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Ren Fail
Renal Failure
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10.1080/0886022X.2024.2394164
2394164
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Research Article
Chronic Kidney Disease and Progression
Association of peripheral eosinophil count with chronic kidney disease progression risk: a retrospective cohort study in Chinese population
Y. Ren et al.
Ren Yan
Zhang Jinshi
Hu Xiao
Yu Rizhen
Tu Qiudi
Li Yiwen
Lin Bo
Zhu Bin
Shao Lina
Wang Minmin
Department of Nephrology, Urology & Nephrology Center, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
Supplemental data for this article is available online at https://doi.org/10.1080/0886022X.2024.2394164.

CONTACT Minmin Wang wangminmin@hmc.edu.cn Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
Lina Shao shaolina@hmc.edu.cn Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
30 8 2024
2024
30 8 2024
46 2 239416416 6 2024
11 8 2024
14 8 2024
KnowledgeWorks Global Ltd.29 8 2024
published online in a building issue29 8 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Background

The role of peripheral eosinophils in chronic kidney disease (CKD) requires further evaluation. We aimed to determine whether an eosinophil count increase is related to the occurrence of end-stage renal disease (ESRD).

Methods

This single-center, observational, retrospective cohort study was conducted between January 2016 and December 2018 in Hangzhou, China, and included 3163 patients, categorized into four groups according to peripheral eosinophil count (PEC) quartile values. The main outcome was ESRD development during follow-up. We evaluated the relationship between the serum eosinophil count, demographic and clinical information, and ESRD incidence. Cox proportional hazards models and Kaplan–Meier survival curves were used.

Results

A total of 3163 patients with CKD were included in this cohort, of whom 1254 (39.6%) were females. The median (interquartile range [IQR]) age was 75 [64, 85] years, and the median (IQR) estimated glomerular filtration rate was 55.16 [45.19, 61.19] mL/min/1.73 m2. The median PEC was 0.1224 × 109/L (IQR, 0.0625–0.212). Among the 3163 patients with CKD, 273 (8.6%) developed ESRD during a median follow-up time of 443.8 [238.8, 764.9] days. Individuals in the highest PEC quartile had a 66.2% higher ESRD risk than those in the lowest quartile (hazard ratio, 1.662; 95% confidence interval, 1.165–2.372). The results from the Kaplan–Meier survival curves confirmed the conclusion.

Conclusions

Alongside traditional risk factors, patients with CKD and an elevated PEC are more likely to develop ESRD. Therefore, more attention should be paid to those patients with CKD who have a high PEC.

Keywords

Chinese
chronic kidney disease
end-stage renal disease
peripheral eosinophil count
Zhejiang Provincial Natural Science Foundation of China LY24H050002 Medical Science and Technology Project of Zhejiang Province 10.13039/501100017594 2023KY454 Traditional Chinese Medicine Science and Technology Project of Zhejiang Province 2023ZL261 Medical Science and Technology Project of Zhejiang Province 10.13039/501100017594 2024KY736 This work was supported by the Zhejiang Provincial Natural Science Foundation of China [Grant number: LY24H050002]; Medical Science and Technology Project of Zhejiang Province [Grant number: 2023KY454]; Traditional Chinese Medicine Science and Technology Project of Zhejiang Province [Grant number: 2023ZL261]; Medical Science and Technology Project of Zhejiang Province [Grant number: 2024KY736].
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pmcIntroduction

There has been a shift in the spectrum of kidney disease in China because of economic advancement and increased life expectancy [1]. Major causes of end-stage renal disease (ESRD) include diabetes, hypertension, hyperuricemia, and other metabolic diseases. The incidence of chronic kidney disease (CKD) in China is 8.2%, imposing a considerable medical burden [2]. CKD is a global health concern because of its high morbidity and mortality rates [3,4]. Proteinuria, blood pressure control, dyslipidemia, and hyperuricemia have been confirmed as the main risk factors for CKD progression [5–9].

CKD progression is a complex pathological process involving several factors. We are particularly interested in peripheral eosinophilic granulocytes, previously considered biomarkers for allergic reactions or parasitic infestations [10]. It has been increasingly recognized that eosinophilic granulocytes serve as immune cells that are crucial in antigen presentation and immune regulation. Previous studies have indicated a higher risk of early mortality within the first 3 months for incident hemodialysis patients with an abnormal peripheral eosinophil count (PEC) [11]. Several studies have indicated a link between PEC and CKD, including aspects of disease onset, advancement, and patient outcomes (Supplementary Table 1). Patients with CKD have a higher PEC than those without CKD [12]. Additionally, a study conducted in the United States reported that eosinophilia was an independent predictor of progression to ESRD among 178 patients [13]. Recently, a study by Kielar et al. about the association between elevated blood eosinophils and CKD progression determined that elevated eosinophils may increase CKD progression risk [14].

Thus, the association between eosinophils and the progression of CKD has received increasing attention. This study used data from Chinese patients; most published studies to date were conducted with North American or European populations. This study aimed to address this gap by including a cohort of over 3000 patients with CKD in China to assess whether the PEC is an independent risk factor for ESRD.

Materials and methods

Study cohort

This was a single-center, observational, retrospective cohort study. In this study, patients with chronic kidney disease were selected. The inclusion criteria were: CKD patients regardless of age or gender. CKD was defined as the presence of proteinuria or hematuria on a spot urine specimen or an estimated glomerular filtration rate (eGFR) of <60 mL/min/1.73 m2 [15]. This retrospective cohort study included 8716 consecutive patients diagnosed with CKD at our hospital in Hangzhou, China, between September 2016 and June 2018. Patients with CKD stage 5 (n = 61), acute kidney injury (n = 12), severe cardiovascular and cerebrovascular diseases (n = 352), malignant tumors (n = 227), hematological diseases (n = 143), pregnancy (n = 85), lactation (n = 48), allergy (n = 140), a follow-up duration of <3 months (n = 1160), and missing key data (n = 3325) were excluded from the study. Finally, 3163 patients with CKD were included in the analyses (Figure 1).

Figure 1. Flowchart of the patients’ screening process in this study.

This study involving humans was approved by the Zhejiang Provincial People’s Hospital Ethics Committee (QT2023043) and conducted in accordance with the principles stated in the Declaration of Helsinki. The ethics committee/institutional review board waived the requirement for written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the study was retrospective cohort in nature.

Exposure measurement

The follow-up populations were grouped according to eosinophilic granulocyte quartiles: Q1 included PECs ≤0.0625 × 109/L; Q2, PECs 0.0625–0.1223 × 109/L; Q3 PECs 0.1224–0.212 × 109/L; and Q4, PECs ≥0.212 × 109/L.

Outcome measurement

The primary outcome was the incidence of progression to ESRD, defined as an eGFR of <15 mL/min/1.73 m2.

Biochemical and hematologic parameters

The patients fasted until after 10 pm before blood collection. The following parameters were determined: routine blood test results (white blood cell count, hemoglobin [Hb] level, and platelet count), renal biochemical parameters (creatinine level and eGFR), lipid profiles, such as triglyceride and total cholesterol levels, liver biochemical parameters (alanine aminotransferase and aspartate aminotransferase levels), and calcium and phosphate levels. All laboratory values were measured using automated and standardized methods. The blood sampling date to test these parameters was defined as t0.

Statistical analyses

The final data were categorized into four groups based on peripheral eosinophilic granulocyte quartiles. Data were analyzed using the SPSS software (version 26.0; IBM Corp., Armonk, NY, USA) and R software (version 3.6.2, R Foundation for Statistical Computing, Auckland, New Zealand). Demographic and clinical data are expressed as proportions or means ± standard deviations for normally distributed data or medians with interquartile ranges (IQRs) for non-normally distributed data. Differences in means for continuous variables were compared using Student’s t-test (two groups) or analysis of variance (multiple groups), while differences in proportions were tested with the chi-square test. Unadjusted and multivariable-adjusted regression models were employed to examine the multivariable-adjusted risks of outcomes according to the PEC quartile as a categorical variable, using Cox proportional hazards regression models after performing the proportional hazards assumption test. Our multivariable adjustment strategy for the proportional hazards models was hierarchical, based on the biological and clinical plausibility of covariates as potential confounders of the association between variables and ESRD. Four models were developed. Model 1 was not adjusted for any covariates. Model 2 was adjusted for age and sex. Model 3 was adjusted for covariates in model 2 plus diabetes and hypertension. Model 4 was adjusted for covariates in model 3 plus Hb, albumin, proteinuria, triglyceride, low-density lipoprotein-cholesterol (LDL-C), serum calcium, and phosphate levels. Subgroup analyses examined the relationships between the PEC and ESRD, stratified by covariates of age, sex, hypertension status, diabetes mellitus status, and CKD stages. p-Values for interaction were derived from the multivariable regression model with an interaction term. Based on the potential role of the CKD stage as a mediator of ESRD in sensitivity analyses, we performed exploratory subgroup analyses based on clinically relevant conditions. We also conducted a sensitivity analysis of competing risk regression for the ESRD endpoints. All statistical tests were two-tailed, with statistical significance set at p < 0.05.

Covariates

All sociodemographic and clinical information was recorded at enrolment. Through literature review, some confounding factors that may affect eosinophils and the progression of chronic kidney disease were included in the study, such as proteinuria [7], hemoglobin, age, albumin [16], blood lipids [6], phosphate, and so on. Hypertension was determined based on self-reported medical diagnosis or systolic blood pressure ≥140mmHg or diastolic blood pressure ≥90mmHg during the physical examination. Diabetes was defined as a medical diagnosis of diabetes mellitus by a doctor or having a glycated hemoglobin A1c level ≥7.0%.

Past medical histories of hypertension and diabetes were defined as binary variables. The eGFR was measured using the Modification of Diet in Renal Disease equation [17]: eGFR (mL/(min × 1.73 m2)) = 175 × (Scr) ^ −1.234 × (age) ^ −0.179 × (0.79 female). Other baseline factors, measured as continuous variables at enrolment, included serum creatinine, serum albumin, Hb, and kidney and liver biochemical parameters. A series of potential confounders were selected a priori based on an interaction graph represented as a directed acyclic graph (DAG) (Supplementary Figure 1). In the DAG, CKD was a mediation variable for ESRD; therefore, the adjustment model did not include the CKD stage.

Results

Baseline characteristics

The baseline characteristics of the patients for the overall population and eosinophilic granulocyte quartiles are shown in Table 1. Among the 3163 patients with CKD, the mean age was 72.01 years and 1254 (39.6%) were female individuals. Overall, 1246 (39.4%) patients had a family history of diabetes and 2306 (72.9%) had hypertension. The median eosinophilic granulocyte count was 0.123 × 109/L (IQR, 0.0625 × 109/L–0.212 × 109/L). Additionally, the eosinophilic granulocyte count was higher in men than in women (2.844 × 109/L vs. 2.368 × 109/L, p < 0.001), and patients whose condition progressed to ESRD had higher eosinophilic granulocyte counts (2.904 × 109/L vs. 2.632 × 109/L, p = 0.026). Statistically significant differences were observed in age; sex; history of hypertension; albumin, aminotransferase, triglyceride, creatinine, proteinuria, and Hb levels; and white blood cell and platelet counts according to eosinophilic granulocyte quartiles.

Table 1. Characteristics of study participants according to quartile of peripheral eosinophil count.

Characteristic	Total (3163)	Quartile 1 (791)	Quartile 2 (791)	Quartile 3 (791)	Quartile 4 (790)	p-Value for group difference	
PEC (×109/L)	0.123 [0.0625–0.212]	0.032 [0–0.0625]	0.091 [0.0625–0.1223]	0.164 [0.1224–0.212]	0.311 [0.212–4.489]	NA	
Age (years)	75.00 [64.00, 85.00]	72.00 [60.00, 82.00]	75.00 [65.00, 85.00]	76.00 [65.00, 86.00]	75.00 [65.00, 86.00]	<0.001*	
Female (%), n (%)	1254 (39.6)	361 (45.6)	350 (44.2)	297 (37.5)	246 (31.1)	<0.001*	
HTN (%), n (%)	2306 (72.9)	543 (68.6)	573 (72.4)	588 (74.3)	602 (76.2)	0.006*	
DM (%), n (%)	1246 (39.4)	287 (36.3)	301 (38.1)	323 (40.8)	335 (42.4)	0.058	
Albumin (g/L)	39.10 [35.50, 42.50]	38.00 [33.70, 42.15]	39.30 [35.85, 42.70]	39.30 [36.20, 42.30]	39.70 [36.20, 42.90]	<0.001*	
ALT (U/L)	18.00 [12.00, 26.00]	18.00 [12.00, 28.00]	17.00 [12.00, 25.00]	17.00 [13.00, 25.00]	18.00 [13.00, 26.00]	0.074	
AST (U/L)	23.00 [18.00, 28.00]	23.00 [19.00, 30.00]	22.00 [18.00, 28.00]	22.00 [18.00, 28.00]	22.00 [18.00, 27.00]	0.001*	
TC (mmol/L)	4.32 [3.63, 5.16]	4.26 [3.51, 5.09]	4.33 [3.67, 5.17]	4.34 [3.64, 5.12]	4.32 [3.68, 5.20]	0.378	
LDLC (mmol/L)	2.37 [1.88, 2.99]	2.33 [1.83, 2.96]	2.40 [1.91, 3.02]	2.35 [1.88, 2.95]	2.40 [1.92, 3.05]	0.273	
TG (mmol/L)	1.27 [0.91, 1.83]	1.22 [0.86, 1.75]	1.27 [0.90, 1.83]	1.28 [0.92, 1.83]	1.33 [1.00, 1.91]	<0.001*	
Serum calcium (mmol/L)	2.25 [2.18, 2.35]	2.25 [2.16, 2.33]	2.26 [2.19, 2.35]	2.25 [2.19, 2.34]	2.26 [2.19, 2.36]	0.002*	
Serum phosphorus (mmol/L)	1.11 [0.99, 1.22]	1.11 [0.97, 1.22]	1.11 [0.99, 1.22]	1.11 [0.99, 1.21]	1.11 [1.00, 1.22]	0.213	
Creatinine (umoll/L)	99.00 [81.70, 120.70]	93.10 [75.65, 114.00]	96.90 [80.90, 117.85]	100.90 [84.15, 122.20]	105.00 [87.38, 125.38]	<0.001*	
Basic eGFR (ml/min/1.73 m2)	55.16 [45.19, 61.19]	56.05 [45.88, 64.03]	56.08 [47.28, 62.93]	54.59 [45.20, 60.44]	53.98 [43.14, 59.57]	<0.001*	
CKD 1, n (%)	163 (5.2)	64 (8.1)	38 (4.8)	29 (3.7)	32 (4.1)	<0.001*	
CKD 2, n (%)	674 (21.3)	169 (21.4)	184 (23.3)	172 (21.7)	149 (18.9)	
CKD 3, n (%)	2087 (66.0)	506 (64.0)	528 (66.8)	529 (66.9)	524 (66.3)	
CKD 4, n (%)	239 (7.6)	52 (6.6)	41 (5.2)	61 (7.7)	85 (10.8)	
WBC (×109/L)	6.35 [5.12, 7.97]	6.38 [4.75, 9.09]	5.95 [4.86, 7.38]	6.17 [5.12, 7.52]	7.01 [5.79, 8.44]	<0.001*	
Hemoglobin (g/L)	129.00 [115.00, 141.00]	127.00 [110.00, 140.00]	128.00 [116.00, 140.00]	128.00 [116.00, 141.00]	130.00 [117.00, 142.00]	0.002*	
PLT(×109/L)	185.00 [146.50, 233.00]	171.00 [126.50, 221.50]	184.00 [145.00, 229.50]	182.00 [149.00, 228.00]	197.50 [162.25, 248.75]	<0.001*	
Proteinuria (negative), n (%)	2311 (73.1)	565 (71.4)	587 (74.2)	595 (75.2)	564 (71.4)	0.435	
Proteinuria (week positive), n (%)	689 (21.8)	179 (22.6)	170 (21.5)	159 (20.1)	181 (22.9)	
Proteinuria (strong positive), n (%)	163 (5.2)	47 (5.9)	34 (4.3)	37 (4.7)	45 (5.7)	
ESRD, n (%)	273 (8.6)	52 (6.6)	62 (7.8)	73 (9.2)	86 (10.9)	0.016*	
The time entering to ESRD (day)	443.8 [238.8, 764.9]	424.3 [223.3, 773.5]	413.1 [227.0, 766.3]	491.5 [275.8, 779.1]	456.8 [245.9, 749.3]	0.94	
PEC: peripheral eosinophil count; DM: diabetes mellitus; HTN: hypertension; ALT: alanine aminotransferase; AST: aspartate aminotransferase; TC: triglyceride; LDLC: low-density lipoprotein cholesterol; TG: total cholesterol; GFR: glomerular filtration rate; CKD: chronic kidney disease; WBC: white blood cell; PLT: platelets; ESRD: end stage renal disease. Values for continuous variables are presented as mean (SD), median (interquartile range), or range (baseline). There was 273 ESRD during the median follow-up time of 443.8 [238.8, 764.9] days.

* p < 0.05.

Eosinophilic granulocytes and risk of ESRD

During the follow-up period of 443.8 [238.8, 764.9] days, 273 (8.6%) individuals developed ESRD (Table 1). The incidence of ESRD was 6.6% in Q1, 7.8% in Q2, 9.2% in Q3, and 10.9% in Q4. The incidence of ESRD was highest in Q4 of the PEC group (p = 0.016).

Table 2 presents the results of multivariate-adjusted models according to the PEC quartiles. A multivariate logistic regression model was established with ESRD as the dependent variable and PEC, age, sex, diabetes, hypertension, proteinuria, Hb, albumin, serum calcium, and phosphate levels as covariates. In model 1, which was stratified by site and adjusted for the PEC, individuals in the highest PEC quartile had a 41.4% higher risk of ESRD than those in the lowest quartile (hazard ratio [HR], 1.414; 95% confidence interval [CI], 1.002–1.996). In model 2, adjusted for model 1 plus age and sex, individuals in the highest quartile had a 45.8% higher ESRD risk (HR, 1.458; 95% CI, 1.031–2.061). In model 3, adjusted for models 1 and 2 plus diabetes and hypertension, individuals in the highest quartile had a 52.8% higher ESRD risk (HR, 1.471; 95% CI, 1.038–2.084). In model 4, adjusted for models 1, 2, and 3 plus Hb, albumin, proteinuria, triglyceride, LDL-C, serum calcium, and phosphate levels, individuals in the highest quartile had a 66.2% higher ESRD risk (HR, 1.662; 95% CI, 1.165–2.372). The Kaplan–Meier survival curves for Q4 (highest PEC) and Q1 (lowest PEC) are shown in Supplementary Figure 2. The HR was 1.42 (95% CI, 1.018–1.988) (p = 0.0435). The trend test findings were significant for each model (p for trend = 0.028, 0.019, 0.017, and 0.006, respectively). When death was treated as a competing factor for ESRD, similar results were found (highest vs. lowest PEC Q: HR, 1.626; 95% CI, 1.129–2.341).

Table 2. Risk of ESRD according to quartiles of peripheral eosinophil count.

Variable	Quartile 1	Quartile 2	Quartile 3	Quartile 4	p For trend	
PEC (×109/L)	0–0. 0625	0.0625–0.1225	0.1225–0.2121	0.211–4.489	 	
No. of participants	791	791	791	790	 	
ESRD	 	
No. of events	52 (6.6)	62 (7.8)	73 (9.2)	86 (10.9)	 	
Event rate per 1000 patient-years	49.76	54.55	63.67	72.98	 	
Hazard ratio (95% CI)	
Multivariable model 1	1 [Reference]	1.071 (0.741–1.549)	1.232 (0.863–1.759)	1.414 (1.002–1.996)a	0.028a	
p-Values	 	0.716	0.250	0.049	 	
Multivariable model 2	1 [Reference]	1.088 (0.752–1.574)	1.261 (0.882–1.801)	1.458 (1.031–2.061)a	0.019a	
p-Values	 	0.655	0.203	0.033	 	
Multivariable model 3	1 [Reference]	1.093 (0.755–1.582)	1.268(0.887–1.812)	1.471 (1.038–2.084)a	0.017a	
p-Values	 	0.638	0.194	0.030	 	
Multivariable model 4	1 [Reference]	1.294 (0.889–1.884)	1.580 (1.096–2.277)a	1.662 (1.165–2.372)b	0.006b	
p-Values	 	0.179	0.014	0.005	 	
Model 1: Stratified by site and adjusted for PEC.

Model 2: Adjusted for model 1 plus age and sex.

Model 3: Adjusted for model 1 and model 2 plus diabetes and hypertension.

Model 4: Adjusted for model 1 and model 2 and model 3 plus hemoglobin, albumin, proteinuria, triglyceride, LDL-C, serum calcium and phosphate levels.

a p < .05.

b p < .01.

Subgroup analyses

The results of the subgroup analyses are presented in Figure 2. The population was classified into two groups based on the PEC (at the 50th percentile), and there was a 41.6% higher risk of ESRD (HR, 1.416; 95% CI, 1.107–1.811). Subgroup analyses were performed by the stratified factors, such as sex (male/female), age (<60, 60–80, and >80 years), hypertension (yes/no), diabetes (yes/no), and CKD stages. The higher PEC group had a higher risk of developing ESRD (HR, 1.416; 95% CI, 1.107–1.811) (p = 0.006). Male individuals with higher PEC had a higher risk (HR, 1.511; 95% CI, 1.090–2.094) (p = 0.013), as did patients without diabetes (HR, 1.538; 95% CI, 1.098–2.156) (p = 0.012) or with hypertension (HR, 1.375; 95% CI, 1.035–1.827) (p = 0.028). However, the interaction effects were not significant between the subgroups.

Figure 2. Subgroup analysis for the risk of ESRD by demographic and primary data category. ESRD: end-stage renal disease.

Discussion

The role of the PEC in CKD progression is unknown; thus, we aimed to determine whether the PEC plays a role in CKD progression. Our study showed that a higher PEC was independently associated with ESRD progression. The data suggest that patients with CKD may on average have a higher PEC in the normal range without being categorized as having eosinophilia. Earlier studies usually dichotomized PEC with cutoffs for eosinophilia; in this study, only a small minority seemed to have a PEC of >0.0004 × 109/mL, although the data suggested more serious (further progressed) CKD in the PECs of Q2 and Q3 compared with those of Q1. In Duk-Hee et al.’s study, higher blood eosinophil levels (≥300 cells/µL) may have contributed to the increased risk of CKD progression in stages 3 and 4 [14].

The development of CKD is influenced by many irreversible factors including race, age, and family history, and modifiable factors like diabetes, hypertension, and hyperlipidemia. Additionally, factors, such as obesity, smoking, poorly controlled blood sugar, blood pressure, and lipid levels are crucial in the early stages of the disease and its progression to ESRD [18]. Once CKD progresses to ESRD, the cost of renal replacement therapy and complication management becomes a substantial medical burden [19]. Therefore, risk stratification is necessary for patients with CKD, especially those with rapid progression. Our study identified a higher PEC level in the peripheral blood as an independent risk factor for CKD progression, alongside traditional risk factors, such as diabetes mellitus, Hb level, and basic renal function.

In our subgroup study, a statistically significant risk of ESRD progression was observed in the hypertension group with a higher PEC. However, the opposite conclusion was made for the diabetes subgroup. We believe that the effect of diabetes itself on ESRD progression was greater, masking the effect of eosinophilic granulocytes and resulting in the absence of a statistically significant difference in the diabetes group.

Eosinophilic granulocytes are common in CKD, and they are associated with systemic disorders that can lead to kidney damage and eosinophilia. Various systemic disorders affecting kidney function, such as cholesterol embolization syndrome (CES), may be accompanied by eosinophilia [20,21]. Generally, the incidence of eosinophilia in patients with CES ranges from 14 to 71% [22]. Patients with CES with kidney involvement showed an even greater increase in the eosinophilic granulocyte count [23], suggesting a potential link between eosinophilic granulocytes and the kidneys. In a prospective cohort study comparing the white blood cell count in veteran and non-veteran Americans, the PEC was slightly elevated in patients with CKD but did not meet the criteria for eosinophilia (PEC > 1.5 × 109/L). In patients with type 2 diabetes, eosinophilia has been associated with albuminuria [24]. Yuan et al. [25] developed a forecasting model demonstrating that low-density lipoprotein level, diabetes mellitus, and eosinophilic granulocyte percentage were risk factors for progression from stage 3B to 5 of CKD.

Eosinophilic granulocytes are commonly observed in renal pathology, with eosinophilic granulocyte interstitial nephritis being the most typical pathology, followed by membranous nephropathy, thrombotic microangiopathy, and diabetic nephropathy. Recently, Hattori et al. [26] found an association between interstitial eosinophilic aggregates and a higher risk of kidney outcomes. Additionally, higher counts of blood eosinophils, which can serve as a surrogate for interstitial eosinophilic aggregates, were linked to worse kidney outcomes. However, the role of eosinophilic granulocytes in CKD development remains unclear.

As research has advanced, there has been a growing understanding of the role of various molecules in the immune system. Macrophages and neutrophils act as the guardians of the immune system. However, eosinophilic granulocytes exhibit very weak phagocytosis. Traditionally, eosinophils have been regarded as biomarkers for allergies and parasitic infections, although recent studies have indicated that eosinophilic granulocytes are not just the endpoint of the inflammatory pathway [26]. Eosinophilic granulocytes are activated by inflammatory mediators, such as interleukin (IL)-3 and IL-5, which increase their circulation for ∼18–24 h before migrating from the blood vessels. The mechanisms of the damage caused by eosinophilic granulocytes include: (1) the release of particulate products, such as alkaline proteins, which can damage epithelial tissue and cause inflammation, and (2) the release of platelet-activating factor, cytokines, and chemokines, further triggering tissue remodeling and fibrosis [22,27]. Makiya et al. [28] observed a correlation between the eosinophilic granulocyte count and CD69 and granule protein concentration. Activated eosinophilic granulocytes lead to thrombotic microangiopathy [29]. Therefore, the eosinophilic granulocyte count may affect the severity of inflammation. It is proposed that eosinophilic granulocytes are mediated by specific types of T helper cells, leading to the production of cytokines, including tumor necrosis factor-α and IL-9, which causes renal interstitial atrophy, irreversible fibrosis, and eventually ESRD [30]. Eosinophilic cationic protein, major alkaline protein, peroxidase, oxygen free radicals, and other cytotoxic factors are released due to the activation of eosinophils accumulated in the kidneys [31], leading to kidney damage. Several studies have demonstrated a correlation between local eosinophilic granulocyte infiltration in the kidney and peripheral eosinophilic granulocytes [13].

It may be due to an excessive number of eosinophils returning to the bloodstream from the renal interstitium, or it may be due to an excessive infiltration of eosinophils into the renal interstitium from the bloodstream.

Recently, substantial advancements have been made in the research of various cytokines in diseases [32,33]. Likewise, the roles and effects of eosinophils in various diseases have been explored. Eosinophils play a crucial pathophysiological role in the body because of their unique degranulation and related secretion of cytokines. Eosinophils can induce corresponding symptoms within the body. Therefore, eosinophilic granulocytes can be used as markers of inflammation, and they correlate with renal progression.

This retrospective cohort study had some limitations. First, it was a single-center, observational, retrospective cohort study, which may introduce statistical bias and limit its generalizability. Although this was a single-center study, a study population of more than 3000 participants would have mitigated some of these statistical problems. Second, we did not routinely assess immunoglobulin E levels, quantify urine protein, or dynamically monitor eosinophilic granulocytes. Thirdly, the comorbidities included in our study were all comorbidities collected at baseline. As for the comorbidities that occurred during the follow-up, we could not collect them comprehensively, which may lead to potential interference with the results. Further prospective studies are required to assess the role of eosinophils in ESRD progression and elucidate the mechanisms linking these bursts of inflammation and eosinophilia to adverse outcomes using appropriate animal and human models.

Conclusions

Our findings demonstrated a significant association between a higher PEC and ESRD progression. Patients with CKD who had high eosinophil counts had a higher risk of entering ESRD. Therefore, apart from traditional risk factors, the increase in eosinophils was a novel risk factor. However, the mechanism underlying this phenomenon needs further investigation. More attention is required for patients with CKD and increased eosinophil levels. In addition, a strengthened follow-up with timely interventions could delay kidney disease progression in this high-risk group and reduce the social burden.

Supplementary Material

supplementary Figure 2 KM curve.jpg

supplementary Figure _DAG.jpg

Acknowledgments

We express our sincere gratitude to our colleagues at Zhejiang Provincial People’s Hospital for their invaluable contributions to this study. We are grateful for the technical support and data platform from Yidu Cloud Technology Company Ltd. We would like to thank Dr. Wu Song for helping with the statistical analysis. Lastly, we would like to thank Editage (www.editage.cn) for English language editing.

Ethical approval

This study involving humans was approved by the Zhejiang Provincial People’s Hospital Ethics Committee (QT2023043) and conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement for written informed consent for participation from the participants or the participants’ legal guardians/next of kin because this study was retrospective cohort in nature.

Consent for publication

Not applicable.

Authors contributions

YR, JZ, YL, BL, BZ, QT, and LS contributed to the conception and design of the study. JZ and XH organized the database. YR and LS performed the statistical analysis. YR wrote the first draft of the manuscript. XH, RY, and MW wrote sections of the manuscript. All authors contributed to the manuscript revision and read and approved the submitted version.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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