Abstract

Background: Long-term peritoneal dialysis (PD) frequently induces chronic peritoneal inflammation, fibrosis, and increased peritoneal solute transport rate (PSTR). This study assessed whether effluent interleukin-17 (IL-17) predicts increased PSTR and relates to peritoneal fibrosis.

Methods: This prospective cohort enrolled 130 PD patients, of whom 115 completed 1-year follow-up. Effluent IL-17, transforming growth factor-β (TGF-β), and fibronectin (FN) were measured by enzyme-linked immunosorbent assay (ELISA). Peritoneal function was assessed by dialysate-to-plasma creatinine ratio (D/P Cr) at baseline and 1 year. We used correlation analysis, multivariable regression, interaction analysis, and Receiver operating characteristic (ROC) analysis to evaluate the predictive value of effluent IL-17 for increased PSTR. A mouse peritoneal fibrosis model was used to evaluate local IL-17 expression by immunohistochemistry and real-time quantitative PCR (RT-qPCR).

Results: Effluent IL-17 was higher in patients with increased PSTR and correlated positively with TGF-β, FN, and ΔD/P Cr. Multivariable analysis showed that IL-17 independently predicted 1-year D/P Cr after adjustment for baseline D/P Cr and clinical covariates. PD vintage significantly modified the association between IL-17 and increased PSTR. Adding IL-17 to baseline D/P Cr and peritoneal creatinine clearance improved prediction of increased PSTR, with the AUC increasing from 0.705 to 0.789. Furthermore, immunohistochemical and qPCR results showed that IL-17 expression was elevated in fibrotic peritoneal tissues of PF mice compared with control mice.

Conclusion: Effluent IL-17 is associated with peritoneal fibrosis and longitudinal PSTR increase, supporting its potential as a noninvasive biomarker for early risk stratification in PD patients.

Keywords: Peritoneal dialysis; Peritoneal fibrosis; Peritoneal function; Biomarker; IL-17

Introduction

Peritoneal dialysis (PD) is a vital treatment option for end-stage renal disease (ESRD) worldwide, as evidenced by its simplicity, cost-effectiveness, and ability to preserve residual renal function [1]. The efficacy of this therapeutic modality relies heavily upon maintaining the architectural and physiological integrity of the peritoneal membrane. However, chronic inflammation and peritoneal injury are induced by chronic exposure to bioincompatible dialysate (characterized by hypertonicity, high glucose, and acidity). These pathological changes include mesothelial cell denudation, neovascularization, and excessive extracellular matrix deposition, eventually progressing to submesothelial fibrosis [2]. Such structural remodelling compromises vascular permeability and disrupts transport kinetics, driving ultrafiltration failure (UFF) and, ultimately, technique failure [3].The peritoneal equilibration test (PET) remains the clinical standard for evaluating peritoneal function through the quantification of solute transport and ultrafiltration capacity. This assessment relies on measuring the dialysate-to-plasma creatinine ratio (D/P Cr), the 4-hour to 0-hour dialysate glucose ratio (D/D0 glucose), and the 4-hour ultrafiltration volume (4 h UF) [4]. A faster peritoneal solute transport rate (PSTR) is predictive of adverse clinical outcomes. Specifically, an increase of 0.1 units in D/P Cr is associated with a significantly elevated risk of technique failure, hospitalization, and all-cause mortality [5,6]. However, while PET effectively reflects current transport characteristics, it lacks the sensitivity required for the early detection of peritoneal function decline.

While specific effluent biomarkers have been identified, their clinical applications remain limited. Interleukin-6 (IL-6) is correlated with solute transport, inflammation, and fibrosis. However, its diagnostic specificity is compromised by interference from systemic inflammation and interindividual variability [7]. Similarly, cancer antigen 125 (CA125) has been recognized as a biomarker for mesothelial cell mass. Nevertheless, CA125 shows limited prognostic value for adverse outcomes, such as peritonitis or ultrafiltration failure, when used as a single marker [8]. Therefore, the identification of highly specific biomarkers to accurately characterize peritoneal pathological mechanisms remains a critical research priority.

Interleukin-17A (IL-17) is the signature cytokine of the T helper 17 (Th17) lineage. It plays a critical role in regulating inflammation and angiogenesis in autoimmune pathologies, such as multiple sclerosis and rheumatoid arthritis [9]. Mechanistically, IL-17 orchestrates a continuous inflammatory response by enhancing Th17 cell differentiation through the IL-6/TGF-β/RORγt (Interleukin-6/ transforming growth factor β/ retinoic acid-related orphan receptor gamma t) axis and recruiting immune cells via CCL20-dependent chemotaxis [10,11]. While IL-17 levels are negligible in the peritoneum of healthy individuals, this cytokine is significantly upregulated in the peritoneum of patients receiving PD. These elevated levels originate primarily from infiltrating Th17 and γδ T cells [12]. Moreover, accumulating evidence suggests that IL-17 promotes fibrosis across various organ systems. For example, it promotes myocardial fibrosis by activating cardiac fibroblasts, induces pulmonary fibrosis through mitochondrial dysfunction-mediated epithelial apoptosis, and exacerbates liver fibrosis by enhancing TGF-β receptor signalling [13-15]. In hypertensive nephropathy and IgA nephropathy, studies have shown that IL-17 activates downstream signalling cascades, notably NF-κB, to induce the release of inflammatory mediators and aberrant extracellular matrix deposition, consequently driving the progression of renal fibrosis [16,17]. However, research concerning the expression of IL-17 in peritoneal tissue and its correlation with impaired peritoneal function remains limited.

Therefore, in this study, the expression of IL-17 in PD effluent was prospectively evaluated, and its ability to predict longitudinal peritoneal function decline was evaluated, with the goal of identifying a novel noninvasive biomarker for early risk stratification.

Methods

Study Design and Population

This prospective cohort study included 130 patients who received regular PD follow-up at the Department of Nephrology, the First Affiliated Hospital of Anhui Medical University, between September 2023 and September 2025. The follow-up duration was 1 year. This research was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (approval number: PJ 2025-08-97). Eligible patients were aged between 18 and 80 years, on stable peritoneal dialysis replacement therapy (PD vintage ≥ 3 months) using only commercially available peritoneal dialysis solutions with 1.5% or 2.5% glucose, and had a stable PD regimen (no adjustments for at least 1 month). Exclusion criteria included peritonitis within the past month, autoimmune diseases (e.g., lupus nephritis, rheumatoid arthritis, Sjögren's syndrome, and chronic lymphocytic thyroiditis), infections (e.g., hepatitis B virus, hepatitis C virus, and syphilis), malignant or benign tumors, and use of antibiotics or hormones at the time of sample collection. All animal procedures were approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20241406). Male C57BL/6 mice (20–24 g, n = 12) were randomly allocated into two groups (control group n = 6, PF group n= 6). To establish a peritoneal fibrosis (PF) model, mice in the experimental group received daily intraperitoneal injections of 2 mL of 4.25% glucose-based dialysis solution for 28 consecutive days. The control group received an equal volume of isotonic saline (0.9% NaCl) following the same schedule.

Data Collection and Variables

General clinical data included the following: (1) demographic characteristics, such as age, sex, height, weight, and body mass index (BMI); (2) PD vintage, defined as the time from PD initiation to study enrolment (years); (3) comorbidities such as diabetes mellitus, hypertension, and cardiovascular disease; and (4) laboratory examinations, including routine biochemical parameters (renal function, blood urea nitrogen (BUN), serum creatinine (Scr), and parathyroid hormone (PTH)), electrolytes (serum calcium and serum phosphate (P)), nutritional indicators (serum albumin [Alb] and haemoglobin [Hb]), lipid metabolism parameters (total cholesterol [TC] and triglyceride [TG]), inflammatory markers (neutrophil count [NE], lymphocyte count [LYM], monocyte count [MONO], and C-reactive protein [CRP]), and corrected serum calcium concentration calculated as: corrected serum calcium (mmol/L) = serum calcium + 0.02 × (40 − Alb [g/L]). Cardiac indices included left ventricular ejection fraction (EF), left atrial diameter (LA), left ventricular diameter (LVD), and left ventricular posterior wall thickness (LVPWD).

Peritoneal function measurement

On the night before the PET, patients were required to retain peritoneal dialysis fluid for 8–12 hours. On the test day morning, after emptying the bladder and bowels, patients assumed a standing position, and the existing PD fluid was completely drained from the peritoneal cavity within 20 minutes. Subsequently, 2 L of 2.5% glucose PD solution was rapidly infused into the peritoneal cavity within 10 minutes, with patients turning from side to side after each 400 mL administration. Upon completion of infusion, the dialysis tubing was clamped, and the PD fluid was retained in the peritoneal cavity for 4 hours.First, original PD fluid was collected immediately (0 minutes) after complete infusion to obtain a 0-hour PD fluid sample. At exactly 2 hours of dwell time, venous blood and PD effluent samples were collected. After maintaining the dialysis position for precisely 4 hours, patients switched to a seated or standing posture, and the PD fluid in the peritoneal cavity was completely drained into a collection bag within 20 minutes. Following drainage, the total volume of effluent was measured and recorded. Subsequently, additional PD effluent samples were collected from the drainage bag [4]. The following parameters were calculated: Dialysate-to-plasma creatinine ratio (D/P Cr) = dialysate creatinine (4 h) / plasma creatinine (2 h) Dialysate glucose ratio (D/D0 glucose) = dialysate glucose (4 h) / dialysate glucose (0 h) At 12 months (±1 month) after baseline, peritoneal function parameters D/P Cr and D/D0 glucose were evaluated again. Definitions: ΔD/P Cr = D/P Cr 1 year later − baseline D/P Cr ΔD/D0 glucose = D/D0 glucose 1 year later − baseline D/D0 glucose Faster PSTR was defined as ΔD/P Cr > 0 at 1-year follow-up.

Biomarker Detection

Peritoneal effluent sample collection: After rigorous application of the inclusion and exclusion criteria, 10 mL of 4-hour peritoneal dialysis effluent was collected from the study subjects. The samples were aliquoted and balanced in a laboratory centrifuge. The centrifuge was set to 3,600 RPM and run for 10 minutes. The resulting precipitate was removed, and the supernatant solution was collected and aliquoted into EP tubes (1 mL per tube). To avoid repeated freeze‒thaw cycles, aliquots were immediately placed in a -80 °C freezer to await analysis. Enzyme-linked immunosorbent assay (ELISA) kits were provided by MeiKe. The supernatant stored at −80 °C was thawed at room temperature, then vortexed and centrifuged again. All experimental procedures were meticulously executed in accordance with the manufacturer’s instructions. With a standard curve R² > 0.99, the concentrations of IL-17, TGF-β, and fibronectin (FN) in the samples were calculated.

Immunohistochemical Staining of Peritoneal Tissue for IL-17

Peritoneal specimens were immediately fixed in 4% paraformaldehyde, followed by sequential dehydration with increasing concentrations of ethanol (70%–100%). The tissues were subsequently cleared in xylene and embedded in paraffin blocks. Thin sections (3 μm) were prepared, deparaffinized, and rehydrated with descending ethanol gradients. Antigen retrieval was performed using heat induction in a citrate buffer solution under pressure for 5 minutes. Endogenous peroxidase activity was quenched using a peroxidase inhibitor (OriGene Tech) at 37 °C for 30 minutes. Prior to antibody incubation, nonspecific epitopes were blocked with 10% goat serum (37 °C, 30 min). Tissue sections were then probed overnight at 4 °C with a rabbit-derived anti-IL-17 primary antibody (1:100 dilution). Following extensive washing, HRP-conjugated secondary antibodies were applied (37 °C, 30 min). Antigen‒antibody interactions were visualized using 3,3′-diaminobenzidine (DAB) as a chromogen, and nuclear counterstaining was performed with Mayer’s haematoxylin. Digital micrographs were acquired using a Leica DM6B brightfield microscope under standardized lighting conditions.

Real-time Quantitative PCR (RT-qPCR)

Total RNA was extracted from mouse peritoneal tissues using a column-based purification method. Before extraction, surgical instruments (forceps and scissors) and grinding beads were disinfected in 75% ethanol and dried in an oven at 60 °C for 15 minutes to ensure an RNase-free environment. Approximately 30–50 mg of peritoneal tissue was placed into an RNase-free EP tube containing 500 µL of lysis buffer (Buffer RL) and two grinding beads. Tissues were homogenized for 5 minutes using a tissue grinder to ensure complete lysis. The homogenate was incubated at room temperature for 5 minutes and then centrifuged at 14,000×g for 5 minutes. The supernatant was transferred to a gDNA-depletion column to remove genomic DNA (14,000×g, 2 min). The flow-through was loaded onto an RNA purification column and washed sequentially with Buffer RW1 and ethanol-diluted Buffer RW2. After discarding the final wash solution, the column was centrifuged at 12,000×g for 2 minutes to remove residual ethanol. Finally, 30 µL of RNase-free water was added to the center of the column membrane. After incubation at room temperature for 2 minutes, total RNA was eluted by centrifugation and stored at −80 °C. The concentration and purity of the isolated RNA were determined using a spectrophotometer. Samples with an A260/A280 ratio between 1.8 and 2.1 were used for subsequent experiments.Reverse transcription was performed using a two-step protocol to synthesize cDNA. First, the RNA concentration of each sample was adjusted to ensure equal template input. Residual genomic DNA was removed using a gDNA remover at 42 °C for 2 minutes. Subsequently, Hifair® III SuperMix plus was added to the reaction mixture. Reverse transcription was performed in a thermal cycler using the following program: 25 °C for 5 min, 55 °C for 15 min, and 85 °C for 5 min. The resulting cDNA was stored at −20°C.Real-time PCR was performed using the QuantStudio 6 Flex Real-time PCR System. The PCR reaction mixture (20 µL total volume) consisted of 10 µL SYBR Green Master Mix, 0.4 µL each of forward and reverse primers, 7.2 µL of DEPC-treated water, and 2 µL of cDNA template. All procedures were performed on ice and protected from direct light.Thermal cycling conditions were as follows:Initial denaturation: 95 °C for 5 min; PCR cycles (40 cycles): 95 °C for 10 s (denaturation) and 60 °C for 30 s (annealing and extension); Melting curve analysis: A default melting curve program was run immediately after the final cycle to verify the specificity of amplification products. The relative mRNA expression levels of target genes were calculated using the 2^−ΔΔCt method, with GAPDH used as the internal reference gene for normalization. Statistical analysis and data visualization were performed using GraphPad Prism 10 software. The overall process of this study is shown in Figure 1.

Figure 1. Flowchart of the overall study design. This prospective cohort study enrolled 130 peritoneal dialysis (PD) patients. Peritoneal effluent samples were collected to measure IL‑17, TGF‑β, and fibronectin concentrations. Peritoneal function parameters were evaluated at baseline and after 1‑year follow‑up. A mouse model of peritoneal fibrosis (PF) was established to verify IL‑17 expression in peritoneal tissues by immunohistochemistry and qPCR. Statistical analyses were performed to explore the association between effluent IL‑17 and increased peritoneal solute transport rate (PSTR).

Statistical Analysis

The collected and experimentally obtained data were analysed and organized. Continuous variables demonstrating a Gaussian distribution are presented as arithmetic means with standard deviations (means ± SDs), whereas parameters violating normality assumptions are presented as median values accompanied by interquartile ranges (IQRs). When variable relationships between indicators followed a normal distribution and satisfied the assumptions of homogeneity of variance and independence, Pearson correlation analysis was applied; otherwise, Spearman correlation analysis was used. We implemented univariate and multivariable linear regression models to assess the predictive variables affecting ΔD/P Cr. In addition, we used binary logistic regression to analyse the interaction effect between IL-17 concentration and PD vintage on the binary outcome of increased PSTR (ΔD/P Cr > 0). We utilized Receiver operating characteristic (ROC) analysis to evaluate the prognostic capability of IL-17 for ΔD/P Cr > 0. Two-tailed tests were performed with α = 0.05, and the results were significant at the 0.05 level. All statistical analyses were performed with SPSS, GraphPad Prism, and R software.

Results

Baseline Characteristics

The study population comprised 130 patients receiving PD. All participants were included in the baseline characteristic analysis. As detailed in Table 1, 42.31% of the initial cohort were male, with a median age of 54 years (IQR 44.25–60.00).The mean BMI was 23.10 ± 3.14 kg/m². At baseline, the median peritoneal effluent IL-17 concentration was 40.49 pg/mL (IQR 39.00–41.79 pg/mL). For peritoneal transport parameters, the mean baseline D/P Cr was 0.62 ± 0.10, and the median baseline D/D0 glucose was 0.42 (IQR 0.36–0.47). After 1 year of follow-up, 15 patients were censored (due to death, transplantation, or loss to follow-up), leaving 115 patients (88.5%) for the final outcome analysis. The baseline characteristics of the analyzed cohort (N = 115) are also presented in Table 1 (second column) and were comparable to the initial cohort (all P > 0.05), indicating no significant selection bias. Detailed comparisons are provided in Supplementary Table 1.

Participants were dichotomized based on PSTR into a non-increased PSTR group (Group A: ΔD/P Cr ≤ 0, n = 50) and an increased PSTR group (Group B: ΔD/P Cr > 0, n = 65). Statistical analysis revealed that baseline D/P Cr (Figure 2A, P = 0.001), D/D0 glucose (P = 0.002), peritoneal creatinine clearance (pCcr) (P = 0.013), and IL-17 concentration (Figure 2B, P < 0.0001) differed significantly between the two groups, whereas PD vintage (Figure 2C) and other parameters showed no significant differences.

Figure 2. Comparisons of baseline indicators and correlation analysis of IL‑17. (A) Comparison of baseline dialysate‑to‑plasma creatinine ratio (D/P Cr) between non‑increased PSTR group (Group A) and increased PSTR group (Group B). (B) Comparison of peritoneal effluent IL‑17 levels between two groups. (C) Comparison of PD vintage between two groups. (D) Correlation between effluent IL‑17 and TGF‑β. (E) Correlation between effluent IL‑17 and fibronectin (FN). (F) Correlation between effluent IL‑17 and ΔD/P Cr. ***P < 0.001, ****P < 0.0001, ns: no significance.

Correlation between effluent IL-17 and changes in PSTR

To quantitatively assess the correlation between cytokines and profibrotic factors, the associations between effluent IL-17 and both TGF-β and FN were calculated. Correlation analysis revealed a marked positive correlation between IL-17 and both TGF-β (r = 0.440, P < 0.001) and FN (r = 0.319, P < 0.01). The scatter plot distributions illustrating these linear associations are depicted in Figure 2D and 2E. Furthermore, the positive correlation between IL-17 and ΔD/P Cr (r = 0.318, P < 0.01) suggests the clinical application value of IL-17 (Figure 2F).

Predictive Value of IL-17 for Peritoneal Transport Status

To identify the vital predictors of PSTR, we constructed univariate and multivariable regression models (Table 2). Univariable linear regression showed that ALB, baseline D/P Cr, and pCcr were associated with 1-year D/P Cr. However, in the multivariable regression analysis incorporating PD vintage, age, CRP, baseline D/P Cr, pCcr and IL-17 as covariates, baseline D/P Cr (β = 0.791, P < 0.001) and IL-17 (β = 0.006, P = 0.022) were identified as independent predictors of D/P Cr one year later. In the multivariable model, after adjustment for other variables, each 1 pg/mL increase in effluent IL-17 concentration predicted an average increase of 0.006 units in 1-year D/P Cr. Similarly, each 1-unit increase in baseline D/P Cr was associated with an average increase of 0.791 units in 1-year D/P Cr.

To further investigate the predictive value of IL-17 for changes in peritoneal function, we evaluated its association with 1-year D/D0 glucose (Table 3). Univariate analysis identified baseline D/P Cr, baseline D/D0 glucose, and pCcr as significant correlates of 1-year D/D0 glucose. However, after adjustment for baseline peritoneal function parameters, only baseline D/D0 glucose (β = 0.474, P < 0.001) and IL-17 (β = −0.005, P = 0.040) remained independent predictors of 1-year D/D0 glucose.

We constructed a binary logistic regression model to assess the joint effect of IL-17 levels and PD vintage on an increased PSTR. To explore this interaction, an interaction term (IL-17 × PD vintage) was included in the model. Notably, PD vintage significantly modified the association between IL-17 and peritoneal transport status (P for interaction = 0.028). As shown in Figure 3A, the predictive effect of IL-17 on faster PSTR was weaker in the short-term dialysis group (1 year). However, this association became significantly stronger in patients with longer dialysis durations (3 and 5 years). The increasingly steep slopes indicate that the deleterious effect of elevated IL-17 on faster PSTR is exacerbated by prolonged PD exposure.

Figure 3. Interaction effect and receiver operating characteristic (ROC) curve analysis. (A) Predicted probability plot showing the interaction between IL‑17 concentration and PD vintage on faster PSTR. (B) ROC curves of baseline model (D/P Cr + pCcr) and combined model (D/P Cr + pCcr + IL‑17) for predicting faster PSTR. AUC: area under the curve.

Incremental Predictive Value of IL-17 over Standard Clinical Metrics

While the standard clinical model (including baseline D/P Cr and pCcr) showed acceptable accuracy in predicting faster PSTR (ΔD/P Cr > 0) (the area under the curve [AUC] = 0.705, 95% CI: [0.609–0.801]), the addition of effluent IL-17 significantly enhanced the accuracy. Specifically, the comprehensive model (IL-17 + baseline D/P Cr + pCcr) achieved the optimal predictive accuracy, with an AUC of 0.789 (95% CI: 0.706–0.872), a sensitivity of 72.3%, and a specificity of 74.0%. DeLong test confirmed that this comprehensive model was statistically superior to the base model (P < 0.05; Figure 3B and Table 4). These findings highlight the incremental predictive value of IL-17, indicating that it captures changes in peritoneal function that cannot be reflected by baseline transport status.

Validation of IL-17 expression in a mouse peritoneal fibrosis model

To investigate the specific role of IL-17 in the pathogenesis of peritoneal fibrosis in vivo, we established a murine PF model by daily intraperitoneal injection of 4.25% glucose-based dialysis fluid. As shown in Figure 4A, haematoxylin and eosin (HE) staining revealed notable thickening of the peritoneum and increased inflammatory cell infiltration in the fibrosis group compared with the control group. Masson staining showed that collagen deposition was significantly increased in fibrotic peritoneal tissue. Moreover, immunohistochemistry (IHC) demonstrated marked infiltration of IL-17-positive cells within the fibrotic tissues. These results verified the successful induction of the PF model and indicated that IL-17 expression was upregulated in the fibrotic peritoneum.

Figure 4. Histological staining and mRNA expression in mouse peritoneal tissues. (A) Representative images of HE staining, Masson staining, and IL-17 immunohistochemical staining in peritoneal tissues from control and PF mice (scale bar = 100 μm, 100× magnification). Relative mRNA expression of (B) α-SMA mRNA, (C) TGF-β1 mRNA, and (D) IL-17 mRNA in peritoneal tissues. *P < 0.05, **P < 0.01. HE, haematoxylin and eosin; PF, peritoneal fibrosis; TGF-β1, transforming growth factor-β1; α-SMA, alpha-smooth muscle actin.

Conclusion

Effluent IL-17 is associated with peritoneal fibrosis and longitudinal PSTR increase, supporting its potential as a noninvasive biomarker for early risk stratification in PD patients.

Abbreviations

4 h UF - 4-hour ultrafiltration volume; Alb - Albumin; AUC - Area Under the Curve; BMI - Body mass index; BUN - Blood urea nitrogen; CA125 - Cancer antigen 125; CRP - C-reactive protein; D/D0 glucose - Dialysate-to-initial dialysate glucose ratio; D/P Cr - Dialysate-to-plasma creatinine ratio; EF - Ejection fraction; ELISA - Enzyme-linked immunosorbent assay; ESRD - End-stage renal disease; FN - Fibronectin; Hb - Haemoglobin; IHC - Immunohistochemistry; IL-17 - Interleukin-17; IL-6 - Interleukin-6; LA - Left atrial diameter; LVD - Left ventricular diameter; LVPWD - Left ventricular posterior wall thickness; LYM - Lymphocyte count; MMT - Mesothelial-to-mesenchymal transition; MONO - Monocyte count; NE - Neutrophil count; NF-κB - Nuclear factor kappa-B; pCcr - Peritoneal creatinine clearance; PD - Peritoneal Dialysis; PET - Peritoneal equilibration test; PF - Peritoneal Fibrosis; PSTR - Peritoneal solute transport rate; PTH - Parathyroid hormone; rCcr - Renal creatinine clearance; ROC - Receiver Operating Characteristic; RORγt - Retinoic acid-related orphan receptor gamma t; RT-qPCR - Real-time Quantitative PCR; SCr - Serum creatinine; TC - Total cholesterol; TG - Triglyceride; TGF-β - Transforming growth factor β; Th17 - T helper 17; UFF - Ultrafiltration failure

Supplementary Materials

Declarations

Author Contributions

Lejia Song: Conceptualization, Methodology, Investigation, Formal analysis, Writing-original draft.
Lu Li: Methodology, Supervision, Resources.
Guang Chen: Validation, Software.
Qiufeng Wang: Resources, Data curation.
Qingqing Rao: Supervision, Investigation.
Huaina Dou: Formal Analysis, Visualization.
Li Zhang: Investigation,Software.
Pei Zhang (Corresponding Author): Conceptualization, Funding Acquisition, Supervision, Validation, Writing-Review & Editing;
All authors read and approved the final manuscript.

Acknowledgements

We would like to thank all the patients who participated in this study and the medical staff at the Department of Nephrology, the First Affiliated Hospital of Anhui Medical University, for their assistance in data collection and patient follow-up.

Funding Information

This work was supported by the Anhui Provincial Natural Science Foundation (Grant No. 2408085MH208).

Ethics Approval and Consent to Participate

The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (Approval No. PJ 2025-08-97). The animal study protocol was approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20241406). The human study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Competing Interests

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 Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

References