Association of serums HIF-1α and HIF-2α with the levels and severity of renal fibrosis: a retrospective cohort study

Abstract

Background and objectives

Renal fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD). Hypoxia is one of the critical factors and plays a role in the development of renal fibrosis. We aim to investigate the relationship between hypoxia-induced factors (HIF-1α and HIF-2α) and the levels and severity of renal fibrosis, and changes in their levels in the CKD population.

Method

We conducted a single-center, retrospective cohort study, that included (n = 204) CKD participants. Participants who were complicated with renal fibrosis were assigned according to the degree of the disease to mild group (n = 61), moderate group (n = 47), and severe group (n = 26). Additionally, (n = 70) healthy participants with normal kidney function were enrolled in the control group. Data and laboratory findings were collected between October 2023 and February 2024.

Results

HIF-1α expression levels increased significantly with the progression of renal fibrosis, the severe group exhibited significantly higher HIF-1α levels compared to the mild group (P < 0.001), moderate group (P < 0.05), and control group (P < 0.001), showing a mild positive correlation coefficient (R = 0.271, 95%CI [0.45–0.49], P < 0.001). HIF-2α, expression levels in the severe group were significantly elevated versus the mild group (P < 0.01), moderate group (P < 0.01), and normal controls (P < 0.001), indicating more pronounced changes during mid-to-late stage fibrosis with a stronger positive correlation coefficient (R = 0.970, 95%CI [0.35–0.38], P < 0.001).

Conclusion

Our findings offer clinical insights into the molecular mechanisms of HIF in renal fibrosis and offer evidence for determining the optimal timing of HIF-related pathways.that to be evaluated for their potential to influence progression in prospective studies.

Introduction

CKD has become one of the most significant public health issues globally, with a median global prevalence of 9.5%, and it is also the 10th leading cause of death worldwide, responsible for over 1.2 million deaths annually [1, 2]. Although early intervention can slow the progression of CKD, many patients remain undiagnosed and untreated due to the lack of obvious symptoms in the early stages. The cost of treating CKD is high, particularly when it progresses to End-Stage Kidney Disease (ESKD), at which point patients often require dialysis or kidney transplantation [3]. With the global aging population and the increasing prevalence of diabetes and hypertension, the burden of CKD is expected to escalate further [4]. Therefore, identifying associated factors to delay the progression of CKD has become a key focus in public health.

The incidence of renal fibrosis is positively correlated with the severity of CKD, and it is particularly pronounced in patients who progress to ESKD [5–7]. In CKD patients, renal fibrosis is often accompanied by irreversible declines in kidney function and is considered a key feature of chronic injury [8]. The characteristic features of renal fibrosis include abnormal deposition of extracellular matrix, infiltration of inflammatory cells, activation of fibroblasts, and vascular remodeling, ultimately leading to the destruction of the renal unit structure [9]. Renal Interstitial Fibrosis (RIF) is regarded as a common final pathway in the progression of various kidney diseases to ESKD [10, 11]. Once renal fibrosis occurs, its process is irreversible. Therefore, identifying new biomarkers associated with fibrosis in this process may be a crucial step in improving disease assessment and prognosis.

An increasing body of research evidence indicates that the progression of CKD exacerbates renal hypoxia, and conversely, renal hypoxia accelerates the course of CKD [12–14]. Fibrosis is considered a critical feature of CKD, and its occurrence and progression are induced by various factors, with hypoxia being one of the key inducers [15]. In 1995, Semenza’s research on the gene regulatory mechanisms of cells responding to hypoxic environments led to the discovery of hypoxia-induced factors (HIFs) [16]. There are three HIF subtypes: HIF-1α, HIF-2α, and HIF-3α, with HIF-1α and HIF-2α being the primary subtypes responsible for mediating the transactivation of hypoxia-related genes [16, 17]. HIF-1α and HIF-2α share 48% amino acid homology and similar structural domains [18, 19]. However, they are not redundant and may play distinct roles in pathophysiological processes. So far, the role of HIF activation in CKD progression remains controversial. In the early stages of renal fibrosis, HIF-1α activation contributes to tissue repair and adaptation to the hypoxic environment. However, in the later stages of the disease, excessive activation of HIF-1α may promote fibroblast proliferation, excessive extracellular matrix (ECM) deposition, and accelerate the progression of renal fibrosis [20, 21]. Meanwhile, in non-diabetic CKD models, depending on the degree of tubulointerstitial hypoxia, activation of HIF-2α in renal tubules has a dual effect on renal fibrosis. When HIF-2α is induced early in CKD, it primarily promotes fibrosis. In contrast, in the later stages of CKD, activation of HIF-2α can protect the kidneys from the progression of fibrosis, thereby preserving kidney function [22, 23]. In this context, the FIB-4 index and the NAFLD fibrosis score (NFS), as non-invasive markers of liver fibrosis, have recently been found to potentially predict the decline in estimated glomerular filtration rate (eGFR). This finding opens up new avenues for identifying biomarkers that can assess and monitor renal fibrosis [24]. Additionally, modulation of endothelial-to-mesenchymal transition (End MT) has been associated with attenuated renal fibrosis and improved kidney function in diabetic kidney disease (DKD) [25].

Further investigation of the expression levels of hypoxia-inducible factors (HIFs) across different stages of renal fibrosis may improve understanding of their associations with fibrotic severity. We hypothesized that serum levels of HIF-1α and HIF-2α are associated with the severity of renal fibrosis. Using enzyme-linked immunosorbent assay (ELISA), we measured the expression levels of HIFs in CKD patients with biopsy-confirmed renal fibrosis. This study aimed to evaluate the association between HIF expression and renal fibrotic severity, and to explore the potential value of HIFs as biomarkers.

Methods

Study design and eligibility

We conducted a single-center retrospective cohort study, n = 204 participants enrolled from the Department of Nephrology, Xuzhou Medical University Affiliated Hospital from October 2023 to February 2024. Based on the degree of renal fibrosis observed from the renal biopsy results, participants were divided into three experimental groups: mild group with (n = 61) participants, moderate group with (n = 47) participants and severe group with (n = 26) participants and (n = 70) healthy participants were enrolled in the control group. The inclusion criteria for the renal fibrosis participates were (1) individuals aged 18–79 years (2) diagnosed with CKD (3) individuals underwent renal biopsy within the past 3 months, with pathological reports clearly documenting renal fibrosis grading (mild/moderate/severe). Exclusion criteria were (1) individuals with other types of kidney diseases or concurrent secondary kidney diseases, (2) severe heart and lung dysfunction, (3) hematologic disorders, (4) autoimmune diseases, (5) endocrine diseases, (6) malignancies, (7) those undergoing renal replacement therapy, (8) use of medications that may affect the expression of HIF-1α or HIF-2α, such as erythropoietin or HIF stabilizers within the past 3 months, (9) pregnant or lactating women. Each investigator conducted the study in compliance with the local or regional regulatory requirements and the ethical standards of the hospital, as well as following the moral principles outlined in the Declaration of Helsinki regarding medical research involving human participants. The research protocol was supervised and regularly reviewed by the Clinical Trial Ethics Committee of Xuzhou Medical University Affiliated Hospital and registered with the Ethics Committee under the registration number (XYFY2024-KL357-01). Due to the retrospective nature of the study, no informed consent was obtained from the participants. The experimental procedure of this study is shown in (Fig. 1).

Fig. 1.

Flowchart of the Study on the Relationship between HIF-1αand HIF-2α Levels and Renal Fibrosis. After assessing patient eligibility, patients were categorized into three groups based on the degree of fibrosis reported in their renal biopsy: mild group (61 cases), moderate group (47 cases), and severe group (26 cases). A healthy control group (70 cases) was also established. Upper serum samples were collected from each group, and the expression levels of HIF-1α and HIF-2α were measured using the Elisa assay

Fibrosis classification data collection

According to the inclusion and exclusion criteria, a total of 204 participants were ultimately enrolled. The study population comprised CKD patients with renal fibrosis confirmed by percutaneous renal biopsy. All biopsy specimens were processed using standard Masson’s trichrome staining. Based on the proportion of fibrotic area within the renal cortical region, patients were classified into three groups: mild fibrosis (< 25%, n = 61), moderate fibrosis (26%–50%, n = 47), and severe fibrosis (> 50%, n = 26). In addition, an age- and sex-matched group of healthy individuals was included as the control group (n = 70).

The clinical and laboratory data were collected from the participants by professionals at our hospital. 5mL of venous blood was collected from the patients. The blood was centrifuged at 3000 rpm for 10 min, and 0.5 mL of the upper serum layer was collected and stored at -80 °C until further analysis. The serum levels of HIF-1α and HIF-2α were measured using a sandwich enzyme-linked immunosorbent assay (ELISA). The ELISA kits were purchased from Xuzhou Meihan Biotechnology Co., Ltd., and the experimental procedures were strictly followed according to the kit’s instructions. The serum levels of HIF-1α and HIF-2α were then compared and analyzed across patients with varying degrees of fibrosis.

Blotting procedure

The specific procedures are as follows: Standards and serum samples were added to the designated wells of the pre-treated ELISA plate, followed by incubation at room temperature for 1 h. After washing, unbound sites were blocked. A biotin-labeled detection antibody was then added and incubated for 30 min. After another wash, an enzyme-labeled antibody was added and incubated for 20 min. Finally, a substrate solution was added for color development. After stopping the reaction, the optical density (OD) values were measured at a wavelength of 450 nm using a microplate reader. A standard curve was plotted, and the concentrations of HIF-1α and HIF-2α were calculated.

Sample size calculation

To determine the adequacy of our study sample size N = 204 for detecting differences among renal fibrosis severity groups (normal, mild, moderate, severe), power analysis was used in a one-way ANOVA context to guide studies, with an effect-size level assumed as being between the medium range (Cohen’s f = 0.25) to 80%, at the alpha level of 0.05; this analysis showed that the exact sample size required was approximately 180 participants. Since our study N = 204 exceeds that threshold, it is considered sufficient to detect medium-sized effects. Retrospective calculations with our observed data may further refine these estimates.

Statistical procedure

SPSS 26.0 and GraphPad Prism were utilized for the analysis. Normally distributed continuous variables were expressed as mean ± standard deviation, while non-normally distributed variables were presented as median and interquartile range. Categorical variables were presented as percentages of observations. When comparing differences between or within groups, we first tested the continuous variables for homogeneity of variances and normality. If the assumptions were met, an independent samples t-test was applied; otherwise, the Mann-Whitney U test was used. The chi-square test was used to analyze categorical variables, continuous variables were expressed as median (interquartile range). Differences among multiple groups were analyzed using non-parametric tests, followed by post-hoc comparisons with adjustment for multiple testing when appropriate. Effect sizes were calculated to evaluate the strength of associations. Spearman correlation analysis was used to evaluate the correlation between two variables. A multinomial logistic regression analysis was conducted to evaluate the association between serum HIF-1α and HIF-2α levels and the severity of renal fibrosis. The degree of renal fibrosis was treated as the dependent variable, with the healthy control group serving as the reference category. Serum HIF-1α and HIF-2α levels were included as independent variables. Odds ratios (ORs) and their 95% confidence intervals (95% CIs) were calculated to estimate the risk of different degrees of renal fibrosis associated with each unit increase in HIF levels. An OR > 1 indicated an increased risk, whereas an OR < 1 indicated a protective effect. A two-tailed ROC curve analysis was performed to evaluate the diagnostic performance of serum HIF-1α and HIF-2α individually and in combination for renal fibrosis, and the area under the curve (AUC), sensitivity, and specificity were calculated. P value of < 0.05 was considered statistically significant.

Results

The baseline characteristics

The patient characteristics are shown in (Table 1). A total of 204 patients were included in this study, with 134 patients in the renal fibrosis group and 70 patients in the control group. There were no significant differences between the two groups in terms of age, gender, weight, height, body mass index (BMI), and hematological parameters (P > 0.05).

Table 1.

Baseline characteristics in the study population stratified according to the levels and severity of renal fibrosis

Characteristics	Mild(n = 61)	Moderate(n = 47)	Severe(n = 26)	Control(n = 70)	P-Value
Age(years)	52.28 ± 12.38	56.09 ± 14.22	54.58 ± 14.73	55.57 ± 12.29	0.181
Sex(male %)	25(41.0%)	25(53.2%)	14(53.8%)	33(47.1%)	-
BMI(kg/m2)	24.44 ± 2.78	24.79 ± 2.89	24.27 ± 4.13	25.19 ± 3.60	0.076
Scr(µmol/L)	105.2 ± 18.6	142.4 ± 28.3	198.5 ± 35.7	76.3 ± 12.1	-
eGFR(ml/min/1.73㎡)	78.5 ± 16.2	54.3 ± 14.7	32.6 ± 9.3	108.7 ± 12.4	-
Hb(g/L)	123.4 ± 11.6	110.5 ± 12.3	96.7 ± 13.2	135.6 ± 10.7	-
Diagnosed diabetes	7/61(11.48%)	6/47(12.66%)	4/2(15.38%)	0	-
Diagnosed hypertension	22/61(36.07)	16/47(34.04)	10/26(38.46)	0	-
CRP(mg/L)	0.90[0.55–2.45]	0.85[0.45–2.30]	0.95[0.60–2.65]	0.85[0.50–2.25]	0.889
Albumin	32.90[27.10-38.75]	32.25[26.55–39.90]	34.10[27.20-39.85]	33.40[26.70–39.80]	0.092
Total cholesterol(mmol/L)	5.64[4.11–7.92]	5.92[4.82–8.01]	5.81[4.49–7.86]	5.93[4.67–7.73]	0.120
Triglycerides(mmol/L)	2.24[1.67–2.96]	2.11[1.59–2.98]	1.86[1.16–2.84]	2.20[1.61–2.92]	0.143
Glycatedhemoglobin(%)	5.80[5.25–6.30]	5.65[5.30–5.95]	5.73[5.40–6.10]	5.78[5.30–6.25]	0.051

Note: Measurement data are given as mean ± SD or number (%), P < 0.05 was deemed statistically significant

Abbreviations: [BMI] body mass index; [CRP] c reactive protein

Expression of HIF-1α in the serum

We analyzed the expression levels of HIF-1α in the serum of patients with varying degrees of renal fibrosis and compared the differences between the mild, moderate, severe, and control groups. As the severity of renal fibrosis increased, serum HIF-1α levels showed a gradual upward trend, with statistically significant differences observed among the groups (P < 0.001). Further pairwise comparisons revealed that HIF-1α levels in the severe fibrosis group were significantly higher than those in the mild fibrosis group, the moderate fibrosis group, and the healthy control group (all P < 0.05). Correlation analysis demonstrated a significant positive association between serum HIF-1α levels and the degree of renal fibrosis (R = 0.271, P < 0.001), indicating that HIF-1α levels increased in parallel with the progression of renal fibrosis (Fig. 2A and C).

Fig. 2.

Distribution of HIF-1α (A) and HIF-2α (B) values in the serum of patients in the mild, moderate, severe, and control groups. The distribution of HIF-1α (C) and HIF-2α (D) values in the serum of patients with different degrees of renal fibrosis (Control, Mild, Moderate, Severe)

These results suggest that HIF-1α levels were significantly higher in the severe fibrosis stage compared to the mild group and control group, while the changes in HIF-1α levels were relatively less noticeable in the mild and moderate stages.

Expression of HIF-2α in the serum

We also analyzed the expression levels of HIF-2α in the serum of patients with varying degrees of renal fibrosis and compared the differences between the mild, moderate, severe, and control groups. We further analyzed the expression levels of serum HIF-2α in patients with different degrees of renal fibrosis. The results showed that HIF-2α levels differed significantly among the fibrosis severity groups (P < 0.001) and exhibited a progressive increase with increasing fibrosis severity. Pairwise comparisons demonstrated that HIF-2α levels in the severe fibrosis group were significantly higher than those in the mild fibrosis group, the moderate fibrosis group, and the healthy control group (all P < 0.01). Correlation analysis revealed a significant positive association between serum HIF-2α levels and the degree of renal fibrosis (R = 0.970, P < 0.001), indicating that HIF-2α levels are closely associated with the progression of renal fibrosis (Fig. 2B and D).

These results suggest that HIF-2α levels were significantly higher in the severe fibrosis stage compared to the mild group and control group, while the changes in HIF-2α levels were relatively less noticeable in the mild and moderate stages.

Multinomial logistic regression analysis

This study employed a multinomial logistic regression analysis, with HIF-1α and HIF-2α as independent variables and the degree of renal fibrosis as the dependent variable. The healthy control group (non-fibrosis group) was used as the reference category for comparative analysis to evaluate the association between serum levels of HIF-1α and HIF-2α and the severity of renal fibrosis (mild, moderate, and severe). The logistic regression results revealed the following associations: In the mild fibrosis group: HIF-1α (OR = 1.00, 95% CI: 0.972–1.028, P = 0.981) showed no significant association, whereas HIF-2α (OR = 1.038, 95% CI: 1.003–1.074, P = 0.035) exhibited a statistically significant positive association. In the moderate fibrosis group: Both HIF-1α (OR = 1.057, 95% CI: 1.025–1.090, P < 0.001) and HIF-2α (OR = 1.050, 95% CI: 1.011–1.091, P = 0.012) demonstrated significant positive associations. In the severe fibrosis group: Both HIF-1α (OR = 1.122, 95% CI: 1.072–1.174, P < 0.001) and HIF-2α (OR = 1.122, 95% CI: 1.059–1.189, P < 0.001) showed strong and statistically significant positive associations (Table 2).

Table 2.

Multinomial logistic regression

Mild	P-Value	OR	OR(95% CI)
HIF-1α	0.981	1.000	0.972 ~ 1.028
HIF-2α	0.035	1.038	1.003 ~ 1.074
Intercept	0.093	0.244	—
Moderate	P-Value	OR	OR(95% CI)
HIF-1α	0.000	1.057	1.025 ~ 1.090
HIF-2α	0.012	1.050	1.011 ~ 1.091
Intercept	0.000	0.008	—
Severe	P-Value	OR	OR(95% CI)
HIF-1α	0.000	1.122	1.072 ~ 1.174
HIF-2α	0.000	1.122	1.059 ~ 1.189
Intercept	0.000	0.000	—

Note: Renal fibrosis severity (mild, moderate, severe) was treated as the dependent variable, with healthy controls (no fibrosis) as the reference category. Serum HIF-1α and HIF-2α levels were included as independent variables in the multinomial logistic regression model. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the association between each unit increase in HIF-1α or HIF-2α and the risk of different degrees of renal fibrosis. A two-tailed P value < 0.05 was considered statistically significant

Abbreviations: OR, odds ratio; CI, confidence interval

Analysis of the predictive value of HIF-1α, HIF-2α, and their combination

In individual testing, HIF-1α and HIF-2α showed AUC values of 0.653 and 0.665, respectively, with relatively high sensitivity. This suggests that HIF-1α and HIF-2α alone have limited diagnostic value for detecting renal fibrosis. However, the combined detection of both markers yielded an improved AUC of 0.700 and higher specificity, demonstrating moderate diagnostic utility for renal fibrosis (Fig. 3).

Fig. 3.

ROC curves of HIF-1α, HIF-2α, and their combination. HIF-1α and HIF-2α yielded AUC values of 0.653 (95% CI: 0.575–0.731) and 0.665 (95% CI: 0.585–0.745), respectively. The combined detection improved the AUC to 0.700 (95% CI: 0.626–0.774) with higher specificity, indicating moderate diagnostic performance. All P values were < 0.01. Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval

Discussion

Our study provides novel insights into the differential expression patterns of HIF-1α and HIF-2α at different stages of kidney fibrosis, both HIF-1α and HIF-2α levelswere positively correlated with the severity of fibrotic changes.

Specifically, the levels of both factors were significantly higher in advanced fibrotic stages compared wjth mild fibrosis and control groups, whereas relatively stable expression was observed during early and intermediate stages. Notably, while HIF-1α demonstrated a moderate positive correlation with fibrosis progression, HIF-2α exhibited a more pronounced association, particularly exhibiting substantial changes during the late fibrotic phase. However, it is noteworthy that we observed an exceptionally high Spearman correlation coefficient (R = 0.970) between HIF-2α levels and the degree of renal fibrosis. This near-perfect correlation may, on the one hand, indicate a strong biological association between HIF-2α and renal fibrosis under chronic hypoxic conditions, supporting its potential value as a biomarker for assessing fibrotic severity. On the other hand, methodological factors cannot be entirely excluded, and further validation in larger, independent cohorts as well as longitudinal studies is required to clarify the temporal dynamics and clinical significance of this exceptionally strong association. HIFs are major transcriptional regulators for hypoxia-driven changes within the cell [16]. From the HIF family members, two major forms, HIF-1α and HIF-2α, orchestrate the renal responses to hypoxic environments possibly contributing to inflammation and fibrotic processes [26]. Although several studies have implicated the role of HIFs in kidney injury and fibrogenesis, the precise expression patterns and regulation of HIFs through different stages of fibrosis remain uncharacterized. Our findings help address this gapsby demonstrating stage-dependent associations between HIFs and fibrotic progression.

The expression levels of HIF-1α showed a positive correlation with the degree of renal fibrosis, which aligns with the pro-fibrotic effects observed by Hao Zhao et al. [27] in a cisplatin-induced CKD model, further supporting the fibrogenic role of HIF-1α. Moreover, our study revealed stage-specific activation of HIF-1α during mid-to-late phase renal fibrosis, suggesting distinct temporal regulation patterns among HIF isoforms in disease progression. Tanaka et al. [28] found that mild activation of HIF-1α at the onset of CKD induces adaptive protective responses, but this becomes a stimulus for renal fibrotic progression in the later stages of the disease. HIF-1α might worsen tubulointerstitial fibrosis through epithelial-mesenchymal transition and inflammation [29]. However, HIF-2α is more closely correlated with the progression of fibrosis, showing a significant change during the advanced fibrotic stage. Kong et al. [22] demonstrated that overexpression of HIF-2α in later stages of CKD suppresses fibrotic progression and preserves renal function [22], which is consistent with the results obtained in our study. Moreover, Kim et al. [30] suggested that sustained activation of HIF-2α not only retards fibrotic progression but also improves renal function, implying that sustained activation of renal HIF-2α expression may be an interesting approach to treatment in CKD.

To our knowledge, there is no data reported regarding the different times of the HIF-1α against HIF-2α across all the stages of renal fibrosis. However, some papers have documented a pattern of differential hypoxic sensitivity in these isoforms. Between the two, HIF-1α reacts quicker in response to hypoxia, facilitating acute adaptive responses to oxygen deprivation. By contrast, HIF-2α shows a slower response to hypoxia but is persistent and contributes to hypoxia-dependent processes employing its distinct physiological and pathological functions [30].

Our study aimed to establish a general framework linking fibrotic severity with differential HIF isoform expression in CKD. The investigation was directed towards exploring the fibrotic aspects of CKD that might assist in guiding more properly targeted therapies and associated factors for certain stages. This is particularly relevant in advanced fibrotic stages, where HIF-2α expression shows a strong association with fibrotic severity; therefore, the authors will attempt to identify optimal time windows and dosage for manipulation of HIF-2α under chronic hypoxic conditions to maximize both anti-fibrotic and restoring kidney function two-fold protective effects by HIF-2α.

Several limitations must be considered within this present study. First, the present study has taken place in one nephrology department with a relatively small sample size and very homogeneous patients, and, accordingly, these factors may influence the generalization of the findings obtained, moreover, a single regional biopsy is not usually equal to the whole fibrotic state of the kidney, thus a bias may come up in the evaluation. Second, the study did not comprehensively explore how factors such as infections, inflammation, metabolism, and genetic predisposition could influence the expression of HIF-1α and HIF-2α. Third, this study did not thoroughly investigate the impact of renal function on serum HIF levels, which may, to some extent, compromise the accuracy of interpreting the association between serum HIF levels and the severity of renal fibrosis. Declining renal function is often accompanied by systemic alterations such as impaired oxygen metabolism, anemia, and inflammation, which may increase or decrease circulating HIF levels. Moreover, renal function itself is closely related to renal fibrosis progression and may therefore act as a potential confounder. Fourth, we measured serum HIF-1α and HIF-2α levels but did not assess their expression in renal biopsy tissue, resulting in a lack of tissue-level validation. Therefore, future studies should comprehensively and systematically control for the aforementioned confounding variables and adopt a larger, multicenter design to validate our findings. Incorporating additional methods for fibrosis assessment may further improve the accuracy of fibrosis evaluation. Potential confounders should be more strictly and systematically controlled to ensure the reliability and specificity of the detection approach. In particular, future research should include renal function and other relevant indices and apply appropriate statistical adjustments to improve the specificity and interpretability of the results. Finally, given that serum HIF levels may be influenced by systemic conditions and renal function, subsequent studies should integrate tissue-based immunohistochemistry/immunofluorescence analyses of renal biopsy specimens to further validate and extend our observations.

Conclusion

This study provides preliminary evidence for the correlation between hypoxia-inducible factors (HIF-1α and HIF-2α) and the degree of renal fibrosis. The findings revealed that serum HIF-1α and HIF-2α levels increased with the severity of renal fibrosis, with HIF-2α demonstrating a more pronounced positive correlation with renal fibrosis progression. However, this study has certain limitations, including the failure to adjust for potential confounding factors such as renal function and the lack of assessment of hypoxia-inducible factor levels in renal tissue. Further research is needed to elucidate the specific cellular and molecular mechanisms of HIF signaling pathways in renal fibrosis, and to assess the feasibility of HIF-related pathways as potential therapeutic targets, as well as to define the timing that should be tested in future prospective or experimental studies.

Abbreviations

BMI

Body Mass Index

BUN

Blood Urea Nitrogen

CKD

Chronic Kidney Disease

CVD

Cardiovascular Disease

eGFR

Estimated Glomerular Filtration Rate

ESKD

End-Stage Kidney Disease

HIF

Hypoxia-Inducible Factor

RIF

Renal Interstitial Fibrosis

ECM

Extracellular Matrix

CPR

C Reactive protein

Author contributions

Xin Zhang: Conceptualization, Methodology, Data Collection, Writing - Original Draft, Writing - Review & Editing, Software. Weiwei Zhang: Data Curation, Methodology. Yousuf Abdulkarim Waheed: Writing - Original Draft, Writing - Review & Editing, Investigation, Supervision, Software. Shulin Li: Writing - Original Draft, Writing - Review & Editing. Dong Sun: Corresponding Author, Investigation, Funding Acquisition, Supervision, Project Administration, Writing - Review & Editing.

Funding

This study was supported by funding from the National Natural Science Foundation of China (82470726, 82270731, 82000703); the Jiangsu Provincial Natural Science Foundation (BK20211054); Science and technology development fund of Affiliated Hospital of Xuzhou Medical University (XYFC2020001; XYFY2020038); The High-Level Hospital Construction Project of Jiangsu Province(LCZX202403);“Paired Assistance Scientific Research Project by The Affiliated Hospital of Xuzhou Medical University(SHJDBF2024104); Xuzhou Basic Research Program (KC22042); The Open Project of Key Laboratory of Higher Education Institutions in Jiangsu Province (XZSYSKF2023019) Xuzhou Medical leading Talent training Project (XWRCHT20210038);Beanstalk talent of Affiliated Hospital of Xuzhou Medical University; the New Technology project of Affiliated Hospital of Xuzhou Medical University (2020301018).

Data availability

The data can be requested from the corresponding author with a reasonable request.

Declarations

Ethical approval

The study was approved by the ethics committees of the participating hospital (Ethics No.XYFY2024-KL357-01). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee.

Consent for publication

All authors accept this work for publication.

Competing interests

The authors declare no competing interests.