HE4 Serves as a Dual-Function Mediator in Chronic Kidney Disease：Biomarker of Renal Dysfunction and Instigator of TGF-β1-Driven Fibrosis

HE4 Serves as a Dual-Function Mediator in Chronic Kidney Disease：Biomarker of Renal Dysfunction and Instigator of TGF-β1-Driven Fibrosis | 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 HE4 Serves as a Dual-Function Mediator in Chronic Kidney Disease：Biomarker of Renal Dysfunction and Instigator of TGF-β1-Driven Fibrosis Bingyu Wang, Huifen Pan, Aihua Zhang, Junhua Mei, Yu Zhong, Zhen Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7377841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chronic kidney disease (CKD) progression is fundamentally driven by renal fibrosis, yet sensitive biomarkers reflecting this pathological process remain elusive. This study systematically investigated the dual roles of human epididymis protein 4 (HE4) as both a functional biomarker and a pathogenic driver in 231 patients with CKD(stages G2-G5)and 66 healthy controls. Quantification of serum biomarkers revealed that HE4 levels increased 22.9-fold, from 48.8 pmol/L in controls to 1117.0 pmol/L in stage G5 CKD, demonstrating a robust inverse correlation with eGFR (r = -0.919, p < 0.001). Multivariate regression identified HE4 as a strong predictor of transforming growth factor-β1 (TGF-β1) elevation (β = 0.665; 5.187 pg/mL TGF-β1 increase per 1 pmol/L HE4), while mediation modeling delineated its stage-specific mechanisms: HE4 promoted α-smooth muscle actin (ACTA2) synthesis exclusively via TGF-β1-mediated pathways (ACME = 0.702), whereas collagen type I (COL1) expression exhibited bidirectional regulation involving TGF-β1-dependent induction (ACME = 0.951) and direct HE4 suppression (ADE = -0.564). Clinically, the HE4-ACTA2 panel achieved superior diagnostic accuracy for early-stage CKD (G2, AUC = 0.946), with 88.9% sensitivity and 89.4% specificity. These findings establish HE4 as a dual-function mediator in CKD pathogenesis. It not only mirrors glomerular filtration decline but also actively drives fibrogenesis through TGF-β1-positive feedback loops, positioning HE4 as both a precision diagnostic tool and a promising therapeutic target for anti-fibrotic interventions. Human epididymis protein 4 (HE4) Chronic kidney disease (CKD) Renal fibrosis TGF-β1 signaling Biomarker Diagnostic accuracy Figures Figure 1 Figure 2 Figure 3 Introduction Chronic kidney disease (CKD), a global public health crisis, is fundamentally driven by renal fibrosis—a pathological hallmark characterized by excessive extracellular matrix (ECM) deposition and myofibroblast activation [ 1 , 2 , 3 ] . While conventional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR) remain cornerstones for assessing renal function, their limited sensitivity to fibrotic activity poses critical diagnostic challenges. Serum creatinine, for instance, is significantly confounded by muscle mass, age, and sex, leading to inaccuracies in patients with sarcopenia or obesity [ 4 , 5 , 6 ] . Similarly, eGFR, as an indirect measure of filtration capacity, fails to capture early tubulointerstitial fibrosis or structural damage [ 7 , 8 ] . This diagnostic gap contributes to the underdiagnosis of approximately 30% of early-stage CKD (stages G1-G2) until irreversible progression to advanced stages (G4-G5) occurs [ 9 ] . Emerging evidence implicates human epididymis protein 4 (HE4), a 25-kDa secreted protease inhibitor, as a pivotal mediator in non-neoplastic fibrotic disorders. In pulmonary fibrosis, alveolar epithelial-derived HE4 activates the signal transducer and activator of transcription 3 /mitogen-activated protein kinase (STAT3/MAPK) pathway via the annexin II receptor, amplifying IL-6 release and synergizing with TGF-β1 to enhance fibrogenic gene expression in fibroblasts [ 10 , 11 ] . Similarly, in hepatic fibrosis, HE4 demonstrates high diagnostic sensitivity and specificity, positioning it as a promising noninvasive biomarker [ 12 , 13 ] . These findings underscore HE4’s conserved role as a "fibrosis amplifier": its whey-acidic protein (WAP) domain stabilizes ECM deposition by inhibiting matrix metalloproteinases (MMP-2/9) [ 14 , 15 ] , while its receptor-mediated signaling reprograms microenvironmental crosstalk. However, the spatiotemporal role of HE4 in renal fibrogenesis—particularly its interplay with the TGF-β1/Smad axis—remains uncharacterized, representing a critical knowledge gap in CKD pathophysiology. Although TGF-β1 is recognized as the master regulator of renal fibrosis [ 16 , 17 , 18 ] , its upstream activators remain incompletely characterized. Notably, HE4's profibrotic actions in extrarenal organs raise three fundamental mechanistic questions: 1. Does hypoxic or inflammatory stimuli induce HE4 secretion from renal tubular epithelial cells? 2. Does HE4 stabilize latent TGF-β1 precursors or amplify TGF-β receptor signaling to establish self-reinforcing profibrotic loops? 3. Does circulating serum HE4 levels concurrently reflect both passive accumulation (due to glomerular filtration decline) and active fibrogenic secretion? Currently, no studies have elucidated HE4’s dual role in CKD—functioning as both a diagnostic biomarker and a pathogenic mediator. This critical knowledge gap significantly limits the development of targeted antifibrotic therapies. This study provides a comprehensive investigation of HE4's dual roles in CKD pathogenesis through three synergistic objectives: 1. To validate HE4 as a sensitive fibrosis biomarker outperforming conventional renal function indices (creatinine, eGFR); 2. To dissect the HE4-TGF-β1 positive feedback loop underlining collagen deposition and myofibroblast transdifferentiation; 3. To develop a novel HE4-ACTA2 biomarker panel for noninvasive early diagnosis in CKD management. Our findings demonstrate that HE4 serves as a critical molecular link between renal functional deterioration and structural damage, presenting significant potential for both improved clinical stratification and targeted therapeutic development in CKD progression.. METHODS 1 Subjects : This single-center, cross-sectional observational study consecutively enrolled 297 participants from the Department of Nephrology at Minhang Hospital, Fudan University between August 2024 and February 2025. The study population comprised: 66 healthy controls (NC) with normal renal function (eGFR ≥ 90 mL/min/1.73m² ), no history of diabetes, hypertension, or CKD. 231 CKD patients stratified by KDIGO 2020 criteria: G2 (n = 54), eGFR 60–79 mL/min/1.73m² with renal damage markers; G3 (n = 70), eGFR 30–59 mL/min/1.73m² ; G4 (n = 56), eGFR 15–29 mL/min/1.73m² ; G5 (n = 51), eGFR < 15 mL/min/1.73m² . Key exclusion criteria: pregnancy/lactation, acute kidney injury, comorbidities associated with HE4 elevation (ovarian cancer, endometriosis, active malignancies), and incomplete clinical data or inadequate biological samples. This study was approved by the Research Ethics Committee of Minhang Hospital, Fudan University (No. 036-01K). All participants provided written informed consent prior to their involvement in the study. 2 Sample Collection and Processing Venous blood samples were collected from all participants in EDTA-anticoagulated tubes after an overnight fast of at least 8 hours. Samples were incubated at room temperature (20–25°C) for 60 minutes and centrifuged at 3000 × g for 10 minutes to isolate serum. Aliquots were immediately stored at − 80°C until analysis, with freeze-thaw cycles rigorously minimized (≤ 2 cycles). 3 Laboratory Measurements Serum creatinine (Scr), urea nitrogen (BUN), and HE4 concentrations were measured using electrochemiluminescence immunoassay (ECLIA) on a Roche cobas e801 analyzer, Procollagen III N-terminal peptide (PIIINP) levels were quantified by chemiluminescence assay (AutoLumo A6200 analyzer). TGF-β1, COLI, and ACTA2 concentrations were assessed by enzyme-linked immunosorbent assay (ELISA) with FineTest® kits (Wuhan Fine Biotech Co., Cat# EH0287, EH7533, EH1509) on a Thermo Multiskan MK3 microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer’s protocols. Assay sensitivities were 18.750 pg/mL (TGF-β1), 0.188 ng/mL (COLI), and 0.188 ng/mL (ACTA2). Both intra- and inter-assay coefficients of variation (CVs) were < 10% for all ELISA measurements. Renal function assessment: Estimated Glomerular Filtration Rate (eGFR) was calculated using the 2021 CKD-EPI creatinine equation without race adjustment. 4 Statistical Analysis Continuous variables were assessed for normality using the Shapiro-Wilk test. Non-normally distributed data presented as median (interquartile range, IDR) and compared across groups using the Kruskal-Wallis H test, with post hoc Dunn-Bonferroni correction for pairwise comparisons. Categorical variables were expressed as frequencies (percentages) and analyzed using the χ² or Fisher’s exact tests, as appropriate. Spearman’s rank correlation evaluated relationships between HE4 and fibrosis biomarkers (e.g., TGF-β1, COLI, ACTA2, PIIINP) or eGFR, with significance thresholds *p < 0.05, **​p < 0.01, ​***p < 0.001. Multivariate linear regression identified predictors of TGF-β1 using stepwise variable selection [variance inflation factor (VIF) < 5 to exclude multicollinearity]. Stratified regression by CKD stage quantified HE4-eGFR relationships, reported as β( 95% coefficients intervals, CI ). The R mediation package (v4.5.0) tested mediation effects using 5,000 bootstrap replicates to estimate average causal mediation effects (ACME), direct effects (ADE), and total effects. ROC curve analysis evaluated HE4’s diagnostic utility, with optimal cutoffs determined by Youden’s index. Analyses were conducted in SPSS 26.0 (IBM) for statistical tests, R 4.3.0 (R Foundation) for mediation analysis, CurveExpert 1.4 for ELISA data processing, GraphPad Prism 9.0 for figure generation, and BioRender.com for pathway diagram creation. RESULTS Clinical characteristics This cross-sectional study enrolled 297 participants,comprising 66 healthy controls (NC) and 231 chronic kidney disease (CKD) patients stratified by disease severity (G2: 54 patients, G3: 70 patients, G4: 56 patients, G5: 51 patients). Demographic analysis revealed comparable median ages across groups (64–68 years; Kruskal-Wallis p > 0.05), with a progressive male predominance increasing from 31.8% in NC to 58.8% in G5. Comorbidity profiles showed declining diabetes prevalence from 46.3% (G2) to 25.4% (G5) ( p < 0.05), while hypertension remained highly prevalent (48.1%–55.4%) without significant stage-dependent differences ( p < 0.05). Renal function markers demonstrated exponential deterioration: eGFR declined from 98.6 mL/min/1.73m² (NC) to 7.2 mL/min/1.73m² (G5) ( p < 0.001), accompanied by 28.5-fold increase in serum creatinine (Scr: 62.0 → 604.0 μmol/L ) and 4.0-fold increase in blood urea nitrogen (BUN: 5.25 → 20.9 mmol/L) , respectively (both p < 0.001). Strikingly, serum HE4 exhibited exponential progression from 48.8 pmol/L (NC) to 1117.0 pmol/L (G5) (22.9-fold, p < 0.05), strongly correlating with renal dysfunction severity. HE4 as a Predictive Biomarker for Renal Function Decline Correlation analysis (Table 2) revealed strong positive associations between HE4 and serum creatinine (r = 0.896) as well as urea nitrogen (r = 0.822) (both p < 0.01), indicating its marked elevation with renal function deterioration. Notably, HE4 exhibited a robust inverse correlation with eGFR (r = −0.919, p < 0.01), highlighting its sensitivity to declining glomerular filtration capacity and the potential utility in monitoring CKD progression. Serum HE4 showed progressive, stepwise elevation across advancing CKD stages (Figure 1A), consistent with escalating renal injury severity. As illustrated in Figure 1B, log₁₀-transformed HE4 levels showed a significant linear inverse relationship with eGFR (log₁₀[HE4] = −0.0132 × eGFR + 2.943; 95% CI: −0.01402 to −0.01238; R² = 0.7727), further validating its predictive value for renal dysfunction. This log-linear association suggests HE4's potential for developing clinical models to estimate eGFR, offering a complementary approach to traditional biomarkers. Inverse Association Between HE4 and eGFR Across CKD Stages: A Stratified Regression Analysis Multivariate linear regression analysis (adjusted for age, diabetes, and hypertension) revealed stage-specific associations between HE4 and eGFR in CKD patients (Table 3). Significant independent inverse correlations were observed in CKD G3 (β = -0.316, p = 0.010) and G4 (β = -0.280, p = 0.035), indicating that each 1 pmol/L HE4 elevation corresponded to eGFR declines of 0.316 and 0.280 mL/min/1.73m² , respectively. No significant associations were detected between HE4 and G2 ( p = 0.136) or G5 ( p = 0.195), potentially reflecting compensatory renal mechanisms in early-stage CKD (G2) and multifactorial confounding in advanced disease (G5). Dynamic Changes in Fibrosis Markers Across CKD Stages Building upon the stratified regression findings showed that HE4’s diagnostic utility in CKD G4-G5 appears to reflect fibrogenic activity rather than real-time renal function, (Table 3). Subsequent analysis of HE4's role in renal fibrogenesis demonstrated stage-dependent elevation of fibrosis biomarkers: TGF-β1 increased from 1,080.82 pg/mL (NC) to 11,557.2 pg/mL (G5), while COL1 rose from 0.94 ng/mL to 112.11 ng/mL (p < 0.01), indicative of accelerated collagen deposition. Concurrent increases in PIIINP (5.25 → 18.40 ng/mL) and ACTA2 (1.33 →5.90 ng/mL , p < 0.01 for all) confirmed progressive myofibroblast activation and extracellular matrix (ECM) remodeling, mirroring CKD stage progression and structural deterioration, (Figure 2) . Spearman’s correlation analysis revealed strong positive associations between HE4 and fibrogenic markers: TGF-β1 (r = 0.939), COL1 (r = 0.683) , PIIINP (r = 0.839), and ACTA2 (r = 0.723, all p < 0.01). Notably, TGF-β1 exhibited the strong correlations with all biomarkers (vs. COL1: r = 0.713; vs. PIIINP: r = 0.805; vs. ACTA2: r = 0.749; p < 0.01), underscoring its central regulatory role in the fibrotic signaling network, (Table 4). Multivariate Regression Reveals HE4-Driven Fibrosis via TGF-β1 Signaling Multivariate linear regression analysis (adjusted R² = 0.903) revealed serum HE4 concentration as the strong independent predictor of TGF-β1 levels, demonstrating a robust positive association (β = 0.665, p < 0.001). Each 1 pmol/L increase in HE4 corresponded to a 5.187-unit elevation in TGF-β1 (95% CI: 4.664–5.710). Renal dysfunction, quantified by eGFR decline, exhibited a significant inverse relationship with TGF-β1 (β = -0.320, p < 0.001), every 1 mL/min/1.73m ² reduction in eGFR predicting 42.339-unit increase in TGF-β1. Among fibrosis biomarkers, ACTA2 showed marginal predictive value (β = 0.051, p = 0.047), associated with an 88.597-unit TGF-β1 increase per ng/mL rise in ACTA2. Traditional renal function indices (serum creatinine, blood urea nitrogen), demographic variables (age, sex), comorbidities (diabetes, hypertension), and collagen markers (COL1, PⅢNP) showed no significant associations (all p > 0.05). Notably,, HE4 and eGFR together accounted for 90.3% of the explained variance in TGF-β1 levels (Table 5), demonstrating their predominant role in TGF-β1 regulation within this cohort. TGF-β1 Mediates the Profibrotic Effects of HE4 on COL1 and ACTA2 To determine whether HE4 promotes fibrosis through TGF-β1-dependent mechanisms, we performed causal mediation analysis (Table 6). The results revealed distinct regulatory patterns for ACTA2 and COL1: For ACTA2 regulation, HE4 significantly promoted myofibroblast activation exclusively through TGF-β1-dependent mechanisms, demonstrating a robust average causal mediation effect (ACME = 0.702, p < 0.001) but a nonsignificant direct effect (ADE = -0.143, p = 0.47). In contrast, COL1 regulation exhibited dual mechanisms: while TGF-β1/ COL1 mediated strong profibrotic effects (ACME = 0.951, p < 0.001), HE4 appeared to suppress COL1 through non-canonical pathways (ADE = -0.564; p < 0.001) , though direct mechanistic validation in renal fibrosis remains necessary. These findings demonstrate bidirectional regulation of COL1 — where TGF-β1-driven ECM deposition is counterbalanced by non-canonical HE4-mediated suppression—in sharp contrast to the unidirectional, TGF-β1-dominant control of ACTA2. Early Diagnosis of Stage 2 CKD: ROC Curve Analysis of HE4 and ACTA2 ROC curve analysis demonstrated significant diagnostic potential for HE4 (AUC = 0.860) and ACTA2 (AUC = 0.860) in detecting renal fibrosis among CKD G2 patients (Figure 3). HE4 exhibited balanced performance (sensitivity = 81.5%, specificity = 83.3%), whereas ACTA2 showed higher specificity (97.0%) but lower sensitivity (63.0%). Serum creatinine (Scr), despite high sensitivity (92.6%), demonstrated limited specificity (63.6%) with elevated false-positive rates (Youden index = 0.562). Among individual biomarkers, HE4 achieved optimal diagnostic performance. Strikingly, combined HE4-ACTA2 detection significantly improved diagnostic accuracy (AUC = 0.946, 95% CI: 0.905–0.986), with enhanced sensitivity (88.9%), specificity (89.4%), and Youden index (0.783), outperforming individual biomarkers. DISCUSSION This study elucidates the dual accumulation mechanisms of HE4, which dynamically reflect renal functional decline during CKD progression. Serum HE4 exhibited exponential increase from 48.8 pmol/L in controls to 1,117.0 pmol/L in CKD G5, demonstrating a strong inverse correlation with eGFR (r = -0.919). The passive filtration-accumulation was driven by HE4's molecular weight (25 kDa), which approaches the renal filtration threshold [ 19 , 20 , 21 ] . It was evidenced by a log-linear relationship with eGFR (R² = 0.773), resembling β2-microglobulin kinetics [ 4 ] . Notably, early-stage HE4 doubling (G2→G3: 75.8→133.0 pmol/L ) sensitively reflected glomerular reserve depletion; Active injury-driven secretion was implicated in post-G4 HE4 surges (215% increase vs. 50% eGFR decline) involving NF-κB-mediated hypersecretion from hypoxic tubular epithelia [ 22 ] . This process was amplified by TGF-β1 positive feedback loops (r = 0.939), which promote pathological collagen deposition through mechanisms similar to ECM remodeling in pulmonary fibrosis ​[ 23 ] , including MMP-9 suppression and dysregulated TGF-β1/Smad2 signaling. While the HE4-eGFR association weakened in advanced CKD (CKD5: β = -0.001), its standardized β (-0.176) still retained clinical relevance. This transitional pattern suggests HE4's role from a "filtration-driven accumulator" in early stages to a "fibrosis amplifier" in late-stage disease. The attenuated statistical significance in G5 may reflect competing pathological processes, wherein HE4's profibrotic effects become partially obscured by synergistic uremic toxin damage (e.g., indoxyl sulfate/p-cresol sulfate) [ 24 , 25 ] , which independently activate fibroblast through oxidative stress pathways. Together, these findings establish HE4 as a dual-axis biomarker that mirrors functional impairment (filtration decline) and structural injury (fibrotic activation), enabling comprehensive CKD staging and progression monitoring. The HE4-TGF-β1 axis forms a self-reinforcing pathological loop (Spearman's r = 0.939, R 2 = 0.882) through three synergistic mechanisms: First, protease inhibition: HE4 stabilizes latent TGF-β1 complexes via its whey-acidic protein (WAP) domain-mediated inhibition of matrix metalloproteinases (MMPs) and serine proteases (e.g., plasmin). This prevents degradation of latent TGF-β binding proteins (LTBP) and amplifies TGF-β1 activation under microenvironmental stimuli [ 26 ] , similar to the role of secretory leukocyte protease inhibitor (SLPI) in pulmonary fibrosis [ 27 ] . Second, Smad3/extracellular signal-regulated kinase (ERK) crosstalk: Active TGF-β1 phosphorylates Smad2/3 via the TGF-β receptor II (TBRII) [ 28 , 29 ] , driving transcription of fibrotic targets (ACTA2, COL1). Simultaneously, HE4 enhances Smad3 signaling through direct interaction with TBRII (analogous to SLPI's regulation of epidermal growth factor receptor [EGFR] [ 30 , 31 ] ) and activates the Annexin II receptor-dependent ERK1/2-STAT3 axis [ 11 ] , creating dual amplification of fibrotic signaling. Third, collagen metabolic reprogramming: TGF-β1 promotes COL1 synthesis (average causal mediation effect [ACME] = 0.951), while HE4 may regulate COL1 degradation through non-canonical pathways, potentially involving ERK-MMP-dependent proteolysis (average direct effect [ADE]=-0.564) [ 32 , 33 ] . This axis establishes a pathogenic feedforward loop akin to advanced glycation end-products (AGEs) in diabetic nephropathy [ 34 ] , where context-dependent signaling switches drive extracellular matrix remodeling through evolutionarily conserved mechanisms, positioning HE4-TGF-β1 as both a therapeutic target and biomarker for fibrotic progression.​ The HE4-ACTA2 biomarker panel demonstrates the potential for early CKD detection and management. In stage G2 CKD, this dual-marker combination achieved superior diagnostic accuracy (AUC = 0.946, 95% CI: 0.905–0.986; sensitivity 88.9%, specificity 89.4%), outperforming conventional serum creatinine (AUC = 0.852). This serum-based approach significantly reduces the inherent drawbacks of traditional invasive diagnostics—including procedural risks (e.g., hemorrhage, infection) [ 35 ] , sampling variability, and substantial healthcare burdens, making it particularly viable for resource-constrained settings. Therapeutically, targeting the HE4-TGF-β1 axis might represent a novel, pathogenesisly grounded strategy to interrupt fibrogenesis via anti-HE4 monoclonal antibodies or WFDC2 (HE4) gene silencing, with HE4 serving as both a biomarker and therapeutic target. By concurrently evaluating functional decline (HE4-eGFR log-linear correlation) and structural injury (ACTA2-driven fibrosis), this strategy aligns with KDIGO 2024 guidelines [ 36 ] , bridging the gap between biomarker discovery and precision nephrology implementation. Limitations and Future Directions While this cross-sectional design precludes causal inference, the robust association between HE4 and TGF-β1 (standardized β = 0.665, p < 0.001) persists after rigorous adjustment for age, sex, diabetes status, and baseline eGFR, underscoring its clinical potential. However, the limited sample size of the G5 subgroup ( n = 51) may reduce statistical power to detect nonlinear trends in advanced CKD, necessitating validation in larger cohorts with balanced CKD stage distribution.​ Future work should incorporate longitudinal cohorts to delineate temporal dynamics and single-cell sequencing to map HE4’s cellular sources (injured tubular epithelium or activated fibroblasts) and receptor targets (Annexin II or TβRII). Multicenter validation across diverse etiologies (e.g., diabetic vs. hypertensive CKD) will clarify whether the HE4-TGF-β1 axis operates as a universal fibrotic driver or exhibits etiology-specific modulation. Conclusion This study repositions HE4 as a dual-function mediator in CKD, serving both as a biomarker and a pathogenic driver. By elucidating its biphasic kinetics and TGF-β1-dependent fibrogenic circuitry, we establish a framework for CKD early diagnosis and targeted anti-fibrotic therapy. These advances position HE4 as a potential cornerstone of precision nephrology, with transformative implications for CKD management. Declarations Research funding: This work has been financially supported by Discipline Development Fund of Minhang Hospital Affiliated to Fudan University（No. YJXK-2023-21-005）. Author contributions: Bingyu Wang: Study design, Data collection, Methodology, Software, Writing – original draft. Huifen Pan, Aihua Zhang, Junhua Mei, and Yu Zhong: Data collection, Methodology. Zhen Zhao: Writing – review & editing, Visualization, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. competing interest ： The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Sharing Statement The raw clinical datasets generated during this study are not publicly available due to patient privacy protections under the ethical approval guidelines of Minhang Hospital, Fudan University (Approval No. 036-01K). De-identified data can be made available upon reasonable request to the corresponding author (email: [ [email protected] ]) with permission from the institutional ethics committee. Processed biomarker data and statistical analysis scripts are included in the supplementary material. References Rockey DC, Bell PD, Hill JA. Fibrosis--a common pathway to organ injury and failure. 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Matrix metalloproteinase-9 regulates afferent arteriolar remodeling and function in hypertension-induced kidney disease. Kidney Int. 2023 Oct;104(4):740-753. doi: 10.1016/j.kint.2023.06.031. Epub 2023 Jul 7. PMID: 37423509; PMCID: PMC10854403. Habgood AN, Tatler AL, Porte J, Wahl SM, Laurent GJ, John AE, Johnson SR, Jenkins G. Secretory leukocyte protease inhibitor gene deletion alters bleomycin-induced lung injury, but not development of pulmonary fibrosis. Lab Invest. 2016 Jun;96(6):623-31. doi: 10.1038/labinvest.2016.40. Epub 2016 Mar 14. PMID: 26974397; PMCID: PMC4884449. Wu W, Wang X, Yu X, Lan HY. Smad3 Signatures in Renal Inflammation and Fibrosis. Int J Biol Sci. 2022 Mar 28;18(7):2795-2806. doi: 10.7150/ijbs.71595. PMID: 35541902; PMCID: PMC9066101. Qu X, Jiang M, Sun YB, Jiang X, Fu P, Ren Y, Wang D, Dai L, Caruana G, Bertram JF, Nikolic-Paterson DJ, Li J. The Smad3/Smad4/CDK9 complex promotes renal fibrosis in mice with unilateral ureteral obstruction. Kidney Int. 2015 Dec;88(6):1323-1335. doi: 10.1038/ki.2015.235. Epub 2015 Jul 29. PMID: 26221756. Nukiwa T, Suzuki T, Fukuhara T, Kikuchi T. Secretory leukocyte peptidase inhibitor and lung cancer. Cancer Sci. 2008 May;99(5):849-55. doi: 10.1111/j.1349-7006.2008.00772.x. PMID: 18380788; PMCID: PMC11159350. Meyer M, Bauer RN, Letang BD, Brighton L, Thompson E, Simmen RC, Bonner J, Jaspers I. Regulation and activity of secretory leukoprotease inhibitor (SLPI) is altered in smokers. Am J Physiol Lung Cell Mol Physiol. 2014 Feb;306(3):L269-76. doi: 10.1152/ajplung.00290.2013. Epub 2013 Nov 2. PMID: 24285265; PMCID: PMC3920198. Marchese V, Juarez J, Patel P, Hutter-Lobo D. Density-dependent ERK MAPK expression regulates MMP-9 and influences growth. Mol Cell Biochem. 2019 Jun;456(1-2):115-122. doi: 10.1007/s11010-019-03496-w. Epub 2019 Jan 28. PMID: 30689107; PMCID: PMC6494698. Yamamoto M, Hanatani S, Araki S, Izumiya Y, Yamada T, Nakanishi N, Ishida T, Yamamura S, Kimura Y, Arima Y, Nakamura T, Takashio S, Yamamoto E, Sakamoto K, Kaikita K, Matsushita K, Morimoto S, Ito T, Tsujita K. HE4 Predicts Progressive Fibrosis and Cardiovascular Events in Patients With Dilated Cardiomyopathy. J Am Heart Assoc. 2021 Aug 3;10(15):e021069. doi: 10.1161/JAHA.120.021069. Epub 2021 Jul 29. PMID: 34320813; PMCID: PMC8475713. Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001 Dec 13;414(6865):813-20. doi: 10.1038/414813a. PMID: 11742414. Marconi L, Dabestani S, Lam TB, Hofmann F, Stewart F, Norrie J, Bex A, Bensalah K, Canfield SE, Hora M, Kuczyk MA, Merseburger AS, Mulders PFA, Powles T, Staehler M, Ljungberg B, Volpe A. Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy. Eur Urol. 2016 Apr;69(4):660-673. doi: 10.1016/j.eururo.2015.07.072. Epub 2015 Aug 29. PMID: 26323946. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024 Apr;105(4S):S117-S314. doi: 10.1016/j.kint.2023.10.018. PMID: 38490803. Tables Table 1. Clinical characteristics and laboratory parameters of study participants variables NC (n=66) CKD G2 （n=54） CKD G3 (n=70) CKD G4 (n=56) CKD G5 (n=51) Age(y) 64 (60.00,67.00) 66 (61.00,69.75) 67 (58.75,71.00) 67 (63.25,71.00) 68 (62.00,71.00) Gender(M/F),n, (n/n) 66(21/45) 54(36/18) 70(39/31) 56(28/28) 51(30/21) Diabetes n(%) - 25（46.3%） 30（42.8%） 25（44.6%） 13（25.4%） Hypertension n(%) - 26（48.1%） 35（50.0%） 31（55.4%） 27（52.9%） eGFR ( ml/min/1.73m 2 ) 98.63 (94.36,109.98) 75.78 (67.42, 82.77) 49.92 (41.14,55.96) 24.29 (20.35,26.71) 7.22 (4.60,6.73) Scr ( μmol/L ) 62 (58.00,72.00) 86 (74.00,96.25) ** 120 (105.75,139.50) *** 194.5 (166.25,251.75) *** 604 (386.00,772.00) *** BUN ( mmol/L ) 5.25 (4.20,6.20) 5.60 (4.60,6.73) 7.50 (6.15,9.73) *** 12.75 (11.43,16.05) *** 20.90 (15.90,25.80) *** HE4( pmol/L ) 48.80 (41.08,57.15) 75.75 (61.75,122.00) ** 133.00 (94.98,176.25) *** 354.00 (250.50,577.50) *** 1117.00 (581.00,1875.00)*** It showed significant difference compared to corresponding normal control (**P<0.01，***P<0.001, Mann-Whitney U test) . Table 2. Correlation analysis of HE4 with renal function parameters. variables eGFR ( ml/min/1.73m 2 ) BUN ( mmol/L ) Scr ( μmol/L ) HE4 ( pmol/L ) Age(y) r -0.177 ** 0.155 ** 0.093 0.212 ** eGFR ( ml/min/1.73m 2 ) r -0.840 ** -0.961 ** -0.919 ** BUN ( mmol/L ) r 0.840 ** 0.822 ** Scr ( μmol/L ) r 0.896 ** HE4 showed strong positive correlations with traditional renal markers (creatinine and BUN) and a strong negative correlation with eGFR (**P<0.01, Spearman's correlation ). Table 3. Multivariable Linear Regression Analysis between HE4 and eGFR. Variable Unstandardized B SE Standardized β 95% CI p -value CKD G2 -0.032 0.021 -0.219 –0.074 to 0.010 0.136 CKD G3 -0.015 0.006 -0.316 –0.027 to –0.004 0.010 CKD G4 -0.005 0.002 -0.280 –0.009 to 0.000 0.035 CKD G5 -0.001 0.001 -0.176 –0.002 to 0.000 0.195 Unstandardized beta coefficients (B) represent the absolute decline in eGFR (ml/min/1.73m²) per 1-unit increase in HE4 (e.g., a 1-unit increase in HE4 was associated with a 0.316 ml/min/1.73m² decline in eGFR in CKD Stage 3). Variance Inflation Factor (VIF) values for all variables were maintained below 3, indicating no substantial multicollinearity interference in the regression model. Table 4. Spearman´s correlation matrix between HE4 and renal fibrosis biomarkers (TGF-β, COL1, PⅢNP, ACTA2) in CKD patients. variables TGF-β1 COL1 PⅢNP ACTA2 HE4 r 0.939 ** 0.683 ** 0.839 ** 0.723 ** TGF-β1 r 0.713 ** 0.805 ** 0.749 ** COL1 r 0.664 ** 0.624 ** PⅢNP r 0.636 ** Table 5. Multivariable Linear Regression Analysis of TGF-β1 and other parameters in CKD Patients. Variable Unstandardized B SE Standardized β 95% CI p-value Diabetes 227.688 189.551 0.023 –145.410 to 600.787 0.231 Hypertension 105.662 181.322 0.011 –251.238 to 462.561 0.561 Male (vs. Female) -103.951 170.922 -0.011 –440.380 to 232.479 0.544 Age (years) -10.702 11.898 -0.017 –34.121 to 12.716 0.369 eGFR( ml/min/1.73m 2 ) -42.339 5.226 -0.320 –52.625 to –32.053 <0.001 BUN( mmol/L ) 6.342 6.538 0.021 –6.526 to 19.210 0.333 Scr( μmol/L ) 0.240 0.546 0.014 –0.834 to 1.314 0.661 HE4 ( pmol/L ) 5.187 0.266 0.665 4.664 to 5.710 <0.001 PⅢNP( ng/mL ) -11.203 19.223 -0.017 –49.040 to 26.634 0.560 COL1( ng/mL ) 1.313 2.100 0.018 –2.821 to 5.446 0.532 ACTA2( ng/mL ) 88.597 44.444 0.051 1.116 to 176.077 0.047 Unstandardized B: Absolute change in TGF-β1 ( pg/mL ) per 1-unit increase in each predictor. Standardized β: Effect size normalized to standard deviation (SD) units. 95% Confidence interval ( CI) intervals excluded 0 indicating statistical significance. Variance Inflation Factor (VIF) < 5 for all variables, confirming minimal multicollinearity. Adjusted R² = 0.903, indicating strong explanatory power. p < 0.05 was considered significant. Table 6. Mediation Effects of HE4 on Fibrosis Biomarkers Through TGF-β1 Signaling Pathways Effect Estimate 95% CI p-value ACTA2 ACME 0.702 0.421 to 1.070 <0.001 *** ADE -0.143 –0.590 to 0.310 0.470 Total Effect 0.559 0.385 to 0.790 <0.001 *** Prop. Mediated 1.256 0.581 to 2.360 <0.001 *** COL1 ACME 0.951 0.721 to 1.260 <0.001 *** ADE -0.564 -0.863 to–0.320 <0.001 *** Total Effect 0.387 0.278 to 0.540 <0.001 *** Prop. Mediated 2.456 1.671 to 3.640 <0.001 *** ACME: Average Causal Mediation Effect (TGF-β1-mediated pathway contribution); ADE: Average Direct Effect (HE4’s non-TGF-β1 pathway contribution); Proportion Mediated: ACME / Total Effect (range >1 indicates suppression paradox); Confidence intervals derived from 5,000 bootstrap replicates. p < 0.001 (two-tailed significance) Additional Declarations No competing interests reported. Supplementary Files Pathdiagramwithmediationeffects.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7377841","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508548808,"identity":"8f54a84e-83ed-40ad-bf35-2bdabdef9ad8","order_by":0,"name":"Bingyu Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Bingyu","middleName":"","lastName":"Wang","suffix":""},{"id":508548809,"identity":"21890b09-8afa-497e-a025-9cfb04c04ae3","order_by":1,"name":"Huifen Pan","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Huifen","middleName":"","lastName":"Pan","suffix":""},{"id":508548810,"identity":"96a5ca70-d934-40fd-b84b-b5b32efc7208","order_by":2,"name":"Aihua Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Zhang","suffix":""},{"id":508548811,"identity":"11d76af1-dfaf-47a6-9270-2e45e3bc32da","order_by":3,"name":"Junhua Mei","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Mei","suffix":""},{"id":508548812,"identity":"3c20c30d-e8a9-412e-a6fe-8887fb04676c","order_by":4,"name":"Yu Zhong","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhong","suffix":""},{"id":508548813,"identity":"c85fb334-dbd5-4a19-bf29-0f6a88adb39f","order_by":5,"name":"Zhen Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCSBmbGBg4GdvP/ggocKGBC2SPWeSDR6cSSNBi8ENBzPJh22HCOuQn9388OHPHTZ5kjMY0ioS2A4w8Ld3J+DVwjjnmLGB5Jm0Yn7pxmM3EnjuMEicObsBrxZmiQQzCcO2w4kz5xxIu5Eg8YzBQCIXvxY2ifRvEolt/xM33EgwK0gwOExYC49EjpnEwbYDYC0MCQlEaJGQyCk2bGxLTpwJDGSJhANpPAT9Ij8jfePDn212if3AqPz485+NHH97L34tmC4lTfkoGAWjYBSMAqwAAOBYTzdbo7gfAAAAAElFTkSuQmCC","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-08-15 02:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7377841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7377841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90543271,"identity":"92c7c93d-90fe-48e5-842a-810a384cf05e","added_by":"auto","created_at":"2025-09-04 00:10:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":410416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression analysis between HE4 levels and eGFR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Box plots of HE4 concentrations across groups (NC, CKD2-5): **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01 NC vs CKD2; ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001 NC vs. CKD3-5. (B) The scatter plot demonstrates a linear increase in log₁₀ (HE4) as eGFR declines (regression equation: log₁₀ (HE4) = - 0.0132×eGFR + 2.943, n=297, Spearman's r= - 0.919, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, R²= 0.7727). The shaded area represents the 95% confidence interval (-0.01402 to -0.01238).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377841/v1/91ae568bd9c9f006d06b40dd.jpg"},{"id":90542277,"identity":"47e12253-c014-4d56-b8cf-04fadfc08c95","added_by":"auto","created_at":"2025-09-04 00:02:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":670913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum Levels of Renal Fibrosis Biomarkers Across CKD Stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) TGF-β1 (key profibrotic cytokine, pg/mL); (B) COL1 (type I collagen, ng/mL); (C) ACTA2 (α-smooth muscle actin, \u003cem\u003eng/mL\u003c/em\u003e); (D) PⅢNP (N-terminal propeptide of type III procollagen, \u003cem\u003eng/mL\u003c/em\u003e). Biomarker concentrations are expressed as median (central line within the box) and interquartile range (IQR; box boundaries). Statistical significance markers (**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) indicate differences compared to the NC group, determined by Mann-Whitney U non-parametric tests.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377841/v1/5236f1d206dcb265f726995a.jpg"},{"id":90543273,"identity":"9a04d29d-e3bd-4ca2-9a73-7717f3f159d4","added_by":"auto","created_at":"2025-09-04 00:10:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":549871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curves and diagnostic performance of HE4 and ACTA2 in CKD Stage 2 patients.\u003c/strong\u003e Red line: ACTA2-HE4 combined detection; Blue line: Serum creatinine (Scr); Black line: HE4; Green line: ACTA2. The Youden Index (Sensitivity + Specificity - 1) reflects the optimal trade-off between sensitivity and specificity.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7377841/v1/0740656973dc036886da8e07.jpg"},{"id":101854080,"identity":"10dfb278-0206-4b0a-81b1-ac895462764b","added_by":"auto","created_at":"2026-02-04 10:28:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2783331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7377841/v1/590cc0cf-3a45-43cf-9c36-20417a72badf.pdf"},{"id":90542289,"identity":"1ca68fed-d389-4520-832d-90106f0acc33","added_by":"auto","created_at":"2025-09-04 00:02:12","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3681420,"visible":true,"origin":"","legend":"","description":"","filename":"Pathdiagramwithmediationeffects.tif","url":"https://assets-eu.researchsquare.com/files/rs-7377841/v1/5170eb33bb44fd3ef1e5f72a.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHE4 Serves as a Dual-Function Mediator in Chronic Kidney Disease：Biomarker of Renal Dysfunction and Instigator of TGF-β1-Driven Fibrosis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD), a global public health crisis, is fundamentally driven by renal fibrosis\u0026mdash;a pathological hallmark characterized by excessive extracellular matrix (ECM) deposition and myofibroblast activation \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. While conventional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR) remain cornerstones for assessing renal function, their limited sensitivity to fibrotic activity poses critical diagnostic challenges. Serum creatinine, for instance, is significantly confounded by muscle mass, age, and sex, leading to inaccuracies in patients with sarcopenia or obesity \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Similarly, eGFR, as an indirect measure of filtration capacity, fails to capture early tubulointerstitial fibrosis or structural damage \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This diagnostic gap contributes to the underdiagnosis of approximately 30% of early-stage CKD (stages G1-G2) until irreversible progression to advanced stages (G4-G5) occurs \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEmerging evidence implicates human epididymis protein 4 (HE4), a 25-kDa secreted protease inhibitor, as a pivotal mediator in non-neoplastic fibrotic disorders. In pulmonary fibrosis, alveolar epithelial-derived HE4 activates the signal transducer and activator of transcription 3 /mitogen-activated protein kinase (STAT3/MAPK) pathway via the annexin II receptor, amplifying IL-6 release and synergizing with TGF-β1 to enhance fibrogenic gene expression in fibroblasts \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Similarly, in hepatic fibrosis, HE4 demonstrates high diagnostic sensitivity and specificity, positioning it as a promising noninvasive biomarker \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These findings underscore HE4\u0026rsquo;s conserved role as a \"fibrosis amplifier\": its whey-acidic protein (WAP) domain stabilizes ECM deposition by inhibiting matrix metalloproteinases (MMP-2/9) \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, while its receptor-mediated signaling reprograms microenvironmental crosstalk. However, the spatiotemporal role of HE4 in renal fibrogenesis\u0026mdash;particularly its interplay with the TGF-β1/Smad axis\u0026mdash;remains uncharacterized, representing a critical knowledge gap in CKD pathophysiology.\u003c/p\u003e\u003cp\u003eAlthough TGF-β1 is recognized as the master regulator of renal fibrosis \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, its upstream activators remain incompletely characterized. Notably, HE4's profibrotic actions in extrarenal organs raise three fundamental mechanistic questions: 1. Does hypoxic or inflammatory stimuli induce HE4 secretion from renal tubular epithelial cells? 2. Does HE4 stabilize latent TGF-β1 precursors or amplify TGF-β receptor signaling to establish self-reinforcing profibrotic loops? 3. Does circulating serum HE4 levels concurrently reflect both passive accumulation (due to glomerular filtration decline) and active fibrogenic secretion? Currently, no studies have elucidated HE4\u0026rsquo;s dual role in CKD\u0026mdash;functioning as both a diagnostic biomarker and a pathogenic mediator. This critical knowledge gap significantly limits the development of targeted antifibrotic therapies.\u003c/p\u003e\u003cp\u003eThis study provides a comprehensive investigation of HE4's dual roles in CKD pathogenesis through three synergistic objectives: 1. To validate HE4 as a sensitive fibrosis biomarker outperforming conventional renal function indices (creatinine, eGFR); 2. To dissect the HE4-TGF-β1 positive feedback loop underlining collagen deposition and myofibroblast transdifferentiation; 3. To develop a novel HE4-ACTA2 biomarker panel for noninvasive early diagnosis in CKD management. Our findings demonstrate that HE4 serves as a critical molecular link between renal functional deterioration and structural damage, presenting significant potential for both improved clinical stratification and targeted therapeutic development in CKD progression..\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003e1 Subjects\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThis single-center, cross-sectional observational study consecutively enrolled 297 participants from the Department of Nephrology at Minhang Hospital, Fudan University between August 2024 and February 2025. The study population comprised: 66 healthy controls (NC) with normal renal function (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e), no history of diabetes, hypertension, or CKD. 231 CKD patients stratified by KDIGO 2020 criteria: G2 (n\u0026thinsp;=\u0026thinsp;54), eGFR 60\u0026ndash;79 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e with renal damage markers; G3 (n\u0026thinsp;=\u0026thinsp;70), eGFR 30\u0026ndash;59 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e; G4 (n\u0026thinsp;=\u0026thinsp;56), eGFR 15\u0026ndash;29 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e; G5 (n\u0026thinsp;=\u0026thinsp;51), eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e. Key exclusion criteria: pregnancy/lactation, acute kidney injury, comorbidities associated with HE4 elevation (ovarian cancer, endometriosis, active malignancies), and incomplete clinical data or inadequate biological samples. This study was approved by the Research Ethics Committee of Minhang Hospital, Fudan University (No. 036-01K). All participants provided written informed consent prior to their involvement in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2 Sample Collection and Processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eVenous blood samples were collected from all participants in EDTA-anticoagulated tubes after an overnight fast of at least 8 hours. Samples were incubated at room temperature (20\u0026ndash;25\u0026deg;C) for 60 minutes and centrifuged at 3000 \u0026times; g for 10 minutes to isolate serum. Aliquots were immediately stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis, with freeze-thaw cycles rigorously minimized (\u0026le;\u0026thinsp;2 cycles).\u003c/p\u003e\u003cp\u003e3 \u003cb\u003eLaboratory Measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSerum creatinine (Scr), urea nitrogen (BUN), and HE4 concentrations were measured using electrochemiluminescence immunoassay (ECLIA) on a Roche cobas e801 analyzer, Procollagen III N-terminal peptide (PIIINP) levels were quantified by chemiluminescence assay (AutoLumo A6200 analyzer). TGF-β1, COLI, and ACTA2 concentrations were assessed by enzyme-linked immunosorbent assay (ELISA) with FineTest\u0026reg; kits (Wuhan Fine Biotech Co., Cat# EH0287, EH7533, EH1509) on a Thermo Multiskan MK3 microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer\u0026rsquo;s protocols. Assay sensitivities were 18.750 \u003cem\u003epg/mL\u003c/em\u003e (TGF-β1), 0.188 \u003cem\u003eng/mL\u003c/em\u003e (COLI), and 0.188 \u003cem\u003eng/mL\u003c/em\u003e (ACTA2). Both intra- and inter-assay coefficients of variation (CVs) were \u0026lt;\u0026thinsp;10% for all ELISA measurements.\u003c/p\u003e\u003cp\u003eRenal function assessment: Estimated Glomerular Filtration Rate (eGFR) was calculated using the 2021 CKD-EPI creatinine equation without race adjustment.\u003c/p\u003e\u003cp\u003e4 \u003cb\u003eStatistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eContinuous variables were assessed for normality using the Shapiro-Wilk test. Non-normally distributed data presented as median (interquartile range, IDR) and compared across groups using the Kruskal-Wallis H test, with post hoc Dunn-Bonferroni correction for pairwise comparisons. Categorical variables were expressed as frequencies (percentages) and analyzed using the χ\u0026sup2; or Fisher\u0026rsquo;s exact tests, as appropriate. Spearman\u0026rsquo;s rank correlation evaluated relationships between HE4 and fibrosis biomarkers (e.g., TGF-β1, COLI, ACTA2, PIIINP) or eGFR, with significance thresholds *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **​p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ​***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003eMultivariate linear regression identified predictors of TGF-β1 using stepwise variable selection [variance inflation factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;5 to exclude multicollinearity]. Stratified regression by CKD stage quantified HE4-eGFR relationships, reported as β( 95% coefficients intervals, \u003cem\u003eCI\u003c/em\u003e). The R mediation package (v4.5.0) tested mediation effects using 5,000 bootstrap replicates to estimate average causal mediation effects (ACME), direct effects (ADE), and total effects. ROC curve analysis evaluated HE4\u0026rsquo;s diagnostic utility, with optimal cutoffs determined by Youden\u0026rsquo;s index.\u003c/p\u003e\u003cp\u003eAnalyses were conducted in SPSS 26.0 (IBM) for statistical tests, R 4.3.0 (R Foundation) for mediation analysis, CurveExpert 1.4 for ELISA data processing, GraphPad Prism 9.0 for figure generation, and BioRender.com for pathway diagram creation.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eClinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study enrolled 297 participants,comprising 66 healthy controls (NC) and 231 chronic kidney disease (CKD) patients stratified by disease severity (G2: 54 patients, G3: 70 patients, G4: 56 patients, G5: 51 patients). Demographic analysis revealed comparable median ages across groups (64\u0026ndash;68 years; Kruskal-Wallis \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), with a progressive male predominance increasing from 31.8% in NC to 58.8% in G5. Comorbidity profiles showed declining diabetes prevalence from 46.3% (G2) to 25.4% (G5) ( \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), while hypertension remained highly prevalent (48.1%\u0026ndash;55.4%) without significant stage-dependent differences ( \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Renal function markers demonstrated exponential deterioration: eGFR declined from 98.6 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e (NC) to 7.2 \u003cem\u003emL/min/1.73m\u0026sup2;\u0026nbsp;\u003c/em\u003e(G5) (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), accompanied by 28.5-fold increase in serum creatinine (Scr: 62.0 \u0026rarr; 604.0 \u003cem\u003e\u0026mu;mol/L\u003c/em\u003e) and 4.0-fold increase in blood urea nitrogen (BUN: 5.25 \u0026rarr; 20.9 \u003cem\u003emmol/L)\u003c/em\u003e, respectively (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Strikingly, serum HE4 exhibited exponential progression from 48.8 \u003cem\u003epmol/L\u003c/em\u003e (NC) to 1117.0 \u003cem\u003epmol/L\u003c/em\u003e (G5) (22.9-fold, p \u0026lt; 0.05), \u0026nbsp;strongly correlating with renal dysfunction severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHE4 as a Predictive Biomarker for Renal Function Decline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis (Table 2) revealed strong positive associations between HE4 and serum creatinine (r = 0.896) as well as urea nitrogen (r = 0.822) (both p \u0026lt; 0.01), indicating its marked elevation with renal function deterioration. Notably, HE4 exhibited a robust inverse correlation with eGFR (r = \u0026minus;0.919, p \u0026lt; 0.01), highlighting its sensitivity to declining glomerular filtration capacity and the potential utility in monitoring CKD progression. Serum HE4 showed progressive, stepwise elevation across advancing CKD stages (Figure 1A), consistent with escalating renal injury severity. As illustrated in Figure 1B, log₁₀-transformed HE4 levels showed a significant linear inverse relationship with eGFR (log₁₀[HE4] = \u0026minus;0.0132 \u0026times; eGFR + 2.943; 95% CI: \u0026minus;0.01402 to \u0026minus;0.01238; R\u0026sup2; = 0.7727), further validating its predictive value for renal dysfunction. This log-linear association suggests HE4\u0026apos;s potential for developing clinical models to estimate eGFR, offering a complementary approach to traditional biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInverse Association Between HE4 and eGFR Across CKD Stages: A Stratified Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate linear regression analysis (adjusted for age, diabetes, and hypertension) \u0026nbsp;revealed stage-specific associations between HE4 and eGFR in CKD patients (Table 3). Significant independent inverse correlations were observed in CKD G3 (\u0026beta; = -0.316, \u003cem\u003ep\u003c/em\u003e = 0.010) and G4 (\u0026beta; = -0.280, \u003cem\u003ep\u003c/em\u003e = 0.035), indicating that each 1 pmol/L HE4 elevation corresponded to eGFR declines of 0.316 and 0.280 \u003cem\u003emL/min/1.73m\u0026sup2;\u003c/em\u003e, respectively. No significant associations were detected between HE4 and G2 (\u003cem\u003ep\u003c/em\u003e = 0.136) or G5 (\u003cem\u003ep\u003c/em\u003e = 0.195), potentially reflecting compensatory renal mechanisms in early-stage CKD (G2) and multifactorial confounding in advanced disease (G5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic Changes in Fibrosis Markers Across CKD Stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding upon the stratified regression findings showed that HE4\u0026rsquo;s diagnostic utility in CKD G4-G5 appears to reflect fibrogenic activity rather than real-time renal function, (Table 3). Subsequent analysis of HE4\u0026apos;s role in renal fibrogenesis demonstrated stage-dependent elevation of fibrosis biomarkers: TGF-\u0026beta;1 increased from 1,080.82 \u003cem\u003epg/mL\u003c/em\u003e (NC) to 11,557.2 \u003cem\u003epg/mL\u003c/em\u003e (G5), while COL1 rose from 0.94 \u003cem\u003eng/mL\u003c/em\u003e to 112.11 \u003cem\u003eng/mL\u003c/em\u003e (p \u0026lt; 0.01), indicative of accelerated collagen deposition. Concurrent increases in PIIINP (5.25\u0026nbsp;\u0026rarr;\u0026nbsp;18.40 \u003cem\u003eng/mL)\u0026nbsp;\u003c/em\u003eand ACTA2 (1.33\u0026nbsp;\u0026rarr;5.90 \u003cem\u003eng/mL\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 for all) confirmed progressive myofibroblast activation and extracellular matrix (ECM) remodeling, mirroring CKD stage progression and structural deterioration, (Figure 2) .\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed strong positive associations between HE4 and fibrogenic markers: TGF-\u0026beta;1 (r = 0.939), COL1 (r = 0.683) , PIIINP (r = 0.839), and ACTA2 (r = 0.723, all\u003cem\u003e\u0026nbsp;p\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). Notably, TGF-\u0026beta;1 exhibited the strong correlations with all biomarkers (vs. COL1: r = 0.713; vs. PIIINP: r = 0.805; vs. ACTA2: r = 0.749; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), underscoring its central regulatory role in the fibrotic signaling network, (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Regression Reveals HE4-Driven Fibrosis via TGF-\u0026beta;1 Signaling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate linear regression analysis (adjusted R\u0026sup2; = 0.903) revealed serum HE4 concentration as the strong independent predictor of TGF-\u0026beta;1 levels, demonstrating a robust positive association (\u0026beta; = 0.665, p \u0026lt; 0.001). Each 1 \u003cem\u003epmol/L\u003c/em\u003e increase in HE4 corresponded to a 5.187-unit elevation in TGF-\u0026beta;1 (95% CI: 4.664\u0026ndash;5.710). Renal dysfunction, quantified by eGFR decline, exhibited a significant inverse relationship with TGF-\u0026beta;1 (\u0026beta;\u0026nbsp;= -0.320, p \u0026lt; 0.001), every 1 \u003cem\u003emL/min/1.73m\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e reduction in eGFR predicting 42.339-unit increase in TGF-\u0026beta;1. Among fibrosis biomarkers, ACTA2 showed marginal predictive value (\u0026beta;\u0026nbsp;= 0.051, p = 0.047), associated with an 88.597-unit TGF-\u0026beta;1 increase per \u003cem\u003eng/mL\u003c/em\u003e rise in ACTA2. Traditional renal function indices (serum creatinine, blood urea nitrogen), demographic variables (age, sex), comorbidities (diabetes, hypertension), and collagen markers (COL1, PⅢNP) showed no significant associations\u0026nbsp;(all\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026gt; 0.05). Notably,, HE4 and eGFR together accounted for 90.3% of the explained variance in TGF-\u0026beta;1 levels (Table 5), demonstrating their predominant role\u0026nbsp;in TGF-\u0026beta;1 regulation within this cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTGF-\u0026beta;1 Mediates the Profibrotic Effects of HE4 on COL1 and ACTA2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether HE4 promotes fibrosis through TGF-\u0026beta;1-dependent mechanisms, we performed causal mediation analysis (Table 6). The results revealed distinct regulatory patterns for ACTA2 and COL1: For ACTA2\u0026nbsp;regulation, HE4 significantly promoted myofibroblast activation exclusively through TGF-\u0026beta;1-dependent mechanisms, demonstrating a robust average causal mediation effect (ACME = 0.702, p \u0026lt; 0.001) but a nonsignificant direct effect (ADE = -0.143, p = 0.47). In contrast, COL1 regulation exhibited dual mechanisms: while TGF-\u0026beta;1/ COL1 mediated strong profibrotic effects (ACME = 0.951, p \u0026lt; 0.001), HE4 appeared to suppress COL1 through non-canonical pathways (ADE = -0.564; p \u0026lt; 0.001) , though direct mechanistic validation in renal fibrosis remains necessary. These findings demonstrate bidirectional regulation of COL1\u0026nbsp;\u0026mdash;\u0026nbsp;where TGF-\u0026beta;1-driven ECM deposition is counterbalanced by non-canonical HE4-mediated suppression\u0026mdash;in sharp contrast to the unidirectional, TGF-\u0026beta;1-dominant control of ACTA2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarly Diagnosis of Stage 2 CKD: ROC Curve Analysis of HE4 and ACTA2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis demonstrated significant diagnostic potential for HE4 (AUC = 0.860) and ACTA2 (AUC = 0.860) \u0026nbsp;in detecting renal fibrosis among CKD G2 patients (Figure 3). HE4 exhibited balanced performance (sensitivity = 81.5%, specificity = 83.3%), whereas ACTA2 showed higher specificity (97.0%) but lower sensitivity (63.0%). Serum creatinine (Scr), despite high sensitivity (92.6%), demonstrated limited specificity (63.6%) with elevated false-positive rates (Youden index = 0.562). Among individual biomarkers, HE4 achieved optimal diagnostic performance. Strikingly, combined HE4-ACTA2 detection significantly improved diagnostic accuracy (AUC = 0.946, 95% CI: 0.905\u0026ndash;0.986), with enhanced sensitivity (88.9%), specificity (89.4%), and Youden index (0.783), outperforming individual biomarkers.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study elucidates the dual accumulation mechanisms of HE4, which dynamically reflect renal functional decline during CKD progression. Serum HE4 exhibited exponential increase from 48.8 \u003cem\u003epmol/L\u003c/em\u003e in controls to 1,117.0 \u003cem\u003epmol/L\u003c/em\u003e in CKD G5, demonstrating a strong inverse correlation with eGFR (r = -0.919). The passive filtration-accumulation was driven by HE4's molecular weight (25 kDa), which approaches the renal filtration threshold \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. It was evidenced by a log-linear relationship with eGFR (R\u0026sup2; = 0.773), resembling β2-microglobulin kinetics\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Notably, early-stage HE4 doubling (G2\u0026rarr;G3: 75.8\u0026rarr;133.0 \u003cem\u003epmol/L\u003c/em\u003e) sensitively reflected glomerular reserve depletion; Active injury-driven secretion was implicated in post-G4 HE4 surges (215% increase vs. 50% eGFR decline) involving NF-κB-mediated hypersecretion from hypoxic tubular epithelia\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This process was amplified by TGF-β1 positive feedback loops (r\u0026thinsp;=\u0026thinsp;0.939), which promote pathological collagen deposition through mechanisms similar to ECM remodeling in pulmonary fibrosis\u003csup\u003e​[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, including MMP-9 suppression and dysregulated TGF-β1/Smad2 signaling.\u003c/p\u003e\u003cp\u003eWhile the HE4-eGFR association weakened in advanced CKD (CKD5: β = -0.001), its standardized β (-0.176) still retained clinical relevance. This transitional pattern suggests HE4's role from a \"filtration-driven accumulator\" in early stages to a \"fibrosis amplifier\" in late-stage disease. The attenuated statistical significance in G5 may reflect competing pathological processes, wherein HE4's profibrotic effects become partially obscured by synergistic uremic toxin damage (e.g., indoxyl sulfate/p-cresol sulfate)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, which independently activate fibroblast through oxidative stress pathways. Together, these findings establish HE4 as a dual-axis biomarker that mirrors functional impairment (filtration decline) and structural injury (fibrotic activation), enabling comprehensive CKD staging and progression monitoring.\u003c/p\u003e\u003cp\u003eThe HE4-TGF-β1 axis forms a self-reinforcing pathological loop (Spearman's r\u0026thinsp;=\u0026thinsp;0.939, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.882) through three synergistic mechanisms: First, protease inhibition: HE4 stabilizes latent TGF-β1 complexes via its whey-acidic protein (WAP) domain-mediated inhibition of matrix metalloproteinases (MMPs) and serine proteases (e.g., plasmin). This prevents degradation of latent TGF-β binding proteins (LTBP) and amplifies TGF-β1 activation under microenvironmental stimuli \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, similar to the role of secretory leukocyte protease inhibitor (SLPI) in pulmonary fibrosis \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Second, Smad3/extracellular signal-regulated kinase (ERK) crosstalk: Active TGF-β1 phosphorylates Smad2/3 via the TGF-β receptor II (TBRII) \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, driving transcription of fibrotic targets (ACTA2, COL1). Simultaneously, HE4 enhances Smad3 signaling through direct interaction with TBRII (analogous to SLPI's regulation of epidermal growth factor receptor [EGFR] \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e) and activates the Annexin II receptor-dependent ERK1/2-STAT3 axis \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, creating dual amplification of fibrotic signaling. Third, collagen metabolic reprogramming: TGF-β1 promotes COL1 synthesis (average causal mediation effect [ACME]\u0026thinsp;=\u0026thinsp;0.951), while HE4 may regulate COL1 degradation through non-canonical pathways, potentially involving ERK-MMP-dependent proteolysis (average direct effect [ADE]=-0.564) \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This axis establishes a pathogenic feedforward loop akin to advanced glycation end-products (AGEs) in diabetic nephropathy\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, where context-dependent signaling switches drive extracellular matrix remodeling through evolutionarily conserved mechanisms, positioning HE4-TGF-β1 as both a therapeutic target and biomarker for fibrotic progression.​\u003c/p\u003e\u003cp\u003eThe HE4-ACTA2 biomarker panel demonstrates the potential for early CKD detection and management. In stage G2 CKD, this dual-marker combination achieved superior diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.946, 95% CI: 0.905\u0026ndash;0.986; sensitivity 88.9%, specificity 89.4%), outperforming conventional serum creatinine (AUC\u0026thinsp;=\u0026thinsp;0.852). This serum-based approach significantly reduces the inherent drawbacks of traditional invasive diagnostics\u0026mdash;including procedural risks (e.g., hemorrhage, infection) \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, sampling variability, and substantial healthcare burdens, making it particularly viable for resource-constrained settings. Therapeutically, targeting the HE4-TGF-β1 axis might represent a novel, pathogenesisly grounded strategy to interrupt fibrogenesis via anti-HE4 monoclonal antibodies or WFDC2 (HE4) gene silencing, with HE4 serving as both a biomarker and therapeutic target.\u003c/p\u003e\u003cp\u003eBy concurrently evaluating functional decline (HE4-eGFR log-linear correlation) and structural injury (ACTA2-driven fibrosis), this strategy aligns with KDIGO 2024 guidelines\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, bridging the gap between biomarker discovery and precision nephrology implementation.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eWhile this cross-sectional design precludes causal inference, the robust association between HE4 and TGF-β1 (standardized β\u0026thinsp;=\u0026thinsp;0.665, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) persists after rigorous adjustment for age, sex, diabetes status, and baseline eGFR, underscoring its clinical potential. However, the limited sample size of the G5 subgroup (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51) may reduce statistical power to detect nonlinear trends in advanced CKD, necessitating validation in larger cohorts with balanced CKD stage distribution.​\u003c/p\u003e\u003cp\u003eFuture work should incorporate longitudinal cohorts to delineate temporal dynamics and single-cell sequencing to map HE4\u0026rsquo;s cellular sources (injured tubular epithelium or activated fibroblasts) and receptor targets (Annexin II or TβRII). Multicenter validation across diverse etiologies (e.g., diabetic vs. hypertensive CKD) will clarify whether the HE4-TGF-β1 axis operates as a universal fibrotic driver or exhibits etiology-specific modulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study repositions HE4 as a dual-function mediator in CKD, serving both as a biomarker and a pathogenic driver. By elucidating its biphasic kinetics and TGF-β1-dependent fibrogenic circuitry, we establish a framework for CKD early diagnosis and targeted anti-fibrotic therapy. These advances position HE4 as a potential cornerstone of precision nephrology, with transformative implications for CKD management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eResearch funding:\u003c/strong\u003e This work has been financially supported by Discipline Development Fund of Minhang Hospital Affiliated to Fudan University（No. YJXK-2023-21-005）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBingyu Wang: Study design, Data collection, Methodology, Software, Writing \u0026ndash; original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuifen Pan, Aihua Zhang, Junhua Mei, and Yu Zhong: Data collection, Methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhen Zhao: Writing \u0026ndash; review \u0026amp; editing, Visualization, Supervision, Resources, Project\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eadministration, Methodology, Funding acquisition, Formal analysis, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ecompeting interest\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw clinical datasets generated during this study are not publicly available due to patient privacy protections under the ethical approval guidelines of Minhang Hospital, Fudan University (Approval No. 036-01K). De-identified data can be made available upon reasonable request to the corresponding author (email: [[email protected]]) with permission from the institutional ethics committee. Processed biomarker data and statistical analysis scripts are included in the supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRockey DC, Bell PD, Hill JA. Fibrosis--a common pathway to organ injury and failure. N Engl J Med. 2015 Mar 19;372(12):1138-49. doi: 10.1056/NEJMra1300575. PMID: 25785971.\u003c/li\u003e\n\u003cli\u003eHinz B, Phan SH, Thannickal VJ, Galli A, Bochaton-Piallat ML, Gabbiani G. The myofibroblast: one function, multiple origins. Am J Pathol. 2007 Jun;170(6):1807-16. doi: 10.2353/ajpath.2007.070112. PMID: 17525249; PMCID: PMC1899462.\u003c/li\u003e\n\u003cli\u003eUlrich D, Noah EM, Burchardt ER, Atkins D, Pallua N. 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Cancer Res. 2005 Mar 15;65(6):2162-9. doi: 10.1158/0008-5472.CAN-04-3924. PMID: 15781627.\u003c/li\u003e\n\u003cli\u003eBingle L, Singleton V, Bingle CD. The putative ovarian tumour marker gene HE4 (WFDC2), is expressed in normal tissues and undergoes complex alternative splicing to yield multiple protein isoforms. Oncogene. 2002 Apr 18;21(17):2768-73. doi: 10.1038/sj.onc.1205363. PMID: 11965550.\u003c/li\u003e\n\u003cli\u003eLeBleu VS, Teng Y, O\u0026apos;Connell JT, Charytan D, M\u0026uuml;ller GA, M\u0026uuml;ller CA, Sugimoto H, Kalluri R. Identification of human epididymis protein-4 as a fibroblast-derived mediator of fibrosis. Nat Med. 2013 Feb;19(2):227-31. doi: 10.1038/nm.2989. Epub 2013 Jan 27. PMID: 23353556; PMCID: PMC4457508.\u003c/li\u003e\n\u003cli\u003eZhang L, Liu L, Bai M, Liu M, Wei L, Yang Z, Qian Q, Ning X, Sun S. Hypoxia-induced HE4 in tubular epithelial cells promotes extracellular matrix accumulation and renal fibrosis via NF-\u0026kappa;B. 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KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024 Apr;105(4S):S117-S314. doi: 10.1016/j.kint.2023.10.018. PMID: 38490803.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Clinical characteristics and laboratory parameters of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003evariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003cp\u003e(n=66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eCKD G2\u003c/p\u003e\n \u003cp\u003e（n=54）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCKD G3\u003c/p\u003e\n \u003cp\u003e(n=70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCKD G4\u003c/p\u003e\n \u003cp\u003e(n=56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCKD G5\u003c/p\u003e\n \u003cp\u003e(n=51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAge(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e(60.00,67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003cp\u003e(61.00,69.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e(58.75,71.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e(63.25,71.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e(62.00,71.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGender(M/F),n,\u003c/p\u003e\n \u003cp\u003e(n/n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e66(21/45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e54(36/18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70(39/31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56(28/28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e51(30/21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDiabetes n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e25（46.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e30（42.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25（44.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13（25.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHypertension n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e26（48.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e35（50.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e31（55.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27（52.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e98.63\u003c/p\u003e\n \u003cp\u003e(94.36,109.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75.78\u003c/p\u003e\n \u003cp\u003e(67.42, 82.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e49.92\u003c/p\u003e\n \u003cp\u003e(41.14,55.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24.29\u003c/p\u003e\n \u003cp\u003e(20.35,26.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003cp\u003e(4.60,6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003e\u0026mu;mol/L\u003c/em\u003e)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e(58.00,72.00)\u003c/p\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003cp\u003e(74.00,96.25)\u003c/p\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003cp\u003e(105.75,139.50)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e194.5\u003c/p\u003e\n \u003cp\u003e(166.25,251.75)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003cp\u003e(386.00,772.00)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003emmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003cp\u003e(4.20,6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003cp\u003e(4.60,6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003cp\u003e(6.15,9.73)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12.75\u003c/p\u003e\n \u003cp\u003e(11.43,16.05)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.90\u003c/p\u003e\n \u003cp\u003e(15.90,25.80)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHE4(\u003cem\u003epmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e48.80\u003c/p\u003e\n \u003cp\u003e(41.08,57.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75.75\u003c/p\u003e\n \u003cp\u003e(61.75,122.00)\u003c/p\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e133.00\u003c/p\u003e\n \u003cp\u003e(94.98,176.25)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e354.00\u003c/p\u003e\n \u003cp\u003e(250.50,577.50)\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1117.00\u003c/p\u003e\n \u003cp\u003e(581.00,1875.00)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIt showed significant difference compared to corresponding normal control \u0026nbsp;(**P\u0026lt;0.01，***P\u0026lt;0.001, Mann-Whitney U test) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u0026nbsp; Correlation analysis of HE4 with renal function parameters.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"87%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003evariables\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003emmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003e\u0026mu;mol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHE4\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003epmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eAge(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e-0.177\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.155\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.212\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.840\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.961\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.919\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003emmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.840\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.822\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003e\u0026mu;mol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHE4 showed strong positive correlations with traditional renal markers (creatinine and BUN) and a strong negative correlation with eGFR (**P\u0026lt;0.01, Spearman\u0026apos;s correlation ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable Linear Regression Analysis between HE4 and eGFR.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eUnstandardized B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eStandardized \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCKD G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026ndash;0.074 to 0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCKD G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026ndash;0.027 to \u0026ndash;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCKD G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026ndash;0.009 to 0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eCKD G5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026ndash;0.002 to 0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnstandardized beta coefficients (B) represent the absolute decline in eGFR (ml/min/1.73m\u0026sup2;) per 1-unit increase in HE4 (e.g., a 1-unit increase in HE4 was associated with a 0.316 ml/min/1.73m\u0026sup2; decline in eGFR in CKD Stage 3). Variance Inflation Factor (VIF) values for all variables were maintained below 3, indicating no substantial multicollinearity interference in the regression model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Spearman\u0026acute;s correlation matrix between HE4 and renal fibrosis biomarkers (TGF-\u0026beta;, COL1, PⅢNP, ACTA2) in CKD patients.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003evariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTGF-\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCOL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePⅢNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eACTA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eHE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.939\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.683\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.839\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.723\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTGF-\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.713\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.805\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.749\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCOL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.664\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.624\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePⅢNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.636\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable Linear Regression Analysis of TGF-\u0026beta;1 and other parameters in CKD Patients.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eUnstandardized B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eStandardized \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e227.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e189.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;145.410 to 600.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e105.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e181.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;251.238 to 462.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMale (vs. Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-103.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e170.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;440.380 to 232.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-10.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e11.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;34.121 to 12.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eeGFR(\u003cem\u003eml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-42.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;52.625 to \u0026ndash;32.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eBUN(\u003cem\u003emmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e6.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e6.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;6.526 to 19.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eScr(\u003cem\u003e\u0026mu;mol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;0.834 to 1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eHE4 (\u003cem\u003epmol/L\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.664 to 5.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePⅢNP(\u003cem\u003eng/mL\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-11.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e19.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;49.040 to 26.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eCOL1(\u003cem\u003eng/mL\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026ndash;2.821 to 5.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eACTA2(\u003cem\u003eng/mL\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e88.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e44.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.116 to 176.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnstandardized B: Absolute change in TGF-\u0026beta;1 (\u003cem\u003epg/mL\u003c/em\u003e) per 1-unit increase in each predictor. Standardized \u0026beta;: Effect size normalized to standard deviation (SD) units. 95% Confidence interval (\u003cem\u003eCI)\u003c/em\u003e intervals excluded 0 indicating statistical significance. Variance Inflation Factor (VIF) \u0026lt; 5 for all variables, confirming minimal multicollinearity. Adjusted R\u0026sup2; = 0.903, indicating strong explanatory power.\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Mediation Effects of HE4 on Fibrosis Biomarkers Through TGF-\u0026beta;1 Signaling Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eACTA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eACME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0.421 to 1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eADE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u0026ndash;0.590 to 0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0.385 to 0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eProp. Mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0.581 to 2.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCOL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eACME\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0.721 to 1.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eADE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e-0.863 to\u0026ndash;0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e0.278 to 0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eProp. Mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e1.671 to 3.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eACME: Average Causal Mediation Effect (TGF-\u0026beta;1-mediated pathway contribution);\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eADE: Average Direct Effect (HE4\u0026rsquo;s non-TGF-\u0026beta;1 pathway contribution);\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eProportion Mediated: ACME / Total Effect (range \u0026gt;1 indicates suppression paradox); Confidence intervals derived from 5,000 bootstrap replicates. \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (two-tailed significance)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human epididymis protein 4 (HE4), Chronic kidney disease (CKD), Renal fibrosis, TGF-β1 signaling, Biomarker, Diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-7377841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7377841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic kidney disease (CKD) progression is fundamentally driven by renal fibrosis, yet sensitive biomarkers reflecting this pathological process remain elusive. This study systematically investigated the dual roles of human epididymis protein 4 (HE4) as both a functional biomarker and a pathogenic driver in 231 patients with CKD(stages G2-G5)and 66 healthy controls. Quantification of serum biomarkers revealed that HE4 levels increased 22.9-fold, from 48.8 \u003cem\u003epmol/L\u003c/em\u003e in controls to 1117.0 \u003cem\u003epmol/L\u003c/em\u003e in stage G5 CKD, demonstrating a robust inverse correlation with eGFR (r = -0.919, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate regression identified HE4 as a strong predictor of transforming growth factor-β1 (TGF-β1) elevation (β\u0026thinsp;=\u0026thinsp;0.665; 5.187 \u003cem\u003epg/mL\u003c/em\u003e TGF-β1 increase per 1 \u003cem\u003epmol/L\u003c/em\u003e HE4), while mediation modeling delineated its stage-specific mechanisms: HE4 promoted α-smooth muscle actin (ACTA2) synthesis exclusively via TGF-β1-mediated pathways (ACME\u0026thinsp;=\u0026thinsp;0.702), whereas collagen type I (COL1) expression exhibited bidirectional regulation involving TGF-β1-dependent induction (ACME\u0026thinsp;=\u0026thinsp;0.951) and direct HE4 suppression (ADE = -0.564). Clinically, the HE4-ACTA2 panel achieved superior diagnostic accuracy for early-stage CKD (G2, AUC\u0026thinsp;=\u0026thinsp;0.946), with 88.9% sensitivity and 89.4% specificity. These findings establish HE4 as a dual-function mediator in CKD pathogenesis. It not only mirrors glomerular filtration decline but also actively drives fibrogenesis through TGF-β1-positive feedback loops, positioning HE4 as both a precision diagnostic tool and a promising therapeutic target for anti-fibrotic interventions.\u003c/p\u003e","manuscriptTitle":"HE4 Serves as a Dual-Function Mediator in Chronic Kidney Disease：Biomarker of Renal Dysfunction and Instigator of TGF-β1-Driven Fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 00:02:07","doi":"10.21203/rs.3.rs-7377841/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c466af4e-111f-48a2-998c-e45254e0aea3","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T10:27:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 00:02:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7377841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7377841","identity":"rs-7377841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}