The role of neutrophil percentage-to-albumin ratio as a biomarker in predicting peripheral arterial disease risk: A National Health and Nutrition Examination Survey-based study

Abstract

Background

Peripheral arterial disease is a common vascular condition characterized by the narrowing of peripheral arteries, leading to reduced blood flow and increased risk of cardiovascular events such as myocardial infarction, stroke, and limb amputation. Despite its impact, peripheral arterial disease remains underdiagnosed, making early identification crucial for improving outcomes. The neutrophil percentage-to-albumin ratio, reflecting systemic inflammation and nutritional status, has been proposed as a potential biomarker for assessing peripheral arterial disease risk.

Methods

This study used data from the National Health and Nutrition Examination Survey from 1999 to 2004, including 2864 participants after excluding those with missing data on peripheral arterial disease status, neutrophil percentage-to-albumin ratio, and covariates. Peripheral arterial disease was diagnosed using the ankle–brachial index, with an ankle–brachial index value less than 0.90 indicating peripheral arterial disease. Neutrophil percentage-to-albumin ratio was calculated as the percentage of neutrophils in total white blood cells divided by serum albumin levels. Logistic regression models were used to examine the association between neutrophil percentage-to-albumin ratio and peripheral arterial disease, adjusting for potential confounders, including age, sex, race, educational level, body mass index, hypertension, hyperlipidemia, diabetes, and smoking. A nonrestricted cubic spline analysis was used to assess the nonlinear relationship between neutrophil percentage-to-albumin ratio and peripheral arterial disease.

Results

Elevated neutrophil percentage-to-albumin ratio was significantly associated with an increased peripheral arterial disease risk. In the unadjusted model, the highest neutrophil percentage-to-albumin ratio quartile (Q4) had an odds ratio of 2.67 (95% confidence interval, 1.78–4.01; P < 0.001). This association remained significant after adjusting for age, sex, and other confounders, with an odds ratio of 1.86 (95% confidence interval, 1.21–2.85; P = 0.004) in the fully adjusted model. Cubic spline analysis revealed a threshold at a neutrophil percentage-to-albumin ratio of 13.71, above which peripheral arterial disease risk increased significantly. Subgroup analyses indicated stronger associations in individuals with hypertension, hyperlipidemia, and diabetes.

Conclusion

Elevated neutrophil percentage-to-albumin ratio is associated with an increased peripheral arterial disease risk, with a threshold effect at a neutrophil percentage-to-albumin ratio of 13.71. Neutrophil percentage-to-albumin ratio may serve as a valuable, cost-effective biomarker for early peripheral arterial disease detection and risk stratification, particularly in high-risk populations.

Keywords

Peripheral arterial disease neutrophil percentage-to-albumin ratio biomarker inflammation risk stratification National Health and Nutrition Examination Survey

Introduction

Peripheral arterial disease (PAD) is a prevalent and serious vascular condition characterized by the narrowing or occlusion of peripheral arteries, most commonly in the lower extremities.¹ This condition reduces blood flow to affected tissues, causing symptoms such as intermittent claudication, pain, and, in severe cases, critical limb ischemia and gangrene.^2,3 PAD is increasingly recognized as a major public health concern because of its association with higher risks of cardiovascular events, such as myocardial infarction, stroke, and limb amputation.^4,5 Despite its substantial impact on public health, PAD remains underdiagnosed and undertreated, largely because many individuals are asymptomatic or present with nonspecific symptoms until the disease has progressed. This delayed diagnosis contributes to poor clinical outcomes and places a significant burden on healthcare system, especially as the global population ages and the prevalence of PAD rises due to increasing rates of risk factors such as diabetes, smoking, and hypertension.^6–8

Given the substantial impact of PAD on quality of life and its high mortality rates, early detection and risk stratification are essential for improving patient outcomes. Although clinical examination and imaging techniques remain important for diagnosing PAD, there is increasing interest in identifying reliable biomarkers that may serve as early indicators of disease onset and progression.⁹ Biomarkers reflecting the underlying pathological processes of PAD—such as inflammation, endothelial dysfunction, and atherosclerosis—could provide valuable insights into an individual’s risk of developing PAD, particularly in asymptomatic individuals who are at high risk due to their medical history or other risk factors.^10–12

The neutrophil percentage-to-albumin ratio (NPAR) is a composite biomarker that combines two key components: neutrophil percentage and albumin levels.¹³ Neutrophils, a type of white blood cell, play an essential role in the body’s immune response.¹⁴ Although their primary function is to combat infection, neutrophils also contribute to chronic inflammation, a major driver of atherosclerosis, which is the underlying cause of PAD.¹⁵ Elevated neutrophil levels are commonly observed in patients with PAD, reflecting a heightened inflammatory state.¹⁶ Neutrophils release various inflammatory mediators, including cytokines and reactive oxygen species (ROS), which contribute to endothelial injury, promote smooth muscle cell proliferation, and enhance the formation of atherosclerotic plaques.¹⁷ The neutrophil percentage, representing the proportion of neutrophils among total white blood cells, thus serves as an effective marker of systemic inflammation. Elevated neutrophil percentages are often associated with greater disease severity and poorer clinical outcomes in patients with PAD.¹⁸

Albumin, the most abundant plasma protein in the human body, is synthesized by the liver and plays a crucial role in maintaining osmotic pressure and transporting various molecules through the bloodstream.¹⁹ Beyond these physiological functions, albumin level is widely used as an indicator of nutritional status. Low albumin levels, also known as hypoalbuminemia, are commonly observed in chronic inflammatory conditions and have been associated with poor cardiovascular outcomes.²⁰ In PAD, hypoalbuminemia reflects not only a deficiency of essential nutrients needed for tissue repair and immune function but also an ongoing inflammatory process. Albumin production is suppressed during systemic inflammation, further contributing to the inflammatory burden and exacerbating endothelial dysfunction, a key event in the development of atherosclerosis.²¹ Although albumin levels provide insights into the inflammatory and nutritional disturbances associated with PAD, albumin is a negative acute-phase reactant, indicating that its levels decrease during systemic inflammation. This inverse relationship between neutrophils and albumin may potentially exaggerate the NPAR. We acknowledge this limitation and emphasize that NPAR likely reflects the inflammatory state rather than serving as an independent causal factor for PAD. Future studies incorporating additional inflammatory markers, such as C-reactive protein (CRP) and interleukin-6 (IL-6), could help clarify the independent role of NPAR in PAD.

When combined, the NPAR provides a more comprehensive measure of both inflammation and nutritional status. Elevated NPAR values indicate a heightened inflammatory response which, together with poor nutritional status, may accelerate atherosclerosis and increase the risk of PAD progression. By integrating these two biomarkers into a single ratio, NPAR enables a more accurate assessment of an individual’s inflammatory and metabolic status, both of which are key contributors to PAD pathogenesis. Previous studies have reported that higher neutrophil and lower albumin levels are associated with an increased risk of PAD and its complications, including critical limb ischemia and amputation.²² The ratio provides a simple, cost-effective tool for the early identification of high-risk individuals, particularly those who may not yet exhibit clinical symptoms but are at elevated risk due to underlying inflammatory or nutritional imbalances. Early intervention in these cases could help prevent the progression of severe PAD and reduce associated healthcare costs.

In conclusion, the NPAR is a promising biomarker for assessing PAD risk. By combining the inflammatory marker neutrophil percentage with the nutritional marker albumin, NPAR provides a more comprehensive view of the underlying mechanisms contributing to PAD. This composite biomarker has potential to improve early detection and risk stratification, particularly in high-risk populations. By reflecting both systemic inflammation and nutritional status, NPAR could become a valuable tool for clinicians to identify PAD at an early stage and intervene before severe complications develop. Further research is required to validate its clinical utility and investigate its potential role in guiding therapeutic strategies for PAD management.

Methods

Study population and participant selection

This study used data from the National Health and Nutrition Examination Survey (NHANES) conducted from 1999 to 2004. NHANES is a large-scale, cross-sectional survey designed to assess the health and nutritional status of the US population. All participants provided written informed consent, and the study was approved by the Institutional Ethics Review Board (ERB) of National Center for Health Statistics (NCHS). The NCHS ERB protocol numbers for the 1999–2004 NHANES is Protocol #98-12 (https://www.cdc.gov/nchs/nhanes/about/erb.html).

Initially, the cohort included a total of 29,608 participants. A series of exclusion criteria were applied to ensure that only individuals with complete and reliable data were included in the final analysis.

First, participants with missing data on PAD status were excluded, reducing the sample size to 7187 individuals. This step was necessary because accurate PAD diagnosis is central to the study’s objective of examining its relationship with the NPAR.

Second, individuals with missing data on NPAR—specifically those lacking complete information on both neutrophil counts and serum albumin levels—were excluded, further reducing the sample size to 4344 participants. Calculating NPAR requires the percentage of neutrophils among total white blood cells and the serum albumin concentration, both of which are essential for assessing participant’s inflammatory and nutritional status.

Finally, individuals with missing data on key covariates—such as demographic characteristics, lifestyle factors, and clinical conditions—were excluded, yielding a final analytical sample of 2864 participants (Figure 1). This ensured that all participants included in the analysis had complete data for adjusting potential confounders in the statistical models. The final sample of 2864 participants is considered sufficient to provide adequate statistical power to detect the association between the NPAR and PAD. Consistent with similar studies in the literature, this sample size allows for meaningful conclusions while minimizing the risk of bias related to sample size limitations. Additionally, the inclusion of a range of demographic and clinical covariates enhances the generalizability of our findings.

Figure 1.

Flowchart depicting participant selection in the study.

Definitions of PAD and NPAR

PAD was diagnosed using the ankle–brachial index (ABI), a noninvasive test comparing blood pressure in the ankle with that in the arm. An ABI value of less than 0.90 in either leg was used to indicate the presence of PAD. This threshold is widely recognized in the literature as a reliable diagnostic criterion for PAD.²³

NPAR is a composite biomarker that combines the percentage of neutrophils among total white blood cells with serum albumin levels.²⁴ Some studies prefer the absolute neutrophil count (ANC) to minimize the influence of other leukocyte populations, whereas we used neutrophil percentage due to the availability of standardized data in NHANES. While acknowledging potential limitations, the robustness of our dataset supports the use of neutrophil percentage as an acceptable proxy for this analysis. Neutrophils serve as a key indicator of systemic inflammation, whereas albumin reflects nutritional status and functions as an acute-phase protein that decreases during inflammatory processes. The ratio was calculated as follows:

NPAR = Neutrophil  Percentage / Serum  Albumin  Level (g / dL)

This ratio provides a comprehensive measure of both inflammation (via neutrophils) and nutritional status (via albumin), both of which contribute to the pathophysiology of PAD.

Statistical analysis

Descriptive statistics were used to examine the baseline characteristics of the study population. Differences between individuals with and without PAD were assessed using t-tests for continuous variables (e.g. age and body mass index (BMI)) and chi-squared tests for categorical variables (e.g. sex, smoking status, and hypertension). These analyses allowed identification of significant differences in demographic and clinical characteristics between the two groups.

The primary objective was to investigate the association between NPAR and PAD. Multivariable logistic regression was used for this analysis because it models the probability of PAD presence, a binary outcome, while adjusting for potential confounding factors.

Three separate regression models were developed:

Model 1. This model included only the exposure variable (NPAR) and the outcome (PAD) with no adjustments for other factors. This unadjusted model assessed the direct association between NPAR and PAD.

Model 2. This model adjusted for age and sex, which can influence both NPAR levels and the likelihood of developing PAD, providing a more accurate estimate of the relationship.

Model 3. The final model included additional covariates, such as race, educational level, marital status, poverty income ratio (PIR), BMI, smoking, alcohol consumption, hypertension, hyperlipidemia, and diabetes. Some of these variables, particularly hypertension and hyperlipidemia, may lie on the same biological pathway as NPAR; future studies may consider refined models to avoid over-adjustment and better isolate the relationship between NPAR and PAD. These covariates were selected for their potential to confound the NPAR–PAD association. For example, smoking and hypertension are established risk factors for PAD, and adjusting for them helps control their influence.

To further investigate the relationship between NPAR and PAD, a nonrestricted cubic spline approach was employed to assess potential nonlinear associations. This method provides flexibility in modeling the relationship, as it does not assume a linear effect between NPAR and PAD, and allows for the identification of threshold effects or nonlinear patterns that may not be detected using traditional linear regression models.

Subgroup analyses were conducted to assess whether the association between NPAR and PAD varied according to key demographic and clinical characteristics, including sex, age group, and the presence of comorbidities. Interaction analyses were also performed to examine whether the effect of NPAR on PAD risk differed based on factors such as hypertension or diabetes.

Covariates

The following covariates were included in the multivariable models based on their potential to confound or modify the relationship between NPAR and PAD:

Demographic variables. Sex, age, race, educational level, and marital status;

Socioeconomic status. PIR;

Lifestyle factors. BMI, smoking status, and alcohol consumption;

Clinical factors. Hypertension, hyperlipidemia, and diabetes.

Statistical software

All statistical analyses were conducted using SPSS version 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria). A significance level of 0.05 was applied for all tests. For nonlinear regression and spline analyses, the ‘splines’ package in R was used to model the cubic spline functions.

Results

Baseline characteristics of the study population

The baseline characteristics of the study participants are summarized in Table 1. A total of 2864 individuals were included in the final analysis, of whom 215 were diagnosed with PAD and 2649 were without PAD. The mean age of the overall cohort was 61.07 ± 12.70 years, with individuals with PAD being significantly older (70.71 ± 11.62 years) than those without PAD (60.28 ± 12.46 years, P < 0.001).

Table 1.

Baseline characteristics of the study participants.

Characteristics	Overall	PAD		P-value
Characteristics	Overall	Yes	No	P-value
n	2864	215	2649
Age, years	61.07 ± 12.70	70.71 ± 11.62	60.28 ± 12.46	<0.001
Sex, n (%)				<0.001
Male	1449 (50.6%)	116 (4.0%)	1333 (46.5%)
Female	1415 (49.4%)	99 (3.5%)	1316 (45.9%)
Race, n (%)				<0.001
Mexican American	555 (19.4%)	43 (1.5%)	512 (17.9%)
Other Hispanic	109 (3.8%)	4 (0.1%)	105 (3.7%)
Non-Hispanic Black	1687 (58.9%)	121 (4.2%)	1566 (54.7%)
Non-Hispanic White	422 (14.7%)	43 (1.5%)	379 (13.2%)
Other races	91 (3.2%)	4 (0.1%)	87 (3.0%)
Educational level, n (%)				<0.001
Less than 9th grade	455 (15.9%)	58 (2.0%)	397 (13.9%)
9–11th grade	403 (14.1%)	32 (1.1%)	371 (13.0%)
High school graduate	680 (23.7%)	59 (2.1%)	621 (21.7%)
Some college or AA degree	730 (25.5%)	41 (1.4%)	689 (24.0%)
College graduate or above	596 (20.8%)	25 (0.9%)	571 (19.9%)
Marital status, n (%)				<0.001
Married	1866 (65.2%)	114 (4.0%)	1752 (61.2%)
Widowed	374 (13.1%)	57 (2.0%)	317 (11.1%)
Divorced	328 (11.4%)	26 (0.9%)	302 (10.5%)
Separated	81 (2.8%)	5 (0.2%)	76 (2.7%)
Never married	134 (4.7%)	6 (0.2%)	128 (4.5%)
Living with partner	81 (2.8%)	7 (0.2%)	74 (2.6%)
PIR, n (%)				<0.001
≤1	371 (13.0%)	46 (1.6%)	325 (11.3%)
1–3	1188 (41.5%)	115 (4.0%)	1073 (37.4%)
>3	1305 (45.6%)	54 (1.9%)	1251 (43.7%)
Alcohol use, n (%)				<0.001
Yes	1915 (66.8%)	135 (4.7%)	1780 (62.2%)
No	949 (33.1%)	80 (2.8%)	869 (30.3%)
Hypertension, n (%)				<0.001
Yes	1353 (47.2%)	138 (4.8%)	1215 (42.4%)
No	1511 (52.8%)	77 (2.7%)	1434 (50.1%)
Hyperlipidemia, n (%)				<0.001
Yes	1349 (47.1%)	114 (4.0%)	1235 (43.1%)
No	1515 (52.9%)	101 (3.5%)	1414 (49.4%)
Diabetes, n (%)				<0.001
Yes	408 (14.2%)	53 (1.9%)	355 (12.4%)
No	2456 (85.7%)	162 (5.7%)	2294 (80.1%)
BMI				<0.001
<19	69 (2.4%)	12 (0.4%)	57 (2.0%)
19–25	676 (23.6%)	47 (1.6%)	629 (22.0%)
25–30	1148 (40.1%)	87 (3.0%)	1061 (37.0%)
≥30	971 (33.9%)	69 (2.4%)	902 (31.5%)

PAD: peripheral artery disease; PIR: poverty income ratio; BMI: body mass index.

Significant differences were also observed in the distribution of sex, race, educational level, and marital status between the PAD and non-PAD groups (all P < 0.001). For example, non-Hispanic Black individuals had the highest prevalence of PAD (4.2%) compared with other racial groups. Additionally, individuals with lower educational levels and a PIR ≤ 1 exhibited higher PAD prevalence, reflecting known socioeconomic disparities in PAD risk.

Clinical characteristics, such as hypertension, hyperlipidemia, and diabetes, were significantly more prevalent in the PAD group (all P < 0.001), with hypertension present in 64.2% of PAD cases compared to 45.9% in the non-PAD group. The prevalence of obesity (BMI ≥ 30) was also higher among individuals with PAD (2.4% in the PAD group vs. 31.5% in the non-PAD group, P < 0.001).

These baseline characteristics confirm the expected associations between traditional risk factors—such as age, sex, BMI, and comorbidities—and PAD, highlighting the importance of these factors in understanding the epidemiology of PAD.

Association between NPAR and PAD

The weighted logistic regression analyses examining the association between the NPAR and PAD are presented in Table 2. Across all three regression models, higher NPAR levels were consistently associated with an increased risk of PAD, with the highest quartile (Q4) demonstrating the strongest association.

Table 2.

Weighted logistic regression analyses of the association between NPAR and PAD

	Model 1		Model 2		Model 3
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Q1	Reference		Reference		Reference
Q2	1.09 (0.68–1.75)	0.709	1.01 (0.63–1.64)	0.956	1.03 (0.63–1.67)	0.908
Q3	1.65 (1.07–2.55)	0.025	1.25 (0.80–1.96)	0.323	1.25 (0.79–1.96)	0.345
Q4	2.67 (1.78–4.01)	<0.001	1.93 (1.27–2.94)	0.002	1.86 (1.21–2.85)	0.004
P for trend		<0.001		<0.001		<0.001

Model 1: Unadjusted.

Model 2: Adjusted for age and sex.

Model 3: Adjusted for age, sex, race, education level, marital status, BMI, PIR, smoking, alcohol use, hypertension, hyperlipidemia, and diabetes.

NPAR: neutrophil percentage-to-albumin ratio; PAD: peripheral artery disease; OR: odds ratio; CI, confidence interval; BMI: body mass index; PIR: poverty income ratio.

In Model 1 (unadjusted), the odds ratio (OR) for PAD in Q4 was 2.67 (95% confidence interval (CI): 1.78–4.01, P < 0.001), indicating that individuals in Q4 were more than twice as likely to have PAD compared with those in the lowest quartile (Q1).

In Model 2 (adjusted for age and sex), the association remained significant, with an OR of 1.93 (95% CI: 1.27–2.94, P = 0.002), suggesting that the relationship between NPAR and PAD remains robust after accounting for these variables.

In Model 3 (fully adjusted for race, educational level, BMI, smoking, alcohol use, hypertension, hyperlipidemia, and diabetes), the association was slightly attenuated but remained significant, with an OR of 1.86 (95% CI: 1.21–2.85, P = 0.004), indicating that higher NPAR continues to be a significant risk factor for PAD after adjusting for multiple potential confounders.

The P for trend was significant in all three models (<0.001), supporting a dose–response relationship between increasing NPAR levels and higher PAD risk. The risk of PAD increased progressively from the first quartile (Q1) to the fourth quartile (Q4), emphasizing the potential utility of NPAR as a marker for assessing PAD risk.

Nonlinear relationship between NPAR and PAD

A nonrestricted cubic spline analysis was performed to examine the potential nonlinear relationship between NPAR and PAD, as shown in Figure 2. The spline curve revealed a threshold effect at an NPAR value of 13.71, above which the risk of PAD increased significantly.

Figure 2.

Restricted cubic spline plot showing the relationship between NPAR and PAD. NPAR: neutrophil percentage-to-albumin ratio; PAD: peripheral arterial disease.

For individuals with NPAR values below 13.71, the association with PAD appeared protective, suggesting that lower NPAR levels may be associated with reduced PAD risk. Once NPAR exceeded 13.71, the risk increased progressively, indicating a nonlinear relationship with a distinct tipping point beyond which higher NPAR values are associated with substantially greater odds of PAD. These findings suggest that NPAR reflects not only systemic inflammation and nutritional status but may also serve as a modifiable risk factor for PAD depending on its level.

Subgroup and interaction analyses

Subgroup and interaction analyses were performed to determine whether the association between NPAR and PAD varied across different subgroups. Key stratification variables included sex, age, race, BMI, PIR, hypertension, hyperlipidemia, and diabetes (Figure 3).

Figure 3.

Subgroup and interaction analysis of the association between NPAR and PAD. NPAR: neutrophil percentage-to-albumin ratio; PAD: peripheral arterial disease.

Sex. In men, the association between higher NPAR and PAD risk was stronger than in women. Men in Q4 had a significantly higher odds of PAD (OR = 2.75, 95% CI: 1.84–4.14) compared with women (OR = 1.99, 95% CI: 1.20–3.28), suggesting that men may be more sensitive to the effects of elevated NPAR on PAD development.

Age. The relationship between NPAR and PAD was more pronounced in individuals aged 65 years and older, indicating that NPAR may be a particularly important predictor of PAD risk in this population. The OR for older individuals in Q4 was 3.21 (95% CI: 2.15–4.76), compared with 1.76 (95% CI: 1.12–2.79) in younger participants.

Hypertension and hyperlipidemia. Among individuals with hypertension and hyperlipidemia, the association between NPAR and PAD was markedly stronger. For example, the OR for PAD in Q4 among hypertensive participants was 3.02 (95% CI: 2.03–4.48), compared with 1.67 (95% CI: 1.11–2.52) in those without hypertension.

Diabetes: A similar pattern was observed for diabetes. Individuals with diabetes and high NPAR values had a substantially higher risk of PAD (OR = 2.88, 95% CI: 1.84–4.53) compared with nondiabetic participants (OR = 1.87, 95% CI: 1.19–2.95).

These findings indicate that individuals with multiple comorbidities—particularly hypertension, hyperlipidemia, and diabetes—face an even higher risk of developing PAD when NPAR levels are elevated. The interaction analyses highlight the importance of accounting for these factors when assessing PAD risk in clinical practice.

Discussion

This study examined the NPAR as a potential biomarker for assessing the risk of PAD. We found that higher NPAR levels are associated with a significantly increased risk of PAD, with a threshold at an NPAR of 13.71, above which the risk rises progressively. These results indicate that NPAR, by integrating systemic inflammation and nutritional status, offers a comprehensive approach to evaluating PAD risk. Subgroup analyses showed that individuals with comorbidities such as hypertension, hyperlipidemia, and diabetes exhibit a stronger association between elevated NPAR and PAD, emphasizing the importance of accounting for these factors in PAD risk assessment.

Although this study supports the potential of NPAR as a predictive biomarker for PAD, we acknowledge the absence of key inflammatory markers, such as CRP, IL-6, or erythrocyte sedimentation rate (ESR), which are commonly used to assess systemic inflammation. Future studies should include these markers to better elucidate the inflammatory mechanisms underlying PAD. Comparisons with other composite inflammatory indices, such as neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), could also help determine whether NPAR provide additional predictive value or primarily reflects systemic inflammation. Importantly, our findings underscore the complex interplay between inflammation, nutritional status, and cardiovascular health, suggesting that NPAR may serve as a valuable tool for early detection and risk stratification in clinical practice.

The results of this study highlight the importance of identifying individuals at high risk for PAD, especially those with multiple comorbidities, such as hypertension and diabetes.²⁵ Moreover, PAD is associated with substantial healthcare costs due to hospitalizations, surgeries, and long-term care for complications, including limb amputation.^26,27 Early detection and intervention are essential for improving outcomes, and biomarkers such as NPAR may play a valuable role in supporting early diagnosis and guiding personalized treatment strategies.

The findings of this study indicate that NPAR could serve as an effective, cost-efficient screening tool for PAD, particularly in individuals at high risk due to underlying inflammatory or metabolic disturbances. Derived from simple, routinely measured biomarkers—neutrophils and albumin—NPAR could be incorporated into existing clinical workflows to identify patients who may benefit from further diagnostic evaluation or preventative interventions. For example, individuals with elevated NPAR could be referred for more detailed vascular assessments, such as ABI testing, to confirm PAD and stratify their risk for cardiovascular events. Although the associations between hypertension, hyperlipidemia, and diabetes with PAD are well established, our study further emphasizes the heightened PAD risk in individuals with these comorbidities when NPAR levels are elevated.

Given the high prevalence of risk factors such as diabetes, hypertension, and obesity—which are closely linked to both inflammation and PAD—integrating NPAR into primary care could help identify asymptomatic individuals at risk of developing PAD before clinical symptoms appear. This approach would enable early lifestyle interventions, pharmacologic treatment, and closer monitoring, potentially preventing PAD progression and reducing related complications.^28,29

At the molecular level, the association between NPAR and PAD is likely driven by the combined effects of systemic inflammation and impaired nutritional status, both of which play central roles in the pathogenesis of atherosclerosis and subsequent vascular diseases such as PAD.

Neutrophils, as key components of the immune system, are often the first responders to vascular injury.³⁰ Although their primary role is pathogen elimination, they also contribute critically to atherosclerotic plaque development and vascular inflammation.³¹ Neutrophils promote endothelial dysfunction and plaque instability through the release of cytokines, ROS, and proteolytic enzymes, which can directly damage endothelial cells and facilitate thrombus formation.^32,33 In PAD, chronic low-grade inflammation, indicated by elevated neutrophil counts, accelerates atherosclerosis, leading to narrowing and stiffening of peripheral arteries. Elevated neutrophil levels are commonly observed in PAD patients and are associated with higher cardiovascular morbidity and mortality.³⁴

Albumin serves not only as a marker of nutritional status but also an acute-phase reactant. During inflammatory states, its production is suppressed by pro-inflammatory cytokines, such as IL-6 and TNF-α, which are central to the inflammatory response.³⁵ Hypoalbuminemia therefore reflects both a deficiency in essential nutrients required for tissue repair and immune function and the presence of an ongoing systemic inflammation. In PAD, low albumin levels are associated with endothelial dysfunction, oxidative stress, and vascular remodeling, all of which contribute to the progression of atherosclerosis.³⁶

The combination of elevated neutrophils and low albumin, as reflected by NPAR, provides an integrated measure of the inflammatory and nutritional disturbances underlying PAD pathophysiology. These factors act synergistically to accelerate endothelial dysfunction and promote plaque instability, increasing susceptibility to the development and progression of PAD in individuals with high NPAR. Moreover, the threshold effect identified in this study—NPAR > 13.71—may represent a critical point at which inflammatory and nutritional imbalances are sufficiently pronounced to drive substantial vascular damage.

Despite the strengths of this study, several limitations should be acknowledged. First, the cross-sectional design limits the ability to infer causality. Although NPAR is strongly associated with PAD, it remains unclear whether elevated NPAR directly contributes to PAD development or primarily reflects underlying conditions such as systemic inflammation or impaired nutritional status.

Second, while adjustments were made for key confounders, including demographic factors, lifestyle behaviors, and clinical conditions, unmeasured or residual confounding may still be present. Factors such as genetic predisposition or vascular endothelial function, which were not included in the models, could potentially influence the observed associations between NPAR and PAD.

Another limitation is the absence of detailed inflammatory markers in the NHANES data. While NPAR was used as a proxy for systemic inflammation, more direct measures, such as CRP or IL-6, could have provided further insights into the molecular mechanisms linking NPAR and PAD.^37,38 Furthermore, the single time-point measurement of NPAR does not capture potential temporal variations in inflammatory and nutritional status, which may influence PAD risk over time.

Lastly, because this study used NHANES data, which represent a US population, the findings may not fully reflect populations in other countries or regions. Therefore, caution is warranted when generalizing these results to non-US populations.

Future research should prioritize prospective cohort studies to establish a causal relationship between NPAR and PAD and to further evaluate its prognostic value, as suggested by studies such as Evsen et al.³⁹ and others examining biomarkers in PAD prognosis.⁴⁰ Longitudinal investigations could clarify whether elevated NPAR serves as an early predictor of PAD development or arises as a consequence of existing vascular disease. Additionally, studies incorporating genetic data and more direct measures of vascular function and inflammatory markers could provide deeper insight into the molecular mechanisms through which NPAR influences PAD risk.

Another promising direction for future research is investigating interventions that could modulate NPAR levels. Because NPAR reflects both systemic inflammation and nutritional status, targeting these pathways may offer therapeutic potential. For example, anti-inflammatory treatments (e.g. statins, TNF-α inhibitors) or nutritional interventions (e.g. increasing intake of anti-inflammatory foods) could help lower NPAR levels and, consequently, reduce the risk of PAD.

Conclusion

This study provides robust evidence regarding an association between NPAR and PAD risk. The observed nonlinear relationship, with a threshold at an NPAR of 13.71, indicates that NPAR may serve as a valuable biomarker for identifying individuals at high risk of PAD. These findings underscore the importance of inflammation and nutritional status as key components of PAD pathogenesis. Future research should investigate the molecular mechanisms underlying this association and evaluate interventions aimed at modulating NPAR to reduce PAD risk. Such efforts could establish NPAR as an important tool in clinical practice and public health strategies for the prevention of PAD and the improvement of cardiovascular outcomes.

Footnotes

Acknowledgments

The authors would thank all the staﬀ and participants involved in the NHANES data collection for their contributions.

Author contributions

H.S: Project development, data collection, data analysis, manuscript writing. G.W, M.H, and L.Q: Data analysis and figure preparation. W.Z: Project development, data collection, manuscript writing. P.J (corresponding author): Provided critical guidance on research direction and reviewed the manuscript. All authors participated in result discussions, contributed to manuscript preparation, and approved the final version.

Data availability

The data used in this study are freely and openly available at .

Declaration of conflicting interests

The authors declare no competing interests.

Ethical approval and informed consent

The NHANES research protocol was approved by the National Center for Health Statistics Ethics Review Board (NCHS ERB) and conducted in accordance with the principles of the Declaration of Helsinki. The NCHS ERB protocol numbers for the 1999–2004 NHANES cycles are “Protocol #98-12.” All participants provided written informed consent at enrollment ().

Funding

This research was supported by the Independent Innovation Policy “Lending to Supplement” project of Hefei (J2019Y06) and the Research Project of the Third People’s Hospital of Hefei in 2024 (SYKF202401).

ORCID iD

Hongzhi Sun

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