Association between Time-Updated Eosinophil Counts and Progression of CKD

Association between Time-Updated Eosinophil Counts and Progression of CKD | 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 Article Association between Time-Updated Eosinophil Counts and Progression of CKD Kohki Hattori, Yusuke Sakaguchi, Tatsufumi Oka, Takayuki Kawaoka, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2003296/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 Patients with chronic kidney disease (CKD) have high blood eosinophil count but its clinical implication is uncertain. Since eosinophils may induce tubulointerstitial injury and arteriosclerosis, eosinophilia might be related to poor clinical outcomes. This retrospective cohort study included 2,877 patients whose estimated glomerular filtration rate (eGFR) was 10–60 mL/min/1.73 m 2 . The exposure was time-updated blood eosinophil counts. The outcomes were 1) initiation of renal replacement therapy (RRT) and 2) cardiovascular events and mortality. We analyzed the associations between eosinophil counts and outcomes using marginal structural models (MSM). Over a median follow-up of 6.5 years, eosinophil counts were measured a median of 22 times per patient (4 times a year per patient). There was a negative correlation between eosinophil count and eGFR. In total, 433 patients initiated RRT, 275 developed cardiovascular events, and 165 died. In MSM, higher eosinophil counts (≥ 289/µL) showed a 1.83-fold (95% confidence interval:1.33–2.51) higher rate of RRT initiation than lower eosinophil counts after adjustment for time-dependent confounders. Higher eosinophil counts were also associated with a higher rate of cardiovascular events and mortality in MSM (hazard ratio, 1.71 [95% confidence interval:1.30–2.25]). In conclusion, patients with CKD who had higher eosinophil counts showed worse kidney outcome. eosinophil chronic kidney disease mortality cardiovascular events marginal structural model Figures Figure 1 Figure 2 Figure 3 Introduction Eosinophils are multifunctional leukocytes involved in an array of pathological processes 1 . Besides their well-known roles in allergic reactions, parasite defense, and autoimmune diseases, eosinophils are also implicated in atherosclerosis 2 – 4 . Marx et al. showed that activated eosinophils in atherosclerotic lesions accelerate plaque formation in concert with platelets by secreting eosinophilic granule proteins and extracellular traps 5 . Population-based cohort studies reported increased cardiovascular risks among those with higher levels of plasma eosinophilic cationic protein (ECP), a marker of eosinophil activity and degranulation 6 , 7 . Patients with advanced chronic kidney disease (CKD) have high blood eosinophil count 8 , 9 although its clinical implication is uncertain. In addition to their proatherogenic property, eosinophils might also contribute to the progression of kidney disease. For example, renal complications sometimes develop in idiopathic hypereosinophilic syndrome, where tubulointerstitial infiltration of eosinophils is typically observed 10 , 11 . Eosinophilic granulomatosis with polyangiitis, characterized by the interstitial infiltration of eosinophils, is also evidentiary to the involvement of eosinophils in kidney injury 12 . Furthermore, interstitial eosinophilic aggregates are found in common kidney diseases, such as diabetic nephropathy, IgA nephropathy, and membranous nephropathy, which are related to interstitial fibrosis and inflammation 13 , 14 . Since there is a positive correlation between eosinophil counts in the blood and renal interstitium 14 , 15 , we hypothesized that increased blood eosinophil counts in patients with CKD reflect eosinophilic inflammation in the kidney and thus indicate a risk of CKD progression. In the current study, we examined the association between time-updated blood eosinophil counts and the risk of kidney failure among patients with advanced CKD. Methods Ethical considerations This study was conducted in accordance with the principles of the Declaration of Helsinki. The Ethics Committee of Osaka University Hospital approved the study protocol and waived the need for a written informed consent given the retrospective nature of the study (no: 20352, 22047). Study design and participants This retrospective cohort study included all patients referred to the outpatient department of nephrology at Osaka University Hospital from January 2005 to January 2018 who met the following inclusion criteria: 1) aged 20 years or older, 2) an estimated glomerular filtration rate (eGFR) of 10–60 mL/min/1.73 m 2 , and 3) not receiving renal replacement therapy (RRT). Patients were excluded if they 1) were followed up for < 1 year or 2) received corticosteroids. Follow-up period was from the first visit to the hospital to death, RRT initiation, loss to follow-up, or February 28th, 2019, whichever occurred first. Data collection The detailed methods have been described elsewhere 16 – 18 . Demographics and comorbidities were extracted from a chart review of patients’ electronic medical records by nephrologists. These included age, sex, body mass index (BMI), blood pressure, diabetes mellitus (DM), cardiovascular comorbidities, chronic respiratory diseases (chronic obstructive pulmonary disease and bronchial asthma), a prior history of arterial catheterization (cardiac catheterization and endovascular treatment for peripheral artery diseases and carotid artery stenosis), and cholesterol embolism. Cardiovascular comorbidities included coronary artery disease, congestive heart failure, valvular heart disease, aortic disease, and stroke (cerebral infarction or intracranial hemorrhage). Time-series data on laboratory measurements and prescriptions were collected using an automated data extraction system of Osaka University Hospital. Laboratory data included serum albumin, creatinine, sodium, potassium, C-reactive protein (CRP), hemoglobin, white blood cell (WBC) counts, and urinary protein-to-creatinine ratio (UPCR). eGFR was calculated using the equation for the Japanese population 19 . The prescription data included loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists (MRAs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), nonsteroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPIs), histamine H 2 receptor antagonists (H 2 blockers), and corticosteroids. We assessed the number of prescription drugs, not limited to those mentioned above. The time-series data were collected at a monthly interval during the study period. We collected information regarding arterial catheterization performed during follow-up (cardiac catheterization and endovascular treatment for peripheral artery diseases and carotid artery stenosis); these procedures may have contributed to the development of eosinophilia via cholesterol embolism. These data were obtained from diagnostic procedure combination (DPC) codes 20 . Exposure The exposure was time-updated blood eosinophil counts (per µL), which were collected at a monthly interval during the study period. Blood eosinophil counts were calculated by multiplying the total WBC count by the percentage of eosinophils as measured using an automated WBC differential counter. Blood eosinophil counts were categorized into quartiles. Study outcomes The study outcome was RRT initiation, defined as the initiation of chronic dialysis or kidney transplantation. We additionally evaluated cardiovascular outcomes, which were a composite of myocardial infarction, stroke, hospitalization for heart failure, and mortality. The dates of these clinical events were ascertained by a chart review of the patients’ electronic medical records by nephrologists. A post-hoc analysis of a randomized controlled trial of oral carbon adsorbent To explore the involvement of uremic toxins in elevated eosinophil counts in CKD, we analyzed the data in a previous randomized controlled trial of the oral carbon adsorbent AST-120 21 , which reduces serum uremic toxin levels such as indoxyl sulfate. This two-year, open-label, randomized, controlled trial enrolled 125 patients with stages 3–4 CKD. Among them, 123 were randomized to either receive AST-120 (6 g/day) or not, in a 3:2 ratio. In this post-hoc analysis, data on eosinophil counts at baseline, 3, 6, and 12 months were added to the original dataset. Statistical analyses The relationship between eosinophil counts and eGFR at baseline was depicted using a restricted cubic spline curve with three knots (10th, 50th, and 90th percentiles of eGFR). The multivariable association between log-transformed eosinophil counts and covariates was assessed by a linear regression analysis with robust standard errors. The following variables were included: age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, a prior history of arterial catheterization, chronic respiratory diseases, ACEIs/ARBs, loop diuretics, thiazide diuretics, MRAs, PPIs, H 2 blockers, NSAIDs, number of drugs prescribed, hemoglobin, albumin, eGFR, CRP, UPCR, and WBC. In the post-hoc analysis of the randomized trial of AST-120 21 , eosinophil counts were compared between the AST-120 and control groups using a linear mixed-effects model for repeated measures with an unstructured covariance matrix. To analyze the longitudinal relationship between time-updated blood eosinophil counts and kidney outcomes, time-dependent confounding should be considered. This is because eosinophil counts increase as kidney function declines. As a result, time-dependent confounding could occur owing to a potential bidirectional relationship between eosinophil counts and kidney function in terms of the development of kidney failure. In order to appropriately account for time-dependent confounding, we used a marginal structural model (MSM). We also performed baseline Cox model, time-average Cox model, and group-based trajectory model (Fig. 1 ). 1) Baseline Cox model Association between baseline eosinophil quartiles and outcomes was analyzed using multivariate Cox proportional hazards models. The following baseline covariates were adjusted in this model: age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, chronic respiratory diseases, a prior history of arterial catheterization and cholesterol embolism, hemoglobin, albumin, eGFR, sodium, potassium, CRP, WBC, UPCR, loop diuretics, thiazide diuretics, MRAs, ACEIs, ARBs, NSAIDs, PPIs, and H2 blockers. The proportional hazards assumption was checked graphically based on the scaled Schoenfeld residuals. 2) Time-average Cox model The average eosinophil count during the first 12 months of follow-up was calculated for each patient. Association between time-average eosinophil quartiles and outcomes was analyzed using a multivariate Cox proportional hazards model adjusted for the same covariates as in the baseline model. In this model, the onset of survival time was set at 12 months. 3) Group-based trajectory model Group-based trajectory model was used to assess the association between eosinophil count trajectories during the first 12 months and subsequent rates of outcomes (STATA command, traj). All available data on eosinophil counts during the first 12 months were used to identify eosinophil count trajectories. In this analysis, the eosinophil counts were log-transformed to normalize their distribution. The group-based trajectory model is a method of data clustering that assumes that a population is composed of a mixture of distinct groups characterized by their longitudinal trajectories 22 – 25 . Potential trajectory groups were estimated from individual longitudinal eosinophil data, using the maximum likelihood estimation method based on the finite mixture model theorem. The patients were divided into one of the trajectory groups according to their estimated probability of group membership. We selected the optimal number of trajectory groups, as well as a function of each trajectory, based on the Bayesian information criterion (BIC), with at least 5% of all patients being in the smallest group. After deriving the eosinophil trajectory groups, multivariate Cox proportional hazards models were used to analyze the association between the trajectory groups and outcomes, adjusting for the same covariates as in the baseline model. In this model, the onset of survival time was set at 12 months. 4) MSM MSM was employed to 1) assess the time-varying eosinophil counts throughout the study period and 2) deal with time-dependent confounding between eosinophil counts and eGFR. MSM is a statistical method that can account for time-dependent confounding 26 – 29 . In the current study, eGFR was considered to be the main time-dependent confounder because it influenced both exposure (eosinophil counts) and renal outcomes, while being possibly affected by previous eosinophil counts. We derived time-varying inverse probability weights (IPWs) from the inverse probability of treatment weights (IPTWs) and the inverse probability of censoring weights (IPCWs). IPTWs were the reciprocal of the predicted probability of each patient having their own exposure history (i.e., high eosinophil count or not). The probability was predicted by a logistic regression model at each of the 1-month follow-up periods, conditional on both baseline and time-dependent covariates, as described below. Two different definitions of high eosinophil counts were adopted: 1) eosinophil count ≥ 289/µL (the top 25th percentile in our cohort) and 2) eosinophil count ≥ 500/µL 30 . Similarly, IPCWs were the reciprocal of the probability of being uncensored, as predicted by a logistic regression model, conditional on both baseline and time-dependent covariates. IPTWs and IPCWs were stabilized by multiplying them with the predicted probabilities based on baseline covariates alone. The IPWs were the product of the stabilized IPTWs and IPCWs, calculated at baseline and for each month. The IPWs were truncated at the 1st and 99th percentiles to reduce the influence of extreme weight values. Baseline covariates included were the same as in the baseline model. Time-dependent covariates included arterial catheterization performed during follow-up, hemoglobin, albumin, eGFR, sodium, potassium, CRP, UPCR, loop diuretics, thiazide diuretics, MRAs, ACEIs, ARBs, NSAIDs, PPIs, H2 blockers, and corticosteroids. MSM created “pseudo-populations” using IPWs, comparing the rate of events if all patients had been continuously exposed to high eosinophil counts with the risk of events if they had never been exposed to it. In MSM, there was no association between measured time-dependent confounders and future exposure. We estimated the hazard ratio (HR) and 95% confidence interval (CI) using an IPW-weighted pooled logistic regression model that produced equivalent estimates to the Cox proportional hazards model. Effect modification was evaluated by incorporating cross-product terms between eosinophil counts and a priori specified baseline covariates into the MSM, including age (< 70 vs. ≥ 70), sex, BMI (< 22 vs. ≥ 22), systolic blood pressure (< 130 vs. ≥ 130 mmHg), DM, cardiovascular comorbidities, hemoglobin (< 12.4 vs. ≥ 12.4 g/dL), albumin (< 3.8 vs. ≥ 3.8 g/dL), CKD stage (stage 3 vs. stage 4–5), UPCR (< 1.0 vs. ≥ 1.0 g/gCr), and ACEIs/ARBs use. Missing data at baseline were imputed using the multiple imputations by chained equation method based on all baseline covariates. Continuous variables with missing data (BMI, systolic blood pressure, eGFR, hemoglobin, sodium, potassium, UPCR, albumin, and CRP) were imputed based on linear regression imputation. We created ten imputed datasets that were analyzed separately and combined using Rubin’s rules. Missing data during follow-up were imputed using the last-observation-carried-forward method. Two sensitivity analyses were conducted. First, the association between eosinophil count and RRT initiation was assessed after excluding patients with chronic respiratory diseases or cholesterol embolism. Second, we reanalyzed MSM after excluding patients who were followed up for less than three months. Statistical analyses were performed using Stata/IC software (version 16.0; Stata Corp, College Station, TX, USA). Results Study population Among 2,889 patients who met the inclusion criteria, 2,877 (99%) had available eosinophil count data (Figure S1). Over the median follow-up period of 6.5 years (interquartile range, 3.5–9.9), eosinophil count was measured a median of 22 (interquartile range, 7–46) times per patient (4 [interquartile range, 2–6] times a year per patient). The clinical characteristics according to eosinophil quartiles are presented in Table 1 . Patients in the highest eosinophil quartile were more likely to be men and to have diabetes mellitus, cardiovascular comorbidities, a lower eGFR, and a higher UPCR. There was a monotonic negative correlation between eosinophil count and eGFR (Fig. 2 ). Table 1 Baseline characteristics according to eosinophil counts quartiles Eosinophil counts quartiles: range (/µL) Total Missing data Q1: 289 Characteristics n = 2,877 n (%) n = 668 n = 684 n = 736 n = 769 P-value Age, year 63(14) 0 63(14) 64(14) 63(14) 63(16) 0.99 Male 1,873(65%) 0 356(52%) 435(64%) 509(69%) 573(75%) < 0.001 Diabetes mellitus 1,173(41%) 0 243(35%) 264(39%) 331(45%) 335(44%) 0.002 BMI, kg/m 2 23(4) 84 (3%) 22(4) 23(4) 23(4) 24(4) < 0.001 SBP, mmHg 131(21) 108 (4%) 130(21) 132(20) 131(20) 131(21) 0.56 Cardiovascular comorbidities 512(18%) 0 78(11%) 118(17%) 136(18%) 180(23%) < 0.001 Prior history of catheterization 415(14%) 0 71(10%) 89(13%) 106(14%) 149(19%) < 0.001 Chronic respiratory diseases 43(1%) 0 7(1%) 6(1%) 9(1%) 21(3%) 0.005 ACEIs/ARBs 485(17%) 0 83(12%) 103(15%) 124(17%) 175(23%) < 0.001 Loop diuretics 299(10%) 0 48(7%) 63(9%) 68(9%) 120(16%) < 0.001 Thiazide diuretics 101(4%) 0 11(2%) 24(4%) 30(4%) 36(5%) 0.001 MRAs 176(6%) 0 29(4%) 41(6%) 39(5%) 67(9%) 0.001 NSAIDs 63(2%) 0 16(2%) 12(2%) 24(3%) 11(1%) 0.59 PPIs 276(10%) 0 52(8%) 56(8%) 61(8%) 107(14%) 0.001 H 2 blockers 285(10%) 0 68(10%) 72(11%) 73(10%) 72(9%) 0.65 Hemoglobin, g/dL 12.3(2.1) 1 (0%) 12.1(2.0) 12.5(2.0) 12.5(2.1) 12.2(2.1) 0.68 Sodium, mEq/L 140(3) 466 (16%) 140(3) 140(3) 140(3) 139(3) 0.002 Potassium, mEq/L 4.4(0.5) 356 (12%) 4.4(0.6) 4.4(0.5) 4.5(0.5) 4.5(0.5) < 0.001 Albumin, g/dL 3.8(0.6) 695 (24%) 3.8(0.6) 3.9(0.6) 3.8(0.6) 3.7(0.6) < 0.001 eGFR, ml/min/1.73m 2 37(14) 50 (2.7%) 39(14) 38(14) 37(14) 35(14) < 0.001 C-reactive protein, mg/dL 0.1[0.0-0.9] 760 (26%) 0.1[0.0-0.7] 0.1[0.0-0.7] 0.1[0.0–1.0] 0.2[0.0-1.1] < 0.001 UPCR, g/gCre 0.7[0.0-2.4] 1070 (37%) 0.4[0.0-1.8] 0.6[0.0-2.2] 0.8[0.0-2.5] 0.9[0.0-2.7] < 0.001 White blood cells, ×10 3 /µL 7.2(3.4) 0 6.7(3.5) 6.4(2.2) 7.3(3.5) 8.3(3.7) < 0.001 Data presented as mean (standard deviation), number (%), or median [25th‒75th] Abbreviations: BMI, body mass index; SBP, systolic blood pressure; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; MRA, mineralocorticoid receptor antagonists; NSAIDs, non-steroidal anti-inflammatory drugs; PPIs, proton pump inhibitors; H 2 blockers, histamine H 2 receptor antagonists; eGFR, estimated glomerular filtration rate; UPCR, urinary protein-to-creatinine ratio Factors associated with higher eosinophil counts Eosinophil counts were positively associated with male, BMI, cardiovascular comorbidities, chronic respiratory diseases, ACEIs/ARBs use, and WBC, and negatively correlated with eGFR (Table 2 ). Table 2 Multivariable linear regression analysis for the association between log-transformed eosinophil counts and clinical factors Variables β [95% CI] P-value Age, per 10 years increase -0.01 [-0.04, 0.02] 0.52 Male 0.34 [0.24, 0.43] < 0.001 BMI, per 1 kg/m 2 increase 0.02 [0.01, 0.03] < 0.001 SBP, per 10 mmHg increase -0.01 [-0.03, 0.01] 0.37 Diabetes mellitus -0.02 [-0.11, 0.06] 0.58 Cardiovascular comorbidities 0.22 [0.11, 0.32] < 0.001 Prior history of catheterization 0.02 [-0.10, 0.14] 0.74 Chronic respiratory diseases 0.40 [0.08, 0.71] 0.01 ACEIs/ARBs use 0.17 [0.07, 0.26] 0.001 Loop diuretics use 0.06 [-0.09, 0.20] 0.46 Thiazide diuretics use 0.07 [-0.10, 0.24] 0.40 MRAs use 0.01 [-0.16, 0.18] 0.91 PPIs use -0.01 [-0.16, 0.13] 0.86 H 2 blockers use -0.11 [-0.23, 0.02] 0.09 NSAIDs use -0.07 [-0.31, 0.16] 0.54 Number of drugs, per 1 drug increase -0.003 [-0.01, 0.01] 0.62 Hemoglobin, per 1 g/dL increase 0.01 [-0.02, 0.03] 0.58 Albumin, per 1 g/dL increase 0.03 [-0.08, 0.14] 0.60 eGFR, per 10 ml/min/1.73 m 2 increase -0.07 [-0.11, -0.04] < 0.001 C-reactive protein, per 1 mg/dL increase -0.01 [-0.03, 0.01] 0.46 UPCR, per 1 g/gCre increase 0.01 [-0.01, 0.03] 0.32 White blood cells, per 1000/µL increase 0.05 [0.03, 0.07] < 0.001 Abbreviations: CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; MRA, mineralocorticoid receptor antagonist; PPI, proton pump inhibitor; H 2 blockers, histamine H 2 receptor antagonist; NSAID, non-steroidal anti-inflammatory drug; eGFR, estimated glomerular filtration rate; UPCR, urinary protein-to-creatinine ratio Baseline and time-average Cox models RRT was initiated in 433 patients (2.1 per 100 patient-years; 95% CI, 1.9 to 2.3). In the baseline Cox model, there was a dose-dependent association between eosinophil quartiles and the rate of RRT initiation (Table 3 ). Patients in the highest eosinophil quartile had a 2.12-fold (95% CI: 1.44 to 3.10) higher rate of RRT initiation than those in the lowest quartile. Similarly, higher time-average eosinophil quartiles were associated with an increased rate of RRT initiation (Table 3 ). The clinical characteristics according to time-average eosinophil quartiles are summarized in Table S1. Table 3 Association between eosinophil quartiles and clinical outcomes Outcome: RRT initiation Baseline Cox model Time-Average Cox model Group-based trajectory model Eosinophil quartiles Q1: 289 (n = 769) Q1: ≤ 108 (n = 696) Q2: 105–183 (n = 669) Q3: 183–302 (n = 723) Q4: ≥ 302 (n = 789) Low (n = 324) Middle (n = 1,430) High (n = 1,123) No. of events 71 79 139 144 62 93 141 134 26 196 208 Incidence rate, 100 p-y (95% CI) 1.4 (1.1–1.7) 1.5 (1.2–1.9) 2.7 (2.3–3.2) 3.0 (2.5–3.5) 1.1 (0.9–1.5) 1.9 (1.5–2.3) 2.9 (2.4–3.4) 2.8 (2.4–3.3) 1.0 (0.7–1.5) 1.9 (1.6–2.1) 3.0 (2.6–3.4) Hazard Ratio (95% CI) 1.00 (ref) 1.18 (0.77–1.78) 1.77 (1.20–2.61) 2.12 (1.44–3.10) 1.00 (ref) 1.42 (0.95–2.11) 1.98 (1.34–2.94) 2.07 (1.40–3.09) 1.00 (ref) 1.74 (1.06–2.85) 2.30 (1.38–3.84) P-value - 0.43 0.004 < 0.001 - 0.09 0.001 < 0.001 - 0.03 0.001 Outcome: CV events and death Baseline Cox model Time-Average Cox model Group-based trajectory model Eosinophil quartiles Q1: 289 (n = 769) Q1: ≤ 108 (n = 696) Q2: 105–183 (n = 669) Q3: 183–302 (n = 723) Q4: ≥ 302 (n = 789) Low (n = 324) Middle (n = 1430) High (n = 1123) No. of events 87 89 104 122 82 70 91 120 37 161 165 Incidence rate, 100 p-y (95% CI) 1.7 (1.4–2.1) 1.7 (1.4–2.1) 2.0 (1.7–2.5) 2.5 (2.1-3.0) 1.5 (1.2–1.9) 1.4 (1.1–1.8) 1.8 (1.5–2.3) 2.5 (2.1-3.0) 1.4 (1.0–2.0) 1.5 (1.3–2.8) 2.3 (2.0-2.7) Hazard Ratio (95% CI) 1.00 (ref) 0.94 (0.63–1.40) 1.17 (0.77–1.76) 1.26 (0.85–1.87) 1.00 (ref) 0.77 (0.54–1.09) 1.10 (0.79–1.52) 1.30 (0.95–1.79) 1.00 (ref) 0.97 (0.65–1.44) 1.32 (0.86-2.00) P-value - 0.8 0.47 0.25 - 0.15 0.58 0.1 - 0.87 0.19 The models were adjusted for age, sex, diabetes mellitus, body mass index, systolic blood pressure, chronic respiratory diseases, cardiovascular comorbidities, prior history of catheterization, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, proton pump inhibitors, histamine H 2 receptor antagonists, non-steroidal anti-inflammatory drugs, hemoglobin, sodium, potassium, albumin, estimated glomerular filtration rate, C-reactive protein, urinary protein-to-creatinine ratio, and white blood cell count. Abbreviations: RRT, renal replacement therapy; p-y, person-years; CI, confidence interval; CV, cardiovascular. Group-based trajectory model Three distinct trajectories of eosinophil counts were identified: low (n = 324), middle (n = 1,430), and high (n = 1,123) (Fig. 3 ). The clinical characteristics in each trajectory group are presented in Table S2. In a multivariable Cox model, patients in the high-trajectory group showed a 2.30-fold (95% CI: 1.38 to 3.84) higher rate of RRT initiation than those in the low-trajectory group (Table 3 ). Msm In MSM, high eosinophil counts (≥ 289 /µL) were associated with a 1.83-fold (95% CI: 1.33 to 2.51) higher rate of RRT initiation than normal eosinophil counts (Table 4 ). There was no significant effect modification by a priori defined baseline covariates, i.e., age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, hemoglobin, albumin, CKD stage, UPCR, and ACEI/ARB use. High eosinophil counts were significantly associated with a higher rate of RRT initiation when using the other definition of high eosinophil count (≥ 500 /µL). Table 4 Marginal structural models for the association between high eosinophil counts and outcomes RRT initiation CV events and mortality High eosinophil counts (/µL) HR (95% CI) P-value HR (95% CI) P-value ≥ 289 (vs.༜289) 1.83 (1.33–2.51) < 0.001 1.71 (1.30–2.25) < 0.001 ≥ 500 (vs.< 500) 1.41 (1.11–1.80) 0.006 1.35 (1.06–1.73) 0.02 The models were adjusted for the baseline and time-dependent covariates. Baseline covariates included age, sex, body mass index, systolic blood pressure, diabetes mellitus, chronic respiratory diseases, cardiovascular comorbidities, prior history of catheterization, hemoglobin, albumin, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, urinary protein-to-creatinine ratio, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, non-steroidal anti-inflammatory drugs, proton pump inhibitors, and histamine H 2 receptor antagonists. Time-dependent covariates included catheterization performed during follow-up, hemoglobin, albumin, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, urinary protein-to-creatinine ratio, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, nonsteroidal anti-inflammatory drugs, proton pump inhibitors, histamine H 2 receptor antagonists, and corticosteroids. Abbreviations: HR, hazard ratio; CI, confidence interval; RRT, renal replacement therapy; CV, cardiovascular. Abbreviations: eGFR, estimated glomerular filtration rate. Cardiovascular events and mortality A total of 275 patients developed cardiovascular events (1.4 per 100 patient-years; 95% CI: 1.2 to 1.5) and 165 died (0.8 per 100 patient-years; 95% CI, 0.7 to 0.9). The association of eosinophil count with cardiovascular events and mortality was not significant in any statistical models except for MSM, where higher eosinophil counts were significantly associated with higher rates of these outcomes. (Tables 3 and 4 ). Sensitivity analysis After excluding patients with chronic respiratory diseases or cholesterol embolism, higher eosinophil counts were still associated with a higher rate of RRT initiation (Table S3). A similar result was obtained when including patients followed up for at least 90 days. Effects of oral carbon adsorbent, AST-120, on eosinophil counts This post-hoc analysis of the randomized controlled trial included 123 patients with stage 3–4 CKD (70 in the AST-120 group and 53 in the control group). The mean (SD) baseline eosinophil counts were 288 (442) /µL and 454 (1,364) /µL in the AST-120 and control groups, respectively. At 12 months, the mean (SD) eosinophil count was 340 (442) /µL and 456 (1,447) /µL in the AST-120 and control groups, respectively. A linear mixed-effects model for repeated measures showed no significant difference in the eosinophil counts between groups (P = 0.44) (Figure S2). Discussion We found a dose-dependent relationship between eosinophil counts and the risk of RRT initiation among patients with advanced CKD. The results were consistent when longitudinal alterations in eosinophil counts were modeled using the group-based trajectory modeling and MSM. Although future mechanistic studies are required to validate our findings, our data suggest the possible involvement of eosinophils in the progression of CKD. Studies that examined the association between eosinophils and CKD progression are sparse 9 , 15 . These studies were limited by the small sample size, highly selective patient population, and insufficient adjustment for relevant confounders. More importantly, they did not consider longitudinal alterations in eosinophil counts despite the fact that eosinophils increase as kidney function declines 8 , 9 . We demonstrated a significant association between eosinophil counts and kidney outcomes in the group-based trajectory modeling and MSM that could capture longitudinal changes in eosinophil counts. Furthermore, MSM revealed that this association was independent of time-dependent confounding factors such as eGFR. Thus, although causality cannot be proven, our study provides plausible evidence regarding the link between eosinophils and CKD progression. Several factors may confound the association between eosinophilia and CKD progression. First, chronic obstructive pulmonary disease and bronchial asthma were strongly associated with high eosinophil counts. They might accelerate CKD progression through hypoxia and inflammation 31 – 33 , and thereby confound the association between eosinophilia and kidney outcomes. Second, drugs such as PPIs, diuretics, and NSAIDs induce both kidney injury and eosinophilia 34 – 37 , and thus could be potential confounders. Finally, arterial catheterization sometimes causes cholesterol embolism, characterized by progressive kidney injury and eosinophilia 38 . Nevertheless, we did not find an association between catheterization and eosinophil count, most likely because cholesterol embolism is a very rare event. It should be emphasized that we demonstrated a significant association between eosinophil count and the kidney outcome after adjustment for these confounders. One of the putative mechanisms linking eosinophils to CKD progression is their pro-atherogenic properties 6 . Since intrarenal arteriosclerosis/arteriolosclerosis leads to glomerulosclerosis and interstitial fibrosis and tubule atrophy (IFTA) 39 – 41 , and is associated with worse renal prognosis 42 , eosinophils might affect kidney outcomes by accelerating nephrosclerotic lesions. Future studies are needed to clarify the effect of eosinophils on intrarenal arteriosclerosis/arteriolosclerosis. Another possible explanation is that eosinophils may be involved in tubulointerstitial injury. Interstitial eosinophilic infiltration is typically observed in drug-induced tubulointerstitial nephritis, eosinophilic granulomatosis with polyangiitis 12 , tubulointerstitial nephritis with uveitis 43 , and idiopathic hypereosinophilic syndrome 10 , 11 . Interestingly, interstitial eosinophilic aggregates are also found in common kidney diseases, and are associated with IFTA, interstitial edema, and eosinophilic tubulitis, suggesting that they may aggravate tubulointerstitial inflammation and fibrosis 13 , 14 . The clinical implications of infiltrating eosinophils in the renal interstitium in terms of renal prognosis requires further detailed investigation. A novel perspective has been proposed that eosinophils exert a tissue-protective effect. Liu et al. reported a cardioprotective role of interleukin-4 and mEar1 (human ECP ortholog) produced by eosinophils in a mouse model of myocardial infarction, suggesting that an increase in eosinophils in the heart and blood after myocardial infarction represents a compensatory protective response 44 . Similarly, eosinophils prevent transverse aortic constriction-induced cardiac hypertrophy by inhibiting cardiomyocyte apoptosis and cardiac fibrosis 45 . A protective role of eosinophils against liver injury has also been reported 46 . Thus, higher eosinophil counts related to an increased risk of CKD progression may indicate a protective response to a more active disease status. However, these studies evaluated the effect of eosinophil deficiency, which may not be extrapolated to that of eosinophilia. Moreover, whether eosinophils play a protective or harmful role may depend on the target organs and the specific pathological context. The precise role of eosinophils in the progression of CKD requires further investigation. Although the exact mechanisms remain elusive as to why eosinophils increase in advanced CKD, evidence has implied a role of uremic toxins. Interleukin-5, a master cytokine for eosinophil development, is elevated in nephrectomized mice 47 . Uremic toxins, such as indoxyl sulfate and p-cresol, upregulate intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 in endothelial cells 48 , 49 , which promote eosinophil migration and degranulation 50 , 51 . Thus, these studies support the involvement of uremic toxins in eosinophil proliferation and activation. Nevertheless, we did not find a significant change in eosinophil counts after AST-120 administration in CKD patients. Although this neutral result may be due to insufficient removal of uremic toxins by the drug, it might indicate that the clinical impact of uremic toxins on eosinophils is trivial. Several factors were related to higher eosinophil counts in our study. Eosinophilia is a well-known side effect of ACEIs, and we confirmed that ACEIs/ARBs users showed higher eosinophil counts. Male had higher eosinophil counts than female. This sex difference has also been observed in healthy individuals 52 . Estrogen inhibits eosinophil production in the bone marrow and induces eosinophil apoptosis 53 , which might explain higher eosinophil counts in men. The positive association between BMI and eosinophil counts may be explained by this sex difference, while there seems to be a complex relationship between body weight and eosinophils 54 . In our study, the association between eosinophil count and kidney outcome was independent of sex and BMI. In addition, there was no significant effect modification by gender or BMI on this association. Notably, even modest elevations in eosinophil counts were associated with CKD progression. This is consistent with previous studies showing that a modest increase in eosinophil count, below the definition of eosinophilia (≥ 500/µL) 30 , is associated with atherosclerotic plaques 55 and albuminuria 56 . Therefore, physicians should be aware of the clinical implications of subclinical eosinophilia, especially among patients with CKD. Higher eosinophil counts were also associated with an increased risk of cardiovascular events and mortality in MSM. However, this association was not confirmed in the other statistical models. This discrepancy may be because only MSM could capture time-series changes in eosinophil counts throughout the study period. Conversely, statistical models other than MSM used eosinophil counts only at baseline or during the first 12 months, and thereby may have introduced a misclassification bias. Additionally, most cardiovascular events in our study were hospitalizations for heart failure. Given that eosinophils contribute to atherosclerotic lesions 57 , the predominance of non-atherosclerotic cardiac events would have compromised the sensitivity to detect the impact of eosinophils. Indeed, a previous cohort study reported that eosinophils are not associated with incident heart failure in the general population 58 . Further exploration of the association between eosinophils and cardiac events in patients with CKD is required. The strengths of this study include large sample size, long-term follow-up period, and abundant data on eosinophil counts repeatedly measured within individuals. Despite the retrospective study design, missing data on eosinophil counts were < 1%. We assessed clinically-meaningful hard outcomes. A variety of covariates potentially related to eosinophilia were adjusted in MSM. Our study had several limitations. First, the observational study design precludes causal inferences between eosinophil and renal prognosis. Second, some patients had a small number of measurements for eosinophils, which might have reduced the accuracy of the exposure. Third, since we enrolled advanced CKD patients in Japan, the generalizability of our findings to patients with more preserved kidney function or other ethnic groups is unknown. Finally, because we did not have data on tissue eosinophils, we could not directly link blood eosinophil counts with kidney eosinophils. However, previous studies showed a correlation between blood and kidney eosinophil counts 14 , 15 . In conclusion, higher eosinophil counts were associated with an increased risk of RRT initiation in CKD patients. This association was robust after adjusting for time-dependent confounders. Even a modest increase in eosinophil count was associated with poorer kidney outcomes. Our findings highlight the possible involvement of eosinophils in the pathogenesis of CKD, which has largely been ignored in this field. Further mechanistic studies are required to elucidate the exact role of eosinophils and their potential as therapeutic targets for CKD. Declarations Acknowledgements Nothing to disclose Author Contributions KH and YS conceptualized the study, were responsible for methodology, data curation, formal analysis, validation, and visualization, and wrote the original draft; TO, TK, SK, and YA were responsible for data collection and revision of the draft; JYK, IM, MM and YI were responsible for supervision and revision of the draft. Data Availability Statement The data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the study. The data will be shared on reasonable request to the corresponding author. Funding Nothing to disclose Conflict of interest statement Nothing to disclose References Rosenberg HF, Dyer KD, Foster PS. Eosinophils: changing perspectives in health and disease. Nat Rev Immunol. 2013; 13: 9–22. 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J Am Soc Nephrol. 2018; 29: 2213–2224. Yanagihara T, Kitamura H, Aki K, et al. Serial renal biopsies in three girls with tubulointerstitial nephritis and uveitis syndrome. Pediatr Nephrol. 2009; 24: 1159–1164. Liu J, Yang C, Liu T, et al. Eosinophils improve cardiac function after myocardial infarction. Nat Commun. 2020; 11: 6396. Yang C, Li J, Deng Z, et al. Eosinophils protect pressure overload- and β-adrenoreceptor agonist-induced cardiac hypertrophy. Cardiovasc Res. 2022 Apr 8:cvac060 doi: 10.1093/cvr/cvac060 . Online ahead of print. Wang Y, Yang Y, Wang M, et al. Eosinophils attenuate hepatic ischemia-reperfusion injury in mice through ST2-dependent IL-13 production. Sci Transl Med. 2021; 13: eabb6576. Andres-Hernando A, Dursun B, Altmann C, et al. Cytokine production increases and cytokine clearance decreases in mice with bilateral nephrectomy. Nephrol Dial Transplant. 2012; 27: 4339–4347. Jing YJ, Ni JW, Ding FH, et al. p-Cresyl sulfate is associated with carotid arteriosclerosis in hemodialysis patients and promotes atherogenesis in apoE-/- mice. Kidney Int. 2016; 89: 439–449. Tumur Z, Shimizu H, Enomoto A, et al. Indoxyl sulfate upregulates expression of ICAM-1 and MCP-1 by oxidative stress-induced NF-kappaB activation. Am J Nephrol. 2010; 31: 435–441. Chihara J, Yamamoto T, Kayaba H, et al. Degranulation of eosinophils mediated by intercellular adhesion molecule-1 and its ligands is involved in adhesion molecule expression on endothelial cells-selective induction of VCAM-1. J Allergy Clin Immunol. 1999; 103: S452-456. Ueki S, Kihara J, Kato H, et al. Soluble vascular cell adhesion molecule-1 induces human eosinophil migration. Allergy. 2009; 64: 718–724. Takami A, Watanabe S, Yamamoto Y, et al. Reference intervals of white blood cell parameters for healthy adults in japan. Int J Lab Hematol. 2021; 43: 948–958. Douin-Echinard V, Calippe B, Billon-Galès A, et al. Estradiol administration controls eosinophilia through estrogen receptor-alpha activation during acute peritoneal inflammation. J Leukoc Biol 2021; 90: 145–154. Calco GN, Fryer AD, Nie Z. Unraveling the connection between eosinophils and obesity. J Leukoc Biol. 2020; 108: 123–128. Kitano T, Nezu T, Shiromoto T, et al. Association Between Absolute Eosinophil Count and Complex Aortic Arch Plaque in Patients With Acute Ischemic Stroke. Stroke 2017; 48: 1074–1076. Fukui M, Tanaka M, Hamaguchi M, et al. Eosinophil count is positively correlated with albumin excretion rate in men with type 2 diabetes. Clin J Am Soc Nephrol. 2009; 4: 1761–1765. Verdoia M, Schaffer A, Cassetti E, et al. Absolute eosinophils count and the extent of coronary artery disease: a single centre cohort study. J Thromb Thrombolysis. 2015; 39: 459–466. Shah AD, Denaxas S, Nicholas O, et al. Low eosinophil and low lymphocyte counts and the incidence of 12 cardiovascular diseases: a CALIBER cohort study. Open Heart. 2016; 3: e000477. Additional Declarations No competing interests reported. Supplementary Files EOSsupplementalSR.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2003296","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":132713382,"identity":"a92b1356-cabb-48c3-a816-6bc5bbe8bd81","order_by":0,"name":"Kohki Hattori","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kohki","middleName":"","lastName":"Hattori","suffix":""},{"id":132713383,"identity":"72dadd59-d6a4-4868-baf8-ad6ae1fe6bd3","order_by":1,"name":"Yusuke Sakaguchi","email":"","orcid":"","institution":"Osaka University Graduate School of 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Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun-Ya","middleName":"","lastName":"Kaimori","suffix":""},{"id":132713391,"identity":"52f9b6f0-f6fa-4a00-8ffa-194de1b100af","order_by":9,"name":"Yoshitaka Isaka","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yoshitaka","middleName":"","lastName":"Isaka","suffix":""}],"badges":[],"createdAt":"2022-08-27 03:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2003296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2003296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25992516,"identity":"4897c505-021e-4c11-aba0-631701fbcc27","added_by":"auto","created_at":"2022-09-02 16:08:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatistical models used in this study\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Baseline Cox proportional hazards model: Association between baseline eosinophil counts and outcomes is analyzed.\u003c/p\u003e\u003cp\u003e(B) Time-average Cox proportional hazards model: Association between average eosinophil counts over the first 12 months and subsequent risk of outcomes is analyzed.\u003c/p\u003e\u003cp\u003e(C) Group-based trajectory model: Eosinophil count trajectories during the first 12 months of follow-up are identified by group-based trajectory modeling. Association between the trajectories and subsequent risk of outcomes is analyzed.\u003c/p\u003e\u003cp\u003e(D) Marginal structural model: Association between time-varying eosinophil counts and outcomes is analyzed with adjustment for baseline and time-dependent confounders.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"EOSFiguresSR1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2003296/v1/2c9c6a2180b97340e7a84b26.jpg"},{"id":25993051,"identity":"aebe7891-eaa1-40ed-ac3d-3d66df11f545","added_by":"auto","created_at":"2022-09-02 16:13:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA restricted cubic spline curve for the association between eosinophil counts and kidney function\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe dashed lines indicate 95% confidence intervals. Three knots (10th, 50th, and 90th percentiles of eGFR) were used in restricted cubic spline regression. The bar graph shows the histogram of the study patients according to eGFR.\u003c/p\u003e\u003cp\u003eAbbreviations: eGFR, estimated glomerular filtration rate.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"EOSFiguresSR2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2003296/v1/d7cedf05a471c44447f667b2.jpg"},{"id":25992519,"identity":"01378747-56e2-4a78-8624-36c6be86a412","added_by":"auto","created_at":"2022-09-02 16:08:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":298624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEosinophil count trajectories identified by group-based trajectory models\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eGroup-based trajectory modeling identified three distinct trajectories of eosinophil counts during the first 12 months of the follow-up period: high, middle, and low. The solid lines and dots represent the averaged estimated trajectory and averaged observed trajectory, respectively. The dashed lines indicate 95% confidence intervals.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"EOSFiguresSR3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2003296/v1/cf7f15f242927c9101f264bf.jpg"},{"id":30672374,"identity":"8d9b2d32-6159-4822-9f6b-7cc87a6ac9e9","added_by":"auto","created_at":"2022-12-22 13:14:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":590875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2003296/v1/76a7e255-8d54-4836-beec-f031b73c2744.pdf"},{"id":25992517,"identity":"2ee8491e-0307-48ad-ae3f-13ee8723cfd2","added_by":"auto","created_at":"2022-09-02 16:08:08","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":198182,"visible":true,"origin":"","legend":"","description":"","filename":"EOSsupplementalSR.docx","url":"https://assets-eu.researchsquare.com/files/rs-2003296/v1/2c6dc7c785cd2cb2def6ffc0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Time-Updated Eosinophil Counts and Progression of CKD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEosinophils are multifunctional leukocytes involved in an array of pathological processes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Besides their well-known roles in allergic reactions, parasite defense, and autoimmune diseases, eosinophils are also implicated in atherosclerosis\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Marx et al. showed that activated eosinophils in atherosclerotic lesions accelerate plaque formation in concert with platelets by secreting eosinophilic granule proteins and extracellular traps\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Population-based cohort studies reported increased cardiovascular risks among those with higher levels of plasma eosinophilic cationic protein (ECP), a marker of eosinophil activity and degranulation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with advanced chronic kidney disease (CKD) have high blood eosinophil count\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e although its clinical implication is uncertain. In addition to their proatherogenic property, eosinophils might also contribute to the progression of kidney disease. For example, renal complications sometimes develop in idiopathic hypereosinophilic syndrome, where tubulointerstitial infiltration of eosinophils is typically observed\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Eosinophilic granulomatosis with polyangiitis, characterized by the interstitial infiltration of eosinophils, is also evidentiary to the involvement of eosinophils in kidney injury\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, interstitial eosinophilic aggregates are found in common kidney diseases, such as diabetic nephropathy, IgA nephropathy, and membranous nephropathy, which are related to interstitial fibrosis and inflammation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Since there is a positive correlation between eosinophil counts in the blood and renal interstitium\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, we hypothesized that increased blood eosinophil counts in patients with CKD reflect eosinophilic inflammation in the kidney and thus indicate a risk of CKD progression. In the current study, we examined the association between time-updated blood eosinophil counts and the risk of kidney failure among patients with advanced CKD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki. The Ethics Committee of Osaka University Hospital approved the study protocol and waived the need for a written informed consent given the retrospective nature of the study (no: 20352, 22047).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included all patients referred to the outpatient department of nephrology at Osaka University Hospital from January 2005 to January 2018 who met the following inclusion criteria: 1) aged 20 years or older, 2) an estimated glomerular filtration rate (eGFR) of 10\u0026ndash;60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and 3) not receiving renal replacement therapy (RRT). Patients were excluded if they 1) were followed up for \u0026lt;\u0026thinsp;1 year or 2) received corticosteroids.\u003c/p\u003e \u003cp\u003eFollow-up period was from the first visit to the hospital to death, RRT initiation, loss to follow-up, or February 28th, 2019, whichever occurred first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe detailed methods have been described elsewhere\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Demographics and comorbidities were extracted from a chart review of patients\u0026rsquo; electronic medical records by nephrologists. These included age, sex, body mass index (BMI), blood pressure, diabetes mellitus (DM), cardiovascular comorbidities, chronic respiratory diseases (chronic obstructive pulmonary disease and bronchial asthma), a prior history of arterial catheterization (cardiac catheterization and endovascular treatment for peripheral artery diseases and carotid artery stenosis), and cholesterol embolism. Cardiovascular comorbidities included coronary artery disease, congestive heart failure, valvular heart disease, aortic disease, and stroke (cerebral infarction or intracranial hemorrhage).\u003c/p\u003e \u003cp\u003eTime-series data on laboratory measurements and prescriptions were collected using an automated data extraction system of Osaka University Hospital. Laboratory data included serum albumin, creatinine, sodium, potassium, C-reactive protein (CRP), hemoglobin, white blood cell (WBC) counts, and urinary protein-to-creatinine ratio (UPCR). eGFR was calculated using the equation for the Japanese population\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The prescription data included loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists (MRAs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), nonsteroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPIs), histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists (H\u003csub\u003e2\u003c/sub\u003e blockers), and corticosteroids. We assessed the number of prescription drugs, not limited to those mentioned above. The time-series data were collected at a monthly interval during the study period.\u003c/p\u003e \u003cp\u003eWe collected information regarding arterial catheterization performed during follow-up (cardiac catheterization and endovascular treatment for peripheral artery diseases and carotid artery stenosis); these procedures may have contributed to the development of eosinophilia via cholesterol embolism. These data were obtained from diagnostic procedure combination (DPC) codes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExposure\u003c/h2\u003e \u003cp\u003eThe exposure was time-updated blood eosinophil counts (per \u0026micro;L), which were collected at a monthly interval during the study period. Blood eosinophil counts were calculated by multiplying the total WBC count by the percentage of eosinophils as measured using an automated WBC differential counter. Blood eosinophil counts were categorized into quartiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy outcomes\u003c/h2\u003e \u003cp\u003eThe study outcome was RRT initiation, defined as the initiation of chronic dialysis or kidney transplantation. We additionally evaluated cardiovascular outcomes, which were a composite of myocardial infarction, stroke, hospitalization for heart failure, and mortality. The dates of these clinical events were ascertained by a chart review of the patients\u0026rsquo; electronic medical records by nephrologists.\u003c/p\u003e \u003cp\u003e \u003cb\u003eA\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003epost-hoc\u003c/span\u003e \u003cb\u003eanalysis of a randomized controlled trial of oral carbon adsorbent\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the involvement of uremic toxins in elevated eosinophil counts in CKD, we analyzed the data in a previous randomized controlled trial of the oral carbon adsorbent AST-120\u003csup\u003e21\u003c/sup\u003e, which reduces serum uremic toxin levels such as indoxyl sulfate. This two-year, open-label, randomized, controlled trial enrolled 125 patients with stages 3\u0026ndash;4 CKD. Among them, 123 were randomized to either receive AST-120 (6 g/day) or not, in a 3:2 ratio. In this \u003cem\u003epost-hoc\u003c/em\u003e analysis, data on eosinophil counts at baseline, 3, 6, and 12 months were added to the original dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe relationship between eosinophil counts and eGFR at baseline was depicted using a restricted cubic spline curve with three knots (10th, 50th, and 90th percentiles of eGFR).\u003c/p\u003e \u003cp\u003eThe multivariable association between log-transformed eosinophil counts and covariates was assessed by a linear regression analysis with robust standard errors. The following variables were included: age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, a prior history of arterial catheterization, chronic respiratory diseases, ACEIs/ARBs, loop diuretics, thiazide diuretics, MRAs, PPIs, H\u003csub\u003e2\u003c/sub\u003e blockers, NSAIDs, number of drugs prescribed, hemoglobin, albumin, eGFR, CRP, UPCR, and WBC.\u003c/p\u003e \u003cp\u003eIn the \u003cem\u003epost-hoc\u003c/em\u003e analysis of the randomized trial of AST-120\u003csup\u003e21\u003c/sup\u003e, eosinophil counts were compared between the AST-120 and control groups using a linear mixed-effects model for repeated measures with an unstructured covariance matrix.\u003c/p\u003e \u003cp\u003eTo analyze the longitudinal relationship between time-updated blood eosinophil counts and kidney outcomes, time-dependent confounding should be considered. This is because eosinophil counts increase as kidney function declines. As a result, time-dependent confounding could occur owing to a potential bidirectional relationship between eosinophil counts and kidney function in terms of the development of kidney failure. In order to appropriately account for time-dependent confounding, we used a marginal structural model (MSM). We also performed baseline Cox model, time-average Cox model, and group-based trajectory model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e1) Baseline Cox model\u003c/p\u003e \u003cp\u003eAssociation between baseline eosinophil quartiles and outcomes was analyzed using multivariate Cox proportional hazards models. The following baseline covariates were adjusted in this model: age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, chronic respiratory diseases, a prior history of arterial catheterization and cholesterol embolism, hemoglobin, albumin, eGFR, sodium, potassium, CRP, WBC, UPCR, loop diuretics, thiazide diuretics, MRAs, ACEIs, ARBs, NSAIDs, PPIs, and H2 blockers. The proportional hazards assumption was checked graphically based on the scaled Schoenfeld residuals.\u003c/p\u003e \u003cp\u003e2) Time-average Cox model\u003c/p\u003e \u003cp\u003eThe average eosinophil count during the first 12 months of follow-up was calculated for each patient. Association between time-average eosinophil quartiles and outcomes was analyzed using a multivariate Cox proportional hazards model adjusted for the same covariates as in the baseline model. In this model, the onset of survival time was set at 12 months.\u003c/p\u003e \u003cp\u003e3) Group-based trajectory model\u003c/p\u003e \u003cp\u003eGroup-based trajectory model was used to assess the association between eosinophil count trajectories during the first 12 months and subsequent rates of outcomes (STATA command, traj). All available data on eosinophil counts during the first 12 months were used to identify eosinophil count trajectories. In this analysis, the eosinophil counts were log-transformed to normalize their distribution.\u003c/p\u003e \u003cp\u003eThe group-based trajectory model is a method of data clustering that assumes that a population is composed of a mixture of distinct groups characterized by their longitudinal trajectories\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Potential trajectory groups were estimated from individual longitudinal eosinophil data, using the maximum likelihood estimation method based on the finite mixture model theorem. The patients were divided into one of the trajectory groups according to their estimated probability of group membership. We selected the optimal number of trajectory groups, as well as a function of each trajectory, based on the Bayesian information criterion (BIC), with at least 5% of all patients being in the smallest group.\u003c/p\u003e \u003cp\u003eAfter deriving the eosinophil trajectory groups, multivariate Cox proportional hazards models were used to analyze the association between the trajectory groups and outcomes, adjusting for the same covariates as in the baseline model. In this model, the onset of survival time was set at 12 months.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4) MSM\u003c/h2\u003e \u003cp\u003eMSM was employed to 1) assess the time-varying eosinophil counts throughout the study period and 2) deal with time-dependent confounding between eosinophil counts and eGFR.\u003c/p\u003e \u003cp\u003eMSM is a statistical method that can account for time-dependent confounding\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In the current study, eGFR was considered to be the main time-dependent confounder because it influenced both exposure (eosinophil counts) and renal outcomes, while being possibly affected by previous eosinophil counts. We derived time-varying inverse probability weights (IPWs) from the inverse probability of treatment weights (IPTWs) and the inverse probability of censoring weights (IPCWs). IPTWs were the reciprocal of the predicted probability of each patient having their own exposure history (i.e., high eosinophil count or not). The probability was predicted by a logistic regression model at each of the 1-month follow-up periods, conditional on both baseline and time-dependent covariates, as described below. Two different definitions of high eosinophil counts were adopted: 1) eosinophil count\u0026thinsp;\u0026ge;\u0026thinsp;289/\u0026micro;L (the top 25th percentile in our cohort) and 2) eosinophil count\u0026thinsp;\u0026ge;\u0026thinsp;500/\u0026micro;L\u003csup\u003e30\u003c/sup\u003e. Similarly, IPCWs were the reciprocal of the probability of being uncensored, as predicted by a logistic regression model, conditional on both baseline and time-dependent covariates. IPTWs and IPCWs were stabilized by multiplying them with the predicted probabilities based on baseline covariates alone. The IPWs were the product of the stabilized IPTWs and IPCWs, calculated at baseline and for each month. The IPWs were truncated at the 1st and 99th percentiles to reduce the influence of extreme weight values.\u003c/p\u003e \u003cp\u003eBaseline covariates included were the same as in the baseline model. Time-dependent covariates included arterial catheterization performed during follow-up, hemoglobin, albumin, eGFR, sodium, potassium, CRP, UPCR, loop diuretics, thiazide diuretics, MRAs, ACEIs, ARBs, NSAIDs, PPIs, H2 blockers, and corticosteroids.\u003c/p\u003e \u003cp\u003eMSM created \u0026ldquo;pseudo-populations\u0026rdquo; using IPWs, comparing the rate of events if all patients had been continuously exposed to high eosinophil counts with the risk of events if they had never been exposed to it. In MSM, there was no association between measured time-dependent confounders and future exposure. We estimated the hazard ratio (HR) and 95% confidence interval (CI) using an IPW-weighted pooled logistic regression model that produced equivalent estimates to the Cox proportional hazards model.\u003c/p\u003e \u003cp\u003eEffect modification was evaluated by incorporating cross-product terms between eosinophil counts and a priori specified baseline covariates into the MSM, including age (\u0026lt;\u0026thinsp;70 vs. \u0026ge; 70), sex, BMI (\u0026lt;\u0026thinsp;22 vs. \u0026ge; 22), systolic blood pressure (\u0026lt;\u0026thinsp;130 vs. \u0026ge; 130 mmHg), DM, cardiovascular comorbidities, hemoglobin (\u0026lt;\u0026thinsp;12.4 vs. \u0026ge; 12.4 g/dL), albumin (\u0026lt;\u0026thinsp;3.8 vs. \u0026ge; 3.8 g/dL), CKD stage (stage 3 vs. stage 4\u0026ndash;5), UPCR (\u0026lt;\u0026thinsp;1.0 vs. \u0026ge; 1.0 g/gCr), and ACEIs/ARBs use.\u003c/p\u003e \u003cp\u003eMissing data at baseline were imputed using the multiple imputations by chained equation method based on all baseline covariates. Continuous variables with missing data (BMI, systolic blood pressure, eGFR, hemoglobin, sodium, potassium, UPCR, albumin, and CRP) were imputed based on linear regression imputation. We created ten imputed datasets that were analyzed separately and combined using Rubin\u0026rsquo;s rules. Missing data during follow-up were imputed using the last-observation-carried-forward method.\u003c/p\u003e \u003cp\u003eTwo sensitivity analyses were conducted. First, the association between eosinophil count and RRT initiation was assessed after excluding patients with chronic respiratory diseases or cholesterol embolism. Second, we reanalyzed MSM after excluding patients who were followed up for less than three months.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using Stata/IC software (version 16.0; Stata Corp, College Station, TX, USA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eAmong 2,889 patients who met the inclusion criteria, 2,877 (99%) had available eosinophil count data (Figure S1). Over the median follow-up period of 6.5 years (interquartile range, 3.5\u0026ndash;9.9), eosinophil count was measured a median of 22 (interquartile range, 7\u0026ndash;46) times per patient (4 [interquartile range, 2\u0026ndash;6] times a year per patient). The clinical characteristics according to eosinophil quartiles are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients in the highest eosinophil quartile were more likely to be men and to have diabetes mellitus, cardiovascular comorbidities, a lower eGFR, and a higher UPCR. There was a monotonic negative correlation between eosinophil count and eGFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics according to eosinophil counts quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eEosinophil counts quartiles: range (/\u0026micro;L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1: \u0026lt; 90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2: 90\u0026ndash;170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3: 170\u0026ndash;289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ4: \u0026gt; 289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,873(65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356(52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e435(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e509(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e573(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,173(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243(35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e264(39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e331(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e335(44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior history of catheterization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89(13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e149(19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic respiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEIs/ARBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e175(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoop diuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiazide diuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176(6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41(6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e107(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e466 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e695 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1[0.0-0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e760 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1[0.0-0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1[0.0-0.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1[0.0\u0026ndash;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2[0.0-1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR, g/gCre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7[0.0-2.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1070 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4[0.0-1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6[0.0-2.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8[0.0-2.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9[0.0-2.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells, \u0026times;10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData presented as mean (standard deviation), number (%), or median [25th‒75th]\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: BMI, body mass index; SBP, systolic blood pressure; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; MRA, mineralocorticoid receptor antagonists; NSAIDs, non-steroidal anti-inflammatory drugs; PPIs, proton pump inhibitors; H\u003csub\u003e2\u003c/sub\u003e blockers, histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists; eGFR, estimated glomerular filtration rate; UPCR, urinary protein-to-creatinine ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with higher eosinophil counts\u003c/h2\u003e \u003cp\u003eEosinophil counts were positively associated with male, BMI, cardiovascular comorbidities, chronic respiratory diseases, ACEIs/ARBs use, and WBC, and negatively correlated with eGFR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression analysis for the association between log-transformed eosinophil counts and clinical factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per 10 years increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 [-0.04, 0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34 [0.24, 0.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, per 1 kg/m\u003csup\u003e2\u003c/sup\u003e increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 [0.01, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, per 10 mmHg increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 [-0.03, 0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02 [-0.11, 0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 [0.11, 0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior history of catheterization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 [-0.10, 0.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic respiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 [0.08, 0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEIs/ARBs use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17 [0.07, 0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoop diuretics use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 [-0.09, 0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiazide diuretics use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 [-0.10, 0.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRAs use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 [-0.16, 0.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPIs use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 [-0.16, 0.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e blockers use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11 [-0.23, 0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07 [-0.31, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of drugs, per 1 drug increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003 [-0.01, 0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, per 1 g/dL increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 [-0.02, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, per 1 g/dL increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 [-0.08, 0.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, per 10 ml/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07 [-0.11, -0.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, per 1 mg/dL increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 [-0.03, 0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPCR, per 1 g/gCre increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 [-0.01, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells, per 1000/\u0026micro;L increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 [0.03, 0.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; MRA, mineralocorticoid receptor antagonist; PPI, proton pump inhibitor; H\u003csub\u003e2\u003c/sub\u003e blockers, histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonist; NSAID, non-steroidal anti-inflammatory drug; eGFR, estimated glomerular filtration rate; UPCR, urinary protein-to-creatinine ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBaseline and time-average Cox models\u003c/h2\u003e \u003cp\u003eRRT was initiated in 433 patients (2.1 per 100 patient-years; 95% CI, 1.9 to 2.3). In the baseline Cox model, there was a dose-dependent association between eosinophil quartiles and the rate of RRT initiation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Patients in the highest eosinophil quartile had a 2.12-fold (95% CI: 1.44 to 3.10) higher rate of RRT initiation than those in the lowest quartile. Similarly, higher time-average eosinophil quartiles were associated with an increased rate of RRT initiation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clinical characteristics according to time-average eosinophil quartiles are summarized in Table S1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between eosinophil quartiles and clinical outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: RRT initiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBaseline Cox model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTime-Average Cox model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eGroup-based trajectory model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil quartiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1: \u0026lt; 90 (n\u0026thinsp;=\u0026thinsp;688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2: 90\u0026ndash;170 (n\u0026thinsp;=\u0026thinsp;684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3: 170\u0026ndash;289 (n\u0026thinsp;=\u0026thinsp;736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4: \u0026gt; 289 (n\u0026thinsp;=\u0026thinsp;769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1: \u0026le; 108\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2: 105\u0026ndash;183\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ3: 183\u0026ndash;302\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ4: \u0026ge; 302 \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMiddle \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;1,123)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate, 100 p-y (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003cp\u003e(1.1\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003cp\u003e(1.2\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003cp\u003e(2.3\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003cp\u003e(2.5\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e(0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003cp\u003e(1.5\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003cp\u003e(2.4\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003cp\u003e(2.4\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e(0.7\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003cp\u003e(1.6\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003cp\u003e(2.6\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003cp\u003e(1.20\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003cp\u003e(1.44\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003cp\u003e(1.34\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003cp\u003e(1.40\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003cp\u003e(1.38\u0026ndash;3.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: CV events and death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBaseline Cox model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTime-Average Cox model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eGroup-based trajectory model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil quartiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1: \u0026lt; 90 (n\u0026thinsp;=\u0026thinsp;688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2: 90\u0026ndash;170 (n\u0026thinsp;=\u0026thinsp;684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3: 170\u0026ndash;289 (n\u0026thinsp;=\u0026thinsp;736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4: \u0026gt; 289 (n\u0026thinsp;=\u0026thinsp;769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1: \u0026le; 108\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2: 105\u0026ndash;183\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ3: 183\u0026ndash;302\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ4: \u0026ge; 302 \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMiddle \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;1123)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate, 100 p-y (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003cp\u003e(1.4\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003cp\u003e(1.4\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003cp\u003e(1.7\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003cp\u003e(2.1-3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003cp\u003e(1.2\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003cp\u003e(1.1\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003cp\u003e(1.5\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003cp\u003e(2.1-3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003cp\u003e(1.0\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003cp\u003e(1.3\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003cp\u003e(2.0-2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e(0.63\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.54\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.79\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e(0.86-2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eThe models were adjusted for age, sex, diabetes mellitus, body mass index, systolic blood pressure, chronic respiratory diseases, cardiovascular comorbidities, prior history of catheterization, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, proton pump inhibitors, histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists, non-steroidal anti-inflammatory drugs, hemoglobin, sodium, potassium, albumin, estimated glomerular filtration rate, C-reactive protein, urinary protein-to-creatinine ratio, and white blood cell count.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: RRT, renal replacement therapy; p-y, person-years; CI, confidence interval; CV, cardiovascular.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGroup-based trajectory model\u003c/h2\u003e \u003cp\u003eThree distinct trajectories of eosinophil counts were identified: low (n\u0026thinsp;=\u0026thinsp;324), middle (n\u0026thinsp;=\u0026thinsp;1,430), and high (n\u0026thinsp;=\u0026thinsp;1,123) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clinical characteristics in each trajectory group are presented in Table S2. In a multivariable Cox model, patients in the high-trajectory group showed a 2.30-fold (95% CI: 1.38 to 3.84) higher rate of RRT initiation than those in the low-trajectory group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \n\u003ch2\u003eMsm\u003c/h2\u003e\n\u003cp\u003eIn MSM, high eosinophil counts (\u0026ge;\u0026thinsp;289 /\u0026micro;L) were associated with a 1.83-fold (95% CI: 1.33 to 2.51) higher rate of RRT initiation than normal eosinophil counts (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). There was no significant effect modification by \u003cem\u003ea priori\u003c/em\u003e defined baseline covariates, i.e., age, sex, BMI, systolic blood pressure, DM, cardiovascular comorbidities, hemoglobin, albumin, CKD stage, UPCR, and ACEI/ARB use. High eosinophil counts were significantly associated with a higher rate of RRT initiation when using the other definition of high eosinophil count (\u0026ge;\u0026thinsp;500 /\u0026micro;L).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMarginal structural models for the association between high eosinophil counts and outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRRT initiation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCV events and mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh eosinophil counts (/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;289 (vs.༜289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83 (1.33\u0026ndash;2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (1.30\u0026ndash;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;500 (vs.\u0026lt; 500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41 (1.11\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.06\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThe models were adjusted for the baseline and time-dependent covariates. Baseline covariates included age, sex, body mass index, systolic blood pressure, diabetes mellitus, chronic respiratory diseases, cardiovascular comorbidities, prior history of catheterization, hemoglobin, albumin, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, urinary protein-to-creatinine ratio, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, non-steroidal anti-inflammatory drugs, proton pump inhibitors, and histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists. Time-dependent covariates included catheterization performed during follow-up, hemoglobin, albumin, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, urinary protein-to-creatinine ratio, loop diuretics, thiazide diuretics, mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, nonsteroidal anti-inflammatory drugs, proton pump inhibitors, histamine H\u003csub\u003e2\u003c/sub\u003e receptor antagonists, and corticosteroids.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; RRT, renal replacement therapy; CV, cardiovascular.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: eGFR, estimated glomerular filtration rate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eCardiovascular events and mortality\u003c/h2\u003e\n \u003cp\u003eA total of 275 patients developed cardiovascular events (1.4 per 100 patient-years; 95% CI: 1.2 to 1.5) and 165 died (0.8 per 100 patient-years; 95% CI, 0.7 to 0.9). The association of eosinophil count with cardiovascular events and mortality was not significant in any statistical models except for MSM, where higher eosinophil counts were significantly associated with higher rates of these outcomes. (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n \u003cp\u003eAfter excluding patients with chronic respiratory diseases or cholesterol embolism, higher eosinophil counts were still associated with a higher rate of RRT initiation (Table S3). A similar result was obtained when including patients followed up for at least 90 days.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003eEffects of oral carbon adsorbent, AST-120, on eosinophil counts\u003c/h2\u003e\n \u003cp\u003eThis \u003cem\u003epost-hoc\u003c/em\u003e analysis of the randomized controlled trial included 123 patients with stage 3\u0026ndash;4 CKD (70 in the AST-120 group and 53 in the control group). The mean (SD) baseline eosinophil counts were 288 (442) /\u0026micro;L and 454 (1,364) /\u0026micro;L in the AST-120 and control groups, respectively. At 12 months, the mean (SD) eosinophil count was 340 (442) /\u0026micro;L and 456 (1,447) /\u0026micro;L in the AST-120 and control groups, respectively. A linear mixed-effects model for repeated measures showed no significant difference in the eosinophil counts between groups (P\u0026thinsp;=\u0026thinsp;0.44) (Figure S2).\u003c/p\u003e\n\u003c/div\u003e\n \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found a dose-dependent relationship between eosinophil counts and the risk of RRT initiation among patients with advanced CKD. The results were consistent when longitudinal alterations in eosinophil counts were modeled using the group-based trajectory modeling and MSM. Although future mechanistic studies are required to validate our findings, our data suggest the possible involvement of eosinophils in the progression of CKD.\u003c/p\u003e \u003cp\u003eStudies that examined the association between eosinophils and CKD progression are sparse\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These studies were limited by the small sample size, highly selective patient population, and insufficient adjustment for relevant confounders. More importantly, they did not consider longitudinal alterations in eosinophil counts despite the fact that eosinophils increase as kidney function declines\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We demonstrated a significant association between eosinophil counts and kidney outcomes in the group-based trajectory modeling and MSM that could capture longitudinal changes in eosinophil counts. Furthermore, MSM revealed that this association was independent of time-dependent confounding factors such as eGFR. Thus, although causality cannot be proven, our study provides plausible evidence regarding the link between eosinophils and CKD progression.\u003c/p\u003e \u003cp\u003eSeveral factors may confound the association between eosinophilia and CKD progression. First, chronic obstructive pulmonary disease and bronchial asthma were strongly associated with high eosinophil counts. They might accelerate CKD progression through hypoxia and inflammation\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and thereby confound the association between eosinophilia and kidney outcomes. Second, drugs such as PPIs, diuretics, and NSAIDs induce both kidney injury and eosinophilia\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and thus could be potential confounders. Finally, arterial catheterization sometimes causes cholesterol embolism, characterized by progressive kidney injury and eosinophilia\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Nevertheless, we did not find an association between catheterization and eosinophil count, most likely because cholesterol embolism is a very rare event. It should be emphasized that we demonstrated a significant association between eosinophil count and the kidney outcome after adjustment for these confounders.\u003c/p\u003e \u003cp\u003eOne of the putative mechanisms linking eosinophils to CKD progression is their pro-atherogenic properties\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Since intrarenal arteriosclerosis/arteriolosclerosis leads to glomerulosclerosis and interstitial fibrosis and tubule atrophy (IFTA)\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and is associated with worse renal prognosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, eosinophils might affect kidney outcomes by accelerating nephrosclerotic lesions. Future studies are needed to clarify the effect of eosinophils on intrarenal arteriosclerosis/arteriolosclerosis.\u003c/p\u003e \u003cp\u003eAnother possible explanation is that eosinophils may be involved in tubulointerstitial injury. Interstitial eosinophilic infiltration is typically observed in drug-induced tubulointerstitial nephritis, eosinophilic granulomatosis with polyangiitis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, tubulointerstitial nephritis with uveitis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and idiopathic hypereosinophilic syndrome\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Interestingly, interstitial eosinophilic aggregates are also found in common kidney diseases, and are associated with IFTA, interstitial edema, and eosinophilic tubulitis, suggesting that they may aggravate tubulointerstitial inflammation and fibrosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The clinical implications of infiltrating eosinophils in the renal interstitium in terms of renal prognosis requires further detailed investigation.\u003c/p\u003e \u003cp\u003eA novel perspective has been proposed that eosinophils exert a tissue-protective effect. Liu et al. reported a cardioprotective role of interleukin-4 and mEar1 (human ECP ortholog) produced by eosinophils in a mouse model of myocardial infarction, suggesting that an increase in eosinophils in the heart and blood after myocardial infarction represents a compensatory protective response\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Similarly, eosinophils prevent transverse aortic constriction-induced cardiac hypertrophy by inhibiting cardiomyocyte apoptosis and cardiac fibrosis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. A protective role of eosinophils against liver injury has also been reported\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Thus, higher eosinophil counts related to an increased risk of CKD progression may indicate a protective response to a more active disease status. However, these studies evaluated the effect of eosinophil deficiency, which may not be extrapolated to that of eosinophilia. Moreover, whether eosinophils play a protective or harmful role may depend on the target organs and the specific pathological context. The precise role of eosinophils in the progression of CKD requires further investigation.\u003c/p\u003e \u003cp\u003eAlthough the exact mechanisms remain elusive as to why eosinophils increase in advanced CKD, evidence has implied a role of uremic toxins. Interleukin-5, a master cytokine for eosinophil development, is elevated in nephrectomized mice\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Uremic toxins, such as indoxyl sulfate and p-cresol, upregulate intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 in endothelial cells\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, which promote eosinophil migration and degranulation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Thus, these studies support the involvement of uremic toxins in eosinophil proliferation and activation. Nevertheless, we did not find a significant change in eosinophil counts after AST-120 administration in CKD patients. Although this neutral result may be due to insufficient removal of uremic toxins by the drug, it might indicate that the clinical impact of uremic toxins on eosinophils is trivial.\u003c/p\u003e \u003cp\u003eSeveral factors were related to higher eosinophil counts in our study. Eosinophilia is a well-known side effect of ACEIs, and we confirmed that ACEIs/ARBs users showed higher eosinophil counts. Male had higher eosinophil counts than female. This sex difference has also been observed in healthy individuals\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Estrogen inhibits eosinophil production in the bone marrow and induces eosinophil apoptosis\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, which might explain higher eosinophil counts in men. The positive association between BMI and eosinophil counts may be explained by this sex difference, while there seems to be a complex relationship between body weight and eosinophils\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In our study, the association between eosinophil count and kidney outcome was independent of sex and BMI. In addition, there was no significant effect modification by gender or BMI on this association.\u003c/p\u003e \u003cp\u003eNotably, even modest elevations in eosinophil counts were associated with CKD progression. This is consistent with previous studies showing that a modest increase in eosinophil count, below the definition of eosinophilia (\u0026ge;\u0026thinsp;500/\u0026micro;L)\u003csup\u003e30\u003c/sup\u003e, is associated with atherosclerotic plaques\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and albuminuria\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Therefore, physicians should be aware of the clinical implications of subclinical eosinophilia, especially among patients with CKD.\u003c/p\u003e \u003cp\u003eHigher eosinophil counts were also associated with an increased risk of cardiovascular events and mortality in MSM. However, this association was not confirmed in the other statistical models. This discrepancy may be because only MSM could capture time-series changes in eosinophil counts throughout the study period. Conversely, statistical models other than MSM used eosinophil counts only at baseline or during the first 12 months, and thereby may have introduced a misclassification bias. Additionally, most cardiovascular events in our study were hospitalizations for heart failure. Given that eosinophils contribute to atherosclerotic lesions\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, the predominance of non-atherosclerotic cardiac events would have compromised the sensitivity to detect the impact of eosinophils. Indeed, a previous cohort study reported that eosinophils are not associated with incident heart failure in the general population\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Further exploration of the association between eosinophils and cardiac events in patients with CKD is required.\u003c/p\u003e \u003cp\u003eThe strengths of this study include large sample size, long-term follow-up period, and abundant data on eosinophil counts repeatedly measured within individuals. Despite the retrospective study design, missing data on eosinophil counts were \u0026lt;\u0026thinsp;1%. We assessed clinically-meaningful hard outcomes. A variety of covariates potentially related to eosinophilia were adjusted in MSM.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, the observational study design precludes causal inferences between eosinophil and renal prognosis. Second, some patients had a small number of measurements for eosinophils, which might have reduced the accuracy of the exposure. Third, since we enrolled advanced CKD patients in Japan, the generalizability of our findings to patients with more preserved kidney function or other ethnic groups is unknown. Finally, because we did not have data on tissue eosinophils, we could not directly link blood eosinophil counts with kidney eosinophils. However, previous studies showed a correlation between blood and kidney eosinophil counts\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, higher eosinophil counts were associated with an increased risk of RRT initiation in CKD patients. This association was robust after adjusting for time-dependent confounders. Even a modest increase in eosinophil count was associated with poorer kidney outcomes. Our findings highlight the possible involvement of eosinophils in the pathogenesis of CKD, which has largely been ignored in this field. Further mechanistic studies are required to elucidate the exact role of eosinophils and their potential as therapeutic targets for CKD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKH and YS conceptualized the study, were responsible for methodology, data curation, formal analysis, validation, and visualization, and wrote the original draft; TO, TK, SK, and YA were responsible for data collection and revision of the draft; JYK, IM, MM and YI were responsible for supervision and revision of the draft. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the study. The data will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to disclose\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRosenberg HF, Dyer KD, Foster PS. Eosinophils: changing perspectives in health and disease. Nat Rev Immunol. 2013; 13: 9\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiegger J, Byrne RA, Joner M, et al. Histopathological evaluation of thrombus in patients presenting with stent thrombosis. A multicenter European study: a report of the prevention of late stent thrombosis by an interdisciplinary global European effort consortium. 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Low eosinophil and low lymphocyte counts and the incidence of 12 cardiovascular diseases: a CALIBER cohort study. Open Heart. 2016; 3: e000477.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"eosinophil, chronic kidney disease, mortality, cardiovascular events, marginal structural model","lastPublishedDoi":"10.21203/rs.3.rs-2003296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2003296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePatients with chronic kidney disease (CKD) have high blood eosinophil count but its clinical implication is uncertain. Since eosinophils may induce tubulointerstitial injury and arteriosclerosis, eosinophilia might be related to poor clinical outcomes. This retrospective cohort study included 2,877 patients whose estimated glomerular filtration rate (eGFR) was 10\u0026ndash;60 mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The exposure was time-updated blood eosinophil counts. The outcomes were 1) initiation of renal replacement therapy (RRT) and 2) cardiovascular events and mortality. We analyzed the associations between eosinophil counts and outcomes using marginal structural models (MSM). Over a median follow-up of 6.5 years, eosinophil counts were measured a median of 22 times per patient (4 times a year per patient). There was a negative correlation between eosinophil count and eGFR. In total, 433 patients initiated RRT, 275 developed cardiovascular events, and 165 died. In MSM, higher eosinophil counts (\u0026ge;\u0026thinsp;289/\u0026micro;L) showed a 1.83-fold (95% confidence interval:1.33\u0026ndash;2.51) higher rate of RRT initiation than lower eosinophil counts after adjustment for time-dependent confounders. Higher eosinophil counts were also associated with a higher rate of cardiovascular events and mortality in MSM (hazard ratio, 1.71 [95% confidence interval:1.30\u0026ndash;2.25]). In conclusion, patients with CKD who had higher eosinophil counts showed worse kidney outcome.\u003c/p\u003e","manuscriptTitle":"Association between Time-Updated Eosinophil Counts and Progression of CKD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-02 16:08:06","doi":"10.21203/rs.3.rs-2003296/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":"1e8bd18f-1947-4198-b314-2244374d4f1e","owner":[],"postedDate":"September 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-22T13:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-02 16:08:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2003296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2003296","identity":"rs-2003296","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}