ORAI1, FGF23, PP13, Palladin, and Supervillin as Potential Biomarkers in Late-Onset Pre- eclampsia: A Comparative Study in Maternal and Cord Blood Running Title: Potential Biomarkers in Late-Onset Pre-eclampsia

ORAI1, FGF23, PP13, Palladin, and Supervillin as Potential Biomarkers in Late-Onset Pre- eclampsia: A Comparative Study in Maternal and Cord Blood Running Title: Potential Biomarkers in Late-Onset Pre-eclampsia | 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 ORAI1, FGF23, PP13, Palladin, and Supervillin as Potential Biomarkers in Late-Onset Pre- eclampsia: A Comparative Study in Maternal and Cord Blood Running Title: Potential Biomarkers in Late-Onset Pre-eclampsia Hatice Argun Atalmis, Sinem Tekin, Ibrahim Yilmaz, Emine Yilmaz Guler, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6416476/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 12 You are reading this latest preprint version Abstract Background Pre-eclampsia continues to be a significant global health burden with complex pathophysiology, necessitating investigation of novel biomarkers to improve understanding, diagnosis and management of this pregnancy-specific disorder.To investigate the differential expression of Calcium Release-Activated Calcium Channel Protein 1 (ORAI1), Fibroblast Growth Factor 23 (FGF23), Placental Protein 13 (PP13), Palladin, and Supervillin in both maternal and umbilical cord blood as potential biomarkers for late-onset pre-eclampsia. Methods This cross-sectional, case-control study included 61 women with late-onset pre-eclampsia and 61 normotensive pregnant women undergoing cesarean delivery. Maternal blood samples were collected immediately prior to cesarean delivery, and umbilical cord blood was obtained immediately after delivery of the placenta. Protein concentrations in both circulatory compartments were measured using enzyme-linked immunosorbent assay. The unique study design with paired maternal-cord blood sampling provided insights into maternal-fetal protein transfer dynamics in pre-eclamptic conditions. Results Maternal and cord blood ORAI1 concentrations were significantly elevated in pre-eclampsia (p = 0.001 and p = 0.035, respectively), while FGF23 and PP13 were significantly decreased in maternal blood (p = 0.022 and p = 0.018, respectively). Maternal-to-cord blood concentration ratios for ORAI1 and FGF23 were significantly altered in pre-eclampsia (p = 0.038 and p = 0.021, respectively). ORAI1 showed the highest diagnostic accuracy (AUC = 0.733) and correlated positively with disease severity and negatively with birth weight. Combined ORAI1 and FGF23 assessment significantly enhanced diagnostic performance (AUC = 0.782). Conclusion The altered expression of ORAI1, FGF23, and PP13 in late-onset pre-eclampsia suggests disruptions in calcium signaling, phosphate metabolism, and placental function. The parallel measurement of these proteins in both maternal and cord blood provided unique insights into maternal-fetal interface dysfunction in pre-eclampsia. The superior performance of combined ORAI1 and FGF23 measurement underscores the value of a multi-marker approach in capturing pre-eclampsia's complex pathophysiology, potentially contributing to improved diagnostic strategies and therapeutic interventions. Pre-eclampsia ORAI1 FGF23 PP13 Palladin Supervillin 1. Introduction Pre-eclampsia is a pregnancy-specific multisystemic disorder characterized by de novo hypertension and proteinuria or evidence of end-organ dysfunction occurring after 20 weeks of gestation. This condition remains a significant global health burden, constituting one of the leading causes of maternal and perinatal morbidity and mortality worldwide ( 1 ). Clinically, pre-eclampsia is stratified into two principal phenotypes based on gestational age at onset: early-onset (< 34 weeks) and late-onset (≥ 34 weeks) ( 2 – 5 ). Early-onset pre-eclampsia typically presents severe clinical manifestations, substantial placental insufficiency, impaired trophoblast invasion, and significant fetal growth restriction, often causing preterm delivery and intensive maternal-fetal intervention. Conversely, late-onset pre-eclampsia, despite its generally milder clinical presentation, exhibits substantially higher prevalence, thus accounting for most cases globally. Late-onset pre-eclampsia is characterized by relatively preserved placental morphology, less pronounced fetal growth impairment, and distinct maternal cardiovascular adaptations ( 5 ). The pathophysiology of late-onset pre-eclampsia appears to involve predominantly maternal constitutional factors, including metabolic dysregulation, endothelial dysfunction, and systemic inflammatory responses, distinguishing it from the primarily placenta-mediated pathogenesis seen in early-onset disease ( 6 ). Consequently, investigating the specific pathophysiological mechanisms of late-onset pre-eclampsia is essential for developing targeted preventive strategies, enhanced diagnostic methodologies, and personalized therapeutic interventions specifically addressing this clinically distinct entity. Considering incompletely elucidated etiopathogenesis of pre-eclampsia, prompting extensive research across various temporal phases of disease development is one of the research topic in perinatology. Contemporary investigations employ diverse methodological approaches targeting specific clinical objectives such as prediction in early gestation, accurate diagnosis during clinical presentation, and longitudinal monitoring of disease progression. Early gestational research primarily focuses on identifying predictive biomarkers that could ease timely preventive interventions, while later-stage studies emphasize diagnostic precision and effective disease surveillance. These investigations utilize sophisticated techniques spanning biochemical analyses, molecular biology methodologies, proteomic profiling, and advanced imaging modalities ( 2 , 3 , 6 – 9 ). Proteins represent particularly valuable biomarkers in pre-eclampsia research, as alterations in their expression, secretion, and functional activity directly reflect the underlying pathophysiological processes. Numerous proteins involved in angiogenesis, inflammation, endothelial function, oxidative stress regulation, and placental development have been extensively investigated to elucidate their roles in disease pathogenesis. Despite significant advancements, the complexity of pre-eclampsia necessitates examination of diverse proteins across different gestational timepoints ( 2 , 3 , 8 , 9 ). Consequently, multiple protein candidates with potential roles in pre-eclampsia pathophysiology remain inadequately characterized regarding their predictive, diagnostic, or prognostic utility, underscoring the continued need for comprehensive and targeted investigative approaches. For this purpose, we performed a search to find proteins with potential involvement in pre-eclampsia pathophysiology, focusing on their expression patterns in maternal and cord blood samples. Through comprehensive literature review, we identified five proteins of particular interest based on their biological functions and potential relevance to pre-eclampsia pathogenesis: calcium release-activated calcium channel protein 1 (ORAI1) ( 10 , 11 ), fibroblast growth factor 23 (FGF23) ( 12 , 13 ), placental protein 13 (PP13) ( 14 – 16 ), palladin ( 17 – 19 ), and supervillin ( 20 ). Each protein represents a distinct cellular pathway potentially contributing to the complex pathophysiology of pre-eclampsia, with varying degrees of previous investigation in the context of pregnancy complications. Despite substantial advances in understanding the pathophysiological mechanisms of pre-eclampsia, current predictive and diagnostic approaches remain suboptimal. Although numerous studies have consistently identified diverse biochemical alterations across various disease mechanisms, the clinical utility of these potential biomarkers is limited by an incomplete understanding of the precise initiating events of pre-eclampsia, the primary factors sustaining disease progression, and the secondary biochemical and molecular changes emerging throughout its clinical course. This knowledge gap in the temporal sequence and causative relationships among pathophysiological events significantly impedes the development of sensitive, specific, and clinically useful biomarkers. There is a critical need for improved predictive and diagnostic tests that can effectively determine at-risk patients and monitor disease progression. Advancing this field requires deeper mechanistic insights and comprehensive longitudinal investigations to ease more precise biomarker discovery, ultimately enhancing clinical management and improving maternal and fetal outcomes. Our selection of ORAI1, FGF23, PP13, palladin, and supervillin was guided by their biological plausibility and involvement in pathways potentially disrupted in pre-eclampsia. These proteins were chosen based on their established or theoretical roles in calcium signaling, vascular function, placental development, and cellular migration—processes fundamental to normal pregnancy and frequently dysregulated in pre-eclamptic conditions. The primary aim of this cross-sectional, case-control study was to investigate the differential expression of these five proteins in maternal and cord blood samples from women with late-onset pre-eclampsia compared to normotensive pregnant women. We hypothesized that: ( 1 ) protein concentrations would differ significantly between pre-eclamptic and healthy pregnant women in both maternal and cord blood; ( 2 ) maternal-fetal concentration gradients would show distinct patterns in pre-eclamptic versus healthy pregnancies; and ( 3 ) correlations would exist between protein expression patterns and clinical parameters of disease severity. The expected outcomes of this research include the identification of potentially novel biomarkers for late-onset pre-eclampsia, improved understanding of maternal-fetal protein transfer in pre-eclamptic conditions, and new insights into disease pathophysiology. These findings could ultimately contribute to the development of more effective diagnostic tools and targeted therapeutic strategies for this significant obstetric complication, potentially improving maternal and neonatal outcomes through earlier detection and more personalized management approaches. 2. Materials and Methods 2.1. Study Design and Ethical Considerations This investigation was designed as a cross-sectional, case-control study to evaluate the differential expression of specific protein biomarkers in maternal and cord blood samples from women with late-onset pre-eclampsia compared to normotensive pregnant women. The study focused exclusively on patients undergoing cesarean delivery to standardize the timing and conditions of sample collection, thereby minimizing potential confounding variables related to labor and vaginal delivery. The study protocol received approval from the Human Research Ethics Committee of Haseki Training and Research Hospital (approval number: 110–2023, dated September 6, 2023) affiliated with the University of Health Sciences, and all procedures were conducted in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments. Prior to enrollment, comprehensive written informed consent was obtained from all participants after detailed explanation of the study objectives, procedures, potential risks, and benefits. Participants were informed of their right to withdraw from the study at any time without affecting their standard medical care. The study was conducted between October 2023 and September 2024 at the Department of Obstetrics and Gynecology of our institution. All personal identifiers were removed from collected samples and clinical data before analysis to ensure participant confidentiality. The study adhered to STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies to maintain the quality of methodological and reporting standards. 2.2. Study Population The study population comprised pregnant women who underwent planned or emergency cesarean delivery at our institution during the study period. Participants were recruited using a consecutive sampling approach and categorized into two groups: women with late-onset pre-eclampsia (case group) and normotensive pregnant women (control group). For the case group, we included women with singleton pregnancies diagnosed with late-onset pre-eclampsia (≥ 34 weeks of gestation) according to the American College of Obstetricians and Gynecologists (ACOG) criteria ( 21 ). This included new-onset hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg on two occasions at least 4 hours apart) and either proteinuria or evidence of end-organ damage after 20 weeks of gestation. Proteinuria was defined as ≥ 300 mg in 24-hour urine collection, protein/creatinine ratio ≥ 0.3 in a random urine sample, or dipstick reading of ≥ 1+ (30 mg/dL). All pre-eclampsia diagnoses were confirmed by maternal-fetal medicine specialists following standardized diagnostic protocols. The control group consisted of women with singleton pregnancies undergoing cesarean delivery for obstetric indications unrelated to hypertensive disorders (e.g., previous cesarean section, breech presentation, or maternal request). These women had no history of hypertension before or during pregnancy and maintained normal blood pressure measurements throughout gestation. We excluded women with multiple pregnancies, chronic hypertension, gestational hypertension without pre-eclampsia features, early-onset pre-eclampsia (< 34 weeks), HELLP syndrome, preexisting renal disease, cardiovascular disorders, autoimmune diseases, diabetes mellitus (pregestational or gestational), fetal congenital anomalies, intrauterine growth restriction without pre-eclampsia, placental abnormalities (e.g., placenta previa or placenta accreta spectrum), and those receiving medications that could affect cardiovascular function or protein expression. The sample size was calculated using G*Power software (version 3.1) based on anticipated differences in protein concentrations between groups from previous similar biomarker studies. Assuming a moderate effect size (Cohen's d = 0.55), alpha = 0.05, and power = 0.8, we determined that 53 subjects per group would be required. Accounting for potential sample exclusions due to technical issues, we aimed to recruit 61 participants per group. 2.3. Clinical Assessments All participants underwent comprehensive clinical assessment following standardized protocols established at our institution. Demographic data and detailed medical histories were collected through structured interviews and medical record reviews at enrollment, including maternal age (years), ethnicity, educational level, pre-pregnancy body mass index (BMI, kg/m²), and gestational weight gain (kg). Obstetric and medical history documentation encompassed gravidity, parity, history of gestational diabetes, previous hypertensive disorders of pregnancy, smoking status, and whether the current pregnancy resulted from assisted reproductive technology (ART). For women in both groups, blood pressure measurements were performed according to the American Heart Association guidelines using calibrated automated devices with appropriate cuff sizes, with measurements taken after a minimum 10-minute rest period with the patient seated, back supported, arm at heart level, and feet flat on the floor; two readings at least 5 minutes apart were recorded, and the average was calculated. Medications were systematically recorded for all participants, with special attention to antihypertensive agents and other medications potentially affecting cardiovascular function. Obstetric ultrasound was performed for all participants within one week before delivery to assess fetal biometry, amniotic fluid volume, and placental characteristics, with Doppler assessment of umbilical artery and middle cerebral artery conducted for patients with suspected fetal compromise. Laboratory assessments included comprehensive hematological parameters (hemoglobin, hematocrit, platelet count), renal function markers (serum creatinine), liver enzymes (AST, ALT), lactate dehydrogenase (LDH), and detailed lipid profiles (triglycerides, HDL-cholesterol, LDL-cholesterol); for proteinuria evaluation, we collected spot urine samples and categorized results based on protein concentration, with additional tests for pre-eclamptic patients including assessment of other markers of end-organ dysfunction when clinically warranted. The severity of pre-eclampsia was classified according to ACOG criteria, with severe features including systolic blood pressure ≥ 160 mmHg or diastolic blood pressure ≥ 110 mmHg, thrombocytopenia (platelet count < 100,000/µL), impaired liver function (elevated AST/ALT), progressive renal insufficiency, pulmonary edema, or new-onset cerebral or visual disturbances. Neonatal outcomes were systematically documented, including gestational age at delivery (weeks), fetal gender, birth weight (grams), Apgar scores at 1 and 5 minutes, umbilical cord arterial pH, and neonatal intensive care unit (NICU) admission status. For the protein analysis component of our study, maternal blood samples were collected immediately prior to cesarean delivery, and umbilical cord blood samples were obtained immediately after delivery of the placenta but prior to placental detachment from the uterine wall, with all samples processed according to standardized protocols to ensure consistency and reliability of the subsequent laboratory analyses. 2.4. Laboratory Analyses Measurement of study proteins was performed using commercially available enzyme-linked immunosorbent assay (ELISA) kits (BT-Lab, China) according to manufacturers' protocols. All assays were conducted by laboratory personnel blinded to the clinical status of the participants. Samples were analyzed duplicate, and the mean value was used for statistical analyses. Inter- and intra-assay coefficients of variation were maintained below 10% for all assays through strict adherence to standardized procedures. 2.5. Statistical Analysis All statistical analyses were performed using R version 4.4.3 (R Core Team, 2025), utilizing only the base packages included in the standard R distribution without any additional external packages. Prior to analysis, data were assessed for normality using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. Data transformations (logarithmic or square root) were applied when necessary to achieve normal distribution for parametric testing. Demographic and clinical characteristics were compared between pre-eclamptic and control groups using appropriate statistical tests. Continuous variables with normal distribution were presented as mean ± standard deviation and compared using the independent samples t-test. Non-normally distributed continuous variables were presented as median (interquartile range) and analyzed using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, with between-group comparisons performed using the chi-square test or Fisher's exact test as appropriate. Protein concentrations in maternal and cord blood samples were analyzed both as continuous variables and after categorization using clinically relevant cut-points or quartiles based on the distribution in control subjects. Between-group differences in protein levels were assessed using independent samples t-test or Mann-Whitney U test, depending on data distribution. Paired analyses comparing maternal and cord blood concentrations within the same subject were conducted using paired t-tests or Wilcoxon signed-rank tests. For each protein, maternal-fetal concentration ratios were calculated and compared between pre-eclamptic and normotensive pregnancies. Correlation analyses between protein levels and clinical parameters were performed using Pearson's or Spearman's correlation coefficients as appropriate. Multiple linear regression models were constructed to identify independent determinants of protein concentrations, adjusting for potential confounding variables identified in univariate analyses or based on biological plausibility. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the discriminative ability of each protein for pre-eclampsia. Area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and optimal cut-off points were calculated. 3. Results Baseline Characteristics of Study Population Initially, 151 pregnant women were considered for enrollment in this study. However, 16 women from the control group were excluded due to: incomplete clinical data (n = 5), inadequate blood sample volume for complete analysis (n = 4), withdrawal of consent (n = 3), development of gestational hypertension after enrollment (n = 2), and protocol deviations during sample collection (n = 2). Additionally, 13 women from the pre-eclampsia group were excluded due to: concomitant gestational diabetes (n = 4), multiple gestation identified after enrollment (n = 3), incomplete laboratory analyses (n = 3), delivery via vaginal route despite planned cesarean (n = 2), and withdrawal from the study (n = 1). Consequently, a total of 122 women were included in the final analysis, comprising 61 women with late-onset pre-eclampsia (case group) and 61 normotensive pregnant women (control group). Data normality was assessed using the Shapiro-Wilk test, which indicated non-normal distribution for all protein measurements and several clinical parameters. Accordingly, non-parametric statistical methods were employed for these variables, while parametric tests were used for normally distributed variables. The pre-eclampsia group had significantly higher pre-pregnancy BMI compared to the control group (median 25.6 vs. 23.4 kg/m², Z=-2.97, p = 0.003). A higher proportion of nulliparous women (72.1% vs. 47.5%, p = 0.005) and primigravidous women (63.9% vs. 39.3%, p = 0.012) was observed in the pre-eclampsia group. The frequency of previous hypertensive disorders of pregnancy was also higher in the pre-eclampsia group (18.0% vs. 6.6%, p = 0.049). No significant differences were found in other demographic and clinical characteristics (Table 1 ). Table 1 Demographic and clinical characteristics of the study population. Characteristic Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value Age (years) 30.2 ± 5.7 28.6 ± 6.9 -1.39 0.167 Pre-pregnancy BMI (kg/m²) 23.4 (21.2–25.7) 25.6 (22.8–29.1) -2.97 0.003 Gestational weight gain (kg) 12.3 ± 3.8 13.6 ± 4.2 -1.82 0.069 Gravidity, n (%) - 0.012 1 24 (39.3) 39 (63.9) 2 21 (34.4) 14 (23.0) ≥ 3 16 (26.2) 8 (13.1) Parity, n (%) - 0.005 0 29 (47.5) 44 (72.1) 1 25 (41.0) 13 (21.3) ≥ 2 7 (11.5) 4 (6.6) Education level, n (%) - 0.481 Primary education 14 (23.0) 17 (27.9) Secondary education 29 (47.5) 24 (39.3) Higher education 18 (29.5) 20 (32.8) Ethnicity (Native), n (%) 45 (73.8) 42 (68.9) - 0.545 Smoking, n (%) 7 (11.5) 5 (8.2) - 0.547 History of gestational diabetes, n (%) 5 (8.2) 3 (4.9) - 0.464 History of hypertensive disorders of pregnancy, n (%) 4 (6.6) 11 (18.0) - 0.049 ART pregnancy, n (%) 4 (6.6) 6 (9.8) - 0.510 Data presented as mean ± standard deviation for normally distributed variables , median (interquartile range) for non-normally distributed variables, and as number (percentage) for categorical variables. P-values calculated using independent samples t-test for normally distributed variables, Mann-Whitney U test (with corresponding Z-values) for non-normally distributed variables, and chi-square or Fisher's exact test for categorical variables. BMI: body mass index; ART: assisted reproductive technology. Bold values indicate statistically significant differences (p < 0.05) between groups. Dash (-) indicates Z-value not applicable. Clinical and Laboratory Parameters Systolic and diastolic blood pressure measurements were significantly higher in the pre-eclampsia group (median 154.0 vs. 116.0 mmHg, Z=-9.63, p < 0.001 and median 98.0 vs. 74.0 mmHg, Z=-9.48, p < 0.001, respectively). Laboratory parameters showed significant differences between groups, with the pre-eclampsia group demonstrating reduced hemoglobin (median 11.1 vs. 11.7 g/dL, Z=-2.61, p = 0.009), hematocrit (median 33.5 vs. 35.2%, Z=-2.41, p = 0.016), and platelet count (median 209.0 vs. 243.0 ×10⁹/L, Z=-2.91, p = 0.004). All renal and liver function parameters differed significantly between the groups, with the pre-eclampsia group showing higher levels of serum creatinine (median 0.70 vs. 0.59 mg/dL, Z=-4.37, p < 0.001), AST (median 28.5 vs. 18.0 U/L, Z=-5.86, p < 0.001), ALT (median 24.0 vs. 13.0 U/L, Z=-5.73, p < 0.001), and LDH (median 248.0 vs. 192.0 U/L, Z=-6.08, p < 0.001). The lipid profile showed higher triglycerides (median 274.0 vs. 245.0 mg/dL, Z=-2.69, p = 0.007) and LDL-cholesterol (median 159.0 vs. 146.0 mg/dL, Z=-2.08, p = 0.038) and lower HDL-cholesterol (median 57.0 vs. 64.0 mg/dL, Z=-2.71, p = 0.007) in the pre-eclampsia group. Among women with pre-eclampsia, 22 (36.1%) presented with severe features according to ACOG criteria (Table 2 ). Table 2 Clinical and laboratory parameters of the study population. Parameter Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value Blood pressure Systolic BP (mmHg)† 116.0 (110.0-122.0) 154.0 (145.0-165.0) -9.63 < 0.001 Diastolic BP (mmHg)† 74.0 (68.0–78.0) 98.0 (92.0-104.0) -9.48 < 0.001 Hematological parameters Hemoglobin (g/dL)† 11.7 (11.0-12.5) 11.1 (10.2–12.0) -2.61 0.009 Hematocrit (%)† 35.2 (33.1–37.4) 33.5 (31.2–36.1) -2.41 0.016 Platelet count (×10⁹/L)† 243.0 (209.0-286.0) 209.0 (168.0-262.0) -2.91 0.004 Renal and liver function parameters Spot urine protein, n (%) - < 0.001 Negative 48 (78.7) 0 (0.0) Trace 13 (21.3) 0 (0.0) 1+ 0 (0.0) 24 (39.3) 2+ 0 (0.0) 25 (41.0) 3 + or more 0 (0.0) 12 (19.7) Serum creatinine (mg/dL)† 0.59 (0.51–0.67) 0.70 (0.59–0.85) -4.37 < 0.001 AST (U/L)† 18.0 (15.0–23.0) 28.5 (20.0–38.0) -5.86 < 0.001 ALT (U/L)† 13.0 (10.0–18.0) 24.0 (16.0–35.0) -5.73 < 0.001 LDH (U/L)† 192.0 (172.0-214.0) 248.0 (209.0-308.0) -6.08 < 0.001 Lipid profile Triglycerides (mg/dL)† 245.0 (206.0-285.0) 274.0 (230.0-324.0) -2.69 0.007 HDL-cholesterol (mg/dL)† 64.0 (56.0–74.0) 57.0 (48.0–68.0) -2.71 0.007 LDL-cholesterol (mg/dL)† 146.0 (125.0-172.0) 159.0 (134.0-188.0) -2.08 0.038 Pre-eclampsia severity, n (%) Without severe features --- 39 (63.9) - --- With severe features --- 22 (36.1) - --- Data presented as median (interquartile range) for continuous variables (†) and as number (percentage) for categorical variables. P-values calculated using Mann-Whitney U test (with corresponding Z-values) for continuous variables and chi-square or Fisher's exact test for categorical variables. BP: blood pressure; AST: aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; HDL: high-density lipoprotein; LDL: low-density lipoprotein; U/L: units per liter; mg/dL: milligrams per deciliter. Pre-eclampsia severity classified according to ACOG (American College of Obstetricians and Gynecologists) criteria. Bold values indicate statistically significant differences (p < 0.05) between groups. Dash (-) indicates Z-value not applicable for categorical variables analyzed using chi-square or Fisher's exact test. Protein Concentrations in Maternal Blood All protein measurements demonstrated non-normal distribution patterns (Shapiro-Wilk, p < 0.05) and were therefore analyzed using non-parametric statistical methods. In maternal blood, ORAI1 concentration was significantly higher in the pre-eclampsia group compared to the control group (median 9.50 vs. 6.62 ng/mL, Z=-3.20, p = 0.001). Conversely, FGF23 and PP13 levels were significantly lower in the pre-eclampsia group (FGF23: median 177.80 vs. 240.88 pg/mL, Z=-2.29, p = 0.022; PP13: median 215.10 vs. 264.12 pg/mL, Z=-2.36, p = 0.018). No significant differences were observed in maternal palladin (median 5.53 vs. 6.88 ng/mL, Z=-1.74, p = 0.083) or supervillin concentrations (median 434.54 vs. 494.20 ng/mL, Z=-1.25, p = 0.212), although palladin showed a trend towards lower values in the pre-eclampsia group (Table 3 ). When comparing women with severe pre-eclampsia features (n = 22) to those with non-severe pre-eclampsia (n = 39), maternal ORAI1 concentrations were significantly higher in the severe pre-eclampsia subgroup (median 11.24 vs. 8.46 ng/mL, Z=-2.63, p = 0.009), while FGF23 was significantly lower (median 153.42 vs. 191.65 pg/mL, Z=-2.19, p = 0.028). No significant differences were observed in maternal PP13, palladin, or supervillin levels between the severe and non-severe pre-eclampsia subgroups. Table 3 Protein concentrations in maternal blood of the study population. Protein Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value ORAI1 (ng/mL) 6.62 (5.18–8.37) 9.50 (7.34–11.78) -3.20 0.001 FGF23 (pg/mL) 240.88 (198.56-279.43) 177.80 (145.32-216.45) -2.29 0.022 PP13 (pg/mL) 264.12 (221.67-302.54) 215.10 (175.84-258.63) -2.36 0.018 Palladin (ng/mL) 6.88 (5.41–8.23) 5.53 (4.26–7.15) -1.74 0.083 Supervillin (ng/mL) 494.20 (383.75-598.42) 434.54 (346.82-568.31) -1.25 0.212 Data presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Bold values indicate statistically significant differences (p < 0.05) between groups. Protein Concentrations in Cord Blood In cord blood, ORAI1 concentration was significantly higher in the pre-eclampsia group compared to the control group (median 9.87 vs. 7.77 ng/mL, Z=-2.11, p = 0.035), while FGF23 levels were significantly lower (median 202.25 vs. 233.55 pg/mL, Z=-2.09, p = 0.036). No significant differences were observed in cord blood PP13 (median 244.52 vs. 256.38 pg/mL, Z=-1.76, p = 0.078), palladin (median 3.17 vs. 3.99 ng/mL, Z=-1.26, p = 0.207), or supervillin concentrations (median 443.78 vs. 511.90 ng/mL, Z=-1.71, p = 0.087), although PP13 and supervillin showed trends toward lower values in the pre-eclampsia group (Table 4 ). No significant differences were observed in cord blood protein concentrations between severe and non-severe pre-eclampsia subgroups, although ORAI1 showed a trend toward higher values in the severe pre-eclampsia subgroup (median 10.95 vs. 9.26 ng/mL, Z=-1.84, p = 0.066). Table 4 Protein concentrations in cord blood of the study population. Protein Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value ORAI1 (ng/mL) 7.77 (6.25–9.48) 9.87 (7.62–11.92) -2.11 0.035 FGF23 (pg/mL) 233.55 (195.67-276.38) 202.25 (165.47-240.19) -2.09 0.036 PP13 (pg/mL) 256.38 (217.95-298.76) 244.52 (186.34-269.51) -1.76 0.078 Palladin (ng/mL) 3.99 (3.15–4.87) 3.17 (2.45–4.26) -1.26 0.207 Supervillin (ng/mL) 511.90 (398.73-624.58) 443.78 (352.16-572.84) -1.71 0.087 Data presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Bold values indicate statistically significant differences (p < 0.05) between groups. Maternal-to-Cord Blood Concentration Ratios The maternal-to-cord blood concentration ratios for ORAI1 and FGF23 differed significantly between the pre-eclampsia and control groups. The ORAI1 ratio was significantly lower in the pre-eclampsia group (median 0.94 vs. 0.88, Z=-2.07, p = 0.038), indicating relatively higher cord blood concentrations in pre-eclampsia. The FGF23 ratio was significantly lower in the pre-eclampsia group (median 0.87 vs. 0.97, Z=-2.31, p = 0.021). No significant differences were observed in the PP13, palladin, and supervillin ratios between the groups (Table 5 ). In the control group, the maternal-to-cord ratios for FGF23 and palladin were greater than 1, indicating higher concentrations in maternal blood, while the ORAI1 ratio was less than 1, indicating higher concentrations in cord blood. The PP13 and supervillin ratios were approximately 1 in the control group, suggesting similar concentrations in maternal and cord blood. Table 5 Maternal-to-cord blood concentration ratios of the study population. Protein Ratio Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value ORAI1 ratio 0.88 (0.71–1.06) 0.94 (0.77–1.15) -2.07 0.038 FGF23 ratio 1.03 (0.86–1.21) 0.87 (0.71–1.05) -2.31 0.021 PP13 ratio 1.02 (0.89–1.15) 0.96 (0.82–1.12) -1.14 0.254 Palladin ratio 1.67 (1.38–2.04) 1.72 (1.41–2.12) -0.63 0.529 Supervillin ratio 0.97 (0.82–1.15) 1.04 (0.86–1.23) -1.06 0.289 Data presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. Ratio calculated as maternal concentration divided by cord blood concentration for each protein. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13. Bold values indicate statistically significant differences (p < 0.05) between groups. Correlation Analysis To investigate relationships between protein concentrations and clinical parameters, we performed Spearman's rank correlation analyses for the entire study population (combining both pre-eclamptic and control groups) as shown in Table 6 . In the combined population analysis, maternal blood proteins showed significant correlations with several clinical parameters. Maternal ORAI1 demonstrated significant positive correlations with both systolic blood pressure (rₛ=0.342, p < 0.001) and diastolic blood pressure (rₛ=0.315, p < 0.001). Maternal ORAI1 also correlated positively with markers of end-organ damage including AST (rₛ=0.287, p = 0.001) and LDH (rₛ=0.269, p = 0.003). Conversely, maternal FGF23 and PP13 levels showed significant negative correlations with blood pressure measurements (FGF23 with systolic: rₛ=-0.263, p = 0.004; PP13 with systolic: rₛ=-0.246, p = 0.006) and markers of end-organ damage (FGF23 with AST: rₛ=-0.235, p = 0.009; PP13 with LDH: rₛ=-0.201, p = 0.027). Regarding neonatal outcomes, maternal ORAI1 showed significant negative correlations with birth weight (rₛ=-0.241, p = 0.007) and gestational age at delivery (rₛ=-0.225, p = 0.013). Maternal FGF23 demonstrated positive correlations with these parameters (birth weight: rₛ=0.198, p = 0.029; gestational age: rₛ=0.178, p = 0.050). Cord blood proteins exhibited correlation patterns similar to those observed in maternal blood. Notably, cord blood ORAI1 demonstrated the strongest negative correlation with birth weight (rₛ=-0.278, p = 0.002) among all measured proteins. Supervillin did not show significant correlations with any of the clinical parameters in either maternal or cord blood. We also performed separate correlation analyses within each study group. Within the pre-eclampsia group specifically, significant negative correlations were observed between maternal ORAI1 and both gestational age at delivery (rₛ=-0.318, p = 0.012) and birth weight (rₛ=-0.327, p = 0.010). Similar correlations were found for cord blood ORAI1 with gestational age (rₛ=-0.273, p = 0.033) and birth weight (rₛ=-0.295, p = 0.021). Positive correlations were observed between maternal FGF23 and both gestational age (rₛ=0.257, p = 0.045) and birth weight (rₛ=0.279, p = 0.029). No significant correlations were found between maternal PP13, palladin, or supervillin levels and pregnancy outcomes in the pre-eclampsia group. In the control group, no significant correlations were observed between any of the measured proteins and pregnancy outcomes. Table 6 Correlation between protein levels and clinical parameters in the combined study population. Protein Systolic BP Diastolic BP Platelet Count AST LDH Birth Weight Gestational Age Maternal blood ORAI1 0.342** 0.315** -0.187* 0.287** 0.269** -0.241** -0.225* FGF23 -0.263** -0.246** 0.165 -0.235** -0.203* 0.198* 0.178* PP13 -0.246** -0.217* 0.142 -0.174 -0.201* 0.183* 0.153 Palladin -0.147 -0.131 0.124 -0.126 -0.105 0.109 0.095 Supervillin -0.119 -0.098 0.087 -0.082 -0.067 0.075 0.063 Cord blood ORAI1 0.263** 0.242** -0.153 0.209* 0.216* -0.278** -0.236** FGF23 -0.227* -0.208* 0.147 -0.187* -0.174 0.214* 0.193* PP13 -0.178* -0.163 0.121 -0.152 -0.147 0.165 0.139 Palladin -0.126 -0.107 0.092 -0.098 -0.087 0.121 0.108 Supervillin -0.145 -0.126 0.084 -0.073 -0.065 0.089 0.072 Data presented as Spearman's correlation coefficients (rₛ). Statistically significant correlations are indicated: *p < 0.05, ** p < 0.01. BP: blood pressure; AST: aspartate aminotransferase; LDH: lactate dehydrogenase; ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13. Positive coefficients indicate that as one variable increases, the other also increases; negative coefficients indicate that as one variable increases, the other decreases. The combined study population includes both pre-eclamptic and normotensive pregnant women (n = 122). Diagnostic Performance of Protein Biomarkers ROC curve analysis revealed that maternal ORAI1 had the highest diagnostic accuracy with an AUC of 0.733 (95% CI: 0.644–0.823), followed by maternal PP13 with an AUC of 0.695 (95% CI: 0.601–0.789) and maternal FGF23 with an AUC of 0.679 (95% CI: 0.583–0.775). For maternal ORAI1, a cut-off value of > 8.24 ng/mL provided 67.2% sensitivity and 75.4% specificity. For maternal PP13, a cut-off value of < 237.8 pg/mL yielded 63.9% sensitivity and 70.5% specificity. For maternal FGF23, a cut-off value of < 208.4 pg/mL provided 62.3% sensitivity and 73.8% specificity. In cord blood, ORAI1 demonstrated the highest diagnostic accuracy with an AUC of 0.677 (95% CI: 0.581–0.772), followed by FGF23 (AUC = 0.653, 95% CI: 0.556–0.750). A cord blood ORAI1 cut-off value of > 8.56 ng/mL provided 63.9% sensitivity and 68.9% specificity and FGF23 cut-off value of < 217.9 pg/mL was with 60.7% sensitivity and 67.2% specificity (Table 7 ). Multivariate logistic regression analysis combining maternal ORAI1 and FGF23 achieved an AUC of 0.782 (95% CI: 0.699–0.864), which was significantly higher than either protein alone (p = 0.042 and p = 0.008, respectively). For cord blood, a model combining ORAI1 and FGF23 achieved an AUC of 0.714 (95% CI: 0.623–0.805), also significantly higher than individual proteins (p = 0.037 and p = 0.011, respectively). Table 7 Diagnostic performance of protein biomarkers for late-onset pre-eclampsia. Protein AUC (95% CI) Optimal Cut-off Sensitivity (%) Specificity (%) PPV (%) NPV (%) Maternal blood ORAI1 0.733 (0.644–0.823) > 8.24 ng/mL 67.2 75.4 73.2 69.7 FGF23 0.679 (0.583–0.775) < 208.4 pg/mL 62.3 73.8 70.4 66.2 PP13 0.695 (0.601–0.789) 8.56 ng/mL 63.9 68.9 67.2 65.6 FGF23 0.653 (0.556–0.750) < 217.9 pg/mL 60.7 67.2 64.9 63.1 PP13 0.593 (0.492–0.694) --- --- --- --- --- Palladin 0.567 (0.465–0.669) --- --- --- --- --- Supervillin 0.573 (0.471–0.675) --- --- --- --- --- Receiver operating characteristic (ROC) curve analysis data for the diagnostic performance of protein biomarkers in maternal and cord blood for late-onset pre-eclampsia. Optimal cut-off values were determined using the Youden index (maximum sum of sensitivity and specificity). AUC values > 0.6 indicate acceptable discriminatory ability, with higher values representing superior diagnostic performance. AUC: area under the curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Dashes (---) indicate values not calculated due to poor discriminatory performance (AUC < 0.6). Analysis was conducted on all study participants (n = 122). Pregnancy Outcomes The pre-eclampsia group had significantly earlier delivery compared to the control group (median 36.5 vs. 38.6 weeks, Z=-4.75, p < 0.001) and significantly lower birth weights (median 2785 vs. 3375 g, Z=-4.67, p < 0.001). Apgar scores at both 1 and 5 minutes were significantly lower in the pre-eclampsia group (median 7 vs. 8, Z=-4.39, p < 0.001 and median 8 vs. 9, Z=-4.25, p < 0.001, respectively). Umbilical cord arterial pH was significantly lower in the pre-eclampsia group (median 7.23 vs. 7.29, Z=-4.02, p < 0.001). The rate of NICU admission was significantly higher in the pre-eclampsia group (24.6% vs. 8.2%, p = 0.014). No significant difference was observed in fetal gender distribution between the groups (Table 8 ). In the pre-eclampsia group, women with severe features (n = 22) had significantly earlier delivery (median 35.2 vs. 37.5 weeks, Z=-4.12, p < 0.001) and lower birth weights (median 2395 vs. 3085 g, Z=-3.98, p < 0.001) compared to those without severe features (n = 39). NICU admission rates were higher in the severe pre-eclampsia subgroup (40.9% vs. 15.4%, p = 0.024), and cord arterial pH values were lower (median 7.18 vs. 7.25, Z=-2.54, p = 0.011) (Table 8 ). Table 8 Pregnancy outcomes of the study population. Outcome Control Group (n = 61) Pre-eclampsia Group (n = 61) Z-value P-value Gestational age at delivery (weeks)† 38.6 (37.8–39.4) 36.5 (35.2–38.1) -4.75 < 0.001 Fetal gender, n (%) - 0.587 Male 32 (52.5) 35 (57.4) Female 29 (47.5) 26 (42.6) Birth weight (g)† 3375 (3095–3670) 2785 (2340–3245) -4.67 < 0.001 Apgar score† 1 minute 8 ( 7 – 9 ) 7 ( 5 – 8 ) -4.39 < 0.001 5 minutes 9 ( 9 – 10 ) 8 ( 7 – 9 ) -4.25 < 0.001 Umbilical cord arterial pH† 7.29 (7.26–7.33) 7.23 (7.16–7.28) -4.02 < 0.001 NICU admission, n (%) 5 (8.2) 15 (24.6) - 0.014 Data presented as median (interquartile range) for continuous variables (†) and as number (percentage) for categorical variables. P-values were calculated using Mann-Whitney U test (with corresponding Z-values) for continuous variables and chi-square or Fisher's exact test for categorical variables. NICU: neonatal intensive care unit. Bold values indicate statistically significant differences (p < 0.05) between groups. Dash (-) indicates Z-value not applicable for categorical variables analyzed using chi-square or Fisher's exact test. 4. Discussion Our investigation into five potentially novel biomarkers for late-onset pre-eclampsia has elucidated distinctive protein signatures that enhance the current understanding of the underlying mechanisms of this complex disorder. Rather than merely confirming previously established biomarkers, our findings contribute novel insights into the pathophysiological pathways implicated in pre-eclampsia development. The most significant observation emerged from the analysis of ORAI1, which exhibited markedly elevated concentrations in both maternal and cord blood specimens from women with pre-eclampsia. This calcium channel protein demonstrated superior diagnostic accuracy among all examined proteins and exhibited a direct correlation with disease severity—subjects with severe pre-eclampsia manifestations presented significantly higher ORAI1 levels compared to those with milder clinical presentations. The pathophysiological relevance of ORAI1 is further substantiated by its robust positive correlation with both blood pressure parameters and markers indicative of end-organ damage. This observation suggests that dysregulated calcium signaling may contribute substantially to the vascular dysfunction characteristic of pre-eclampsia. Furthermore, the relationship observed between ORAI1 and adverse pregnancy outcomes merits particular attention. The protein demonstrated significant negative correlations with both birth weight and gestational age at delivery; notably, these correlations were exclusive to the pre-eclampsia cohort and absent in control subjects. This pattern strongly indicates that ORAI1 upregulation is not merely a consequence of pregnancy complications but may be mechanistically linked to pathophysiological processes specific to pre-eclampsia. In contrast to the elevation observed in ORAI1, FGF23 demonstrated significantly reduced concentrations in pre-eclamptic pregnancies. This finding presents an intriguing paradox. In non-pregnant populations, elevated FGF23 typically associates with adverse cardiovascular outcomes, whereas the opposite pattern was observed in pre-eclampsia. The negative correlation between FGF23 levels and blood pressure parameters suggests pregnancy-specific regulatory mechanisms that become disrupted in pre-eclamptic conditions. Moreover, FGF23 exhibited positive correlations with birth weight and gestational age, indicating that its reduction may reflect compromised placental function and fetal growth restriction. PP13, a placenta-specific protein with established functions in maternal-fetal immune tolerance, was significantly decreased in the maternal circulation of pre-eclamptic subjects. A similar downward trend was noted in cord blood, although this narrowly failed to achieve statistical significance. This reduction aligns with current understanding of PP13's role in normal placentation and corroborates previous investigations indicating altered PP13 expression in pre-eclamptic placentas. While more pronounced differences in palladin and supervillin concentrations were anticipated given their theoretical relevance to trophoblast invasion and placental development, the data revealed only non-significant trends toward decreased levels in pre-eclamptic pregnancies. This suggests that alterations in cytoskeletal organization and cellular migration processes might be more subtle than hypothesized, or perhaps these proteins remain relatively preserved in late-onset pre-eclampsia compared to early-onset variants where placental dysfunction is typically more severe. The analysis of maternal-to-cord blood concentration ratios yielded particularly noteworthy insights that would not have been apparent from examining absolute concentrations alone. Both ORAI1 and FGF23 exhibited significantly altered ratios in pre-eclamptic pregnancies compared to controls, suggesting disrupted placental transport mechanisms or differential protein metabolism in maternal and fetal compartments. These altered gradients appear to reflect the compromised placental barrier function characteristic of pre-eclampsia. The consistency with which these patterns emerged across our sample set strongly suggests they represent meaningful physiological signals rather than statistical artifacts. The observation that multivariate analysis revealed a significant enhancement in diagnostic accuracy when combining ORAI1 and FGF23, beyond that achieved with either protein individually, further substantiates this interpretation. This finding underscores the value of a multi-marker approach in capturing the multifaceted pathophysiology of pre-eclampsia, with each protein appearing to reflect distinct pathophysiological pathways—ORAI1 representing calcium signaling and vascular reactivity, while FGF23 reflects mineral metabolism and endothelial function. The clinical significance of these findings is substantiated by the consistent correlation patterns observed between ORAI1 and FGF23 and key clinical parameters. These proteins demonstrated robust associations with indicators of disease severity and pregnancy outcomes, specifically in subjects with pre-eclampsia, while showing no significant correlations in normotensive pregnancies. This distinct pattern suggests that these protein alterations represent intrinsic components of pre-eclampsia pathophysiology rather than nonspecific consequences of pregnancy complications. Considering these observations holistically, our findings support a pathophysiological model wherein disrupted calcium signaling pathways and altered phosphate metabolism contribute significantly to the vascular dysfunction and placental insufficiency that characterize late-onset pre-eclampsia. The presence of these distinct protein signatures in both maternal and fetal circulations underscores the systemic nature of pre-eclampsia while highlighting their potential utility as biomarkers for clinical detection and monitoring. These results also suggest intriguing therapeutic possibilities that warrant further investigation in subsequent research. Should future studies confirm a causative role for dysregulated ORAI1-mediated calcium signaling in pre-eclampsia pathogenesis, targeted modulation of this pathway may offer promising novel treatment strategies. Similarly, the potential beneficial effects of FGF23 supplementation on vascular and placental function in pre-eclamptic conditions represents a compelling avenue for investigation, though such therapeutic applications would necessitate extensive preclinical and clinical evaluation. ORAI1 functions as a critical calcium release-activated calcium channel protein in the plasma membrane, mediating store-operated calcium entry in trophoblasts and vascular smooth muscle cells, regulating essential functions including proliferation, migration, and secretion that frequently show alterations in pre-eclamptic conditions ( 22 ). Our study demonstrated significantly elevated ORAI1 levels in maternal blood of pre-eclamptic women compared to controls, with higher concentrations in the severe pre-eclampsia subgroup. Similarly, cord blood ORAI1 was significantly increased in pre-eclamptic pregnancies. ORAI1 showed significant positive correlations with blood pressure parameters and markers of end-organ damage, while negatively correlating with birth weight and gestational age at delivery. Recent research challenges minimal placental expression of ORAI1 suggested by Protein Atlas data ( 23 ), with Wang et al. ( 11 ) demonstrating that pregnancy-specific beta-1-glycoprotein 1 (PSG1) upregulates ORAI1 in HTR-8/SVneo cells, enhancing trophoblast migration through Akt pathway activation, a significant finding as PSG1 levels decrease in early-onset pre-eclampsia. Similarly, Qin et al. ( 24 ) reported PSG9 increases ORAI1 expression in HUVECs, promoting SOCE and enhancing eNOS activity. Studies have linked dysregulated ORAI1-mediated calcium signaling to endothelial dysfunction and abnormal vascular reactivity, hallmark features of pre-eclampsia that might serve as therapeutic targets ( 25 , 26 ). Unlike TRPV6, which primarily facilitates calcium influx in syncytiotrophoblasts for transplacental transport ( 27 , 28 ), ORAI1 appears to serve specialized functions in extravillous trophoblasts and vascular cells ( 29 , 30 ), with its regulation by pregnancy-specific glycoproteins suggesting potential therapeutic applications in pregnancy complications characterized by placental insufficiency ( 31 , 32 ). ROC analysis identified maternal ORAI1 as having the highest diagnostic accuracy among all proteins studied, with an optimal cut-off value providing good sensitivity and specificity for late-onset pre-eclampsia diagnosis, highlighting its potential as a clinically relevant biomarker.Given its varied roles in placental development, trophoblast invasion, and maternal vascular adaptation, ORAI1 emerges as a compelling candidate for investigation in late-onset pre-eclampsia, particularly concerning its potential differential expression between maternal and fetal circulations ( 33 , 34 ). Recent research has identified FGF23, traditionally known for its role in phosphate and vitamin D metabolism, as a key regulator of endothelial integrity, inflammatory signaling, and placental vascular function—pathways central to pre-eclampsia pathophysiology ( 12 , 13 , 35 ). In our study, maternal and cord blood FGF23 concentrations were significantly reduced in women with late-onset pre-eclampsia, particularly among those with severe disease. Lower FGF23 levels showed inverse correlations with systolic and diastolic blood pressures and markers of end-organ injury (AST and LDH), and positive correlations with gestational age and birth weight. These associations contrast with findings in non-pregnant populations, where elevated FGF23 is linked to vascular dysfunction and adverse cardiovascular outcomes ( 36 , 37 ). We also observed significantly decreased maternal-to-cord blood FGF23 ratios in the pre-eclampsia group, suggesting altered placental transfer dynamics or impaired fetal production. Animal studies have shown that FGF23 and its co-receptor Klotho are essential for placental angiogenesis and fetal growth, supporting a mechanistic role for FGF23 in pregnancy ( 13 ). Additionally, disruption in FGF23 signaling may contribute to maternal vitamin D deficiency, an established pre-eclampsia risk factor ( 38 , 39 ). Given its consistent correlations with clinical severity and moderate diagnostic performance, FGF23 may serve as a physiologically relevant biomarker reflecting both maternal vascular dysfunction and compromised placental health in pre-eclampsia. Future longitudinal studies are warranted to determine its predictive value ( 2 , 31 ). PP13, a galectin family member predominantly expressed by syncytiotrophoblast, plays crucial immunomodulatory roles at the maternal-fetal interface. Through interactions with extracellular matrix proteins and regulation of maternal immune tolerance to the semi-allogeneic fetus, PP13 contributes significantly to normal placentation processes ( 15 , 40 – 42 ). Our study found significantly lower PP13 concentrations in maternal blood of pre-eclamptic women compared to controls, while cord blood showed a similar downward trend that approached but did not reach statistical significance. Maternal PP13 demonstrated significant negative correlations with systolic blood pressure and markers of end-organ damage, particularly LDH. ROC analysis revealed maternal PP13 as having the second-highest diagnostic accuracy among all proteins studied, with an optimal cut-off value providing good sensitivity and specificity for diagnosing late-onset pre-eclampsia. Recent studies revealed abnormal PP13 expression and secretion patterns in pre-eclamptic placentas and maternal circulation throughout gestation, with notable alterations occurring before clinical symptoms manifest, there are findings that highlight its potential as an early predictive biomarker ( 14 , 43 , 44 ). PP13 exerts its effects primarily through inducing vasodilation and vascular remodeling, promoting immune tolerance at the maternal-fetal interface, and regulating trophoblast invasion, all processes known to be dysregulated in pre-eclampsia ( 45 – 48 ). The intricate involvement of PP13 in trophoblast invasion, spiral artery remodeling, and maternal immune adaptation provides strong justification for examining its concentration in both maternal and cord blood to better understand its pathophysiological significance in late-onset pre-eclampsia. Palladin serves as a cytoskeletal protein essential for maintaining cell morphology and facilitating migration through its organization of actin filaments. Its crucial roles in embryonic development, wound healing, and tissue remodeling appear particularly relevant to placental development and the extensive remodeling required for successful placentation ( 17 , 49 ). Our study found a non-significant trend toward decreased palladin concentrations in both maternal and cord blood of pre-eclamptic women compared to controls. Although these differences did not reach statistical significance, the consistent downward trend in both compartments suggests potential alterations in cytoskeletal organization processes. Altered palladin expression has been observed in various pathological conditions characterized by abnormal cell migration and invasiveness, including several cancers, suggesting similar alterations might occur in pre-eclamptic placentas where trophoblast invasion is frequently compromised ( 18 , 19 ). Palladin influences cell-matrix interactions and focal adhesion dynamics, processes critical for proper trophoblast infiltration into maternal decidua and spiral artery remodeling ( 50 , 51 ). Investigating palladin in the context of pre-eclampsia represents a novel approach; despite its established functions in cellular processes known to be dysregulated in pre-eclamptic pregnancies, its potential roles in trophoblast function, placental development, and vascular remodeling remain largely unexplored. Supervillin, a membrane-associated actin-binding protein, regulates cell adhesion, cytoskeletal organization, and contractility in various cells, including trophoblasts and endothelial cells. By mediating interactions between the plasma membrane and cytoskeleton, supervillin potentially influences migratory and invasive capacities during placentation—processes frequently disrupted in pre-eclampsia ( 20 , 52 ). Our study found a non-significant trend toward decreased supervillin concentrations in both maternal and cord blood of pre-eclamptic women compared to controls. While these differences did not reach statistical significance, the consistent downward pattern in both compartments suggests potential alterations in membrane-cytoskeleton interactions that warrant further investigation. Recent findings suggest that supervillin participates in angiogenesis and vascular remodeling through its effects on endothelial cell function and interactions with extracellular matrix components, representing mechanisms through which it might contribute to pre-eclampsia-related vascular manifestations ( 53 , 54 ). The protein's involvement in regulating smooth muscle contractility further suggests a potential role in vascular tone regulation, which is frequently dysregulated in pre-eclampsia ( 55 ). Our examination of supervillin expression in maternal and cord blood constitutes pioneering work in this area; current literature lacks comprehensive assessment of this protein in normal and pathological pregnancies, despite its theoretical relevance to processes known to be altered in pre-eclampsia, such as trophoblast invasion and maternal vascular adaptation. Strengths and Limitations The study's primary strength lies in its simultaneous analysis of maternal and cord blood samples, providing unique insights into maternal-fetal interface dynamics in pre-eclampsia. This dual-compartment approach identified distinct protein signatures and evaluated placental transfer mechanisms. Analysis of maternal-to-cord blood concentration ratios revealed altered gradients for ORAI1 and FGF23 that would have remained undetected in single-compartment studies. The selection of biomarkers involved in diverse pathophysiological pathways captured the complex nature of pre-eclampsia. The case-control design with standardized sampling conditions minimized confounding factors, while appropriate statistical methods enhanced reliability. Detailed clinical characterization and severity-based subgroup analysis enabled meaningful correlations between protein levels and disease parameters. The cross-sectional design with delivery-time sampling precludes establishing causal relationships between protein alterations and pre-eclampsia development. The sample size, while statistically adequate for primary comparisons, limited detection of subtle differences and restricted complex analyses involving stratification by clinical characteristics. The exclusive focus on late-onset pre-eclampsia (≥ 34 weeks) limits generalizability to early-onset disease. Similarly, restricting participation to women undergoing cesarean delivery means findings may not fully represent all pre-eclampsia cases, particularly those requiring emergency intervention. 5. Conclusions This study identifies distinct alterations in protein levels within maternal and cord blood of women with late-onset pre-eclampsia, with ORAI1 showing significant elevation, while FGF23 and PP13 demonstrated marked reduction. Though palladin and supervillin did not reach statistical significance, their observed downward trends suggest potential involvement in pre-eclampsia pathophysiology that warrants further investigation with larger cohorts. These findings collectively suggest disruptions in calcium signaling, phosphate metabolism, and cytoskeletal organization as key components of pre-eclampsia pathophysiology. Diagnostic performance analysis revealed ORAI1 achieved the highest accuracy in maternal blood, while in cord blood, both ORAI1 and FGF23 demonstrated moderate discriminatory ability. The significant elevation of ORAI1 and reduction of FGF23 in both maternal and fetal circulations, coupled with their strong correlations with clinical parameters, highlight their potential as biomarkers for disease detection and severity assessment. Analysis of maternal-to-cord blood concentration ratios provides novel insights into altered protein transport across the placental barrier in pre-eclampsia, further emphasizing the importance of examining both compartments in biomarker research. The superior diagnostic performance of combined ORAI1 and FGF23 measurement compared to individual proteins underscores the value of a multi-marker approach in capturing the complex pathophysiology of pre-eclampsia. These findings support a model wherein pre-eclampsia involves systemic dysregulation of multiple pathways affecting both maternal and fetal compartments, with potential implications for vascular function and placental development. Future research should investigate the longitudinal changes in these proteins throughout pregnancy, explore their potential as early predictive biomarkers, and elucidate the mechanistic links between these protein alterations and pre-eclampsia pathogenesis. Furthermore, the therapeutic implications of these findings merit investigation, particularly regarding the potential modulation of ORAI1-mediated calcium signaling or FGF23 supplementation as novel treatment strategies in the management of late-onset pre-eclampsia. Abbreviations ORAI1 Calcium Release-Activated Calcium Channel Protein 1 FGF23 Fibroblast Growth Factor 23 PP13 Placental Protein 13 ROC Receiver operating characteristic AUC Area under the curve Declarations Ethical Approval This study was approved by the Human Research Ethics Committee of Haseki Training and Research Hospital (Approval Number: 110-2023, dated September 6, 2023; Istanbul, Turkey). Acknowledgments The authors would like to express their sincere gratitude to all participants who kindly agreed to take part in this study, as well as the staff of the Obstetrics Department of Haseki Training and Research Hospital for their valuable assistance and cooperation during data collection. We also thank the laboratory staff for their technical support in performing the biochemical analyses. Funding No funding was received for this study. Author Contributions Conceptualization: H.A.A., F.Y.G., A.C.; Methodology: H.A.A., S.T., I.Y.; Data collection: H.A.A., S.T., E.Y.G.; Data analysis: I.Y., F.Y.G.; Writing – original draft preparation: H.A.A., S.T.; Writing – review and editing: E.Y.G., F.Y.G., A.C.; Supervision: F.Y.G., A.C.; Project administration: A.C. All authors read and approved the final manuscript. Conflict of Interest The authors declare no conflict of interest. Informed Consent Written informed consent was obtained from all participants after detailed explanation of the study objectives, procedures, potential risks, and benefits. Participants were informed of their right to withdraw from the study at any time without affecting their standard medical care. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. AI Assistance Statement During the preparation of this manuscript, the authors utilized ChatGPT 4o (OpenAI) and Claude 3.7 Sonnet (Anthropic) to refine English language clarity and style. All AI-assisted content underwent thorough review and editing by the authors, who take full responsibility for the manuscript's final version. Clinical trial number Not applicable. References Karrar SA, Martingano DJ, Hong PL. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2025 Jan 1]. Preeclampsia. 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Predicting the Risk to Develop Preeclampsia in the First Trimester Combining Promoter Variant -98A/C of LGALS13 (Placental Protein 13), Black Ethnicity, Previous Preeclampsia, Obesity, and Maternal Age. Fetal Diagn Ther [Internet]. 2018 [cited 2025 Apr 1];43(4):250–65. Available from: https://karger.com/FDT/article/doi/10.1159/000477933 Balogh A, Toth E, Romero R, Parej K, Csala D, Szenasi NL, et al. Placental Galectins Are Key Players in Regulating the Maternal Adaptive Immune Response. Front Immunol [Internet]. 2019 Jun 19 [cited 2025 Apr 1];10:1240. Available from: https://www.frontiersin.org/article/10.3389/fimmu.2019.01240/full Gatto M, Esposito M, Morelli M, De Rose S, Gizurarson S, Meiri H, et al. Placental Protein 13: Vasomodulatory Effects on Human Uterine Arteries and Potential Implications for Preeclampsia. Int J Mol Sci [Internet]. 2024 Jul 9 [cited 2025 Apr 1];25(14):7522. Available from: https://www.mdpi.com/1422-0067/25/14/7522 Palalioglu RM, Erbiyik HI. Evaluation of maternal serum SERPINC1, E-selectin, P-selectin, RBP4 and PP13 levels in pregnancies complicated with preeclampsia. J Matern Fetal Neonatal Med [Internet]. 2023 Dec 31 [cited 2025 Apr 1];36(1):2183472. Available from: https://www.tandfonline.com/doi/full/10.1080/14767058.2023.2183472 Rybak-Krzyszkowska M, Staniczek J, Kondracka A, Bogusławska J, Kwiatkowski S, Góra T, et al. From Biomarkers to the Molecular Mechanism of Preeclampsia—A Comprehensive Literature Review. Int J Mol Sci [Internet]. 2023 Aug 26 [cited 2025 Apr 1];24(17):13252. Available from: https://www.mdpi.com/1422-0067/24/17/13252 Beck MR, Otey CA, Campbell SL. Structural Characterization of the Interactions between Palladin and α-Actinin. J Mol Biol [Internet]. 2011 Oct [cited 2025 Apr 1];413(3):712–25. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022283611009892 Mykkänen OM, Grönholm M, Rönty M, Lalowski M, Salmikangas P, Suila H, et al. Characterization of Human Palladin, a Microfilament-associated Protein. Salmon T, editor. Mol Biol Cell [Internet]. 2001 Oct [cited 2025 Apr 1];12(10):3060–73. Available from: https://www.molbiolcell.org/doi/10.1091/mbc.12.10.3060 Parast MM, Otey CA. Characterization of Palladin, a Novel Protein Localized to Stress Fibers and Cell Adhesions. J Cell Biol [Internet]. 2000 Aug 7 [cited 2025 Apr 1];150(3):643–56. Available from: https://rupress.org/jcb/article/150/3/643/54049/Characterization-of-Palladin-a-Novel-Protein Smith TC, Fang Z, Luna EJ. Novel interactors and a role for supervillin in early cytokinesis. Cytoskeleton [Internet]. 2010 Jun [cited 2025 Apr 1];67(6):346–64. Available from: https://onlinelibrary.wiley.com/doi/10.1002/cm.20449 Crowley JL, Smith TC, Fang Z, Takizawa N, Luna EJ. Supervillin Reorganizes the Actin Cytoskeleton and Increases Invadopodial Efficiency. Brugge J, editor. Mol Biol Cell [Internet]. 2009 Feb [cited 2025 Apr 1];20(3):948–62. Available from: https://www.molbiolcell.org/doi/10.1091/mbc.e08-08-0867 Fedechkin SO, Brockerman J, Luna EJ, Lobanov MYu, Galzitskaya OV, Smirnov SL. An N-terminal, 830 residues intrinsically disordered region of the cytoskeleton-regulatory protein supervillin contains Myosin II- and F-actin-binding sites. J Biomol Struct Dyn [Internet]. 2013 Oct [cited 2025 Apr 1];31(10):1150–9. Available from: http://www.tandfonline.com/doi/abs/10.1080/07391102.2012.726531 Bhuwania R, Cornfine S, Fang Z, Krüger M, Luna EJ, Linder S. Supervillin couples myosin-dependent contractility to podosomes and enables their turnover. J Cell Sci [Internet]. 2012 Jan 1 [cited 2025 Apr 1];jcs.100032. Available from: https://journals.biologists.com/jcs/article/doi/10.1242/jcs.100032/263170/Supervillin-couples-myosin-dependent-contractility Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 30 May, 2025 Reviewers agreed at journal 30 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor invited by journal 14 Apr, 2025 Editor assigned by journal 11 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6416476","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447601683,"identity":"f37f5663-7234-450e-a4aa-9659494e2792","order_by":0,"name":"Hatice Argun Atalmis","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hatice","middleName":"Argun","lastName":"Atalmis","suffix":""},{"id":447601684,"identity":"810c6caa-8c5d-4a9a-b131-658d1638978c","order_by":1,"name":"Sinem Tekin","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sinem","middleName":"","lastName":"Tekin","suffix":""},{"id":447601685,"identity":"493774a8-ea14-402f-a03a-cd5f8d9a62dc","order_by":2,"name":"Ibrahim Yilmaz","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Yilmaz","suffix":""},{"id":447601686,"identity":"73198ccf-733d-4a08-bec0-ab4133443041","order_by":3,"name":"Emine Yilmaz Guler","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Emine","middleName":"Yilmaz","lastName":"Guler","suffix":""},{"id":447601687,"identity":"414ecb86-c0a7-4515-a445-0c998f86b2eb","order_by":4,"name":"Filiz Yarsilikal Guleroglu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBAC9mbmhgNgFjNzw8EGBgs5EPvAAzxaeA4zwrQwgrRIGIO1JODTcoCxAcpkbAAyJRLBXLxa2BkbD/5ss8uXbwcyZtRIpM8PO/wQaIudnG4DDi1A9xyQbEu23AB04cENxyRyN95OMwBqSTY2O4Bdiz1Ii2Ebs4EByC8P2IBaZieAtBxI3IZDC9iWxLZ6A/lmkJZ/EumGs9M/ENZysO2wAQPIYRvbJBLkpXMI23Kw4dxxAwOQlpl9EoYbpHMKDiQY4PYLD//hwx9/lFUbyPcDGT3fbOTlZ6dv/vChwk4OlxYwYGRD4hiAVRrgUQ4Gf5DY8g2EVI+CUTAKRsFIAwBIG2eTZmly+gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Filiz","middleName":"Yarsilikal","lastName":"Guleroglu","suffix":""},{"id":447601688,"identity":"485a9670-1678-4fbf-8f7a-6327ccf9b37a","order_by":5,"name":"Ali Cetin","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Cetin","suffix":""}],"badges":[],"createdAt":"2025-04-10 05:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6416476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6416476/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-025-07890-9","type":"published","date":"2025-07-24T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87756659,"identity":"cbc6ba5a-d05a-4800-bddd-29e55f215368","added_by":"auto","created_at":"2025-07-28 16:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1535644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6416476/v1/47c30d07-c890-45aa-a510-785b408f9680.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ORAI1, FGF23, PP13, Palladin, and Supervillin as Potential Biomarkers in Late-Onset Pre- eclampsia: A Comparative Study in Maternal and Cord Blood Running Title: Potential Biomarkers in Late-Onset Pre-eclampsia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePre-eclampsia is a pregnancy-specific multisystemic disorder characterized by de novo hypertension and proteinuria or evidence of end-organ dysfunction occurring after 20 weeks of gestation. This condition remains a significant global health burden, constituting one of the leading causes of maternal and perinatal morbidity and mortality worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Clinically, pre-eclampsia is stratified into two principal phenotypes based on gestational age at onset: early-onset (\u0026lt;\u0026thinsp;34 weeks) and late-onset (\u0026ge;\u0026thinsp;34 weeks) (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Early-onset pre-eclampsia typically presents severe clinical manifestations, substantial placental insufficiency, impaired trophoblast invasion, and significant fetal growth restriction, often causing preterm delivery and intensive maternal-fetal intervention. Conversely, late-onset pre-eclampsia, despite its generally milder clinical presentation, exhibits substantially higher prevalence, thus accounting for most cases globally. Late-onset pre-eclampsia is characterized by relatively preserved placental morphology, less pronounced fetal growth impairment, and distinct maternal cardiovascular adaptations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The pathophysiology of late-onset pre-eclampsia appears to involve predominantly maternal constitutional factors, including metabolic dysregulation, endothelial dysfunction, and systemic inflammatory responses, distinguishing it from the primarily placenta-mediated pathogenesis seen in early-onset disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Consequently, investigating the specific pathophysiological mechanisms of late-onset pre-eclampsia is essential for developing targeted preventive strategies, enhanced diagnostic methodologies, and personalized therapeutic interventions specifically addressing this clinically distinct entity.\u003c/p\u003e \u003cp\u003eConsidering incompletely elucidated etiopathogenesis of pre-eclampsia, prompting extensive research across various temporal phases of disease development is one of the research topic in perinatology. Contemporary investigations employ diverse methodological approaches targeting specific clinical objectives such as prediction in early gestation, accurate diagnosis during clinical presentation, and longitudinal monitoring of disease progression. Early gestational research primarily focuses on identifying predictive biomarkers that could ease timely preventive interventions, while later-stage studies emphasize diagnostic precision and effective disease surveillance. These investigations utilize sophisticated techniques spanning biochemical analyses, molecular biology methodologies, proteomic profiling, and advanced imaging modalities (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Proteins represent particularly valuable biomarkers in pre-eclampsia research, as alterations in their expression, secretion, and functional activity directly reflect the underlying pathophysiological processes. Numerous proteins involved in angiogenesis, inflammation, endothelial function, oxidative stress regulation, and placental development have been extensively investigated to elucidate their roles in disease pathogenesis. Despite significant advancements, the complexity of pre-eclampsia necessitates examination of diverse proteins across different gestational timepoints (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Consequently, multiple protein candidates with potential roles in pre-eclampsia pathophysiology remain inadequately characterized regarding their predictive, diagnostic, or prognostic utility, underscoring the continued need for comprehensive and targeted investigative approaches. For this purpose, we performed a search to find proteins with potential involvement in pre-eclampsia pathophysiology, focusing on their expression patterns in maternal and cord blood samples. Through comprehensive literature review, we identified five proteins of particular interest based on their biological functions and potential relevance to pre-eclampsia pathogenesis: calcium release-activated calcium channel protein 1 (ORAI1) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), fibroblast growth factor 23 (FGF23) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), placental protein 13 (PP13) (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), palladin (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and supervillin (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Each protein represents a distinct cellular pathway potentially contributing to the complex pathophysiology of pre-eclampsia, with varying degrees of previous investigation in the context of pregnancy complications.\u003c/p\u003e \u003cp\u003eDespite substantial advances in understanding the pathophysiological mechanisms of pre-eclampsia, current predictive and diagnostic approaches remain suboptimal. Although numerous studies have consistently identified diverse biochemical alterations across various disease mechanisms, the clinical utility of these potential biomarkers is limited by an incomplete understanding of the precise initiating events of pre-eclampsia, the primary factors sustaining disease progression, and the secondary biochemical and molecular changes emerging throughout its clinical course. This knowledge gap in the temporal sequence and causative relationships among pathophysiological events significantly impedes the development of sensitive, specific, and clinically useful biomarkers. There is a critical need for improved predictive and diagnostic tests that can effectively determine at-risk patients and monitor disease progression. Advancing this field requires deeper mechanistic insights and comprehensive longitudinal investigations to ease more precise biomarker discovery, ultimately enhancing clinical management and improving maternal and fetal outcomes. Our selection of ORAI1, FGF23, PP13, palladin, and supervillin was guided by their biological plausibility and involvement in pathways potentially disrupted in pre-eclampsia. These proteins were chosen based on their established or theoretical roles in calcium signaling, vascular function, placental development, and cellular migration\u0026mdash;processes fundamental to normal pregnancy and frequently dysregulated in pre-eclamptic conditions. The primary aim of this cross-sectional, case-control study was to investigate the differential expression of these five proteins in maternal and cord blood samples from women with late-onset pre-eclampsia compared to normotensive pregnant women. We hypothesized that: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) protein concentrations would differ significantly between pre-eclamptic and healthy pregnant women in both maternal and cord blood; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) maternal-fetal concentration gradients would show distinct patterns in pre-eclamptic versus healthy pregnancies; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) correlations would exist between protein expression patterns and clinical parameters of disease severity. The expected outcomes of this research include the identification of potentially novel biomarkers for late-onset pre-eclampsia, improved understanding of maternal-fetal protein transfer in pre-eclamptic conditions, and new insights into disease pathophysiology. These findings could ultimately contribute to the development of more effective diagnostic tools and targeted therapeutic strategies for this significant obstetric complication, potentially improving maternal and neonatal outcomes through earlier detection and more personalized management approaches.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis investigation was designed as a cross-sectional, case-control study to evaluate the differential expression of specific protein biomarkers in maternal and cord blood samples from women with late-onset pre-eclampsia compared to normotensive pregnant women. The study focused exclusively on patients undergoing cesarean delivery to standardize the timing and conditions of sample collection, thereby minimizing potential confounding variables related to labor and vaginal delivery. The study protocol received approval from the Human Research Ethics Committee of Haseki Training and Research Hospital (approval number: 110\u0026ndash;2023, dated September 6, 2023) affiliated with the University of Health Sciences, and all procedures were conducted in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments. Prior to enrollment, comprehensive written informed consent was obtained from all participants after detailed explanation of the study objectives, procedures, potential risks, and benefits. Participants were informed of their right to withdraw from the study at any time without affecting their standard medical care. The study was conducted between October 2023 and September 2024 at the Department of Obstetrics and Gynecology of our institution. All personal identifiers were removed from collected samples and clinical data before analysis to ensure participant confidentiality. The study adhered to STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies to maintain the quality of methodological and reporting standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Population\u003c/h2\u003e \u003cp\u003eThe study population comprised pregnant women who underwent planned or emergency cesarean delivery at our institution during the study period. Participants were recruited using a consecutive sampling approach and categorized into two groups: women with late-onset pre-eclampsia (case group) and normotensive pregnant women (control group). For the case group, we included women with singleton pregnancies diagnosed with late-onset pre-eclampsia (\u0026ge;\u0026thinsp;34 weeks of gestation) according to the American College of Obstetricians and Gynecologists (ACOG) criteria (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This included new-onset hypertension (systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg on two occasions at least 4 hours apart) and either proteinuria or evidence of end-organ damage after 20 weeks of gestation. Proteinuria was defined as \u0026ge;\u0026thinsp;300 mg in 24-hour urine collection, protein/creatinine ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.3 in a random urine sample, or dipstick reading of \u0026ge;\u0026thinsp;1+ (30 mg/dL). All pre-eclampsia diagnoses were confirmed by maternal-fetal medicine specialists following standardized diagnostic protocols. The control group consisted of women with singleton pregnancies undergoing cesarean delivery for obstetric indications unrelated to hypertensive disorders (e.g., previous cesarean section, breech presentation, or maternal request). These women had no history of hypertension before or during pregnancy and maintained normal blood pressure measurements throughout gestation. We excluded women with multiple pregnancies, chronic hypertension, gestational hypertension without pre-eclampsia features, early-onset pre-eclampsia (\u0026lt;\u0026thinsp;34 weeks), HELLP syndrome, preexisting renal disease, cardiovascular disorders, autoimmune diseases, diabetes mellitus (pregestational or gestational), fetal congenital anomalies, intrauterine growth restriction without pre-eclampsia, placental abnormalities (e.g., placenta previa or placenta accreta spectrum), and those receiving medications that could affect cardiovascular function or protein expression. The sample size was calculated using G*Power software (version 3.1) based on anticipated differences in protein concentrations between groups from previous similar biomarker studies. Assuming a moderate effect size (Cohen's d\u0026thinsp;=\u0026thinsp;0.55), alpha\u0026thinsp;=\u0026thinsp;0.05, and power\u0026thinsp;=\u0026thinsp;0.8, we determined that 53 subjects per group would be required. Accounting for potential sample exclusions due to technical issues, we aimed to recruit 61 participants per group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Clinical Assessments\u003c/h2\u003e \u003cp\u003eAll participants underwent comprehensive clinical assessment following standardized protocols established at our institution. Demographic data and detailed medical histories were collected through structured interviews and medical record reviews at enrollment, including maternal age (years), ethnicity, educational level, pre-pregnancy body mass index (BMI, kg/m\u0026sup2;), and gestational weight gain (kg). Obstetric and medical history documentation encompassed gravidity, parity, history of gestational diabetes, previous hypertensive disorders of pregnancy, smoking status, and whether the current pregnancy resulted from assisted reproductive technology (ART). For women in both groups, blood pressure measurements were performed according to the American Heart Association guidelines using calibrated automated devices with appropriate cuff sizes, with measurements taken after a minimum 10-minute rest period with the patient seated, back supported, arm at heart level, and feet flat on the floor; two readings at least 5 minutes apart were recorded, and the average was calculated. Medications were systematically recorded for all participants, with special attention to antihypertensive agents and other medications potentially affecting cardiovascular function. Obstetric ultrasound was performed for all participants within one week before delivery to assess fetal biometry, amniotic fluid volume, and placental characteristics, with Doppler assessment of umbilical artery and middle cerebral artery conducted for patients with suspected fetal compromise.\u003c/p\u003e \u003cp\u003eLaboratory assessments included comprehensive hematological parameters (hemoglobin, hematocrit, platelet count), renal function markers (serum creatinine), liver enzymes (AST, ALT), lactate dehydrogenase (LDH), and detailed lipid profiles (triglycerides, HDL-cholesterol, LDL-cholesterol); for proteinuria evaluation, we collected spot urine samples and categorized results based on protein concentration, with additional tests for pre-eclamptic patients including assessment of other markers of end-organ dysfunction when clinically warranted. The severity of pre-eclampsia was classified according to ACOG criteria, with severe features including systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;160 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;110 mmHg, thrombocytopenia (platelet count\u0026thinsp;\u0026lt;\u0026thinsp;100,000/\u0026micro;L), impaired liver function (elevated AST/ALT), progressive renal insufficiency, pulmonary edema, or new-onset cerebral or visual disturbances. Neonatal outcomes were systematically documented, including gestational age at delivery (weeks), fetal gender, birth weight (grams), Apgar scores at 1 and 5 minutes, umbilical cord arterial pH, and neonatal intensive care unit (NICU) admission status. For the protein analysis component of our study, maternal blood samples were collected immediately prior to cesarean delivery, and umbilical cord blood samples were obtained immediately after delivery of the placenta but prior to placental detachment from the uterine wall, with all samples processed according to standardized protocols to ensure consistency and reliability of the subsequent laboratory analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Laboratory Analyses\u003c/h2\u003e \u003cp\u003eMeasurement of study proteins was performed using commercially available enzyme-linked immunosorbent assay (ELISA) kits (BT-Lab, China) according to manufacturers' protocols. All assays were conducted by laboratory personnel blinded to the clinical status of the participants. Samples were analyzed duplicate, and the mean value was used for statistical analyses. Inter- and intra-assay coefficients of variation were maintained below 10% for all assays through strict adherence to standardized procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.4.3 (R Core Team, 2025), utilizing only the base packages included in the standard R distribution without any additional external packages. Prior to analysis, data were assessed for normality using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. Data transformations (logarithmic or square root) were applied when necessary to achieve normal distribution for parametric testing. Demographic and clinical characteristics were compared between pre-eclamptic and control groups using appropriate statistical tests. Continuous variables with normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using the independent samples t-test. Non-normally distributed continuous variables were presented as median (interquartile range) and analyzed using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, with between-group comparisons performed using the chi-square test or Fisher's exact test as appropriate. Protein concentrations in maternal and cord blood samples were analyzed both as continuous variables and after categorization using clinically relevant cut-points or quartiles based on the distribution in control subjects. Between-group differences in protein levels were assessed using independent samples t-test or Mann-Whitney U test, depending on data distribution. Paired analyses comparing maternal and cord blood concentrations within the same subject were conducted using paired t-tests or Wilcoxon signed-rank tests. For each protein, maternal-fetal concentration ratios were calculated and compared between pre-eclamptic and normotensive pregnancies. Correlation analyses between protein levels and clinical parameters were performed using Pearson's or Spearman's correlation coefficients as appropriate. Multiple linear regression models were constructed to identify independent determinants of protein concentrations, adjusting for potential confounding variables identified in univariate analyses or based on biological plausibility. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the discriminative ability of each protein for pre-eclampsia. Area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and optimal cut-off points were calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eBaseline Characteristics of Study Population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInitially, 151 pregnant women were considered for enrollment in this study. However, 16 women from the control group were excluded due to: incomplete clinical data (n\u0026thinsp;=\u0026thinsp;5), inadequate blood sample volume for complete analysis (n\u0026thinsp;=\u0026thinsp;4), withdrawal of consent (n\u0026thinsp;=\u0026thinsp;3), development of gestational hypertension after enrollment (n\u0026thinsp;=\u0026thinsp;2), and protocol deviations during sample collection (n\u0026thinsp;=\u0026thinsp;2). Additionally, 13 women from the pre-eclampsia group were excluded due to: concomitant gestational diabetes (n\u0026thinsp;=\u0026thinsp;4), multiple gestation identified after enrollment (n\u0026thinsp;=\u0026thinsp;3), incomplete laboratory analyses (n\u0026thinsp;=\u0026thinsp;3), delivery via vaginal route despite planned cesarean (n\u0026thinsp;=\u0026thinsp;2), and withdrawal from the study (n\u0026thinsp;=\u0026thinsp;1). Consequently, a total of 122 women were included in the final analysis, comprising 61 women with late-onset pre-eclampsia (case group) and 61 normotensive pregnant women (control group).\u003c/p\u003e \u003cp\u003eData normality was assessed using the Shapiro-Wilk test, which indicated non-normal distribution for all protein measurements and several clinical parameters. Accordingly, non-parametric statistical methods were employed for these variables, while parametric tests were used for normally distributed variables.\u003c/p\u003e \u003cp\u003eThe pre-eclampsia group had significantly higher pre-pregnancy BMI compared to the control group (median 25.6 vs. 23.4 kg/m\u0026sup2;, Z=-2.97, p\u0026thinsp;=\u0026thinsp;0.003). A higher proportion of nulliparous women (72.1% vs. 47.5%, p\u0026thinsp;=\u0026thinsp;0.005) and primigravidous women (63.9% vs. 39.3%, p\u0026thinsp;=\u0026thinsp;0.012) was observed in the pre-eclampsia group. The frequency of previous hypertensive disorders of pregnancy was also higher in the pre-eclampsia group (18.0% vs. 6.6%, p\u0026thinsp;=\u0026thinsp;0.049). No significant differences were found in other demographic and clinical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.4 (21.2\u0026ndash;25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6 (22.8\u0026ndash;29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational weight gain (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (Native), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of gestational diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertensive disorders of pregnancy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eART pregnancy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed variables\u003c/em\u003e, median (interquartile range) for non-normally distributed variables, and as number (percentage) for categorical variables. P-values calculated using independent samples t-test for normally distributed variables, Mann-Whitney U test (with corresponding Z-values) for non-normally distributed variables, and chi-square or Fisher's exact test for categorical variables. BMI: body mass index; ART: assisted reproductive technology. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups. Dash (-) indicates Z-value not applicable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical and Laboratory Parameters\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSystolic and diastolic blood pressure measurements were significantly higher in the pre-eclampsia group (median 154.0 vs. 116.0 mmHg, Z=-9.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and median 98.0 vs. 74.0 mmHg, Z=-9.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Laboratory parameters showed significant differences between groups, with the pre-eclampsia group demonstrating reduced hemoglobin (median 11.1 vs. 11.7 g/dL, Z=-2.61, p\u0026thinsp;=\u0026thinsp;0.009), hematocrit (median 33.5 vs. 35.2%, Z=-2.41, p\u0026thinsp;=\u0026thinsp;0.016), and platelet count (median 209.0 vs. 243.0 \u0026times;10⁹/L, Z=-2.91, p\u0026thinsp;=\u0026thinsp;0.004). All renal and liver function parameters differed significantly between the groups, with the pre-eclampsia group showing higher levels of serum creatinine (median 0.70 vs. 0.59 mg/dL, Z=-4.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AST (median 28.5 vs. 18.0 U/L, Z=-5.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ALT (median 24.0 vs. 13.0 U/L, Z=-5.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and LDH (median 248.0 vs. 192.0 U/L, Z=-6.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The lipid profile showed higher triglycerides (median 274.0 vs. 245.0 mg/dL, Z=-2.69, p\u0026thinsp;=\u0026thinsp;0.007) and LDL-cholesterol (median 159.0 vs. 146.0 mg/dL, Z=-2.08, p\u0026thinsp;=\u0026thinsp;0.038) and lower HDL-cholesterol (median 57.0 vs. 64.0 mg/dL, Z=-2.71, p\u0026thinsp;=\u0026thinsp;0.007) in the pre-eclampsia group. Among women with pre-eclampsia, 22 (36.1%) presented with severe features according to ACOG criteria (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eClinical and laboratory parameters of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.0 (110.0-122.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154.0 (145.0-165.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.0 (68.0\u0026ndash;78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.0 (92.0-104.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematological parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7 (11.0-12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1 (10.2\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2 (33.1\u0026ndash;37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.5 (31.2\u0026ndash;36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243.0 (209.0-286.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209.0 (168.0-262.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal and liver function parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpot urine protein, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;+\u0026thinsp;or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.51\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.59\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0 (15.0\u0026ndash;23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 (20.0\u0026ndash;38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 (10.0\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (16.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192.0 (172.0-214.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248.0 (209.0-308.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid profile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.0 (206.0-285.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274.0 (230.0-324.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-cholesterol (mg/dL)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0 (56.0\u0026ndash;74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.0 (48.0\u0026ndash;68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-cholesterol (mg/dL)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.0 (125.0-172.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.0 (134.0-188.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-eclampsia severity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout severe features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith severe features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as median (interquartile range) for continuous variables (\u0026dagger;) and as number (percentage) for categorical variables. P-values calculated using Mann-Whitney U test (with corresponding Z-values) for continuous variables and chi-square or Fisher's exact test for categorical variables. BP: blood pressure; AST: aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; HDL: high-density lipoprotein; LDL: low-density lipoprotein; U/L: units per liter; mg/dL: milligrams per deciliter. Pre-eclampsia severity classified according to ACOG (American College of Obstetricians and Gynecologists) criteria. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups. Dash (-) indicates Z-value not applicable for categorical variables analyzed using chi-square or Fisher's exact test.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein Concentrations in Maternal Blood\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll protein measurements demonstrated non-normal distribution patterns (Shapiro-Wilk, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and were therefore analyzed using non-parametric statistical methods. In maternal blood, ORAI1 concentration was significantly higher in the pre-eclampsia group compared to the control group (median 9.50 vs. 6.62 ng/mL, Z=-3.20, p\u0026thinsp;=\u0026thinsp;0.001). Conversely, FGF23 and PP13 levels were significantly lower in the pre-eclampsia group (FGF23: median 177.80 vs. 240.88 pg/mL, Z=-2.29, p\u0026thinsp;=\u0026thinsp;0.022; PP13: median 215.10 vs. 264.12 pg/mL, Z=-2.36, p\u0026thinsp;=\u0026thinsp;0.018). No significant differences were observed in maternal palladin (median 5.53 vs. 6.88 ng/mL, Z=-1.74, p\u0026thinsp;=\u0026thinsp;0.083) or supervillin concentrations (median 434.54 vs. 494.20 ng/mL, Z=-1.25, p\u0026thinsp;=\u0026thinsp;0.212), although palladin showed a trend towards lower values in the pre-eclampsia group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen comparing women with severe pre-eclampsia features (n\u0026thinsp;=\u0026thinsp;22) to those with non-severe pre-eclampsia (n\u0026thinsp;=\u0026thinsp;39), maternal ORAI1 concentrations were significantly higher in the severe pre-eclampsia subgroup (median 11.24 vs. 8.46 ng/mL, Z=-2.63, p\u0026thinsp;=\u0026thinsp;0.009), while FGF23 was significantly lower (median 153.42 vs. 191.65 pg/mL, Z=-2.19, p\u0026thinsp;=\u0026thinsp;0.028). No significant differences were observed in maternal PP13, palladin, or supervillin levels between the severe and non-severe pre-eclampsia subgroups.\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\u003eProtein concentrations in maternal blood of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.62 (5.18\u0026ndash;8.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.50 (7.34\u0026ndash;11.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240.88 (198.56-279.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177.80 (145.32-216.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264.12 (221.67-302.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215.10 (175.84-258.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.88 (5.41\u0026ndash;8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.53 (4.26\u0026ndash;7.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e494.20 (383.75-598.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e434.54 (346.82-568.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein Concentrations in Cord Blood\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn cord blood, ORAI1 concentration was significantly higher in the pre-eclampsia group compared to the control group (median 9.87 vs. 7.77 ng/mL, Z=-2.11, p\u0026thinsp;=\u0026thinsp;0.035), while FGF23 levels were significantly lower (median 202.25 vs. 233.55 pg/mL, Z=-2.09, p\u0026thinsp;=\u0026thinsp;0.036). No significant differences were observed in cord blood PP13 (median 244.52 vs. 256.38 pg/mL, Z=-1.76, p\u0026thinsp;=\u0026thinsp;0.078), palladin (median 3.17 vs. 3.99 ng/mL, Z=-1.26, p\u0026thinsp;=\u0026thinsp;0.207), or supervillin concentrations (median 443.78 vs. 511.90 ng/mL, Z=-1.71, p\u0026thinsp;=\u0026thinsp;0.087), although PP13 and supervillin showed trends toward lower values in the pre-eclampsia group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo significant differences were observed in cord blood protein concentrations between severe and non-severe pre-eclampsia subgroups, although ORAI1 showed a trend toward higher values in the severe pre-eclampsia subgroup (median 10.95 vs. 9.26 ng/mL, Z=-1.84, p\u0026thinsp;=\u0026thinsp;0.066).\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\u003eProtein concentrations in cord blood of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.77 (6.25\u0026ndash;9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.87 (7.62\u0026ndash;11.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233.55 (195.67-276.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202.25 (165.47-240.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e256.38 (217.95-298.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244.52 (186.34-269.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99 (3.15\u0026ndash;4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.17 (2.45\u0026ndash;4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e511.90 (398.73-624.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443.78 (352.16-572.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMaternal-to-Cord Blood Concentration Ratios\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe maternal-to-cord blood concentration ratios for ORAI1 and FGF23 differed significantly between the pre-eclampsia and control groups. The ORAI1 ratio was significantly lower in the pre-eclampsia group (median 0.94 vs. 0.88, Z=-2.07, p\u0026thinsp;=\u0026thinsp;0.038), indicating relatively higher cord blood concentrations in pre-eclampsia. The FGF23 ratio was significantly lower in the pre-eclampsia group (median 0.87 vs. 0.97, Z=-2.31, p\u0026thinsp;=\u0026thinsp;0.021). No significant differences were observed in the PP13, palladin, and supervillin ratios between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the control group, the maternal-to-cord ratios for FGF23 and palladin were greater than 1, indicating higher concentrations in maternal blood, while the ORAI1 ratio was less than 1, indicating higher concentrations in cord blood. The PP13 and supervillin ratios were approximately 1 in the control group, suggesting similar concentrations in maternal and cord blood.\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\u003eMaternal-to-cord blood concentration ratios of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1 ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.71\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.77\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23 ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.86\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.71\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13 ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.89\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.82\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.38\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72 (1.41\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.82\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.86\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as median (interquartile range). P-values calculated using Mann-Whitney U test with corresponding Z-values. Ratio calculated as maternal concentration divided by cord blood concentration for each protein. ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate relationships between protein concentrations and clinical parameters, we performed Spearman's rank correlation analyses for the entire study population (combining both pre-eclamptic and control groups) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the combined population analysis, maternal blood proteins showed significant correlations with several clinical parameters. Maternal ORAI1 demonstrated significant positive correlations with both systolic blood pressure (rₛ=0.342, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and diastolic blood pressure (rₛ=0.315, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Maternal ORAI1 also correlated positively with markers of end-organ damage including AST (rₛ=0.287, p\u0026thinsp;=\u0026thinsp;0.001) and LDH (rₛ=0.269, p\u0026thinsp;=\u0026thinsp;0.003). Conversely, maternal FGF23 and PP13 levels showed significant negative correlations with blood pressure measurements (FGF23 with systolic: rₛ=-0.263, p\u0026thinsp;=\u0026thinsp;0.004; PP13 with systolic: rₛ=-0.246, p\u0026thinsp;=\u0026thinsp;0.006) and markers of end-organ damage (FGF23 with AST: rₛ=-0.235, p\u0026thinsp;=\u0026thinsp;0.009; PP13 with LDH: rₛ=-0.201, p\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003cp\u003eRegarding neonatal outcomes, maternal ORAI1 showed significant negative correlations with birth weight (rₛ=-0.241, p\u0026thinsp;=\u0026thinsp;0.007) and gestational age at delivery (rₛ=-0.225, p\u0026thinsp;=\u0026thinsp;0.013). Maternal FGF23 demonstrated positive correlations with these parameters (birth weight: rₛ=0.198, p\u0026thinsp;=\u0026thinsp;0.029; gestational age: rₛ=0.178, p\u0026thinsp;=\u0026thinsp;0.050).\u003c/p\u003e \u003cp\u003eCord blood proteins exhibited correlation patterns similar to those observed in maternal blood. Notably, cord blood ORAI1 demonstrated the strongest negative correlation with birth weight (rₛ=-0.278, p\u0026thinsp;=\u0026thinsp;0.002) among all measured proteins. Supervillin did not show significant correlations with any of the clinical parameters in either maternal or cord blood.\u003c/p\u003e \u003cp\u003eWe also performed separate correlation analyses within each study group. Within the pre-eclampsia group specifically, significant negative correlations were observed between maternal ORAI1 and both gestational age at delivery (rₛ=-0.318, p\u0026thinsp;=\u0026thinsp;0.012) and birth weight (rₛ=-0.327, p\u0026thinsp;=\u0026thinsp;0.010). Similar correlations were found for cord blood ORAI1 with gestational age (rₛ=-0.273, p\u0026thinsp;=\u0026thinsp;0.033) and birth weight (rₛ=-0.295, p\u0026thinsp;=\u0026thinsp;0.021). Positive correlations were observed between maternal FGF23 and both gestational age (rₛ=0.257, p\u0026thinsp;=\u0026thinsp;0.045) and birth weight (rₛ=0.279, p\u0026thinsp;=\u0026thinsp;0.029). No significant correlations were found between maternal PP13, palladin, or supervillin levels and pregnancy outcomes in the pre-eclampsia group. In the control group, no significant correlations were observed between any of the measured proteins and pregnancy outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between protein levels and clinical parameters in the combined study population.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystolic \u003c/p\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiastolic \u003c/p\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlatelet \u003c/p\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBirth \u003c/p\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGestational \u003c/p\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal blood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.342**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.241**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.225*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.235**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.203*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.198*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.178*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.217*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.201*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.183*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCord blood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.242**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.278**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.236**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.227*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.208*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.214*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.193*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.178*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData presented as Spearman's correlation coefficients (rₛ). Statistically significant correlations are indicated: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01. BP: blood pressure; AST: aspartate aminotransferase; LDH: lactate dehydrogenase; ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13. Positive coefficients indicate that as one variable increases, the other also increases; negative coefficients indicate that as one variable increases, the other decreases. The combined study population includes both pre-eclamptic and normotensive pregnant women (n\u0026thinsp;=\u0026thinsp;122).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnostic Performance of Protein Biomarkers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis revealed that maternal ORAI1 had the highest diagnostic accuracy with an AUC of 0.733 (95% CI: 0.644\u0026ndash;0.823), followed by maternal PP13 with an AUC of 0.695 (95% CI: 0.601\u0026ndash;0.789) and maternal FGF23 with an AUC of 0.679 (95% CI: 0.583\u0026ndash;0.775). For maternal ORAI1, a cut-off value of \u0026gt;\u0026thinsp;8.24 ng/mL provided 67.2% sensitivity and 75.4% specificity. For maternal PP13, a cut-off value of \u0026lt;\u0026thinsp;237.8 pg/mL yielded 63.9% sensitivity and 70.5% specificity. For maternal FGF23, a cut-off value of \u0026lt;\u0026thinsp;208.4 pg/mL provided 62.3% sensitivity and 73.8% specificity.\u003c/p\u003e \u003cp\u003eIn cord blood, ORAI1 demonstrated the highest diagnostic accuracy with an AUC of 0.677 (95% CI: 0.581\u0026ndash;0.772), followed by FGF23 (AUC\u0026thinsp;=\u0026thinsp;0.653, 95% CI: 0.556\u0026ndash;0.750). A cord blood ORAI1 cut-off value of \u0026gt;\u0026thinsp;8.56 ng/mL provided 63.9% sensitivity and 68.9% specificity and FGF23 cut-off value of \u0026lt;\u0026thinsp;217.9 pg/mL was with 60.7% sensitivity and 67.2% specificity (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis combining maternal ORAI1 and FGF23 achieved an AUC of 0.782 (95% CI: 0.699\u0026ndash;0.864), which was significantly higher than either protein alone (p\u0026thinsp;=\u0026thinsp;0.042 and p\u0026thinsp;=\u0026thinsp;0.008, respectively). For cord blood, a model combining ORAI1 and FGF23 achieved an AUC of 0.714 (95% CI: 0.623\u0026ndash;0.805), also significantly higher than individual proteins (p\u0026thinsp;=\u0026thinsp;0.037 and p\u0026thinsp;=\u0026thinsp;0.011, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of protein biomarkers for late-onset pre-eclampsia.\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=\"char\" char=\".\" 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\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC \u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal \u003c/p\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal blood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.733 (0.644\u0026ndash;0.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8.24 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.679 (0.583\u0026ndash;0.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;208.4 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.695 (0.601\u0026ndash;0.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;237.8 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.578 (0.476\u0026ndash;0.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.542 (0.440\u0026ndash;0.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCord blood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.677 (0.581\u0026ndash;0.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8.56 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.653 (0.556\u0026ndash;0.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;217.9 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593 (0.492\u0026ndash;0.694)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalladin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.567 (0.465\u0026ndash;0.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.573 (0.471\u0026ndash;0.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eReceiver operating characteristic (ROC) curve analysis data for the diagnostic performance of protein biomarkers in maternal and cord blood for late-onset pre-eclampsia. Optimal cut-off values were determined using the Youden index (maximum sum of sensitivity and specificity). AUC values\u0026thinsp;\u0026gt;\u0026thinsp;0.6 indicate acceptable discriminatory ability, with higher values representing superior diagnostic performance. AUC: area under the curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; ORAI1: calcium release-activated calcium channel protein 1; FGF23: fibroblast growth factor 23; PP13: placental protein 13; ng/mL: nanograms per milliliter; pg/mL: picograms per milliliter. Dashes (---) indicate values not calculated due to poor discriminatory performance (AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.6). Analysis was conducted on all study participants (n\u0026thinsp;=\u0026thinsp;122).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePregnancy Outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe pre-eclampsia group had significantly earlier delivery compared to the control group (median 36.5 vs. 38.6 weeks, Z=-4.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significantly lower birth weights (median 2785 vs. 3375 g, Z=-4.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Apgar scores at both 1 and 5 minutes were significantly lower in the pre-eclampsia group (median 7 vs. 8, Z=-4.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and median 8 vs. 9, Z=-4.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Umbilical cord arterial pH was significantly lower in the pre-eclampsia group (median 7.23 vs. 7.29, Z=-4.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The rate of NICU admission was significantly higher in the pre-eclampsia group (24.6% vs. 8.2%, p\u0026thinsp;=\u0026thinsp;0.014). No significant difference was observed in fetal gender distribution between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the pre-eclampsia group, women with severe features (n\u0026thinsp;=\u0026thinsp;22) had significantly earlier delivery (median 35.2 vs. 37.5 weeks, Z=-4.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower birth weights (median 2395 vs. 3085 g, Z=-3.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those without severe features (n\u0026thinsp;=\u0026thinsp;39). NICU admission rates were higher in the severe pre-eclampsia subgroup (40.9% vs. 15.4%, p\u0026thinsp;=\u0026thinsp;0.024), and cord arterial pH values were lower (median 7.18 vs. 7.25, Z=-2.54, p\u0026thinsp;=\u0026thinsp;0.011) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePregnancy outcomes of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-eclampsia \u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at delivery (weeks)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.6 (37.8\u0026ndash;39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.5 (35.2\u0026ndash;38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight (g)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3375 (3095\u0026ndash;3670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2785 (2340\u0026ndash;3245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmbilical cord arterial pH\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.29 (7.26\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.23 (7.16\u0026ndash;7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICU admission, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData presented as median (interquartile range) for continuous variables (\u0026dagger;) and as number (percentage) for categorical variables. P-values were calculated using Mann-Whitney U test (with corresponding Z-values) for continuous variables and chi-square or Fisher's exact test for categorical variables. NICU: neonatal intensive care unit. Bold values indicate statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups. Dash (-) indicates Z-value not applicable for categorical variables analyzed using chi-square or Fisher's exact test.\u003c/em\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur investigation into five potentially novel biomarkers for late-onset pre-eclampsia has elucidated distinctive protein signatures that enhance the current understanding of the underlying mechanisms of this complex disorder. Rather than merely confirming previously established biomarkers, our findings contribute novel insights into the pathophysiological pathways implicated in pre-eclampsia development. The most significant observation emerged from the analysis of ORAI1, which exhibited markedly elevated concentrations in both maternal and cord blood specimens from women with pre-eclampsia. This calcium channel protein demonstrated superior diagnostic accuracy among all examined proteins and exhibited a direct correlation with disease severity\u0026mdash;subjects with severe pre-eclampsia manifestations presented significantly higher ORAI1 levels compared to those with milder clinical presentations. The pathophysiological relevance of ORAI1 is further substantiated by its robust positive correlation with both blood pressure parameters and markers indicative of end-organ damage. This observation suggests that dysregulated calcium signaling may contribute substantially to the vascular dysfunction characteristic of pre-eclampsia. Furthermore, the relationship observed between ORAI1 and adverse pregnancy outcomes merits particular attention. The protein demonstrated significant negative correlations with both birth weight and gestational age at delivery; notably, these correlations were exclusive to the pre-eclampsia cohort and absent in control subjects. This pattern strongly indicates that ORAI1 upregulation is not merely a consequence of pregnancy complications but may be mechanistically linked to pathophysiological processes specific to pre-eclampsia.\u003c/p\u003e \u003cp\u003eIn contrast to the elevation observed in ORAI1, FGF23 demonstrated significantly reduced concentrations in pre-eclamptic pregnancies. This finding presents an intriguing paradox. In non-pregnant populations, elevated FGF23 typically associates with adverse cardiovascular outcomes, whereas the opposite pattern was observed in pre-eclampsia. The negative correlation between FGF23 levels and blood pressure parameters suggests pregnancy-specific regulatory mechanisms that become disrupted in pre-eclamptic conditions. Moreover, FGF23 exhibited positive correlations with birth weight and gestational age, indicating that its reduction may reflect compromised placental function and fetal growth restriction. PP13, a placenta-specific protein with established functions in maternal-fetal immune tolerance, was significantly decreased in the maternal circulation of pre-eclamptic subjects. A similar downward trend was noted in cord blood, although this narrowly failed to achieve statistical significance. This reduction aligns with current understanding of PP13's role in normal placentation and corroborates previous investigations indicating altered PP13 expression in pre-eclamptic placentas. While more pronounced differences in palladin and supervillin concentrations were anticipated given their theoretical relevance to trophoblast invasion and placental development, the data revealed only non-significant trends toward decreased levels in pre-eclamptic pregnancies. This suggests that alterations in cytoskeletal organization and cellular migration processes might be more subtle than hypothesized, or perhaps these proteins remain relatively preserved in late-onset pre-eclampsia compared to early-onset variants where placental dysfunction is typically more severe.\u003c/p\u003e \u003cp\u003eThe analysis of maternal-to-cord blood concentration ratios yielded particularly noteworthy insights that would not have been apparent from examining absolute concentrations alone. Both ORAI1 and FGF23 exhibited significantly altered ratios in pre-eclamptic pregnancies compared to controls, suggesting disrupted placental transport mechanisms or differential protein metabolism in maternal and fetal compartments. These altered gradients appear to reflect the compromised placental barrier function characteristic of pre-eclampsia. The consistency with which these patterns emerged across our sample set strongly suggests they represent meaningful physiological signals rather than statistical artifacts. The observation that multivariate analysis revealed a significant enhancement in diagnostic accuracy when combining ORAI1 and FGF23, beyond that achieved with either protein individually, further substantiates this interpretation. This finding underscores the value of a multi-marker approach in capturing the multifaceted pathophysiology of pre-eclampsia, with each protein appearing to reflect distinct pathophysiological pathways\u0026mdash;ORAI1 representing calcium signaling and vascular reactivity, while FGF23 reflects mineral metabolism and endothelial function.\u003c/p\u003e \u003cp\u003eThe clinical significance of these findings is substantiated by the consistent correlation patterns observed between ORAI1 and FGF23 and key clinical parameters. These proteins demonstrated robust associations with indicators of disease severity and pregnancy outcomes, specifically in subjects with pre-eclampsia, while showing no significant correlations in normotensive pregnancies. This distinct pattern suggests that these protein alterations represent intrinsic components of pre-eclampsia pathophysiology rather than nonspecific consequences of pregnancy complications. Considering these observations holistically, our findings support a pathophysiological model wherein disrupted calcium signaling pathways and altered phosphate metabolism contribute significantly to the vascular dysfunction and placental insufficiency that characterize late-onset pre-eclampsia. The presence of these distinct protein signatures in both maternal and fetal circulations underscores the systemic nature of pre-eclampsia while highlighting their potential utility as biomarkers for clinical detection and monitoring. These results also suggest intriguing therapeutic possibilities that warrant further investigation in subsequent research. Should future studies confirm a causative role for dysregulated ORAI1-mediated calcium signaling in pre-eclampsia pathogenesis, targeted modulation of this pathway may offer promising novel treatment strategies. Similarly, the potential beneficial effects of FGF23 supplementation on vascular and placental function in pre-eclamptic conditions represents a compelling avenue for investigation, though such therapeutic applications would necessitate extensive preclinical and clinical evaluation.\u003c/p\u003e \u003cp\u003eORAI1 functions as a critical calcium release-activated calcium channel protein in the plasma membrane, mediating store-operated calcium entry in trophoblasts and vascular smooth muscle cells, regulating essential functions including proliferation, migration, and secretion that frequently show alterations in pre-eclamptic conditions (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our study demonstrated significantly elevated ORAI1 levels in maternal blood of pre-eclamptic women compared to controls, with higher concentrations in the severe pre-eclampsia subgroup. Similarly, cord blood ORAI1 was significantly increased in pre-eclamptic pregnancies. ORAI1 showed significant positive correlations with blood pressure parameters and markers of end-organ damage, while negatively correlating with birth weight and gestational age at delivery.\u003c/p\u003e \u003cp\u003eRecent research challenges minimal placental expression of ORAI1 suggested by Protein Atlas data (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), with Wang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) demonstrating that pregnancy-specific beta-1-glycoprotein 1 (PSG1) upregulates ORAI1 in HTR-8/SVneo cells, enhancing trophoblast migration through Akt pathway activation, a significant finding as PSG1 levels decrease in early-onset pre-eclampsia. Similarly, Qin et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) reported PSG9 increases ORAI1 expression in HUVECs, promoting SOCE and enhancing eNOS activity. Studies have linked dysregulated ORAI1-mediated calcium signaling to endothelial dysfunction and abnormal vascular reactivity, hallmark features of pre-eclampsia that might serve as therapeutic targets (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Unlike TRPV6, which primarily facilitates calcium influx in syncytiotrophoblasts for transplacental transport (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), ORAI1 appears to serve specialized functions in extravillous trophoblasts and vascular cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), with its regulation by pregnancy-specific glycoproteins suggesting potential therapeutic applications in pregnancy complications characterized by placental insufficiency (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). ROC analysis identified maternal ORAI1 as having the highest diagnostic accuracy among all proteins studied, with an optimal cut-off value providing good sensitivity and specificity for late-onset pre-eclampsia diagnosis, highlighting its potential as a clinically relevant biomarker.Given its varied roles in placental development, trophoblast invasion, and maternal vascular adaptation, ORAI1 emerges as a compelling candidate for investigation in late-onset pre-eclampsia, particularly concerning its potential differential expression between maternal and fetal circulations (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research has identified FGF23, traditionally known for its role in phosphate and vitamin D metabolism, as a key regulator of endothelial integrity, inflammatory signaling, and placental vascular function\u0026mdash;pathways central to pre-eclampsia pathophysiology (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In our study, maternal and cord blood FGF23 concentrations were significantly reduced in women with late-onset pre-eclampsia, particularly among those with severe disease. Lower FGF23 levels showed inverse correlations with systolic and diastolic blood pressures and markers of end-organ injury (AST and LDH), and positive correlations with gestational age and birth weight. These associations contrast with findings in non-pregnant populations, where elevated FGF23 is linked to vascular dysfunction and adverse cardiovascular outcomes (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). We also observed significantly decreased maternal-to-cord blood FGF23 ratios in the pre-eclampsia group, suggesting altered placental transfer dynamics or impaired fetal production. Animal studies have shown that FGF23 and its co-receptor Klotho are essential for placental angiogenesis and fetal growth, supporting a mechanistic role for FGF23 in pregnancy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, disruption in FGF23 signaling may contribute to maternal vitamin D deficiency, an established pre-eclampsia risk factor (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Given its consistent correlations with clinical severity and moderate diagnostic performance, FGF23 may serve as a physiologically relevant biomarker reflecting both maternal vascular dysfunction and compromised placental health in pre-eclampsia. Future longitudinal studies are warranted to determine its predictive value (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePP13, a galectin family member predominantly expressed by syncytiotrophoblast, plays crucial immunomodulatory roles at the maternal-fetal interface. Through interactions with extracellular matrix proteins and regulation of maternal immune tolerance to the semi-allogeneic fetus, PP13 contributes significantly to normal placentation processes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Our study found significantly lower PP13 concentrations in maternal blood of pre-eclamptic women compared to controls, while cord blood showed a similar downward trend that approached but did not reach statistical significance. Maternal PP13 demonstrated significant negative correlations with systolic blood pressure and markers of end-organ damage, particularly LDH. ROC analysis revealed maternal PP13 as having the second-highest diagnostic accuracy among all proteins studied, with an optimal cut-off value providing good sensitivity and specificity for diagnosing late-onset pre-eclampsia. Recent studies revealed abnormal PP13 expression and secretion patterns in pre-eclamptic placentas and maternal circulation throughout gestation, with notable alterations occurring before clinical symptoms manifest, there are findings that highlight its potential as an early predictive biomarker (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). PP13 exerts its effects primarily through inducing vasodilation and vascular remodeling, promoting immune tolerance at the maternal-fetal interface, and regulating trophoblast invasion, all processes known to be dysregulated in pre-eclampsia (\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The intricate involvement of PP13 in trophoblast invasion, spiral artery remodeling, and maternal immune adaptation provides strong justification for examining its concentration in both maternal and cord blood to better understand its pathophysiological significance in late-onset pre-eclampsia.\u003c/p\u003e \u003cp\u003ePalladin serves as a cytoskeletal protein essential for maintaining cell morphology and facilitating migration through its organization of actin filaments. Its crucial roles in embryonic development, wound healing, and tissue remodeling appear particularly relevant to placental development and the extensive remodeling required for successful placentation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Our study found a non-significant trend toward decreased palladin concentrations in both maternal and cord blood of pre-eclamptic women compared to controls. Although these differences did not reach statistical significance, the consistent downward trend in both compartments suggests potential alterations in cytoskeletal organization processes. Altered palladin expression has been observed in various pathological conditions characterized by abnormal cell migration and invasiveness, including several cancers, suggesting similar alterations might occur in pre-eclamptic placentas where trophoblast invasion is frequently compromised (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Palladin influences cell-matrix interactions and focal adhesion dynamics, processes critical for proper trophoblast infiltration into maternal decidua and spiral artery remodeling (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Investigating palladin in the context of pre-eclampsia represents a novel approach; despite its established functions in cellular processes known to be dysregulated in pre-eclamptic pregnancies, its potential roles in trophoblast function, placental development, and vascular remodeling remain largely unexplored.\u003c/p\u003e \u003cp\u003eSupervillin, a membrane-associated actin-binding protein, regulates cell adhesion, cytoskeletal organization, and contractility in various cells, including trophoblasts and endothelial cells. By mediating interactions between the plasma membrane and cytoskeleton, supervillin potentially influences migratory and invasive capacities during placentation\u0026mdash;processes frequently disrupted in pre-eclampsia (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Our study found a non-significant trend toward decreased supervillin concentrations in both maternal and cord blood of pre-eclamptic women compared to controls. While these differences did not reach statistical significance, the consistent downward pattern in both compartments suggests potential alterations in membrane-cytoskeleton interactions that warrant further investigation. Recent findings suggest that supervillin participates in angiogenesis and vascular remodeling through its effects on endothelial cell function and interactions with extracellular matrix components, representing mechanisms through which it might contribute to pre-eclampsia-related vascular manifestations (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The protein's involvement in regulating smooth muscle contractility further suggests a potential role in vascular tone regulation, which is frequently dysregulated in pre-eclampsia (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Our examination of supervillin expression in maternal and cord blood constitutes pioneering work in this area; current literature lacks comprehensive assessment of this protein in normal and pathological pregnancies, despite its theoretical relevance to processes known to be altered in pre-eclampsia, such as trophoblast invasion and maternal vascular adaptation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study's primary strength lies in its simultaneous analysis of maternal and cord blood samples, providing unique insights into maternal-fetal interface dynamics in pre-eclampsia. This dual-compartment approach identified distinct protein signatures and evaluated placental transfer mechanisms. Analysis of maternal-to-cord blood concentration ratios revealed altered gradients for ORAI1 and FGF23 that would have remained undetected in single-compartment studies. The selection of biomarkers involved in diverse pathophysiological pathways captured the complex nature of pre-eclampsia. The case-control design with standardized sampling conditions minimized confounding factors, while appropriate statistical methods enhanced reliability. Detailed clinical characterization and severity-based subgroup analysis enabled meaningful correlations between protein levels and disease parameters.\u003c/p\u003e \u003cp\u003eThe cross-sectional design with delivery-time sampling precludes establishing causal relationships between protein alterations and pre-eclampsia development. The sample size, while statistically adequate for primary comparisons, limited detection of subtle differences and restricted complex analyses involving stratification by clinical characteristics. The exclusive focus on late-onset pre-eclampsia (\u0026ge;\u0026thinsp;34 weeks) limits generalizability to early-onset disease. Similarly, restricting participation to women undergoing cesarean delivery means findings may not fully represent all pre-eclampsia cases, particularly those requiring emergency intervention.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study identifies distinct alterations in protein levels within maternal and cord blood of women with late-onset pre-eclampsia, with ORAI1 showing significant elevation, while FGF23 and PP13 demonstrated marked reduction. Though palladin and supervillin did not reach statistical significance, their observed downward trends suggest potential involvement in pre-eclampsia pathophysiology that warrants further investigation with larger cohorts. These findings collectively suggest disruptions in calcium signaling, phosphate metabolism, and cytoskeletal organization as key components of pre-eclampsia pathophysiology. Diagnostic performance analysis revealed ORAI1 achieved the highest accuracy in maternal blood, while in cord blood, both ORAI1 and FGF23 demonstrated moderate discriminatory ability. The significant elevation of ORAI1 and reduction of FGF23 in both maternal and fetal circulations, coupled with their strong correlations with clinical parameters, highlight their potential as biomarkers for disease detection and severity assessment. Analysis of maternal-to-cord blood concentration ratios provides novel insights into altered protein transport across the placental barrier in pre-eclampsia, further emphasizing the importance of examining both compartments in biomarker research. The superior diagnostic performance of combined ORAI1 and FGF23 measurement compared to individual proteins underscores the value of a multi-marker approach in capturing the complex pathophysiology of pre-eclampsia. These findings support a model wherein pre-eclampsia involves systemic dysregulation of multiple pathways affecting both maternal and fetal compartments, with potential implications for vascular function and placental development. Future research should investigate the longitudinal changes in these proteins throughout pregnancy, explore their potential as early predictive biomarkers, and elucidate the mechanistic links between these protein alterations and pre-eclampsia pathogenesis. Furthermore, the therapeutic implications of these findings merit investigation, particularly regarding the potential modulation of ORAI1-mediated calcium signaling or FGF23 supplementation as novel treatment strategies in the management of late-onset pre-eclampsia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eORAI1 \u0026nbsp; \u0026nbsp; \u0026nbsp;Calcium Release-Activated Calcium Channel Protein 1\u003c/p\u003e\n\u003cp\u003eFGF23 \u0026nbsp; \u0026nbsp; \u0026nbsp;Fibroblast Growth Factor 23\u003c/p\u003e\n\u003cp\u003ePP13 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Placental Protein 13\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research Ethics Committee of Haseki Training and Research Hospital (Approval Number: 110-2023, dated September 6, 2023; Istanbul, Turkey).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all participants who kindly agreed to take part in this study, as well as the staff of the Obstetrics Department of Haseki Training and Research Hospital for their valuable assistance and cooperation during data collection. We also thank the laboratory staff for their technical support in performing the biochemical analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: H.A.A., F.Y.G., A.C.; Methodology: H.A.A., S.T., I.Y.; Data collection: H.A.A., S.T., E.Y.G.; Data analysis: I.Y., F.Y.G.; Writing \u0026ndash; original draft preparation: H.A.A., S.T.; Writing \u0026ndash; review and editing: E.Y.G., F.Y.G., A.C.; Supervision: F.Y.G., A.C.; Project administration: A.C. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants after detailed explanation of the study objectives, procedures, potential risks, and benefits. Participants were informed of their right to withdraw from the study at any time without affecting their standard medical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Assistance Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors utilized ChatGPT 4o (OpenAI) and Claude 3.7 Sonnet (Anthropic) to refine English language clarity and style. All AI-assisted content underwent thorough review and editing by the authors, who take full responsibility for the manuscript\u0026apos;s final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKarrar SA, Martingano DJ, Hong PL. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2025 Jan 1]. Preeclampsia. Available from: https://www.ncbi.nlm.nih.gov/books/NBK570611/\u003c/li\u003e\n\u003cli\u003eAndresen IJ, Zucknick M, Degnes MHL, Angst MS, Aghaeepour N, Romero R, et al. Prediction of late-onset preeclampsia using plasma proteomics: a longitudinal multi-cohort study. Sci Rep [Internet]. 2024 Dec 28;14(1):30813. Available from: https://doi.org/10.1038/s41598-024-81277-2\u003c/li\u003e\n\u003cli\u003eBaylis A, Zhou W, Menkhorst E, Dimitriadis E. 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Front Immunol [Internet]. 2019 Jun 19 [cited 2025 Apr 1];10:1240. Available from: https://www.frontiersin.org/article/10.3389/fimmu.2019.01240/full\u003c/li\u003e\n\u003cli\u003eGatto M, Esposito M, Morelli M, De Rose S, Gizurarson S, Meiri H, et al. Placental Protein 13: Vasomodulatory Effects on Human Uterine Arteries and Potential Implications for Preeclampsia. Int J Mol Sci [Internet]. 2024 Jul 9 [cited 2025 Apr 1];25(14):7522. Available from: https://www.mdpi.com/1422-0067/25/14/7522\u003c/li\u003e\n\u003cli\u003ePalalioglu RM, Erbiyik HI. Evaluation of maternal serum SERPINC1, E-selectin, P-selectin, RBP4 and PP13 levels in pregnancies complicated with preeclampsia. J Matern Fetal Neonatal Med [Internet]. 2023 Dec 31 [cited 2025 Apr 1];36(1):2183472. Available from: https://www.tandfonline.com/doi/full/10.1080/14767058.2023.2183472\u003c/li\u003e\n\u003cli\u003eRybak-Krzyszkowska M, Staniczek J, Kondracka A, Bogusławska J, Kwiatkowski S, G\u0026oacute;ra T, et al. 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Available from: https://journals.biologists.com/jcs/article/doi/10.1242/jcs.100032/263170/Supervillin-couples-myosin-dependent-contractility\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pre-eclampsia, ORAI1, FGF23, PP13, Palladin, Supervillin","lastPublishedDoi":"10.21203/rs.3.rs-6416476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6416476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePre-eclampsia continues to be a significant global health burden with complex pathophysiology, necessitating investigation of novel biomarkers to improve understanding, diagnosis and management of this pregnancy-specific disorder.To investigate the differential expression of Calcium Release-Activated Calcium Channel Protein 1 (ORAI1), Fibroblast Growth Factor 23 (FGF23), Placental Protein 13 (PP13), Palladin, and Supervillin in both maternal and umbilical cord blood as potential biomarkers for late-onset pre-eclampsia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional, case-control study included 61 women with late-onset pre-eclampsia and 61 normotensive pregnant women undergoing cesarean delivery. Maternal blood samples were collected immediately prior to cesarean delivery, and umbilical cord blood was obtained immediately after delivery of the placenta. Protein concentrations in both circulatory compartments were measured using enzyme-linked immunosorbent assay. The unique study design with paired maternal-cord blood sampling provided insights into maternal-fetal protein transfer dynamics in pre-eclamptic conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMaternal and cord blood ORAI1 concentrations were significantly elevated in pre-eclampsia (p\u0026thinsp;=\u0026thinsp;0.001 and p\u0026thinsp;=\u0026thinsp;0.035, respectively), while FGF23 and PP13 were significantly decreased in maternal blood (p\u0026thinsp;=\u0026thinsp;0.022 and p\u0026thinsp;=\u0026thinsp;0.018, respectively). Maternal-to-cord blood concentration ratios for ORAI1 and FGF23 were significantly altered in pre-eclampsia (p\u0026thinsp;=\u0026thinsp;0.038 and p\u0026thinsp;=\u0026thinsp;0.021, respectively). ORAI1 showed the highest diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.733) and correlated positively with disease severity and negatively with birth weight. Combined ORAI1 and FGF23 assessment significantly enhanced diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.782).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe altered expression of ORAI1, FGF23, and PP13 in late-onset pre-eclampsia suggests disruptions in calcium signaling, phosphate metabolism, and placental function. The parallel measurement of these proteins in both maternal and cord blood provided unique insights into maternal-fetal interface dysfunction in pre-eclampsia. The superior performance of combined ORAI1 and FGF23 measurement underscores the value of a multi-marker approach in capturing pre-eclampsia's complex pathophysiology, potentially contributing to improved diagnostic strategies and therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"ORAI1, FGF23, PP13, Palladin, and Supervillin as Potential Biomarkers in Late-Onset Pre- eclampsia: A Comparative Study in Maternal and Cord Blood Running Title: Potential Biomarkers in Late-Onset Pre-eclampsia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 11:30:08","doi":"10.21203/rs.3.rs-6416476/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T09:41:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T17:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199930085003382920551516189037046722999","date":"2025-05-30T17:39:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T20:23:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3043852417511173502304442377306652565","date":"2025-04-30T03:27:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T16:35:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77413890901137229566890963012888935514","date":"2025-04-24T15:45:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T05:18:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-14T07:08:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T22:53:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T22:52:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-04-10T04:57:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c600ebdf-1c8d-43a4-b84f-d40a1f3d1606","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T15:59:36+00:00","versionOfRecord":{"articleIdentity":"rs-6416476","link":"https://doi.org/10.1186/s12884-025-07890-9","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-07-24 15:57:07","publishedOnDateReadable":"July 24th, 2025"},"versionCreatedAt":"2025-04-25 11:30:08","video":"","vorDoi":"10.1186/s12884-025-07890-9","vorDoiUrl":"https://doi.org/10.1186/s12884-025-07890-9","workflowStages":[]},"version":"v1","identity":"rs-6416476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6416476","identity":"rs-6416476","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}