Interplay between Systemic Immune-Metabolic Signatures and Endometrial Immune Profiles in Women with Recurrent Pregnancy Loss

Interplay between Systemic Immune-Metabolic Signatures and Endometrial Immune Profiles in Women with Recurrent Pregnancy Loss | 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 Interplay between Systemic Immune-Metabolic Signatures and Endometrial Immune Profiles in Women with Recurrent Pregnancy Loss Amin Kamrani, Shiva Pourvahdani, Mohammadbagher Pirouzpanah, Aysan Salamati, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9523337/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Recurrent pregnancy loss (RPL) is a complex disorder fundamentally linked to immune dysregulation at the maternal-fetal interface. While endometrial immune profiling provides critical diagnostic insights for managing RPL, its clinical application is limited by the invasive nature of endometrial biopsies. Objective This study aimed to identify non-invasive, serum-based immunological and metabolic markers that accurately reflect local endometrial immune profiles, facilitating a less invasive risk assessment and patient categorization. Methods The study enrolled 106 participants, including 81 women with RPL and 25 fertile controls. Endometrial biopsies were analyzed for IL-15/Fn-14, IL-18/TWEAK, and CD56 expression to stratify patients into balanced, high immune dysregulation (over-activated), and low immune dysregulation profiles. Corresponding peripheral blood samples were evaluated for Th1/Th2 ratios, natural killer (NK) cell frequencies, autoantibodies, and metabolic biomarkers including adiponectin, prostaglandin E2 (PGE-2), insulin-like growth factor-1 (IGF-1), and total phospholipids. Results All control subjects exhibited a balanced endometrial immune profile. In contrast, approximately 71% of RPL patients demonstrated immune dysregulation, with 46.9% showing an over-activated profile and 24.7% a low-activated profile. Systemically, the high immune dysregulation group exhibited significantly elevated peripheral NK cell frequencies and Th1/Th2 ratios compared to the balanced group. Furthermore, this over-activated group demonstrated a substantially higher prevalence of serum autoantibodies. Metabolically, high immune dysregulation was associated with significantly decreased serum adiponectin and IGF-1 levels, alongside markedly elevated PGE-2 and total phospholipid concentrations. Conclusion Endometrial immune dysregulation in RPL is tightly correlated with distinct systemic immune and metabolic signatures. Utilizing these corresponding serum biomarkers offers a highly promising, non-invasive alternative to endometrial biopsy, paving the way for individualized precision medicine and targeted therapeutic strategies for women experiencing RPL. Recurrent pregnancy loss endometrial immune profiling uterine NK cells IL-15 IL-18 autoantibodies metabolic biomarkers immune dysregulation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Recurrent pregnancy loss (RPL) can be understood as a complicated pregnancy-related disorder with a multifactorial cause, and among those factors, the compromised regulation of the mother–fetal interface by the immune system has been shown to play an important part (Dashti et al. 2025). The evidence has been mounting lately that the implantation process and the survival of pregnancy should be accompanied by a precisely calibrated endometrial immune environment, rather than by an immune tolerance mechanism. An imprecise regulation of this state could lead to implantation failure, early pregnancy loss, or repeated miscarriage (Aslanian-Kalkhoran et al. 2023). An endometrial immune profiling has been promoted as a relevant tool with which the immune deregulation state specific to women with pregnancy-related disorders might be described. On the premise of the expression level of major immunological factors such as cytokines, cytokine receptors, and natural killer cell numbers, patients could be categorized according to tightly defined endometrial immune profiles (Ebrahimi et al. 2025). These endometrial immune profiles include those with excessive immune deregulation, those with reduced immune deregulation, those with mixed deregulation, and those with the normal profile. Importantly, every particular state has been related to different underlying factors with, accordingly, an appropriate distinct therapeutic target, making it an important aim to prioritize the accurate immune typing for patients with an entity like RPL. Amongst these immune markers, the roles of IL-15 and IL-18 are pivotal in modulating the activation, proliferation, and polarization of uNK cells, and the ratios of mRNA expression of IL-15/Fn-14 and IL-18/TWEAK have been demonstrated to represent the relative equilibrium of immune activation, tissue remodeling, and angiogenesis within the endometrium (Cheloufi et al. 2021). Additionally, the expression of CD56 is a crucial marker of the activity and number of uNK cells (Пастущек et al. 2020 ). Collectively, these markers allow a sensitive delineation of endometrial immune profiles and immune dysregulation within women with RPL. Although endometrial immune profiling holds immense potential within clinical practice, it remains hindered by the invasive nature of endometrial biopsy (Elkarhat et al. 2019 ). Hence, there remains a pressing need to transfer the translation of immune profiling from tissues to less invasive, serum-based markers within the field of reproductive medicine. The discovery of immune- and metabolism-related profiles within peripheral blood which reflect endometrial immune homeostasis would provide potential avenues for non-invasive risk assessment, prognosis, and individualized management strategies within women with high RPL risks (Stefanidou et al. 2011 ; Robinson et al. 2012). Besides localized endometrial immune system dysregulation, systemic immune system imbalances, which involve endometrial Th1/Th2 ratios, presence of autoantibodies, and endometrial environment-based metabolic imbalances, play pivotal roles in pathogenesis of recurrent pregnancy loss (Park, Han, and Lee 2022). There is potential interplay between systemic immunity and localized endometrial immunity, which could lead to immune system dysregulation depending on diverse levels of immune profiles. Conversely, their connection to diverse endometrial immune profiles has not been adequately explored. This current study was aimed at exploring immunological and metabolic imbalances characteristically exhibited by women experiencing recurrent pregnancy loss; the study considered fertile women as controls. This research aims to define immunological dysregulation depending on endometrial markers and identify detectable serum markers that could reflect endometrial immunity by characterizing immunological dysregulation depending on diverse immune profiles that include high immune profiles, low immune profiles, mixed immune profiles, and normal immune profiles. This could ease implementation of serum-based immune profiling over endometrial biopsy-based diagnostic procedures for women experiencing recurrent pregnancy loss. 2. Methods and Materials 2.1 Study Participants and Ethical Considerations In total, 106 women were included in this study, 81 of whom had experienced recurrent pregnancy losses (RPL) and 25 of whom were control participants (C) of a similar age. Control participants had experienced at least one successful pregnancy before and had never experienced miscarriage or fertility treatments. All participants provided written consent to the research. The ethics committee of our institution approved this study. Clinical and demographic information, such as patient age, BMI, infertility duration and character, hormone status (FSH, LH, AMH), endometrial thickness, and reproductive history, were collected. (Demographic and clinical parameters of the patients are shown in Table 1 ). All participants were chosen from infertility clinics in Tabriz, East Azerbaijan Province, Iran, and had regular menstrual cycles, no history of endometrial disease, and no use of hormonal medications in the three months preceding the procedure. Written informed consent was obtained from all individual participants included in this study, in accordance with the ethical principles of the Declaration of Helsinki. Peripheral blood samples were obtained alongside endometrial biopsies to examine immunologic and metabolic aspects. Patients with RPL were identified if they lost at least two consecutive pregnancies. Results were expressed using means ± SD for continuous data and frequency and percentage for categorical data. Table 1 / Demographic and Clinical Characteristics of RPL Patients and Healthy Controls Variable RPL patients (n = 81) Healthy controls (n = 25) P value Age (years) 32.05 ± 2.81 30.88 ± 2.52 0.065 Body mass index (BMI, kg/m²) 25.68 ± 1.80 24.86 ± 1.96 0.056 Duration of infertility (years) 4.2 ± 1.9 — — Type of infertility (Primary/Secondary), n (%) 49 (60.5) / 32 (39.5) — — Number of previous miscarriages 2.9 ± 0.8 0 < 0.0001 FSH (IU/L) 7.36 ± 1.02 6.91 ± 1.04 0.057 LH (IU/L) 6.22 ± 0.642 5.92 ± 0.520 0.037 AMH (ng/mL) 2.4 ± 1.1 2.7 ± 1.2 0.298 Endometrial thickness (mm) 8.7 ± 1.3 9.0 ± 1.2 0.214 Regular menstrual cycle, n (%) 81 (100) 25 (100) — Data are presented as mean ± SD or number (percentage). RPL : recurrent pregnancy loss; FSH : follicle-stimulating hormone; LH : luteinizing hormone; AMH : anti-Müllerian hormone. 2.2 Endometrial Sample Collection and Processing The endometrial biopsy was performed during the mid-luteal phase (LH + 7 to LH + 9 days) by suction cannula (Nexodis; Meringer, Kalisz, Poland). This was done immediately after collection, when it was placed into 5 mL of phosphate-buffered saline (PBS; Gibco; Thermo Fisher Scientific) comprising 4% fetal bovine serum (FBS; Sigma-Aldrich). The biopsy was then transported on ice. Samples were processed for evaluating gene expression levels of interleukin-15 (IL-15), interleukin-18 (IL-18), TWEAK, Fn-14, or CD56 by quantitative real-time PCR (qRT-PCR) and flow cytometric analysis of immune cell infiltrates. Based on ratios of IL-18/TWEAK, IL-15/FN-14, and CD56 gene expression levels, 81 patients with RPL were subdivided into three groups of endometrial immune profiles: BIR-P patients having a ‘Balanced Immune Regulation,’ HID-P patients having ‘high immune dysregulation,’ and LID-P patients having ‘low immune dysregulation’ based on defined standardized criteria by Lédée et al. (Cheloufi et al. 2021). This classification allowed the patients to be stratified for further immunologic and metabolic studies. Endometrial immune profiling was done based on the ratio of IL-15/Fn-14 and IL-18/TWEAK genes, which reflected the activation state of the uNK cells and the immune balance state of the endometrium. According to the criteria set forth by Lédée et al., a balanced pattern had IL-15/Fn-14 ratios between 0.36 and 3.4, an IL-18/TWEAK ratio between 0.02 and 0.13, and CD56 gene expression between 0.5 and 1.5. Over-immune activation showed high levels of IL-15/Fn-14,IL-18/TWEAK, and CD56, while low immune activation presented low levels of the three genes. 2.3 Endometrial Immune Biomarker Measurement and Classification Relative mRNA expression levels of IL-18, TWEAK, IL-15, Fn-14, and CD56 were quantified in endometrial samples by real-time PCR. Total RNA was extracted using a commercial RNA extraction kit (RNeasy Mini Kit, Qiagen, Germany) and reverse-transcribed into cDNA (High-Capacity cDNA Reverse Transcription Kit, Applied Biosystems, USA). Quantitative PCR was performed using SYBR Green chemistry (PowerUp™ SYBR Green Master Mix, Applied Biosystems, USA) on a real-time PCR system (ABI StepOnePlus™, Applied Biosystems, USA). Gene expression was normalized to GAPDH and calculated using the 2⁻ΔΔCt method. Immune profiles were classified according to established reference ranges: balanced (IL-18/TWEAK: 0.02–0.13; IL-15/Fn-14: 0.36–3.4; CD56: 0.5–1.5), over-activated (> 0.13, > 3.4, > 1.5), and low-activated (< 0.02, < 0.36, < 0.5), respectively. 2.4 Flow Cytometric Analysis of Th1/Th2 Ratio and NK Cells Flow cytometric analysis of Th1/Th2 ratio and natural killer (NK) cells was performed in the three study groups. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation using Ficoll-Paque (Yekta Tajhiz Azma, Tehran, Iran). For intracellular cytokine staining, PBMCs were stimulated with phorbol 12-myristate 13-acetate and ionomycin (PMA/Ionomycin; Sigma-Aldrich, USA) in the presence of Brefeldin A (Sigma-Aldrich, USA). Cells were first stained for surface markers using anti-human CD3 (PerCP-Cy5.5, clone UCHT1, BD Biosciences, USA) and anti-human CD4 (APC, clone RPA-T4, BD Biosciences, USA), followed by fixation and permeabilization (Cytofix/Cytoperm™, BD Biosciences, USA). Intracellular staining was then performed using anti-human IFN-γ (FITC, clone B27, BD Biosciences, USA) and anti-human IL-4 (PE, clone MP4-25D2, BD Biosciences, USA) antibodies to identify Th1 and Th2 cells, respectively. The Th1/Th2 ratio was calculated as the percentage of IFN-γ⁺ to IL-4⁺ cells within the CD3⁺CD4⁺ T-cell population. NK cells were identified by surface staining with anti-human CD56 (PE, clone NCAM16.2, BD Biosciences, USA) and anti-human CD16 (FITC, clone 3G8, BioLegend, USA), and defined as CD3⁻CD16⁺CD56⁺ cells. Data acquisition was performed using a flow cytometer (FACSCalibur™, BD Biosciences, USA) and analyzed with FlowJo software (Tree Star Inc., USA). 2.5 Measurement of Autoantibodies and Lipid-Related Metabolites by ELISA Serum levels of autoantibodies, including anti-nuclear antibody (ANA), anti-cardiolipin IgG, anti-double-stranded DNA (anti-dsDNA IgG), lupus anticoagulant (LA), anti-thyroglobulin (anti-TG IgG), anti-thyroid peroxidase (anti-TPO), anti-tissue transglutaminase IgA (anti-tTG IgA), anti-β2-glycoprotein I IgG, and anti-phosphatidylserine IgG, were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturers’ instructions (Pishtaz Teb Co, Iran; Idealdiag, Iran; Niaztebsalamat, Iran; MyBiosource, USA). Serum concentrations of lipid-related metabolites and mediators, including sphingosine-1-phosphate (S1P), prostaglandin E2 (PGE-2), insulin-like growth factor-1 (IGF-1), phosphatidylserine, adiponectin, and leptin, were also quantified using specific ELISA kits (MyBiosource, USA; Invitrogen, Thermofisher, USA; Enzo, Belgium). Optical density was measured at 450 nm using a microplate reader, and analyte concentrations were calculated from standard curves provided with each kit. Positivity for autoantibodies was defined according to the cutoff values recommended by the manufacturers. 2.6 Total Phospholipid Measurement by Spectrophotometry Total phospholipid content was measured using a spectrophotometric method following lipid extraction by the Bligh and Dyer procedure. Briefly, total lipids were extracted from serum samples using a chloroform:methanol (2:1, v/v) solution, followed by multiple washing steps. Phospholipid standards (10, 20, 30, and 40 mg/dL) were prepared using perchloric acid (70%), ammonium molybdate (2.5%), and ascorbic acid (10%). After acid digestion, ammonium molybdate and ascorbic acid were added to both standards and samples to allow color development. Optical density was measured at 800 nm using a spectrophotometer (Pharmacia LKB Novaspec II), and phospholipid concentrations were calculated based on the standard curve. 2.7 Statistical Analysis All statistical analyses were performed using GraphPad Prism software (version 10.6.0; GraphPad Software, La Jolla, CA, USA). Comparisons between groups were conducted using one-way analysis of variance (ANOVA) or Student’s t-test, as appropriate. A p-value less than 0.05 was considered statistically significant. 3. Results 3.1 Endometrial Immune Profiling Results Endometrial immune profiling based on IL-18/TWEAK, IL-15/Fn-14 ratios, and CD56 mRNA expression revealed distinct patterns among RPL patients compared to controls. All control subjects (n = 25) exhibited balanced profiles, with mean IL-18/TWEAK, IL-15/Fn-14, and CD56 values of 0.076, 1.93, and 1.01, respectively. In contrast, among RPL patients (n = 81), 38 individuals (46.9%) showed an over-activated profile characterized by IL-18/TWEAK > 0.13, IL-15/Fn-14 > 3.4, and CD56 > 1.5 (mean values: 0.165, 3.98, and 1.74, respectively; p < 0.0001). Twenty patients (24.7%) displayed low-activated profiles with IL-18/TWEAK < 0.02, IL-15/Fn-14 < 0.36, and CD56 < 0.5 (mean values: 0.012, 0.23, and 0.30; p < 0.0001). Only 23 patients (28.4%) fell within the balanced range (IL-18/TWEAK 0.02–0.13, IL-15/Fn-14 0.36–3.4, CD56 0.5–1.5; mean values: 0.071, 1.92, 0.94), showing no significant difference compared to controls (p = 0.947, 0.999, and 0.589, respectively). Figure 1 and Table 2 show distinct endometrial immune activation patterns in RPL patients compared with controls based on IL-18/TWEAK, IL-15/Fn-14, and CD56 expression. These findings demonstrate that nearly 71% of RPL patients exhibit either excessive or insufficient endometrial immune activation, highlighting the heterogeneity of uNK cell-mediated immune responses in recurrent pregnancy loss. Table 2 / Immune Biomarker Profiles in RPL Patients and Healthy Controls Biomarker Immune profile Interpretation range Control group (n = 25) RPL group (n = 81) P value IL-18 / TWEAK Reference mean 0.02–0.13 0.076 – – Over-activation > 0.13 – 38 (46.9%) Mean: 0.165 < 0.0001 * Low activation < 0.02 – 20 (24.7%) Mean: 0.012 3.4 – 38 (46.9%) Mean: 3.98 < 0.0001 * Low activation < 0.36 – 20 (24.7%) Mean: 0.23 1.5 – 38 (46.9%) Mean: 1.74 < 0.0001 * Low activation < 0.5 – 20 (24.7%) Mean: 0.30 < 0.0001 * Balanced 0.5–1.5 25 (100%) Mean: 1.01 23 (28.4%) Mean: 0.94 0.589 3.2 Serum Autoantibody Profiles across Endometrial Immune Profiles Serum autoantibody positivity differed notably among the three endometrial immune profile groups. In the Balanced group (n = 23), autoantibody positivity was generally low, with only Anti-TG (2/23, 8.69%), Anti-TPO (1/23, 4.34%), Anti-tTG (1/23, 4.34%), and Anti-Phosphatidylserine IgG (5/23, 21.73%) detected. The Low-activated group (n = 20) showed modest positivity, including ANA (1/20, 5.0%), anti-dsDNA (2/20, 10.0%), Anti-TPO (3/20, 15.0%), Anti-Phosphatidylserine IgG (5/20, 25.0%), and Anti-TG (0%). In contrast, the Over-activated group (n = 38) demonstrated significantly higher prevalence of multiple autoantibodies, including anti-cardiolipin IgG (19/38, 50.0%; p = 0.0002 vs. Balanced), LA (23/38, 60.53%; p < 0.0001), Anti-TG (26/38, 68.42%; p < 0.0001), anti-β2-Glycoprotein I IgG (20/38, 52.63%; p = 0.0001), and Anti-Phosphatidylserine IgG (24/38, 63.15%; p = 0.0072). ANA, anti-dsDNA, Anti-TPO, and anti-tTG positivity did not differ significantly across groups. These findings indicate a strong association between over-activated endometrial immune profiles and elevated serum autoantibody levels, whereas low and balanced profiles exhibited limited autoantibody positivity. Table 3 shows that serum autoantibody positivity is markedly higher in the over-activated endometrial immune profile compared with balanced and low-activated groups, indicating a strong association between excessive endometrial immune activation and elevated autoantibody levels in RPL patients. Table 3 demonstrates that serum autoantibody positivity is substantially elevated in the over-activated endometrial immune profile compared with balanced and low-activated groups, supporting a strong association between excessive endometrial immune activation and increased autoantibody levels in RPL patients. Table 3 /Autoantibody Profiles and Positivity Rates in RPL Patients Variable Reference Range / Cut-off Balanced Pos / Total (%) Low Pos / Total (%) Over Pos / Total (%) Balanced vs. Low P value Balanced vs. Over P value Low vs. Over P value Anti-Nuclear Antibody (ANA) Index 1.1: Positive 0/23 (0%) 1/20 (5.0%) 3/38 (7.89%) 0.5551 0.3849 0.9181 Anti-cardiolipin (IgG) 18: Positive (GPL/MPL) 0/23 (0%) 1/20 (5.0%) 19/38 (50.0%) 0.5551 0.0002 0.0028 Anti-dsDNA (IgG) 100 IU/mL: Positive 0/23 (0%) 2/20 (10.0%) 3/38 (7.89%) 0.2994 0.3849 0.9638 Lupus Anticoagulant (LA) Ratio 1.30: Positive 0/23 (0%) 1/20 (5.0%) 23/38 (60.53%) 0.5551 < 0.0001 0.0002 Anti-Thyroglobulin (Anti-TG, IgG) 100 IU/mL: Positive 2/23 (8.69%) 4/20 (0%) 26/38 (68.42%) 0.4924 < 0.0001 0.0021 Anti-Thyroid Peroxidase (Anti-TPO) 35 IU/mL: Positive 1/23 (4.34%) 3/20 (15.0%) 4/38 (10.52%) 0.4871 0.6953 0.8838 Anti-tTG (IgA) 20 IU/mL: Positive 1/23 (4.34%) 1/20 (5.0%) 3/38 (7.89%) 0.9949 0.8632 0.9181 Anti-β2-Glycoprotein I (IgG) 8 U/mL: Positive 0/23 (0%) 0/20 (0%) 20/38 (52.63%) > 0.9999 0.0001 0.0003 Anti-Phosphatidylserine (IgG) 25 U/mL: Positive 5/23 (21.73%) 5/20 (25.0%) 24/38 (63.15) 0.0637 0.0072 0.022 3.3 Immune Cell Profiles and Th1/Th2 Balance across Immune Regulation Groups Analysis of immune cell profiles revealed that the frequency of NK cells (CD3⁻CD16⁺CD56⁺) was 6.95 ± 4.18% in the Balanced Immune Regulation group, 8.81 ± 2.34% in the Low Immune Dysregulation group, and 11.14 ± 2.80% in the High Immune Dysregulation group. The difference between the Balanced Immune Regulation and High Immune Dysregulation groups was statistically significant (P < 0.0001), while no significant difference was observed between the Balanced and Low Immune Dysregulation groups (P = 0.149). The comparison between Low and High Immune Dysregulation groups also reached statistical significance (P = 0.030). Figure 2 A shows the flow cytometry gating strategy for the three immune regulation groups, while Fig. 2 B presents the comparative analysis of NK cell (CD3⁻CD16⁺CD56⁺) frequencies among the balanced, low dysregulation, and high dysregulation groups. Similarly, the Th1/Th2 ratio (CD4⁺IFN-γ⁺/CD4⁺IL-4⁺) was 12.68 ± 1.51 in the Balanced Immune Regulation group, 13.18 ± 1.20 in the Low Immune Dysregulation group, and 22.42 ± 2.26 in the High Immune Dysregulation group. The Th1/Th2 ratio was significantly higher in the High Immune Dysregulation group compared to both the Balanced and Low Immune Dysregulation groups (P < 0.0001 for both comparisons), whereas no significant difference was observed between the Balanced and Low Immune Dysregulation groups (P = 0.656). Figure 3 A shows the flow cytometry gating strategy for Th1 and Th2 cells, while Fig. 3 B presents the comparative analysis of the Th1/Th2 ratio among the balanced, low dysregulation, and high dysregulation groups. These findings indicate that immune dysregulation is associated with an increased proportion of NK cells and a Th1-biased response, while individuals with balanced immune regulation exhibit lower levels of these immune markers. Table 4 summarizes the comparative analysis of NK cell frequency and Th1/Th2 ratio among the balanced, low immune dysregulation, and high immune dysregulation groups, demonstrating significantly elevated NK cells and Th1/Th2 ratio in the high immune dysregulation group. Table 4 / Comparison of Immune Cell Profiles Among RPL Patients with Different Immune Regulation Status Variables Balanced Immune Regulation group (G1) (Mean ± SD) Low Immune Dysregulation group (G2) (Mean ± SD) High Immune Dysregulation group (G3) (Mean ± SD) G1 vs. G2 (P value) G1 vs. G3 (P value) G2 vs. G3 (P value) NK Cells (CD3 − CD16 + D56 + ) 6.95 ± 4.18 8.81 ± 2.34 11.14 ± 2.80 0.149 < 0.0001 0.030 Th1/Th2 (CD4 + IFN-γ + /CD4 + IL4 + ) 12.68 + 1.51 13.18 + 1.20 22.42 + 2.26 0.656 < 0.0001 < 0.0001 3.4 Comparison of Metabolic Biomarkers between Balanced and High Immune Dysregulation Groups Direct comparison between the balanced immune regulation group (G1) and the high immune dysregulation group (G3) demonstrated significant alterations in several metabolic biomarkers. Serum adiponectin levels were significantly reduced in the high immune dysregulation group compared with the balanced group (13.62 ± 0.92 vs. 16.88 ± 1.47 ng/mL, P < 0.0001). Similarly, prostaglandin E2 (PGE-2) concentrations were markedly elevated in the high immune dysregulation group (390.5 ± 22.9 pg/mL) relative to the balanced group (321.9 ± 28.7 pg/mL, P < 0.0001). Insulin-like growth factor-1 (IGF-1) levels were significantly lower in the high immune dysregulation group compared with the balanced immune regulation group (2.51 ± 0.85 vs. 5.52 ± 0.86 ng/mL, P < 0.0001). In addition, total phospholipid concentrations were substantially increased in the high immune dysregulation group (358.9 ± 13.3 mg/dL) compared with the balanced group (250.6 ± 18.4 mg/dL, P < 0.0001). In contrast, no statistically significant differences were observed between the balanced and high immune dysregulation groups in serum leptin levels (6.63 ± 1.94 vs. 8.66 ± 3.94 ng/mL, P = 0.334), sphingosine-1-phosphate (S1P) concentrations (34.67 ± 2.04 vs. 36.96 ± 8.61 ng/mL, P = 0.311), or phosphatidylserine levels (33.58 ± 1.87 vs. 33.95 ± 1.18 ng/mL, P = 0.0568). Overall, these findings indicate that high endometrial immune dysregulation in RPL patients is associated with pronounced disturbances in adipokine balance, inflammatory lipid mediators, growth-related factors, and phospholipid metabolism, while certain lipid signaling molecules remain relatively preserved. Figure 4 and Table 5 illustrates the direct comparison of key metabolic biomarkers between the balanced immune regulation group and the high immune dysregulation group, highlighting significant alterations in adiponectin, PGE-2, IGF-1, and total phospholipids, while leptin, S1P, and phosphatidylserine levels remain relatively unchanged. Table 5 / Comparison of Metabolic and Signaling Biomarkers Among RPL Patients with Different Immune Regulation Status Variable Balanced Immune Regulation group (Mean + SD) (G1) Low Immune Dysregulation (Mean + SD) group (G2) High Immune Dysregulation group (Mean + SD) (G3) G1 vs. G2 P value G1 vs. G3 P value G2 vs. G3 P value Leptin (ng/ml) 6.629 ± 1.942 6.060 ± 1.914 8.655 ± 3.936 > 0.999 0.334 0.0553 Adiponectin (ng/ml) 16.88 ± 1.466 22.51 ± 2.509 13.62 ± 0.916 < 0.0001 < 0.0001 0.999 PGE-2 (pg/ml) 321.9 ± 28.65 205.4 ± 10.35 390.5 ± 22.92 < 0.0001 < 0.0001 0.999 0.0568 0.0640 IGF-1 (ng/ml) 5.518 ± 0.862 3.308 ± 0.500 2.51 ± 0.853 < 0.0001 0.999 < 0.0001 < 0.0001 Data are presented as mean ± SD. G1: Balanced Immune Regulation group; G2: Low Immune Dysregulation group; G3: High Immune Dysregulation group; S1P: sphingosine-1-phosphate; PGE-2: prostaglandin E2; IGF-1: insulin-like growth factor-1. 4. Discussion This current study offers compelling evidence of a significant relationship between recurrent pregnancy loss and endometrial immune profiling, which extends beyond local uterine immunity and encompasses systemic immune and metabolic abnormalities. The results of our current investigation support the notion of recurrent pregnancy loss as a complex syndrome of immune-related phenotypes, each of which may require individualized approaches to diagnosis and treatment. One of the most striking findings of our current investigation was that approximately 71% of recurrent pregnancy loss patients displayed either a high or low endometrial immune dysregulation profile, while all of the fertile controls displayed a balanced immune profile. This finding supports a study reports by Lédée and colleagues, who proposed a model of recurrent pregnancy loss as a result of inappropriate or insufficient uNK cell priming, compromising implantation and early placental development (Cheloufi et al. 2021). However, our current findings extend their model by demonstrating a relationship between endometrial immune imbalance and systemic immune activation, autoantibody production, and metabolic remodeling, suggesting a potential interaction or crosstalk between uterine immunity and systemic immune and metabolic homeostasis. The over-activated immune profile was characterized by a higher level of IL-15/Fn-14 and IL-18/TWEAK ratios, as well as CD56 expression, indicating excessive uNK cell activation (Lédée et al. 2020a dée et al. 2020b). IL-15 is a key cytokine involved in uNK cell proliferation and cytotoxicity, while IL-18 regulates Th1 cell polarization through IFN-γ production (Iudicone et al. 2016 ; Martynova et al. 2022 ). Recent studies have highlighted excessive IL-15/IL-18 signaling as a key pathogenic event in recurrent pregnancy loss, skewing uNK cell responses towards a pro-inflammatory, cytotoxic rather than their physiological pro-angiogenic, tissue-remodeling phenotype (Yang et al. 2022 b et al. 2021; Gordon 2021 ). The current findings of increased peripheral NK cell frequency and a highly elevated Th1/Th2 ratio in the over-activated immune profile strongly support this model of recurrent pregnancy loss pathogenesis, suggesting a relationship between endometrial over-activation and systemic Th1 immunity. Most importantly, the patient group with high immune dysregulation had a significantly higher prevalence of antiphospholipid-related autoantibodies, including anti-cardiolipin, lupus anticoagulant, anti-β2-glycoprotein I, and anti-phosphatidylserine. These observations are supported by recent evidence suggesting that antiphospholipid autoantibodies may play a direct role in the activation of NK cells, trophoblast invasion, and the remodeling of the spiral arteries (Martirosyan et al. 2024 ; Ma et al. 2025 ). In addition, the role of IL-18-induced hyperinflammatory responses in the activation of autoantibody-producing B cells provides evidence of the link between endometrial immune hyper-activation and autoimmunity (Chen et al. 2024 ). Overall, the present study suggests that the over-activated endometrial immune phenotype may be the common end-point of hyperactivated innate immune responses, Th1 dominance, and autoantibody production. In contrast, the low immune activation phenotype had low ratios of IL-15/Fn-14 and IL-18/TWEAK and low CD56 expression, indicating insufficient uNK cell recruitment and activation. Although these patients did not demonstrate evidence of systemic immune hyper-activation and autoantibody production, the insufficient cytokine and growth factor production by uNK cells may compromise decidualization, angiogenesis, and trophoblast support. Recent studies have suggested that insufficient uNK cell activity may be as harmful as hyperactivated uNK cells, resulting in poor vascular remodeling and pregnancy failure (Wei et al. 2022 ). The relative balance of Th1 and Th2 responses in the low immune activation phenotype also supports the concept of local immune insufficiency (Bretscher 2014 ). In addition to the immune system, the present study also shows a strong link between high immune dysregulation and metabolic and lipid-related markers. The substantial reduction in adiponectin observed in the over-activated group is particularly interesting, given the anti-inflammatory, Th1-suppressive, and regulatory roles of adiponectin. Adiponectin has recently been implicated in pregnancy complications, as reduced levels are associated with increased NF-κB activation and subsequent elevated production of pro-inflammatory cytokines (Gutaj et al. 2020 ). Consequently, low levels of adiponectin may enhance immune activation in RPL patients. On the contrary, the elevated levels of PGE-2 in the high immune dysregulation group may be a compensatory response to the overactive state of inflammation. Although PGE-2 is traditionally regarded as an immunosuppressant and tolerogen, it has been shown to induce dysregulated angiogenesis and trophoblast invasion in the setting of prolonged overexpression (Lin et al. 2018 ). Similarly, the decreased levels of IGF-1 may impair endometrial receptivity and placenta development, as it has a crucial role in trophoblast cell proliferation, decidualization, and immune modulation at the fetal–maternal interface (Pollheimer et al. 2018 ). The elevated levels of total phospholipids may be a reflection of the significant changes in lipid metabolism, which may play a role in the altered membrane signaling, autoantigen exposure, and inflammatory response. Interestingly, the levels of sphingosine-1-phosphate and leptin were found to be comparable between the groups, suggesting that lipid mediators may not be equally affected in the process of immune-mediated RPL (Liu et al. 2024 ). In a clinical setting, the implications of the current study are considerable. The close association between endometrial immune profiles and peripheral immune-metabolic signatures may provide a new tool in the management of RPL by allowing the use of serum biomarkers as a substitute for endometrial biopsy in the assessment of the immune status (Blackwood et al. 2016 ). This may be a more convenient method of monitoring changes in the immune status in the setting of RPL. In addition, the immune profiling may provide a tool in the management of RPL by allowing the use of specific therapies, i.e., immunosuppression in the setting of overactive immune profiles, immune activation in low immune activation profiles, and metabolic therapies in the setting of metabolic imbalances. In conclusion, the current study supports the paradigm of RPL as a heterogeneous condition caused by different immune–metabolic endotypes rather than a single pathological process. The endometrial immune profiling, in combination with systemic biomarkers, provides a comprehensive understanding of the heterogeneity of RPL and opens the door for the development of precision medicine in the field of reproductive immunology. Future longitudinal and interventional studies are required to confirm the usefulness of serum-based immune profiling in RPL and determine if the correction of the imbalances in the immune and metabolic status can improve pregnancy outcomes in RPL. Declarations Funding Statement "This study was supported by a research grant (Grant No. 73540 ) from the Immunology Research Center, Tabriz University of Medical Sciences , and Tabriz, Iran . The authors express their gratitude for the financial and technical support provided by the center throughout the duration of this research. Human Ethics and Consent to Participate declarations This study was approved by the Ethics Committee of [ Tabriz University of Medical Sciences , Tabriz, Iran ] (Approval No: 73540). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki. Consent to participate declaration Written informed consent was obtained from all individual participants included in this study, in accordance with the ethical principles of the Declaration of Helsinki. Clinical trial number Not applicable Ethics Approval declaration This study was approved by the Ethics Committee of Tabriz University of Medical Sciences (Approval No: IR.TBZMED.REC.73540). Conflict of Interests Not Applicable. Acknowledgment Not Applicable. Author Contribution A.K. and M.Y. designed the study.A.K., S.P., and S.B.A. performed experiments.M.P., A.S., and A.H. collected clinical data and samples.A.A. and A.M. conducted statistical and bioinformatic analyses.M.A. and J.A.H. contributed to data interpretation and validation.A.K. wrote the main manuscript text.S.P., S.B.A., and M.Y. prepared Figures 1–3.All authors reviewed and approved the final manuscript. References Aslanian-Kalkhoran, Lida, Amin Kamrani, Iraj Alipourfard, Forough Chakari-Khiavi, Aref Chakari-Khiavi, Leili Aghebati-Maleki, Ali Akbar Shekarchi, Amir Mehdizadeh, Maryam Mojahedi, and Shahla Danaii. 2023. 'The effect of lymphocyte immunotherapy (LIT) in modulating immune responses in patients with recurrent pregnancy loss (RPL)', International Immunopharmacology , 121: 110326. Blackwood, J. C., D. A. Cummings, S. Iamsirithaworn, and P. Rohani. 2016. 'Using age-stratified incidence data to examine the transmission consequences of pertussis vaccination', Epidemics , 16: 1-7. Bretscher, PA. 2014. 'On the mechanism determining the TH1/TH2 phenotype of an immune response, and its pertinence to strategies for the prevention, and treatment, of certain infectious diseases', Scandinavian journal of immunology , 79: 361-76. Cheloufi, Meryam, Alaa Kazhalawi, Anne Pinton, Mona Rahmati, Lucie Chevrier, Laura Prat-Ellenberg, Anne-Sophie Michel, Geraldine Dray, Arsène Mekinian, and Gilles Kayem. 2021. 'The endometrial immune profiling may positively affect the management of recurrent pregnancy loss', Frontiers in immunology , 12: 656701. Chen, Jian, Jing Yue, Yu Lu, Ting Li, Xue Li, and Jian-Yu Zhang. 2024. 'Recurrent miscarriage and low-titer antiphospholipid antibodies', Clinical Rheumatology , 43: 1327-34. Dashti, Mohsen, Amin Kamrani, Zahra Shahir-Khajeh, Javad Ahmadian Heris, Leili Aghebati-Maleki, Shahla Danaii, Forough Chakari-Khiavi, Behnam Shahriar, Mehrin Sadough, and Sina Baharaghdam. 2025. 'Impact of lymphocyte immunotherapy (LIT) on fertility rates in recurrent pregnancy loss (RPL) women with antinuclear antibodies: A randomized clinical trial', Journal of reproductive immunology , 168: 104432. Ebrahimi, Rezvan, Kimia Motlagh Asghari, Sina Janbaz Alamdary, Amin Kamrani, Mohammad Sadegh Soltani-Zangbar, Shahla Danaii, Akbar Aliasgharzadeh, Javad Ahmadian Heris, Leili Aghebati-Maleki, and Mohammad Hojjat-Farsangi. 2025. 'Intradermal lymphocyte therapy: A promising treatment for recurrent pregnancy loss in patients without anti-TPO antibodies', Human Immunology , 86: 111229. Elkarhat, Zouhair, Zineb Kindil, Latifa Zarouf, Lunda Razoki, Jamila Aboulfaraj, Chadli Elbakay, Sanaa Nassereddine, Boubker Nasser, Abdelhamid Barakat, and Hassan Rouba. 2019. 'Chromosomal abnormalities in couples with recurrent spontaneous miscarriage: a 21-year retrospective study, a report of a novel insertion, and a literature review', Journal of assisted reproduction and genetics , 36: 499-507. Gordon, Scott M. 2021. 'Interleukin-15 in outcomes of pregnancy', International Journal of Molecular Sciences , 22: 11094. Gutaj, Paweł, Rafał Sibiak, Maurycy Jankowski, Karina Awdi, Rut Bryl, Paul Mozdziak, Bartosz Kempisty, and Ewa Wender-Ozegowska. 2020. 'The role of the adipokines in the most common gestational complications', International Journal of Molecular Sciences , 21: 9408. Iudicone, Paola, Daniela Fioravanti, Elisabetta Cicchetti, Ilaria Grazia Zizzari, Annino Pandolfi, Rita Scocchera, Raffaella Fazzina, and Luca Pierelli. 2016. 'Interleukin-15 enhances cytokine induced killer (CIK) cytotoxic potential against epithelial cancer cell lines via an innate pathway', Human immunology , 77: 1239-47. Lédée, N, M Petitbarat, L Prat-Ellenberg, G Dray, GN Cassuto, L Chevrier, A Kazhalawi, K Vezmar, and G Chaouat. 2020a. 'The uterine immune profile: A method for individualizing the management of women who have failed to implant an embryo after IVF/ICSI', Journal of Reproductive Immunology , 142: 103207. Lédée, Nathalie, Marie Petitbarat, Laura Prat-Ellenberg, Géraldine Dray, Guy N Cassuto, Lucie Chevrier, Alaa Kazhalawi, Katia Vezmar, and Gerard Chaouat. 2020b. 'Endometrial immune profiling: a method to design personalized care in assisted reproductive medicine', Frontiers in Immunology , 11: 1032. Lin, Yi-Heng, Ya-Hsin Chen, Heng-Yu Chang, Heng-Kien Au, Chii-Ruey Tzeng, and Yen-Hua Huang. 2018. 'Chronic niche inflammation in endometriosis-associated infertility: current understanding and future therapeutic strategies', International Journal of Molecular Sciences , 19: 2385. Liu, Kun, Xiaojuan Xu, Liang Sun, Hongxing Li, Yi Jin, Xiaoling Ma, Bairong Shen, and Cesar Martin. 2024. 'Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss', Frontiers in Immunology , 15: 1397633. Löb, Sanja, Beate Ochmann, Zhi Ma, Theresa Vilsmaier, Christina Kuhn, Elisa Schmoeckel, Saskia-Laureen Herbert, Thomas Kolben, Achim Wöckel, and Sven Mahner. 2021. 'The role of Interleukin-18 in recurrent early pregnancy loss', Journal of Reproductive Immunology , 148: 103432. Ma, Guangyu, Jinbiao Han, Rui Gao, and Lang Qin. 2025. 'Immune-mediated mechanisms and maternal-fetal interface dysfunction in obstetric antiphospholipid syndrome', Frontiers in Immunology , 16: 1722080. Martirosyan, A, E Kriegova, J Savara, L Abroyan, S Ghonyan, Z Slobodova, R Nesnadna, and Gayane Manukyan. 2024. 'Impact of antiphospholipid syndrome on placenta and uterine NK cell function: insights from a mouse model', Scientific Reports , 14: 31163. Martynova, Ekaterina, Albert Rizvanov, Richard A Urbanowicz, and Svetlana Khaiboullina. 2022. 'Inflammasome contribution to the activation of Th1, Th2, and Th17 immune responses', Frontiers in microbiology , 13: 851835. Park, Joon Cheol, Jae Won Han, and Sung Ki Lee. 2022. 'T helper cell pathology and recurrent pregnancy losses; Th1/Th2, Treg/Th17, and other T cell responses', Immunology of Recurrent Pregnancy Loss and Implantation Failure : 27-53. Pollheimer, Jürgen, Sigrid Vondra, Jennet Baltayeva, Alexander Guillermo Beristain, and Martin Knöfler. 2018. 'Regulation of placental extravillous trophoblasts by the maternal uterine environment', Frontiers in Immunology , 9: 2597. Robinson, Lynne, Ioannis D Gallos, Sarah J Conner, Madhurima Rajkhowa, David Miller, Sheena Lewis, Jackson Kirkman-Brown, and Arri Coomarasamy. 2012. 'The effect of sperm DNA fragmentation on miscarriage rates: a systematic review and meta-analysis', Human Reproduction , 27: 2908-17. Stefanidou, Erato M, Laura Caramellino, Ambra Patriarca, and Guido Menato. 2011. 'Maternal caffeine consumption and sine causa recurrent miscarriage', European Journal of Obstetrics & Gynecology and Reproductive Biology , 158: 220-24. Wei, Xiao-Wei, Yu-Chen Zhang, Fan Wu, Fu-Ju Tian, and Yi Lin. 2022. 'The role of extravillous trophoblasts and uterine NK cells in vascular remodeling during pregnancy', Frontiers in Immunology , 13: 951482. Yang, Xiuhua, Yingying Tian, Linlin Zheng, Thanh Luu, and Joanne Kwak-Kim. 2022. 'The update immune-regulatory role of pro-and anti-inflammatory cytokines in recurrent pregnancy losses', International Journal of Molecular Sciences , 24: 132. Пастущек, Я, УР Маркерт, БВ Донськой, Е Шлебнер, К Бар, М Чіокадзе, and КГ Хажиленко. 2020. 'Beyond Uterine Natural Killer Cell Numbers in Unexplained Recurrent Pregnancy Loss: Combined Analysis of CD45, CD56, CD16, CD57, and CD138'. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 06 May, 2026 Editor assigned by journal 06 May, 2026 Submission checks completed at journal 06 May, 2026 First submitted to journal 25 Apr, 2026 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-9523337","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638182069,"identity":"9aaed047-424a-4224-a680-8ce6d4fd2b90","order_by":0,"name":"Amin Kamrani","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"","lastName":"Kamrani","suffix":""},{"id":638182070,"identity":"6d6a0c03-033e-4a27-81bb-be39192672ca","order_by":1,"name":"Shiva Pourvahdani","email":"","orcid":"","institution":"Tabriz University of Medical 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Yousefi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACHgaJBDCD8cGBDwwMCaRoYTY4OINoLQxQLcw8xGjRbe89eOMBw+HE+e3NjIdt2+zy+NkbGD98zMGtxezMuWSLBKCWDWcOMxzObUsuluw5wCw5cxseLTdyzCTAWiTyDwC1MCduuJHAxsyLT8v9NxAt8+c/Zjhs2VZPhJYbPBAtDTeYGQ4zth0mQsuZHGOgX9KNN5xJZjjYc+544syeg834/XL8jOHNHwzWsvPbDzN/+FFWndjP3nzww0c8WsCA8V8zlMEGJhsIqAeDOij9hxjFo2AUjIJRMNIAAPCOWVyDzWBKAAAAAElFTkSuQmCC","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Yousefi","suffix":""}],"badges":[],"createdAt":"2026-04-25 07:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9523337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9523337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109331297,"identity":"970a853e-7c0a-49f7-b6f9-b9bd3a7459c0","added_by":"auto","created_at":"2026-05-15 16:09:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative mRNA expression levels of IL-18/TWEAK, IL-15/Fn-14, and CD56⁺ in RPL and control groups.\u003c/strong\u003e\u003cbr\u003e\nScatter dot plots showing the relative mRNA expression ratios of IL-18/TWEAK, IL-15/Fn-14, and CD56⁺ in samples obtained from patients with recurrent pregnancy loss (RPL) and healthy controls. Each symbol represents an individual sample (circles: RPL; squares: Control). Gene expression levels are presented as relative mRNA ratios on a logarithmic scale (0.001–10). Differences between groups were analyzed using appropriate statistical tests as described in the Methods section.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9523337/v1/c9d5303eb207b3e8dc2a3fae.png"},{"id":109331255,"identity":"92f479fb-ddcf-429d-b0a6-33d9ef94edf9","added_by":"auto","created_at":"2026-05-15 16:09:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":544064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow cytometric analysis of peripheral blood NK cells in study groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative flow cytometry dot plots showing the gating strategy for identification of NK cells. Lymphocytes were analyzed based on CD3 (PerCP) and CD56 (PE) expression, and NK cells were defined as CD3⁻CD56⁺ cells in the BIR-P, HID-P, and LID-P groups.\u003c/p\u003e\n\u003cp\u003e(B) Quantitative analysis of peripheral blood NK cell percentages among the three groups. Data are presented as mean ± SD, with each dot representing an individual sample. Statistical comparisons between groups were performed using the appropriate test as described in the Methods section. A significant increase in NK cell percentage was observed in the HID-P group compared to BIR-P (p \u0026lt; 0.0001) and LID-P (p = 0.030), while no significant difference was detected between HID-P and LID-P (p = 0.149).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9523337/v1/a11c7a709ca5be29127a42a1.png"},{"id":109331279,"identity":"dbe1f9f7-fe43-4aed-8008-2c55c62d61cd","added_by":"auto","created_at":"2026-05-15 16:09:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of Th1 cell frequency in different study groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative flow cytometry dot plots showing the percentage of CD4⁺IFN-γ⁺ (Th1) cells in peripheral blood samples of the three groups: BIR-P, HID-P, and LID-P. Cells were first gated on CD4⁺ lymphocytes and subsequently analyzed for intracellular IFN-γ expression to determine Th1 populations.\u003c/p\u003e\n\u003cp\u003e(B) Quantitative analysis of Th1/Th2 (%) or Th1 frequency among the study groups. Data are presented as mean ± SEM with individual data points for each subject. Statistical comparisons were performed between groups, demonstrating a significant increase in Th1 frequency in the HID-P group compared with BIR-P and LID-P (p\u0026lt;0.0001), while no significant difference was observed between BIR-P and LID-P (p=0.656).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9523337/v1/5536461abe0b08f50aff7ffe.png"},{"id":109331309,"identity":"fa201473-4553-4a24-9b67-8a93abbb7d07","added_by":"auto","created_at":"2026-05-15 16:09:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8020062,"visible":true,"origin":"","legend":"\u003cp\u003eDirect comparison of key metabolic biomarkers between the balanced immune regulation group (G1) and the high immune dysregulation group (G3) in RPL patients. Serum adiponectin and IGF-1 levels were significantly decreased in the high immune dysregulation group, whereas PGE-2 and total phospholipid concentrations were significantly increased compared with the balanced group (P \u0026lt; 0.0001 for all). No significant differences were observed in leptin, S1P, or phosphatidylserine levels between the two groups. Data are presented as mean ± SD. Statistical significance was determined using an independent t-test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9523337/v1/e23b602c3f7ef0d4675dff76.png"},{"id":109331689,"identity":"d52995f0-7e83-494e-a2d1-a04dad79505e","added_by":"auto","created_at":"2026-05-15 16:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7508584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9523337/v1/6bb9dbe4-1cf3-4660-8266-f32b707782d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interplay between Systemic Immune-Metabolic Signatures and Endometrial Immune Profiles in Women with Recurrent Pregnancy Loss","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecurrent pregnancy loss (RPL) can be understood as a complicated pregnancy-related disorder with a multifactorial cause, and among those factors, the compromised regulation of the mother\u0026ndash;fetal interface by the immune system has been shown to play an important part (Dashti et al. 2025). The evidence has been mounting lately that the implantation process and the survival of pregnancy should be accompanied by a precisely calibrated endometrial immune environment, rather than by an immune tolerance mechanism. An imprecise regulation of this state could lead to implantation failure, early pregnancy loss, or repeated miscarriage (Aslanian-Kalkhoran et al. 2023). An endometrial immune profiling has been promoted as a relevant tool with which the immune deregulation state specific to women with pregnancy-related disorders might be described. On the premise of the expression level of major immunological factors such as cytokines, cytokine receptors, and natural killer cell numbers, patients could be categorized according to tightly defined endometrial immune profiles (Ebrahimi et al. 2025). These endometrial immune profiles include those with excessive immune deregulation, those with reduced immune deregulation, those with mixed deregulation, and those with the normal profile. Importantly, every particular state has been related to different underlying factors with, accordingly, an appropriate distinct therapeutic target, making it an important aim to prioritize the accurate immune typing for patients with an entity like RPL.\u003c/p\u003e \u003cp\u003eAmongst these immune markers, the roles of IL-15 and IL-18 are pivotal in modulating the activation, proliferation, and polarization of uNK cells, and the ratios of mRNA expression of IL-15/Fn-14 and IL-18/TWEAK have been demonstrated to represent the relative equilibrium of immune activation, tissue remodeling, and angiogenesis within the endometrium (Cheloufi et al. 2021). Additionally, the expression of CD56 is a crucial marker of the activity and number of uNK cells (Пастущек et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Collectively, these markers allow a sensitive delineation of endometrial immune profiles and immune dysregulation within women with RPL. Although endometrial immune profiling holds immense potential within clinical practice, it remains hindered by the invasive nature of endometrial biopsy (Elkarhat et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, there remains a pressing need to transfer the translation of immune profiling from tissues to less invasive, serum-based markers within the field of reproductive medicine. The discovery of immune- and metabolism-related profiles within peripheral blood which reflect endometrial immune homeostasis would provide potential avenues for non-invasive risk assessment, prognosis, and individualized management strategies within women with high RPL risks (Stefanidou et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Robinson et al. 2012).\u003c/p\u003e \u003cp\u003eBesides localized endometrial immune system dysregulation, systemic immune system imbalances, which involve endometrial Th1/Th2 ratios, presence of autoantibodies, and endometrial environment-based metabolic imbalances, play pivotal roles in pathogenesis of recurrent pregnancy loss (Park, Han, and Lee 2022). There is potential interplay between systemic immunity and localized endometrial immunity, which could lead to immune system dysregulation depending on diverse levels of immune profiles. Conversely, their connection to diverse endometrial immune profiles has not been adequately explored. This current study was aimed at exploring immunological and metabolic imbalances characteristically exhibited by women experiencing recurrent pregnancy loss; the study considered fertile women as controls. This research aims to define immunological dysregulation depending on endometrial markers and identify detectable serum markers that could reflect endometrial immunity by characterizing immunological dysregulation depending on diverse immune profiles that include high immune profiles, low immune profiles, mixed immune profiles, and normal immune profiles. This could ease implementation of serum-based immune profiling over endometrial biopsy-based diagnostic procedures for women experiencing recurrent pregnancy loss.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Participants and Ethical Considerations\u003c/h2\u003e \u003cp\u003eIn total, 106 women were included in this study, 81 of whom had experienced recurrent pregnancy losses (RPL) and 25 of whom were control participants (C) of a similar age. Control participants had experienced at least one successful pregnancy before and had never experienced miscarriage or fertility treatments. All participants provided written consent to the research. The ethics committee of our institution approved this study. Clinical and demographic information, such as patient age, BMI, infertility duration and character, hormone status (FSH, LH, AMH), endometrial thickness, and reproductive history, were collected. (Demographic and clinical parameters of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants were chosen from infertility clinics in Tabriz, East Azerbaijan Province, Iran, and had regular menstrual cycles, no history of endometrial disease, and no use of hormonal medications in the three months preceding the procedure. Written informed consent was obtained from all individual participants included in this study, in accordance with the ethical principles of the Declaration of Helsinki. Peripheral blood samples were obtained alongside endometrial biopsies to examine immunologic and metabolic aspects. Patients with RPL were identified if they lost at least two consecutive pregnancies. Results were expressed using means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous data and frequency and percentage for categorical data.\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\u003e/ Demographic and Clinical Characteristics of RPL Patients and Healthy Controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRPL patients (n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy controls (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI, kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of infertility (Primary/Secondary), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (60.5) / 32 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of previous miscarriages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMH (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular menstrual cycle, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (percentage). RPL\u003c/b\u003e: recurrent pregnancy loss; \u003cb\u003eFSH\u003c/b\u003e: follicle-stimulating hormone; \u003cb\u003eLH\u003c/b\u003e: luteinizing hormone; \u003cb\u003eAMH\u003c/b\u003e: anti-M\u0026uuml;llerian hormone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Endometrial Sample Collection and Processing\u003c/h2\u003e \u003cp\u003eThe endometrial biopsy was performed during the mid-luteal phase (LH\u0026thinsp;+\u0026thinsp;7 to LH\u0026thinsp;+\u0026thinsp;9 days) by suction cannula (Nexodis; Meringer, Kalisz, Poland). This was done immediately after collection, when it was placed into 5 mL of phosphate-buffered saline (PBS; Gibco; Thermo Fisher Scientific) comprising 4% fetal bovine serum (FBS; Sigma-Aldrich). The biopsy was then transported on ice. Samples were processed for evaluating gene expression levels of interleukin-15 (IL-15), interleukin-18 (IL-18), TWEAK, Fn-14, or CD56 by quantitative real-time PCR (qRT-PCR) and flow cytometric analysis of immune cell infiltrates. Based on ratios of IL-18/TWEAK, IL-15/FN-14, and CD56 gene expression levels, 81 patients with RPL were subdivided into three groups of endometrial immune profiles: BIR-P patients having a \u0026lsquo;Balanced Immune Regulation,\u0026rsquo; HID-P patients having \u0026lsquo;high immune dysregulation,\u0026rsquo; and LID-P patients having \u0026lsquo;low immune dysregulation\u0026rsquo; based on defined standardized criteria by L\u0026eacute;d\u0026eacute;e et al. (Cheloufi et al. 2021). This classification allowed the patients to be stratified for further immunologic and metabolic studies. Endometrial immune profiling was done based on the ratio of IL-15/Fn-14 and IL-18/TWEAK genes, which reflected the activation state of the uNK cells and the immune balance state of the endometrium. According to the criteria set forth by L\u0026eacute;d\u0026eacute;e et al., a balanced pattern had IL-15/Fn-14 ratios between 0.36 and 3.4, an IL-18/TWEAK ratio between 0.02 and 0.13, and CD56 gene expression between 0.5 and 1.5. Over-immune activation showed high levels of IL-15/Fn-14,IL-18/TWEAK, and CD56, while low immune activation presented low levels of the three genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Endometrial Immune Biomarker Measurement and Classification\u003c/h2\u003e \u003cp\u003eRelative mRNA expression levels of IL-18, TWEAK, IL-15, Fn-14, and CD56 were quantified in endometrial samples by real-time PCR. Total RNA was extracted using a commercial RNA extraction kit (RNeasy Mini Kit, Qiagen, Germany) and reverse-transcribed into cDNA (High-Capacity cDNA Reverse Transcription Kit, Applied Biosystems, USA). Quantitative PCR was performed using SYBR Green chemistry (PowerUp\u0026trade; SYBR Green Master Mix, Applied Biosystems, USA) on a real-time PCR system (ABI StepOnePlus\u0026trade;, Applied Biosystems, USA). Gene expression was normalized to GAPDH and calculated using the 2⁻ΔΔCt method. Immune profiles were classified according to established reference ranges: balanced (IL-18/TWEAK: 0.02\u0026ndash;0.13; IL-15/Fn-14: 0.36\u0026ndash;3.4; CD56: 0.5\u0026ndash;1.5), over-activated (\u0026gt;\u0026thinsp;0.13, \u0026gt;\u0026thinsp;3.4, \u0026gt;\u0026thinsp;1.5), and low-activated (\u0026lt;\u0026thinsp;0.02, \u0026lt;\u0026thinsp;0.36, \u0026lt;\u0026thinsp;0.5), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Flow Cytometric Analysis of Th1/Th2 Ratio and NK Cells\u003c/h2\u003e \u003cp\u003eFlow cytometric analysis of Th1/Th2 ratio and natural killer (NK) cells was performed in the three study groups. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation using Ficoll-Paque (Yekta Tajhiz Azma, Tehran, Iran). For intracellular cytokine staining, PBMCs were stimulated with phorbol 12-myristate 13-acetate and ionomycin (PMA/Ionomycin; Sigma-Aldrich, USA) in the presence of Brefeldin A (Sigma-Aldrich, USA). Cells were first stained for surface markers using anti-human CD3 (PerCP-Cy5.5, clone UCHT1, BD Biosciences, USA) and anti-human CD4 (APC, clone RPA-T4, BD Biosciences, USA), followed by fixation and permeabilization (Cytofix/Cytoperm\u0026trade;, BD Biosciences, USA). Intracellular staining was then performed using anti-human IFN-γ (FITC, clone B27, BD Biosciences, USA) and anti-human IL-4 (PE, clone MP4-25D2, BD Biosciences, USA) antibodies to identify Th1 and Th2 cells, respectively. The Th1/Th2 ratio was calculated as the percentage of IFN-γ⁺ to IL-4⁺ cells within the CD3⁺CD4⁺ T-cell population. NK cells were identified by surface staining with anti-human CD56 (PE, clone NCAM16.2, BD Biosciences, USA) and anti-human CD16 (FITC, clone 3G8, BioLegend, USA), and defined as CD3⁻CD16⁺CD56⁺ cells. Data acquisition was performed using a flow cytometer (FACSCalibur\u0026trade;, BD Biosciences, USA) and analyzed with FlowJo software (Tree Star Inc., USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Measurement of Autoantibodies and Lipid-Related Metabolites by ELISA\u003c/h2\u003e \u003cp\u003eSerum levels of autoantibodies, including anti-nuclear antibody (ANA), anti-cardiolipin IgG, anti-double-stranded DNA (anti-dsDNA IgG), lupus anticoagulant (LA), anti-thyroglobulin (anti-TG IgG), anti-thyroid peroxidase (anti-TPO), anti-tissue transglutaminase IgA (anti-tTG IgA), anti-β2-glycoprotein I IgG, and anti-phosphatidylserine IgG, were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturers\u0026rsquo; instructions (Pishtaz Teb Co, Iran; Idealdiag, Iran; Niaztebsalamat, Iran; MyBiosource, USA). Serum concentrations of lipid-related metabolites and mediators, including sphingosine-1-phosphate (S1P), prostaglandin E2 (PGE-2), insulin-like growth factor-1 (IGF-1), phosphatidylserine, adiponectin, and leptin, were also quantified using specific ELISA kits (MyBiosource, USA; Invitrogen, Thermofisher, USA; Enzo, Belgium). Optical density was measured at 450 nm using a microplate reader, and analyte concentrations were calculated from standard curves provided with each kit. Positivity for autoantibodies was defined according to the cutoff values recommended by the manufacturers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Total Phospholipid Measurement by Spectrophotometry\u003c/h2\u003e \u003cp\u003eTotal phospholipid content was measured using a spectrophotometric method following lipid extraction by the Bligh and Dyer procedure. Briefly, total lipids were extracted from serum samples using a chloroform:methanol (2:1, v/v) solution, followed by multiple washing steps. Phospholipid standards (10, 20, 30, and 40 mg/dL) were prepared using perchloric acid (70%), ammonium molybdate (2.5%), and ascorbic acid (10%). After acid digestion, ammonium molybdate and ascorbic acid were added to both standards and samples to allow color development. Optical density was measured at 800 nm using a spectrophotometer (Pharmacia LKB Novaspec II), and phospholipid concentrations were calculated based on the standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism software (version 10.6.0; GraphPad Software, La Jolla, CA, USA). Comparisons between groups were conducted using one-way analysis of variance (ANOVA) or Student\u0026rsquo;s t-test, as appropriate. A p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Endometrial Immune Profiling Results\u003c/h2\u003e \u003cp\u003eEndometrial immune profiling based on IL-18/TWEAK, IL-15/Fn-14 ratios, and CD56 mRNA expression revealed distinct patterns among RPL patients compared to controls. All control subjects (n\u0026thinsp;=\u0026thinsp;25) exhibited balanced profiles, with mean IL-18/TWEAK, IL-15/Fn-14, and CD56 values of 0.076, 1.93, and 1.01, respectively. In contrast, among RPL patients (n\u0026thinsp;=\u0026thinsp;81), 38 individuals (46.9%) showed an over-activated profile characterized by IL-18/TWEAK\u0026thinsp;\u0026gt;\u0026thinsp;0.13, IL-15/Fn-14\u0026thinsp;\u0026gt;\u0026thinsp;3.4, and CD56\u0026thinsp;\u0026gt;\u0026thinsp;1.5 (mean values: 0.165, 3.98, and 1.74, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Twenty patients (24.7%) displayed low-activated profiles with IL-18/TWEAK\u0026thinsp;\u0026lt;\u0026thinsp;0.02, IL-15/Fn-14\u0026thinsp;\u0026lt;\u0026thinsp;0.36, and CD56\u0026thinsp;\u0026lt;\u0026thinsp;0.5 (mean values: 0.012, 0.23, and 0.30; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Only 23 patients (28.4%) fell within the balanced range (IL-18/TWEAK 0.02\u0026ndash;0.13, IL-15/Fn-14 0.36\u0026ndash;3.4, CD56 0.5\u0026ndash;1.5; mean values: 0.071, 1.92, 0.94), showing no significant difference compared to controls (p\u0026thinsp;=\u0026thinsp;0.947, 0.999, and 0.589, respectively). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show distinct endometrial immune activation patterns in RPL patients compared with controls based on IL-18/TWEAK, IL-15/Fn-14, and CD56 expression. These findings demonstrate that nearly 71% of RPL patients exhibit either excessive or insufficient endometrial immune activation, highlighting the heterogeneity of uNK cell-mediated immune responses in recurrent pregnancy loss.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e/ Immune Biomarker Profiles in RPL Patients and Healthy Controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRPL group (n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-18 / TWEAK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver-activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (46.9%) Mean: 0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (24.7%) Mean: 0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (100%) Mean: 0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (28.4%)\u003c/p\u003e \u003cp\u003eMean: 0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-15 / Fn-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver-activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (46.9%) Mean: 3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (24.7%) Mean: 0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (100%) Mean: 1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (28.4%) Mean: 1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD56 mRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver-activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (46.9%) Mean: 1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (24.7%) Mean: 0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (100%) Mean: 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (28.4%) Mean: 0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Serum Autoantibody Profiles across Endometrial Immune Profiles\u003c/h2\u003e \u003cp\u003eSerum autoantibody positivity differed notably among the three endometrial immune profile groups. In the Balanced group (n\u0026thinsp;=\u0026thinsp;23), autoantibody positivity was generally low, with only Anti-TG (2/23, 8.69%), Anti-TPO (1/23, 4.34%), Anti-tTG (1/23, 4.34%), and Anti-Phosphatidylserine IgG (5/23, 21.73%) detected. The Low-activated group (n\u0026thinsp;=\u0026thinsp;20) showed modest positivity, including ANA (1/20, 5.0%), anti-dsDNA (2/20, 10.0%), Anti-TPO (3/20, 15.0%), Anti-Phosphatidylserine IgG (5/20, 25.0%), and Anti-TG (0%). In contrast, the Over-activated group (n\u0026thinsp;=\u0026thinsp;38) demonstrated significantly higher prevalence of multiple autoantibodies, including anti-cardiolipin IgG (19/38, 50.0%; p\u0026thinsp;=\u0026thinsp;0.0002 vs. Balanced), LA (23/38, 60.53%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), Anti-TG (26/38, 68.42%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), anti-β2-Glycoprotein I IgG (20/38, 52.63%; p\u0026thinsp;=\u0026thinsp;0.0001), and Anti-Phosphatidylserine IgG (24/38, 63.15%; p\u0026thinsp;=\u0026thinsp;0.0072). ANA, anti-dsDNA, Anti-TPO, and anti-tTG positivity did not differ significantly across groups. These findings indicate a strong association between over-activated endometrial immune profiles and elevated serum autoantibody levels, whereas low and balanced profiles exhibited limited autoantibody positivity. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that serum autoantibody positivity is markedly higher in the over-activated endometrial immune profile compared with balanced and low-activated groups, indicating a strong association between excessive endometrial immune activation and elevated autoantibody levels in RPL patients. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates that serum autoantibody positivity is substantially elevated in the over-activated endometrial immune profile compared with balanced and low-activated groups, supporting a strong association between excessive endometrial immune activation and increased autoantibody levels in RPL patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e/Autoantibody Profiles and Positivity Rates in RPL Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference Range / Cut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBalanced Pos / Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Pos / Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOver Pos / Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBalanced vs. Low P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBalanced vs. Over P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow vs. Over P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Nuclear Antibody (ANA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u0026thinsp;\u0026lt;\u0026thinsp;0.9: Negative0.9\u0026ndash;1.1: Equivocal\u0026thinsp;\u0026gt;\u0026thinsp;1.1: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/23 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/20 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3/38 (7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-cardiolipin (IgG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12: Negative12\u0026ndash;18: Equivocal\u0026thinsp;\u0026gt;\u0026thinsp;18: Positive (GPL/MPL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/23 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/20 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19/38 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-dsDNA (IgG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100 IU/mL: Normal\u0026thinsp;\u0026gt;\u0026thinsp;100 IU/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/23 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/20 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3/38 (7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLupus Anticoagulant (LA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio\u0026thinsp;\u0026lt;\u0026thinsp;1.20: Negative\u0026thinsp;\u0026gt;\u0026thinsp;1.30: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/23 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/20 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23/38 (60.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Thyroglobulin (Anti-TG, IgG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100 IU/mL: Negative\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100 IU/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/23 (8.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/20 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26/38 (68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Thyroid Peroxidase (Anti-TPO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35 IU/mL: Negative\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35 IU/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/23 (4.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/20 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4/38 (10.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-tTG (IgA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 IU/mL: Negative\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 IU/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/23 (4.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/20 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3/38 (7.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-β2-Glycoprotein I (IgG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8 U/mL: Negative\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8 U/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/23 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/20 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20/38 (52.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Phosphatidylserine (IgG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25U/mL: Negative\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25 U/mL: Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/23 (21.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/20 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24/38 (63.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0072\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immune Cell Profiles and Th1/Th2 Balance across Immune Regulation Groups\u003c/h2\u003e \u003cp\u003eAnalysis of immune cell profiles revealed that the frequency of NK cells (CD3⁻CD16⁺CD56⁺) was 6.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18% in the Balanced Immune Regulation group, 8.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34% in the Low Immune Dysregulation group, and 11.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80% in the High Immune Dysregulation group. The difference between the Balanced Immune Regulation and High Immune Dysregulation groups was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while no significant difference was observed between the Balanced and Low Immune Dysregulation groups (P\u0026thinsp;=\u0026thinsp;0.149). The comparison between Low and High Immune Dysregulation groups also reached statistical significance (P\u0026thinsp;=\u0026thinsp;0.030). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the flow cytometry gating strategy for the three immune regulation groups, while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB presents the comparative analysis of NK cell (CD3⁻CD16⁺CD56⁺) frequencies among the balanced, low dysregulation, and high dysregulation groups. Similarly, the Th1/Th2 ratio (CD4⁺IFN-γ⁺/CD4⁺IL-4⁺) was 12.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 in the Balanced Immune Regulation group, 13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 in the Low Immune Dysregulation group, and 22.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26 in the High Immune Dysregulation group. The Th1/Th2 ratio was significantly higher in the High Immune Dysregulation group compared to both the Balanced and Low Immune Dysregulation groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for both comparisons), whereas no significant difference was observed between the Balanced and Low Immune Dysregulation groups (P\u0026thinsp;=\u0026thinsp;0.656). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA shows the flow cytometry gating strategy for Th1 and Th2 cells, while Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB presents the comparative analysis of the Th1/Th2 ratio among the balanced, low dysregulation, and high dysregulation groups. These findings indicate that immune dysregulation is associated with an increased proportion of NK cells and a Th1-biased response, while individuals with balanced immune regulation exhibit lower levels of these immune markers. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the comparative analysis of NK cell frequency and Th1/Th2 ratio among the balanced, low immune dysregulation, and high immune dysregulation groups, demonstrating significantly elevated NK cells and Th1/Th2 ratio in the high immune dysregulation group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e/ Comparison of Immune Cell Profiles Among RPL Patients with Different Immune Regulation Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eBalanced Immune Regulation group (G1) (Mean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Immune Dysregulation group (G2) (Mean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Immune Dysregulation group (G3) (Mean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG1\u0026nbsp;vs.\u0026nbsp;G2\u003c/p\u003e \u003cp\u003e(P value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG1\u0026nbsp;vs.\u0026nbsp;G3\u003c/p\u003e \u003cp\u003e(P value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG2\u0026nbsp;vs.\u0026nbsp;G3\u003c/p\u003e \u003cp\u003e(P value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK Cells\u003c/p\u003e \u003cp\u003e(CD3\u003csup\u003e\u0026minus;\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003eD56\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.81\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.14\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh1/Th2\u003c/p\u003e \u003cp\u003e(CD4\u003csup\u003e+\u003c/sup\u003eIFN-γ\u003csup\u003e+\u003c/sup\u003e/CD4\u003csup\u003e+\u003c/sup\u003eIL4\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.68\u0026thinsp;+\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.18\u0026thinsp;+\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.42\u0026thinsp;+\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparison of Metabolic Biomarkers between Balanced and High Immune Dysregulation Groups\u003c/h2\u003e \u003cp\u003eDirect comparison between the balanced immune regulation group (G1) and the high immune dysregulation group (G3) demonstrated significant alterations in several metabolic biomarkers. Serum adiponectin levels were significantly reduced in the high immune dysregulation group compared with the balanced group (13.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 vs. 16.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, prostaglandin E2 (PGE-2) concentrations were markedly elevated in the high immune dysregulation group (390.5\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9 pg/mL) relative to the balanced group (321.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.7 pg/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Insulin-like growth factor-1 (IGF-1) levels were significantly lower in the high immune dysregulation group compared with the balanced immune regulation group (2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 vs. 5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In addition, total phospholipid concentrations were substantially increased in the high immune dysregulation group (358.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3 mg/dL) compared with the balanced group (250.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4 mg/dL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In contrast, no statistically significant differences were observed between the balanced and high immune dysregulation groups in serum leptin levels (6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94 vs. 8.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.334), sphingosine-1-phosphate (S1P) concentrations (34.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04 vs. 36.96\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.311), or phosphatidylserine levels (33.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 vs. 33.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 ng/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0568). Overall, these findings indicate that high endometrial immune dysregulation in RPL patients is associated with pronounced disturbances in adipokine balance, inflammatory lipid mediators, growth-related factors, and phospholipid metabolism, while certain lipid signaling molecules remain relatively preserved. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the direct comparison of key metabolic biomarkers between the balanced immune regulation group and the high immune dysregulation group, highlighting significant alterations in adiponectin, PGE-2, IGF-1, and total phospholipids, while leptin, S1P, and phosphatidylserine levels remain relatively unchanged.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e/ Comparison of Metabolic and Signaling Biomarkers Among RPL Patients with Different Immune Regulation Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalanced Immune Regulation group (Mean\u0026thinsp;+\u0026thinsp;SD) (G1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Immune Dysregulation (Mean\u0026thinsp;+\u0026thinsp;SD) group (G2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Immune Dysregulation group (Mean\u0026thinsp;+\u0026thinsp;SD) (G3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG1 vs. G2 P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG1 vs. G3 P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG2 vs. G3 P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeptin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.629\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e1.942\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.060\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e1.914\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.655\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e3.936\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16.88\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e1.466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22.51\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e2.509\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.62\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e0.916\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingosine 1 phosphate (S1P) (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e34.67\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e2.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37.73\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e4.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e36.96\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e8.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGE-2 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e321.9\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e28.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e205.4\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e10.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e390.5\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e22.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylserine (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e33.58\u0026thinsp;+\u0026thinsp;1.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e33.70\u0026thinsp;+\u0026thinsp;1.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e33.95\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0568\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0640\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.518\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e0.862\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.308\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e0.500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.51\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e0.853\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Phospholipid (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e250.6\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e18.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e261.3\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e20.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e358.9\u003c/b\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;\u003cb\u003e13.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. G1: Balanced Immune Regulation group; G2: Low Immune Dysregulation group; G3: High Immune Dysregulation group; S1P: sphingosine-1-phosphate; PGE-2: prostaglandin E2; IGF-1: insulin-like growth factor-1.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis current study offers compelling evidence of a significant relationship between recurrent pregnancy loss and endometrial immune profiling, which extends beyond local uterine immunity and encompasses systemic immune and metabolic abnormalities. The results of our current investigation support the notion of recurrent pregnancy loss as a complex syndrome of immune-related phenotypes, each of which may require individualized approaches to diagnosis and treatment. One of the most striking findings of our current investigation was that approximately 71% of recurrent pregnancy loss patients displayed either a high or low endometrial immune dysregulation profile, while all of the fertile controls displayed a balanced immune profile. This finding supports a study reports by L\u0026eacute;d\u0026eacute;e and colleagues, who proposed a model of recurrent pregnancy loss as a result of inappropriate or insufficient uNK cell priming, compromising implantation and early placental development (Cheloufi et al. 2021). However, our current findings extend their model by demonstrating a relationship between endometrial immune imbalance and systemic immune activation, autoantibody production, and metabolic remodeling, suggesting a potential interaction or crosstalk between uterine immunity and systemic immune and metabolic homeostasis. The over-activated immune profile was characterized by a higher level of IL-15/Fn-14 and IL-18/TWEAK ratios, as well as CD56 expression, indicating excessive uNK cell activation (L\u0026eacute;d\u0026eacute;e et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020a\u003c/span\u003ed\u0026eacute;e et al. 2020b). IL-15 is a key cytokine involved in uNK cell proliferation and cytotoxicity, while IL-18 regulates Th1 cell polarization through IFN-γ production (Iudicone et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martynova et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent studies have highlighted excessive IL-15/IL-18 signaling as a key pathogenic event in recurrent pregnancy loss, skewing uNK cell responses towards a pro-inflammatory, cytotoxic rather than their physiological pro-angiogenic, tissue-remodeling phenotype (Yang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003eb et al. 2021; Gordon \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The current findings of increased peripheral NK cell frequency and a highly elevated Th1/Th2 ratio in the over-activated immune profile strongly support this model of recurrent pregnancy loss pathogenesis, suggesting a relationship between endometrial over-activation and systemic Th1 immunity.\u003c/p\u003e \u003cp\u003eMost importantly, the patient group with high immune dysregulation had a significantly higher prevalence of antiphospholipid-related autoantibodies, including anti-cardiolipin, lupus anticoagulant, anti-β2-glycoprotein I, and anti-phosphatidylserine. These observations are supported by recent evidence suggesting that antiphospholipid autoantibodies may play a direct role in the activation of NK cells, trophoblast invasion, and the remodeling of the spiral arteries (Martirosyan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, the role of IL-18-induced hyperinflammatory responses in the activation of autoantibody-producing B cells provides evidence of the link between endometrial immune hyper-activation and autoimmunity (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, the present study suggests that the over-activated endometrial immune phenotype may be the common end-point of hyperactivated innate immune responses, Th1 dominance, and autoantibody production. In contrast, the low immune activation phenotype had low ratios of IL-15/Fn-14 and IL-18/TWEAK and low CD56 expression, indicating insufficient uNK cell recruitment and activation. Although these patients did not demonstrate evidence of systemic immune hyper-activation and autoantibody production, the insufficient cytokine and growth factor production by uNK cells may compromise decidualization, angiogenesis, and trophoblast support. Recent studies have suggested that insufficient uNK cell activity may be as harmful as hyperactivated uNK cells, resulting in poor vascular remodeling and pregnancy failure (Wei et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relative balance of Th1 and Th2 responses in the low immune activation phenotype also supports the concept of local immune insufficiency (Bretscher \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition to the immune system, the present study also shows a strong link between high immune dysregulation and metabolic and lipid-related markers. The substantial reduction in adiponectin observed in the over-activated group is particularly interesting, given the anti-inflammatory, Th1-suppressive, and regulatory roles of adiponectin. Adiponectin has recently been implicated in pregnancy complications, as reduced levels are associated with increased NF-κB activation and subsequent elevated production of pro-inflammatory cytokines (Gutaj et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, low levels of adiponectin may enhance immune activation in RPL patients.\u003c/p\u003e \u003cp\u003eOn the contrary, the elevated levels of PGE-2 in the high immune dysregulation group may be a compensatory response to the overactive state of inflammation. Although PGE-2 is traditionally regarded as an immunosuppressant and tolerogen, it has been shown to induce dysregulated angiogenesis and trophoblast invasion in the setting of prolonged overexpression (Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, the decreased levels of IGF-1 may impair endometrial receptivity and placenta development, as it has a crucial role in trophoblast cell proliferation, decidualization, and immune modulation at the fetal\u0026ndash;maternal interface (Pollheimer et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The elevated levels of total phospholipids may be a reflection of the significant changes in lipid metabolism, which may play a role in the altered membrane signaling, autoantigen exposure, and inflammatory response. Interestingly, the levels of sphingosine-1-phosphate and leptin were found to be comparable between the groups, suggesting that lipid mediators may not be equally affected in the process of immune-mediated RPL (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In a clinical setting, the implications of the current study are considerable. The close association between endometrial immune profiles and peripheral immune-metabolic signatures may provide a new tool in the management of RPL by allowing the use of serum biomarkers as a substitute for endometrial biopsy in the assessment of the immune status (Blackwood et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This may be a more convenient method of monitoring changes in the immune status in the setting of RPL. In addition, the immune profiling may provide a tool in the management of RPL by allowing the use of specific therapies, i.e., immunosuppression in the setting of overactive immune profiles, immune activation in low immune activation profiles, and metabolic therapies in the setting of metabolic imbalances.\u003c/p\u003e \u003cp\u003eIn conclusion, the current study supports the paradigm of RPL as a heterogeneous condition caused by different immune\u0026ndash;metabolic endotypes rather than a single pathological process. The endometrial immune profiling, in combination with systemic biomarkers, provides a comprehensive understanding of the heterogeneity of RPL and opens the door for the development of precision medicine in the field of reproductive immunology. Future longitudinal and interventional studies are required to confirm the usefulness of serum-based immune profiling in RPL and determine if the correction of the imbalances in the immune and metabolic status can improve pregnancy outcomes in RPL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eFunding Statement\u003c/h3\u003e\n\u003cp\u003e\"This study was supported by a research grant (Grant No. \u003cstrong\u003e73540\u003c/strong\u003e) from the \u003cstrong\u003eImmunology Research Center, Tabriz University of Medical Sciences\u003c/strong\u003e, \u003cstrong\u003eand Tabriz, Iran\u003c/strong\u003e. The authors express their gratitude for the financial and technical support provided by the center throughout the duration of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of [\u003cstrong\u003eTabriz University of Medical Sciences\u003c/strong\u003e, \u003cstrong\u003eTabriz, Iran\u003c/strong\u003e] (Approval No: 73540). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants included in this study, in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tabriz University of Medical Sciences (Approval No: IR.TBZMED.REC.73540).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.K. and M.Y. designed the study.A.K., S.P., and S.B.A. performed experiments.M.P., A.S., and A.H. collected clinical data and samples.A.A. and A.M. conducted statistical and bioinformatic analyses.M.A. and J.A.H. contributed to data interpretation and validation.A.K. wrote the main manuscript text.S.P., S.B.A., and M.Y. prepared Figures 1\u0026ndash;3.All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAslanian-Kalkhoran, Lida, Amin Kamrani, Iraj Alipourfard, Forough Chakari-Khiavi, Aref Chakari-Khiavi, Leili Aghebati-Maleki, Ali Akbar Shekarchi, Amir Mehdizadeh, Maryam Mojahedi, and Shahla Danaii. 2023. \u0026apos;The effect of lymphocyte immunotherapy (LIT) in modulating immune responses in patients with recurrent pregnancy loss (RPL)\u0026apos;, \u003cem\u003eInternational Immunopharmacology\u003c/em\u003e, 121: 110326.\u003c/li\u003e\n\u003cli\u003eBlackwood, J. 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Immunology\u003c/em\u003e, 13: 951482.\u003c/li\u003e\n\u003cli\u003eYang, Xiuhua, Yingying Tian, Linlin Zheng, Thanh Luu, and Joanne Kwak-Kim. 2022. \u0026apos;The update immune-regulatory role of pro-and anti-inflammatory cytokines in recurrent pregnancy losses\u0026apos;, \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, 24: 132.\u003c/li\u003e\n\u003cli\u003eПастущек, Я, УР Маркерт, БВ Донськой, Е Шлебнер, К Бар, М Чіокадзе, and КГ Хажиленко. 2020. \u0026apos;Beyond Uterine Natural Killer Cell Numbers in Unexplained Recurrent Pregnancy Loss: Combined Analysis of CD45, CD56, CD16, CD57, and CD138\u0026apos;.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recurrent pregnancy loss, endometrial immune profiling, uterine NK cells, IL-15, IL-18, autoantibodies, metabolic biomarkers, immune dysregulation","lastPublishedDoi":"10.21203/rs.3.rs-9523337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9523337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecurrent pregnancy loss (RPL) is a complex disorder fundamentally linked to immune dysregulation at the maternal-fetal interface. While endometrial immune profiling provides critical diagnostic insights for managing RPL, its clinical application is limited by the invasive nature of endometrial biopsies.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to identify non-invasive, serum-based immunological and metabolic markers that accurately reflect local endometrial immune profiles, facilitating a less invasive risk assessment and patient categorization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study enrolled 106 participants, including 81 women with RPL and 25 fertile controls. Endometrial biopsies were analyzed for IL-15/Fn-14, IL-18/TWEAK, and CD56 expression to stratify patients into balanced, high immune dysregulation (over-activated), and low immune dysregulation profiles. Corresponding peripheral blood samples were evaluated for Th1/Th2 ratios, natural killer (NK) cell frequencies, autoantibodies, and metabolic biomarkers including adiponectin, prostaglandin E2 (PGE-2), insulin-like growth factor-1 (IGF-1), and total phospholipids.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll control subjects exhibited a balanced endometrial immune profile. In contrast, approximately 71% of RPL patients demonstrated immune dysregulation, with 46.9% showing an over-activated profile and 24.7% a low-activated profile. Systemically, the high immune dysregulation group exhibited significantly elevated peripheral NK cell frequencies and Th1/Th2 ratios compared to the balanced group. Furthermore, this over-activated group demonstrated a substantially higher prevalence of serum autoantibodies. Metabolically, high immune dysregulation was associated with significantly decreased serum adiponectin and IGF-1 levels, alongside markedly elevated PGE-2 and total phospholipid concentrations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEndometrial immune dysregulation in RPL is tightly correlated with distinct systemic immune and metabolic signatures. Utilizing these corresponding serum biomarkers offers a highly promising, non-invasive alternative to endometrial biopsy, paving the way for individualized precision medicine and targeted therapeutic strategies for women experiencing RPL.\u003c/p\u003e","manuscriptTitle":"Interplay between Systemic Immune-Metabolic Signatures and Endometrial Immune Profiles in Women with Recurrent Pregnancy Loss","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:08:30","doi":"10.21203/rs.3.rs-9523337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T11:03:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:28:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234228265348229899868659118892347582152","date":"2026-05-11T07:20:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T00:04:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300459141436372434827941224393027217291","date":"2026-05-10T12:39:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T11:28:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T05:44:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T05:43:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2026-04-25T07:24:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8014c224-19b8-411f-a4ff-aeb3cffea66a","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T11:03:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:28:21+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"234228265348229899868659118892347582152","date":"2026-05-11T07:20:10+00:00","index":18,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T00:04:41+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"300459141436372434827941224393027217291","date":"2026-05-10T12:39:37+00:00","index":16,"fulltext":""},{"type":"reviewersInvited","content":"6","date":"2026-05-06T11:28:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T05:44:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T05:43:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:08:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9523337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9523337","identity":"rs-9523337","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}