Association of Neutrophil Percentage-To-Albumin Ratio With All-Cause and Cardiovascular Mortality in Chronic Kidney Disease

Abstract

Introduction

This research sought to understand the relationship of the neutrophil percentage-to-albumin ratio (NPAR) and mortality from cardiovascular (CVD) and all causes in populations with CKD.

Methods

7,553 CKD patients were included and grouped into quartiles based on NPAR. Kaplan–Meier curves were utilized for survival analysis, while a multivariate Cox proportional hazards model calculated the risk ratio of NPAR on CVD and all-cause mortality. The potential linear connection of NPAR with CVD/all-cause mortality was analyzed through restricted cubic spline (RCS) analysis, and receiver operating characteristic (ROC) analysis was used to assess predictive performance. Subgroup analysis was executed with stratification according to demographic characteristics.

Results

The Kaplan–Meier curve indicated that a higher NPAR was linked to a greater risk of CVD/all-cause mortality (p < 0.0001). In fully adjusted models, a one-unit rise in NPAR correlated with a 185% rise in the likelihood of dying from CVD diseased (HR = 2.85, 95% CI 2.26–3.47) and a 154% rise in the likelihood of dying from any cause (HR = 2.54, 95% CI 2.22–2.91). A non-linear link for NPAR and CVD/all-cause mortality was validated through RCS analysis, with a P value <0.001. ROC analysis showed that NPAR had modest discriminative ability for predicting all-cause mortality (AUC = 0.601), with an optimal cutoff value of 1.48. Across different subgroups, NPAR reliably predicted CVD and all-cause mortality in CKD patients.

Conclusion

Elevated NPAR were linked with a higher likelihood of CVD mortality and all-cause mortality in patients with CKD and could serve as an effective prognostic biomarker.

Keywords

national health and nutrition examination survey chronic kidney disease neutrophil percentage-to-albumin ratio mortality CVD mortality

Introduction

Chronic kidney disease (CKD) refers to a continuous, advancing, and non-reversible dysfunction of kidney function lasting at least three months. The 2017 Global Burden of Disease Study reported that CKD impacted around 697.5 million people globally, with a prevalence rate of 9.1%.¹ Between 1990 and 2017, CKD-related mortality increased by a concerning 41.5%, reaching 1.2 million deaths annually. Projections estimate that this figure will rise to 1.81 million deaths per year by 2030, underscoring the rapid emergence of CKD as a major global health priority over the past decades.^2,3 Despite the growing awareness of CKD as a significant global health burden, efforts to establish effective risk stratification strategies for improving clinical outcomes in individuals with CKD remain limited.

Systemic inflammation and nutritional status are key determinants of disease progression and prognosis in chronic disorders, including CKD.^4-7 However, reliable biomarkers that simultaneously reflect both inflammatory burden and nutritional condition for predicting CKD outcomes are still lacking. Neutrophils, as primary mediators of the innate immune response, are widely used indicators of systemic inflammation, and elevated neutrophil levels have been associated with poor prognosis in CKD.^8,9 Serum albumin, which constitutes more than half of total circulating protein, is a well-established marker of nutritional status and systemic health; decreased albumin levels are associated with heightened inflammation, increased susceptibility to infection, and adverse clinical complications.¹⁰

The neutrophil percentage-to-albumin ratio (NPAR), calculated by dividing neutrophil percentage by serum albumin concentration, integrates inflammatory and nutritional information into a single, easily obtainable biomarker. Population-based analyses have shown that elevated NPAR levels are associated with an increased risk of CKD in the general population, as well as a higher risk of diabetic kidney disease among individuals with type 2 diabetes.^11,12 Emerging evidence further suggests that elevated NPAR is independently associated with adverse outcomes in several chronic conditions, including cardiovascular disease, diabetes, chronic heart failure, and in patients undergoing peritoneal dialysis.^13-16However, whether NPAR can predict all-cause and cardiovascular (CVD) mortality across the CKD population remains unclear.

The goal of this investigation sought to explore the predictive role of NPAR in cardiovascular CVD and all-cause mortality in individuals with CKD, based on National Health and Nutrition Examination Survey (NHANES) data.

Methods

Study Population

Data spanning ten NHANES cycles from 1999 to 2018 were downloaded for this cohort study. NHANES is a nationwide population survey aimed to evaluate the health and nutrition of non-institutional populations every two years by the National Centre for Health Statistics (NCHS). All data collected for NHANSE were processed according to standard procedures and granted approval by the NCHS Ethics Committee. Each participant involved was aware of the project and provided written informed consent. As a secondary analysis of pre-anonymized datasets, the research did not engage directly with human subjects. Under these conditions, both informed consent and institutional review board approval were therefore deemed exempt. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement from the EQUATOR Network,¹⁷ and the completed STROBE checklist is provided as a supplemental file.

From 1999 to 2018, 10 cycles NHANES dataset recorded 13,114 patients were diagnosed with CKD. As shown in Figure 1, the following individuals were excluded: those aged <20 years (n = 3,605); pregnant women (n = 83); those lacking follow-up information (n = 9); those without serum albumin measurements (n = 462); those without neutrophil percentage data (n = 56); and those with missing data on covariates (marital status, n = 82; education level, n = 19; smoking status, n = 5; drinking status, n = 902; body mass index [BMI], n = 309; hypertension, n = 2; alanine aminotransferase [ALT], n = 20; uric acid, n = 2; total cholesterol, n = 4; high-density lipoprotein cholesterol [HDL-C], n = 1). The final analysis cohort included 7,553 participants. All laboratory measurements, including neutrophil percentage and serum albumin used for the calculation of NPAR, were obtained during the Mobile Examination Center examination under standardized conditions after a minimum 9-hour fast. NPAR was calculated as neutrophil percentage (%) × 100 divided by serum albumin (g/dL).

Figure 1.

The flow chart of study participants

Definition of CKD and Clinical Outcomes

According to the KDIGO guidelines, a CKD diagnosis is primarily based on laboratory tests, including the eGFR (estimated glomerular filtration rate) and the UACR (urine protein to creatinine ratio).³ The eGFR is determined using the CKD-EPI method, which classifies kidney function into five stages.¹⁸ The following five stages are delineated: Stage G1 with an eGFR of 90 mL/min/1.73 m² or more, Stage G2 with an eGFR of 60 to 89 mL/min/1.73 m², Stage G3a with an eGFR of 45 to 59 mL/min/1.73 m², Stage G3b with an eGFR of 30 to 44 mL/min/1.73 m², Stage G4 with an eGFR of 15 to 29 mL/min/1.73 m², and Stage G5 with an eGFR under 15 mL/min/1.73 m²¹⁸. The formula for calculating UACR is urine microalbumin (mg/L) divided by urine creatinine (g/L). The outcomes specify three stages: A1 (UACR under 30 mg/g), A2 (UACR ranging from 30 to 300 mg/g), and A3 (UACR over 300 mg/g). CKD is diagnosed when meeting either of the following criteria: an eGFR of stage 3a or worse, or a UACR of stage A2 or worse.

All individuals were prospectively tracked until December 31, 2019 to identify whether the subjects died during the follow-up period, with special attention to whether they died of cardiovascular causes. Death data were available at National Death Index.

Covariates

This study gathered participants’ demographics (age, sex, race, marital status, education level, and BMI); lifestyle factors (smoking status and drinking status); comorbidities (hypertension and diabetes); as well as laboratory test indicators (lymphocyte, monocyte, red blood cell, platelet, ALT, aspartate aminotransferase (AST), creatinine, uric acid, blood urea nitrogen (BUN), total cholesterol, HDL-C. These factors were corrected for as covariates in this study. CVD diagnosis was based on a questionnaire asking, “Has a doctor or other healthcare professional ever informed you that you have heart failure, chronic coronary heart disease, myocardial infarction, angina pectoris, or stroke?” If a positive answer was given by the participant, then they were regarded as having CVD.

Statistical Analysis

After summarizing the data, the presentation of continuous variables was mean ± standard deviation (SD), and for categorical variables, the presentation was shown in terms of frequency and percentage. Participants were divided into quartiles according to ascending NPAR values, where Quartile 1 (Q1) indicating the group with the lowest NPAR values and Q4 representing the group with the highest NPAR values. The comparison of the baseline characteristics of the subjects among different NPAR groups was then undertaken employing one-way ANOVA for continuous data and chi-square tests were utilized for categorical data. Kaplan-Meier estimates were employed to analyze survival data across NPAR quartile groups, and differences among groups were assessed using log-rank tests. The independent predictive impact of NPAR on CVD/all-causes mortality was analyzed through a multivariate Cox proportional hazards regression. This analysis considered NPAR as a continuous variable and divided it into four quartiles as a categorical variable, using four models to control for potential confounders. Specifically: Model 1: Analyzed without adjusting for any covariates. Model 2: Incorporated demographic characteristics as covariates, including age, sex, race, marital status, and education level. Model 3: Incorporated lifestyle factors including smoking and drinking status, along with comorbidities including hypertension and diabetes, and BMI as covariates, based on Model 2. Model 4: Expanded upon Model 3 by incorporating additional all laboratory test indicators. Further analysis using restricted cubic splines (RCS) was conducted to determine whether NPAR was linearly correlated with both CVD and all-cause mortality in CKD patients. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of NPAR to predict all-cause mortality in participants with CKD. The area under the curve (AUC) was used to assess its discriminative performance, and the optimal cutoff value was determined using the Youden index. The reliability of the association between NPAR and CVD/all-cause mortality was explored using stratified analysis in different subgroups, which were stratified based on demographic, lifestyle, and comorbidity factors. Additionally, the interaction between NPAR and each subgroup variable was examined to determine whether there was any effect modification. The analyses were performed with R software, considering a P-value < 0.05 as statistically significant.

Results

Baseline Data of the Participants

Table 1 showed the overall basic characteristics of CKD patients, as well as the basic characteristics of each subgroup population after stratification by NPAR quartiles. NPAR ranged from 0.017 to 3.608, with a mean ± SD of 1.457 ± 0.288. Median follow-up duration was 7.25(4.08,11.58) years. Total 2981of events were occurred and the overall mortality rate was 39.47%. Following the screening process depicted in Figure 1, the study included 7,553 CKD patients, with an average age of 64.00 ± 16.53 years, and 48.21% of the participants were male. With exception of sex and education level, the NPAR quartile groups demonstrated notable differences in other covariates (p < 0.05). Compared with CKD patients in the NPAR Q1 group, patients in the Q4 group were older and exhibited higher BMI, smoking and drinking histories, and had a notably higher incidence of hypertension, diabetes and mortality. Laboratory tests indicated that the Q4 group had significantly elevated levels of serum creatinine, uric acid, and BUN compared to the Q1 group.

Table 1.

Baseline Characteristics of Participants

Variables	Total (n=7553)	Neutrophil percentage-to-albumin ratio				P
Variables	Total (n=7553)	Quartile 1 (n=1887) (<1.277)	Quartile 2 (n=1890) (1.277–1.452)	Quartile 3 (n=1890) (1.453–1.627)	Quartile 4 (n=1886) (>1.627)	P
Age, years	64.00 ± 16.53	60.98 ± 17.02	63.82 ± 16.32	64.72 ± 16.59	66.48 ± 15.70	<0.001
Age groups						<0.001
<60 years	2384 (31.56)	706 (37.41)	599 (31.69)	574 (30.37)	505 (26.78)
≥60 years	5169 (68.44)	1181 (62.59)	1291 (68.31)	1316 (69.63)	1381 (73.22)
Sex						0.86
Male	3641 (48.21)	915 (48.49)	907 (47.99)	923 (48.84)	896 (47.51)
Female	3912 (51.79)	972 (51.51)	983 (52.01)	967 (51.16)	990 (52.49)
Race						<0.001
Non-Hispanic White	3824 (50.63)	744 (39.43)	958 (50.69)	1059 (56.03)	1063 (56.36)
Non-Hispanic Black	1595 (21.12)	585 (31.00)	360 (19.05)	319 (16.88)	331 (17.55)
Mexican-American	1128 (14.93)	241 (12.77)	318 (16.83)	286 (15.13)	283 (15.01)
Other	1006 (13.32)	317 (16.80)	254 (13.44)	226 (11.96)	209 (11.08)
Marital status						<0.001
Married	3824 (50.63)	925 (49.02)	997 (52.75)	1003 (53.07)	899 (47.67)
Other	3729 (49.37)	962 (50.98)	893 (47.25)	887 (46.93)	987 (52.33)
Education level						0.22
Below high school	2574 (34.08)	624 (33.07)	650 (34.39)	646 (34.18)	654 (34.68)
High school	1816 (24.04)	448 (23.74)	490 (25.93)	449 (23.76)	429 (22.75)
Above high school	3163 (41.88)	815 (43.19)	750 (39.68)	795 (42.06)	803 (42.58)
Smoking status						<0.001
Never	3737 (49.48)	982 (52.04)	965 (51.06)	918 (48.57)	872 (46.24)
Former	2534 (33.55)	570 (30.21)	636 (33.65)	634 (33.54)	694 (36.80)
Current	1282 (16.97)	335 (17.75)	289 (15.29)	338 (17.88)	320 (16.97)
Drinking status						<0.001
Never	1370 (18.14)	352 (18.65)	358 (18.94)	331 (17.51)	329 (17.44)
Former	2040 (27.01)	463 (24.54)	474 (25.08)	507 (26.83)	596 (31.60)
Current	4143 (54.85)	1072 (56.81)	1058 (55.98)	1052 (55.66)	961 (50.95)
BMI, kg/m²	29.80 ± 7.16	28.86 ± 6.41	29.57 ± 6.65	29.87 ± 7.09	30.91 ± 8.21	<0.001
BMI groups						<0.001
<25 kg/m²	1920 (25.42)	538 (28.51)	461 (24.39)	478 (25.29)	443 (23.49)
25–30 kg/m²	2440 (32.31)	647 (34.29)	648 (34.29)	612 (32.38)	533 (28.26)
≥30 kg/m²	3193 (42.27)	702 (37.20)	781 (41.32)	800 (42.33)	910 (48.25)
Hypertension						<0.001
No	2108 (27.91)	602 (31.90)	516 (27.30)	533 (28.20)	457 (24.23)
Yes	5445 (72.09)	1285 (68.10)	1374 (72.70)	1357 (71.80)	1429 (75.77)
Diabetes						<0.001
No	4652 (61.59)	1260 (66.77)	1201 (63.54)	1165 (61.64)	1026 (54.40)
Yes	2901 (38.41)	627 (33.23)	689 (36.46)	725 (38.36)	860 (45.60)
Lymphocyte, 1000 cells/uL	2.15 ± 4.67	3.05 ± 9.21	2.10 ± 0.65	1.85 ± 0.62	1.60 ± 0.60	<0.001
Monocyte, 1000 cells/uL	0.59 ± 0.26	0.61 ± 0.39	0.58 ± 0.20	0.59 ± 0.20	0.60 ± 0.21	0.02
Red blood cell, millioncells/uL	4.54 ± 0.56	4.60 ± 0.56	4.60 ± 0.55	4.54 ± 0.55	4.44 ± 0.58	<0.001
Platelet, 1000 cells/uL	244.84 ± 73.76	242.18 ± 69.09	242.81 ± 67.29	244.96 ± 73.93	249.43 ± 83.51	0.01
ALT, U/L	24.01 ± 32.68	25.38 ± 19.62	24.13 ± 15.16	24.41 ± 50.08	22.14 ± 33.82	0.02
AST, U/L	26.05 ± 16.97	27.51 ± 18.49	26.03 ± 12.60	25.79 ± 17.01	24.84 ± 18.95	<0.001
Creatinine, umol/L	105.43 ± 82.76	97.20 ± 64.22	99.82 ± 73.70	106.10 ± 83.38	118.61 ± 103.10	<0.001
Uric acid, umol/L	359.83 ± 99.03	354.23 ± 92.84	355.45 ± 94.29	360.53 ± 98.19	369.11 ± 109.37	<0.001
BUN, mmol/L	6.59 ± 3.42	5.93 ± 2.90	6.29 ± 2.93	6.71 ± 3.42	7.43 ± 4.11	<0.001
Total cholesterol, mmol/L	5.04 ± 1.18	5.22 ± 1.19	5.12 ± 1.17	5.00 ± 1.13	4.83 ± 1.18	<0.001
HDL-C, mmol/L	1.34 ± 0.43	1.37 ± 0.44	1.34 ± 0.43	1.33 ± 0.42	1.33 ± 0.42	<0.01
Median follow-up duration, years	7.25(4.08,11.58)	8.08(4.42,12.42)	7.83(4.33,12.00)	7.38(4.25,11.50)	5.92(3.17,10.08)	<0.001
Overall mortality	2981(39.47)	548(29.04)	676(35.77)	805(42.59)	952(50.48)	<0.001

BMI, body mass index; AST, aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; HDL-C, high-density lipoprotein cholesterol.

Relationship Between NPAR and the Risk of CVD/All-Cause Mortality in CKD Patients

The Kaplan-Meier survival curve revealed that the NPAR Q1 group experienced the lowest mortality rates for CVD and all causes, which increased in the Q2 and Q3 groups, while the NPAR Q4 group had the worst survival rates (P<0.0001) (Figure 2). This indicated that higher NPAR levels were linked to a greater risk of mortality.

Figure 2.

Kaplan–Meier curves of the survival rate of patients with CKD for (A) all-cause mortality and (B) cardiovascular mortality

When NPAR was used as a continuous variable, Cox proportional hazards model showed that higher NPAR levels were connected to a higher risk of mortality (Table 2). In Model 1, which was not adjusted for covariates, an increase of one unit in NPAR was linked to a 271% rise in the risk of dying from CVD (HR = 3.71, 95% CI: 3.03–4.54; P<0.001) and a 223% rise in the risk of dying from any cause (HR = 3.23, 95% CI: 2.85–3.66; P<0.001). This link continued to be robust after controlling for covariates. For CVD mortality, a 1-unit increment in NPAR was linked to a 220% rise of HR in Model 2 (HR = 3.20, 95% CI: 2.59–3.97), a 201% rise in Model 3 (HR = 3.01, 95% CI: 2.43–3.74), and a 185% rise in Model 4 (HR = 2.85, 95% CI: 2.25–3.61). For all-cause mortality, the corresponding risk increases were 176% (HR = 2.76, 95% CI: 2.43–3.15) in Model 2, 160% (HR = 2.60, 95% CI: 2.28–2.96) in Model 3, and 154% (HR = 2.54, 95% CI: 2.22–2.91) in Model 4, respectively (Table 2). When treating NPAR as a categorical variable, patients in Q4 were at greater risk of both CVD and all-cause mortality after full covariate adjustment (Model 4) compared to those in Q1, with HR values of 1.99 (95%CI: 1.64–2.42) and 1.84 (95% CI: 1.64–2.04), respectively (Table 3). RCS analysis uncovered a notable non-linear correlation between NPAR and both CVD mortality and all-cause mortality (P non-linear <0.001) (Figure 3). At low NPAR levels, the risk of death was stable, but it significantly increased with higher NPAR levels. ROC analysis was performed to evaluate predictive performance of NPAR for all-cause mortality, with an AUC of 0.601 and an optimal cutoff value of 1.48 (Figure 4). Stratification based on this cutoff revealed that participants with NPAR ≥1.48 had significantly higher mortality risks than those with NPAR <1.48. After full adjustment (Model 4), elevated NPAR remained independently associated with increased risks of all-cause mortality (HR: 1.44, 95% CI: 1.33–1.55) and CVD mortality (HR: 1.43, 95% CI: 1.26–1.63), supporting the prognostic value of this threshold (Table 4).

Table 2.

Hazard Ratios of All-Cause and CVD Mortality by Categories of NPAR Among Adults With CKD

	NPAR Q1	NPAR Q2	NPAR Q3	NPAR Q4	P _trend
	HR (95% CI)	HR (95% CI)	HR (95% CI)	HR (95% CI)	P _trend
All-cause mortality
Model 1	Ref	1.32 (1.18,1.47)	1.61 (1.45,1.80)	2.38 (2.14,2.65)	< 0.001
Model 2	Ref	1.19 (1.06,1.33)	1.36 (1.22,1.52)	1.96 (1.76,2.19)	< 0.001
Model 3	Ref	1.16 (1.04,1.30)	1.33 (1.19,1.49)	1.88 (1.68,2.09)	< 0.001
Model 4	Ref	1.20 (1.07,1.34)	1.34 (1.19,1.50)	1.84 (1.64,2.04)	< 0.001
CVD mortality
Model 1	Ref	1.41 (1.17,1.71)	1.76 (1.46,2.12)	2.74 (2.29,3.27)	< 0.001
Model 2	Ref	1.29 (1.06,1.56)	1.49 (1.23,1.80)	2.25 (1.88,2.70)	< 0.001
Model 3	Ref	1.26 (1.03,1.52)	1.46 (1.21,1.76)	2.14 (1.78,2.56)	< 0.001
Model 4	Ref	1.24 (1.02,1.51)	1.39 (1.15,1.70)	1.99 (1.64,2.42)	< 0.001

CVD, cardiovascular disease; NPAR, neutrophil percentage-to-albumin ratio; HR, hazard ratio; CI, confidence interval; AST, aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; HDL-C, high-density lipoprotein cholesterol.

Model 1: crude model.

Model 2: adjusted for age, sex, race, marital status and education.

Model 3: model 2 + smoking status, drinking status, hypertension, diabetes, and BMI.

Model 4: model 3 + lymphocyte, monocyte, red blood cell, platelet, ALT, AST, creatinine, uric acid, BUN, total cholesterol, and HDL-C.

Table 3.

Hazard Ratios of All-Cause and CVD Mortality by Continuous NPAR Among Adults With CKD

	Model 1	Model 2	Model 3	Model 4
	HR (95% CI)	HR (95% CI)	HR (95% CI)	HR (95% CI)
All-cause mortality	3.23^* (2.85,3.66)	2.76^* (2.43,3.15)	2.60^* (2.28,2.96)	2.54^* (2.22,2.91)
CVD mortality	3.71^* (3.03,4.54)	3.20^* (2.59,3.97)	3.01^* (2.43,3.74)	2.85^* (2.25,3.61)

^*Statistically significant (P < 0.001).

Model 1: crude model.

Model 2: adjusted for age, sex, race, marital status and education.

Model 3: model 2 + smoking status, drinking status, hypertension, diabetes, and BMI.

Model 4: model 3 + lymphocyte, monocyte, red blood cell, platelet, ALT, AST, creatinine, uric acid, BUN, total cholesterol, and HDL-C.

Figure 3.

Receiver operator characteristic curve analysis for the NPAR and all-cause mortality

Figure 4.

Restricted cubic spline of HR and 95% CI for the association between NPAR and (A) All-cause mortality and (B) CVD mortality

Table 4.

Hazard Ratios of All-Cause and CVD Mortality by Optimal Cut-Off of NPAR Among Adults With CKD

	Model 1	Model 2	Model 3	Model 4
	HR (95% CI)	HR (95% CI)	HR (95% CI)	HR (95% CI)
All-cause mortality
Low	Ref	Ref	Ref	Ref
High	1.73^* (1.61,1.86)	1.53^* (1.42,1.64)	1.49^* (1.39,1.61)	1.44^* (1.33,1.55)
CVD mortality
Low	Ref	Ref	Ref	Ref
High	1.81^* (1.60,2.04)	1.58^* (1.40,1.79)	1.54^* (1.36,1.74)	1.43^* (1.26,1.63)

^*Statistically significant (P < 0.001).

Model 1: crude model.

Model 2: adjusted for age, sex, race, marital status and education.

Model 3: model 2 + smoking status, drinking status, hypertension, diabetes, and BMI.

Model 4: model 3 + lymphocyte, monocyte, red blood cell, platelet, ALT, AST, creatinine, uric acid, BUN, total cholesterol, and HDL-C.

Subgroup Analysis

To determine the effect of covariates on the NPAR and CVD/all-cause mortality relationship, interaction analysis identified significant interactions with age, race, BMI, smoking status and drinking status (interaction P<0.05). More importantly, in all subgroups stratified by demographic characteristics and comorbidities, higher NPAR were consistently and significantly linked to a heightened CVD/all-cause mortality risk (P<0.05), demonstrating its broad applicability and stability as a predictive indicator (Table 5).

Table 5.

Subgroup Analyses of the Association Between NPAR and Mortality Among Adults With CKD

Variables	All-cause mortality		CVD mortality
Variables	OR (95% CI)	P for interaction	OR (95% CI)	P for interaction
Age		0.008		0.010
<60 years	3.60 (2.40–5.40)		4.28 (2.33–7.88)
≥60 years	2.40 (2.07–2.78)		2.56 (1.98–3.32)
Sex		0.476		0.358
Male	2.58 (2.14–3.11)		3.08 (2.24–4.23)
Female	2.44 (2.00–2.99)		2.60 (1.79–3.78)
Race		0.033		0.147
Non-Hispanic White	2.26 (1.88–2.72)		2.64 (1.91–3.65)
Non-Hispanic Black	2.40 (1.80–3.19)		2.43 (1.54–3.82)
Mexican-American	4.69 (2.94–7.48)		4.98 (2.19–11.35)
Other	4.77 (2.49–9.13)		4.63 (1.53–14.02)
Marital status		0.81		0.263
Married	2.31 (1.86–2.86)		2.40 (1.64–3.50)
Other	2.59 (2.16–3.10)		3.22 (2.38–4.34)
Education level		0.377		0.053
Below high school	2.84 (2.28–3.54)		2.32 (1.59–3.40)
High school	2.94 (2.20–3.94)		4.20 (2.58–6.84)
Above high school	2.13 (1.68–2.71)		3.15 (2.11–4.71)
Smoking status		0.042		0.410
Never	2.34 (1.82–3.00)		2.66 (1.73–4.09)
Former	2.29 (1.78–2.95)		2.69 (1.72–4.22)
Current	3.14 (2.48–3.96)		3.08 (2.07–4.58)
Drinking status		0.505		0.026
Never	2.87 (2.32–3.55)		3.14 (2.16–4.54)
Former	2.25 (1.82–2.79)		2.17 (1.50–3.15)
Current	2.67 (1.84–3.88)		5.61 (2.94–10.69)
BMI		0.154		0.021
< 25 kg/m²	3.45 (2.49–4.79)		3.33 (1.89–5.86)
25–30 kg/m²	2.17 (1.72–2.74)		1.70 (1.15–2.52)
≥ 30 kg/m²	2.68 (2.16–3.31)		3.83 (2.67–5.49)
Hypertension		0.510		0.770
No	1.87 (1.32–2.65)		2.41 (1.33–4.37)
Yes	2.67 (2.30–3.09)		2.82 (2.17–3.67)
Diabetes		0.313		0.914
No	2.29 (1.90–2.76)		2.53 (1.79–3.57)
Yes	3.07 (2.48–3.78)		3.64 (2.56–5.15)

Adjusted for age, sex, race, marital status, education, smoking status, drinking status, hypertension, diabetes, BMI, lymphocyte, monocyte, red blood cell, platelet, ALT, AST, creatinine, uric acid, BUN, total cholesterol, and HDL-C. Stratified variables themselves were not adjusted in the subgroup analy.

Discussion

In this NHANES-based cohort study, we investigated the association between the NPAR and survival outcomes among patients with CKD. We found that higher NPAR levels were significantly associated with increased risks of both CVD and all-cause mortality. These associations remained robust after adjustment for multiple clinical variables, further supporting the potential prognostic value of NPAR in CKD.

NPAR refers to the neutrophil percentage-to-albumin ratio and reflects the combined influence of systemic inflammation and nutritional status; thus, elevated NPAR levels tend to cluster with clinical characteristics associated with poorer overall health. Consistent with this interpretation, individuals in the highest NPAR quartile in our study were older and had a higher prevalence of smoking and drinking history, higher BMI, and a greater burden of hypertension and diabetes. The unequal distribution of race across NPAR quartiles may reflect underlying health disparities related to differences in comorbidity burden, socioeconomic conditions, and access to healthcare rather than inherent biological differences. These factors were adjusted for in multivariable regression models to minimize potential confounding. Importantly, the association between NPAR and both cardiovascular and all-cause mortality remained significant after adjustment for these potential confounders.

Various biomarkers have been explored as simple and cost-effective tools for clinical risk stratification. Among them, NPAR may be more effective in predicting adverse disease outcomes, a concept that has been supported in several non-renal diseases.^19-21 Previous studies have also evaluated the neutrophil-to-albumin ratio (NAR) as a prognostic marker in patients with CKD and reported a complex nonlinear association between NAR and mortality risk, whereas NPAR demonstrated a more consistent linear relationship with mortality.^22,23 One possible explanation is that, compared with NAR, NPAR uses neutrophil percentage rather than absolute neutrophil counts, which may provide a more stable indicator of systemic inflammation and be less susceptible to fluctuations in total leukocyte levels. Another advantage of NPAR over NAR is its superior predictive performance for mortality after adjustment for multiple covariates, highlighting its potential as a more reliable prognostic marker in patients with CKD.

Previous studies have increasingly recognized the prognostic significance of the NPAR in kidney-related diseases. Elevated NPAR levels have been associated with increased all-cause mortality in critically ill patients with acute kidney injury.²⁴ Similarly, higher NPAR has been linked to increased CVD and all-cause mortality among individuals with diabetic kidney disease and patients with CKD stages G3a–G5.^25,26 In patients with end-stage renal disease receiving renal replacement therapy, elevated NPAR has also been reported as an independent predictor of mortality, including those undergoing hemodialysis and peritoneal dialysis.^27,28 In addition, studies in dialysis populations have shown that preprocedural NPAR is associated with poorer survival in patients undergoing tunneled hemodialysis catheter placement.²⁹ Consistent with these findings, our study further demonstrates that higher NPAR levels are associated with increased risks of both CVD and all-cause mortality in patients with CKD.

The mechanism linking NPAR to CKD is not yet understood. The reactive oxygen species (ROS) produced by neutrophils had a direct toxic effect on renal tubular epithelial cells and endothelial cells, triggering cell apoptosis or necrotic death.^30,31 In addition, proinflammatory cytokines released by neutrophils, such as TNF-α, IL-1β and IL-6, could recruit and stimulate additional immune cells, which would drive inflammatory cell infiltration and exacerbate kidney injury.^32,33 Activation of inflammatory pathways or thrombogenic processes might directly impair glomerular and tubular epithelial cells, thereby driving fibrotic remodeling and progressive deterioration of renal function.^34,35 Serum albumin is an important indicator of nutritional status and is commonly used to assess protein and energy reserves in the body.³⁶ Therefore, reduced serum albumin levels may reflect malnutrition and a catabolic state. Previous studies have shown that hypoalbuminemia is an independent risk factor for all-cause mortality in patients with CKD.³⁷ In addition, albumin plays an important role in maintaining renal function through multiple biological mechanisms. One of its key physiological functions is maintaining plasma colloid osmotic pressure, which is essential for preserving effective circulating blood volume and GFR.^38,39 Albumin also possesses antioxidant and anticoagulant properties and can protect renal tissues by scavenging ROS and preventing oxidative damage.^22,39 Conversely, hypoalbuminemia may increase the re-absorptive burden on renal tubules and contribute to the decline in eGFR. Moreover, low albumin levels have been reported to activate the renin–angiotensin–aldosterone system and the sympathetic nervous system, potentially inducing intraglomerular hypertension and further accelerating renal dysfunction.^40,41

From a public health perspective, as both neutrophil percentage and serum albumin are routinely measured in standard clinical practice and large-scale health surveys, NPAR can be calculated without additional cost or specialized testing. This practical advantage may facilitate the rapid identification of individuals with increased inflammatory burden and poor nutritional status who may be at higher risk of adverse outcomes. At the population level, incorporating such composite biomarkers into routine clinical assessment may support risk stratification, targeted monitoring, timely lifestyle or nutritional interventions, and optimization of chronic disease management strategies. However, given the observational nature of this study and the reliance on single baseline measurements, NPAR should be interpreted as a risk indicator rather than a causal determinant or a standalone clinical decision-making tool. Future prospective studies are warranted to determine whether NPAR-guided risk stratification can improve clinical outcomes or inform public health prevention strategies in CKD populations.

Although the prognostic efficacy of NPAR has been demonstrated, several limitations warrant cautious interpretation of the findings. First, the observational design inherently precludes definitive causal inferences. Second, reliance on a single measurement of NPAR and potential recall bias in retrospective data collection could obscure its true association with mortality outcomes. To strengthen clinical relevance, future investigations should prioritize longitudinal cohorts with serial NPAR assessments across ethnically diverse populations, coupled with mechanistic studies to elucidate its pathophysiological role in specific disease contexts.

Conclusion

Our study provides robust evidence supporting the potential clinical significance of the NPAR in risk assessment for CKD patients, and its integration into routine clinical practice may improve patient management strategies in the future.

Supplemental Material

Supplemental Material - Association of Neutrophil Percentage-to-Albumin Ratio with All-Cause and Cardiovascular Mortality in Chronic Kidney Disease

Supplemental Materal for Association of Neutrophil Percentage-to-Albumin Ratio with All-Cause and Cardiovascular Mortality in Chronic Kidney Disease by Yingying Wu and Siqi Ding in Inquiry: The Journal of Health Care Organization, Provision, and Financing.

Footnotes

Acknowledgment

We appreciate the staff and participants of the National Health and Nutrition Examination Survey (NHANES).

ORCID iD

Siqi Ding

Ethical Considerations

The study protocol was approved by the NCHS Ethics Review Board (. htm), and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing their written informed consent.

Author Contributions

Yingying Wu developed the methodology, analyzed the data, and wrote the original draft. Siqi Ding oversaw project administration, led the conceptualization, and managed the manuscript review and editing.

Funding

This work was supported by the Jinhua Science and Technology Bureau Key Project (Grant No. 2026-3-127) and the Zhejiang Provincial Natural Science Foundation, Regional Youth Project (Grant No. HSQY26H0900).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found below: .

Supplemental Material

Supplemental material for this article is available online.

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