Neutrophil percentage-to-albumin ratio is associated with cognitive function in adults aged over 60 years: An analysis of data from the NHANES 2011

Abstract

Background:

The aging global population is increasing the attention to cognitive decline in older individuals.

Objective:

This study sought to examine the potential link between the neutrophil percentage-to-albumin ratio (NPAR) and cognitive function.

Methods:

We analyzed data from the National Health and Nutrition Examination Survey 2011–2014 using multivariate logistic regression and smooth curve fitting, to investigate the correlation between NPAR and cognitive performance. Restricted cubic spline analysis assessed the linear relationship with high-risk cognitive dysfunction, while piecewise linear regression identified thresholds. Subgroup analyses confirmed the consistency and reliability of our findings.

Results:

Our study included data from 2759 individuals aged >60 years. NPAR showed a significant correlation with Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word learning score, CERAD delayed recall score, total z-score and a high risk of cognitive dysfunction. Furthermore, there were statistically significant trends in the changes in CERAD word learning, digit symbol substitution test, and CERAD delayed recall scores as the NPAR quartile increased, these trends were inverted U-shaped. When the NPAR exceeded 14.57, there was a positive association with the likelihood of a high risk of cognitive impairment. The link between NPAR and cognitive performance was notably stronger in individuals with moderate body mass index and those aged 73–80 years.

Conclusions:

A strong link was observed between the NPAR and cognitive function. NPAR may serve as a tool to identify individuals at increased risk of cognitive decline.

Keywords

Alzheimer's disease cognitive function cross-sectional study neutrophil percentage-to-albumin ratio NHANES

Introduction

Cognitive abilities, including learning, memory, language, and executive functions, are significantly affected by age, the primary factor in cognitive decline.^1,2 As birth rates decline and life expectancies rise, the global population ages, making age-related cognitive impairments such as Alzheimer's disease significant public health issues. In the United States, the number of people with cognitive impairments is projected to increase from 12.23 million in 2020 to 21.55 million in 2060, significantly increasing the health and economic burden on individuals and society.³ Interventions aimed at reversing cognitive decline have been developed, although their long-term effectiveness remains uncertain.⁴ Therefore, early detection of cognitive decline, and its effective management, is crucial, particularly in high-risk individuals.

Cognitive decline is influenced by several factors, including metabolic abnormalities, chronic inflammation, genetics, and nutrition.^5–8 Recently, the association between inflammation and cognitive function has received increasing attention. Neutrophils, the most abundant immune cell type in mammals, play a critical role in initiating inflammation, and in the neurodegenerative changes observed in conditions such as Alzheimer's disease.^9,10 Albumin, a marker of nutritional status and inflammation, is the most prevalent protein in human plasma. It acts as a carrier protein, osmotic regulator, and antioxidant, and is essential to overall health, aging, and neurodegenerative disease progression.^11,12 A seven-year retrospective cohort study identified lower serum albumin levels independently predict mild cognitive impairment in older adult.¹³ Moreover, a study from China revealed that serum albumin levels, as well as kidney function and hemoglobin levels, were notably linked to the severity of Alzheimer's disease.¹⁴

The neutrophil percentage-to-albumin ratio (NPAR), integrating these two factors, has therefore recently been proposed as an indicator of systemic inflammation and infection and has been correlated with disease mortality, infection risk, and cancer prognosis.^15–18 However, until now, no studies have explored the relationship between the NPAR and cognitive function. Data from the National Health and Nutrition Examination Survey (NHANES) for the years 2011 to 2014 were used to investigate this link.

Materials and methods

Data source and study population

The NHANES is a biennial initiative led by the National Center for Health Statistics (NCHS) to assess the health and dietary condition of the population of the United States, using a multistage probability sampling methodology to ensure an accurate representation of the population. Data are collected through organized interviews, clinical evaluations, and laboratory analyses conducted by skilled healthcare personnel. Additional information is available on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm). The NHANES was approved by the NCHS Ethics Review Board, and all participants provided written informed consent. Detailed information on approval is available on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm).

NPAR calculation

The NPAR is calculated by dividing the neutrophil percentage of total white blood cells by the albumin concentration, both measured from blood samples: Neutrophil percentage (%) / Albumin (g/dL). The NPAR (the exposure variable) was analyzed as continuous values and after categorization into four quartiles (Q).

Cognitive function assessment

Cognitive performance was evaluated using four tests: the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word learning (CERAD-WL), digit symbol substitution test (DSST), animal fluency test (AFT), and CERAD delayed recall (CERAD-DR).^19–21 CERAD-WL measures verbal learning capabilities through three progressive trials, with a total score between 0 and 30. The DSST assesses processing speed, attention span, and working memory, with scores ranging from 0 to 105. The AFT, an element of the executive function evaluation, measures categorical verbal fluency with a scoring range of 3 to 39. CERAD-DR, performed after the AFT and DSST, evaluates delayed recall by asking patients to name the ten words from CERAD-WL, with scores ranging from 0 to 10. Considering the floor and learning effects, we assessed cognitive function using standardized z-scores (z = (x-μ)/σ), where z represents the standardized score, x is the test-specific score, μ is the mean, and σ is the standard deviation.^20,22,23 Z-scores normalize the scores from different cognitive function tests, translating diverse test results into a standardized scale to enhance their comparability. By concentrating on relative changes, z-scores effectively reduce biases arising from baseline differences among test participants. This feature makes z-scores especially valuable in studies that include diverse populations, ensuring that comparisons are both meaningful and statistically robust. To date, there is no standardized method of measuring cognitive dysfunction. Therefore, we used the total z-scores from the four tests to determine total cognitive function. Furthermore, based on previous studies,^24,25 we differentiated between normal cognitive function and cognitive impairment (lowest quartile of scores from each test) by assigning scores of 0 or 1. Finally, we calculated an overall cognitive impairment score by aggregating the scores from all four tests, with ≤2 and >2 indicating low and high risks of cognitive dysfunction, respectively.

Covariates assessment

We selected the following covariates according to previous studies on NPAR and cognitive function:^19,26,27 age; sex; race; education level; marital status; poverty income ratio (PIR); body mass index (BMI); depression score; sleep duration; alcohol intake (more or less than 12 drinks per year); smoking status (never, former, current); sleep quality (whether reported sleeping problems to a doctor); the Healthy Eating Index-2015 (HEI-2015, with a score range from 0 to 100, where higher scores indicate better overall dietary quality, includes 13 dietary components: total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acid ratio, refined grains, sodium, saturated fats, and added sugars); physical activity status (whether engaging in moderate recreational activities); and diagnoses of congestive heart failure, coronary heart disease, heart attack, stroke, diabetes mellitus and hypertension. For subsequent subgroup analysis, ages are categorized into three equal ranges: 60–69 years, 69–73 years, and 73–80 years. Similarly, participants are equally divided into three BMI categories (in kg/m²): 13.4–24.1, 24.1–29.6, and 29.6–71.5.

Statistical analysis

Data are represented as mean ± standard deviation or count (percentage). We categorized the participant demographic characteristics into quartiles based on NPARs with a weighted Student's t-test applied to continuous variables and a weighted chi-squared test used for categorical variables. As the NHANES aims to reflect the civilian population of the United States, our statistical methods incorporated NHANES-specific sample weights. Initially, we applied weighted multivariate linear regression analysis to explore the correlation between the NPAR and the four cognitive function test scores, as well as the total z-scores, through curve fitting. Restricted cubic splines (RCS) were used to explore the nonlinear relationship between the NPAR and a high risk of cognitive dysfunction, and a two-piecewise linear regression model was constructed to calculate the turning point. We also conducted stratified and interaction analyses to examine how the association between the NPAR and cognitive function varied with age, sex, race, and BMI. For this purpose, we included interaction terms in the model and used weighted linear regression analysis and logistic regression models for model fitting. The significance of the interaction terms was assessed using analysis of variance (ANOVA). The analytical framework included three models: Model 1, unadjusted; Model 2, adjusted for age, sex, and race; and Model 3, which considered all covariates listed in Table 1, excluding those directly linked to the NPAR and cognitive function. Model 3 served as the basis for RCS, segmented linear regression, subgroup assessments, and curve fitting. All statistical analyses were conducted using R version 4.3.3 and Empower Stats 4.0. A two-tailed p-value < 0.05 was considered statistically significant.

Table 1.

Baseline participant characteristics.

	Neutrophil percentage-to-albumin ratio
Characteristic	Q1(0.18–12.34)	Q2(12.35–14.07)	Q3(14.08–15.80)	Q4(15.81–27.08)	p
Age (y)	68.51 ± 6.55	69.31 ± 6.70	69.71 ± 6.71	69.41 ± 6.81	0.0076
Sex (%)					0.0035
Male	40.44	50.23	46.37	46.70
Female	59.56	49.77	53.63	53.30
Race/ethnicity (%)					0.0905
Mexican American	4.70	3.11	3.23	3.35
Other Hispanic	4.27	3.37	3.08	3.55
Non-Hispanic white	76.59	82.50	82.04	76.62
Non-Hispanic black	8.76	6.30	7.08	10.87
Other	5.69	4.72	4.58	5.61
Education level (%)					0.4306
Below 9th grade	6.48	4.87	5.27	8.33
9–11th grade	9.86	11.07	10.41	10.05
High school graduate or equivalent	20.52	24.76	22.05	22.25
Some college or associate degree	32.51	31.29	32.11	28.23
College graduate or above	30.63	28.01	30.16	31.14
Marital status (%)					0.2506
Married	61.09	66.69	60.29	62.27
Widowed	18.03	14.29	18.57	17.32
Divorced	14.27	10.84	13.13	10.88
Separated	1.08	1.36	0.86	1.31
Never married	3.59	3.60	4.48	5.93
Living with partner	1.93	3.21	2.68	2.29
PIR	2.75 ± 1.59	2.71 ± 1.51	2.81 ± 1.57	2.56 ± 1.44	0.0172
BMI (kg/m²)	27.97 ± 7.01	28.36 ± 7.28	28.81 ± 7.74	27.30 ± 6.59	0.0010
Depression score	2.92 ± 4.19	2.79 ± 3.99	2.99 ± 4.08	3.64 ± 4.82	0.0015
Sleep duration (h)	6.96 ± 1.36	7.01 ± 1.37	7.07 ± 1.39	7.11 ± 1.54	0.2134
Alcohol intake (%)					0.0727
≥12 drinks per year	65.14	69.19	73.01	68.12
<12 drinks per year	34.86	30.81	26.99	31.88
Smoking status (%)					0.0112
Never	52.96	53.24	49.65	43.48
Former	35.48	35.85	38.27	42.56
Current	11.57	10.91	12.09	13.96
Congestive heart failure (%)					<0.0001
Yes	4.50	3.91	5.48	13.51
No	95.50	96.09	94.53	86.49
Coronary heart disease (%)					<0.0001
Yes	4.30	8.72	7.75	14.01
No	95.70	91.28	92.25	85.99
Heart attack (%)					0.0367
Yes	6.41	7.29	6.62	10.61
No	93.59	92.71	93.38	89.39
Stroke (%)					<0.0001
Yes	3.70	4.06	6.36	10.10
No	96.30	95.84	93.64	89.90
Diabetes mellitus (%)					<0.0001
Yes	17.50	17.03	21.11	31.22
No	77.33	78.42	74.86	65.32
Borderline	5.17	4.54	4.03	3.46
Hypertension (%)					<0.0001
Yes	52.92	60.02	63.01	69.98
No	47.08	39.98	36.99	30.02
Healthy Eating Index	51.05 ± 13.29	51.29 ± 12.79	50.22 ± 13.60	50.41 ± 12.64	0.3587
Moderate recreational activities					0.3907
Yes	45.85	43.24	41.50	42.21
No	54.15	56.76	58.50	57.79
Sleep trouble reported					0.2451
Yes	21.97	19.54	23.06	19.40
No	78.03	80.46	76.94	80.60
CERAD W-L score	20.15 ± 4.35	19.86 ± 4.23	19.73 ± 4.38	18.81 ± 4.77	<0.0001
DSST score	48.45 ± 16.95	49.50 ± 17.44	48.37 ± 16.91	44.09 ± 16.90	<0.0001
AFT score	17.32 ± 5.67	17.37 ± 5.50	17.45 ± 5.62	16.47 ± 5.44	0.0039
CERAD D-R score	6.61 ± 2.23	6.32 ± 2.21	6.31 ± 2.23	5.78 ± 2.41	<0.0001
Total z-score	0.82 ± 3.07	0.70 ± 3.05	0.62 ± 3.00	−0.23 ± 3.31	<0.0001
High risk of cognitive dysfunction (%)
Yes	88.41	87.88	86.29	79.31	<0.0001
No	11.59	12.12	13.71	20.69

Continuous variables are expressed as mean ± standard deviation; p-values were calculated using weighted linear regression. Categorical variables are expressed as percentages; p-values were calculated using weighted chi-squared tests. BMI, body mass index; CERAD-WL, Consortium to Establish a Registry for Alzheimer's Disease word learning; DSST, digit symbol substitution test; AFT, animal fluency test; CERAD-DR, Consortium to Establish a Registry for Alzheimer's Disease delayed recall; Q, quartile; PIR, poverty income ratio.

Results

Baseline participant characteristics

We included 19,931 participants from the NHANES 2011–2014 and sequentially excluded participants aged <60 years (16,299), those with missing cognitive function scores (698), and those with missing NPAR data (175). A total of 2759 adults were ultimately included in the analysis (Figure 1).

Figure 1.

Flowchart of the study.

Table 1 presents the participant characteristics, including demographic details, anthropometric measures, and questionnaire scores. The participants had an average age of 69.60 ± 6.82 years; 1417 (51.36%) were female and 1327 (48.10%) were non-Hispanic white. Participants were categorized into four quartiles based on their NPARs: Q1, 0.18–12.34; Q2, 12.35–14.07; Q3, 14.08–15.80; and Q4, 15.81–27.08. The NPAR was associated with age, sex, PIR, BMI, smoking status, and depression scores (p < 0.05). An elevation in the NPAR corresponded to a decline in the individual cognitive function test scores and total z-scores (p < 0.05). Additionally, heightened NPARs were linked to a greater probability of congestive heart failure, coronary heart disease, heart attack, stroke, diabetes and hypertension (p < 0.05).

Association between the NPAR and cognitive function

Table 2 illustrates the relationship between the NPAR and cognitive function. In Model 1, the NPAR was significantly associated with CERAD-WL, DSST, AFT, and CERAD-DR scores, and the total z-score (p < 0.001). In Model 3, the NPAR as a continuous variable was significantly associated with CERAD-WL (β = −0.11; 95% confidence interval [CI]: −0.17, −0.05; p < 0.001) and CERAD-DR (β = −0.06; 95% CI: −0.08, −0.03; p < 0.001) scores, and the total z-score (β = −0.06; 95% CI: −0.10, −0.03; p < 0.05), but not with DSST or AFT scores (p > 0.05). NPAR quartiles showed significant trends in the changes in CERAD-WL, DSST, and CERAD-DR scores, and the total z-score (p for trend < 0.05), however, the change in the AFT score was not significant (p for trend = 0.480). In Figure 2, smoothed curve fitting illustrated the relationship between NPAR and cognitive function scores. As NPAR increased, the curve ascended and subsequently descended upon reaching a specific threshold, forming an inverted U-shaped pattern.

Figure 2.

Association between NPAR and cognitive function test scores.

Table 2.

Association between the NPAR and cognitive function scores.

Cognitive function	Model 1β (95% CI)	Model 2β (95% CI)	Model 3β (95% CI)
CERAD W-L score
NPAR (continuous)	−0.20 (−0.27, −0.14)*	−0.16 (−0.22, −0.10)*	−0.11 (−0.17, −0.05)*
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	−0.29 (−0.76, 0.18)	−0.05 (−0.49, 0.38)	0.02 (−0.40, 0.44)
Quartile 3	−0.43 (−0.89, 0.04)	−0.16 (−0.59, 0.28)	−0.09 (−0.51, 0.32)
Quartile 4	−1.34 (−1.82, −0.86)*	−1.06 (−1.51, −0.62)*	−0.69 (−1.13, −0.25)*
p for trend	<0.0001	<0.0001	0.0019
DSST score
NPAR (continuous)	−0.58 (−0.82, −0.34)*	−0.49 (−0.72, −0.25)*	−0.17 (−0.39, 0.05)
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	1.05 (−0.75, 2.85)	1.49 (−0.27, 3.26)	1.63 (0.01, 3.24)
Quartile 3	−0.08 (−1.88, 1.72)	0.44 (−1.32, 2.20)	0.45 (−1.16, 2.07)
Quartile 4	−4.35 (−6.21, −2.50)*	−3.73 (−5.54, −1.91)*	−1.70 (−3.40, −0.01)*
p for trend	<0.0001	<0.0001	0.0248
AFT score
NPAR (continuous)	−0.14 (−0.22, −0.06)*	−0.11 (−0.19, −0.04)*	−0.04 (−0.12, 0.03)
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	0.06 (−0.53, 0.65)	0.12 (−0.46, 0.70)	0.17 (−0.39, 0.72)
Quartile 3	0.13 (−0.45, 0.72)	0.25 (−0.33, 0.83)	0.23 (−0.32, 0.79)
Quartile 4	−0.84 (−1.45, −0.24)*	−0.70 (−1.30, −0.11)*	−0.25 (−0.83, 0.34)
p for trend	0.0103	0.0341	0.4804
CERAD D-R score
NPAR (continuous)	−0.11 (−0.14, −0.08)*	−0.09 (−0.12, −0.06)*	−0.06 (−0.08, −0.03)*
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	−0.29 (−0.53, −0.05)*	−0.17 (−0.39, 0.06)	−0.12 (−0.34, 0.10)
Quartile 3	−0.30 (−0.54, −0.06)*	−0.16 (−0.38, 0.07)	−0.10 (−0.32, 0.11)
Quartile 4	−0.83 (−1.08, −0.58)*	−0.69 (−0.92, −0.46)*	−0.45 (−0.68, −0.22)*
p for trend	<0.0001	<0.0001	0.0002
Total z-score
NPAR (continuous)	−0.15 (−0.19, −0.11)*	−0.12 (−0.16, −0.08)*	−0.06 (−0.10, −0.03)*
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	−0.12 (−0.44, 0.21)	0.03 (−0.28, 0.33)	0.08 (−0.21, 0.36)
Quartile 3	−0.20 (−0.53, 0.13)	−0.03 (−0.34, 0.28)	0.01 (−0.28, 0.29)
Quartile 4	−1.05 (−1.39, −0.71)*	−0.87 (−1.18, −0.55)*	−0.49 (−0.79, −0.19)*
P for trend	<0.0001	<0.0001	0.0016

Model 1: unadjusted. Model 2: adjusted for age, sex, and race. Model 3: adjusted for all covariates presented in Table 1 except the stratification components (age, sex, race, poverty income ratio, body mass index, education level, marital status, alcohol intake, smoking status, sleep duration, depression score, congestive heart failure, coronary heart disease, heart attack, stroke, hypertension, and diabetes mellitus). CERAD-WL, Consortium to Establish a Registry for Alzheimer's Disease word learning; DSST, digit symbol substitution test; AFT, animal fluency test; CERAD-DR, Consortium to Establish a Registry for Alzheimer's Disease delayed recall; CI, confidence interval; NPAR, neutrophil percentage-to-albumin ratio. *p < 0.05.

The NPARs of 401 patients at high risk of cognitive dysfunction and 2358 patients at low risk were further analyzed in Table 3. In Model 1, an increase in NPAR as a continuous variable was associated with an increased likelihood of a high risk of cognitive dysfunction (odds ratio [OR] = 1.11; 95% CI: 1.07, 1.15; p < 0.001). This association was consistent across Models 2 (OR = 1.10; 95% CI: 1.06, 1.15; p < 0.001) and 3 (OR = 1.08; 95% CI: 1.04, 1.13; p < 0.001). When the NPAR was analyzed as a categorical variable, higher quartiles were linked to a greater likelihood of a high risk of cognitive dysfunction (p for trend < 0.05). RCS suggested a linear relationship between NPAR and a high risk of cognitive dysfunction (p for nonlinearity = 0.253; Figure 3A). A threshold effect analysis was performed using two-piecewise linear regression, with an inflection point of 14.57 (Figure 3B). Above this threshold, each unit increase in NPAR increased the likelihood of a high risk of cognitive dysfunction by 3% (OR = 1.03; 95% CI: 1.00, 1.02; p < 0.001), below this threshold, the association was not statistically significant (OR = 1.01; 95% CI: 0.99, 1.00; p = 0.609). The log-likelihood ratio was < 0.001.

Figure 3.

Association between the NPAR and a high risk of cognitive dysfunction using Model 3. (A) Restricted cubic spline plots. (B) Two-piecewise linear regression. (C) confidence interval; NPAR: neutrophil percentage-to-albumin ratio; OR: odds ratio.

Table 3.

Association between the NPAR and a high risk of cognitive dysfunction.

Participants at a high risk of cognitive dysfunction	Model 1OR (95% CI)p	Model 2OR (95% CI)p	Model 3OR (95% CI)p
NPAR (continuous)	1.11 (1.07, 1.15)*	1.10 (1.06, 1.15)*	1.08 (1.04, 1.13)*
NPAR group
Quartile 1	Reference	Reference	Reference
Quartile 2	1.05 (0.76, 1.46)	0.97 (0.69, 1.36)	1.02 (0.72, 1.45)
Quartile 3	1.21 (0.88, 1.66)	1.12 (0.80, 1.55)	1.16 (0.82, 1.63)
Quartile 4	1.99 (1.48, 2.68)*	1.90 (1.40, 2.59)*	1.76 (1.27, 2.44)*
P for trend	<0.0001	<0.0001	0.0002

Model 1: unadjusted. Model 2: adjusted for age, sex, and race. Model 3: adjusted for all covariates presented in Table 1 except the stratification components (age, sex, race, poverty income ratio, body mass index, education level, marital status, alcohol intake, smoking status, sleep duration, depression score, congestive heart failure, coronary heart disease, heart attack, stroke, hypertension, and diabetes mellitus). CI, confidence interval; NPAR, neutrophil percentage-to-albumin ratio; OR, odds ratio. *p < 0.05.

Subgroup analysis

Stratified analyses were also conducted according to age, sex, race, and BMI. Individuals with a moderate BMI showed a stronger association between the NPAR and the DSST and AFT scores (p = 0.0236 and p = 0.0215, respectively). Additionally, participants aged 73–80 years exhibited a greater association between the NPAR and DSST score (p = 0.0193). The relationship between the NPAR and cognitive function was mostly consistent and stable across the characteristics analyzed (Figure 4).

Figure 4.

Subgroup analysis of the association between the NPAR and cognitive function using Model 3. CERAD-WL, Consortium to Establish a Registry for Alzheimer's Disease word learning; DSST, digit symbol substitution test; AFT, animal fluency test; CERAD-DR, Consortium to Establish a Registry for Alzheimer's Disease delayed recall; CI, confidence interval; NPAR, neutrophil percentage-to-albumin ratio; OR, odds ratio.

Discussion

Cognitive dysfunction in older individuals is often an initial clinical indicator of Alzheimer's disease and other forms of dementia, it is therefore important to identify individuals at high risk of cognitive decline early to allow close monitoring of cognitive performance. To the best of our knowledge, this study represents the first attempt to explore the connection between NPAR and cognitive function, aiming to ascertain its potential in identifying individuals at risk. Our cross-sectional analysis found a significant association between the NPAR and cognitive performance, especially learning, memory, and overall cognitive ability. When the NPAR exceeded 14.57, there was a positive association with the likelihood of a high risk of cognitive impairment, even after rigorous adjustment for potential confounders. From a clinical standpoint, monitoring NPAR as a convenient blood biomarker offers practical advantages over complex cognitive function tests or imaging studies. It provides clinicians with an early warning system to identify patients who require closer monitoring of changes in cognitive function, allowing for timely interventions to improve and delay cognitive decline. Moreover, studies have shown that NPAR values near this threshold correlate with outcomes in other diseases, such as survival rates in oral squamous cell carcinoma¹⁷ and spontaneous bacterial peritonitis,²⁸ underscoring its broad predictive utility beyond cognitive impairment. Our study also investigated the association between NPAR and cognitive performance across different demographic subgroups. We found a more pronounced association between NPAR levels and cognitive function in individuals aged 73–80 years with moderate BMI. This finding suggests that NPAR, may exert a more significant impact on cognitive health in older adults with moderate BMI. The stronger association in the 73–80 age group may be attributed to age-related increases in systemic inflammation and nutritional deficits, leading to further decline in cognitive function. Conversely, the lack of significant association in the 69–73 age group may reflect relatively stable physiological conditions or insufficient sample size to detect minor effects. Additionally, our study revealed a stronger association between NPAR and cognitive function in individuals with moderate BMI. This observation may reflect specific physiological states related to inflammation response and nutritional status in these groups, thereby enhancing NPAR's predictive capability for changes in cognitive function. Conversely, extreme BMI (either high or low) might lead to other complex metabolic and physiological states that could obscure the relationship between NPAR and cognitive function.

Inflammation is increasingly being recognized as a critical factor in cognitive decline. Walker et al.⁸ described aging as the result of cumulative damage to macromolecular and cellular structures causing inflammation in blood and solid tissues. This inflammatory response is associated with numerous age-related chronic diseases and the deterioration of cognitive function. A study involving 367 patients who had experienced a stroke demonstrated that the NPAR at admission correlated with post-stroke cognitive dysfunction.²⁹ Further studies^30,31 also provide evidence to support the use of the NPAR as a prognostic indicator of cognitive impairment following carotid endarterectomy and chemotherapy for breast cancer, underscoring the ability of inflammatory markers in the blood to signal cognitive dysfunction.

The NPAR is a relatively new inflammatory biomarker that reflects the dynamic balance between immunity, inflammation, and disease activity. An analysis of a public database found that a higher NPAR predicts poor prognosis in sepsis,¹⁵ whereas another retrospective analysis showed that a higher NPAR predicts the occurrence of infections in patients who have experienced a stroke.¹⁸ Xu et al.¹⁶ suggested that the NPAR may reflect the inflammatory status of acute schizophrenia and bipolar disorder, implying a link between the NPAR and neurological diseases, consistent with our findings.

NPAR, a comprehensive inflammatory biomarker, effectively assesses inflammation levels and nutritional status. Although its impact on cognitive function remains underexplored in basic research, several hypotheses propose potential mechanisms of NPAR. Elevated NPAR indicates a stronger systemic inflammatory response and potential malnutrition, both closely linked to cognitive decline. High NPAR values suggest lower serum albumin levels, which are widely recognized to indicate severe inflammation and susceptibility to infectious complications. Several studies have shown an association between low albumin levels and cognitive impairment in older individuals.^11,13,14 Hypoalbuminemia, reflecting overall nutritional deterioration, may contribute to cognitive function decline by impairing brain tissue repair and maintenance.³² Albumin's anti-inflammatory and antioxidant properties are critical for cognitive health, and reduced levels may decrease the central nervous system's antioxidant capacity, increasing neuroinflammation and impairing cognitive function.³³ Additionally, the misfolding of serum albumin due to aging and disease may further impede cognitive function.³⁴ Systemic inflammation can affect central nervous system function through various pathways, particularly chronic low-grade inflammation, which exacerbates neuroinflammatory burden³⁵ and increases microvascular inflammation,³⁶ directly impacting cognitive function. Furthermore, neutrophils play a pivotal role in both the preservation and deterioration of cognitive function.³⁷ Their activation and oxidative stress can lead to neuropathological changes observed in Alzheimer's disease models,^38,39 further impairing cognitive function and being closely associated with neurodegenerative diseases such as Alzheimer's disease.⁴⁰ A study found that higher NPAR levels in patients with acute ischemic stroke undergoing reperfusion therapy were associated with poorer functional outcomes,⁴¹ suggesting that changes in NPAR may be closely related to brain vascular health, with systemic inflammation and malnutrition potentially damaging the brain's vascular system and thereby affecting cognitive function. The immune system plays a crucial role in maintaining healthy neurocognitive function.^42,43 Neutrophils, as key players in various autoimmune and autoinflammatory diseases, are central to the initiation and continuation of these conditions.⁴⁴ Therefore, we hypothesize that NPAR may reflect immune system dysregulation, negatively impacting cognitive abilities. Consequently, elevated NPAR significantly impacts cognitive health by weakening antioxidant defenses, exacerbating inflammation, affecting immune function and brain vascular health, and leading to malnutrition.

In summary, an increase in the NPAR, caused by an increase in neutrophils and/or a decrease in serum albumin, could be used for screening individuals at a high risk of cognitive dysfunction. Its clinical utility lies in being an easily accessible and cost-effective indicator that aids in the early identification and stratification of patients at risk of cognitive decline. This allows for timely detection of cognitive impairment among older adults and facilitates early intervention. The threshold can be applied in various clinical settings, such as routine health check-ups, preoperative evaluations, and monitoring of chronic disease patients. Particularly in preoperative evaluations related to anesthesia, which closely align with our daily work, patients with NPAR values above the threshold may receive more intensive cognitive monitoring and preventive strategies. To ensure the clinical applicability of the NPAR threshold, in future research, we will conduct internal and external validation, evaluate predictive accuracy through long-term follow-up studies, and finally establish standardized procedures to ensure consistency across studies.

This study boasts numerous strengths. It is the first to illustrate the association between NPAR and cognitive performance in adults over the age of 60. Furthermore, the nationally representative nature of the sample bolsters the robustness of the statistical outcomes. In addition, adjustment for a broad spectrum of confounding factors enhances the reliability of the conclusions. Nonetheless, this study does have some limitations. First, its cross-sectional design precludes the establishment of causality between NPAR and cognitive function, underscoring the necessity for additional longitudinal studies. We plan to conduct a longitudinal study to validate the predictive value of NPAR in cognitive dysfunction among patients undergoing anesthesia in the future. Second, although significant efforts were made to adjust for confounding factors, the possibility of residual confounding factors cannot be completely eliminated. Thus, future studies could utilize methodologies such as instrumental variable analysis, propensity score matching, and multilevel modeling to estimate the impacts of potential confounders. Finally, the sample comprised only older adults from the United States, potentially constraining the applicability of the results. Therefore, interpretations of the findings should be approached with caution.

Conclusions

In this study, we observed an association between the NPAR and cognitive function, particularly learning, memory, and overall cognitive ability. When the NPAR exceeded 14.57, there was a positive association with the likelihood of a high risk of cognitive impairment, suggesting that the NPAR may be useful in the identification cognitive dysfunction.

Footnotes

Acknowledgments

We are grateful for the linguistic assistance provided by Editage () during the preparation of this manuscript and extend our thanks to all NHANES staff for their support.

Author contributions

Di Fan (Conceptualization; Methodology; Writing- Original draft; Data curation; Formal Analysis); Tingfan Wang (Writing- Original draft; Data curation; Formal Analysis); Jinxian Xiang (Data curation; Writing- Review & Editing); Yiping Bai (Visualization; Investigation); Liling Zhang (Visualization; Investigation); Xiaobin Wang (Supervision; Writing - Review & Editing; Project administration).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work received support from the Sichuan Province Science and Technology Support Program (Grant No. 2022YFS0632) and the scientific research project sponsored by the Luzhou Science and Technology Bureau (2021LZXNYD-Z06). The funders did not participate in the study design, data collection and analysis, the decision to publish, or the manuscript preparation.

Declaration of conflicting interests

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

Data availability

The data supporting the conclusions of this study are publicly available through the NHANES and can be accessed at the following link: .

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Neutrophil percentage-to-albumin ratio is associated with cognitive function in adults aged over 60 years: An analysis of data from the NHANES 2011–2014

Abstract

Background:

Objective:

Methods:

Results:

Conclusions:

Keywords

Introduction

Materials and methods

Data source and study population

NPAR calculation

Cognitive function assessment

Covariates assessment

Statistical analysis

Results

Baseline participant characteristics

Association between the NPAR and cognitive function

Subgroup analysis

Discussion

Conclusions

Footnotes

Acknowledgments

Author contributions

Funding

Declaration of conflicting interests

Data availability

References