstrated that immune-inflammatory dysregulation is closely associated with cognitive impairments across various psychiatric disorders (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). While preliminary studies have linked composite markers such as NLR, PLR, and SII with depressive severity, their direct relationship with cognitive performance in MDD remains limited. In light of these gaps, our study explores potential associations between immune-inflammatory indices and cognitive function in adolescents with first-episode, drug-na\u0026iuml;ve MDD.\u003c/p\u003e\u003cp\u003eRecent research from SZ populations has shown that higher NLR, PLR, and SII levels are significantly associated with poorer cognitive deficits, as measured by the Mini-Mental State Examination (MMSE) (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). These findings align with a broader trend across clinical populations. For instance, elevated NLR, MLR, and SII were linked to cognitive decline in surgical patients (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e), whereas lower levels of NLR and MLR served as protective factors for post-stroke cognitive impairment (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). These findings show that systemic immune-inflammatory responses may play a generalized role in cognitive dysfunction. Our findings build on this emerging literature by demonstrating that SII and SIRI levels were negatively correlated with working memory, while MLR was negatively associated with reasoning and problem solving in MDD. These results suggest that peripheral immune markers could serve as useful indicators for early detection and individualized treatment strategies targeting cognitive dysfunction in depression, possibly reflecting shared immunopathological mechanisms across neuropsychiatric and somatic conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Association between NK cell surface receptors and cognitive function\u003c/h2\u003e\u003cp\u003eRecently, evidence shows that phenotypically distinct NK cells can infiltrate the central nervous system (CNS) and influence cognition and behavior (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). However, few studies have examined the link between NK cell surface receptor profiles and cognitive function in psychiatric disorders (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). Consistent with our initial hypothesis of an immune\u0026ndash;cognitive regulatory axis in MDD, our findings suggest that altered NK cell phenotypes may contribute to cognitive impairment.\u003c/p\u003e\u003cp\u003eSpecifically, MDD patients showed a general pattern of upregulated NK cell surface receptors (except for NKG2C, NKp30, and CD57), paralleled by a global decline in cognitive performance. As shown in the z-score plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), cognitive decline followed a uniform downward trend across domains, whereas NK receptor expression varied in direction and magnitude, highlighting the complexity of peripheral immune responses in MDD. Notably, HLA-DR expression was significantly higher in the MDD group and negatively correlated with speed of processing. Similarly, decreased NKG2C expression was paralleled by poor performance in working memory, and reduced CD57 was linked to deficits in visual learning. These inverse correlations may reflect an immune dysregulation state that contributes to neurocognitive dysfunction. Interestingly, NKp30 expression was positively correlated with attention/vigilance, though without significant between-group differences in either expression or correlation strength. It suggests that the regulatory role of NKp30 in cognition may not be disease-specific, but rather a general feature present across populations.\u003c/p\u003e\u003cp\u003eMechanistically, animal studies demonstrate that NK cell deficiency impairs both short- and long-term memory in mice (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e), supporting a possible link between NK activity and cognition. In humans, HLA-DR expression correlates with structural language and social awareness in ASD patients (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), suggesting a broader neuroimmune influence. NKG2C\u003csup\u003e+\u003c/sup\u003e NK cells produce higher levels of IFN-γ and IL-17, and have been linked to neurotoxicity in neurodegenerative diseases (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Systemic NK cell depletion significantly reduces IFN-γ levels in the prefrontal cortex, leading to impaired gamma-aminobutyric acid (GABA) signaling and poor performance on working memory (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). Collectively, these findings underscore the dual role of NK cells in peripheral immune regulation and central cognitive processes. Our study further contributes to the hypothesis, suggesting that NK cell surface phenotypes may serve as peripheral indicators of cognitive dysfunction in MDD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Association between NK cell surface receptors and immune-inflammatory markers\u003c/h2\u003e\u003cp\u003eTo our knowledge, no prior studies have directly examined the relationship between NK cell surface receptor expression and composite immune-inflammatory markers in MDD. To address this gap, we examined their correlations in both the MDD and HC groups. In our study, NKp46 expression was negatively associated with LYM and positively associated with PLR, and NKG2A also showed a negative correlation with LYM in the MDD group. Existing literature has suggested that NK receptor expression may influence systemic inflammation. For example, elevated NLR is linked to reduced IFN‑γ release from NK cells in healthy individuals (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e), and the receptor such as HLA-DR, NKG2C, or CD57, regulates IFN-γ levels during activation (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e), suggesting a potential pathway through which NK receptors may regulate systemic immune responses via cytokine signaling. These findings provide a theoretical basis for exploring the interplay between NK phenotypes and peripheral inflammation.\u003c/p\u003e\u003cp\u003eOur correlation analysis revealed a notable divergence across immune and cognitive domains. Specifically, all inflammation\u0026ndash;cognition correlations showed significant group differences, while no such differences were observed in NK receptor\u0026ndash;inflammation associations. Only partial NK receptor\u0026ndash;cognition correlations differed between groups. This pattern suggests that inflammation\u0026ndash;cognition links are more sensitive to MDD status, supporting a state-dependent role of systemic inflammation in cognitive outcomes. Meanwhile, NK cells may contribute to cognitive dysfunction through inflammation-independent pathways, such as direct CNS infiltration or neuroimmune signaling. Together, these findings refine the conceptual model of the immune\u0026ndash;cognitive axis in adolescent depression. Rather than serving solely as intermediaries between peripheral inflammation and cognitive dysfunction, NK cells may play a more direct neuroimmune role.\u003c/p\u003e\u003cp\u003eDespite several immune-inflammatory markers and NK receptors exhibiting significant bivariate associations with cognitive domains, none remained significant in multivariate models. This discrepancy may reflect limited statistical power due to the modest sample size, shared variance among predictors, or the influence of unmeasured intermediate mechanisms, highlighting the need for further mechanistic investigation.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn essence, our study supports a multi-level integrative model in which NK cells serve as critical immunological intermediaries within the immune\u0026ndash;cognitive axis of adolescent MDD. The convergence of findings across molecular (NK cell surface receptor), cellular (blood-based immune markers), and functional (cognitive domains) levels strengthens the plausibility of this network. Our findings suggest that NK cell phenotypes may not only bridge systemic inflammation and cognitive impairment but also influence cognition through inflammation-independent pathways such as neuroimmune signaling. This is the first study to implicate NK receptor dysregulation in adolescent MDD, suggesting a potential age-specific immune mechanism distinct from adults. These findings not only provide support for the biopsychosocial model of depression but also identify NK cell receptors as promising targets for future neuroimmune and immunomodulatory therapies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations of this study should be noted. First, the relatively small sample size may have limited the power to detect subtle group differences and correlations. Second, the cross-sectional design precludes a causal interpretation of the association between immune alterations and cognitive impairment. Third, immune-inflammatory and NK cell phenotypic analysis were obtained from peripheral blood, which may not accurately represent the immune status of the CNS. Lastly, potential confounding factors such as lifestyle, circadian rhythm, or perceived stress were not systematically controlled and should be addressed in future longitudinal studies to enhance the robustness and generalizability of the findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eautism spectrum disorders\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBDNF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebrain-derived neurotrophic factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebipolar disorder\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecentral nervous system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSM-5\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelectroconvulsive therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eethylenediaminetetraacetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGABA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egamma-aminobutyric acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHAMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Hamilton Depression Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHLA-DR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehuman leukocyte antigen-DR isotype\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK cells\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural killer cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNKCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNK cell activity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehealthy control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHLA-DR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehuman leukocyte antigen-DR isotype\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eILT2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eimmunoglobulin-like transcript 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLYM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elymphocytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCCB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMATRICS Consensus Cognitive Battery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emajor depressive disorder\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocyte-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMini Mental State Examination\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMONO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNEU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophils\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIMH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe National Institutes of Mental Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNKG2A/NKG2C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural killer group 2 member A/C\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK cell\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural killer cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNKp30\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural cytotoxicity triggering receptor 3\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNKp46\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enatural cytotoxicity triggering receptor 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eperipheral blood mononuclear cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphate Buffered Saline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplatelets\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCID\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Structured Clinical Interview for DSM-5\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic immune-inflammation index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic inflammation response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSZ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eschizophrenia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTRAIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor necrosis factor related apoptosis-inducing ligand.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2023-KY-0770-002). Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to confidentiality, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported\u0026nbsp;by the National Natural Science Foundation of China (Grant NO. 81801325), Henan Provincial Fund for the Cultivation of Outstanding Young Talents in Health Science and Technology Innovation (Grant NO. YXKC2020035), Henan Scientific and Technological Development Program (Grant NO. 252102311004), Key Research and Development Program of Henan Province (Grant NO. 251111313200), National Natural Science Foundation of China (Grant NO. 81801335), Henan Scientific and Technological Development Program (Grant NO. 232102311051), and Henan Scientific and Technological Development Program (Grant NO. 242102311050).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJiahui Wang: Data curation, Formal analysis,\u0026nbsp;Investigation, Software, Visualization,\u0026nbsp;Writing- original draft.\u0026nbsp;Lingzhi Hou:\u0026nbsp;Data curation,\u0026nbsp;Investigation,\u0026nbsp;Validation, Writing- original draft;\u0026nbsp;Cai Li: Funding acquisition; Project administration,\u0026nbsp;Resources; Supervision.\u0026nbsp;Yitong Liu: Investigation, Software, Validation; Yan Xu: Data curation; Investigation; Yang He: Resources; Software; Lei Yang: Funding acquisition; Resources; Li Wang: Resources; Qidong Liu: Resources; Jun Cheng: Resources; Yanyan Zhang: Resources; Yunmiao Ma: Investigation; Haiwei Xu: Funding acquisition;\u0026nbsp;Methodology, Project administration; Resources; Hong Li:\u0026nbsp;Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to all participants for their involvement in this study. We also appreciate the language editing assistance provided by ChatGPT (OpenAI), under the full supervision of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThapar A, Eyre O, Patel V, Brent D. Depression in young people. Lancet. 2022 Aug 20;400(10352):617\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eZhang Y, Li Z, Feng Q, Xu Y, Yu R, Chen J, et al. Global, regional and national burdens of major depression disorders and its attributable risk factors in adolescents and young adults aged 10\u0026ndash;24 years from 1990 to 2021. BMC Psychiatry. 2025 Apr 18;25(1):399. \u003c/li\u003e\n\u003cli\u003eHarris IM, Beese S, Moore D. Predicting future self-harm or suicide in adolescents: a systematic review of risk assessment scales/tools. BMJ Open. 2019 Sep;9(9):e029311. \u003c/li\u003e\n\u003cli\u003eJohnson D, Dupuis G, Piche J, Clayborne Z, Colman I. Adult mental health outcomes of adolescent depression: A systematic review. 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Natural killer cells and innate lymphoid cells 1 tune anxiety-like behavior and memory in mice via interferon-\u0026gamma; and acetylcholine. Nature Communications. 2023;14(1):3103. \u003c/li\u003e\n\u003cli\u003eTerr\u0026eacute;n I, Orrantia A, Vitall\u0026eacute; J, Astarloa-Pando G, Zenarruzabeitia O, Borrego F. Modulating NK cell metabolism for cancer immunotherapy. Seminars in Hematology. 2020 Oct 1;57(4):213\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eKemeny ME. Psychobiological responses to social threat: Evolution of a psychological model in psychoneuroimmunology. Brain, behavior, and immunity. 2009;23(1):1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eGoldeck D, Schulte C, Teixeira dos Santos MC, Scheller D, \u0026Ouml;ttinger L, Pawelec G, et al. Higher Frequencies of T-Cells Expressing NK-Cell Markers and Chemokine Receptors in Parkinson\u0026rsquo;s Disease. Journal of Ageing and Longevity. 2023 Mar;3(1):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eKim BR, Chun S, Cho D, Kim KH. Association of neutrophil-to-lymphocyte ratio and natural killer cell activity revealed by measurement of interferon-gamma levels in a healthy population. J Clin Lab Anal. 2019 Jan 1;33(1):e22640. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"major depressive disorder, natural killer cells, immune-inflammation, cognitive impairment, NK cell surface receptors, boimarker","lastPublishedDoi":"10.21203/rs.3.rs-7186161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7186161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eImmune dysregulation and cognitive deficits are increasingly recognized in adolescent major depressive disorder (MDD), yet their interrelationship remains unclear. This study aimed to investigate peripheral immune-inflammatory alterations and natural killer (NK) cell phenotypes, and explore their association with cognitive function in adolescent MDD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFifty-four first-episode, drug-na\u0026iuml;ve adolescents with MDD and 33 matched healthy controls (HCs) were enrolled. Group differences in peripheral blood immune-inflammatory indices (NLR, PLR, MLR, SII, SIRI), NK cell surface receptors (HLA-DR, NKp46, NKp30, NKG2A, NKG2C, KIR2DL1, ILT2, CD57), and cognitive function were analyzed, along with their intercorrelations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared with HCs, patients with MDD showed lower NEU, NLR, PLR, and SII levels, alongside elevated LYM counts. NK cells exhibited reduced overall proportions but increased expression of HLA-DR, NKp46, NKG2A, and ILT2, with decreased CD57 expression in the MDD group. Significant cognitive impairments were observed in speed of processing, reasoning and problem solving, and social cognition. Furthermore, several immune-inflammatory markers (MLR, SII, SIRI) and NK cell receptors (HLA-DR, NKG2C, NKp30, CD57) were significantly correlated with performance across multiple cognitive domains.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings reveal significant associations between NK cell phenotypes, systemic immune-inflammatory markers, and cognitive function in adolescent MDD. These results suggest a potential regulatory role of NK cells within the immune\u0026ndash;cognitive axis, possibly reflecting both intermediary functions and inflammation-independent neuroimmune mechanisms. This study provides novel insight into potential biomarkers and immunomodulatory targets for early intervention in adolescent MDD.\u003c/p\u003e","manuscriptTitle":"Altered NK Cell Receptor Profiles and Immune-Inflammatory Markers in Adolescent Major Depressive Disorder: Associations with Cognitive Impairment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:33:09","doi":"10.21203/rs.3.rs-7186161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T08:34:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T01:29:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T07:48:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62283656150970674397942782153596191361","date":"2025-08-21T09:28:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154452365535322764537557331328126104961","date":"2025-08-19T05:11:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T08:28:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T06:15:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-06T04:26:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T16:18:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-08-05T12:56:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"111dc18a-0448-4c00-a131-2487e63f997a","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:10:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:33:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7186161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7186161","identity":"rs-7186161","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}