Skip to main content
Download PDF
Download ePub

Combined impact of neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) and cognitive function on all-cause mortality in older adults: a population-based study

Lipids in Health and Disease volume 24, Article number: 83 (2025) Cite this article

Abstract

Background

The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) has emerged as a potential biomarker for chronic disease outcomes. Cognitive impairment is a major contributor to mortality in older adults. However, the combined effect of NHR and cognitive function on all-cause mortality remains unclear. This study aims to investigate the joint impact of NHR and cognitive impairment on all-cause mortality in this population.

Methods

We analyzed participants in the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2014. Participants were grouped according to NHR levels, DSST scores, and the combined NHR and DSST. Weighted Cox regression models assessed the association between NHR, cognitive impairment, and all-cause mortality. Weighted Kaplan-Meier curves estimated survival probabilities.

Results

The study involved 1,486 participants (weighted sample was 54,078,084) aged 60 years and older, of whom 81.76% (n = 1,180) survived and 18.24% (n = 306) died by the end of follow-up. The median follow-up time was 78 months (IQR: 68–94). Weighted multivariable Cox regression revealed that high NHR (HR = 1.82, 95% CI: 1.21–2.74; P = 0.004), cognitive impairment (HR = 1.87, 95% CI: 1.25–2.79; P = 0.002), and the combination of high NHR and cognitive impairment (HR = 2.98, 95% CI: 1.45–6.14; P = 0.003) were independently associated with higher all-cause mortality, after full adjustment in model 3. Kaplan-Meier curves revealed significant survival differences, with the highest survival rate in the NHR Low & Normal cognition and the lowest in the NHR High & Cognitive impairment (P < 0.001).

Conclusions

High NHR and cognitive impairment in aged 60 years and older have an increased risk of all-cause mortality. These findings underscore the importance of integrating both NHR and cognitive assessments in mortality risk evaluations, offering a potential strategy for early intervention in aging populations.

Introduction

Cognitive impairment in older adults has become a significant public health challenge, especially given that aging is one of the primary risk factors for cognitive decline. Cognitive decline has emerged as a significant determinant of mortality among older adults. It is already well established that cognitive impairment and dementia are associated with increased risk of death [1, 2]. Specifically, cognitive decline often predicts adverse outcomes such as decreased functional independence, reduced quality of life, and heightened vulnerability to chronic diseases [3,4,5]. This relationship is particularly pronounced in aging populations, where the natural course of aging is compounded by underlying medical conditions, frailty, and inflammation [6]. Therefore, understanding the relationship between cognitive function and mortality in older adults is essential.

In recent years, the Neutrophil-to-High-Density Lipoprotein Cholesterol Ratio (NHR) has emerged as a novel biomarker of inflammation and cardiovascular risk. This ratio reflects the balance between neutrophil activity, which plays a role in inflammatory processes, and high-density lipoprotein cholesterol (HDL-C), which is known for its anti-inflammatory and protective effects [7, 8]. Studies indicate that an elevated NHR is a predictor of poor prognosis in several populations, including those with metabolic syndrome, cardiovascular diseases, diabetes, and cancer, and has been associated with increased mortality [9,10,11]. Increased NHR was also identified as a strong and independent predictor of all-cause mortality in the general population, further supporting its potential role in risk assessment [12]. However, the specific impact of NHR on all-cause mortality in older adults, especially in relation to cognitive function, remains poorly understood.

The NHR reflects systemic inflammation and lipid metabolism imbalance, while cognitive impairment is associated with neurodegeneration and neuroinflammation. Both elevated NHR and cognitive impairment are linked to poor health outcomes through inflammatory pathways. Although these factors are individually recognized as predictors of mortality, their potential synergistic effect remains unclear. Their coexistence may signify a higher-risk phenotype that warrants further investigation. However, few studies have explored their combined impact on health outcomes. Therefore, this study will utilize large-scale databases such as the National Health and Nutrition Examination Survey (NHANES) to explore the combined impact of NHR and cognitive function and on all-cause mortality in older adults. The findings aim to provide evidence on the association between NHR, cognitive function, and all-cause mortality, contributing to a better understanding of mortality risk in older adults. This may provide evidence for early risk assessment and intervention in clinical practice. Furthermore, understanding the interplay between these factors could inform public health strategies aimed at reducing mortality and improving the quality of life in aging populations.

Methods

Study population

NHANES is a large, complex, multistage survey of the noninstitutionalized U.S. population, conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS). Data used in this study were derived from a de-identified and publicly database (https://www.cdc.gov/nchs/nhanes/index.htm). The survey provides nationally representative estimates of health and nutritional status. The NHANES protocol was approved by the NCHS Research Ethics Review Board, and all participants provided informed consent. Data collection occurred through the Mobile Examination Center (MEC), which included face-to-face interviews, physical examinations, and laboratory tests. Our analysis focused on 3,632 participants aged 60 and older, selected from the 19,931 individuals who participated in NHANES between 2011 and 2014. We excluded participants with missing data, resulting in a final sample of 1,486 individuals. The participant selection process is illustrated in Fig. 1.

Fig. 1
figure 1

Flowchart of participant selection from NHANES 2011–2014

Full size image

Baseline characteristics

Demographic, examination, laboratory and questionnaire data were downloaded from the NHANES website. Covariates considered include several demographic characteristics: sex, age (years), race, body mass index (BMI), family income (poverty income ratio, PIR), educational attainment (less than a high school education, some high school, high school graduate/General educational development (GED), some college or associate’s degree, college graduate or more). Race was categorized as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black or other races. Smoking status was classified as never-smoker, former smoker or current smoker. Individuals were considered nondrinkers, 1 to < 5 drinks/month, 5 to < 10 drinks/month, or ≥ 10 drinks/month. Hypertension status was determined from a self-reported medical history of high blood pressure, antihypertensive medicine, or non-same-day randomized records of 3 times of systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [13]. Participants with diabetes were identified as having any of the following: hemoglobin A1C concentration ≥ 6.5% or a fasting plasma glucose level ≥ 126 mg/dL; for those who responded “yes” to the question: “Have you ever been told by a doctor that you have diabetes?”, “Are you now taking insulin?”, “Are you now taking diabetic pills to lower your blood sugar?” [14]. Creatinine, Neutrophil count and high-density lipoprotein cholesterol (HDL-C) were measured in the NHANES laboratory. Cardiovascular disease was defined as a self-reported diagnosis of heart failure, coronary heart disease, angina, heart attack. The eGFR was calculated from the serum creatinine (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] Eq. 2009) [15]. Chronic kidney disease (CKD) is defined by eGFR < 60 mL/min/1.73 m².

Calculation of NHR

The NHR data used in this study were collected during the NHANES cycles from 2011 to 2014. Neutrophil counts were determined by total blood count using an automated blood analysis system and displayed as 10 ^ 3 cells / µL. HDL-C levels were assessed using an automatic device with venous blood samples obtained after 8-hour fasting. NHR is calculated as the neutrophil count (10^3 cells/µL) divided by the HDL-C level (mmol/L) [16]. The optimal cut-off value of NHR was 3.65 determined by the“surv_cutpoint” function in the “survminer” package [17]. Participants were categorized into NHR Low (< 3.65) and NHR High (≥ 3.65) as NHR group.

Calculation of cognitive function

Cognitive function was assessed using the Digit Symbol Substitution Test (DSST) for participants aged 60 and older from the NHANES 2011–2012 and 2013–2014 cycles. DSST evaluates important aspects of executive function, including processing speed, sustained attention, and working memory [18]. Participants match numerals to symbols based on a provided key, scoring one point for each correct match within 120 s, with a maximum score of 133, higher scores on these tests indicate better cognitive function, a result supported by extensive research [19, 20]. There is no universally accepted “gold standard” for defining low cognitive performance using the DSST. Cognitive impairment was defined using the 25th percentile cut-off, consistent with previous research [21,22,23]. Participants scoring below this threshold in any of the three tests were classified as cognitively impaired. The DSST groups were then divided into normal cognition and cognitive impairment categories.

Mortality data

All-cause mortality refers to death from any cause were the primary outcomes. Mortality data were obtained by linking the study dataset with the National Death Index (NDI) through the Public-use Linked Mortality Files, available from the Centers for Disease Control and Prevention (CDC) (https://www.cdc.gov/nchs/data-linkage/mortality.htm). Specifically, mortality outcomes were derived from the 2011–2014 NHANES public-use linked mortality files, with causes of death classified according to the International Classification of Diseases, Tenth Revision (ICD-10). Follow-up time was calculated from the date of interview to the date of death, or the end of follow-up (December 31, 2019), whichever came first [24].

Statistical analysis

NHANES utilized a complex, multistage, probability sampling design for representativeness of the US civilian non-institutionalized population. Sample weights, stratification, and clustering were incorporated in the analysis. A 2-year weight (wtsa2 year) was selected for the DSST score subsamples from the 2011–2012 and 2013–2014 cycles, with the final weight adjusted to 1/2 wtsa2 year for NHANES 2011–2014. Baseline characteristics were compared between the NHR and DSST groups, with continuous variables presented as medians (Q1, Q3) and categorical variables as counts (weighted percentages). Pearson’s chi-square tests, with Rao & Scott adjustments for complex survey design, were used for categorical variables, and design-based Kruskal-Wallis tests for continuous variables. Log-rank and weighted Kaplan-Meier survival analyses were applied to compare survival probabilities. We created four subgroup (NHR Low & Normal cognition, NHR High & Normal cognition, NHR Low & Cognitive impairment, and NHR High & Cognitive impairment) to assess the combined effect of NHR and cognitive function on mortality using three weighted Cox regression models: Model 1 (unadjusted), Model 2 (adjusted for sex, age, race, education, PIR, alcohol intake, and smoking), and Model 3 (further adjusted for hypertension, diabetes, stroke, cardiovascular disease, BMI, and CKD) [25]. The NHR group, DSST group, and NHR & DSST group were analyzed separately in different models to assess their independent associations with all-cause mortality. Each model was adjusted for potential confounders as outlined. All analyses were conducted using R version 4.3.3, with statistical significance defined as P < 0.05.

Results

Baseline characteristics of the participants

A total of 1,486 participants aged 60 years and older were enrolled in this study from the NHANES 2011–2014 database. Based on the NHANES sampling design, the weighted data analysis provided a population estimate of 54,078,084, representing the US population aged 60 and older. By the end of follow-up, 81.76% (n = 1,180) survived, while 18.24% (n = 306) passed away. The median follow-up time was 78 months (IQR: 68–94) for the overall population, 81 months (IQR: 71–95) for the survivors, and 54 months (IQR: 32–72) for those who passed away.

In terms of NHR group, 33.16% of participants were in the NHR High group. The NHR High group had higher neutrophil counts, lower HDL-C levels, and higher NHR scores. The NHR High group also had a higher proportion of smokers, increased alcohol consumption, BMI ≥ 30, and higher rates of hypertension, diabetes, and cardiovascular disease (P < 0.001) (Table 1). Additionally, the NHR High group had lower DSST score and a higher all-cause mortality rate (P < 0.001).

Table 1 Characteristics of the participants based on NHR group
Full size table

In terms of cognitive function, 84.03% were in the normal cognition group and 15.97% in the cognitive impairment group (Table 2). The cognitive impairment group was older, with higher proportions in the 70–79 (36.21%) and ≥ 80 (33.63%) age groups (P < 0.001). Significant differences were found in race, education (P < 0.001), and smoking status (P = 0.029). Hypertension (P = 0.001), diabetes (P = 0.002), and cardiovascular disease (P < 0.001) were more common in this group, which also had a higher all-cause mortality rate.

Table 2 Characteristics of the participants based on cognitive function
Full size table

Impact on all-cause mortality across different groups

Weighted Cox regression results are shown in Table 3. The NHR group, DSST group, and NHR & DSST group were examined separately across different models to evaluate their independent associations with all-cause mortality, with adjustments for potential confounders. Higher NHR and cognitive impairment were independently associated with increased mortality risk. After full adjustment in model 3, high NHR (HR = 1.82, 95% CI: 1.21–2.74, P = 0.004) remained significantly associated with higher mortality. Similarly, cognitive impairment (HR = 1.87, 95% CI: 1.25–2.79, P = 0.002) also remained significantly linked to higher mortality. The combination of high NHR and cognitive impairment showed the highest mortality risk (HR = 2.98, 95% CI: 1.45–6.14, P = 0.003), this finding is further supported by the weighted multivariable Cox regression analysis presented in Fig. 2.

Table 3 Weighted multivariable Cox regression models to all-cause mortality
Full size table

The forest plot (Fig. 2) illustrates the association between the NHR & DSST group and each risk factor with all-cause mortality, based on the weighted multivariable Cox regression analysis in the fully adjusted Model 3. Hazard ratio (HR) was calculated using weighted multivariable Cox regression, with NHR Low & normal cognition as the reference. NHR High & Normal cognition (HR = 2.12, 95% CI: 1.42–3.17, P < 0.001), NHR Low & Cognitive impairment (HR = 2.44, 95% CI: 1.46–4.08, P < 0.001), and NHR High & Cognitive impairment (HR = 2.98, 95% CI: 1.45–6.14, P = 0.003) all showed significantly higher mortality risk. Age was a significant risk factor, with those aged 70–79 (HR = 2.20, P < 0.001) and those aged 80 and older (HR = 5.01, P < 0.001). CKD (HR = 1.49, P = 0.01) increased mortality risk. However, a higher PIR > 3.5 was associated with reduced mortality (HR = 0.60, P = 0.041).

Fig. 2
figure 2

Forest plot of the weighted multivariable Cox regression analysis for the association between characteristic groups and all-cause mortality

Full size image

Kaplan-Meier survival curves for NHR and DSST

The weighted Kaplan-Meier survival curves were depicted in Fig. 3. The curves for the NHR group (Fig. 3A) illustrated those individuals with high NHR exhibited a significantly lower survival probability than those with low NHR (P < 0.001). The survival curves for the DSST group (Fig. 3B) indicated a significantly higher survival probability for individuals with normal cognition compared to those with cognitive impairment (P < 0.001). The combined analysis of NHR & DSST group (Fig. 3C) showed significant differences in survival probabilities (P < 0.001), with the highest survival in the NHR Low & normal cognition, the lowest in the NHR High & Cognitive impairment, and intermediate survival in the NHR High & Normal cognition and NHR Low & Cognitive impairment, with the former slightly higher.

Fig. 3
figure 3

Weighted Kaplan-Meier analyses by Log-rank for all-cause mortality. A NHR Group, B DSST Group, C NHR & DSST Group

Full size image

Discussion

This study highlights the combined impact of NHR and cognitive function on all-cause mortality in older adults. Participants with cognitive impairment were older, had lower education levels, and higher rates of hypertension, diabetes, and cardiovascular disease, leading to higher mortality. Similarly, individuals with the high NHR had higher rates of smoking, BMI, hypertension, diabetes, and cardiovascular disease, with a higher mortality rate. Both high NHR and cognitive impairment were independently associated with increased mortality risk, with their combination showing the highest risk. Weighted multivariable Cox regression analysis confirmed the combined impact of NHR and cognitive function on mortality. Additionally, age and chronic kidney disease increased mortality risk, while a higher PIR > 3.5 was protective. Increasing age is associated with higher mortality risk due to the cumulative effects of age-related decline in physiological function, increased comorbidities, and reduced resilience to illness. Similarly, CKD increases mortality risk as it is often accompanied by deteriorating kidney function, metabolic imbalances, and a higher incidence of cardiovascular complications. In contrast, a higher PIR > 3.5 is protective, likely reflecting better socioeconomic status, which facilitates access to quality healthcare, healthier lifestyles, and lower levels of stress, ultimately contributing to a lower risk of death. The weighted Kaplan-Meier survival curves showed that the highest survival in the NHR Low & normal cognition, the lowest in the NHR High & Cognitive impairment. The findings indicate that the combination of high NHR and cognitive impairment significantly increases the risk of all-cause mortality in older adults, with the highest risk observed when both factors are present.

NHR, as an inflammatory marker, represents a risk factor for predicting mortality [26]. The “survminer: surv_cutpoint” package is widely used in survival prognosis analysis [17, 27]. This function employs the maximally selected rank statistics (also known as the maximization of the log-rank test statistic) to identify the cut-off that best separates the survival groups [28]. By calculating the optimal threshold, it ensures that the dichotomization of the continuous variable provides the most significant survival difference between the groups. This methodology is based on well-established statistical principles and has been widely used in survival analysis to define meaningful cut-offs for continuous variables. We used this method to determine a cutoff value of 3.65, dividing NHR into NHR group including NHR Low and NHR high. In previous studies, NHR is often divided into tertile or quartile for analysis [29, 30]. By selecting the optimal cutoff value using this method, the grouping boundaries can be determined based on the data itself, rather than a predefined standard, which helps provide more objective and practical results. Our study found that high NHR was significantly associated with all-cause mortality after full adjustment (HR = 1.82). Similarly, Jiang et al. [12] reported a 29% higher risk of all-cause mortality for participants in the highest tertile of NHR compared to the lowest tertile (HR = 1.29), while Chuang et al. [31] observed that the highest quartile of NHR was associated with an increased risk of all-cause mortality (HR = 1.48). In addition, a study by Wu et al. demonstrated that elevated NHR levels were independently associated with a 40% increased risk of all-cause mortality, further supporting the role of NHR as a predictor of mortality risk in older adults [32]. Similarly, Chen et al. found that higher NHR levels were significantly associated with an increased risk of in-hospital major adverse cardiac events (MACE) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (HR = 2.211, 95% CI: 1.092–4.479, P = 0.027), suggesting that NHR may serve as a valuable prognostic marker for predicting adverse outcomes in cardiovascular diseases [33]. Furthermore, a recent study by Ren et al. found that the diagnostic power of NHR was stronger in ST-segment elevated acute coronary syndrome (STE-ACS) patients compared to non-ST-segment elevated acute coronary syndrome (NSTE-ACS) patients (P < 0.001), highlighting the potential of NHR as a convenient and effective marker for predicting the presence, progression, and severity of acute coronary syndrome in type 2 diabetes mellitus (T2DM) patients [34]. These studies support NHR as a marker for all-cause mortality risk across different populations. Our study found that participants in the high NHR had higher rates of smoking, BMI ≥ 30, hypertension, diabetes, and cardiovascular disease. Other study had shown that high NHR was linked to the development of metabolic syndrome such as obesity, hypertension, and diabetes [35]. High NHR may thus be associated with the cumulative effects of chronic diseases. NHR independently predicted all-cause mortality, with higher levels linked to increased risk.

DSST, a widely used tool for assessing cognitive function, has been linked to mortality in multiple studies [36,37,38]. In our study, cognitive impairment was defined using the 25th percentile cut-off, with 15.97% of older adults classified as impaired, aligning with previous reports estimating 10–20% of U.S. adults aged ≥ 65 having mild cognitive impairment [39]. Using the 25th percentile threshold offers a more reasonable method for estimating prevalence. Some studies have demonstrated a significant association between cognitive impairment and all-cause mortality. A population-based cohort study [40] found that in the 2002–2008 cohort, the risk of all-cause mortality due to cognitive impairment was 1.32 times higher, while in the 2008–2014 cohort, it was 1.26 times higher. Another study using data from the English Longitudinal Study of Ageing found that mild cognitive impairment was associated with a 1.40 times higher mortality risk, and moderate to severe cognitive impairment with a 2.49 times higher risk, compared to normal cognitive function [41]. In our study, after adjusting for confounders, the all-cause mortality risk was 1.87 times higher in the cognitive impairment group than in the normal cognition group, which reveals that impaired cognitive function is significantly linked to all-cause mortality in older adults, consistent with findings from other studies. Zhao et al. found that impairments in various cognitive subdomains, such as naming foods, registration, attention and calculation, copy figure, delayed recall, and language, were independently associated with increased mortality risk among participants [42]. Georgakis et al. reported that the coexistence of cognitive impairment (COGI) and depression increased all-cause and cardiovascular mortality risks by 66% and 72%, respectively, highlighting the synergistic impact of cognitive and mental health disorders on mortality outcomes [43]. These findings highlight the importance of early detection and management of cognitive impairment in the elderly to reduce mortality risk.

Our research provides new insights into the relationship between cognitive function and mortality by including the analysis of NHR. In our study, the results showed that the group with NHR Low & Normal cognition had the highest survival probability, while that with NHR High & Cognitive impairment had the lowest survival probability. This suggests that the combined effect of cognitive function and NHR is significant in predicting survival probability. Previous researches suggest that there is a close relationship between NHR and oxidative stress, as NHR reflects the activation state of neutrophils and the level of high-density lipoprotein cholesterol, which indirectly indicates the level of oxidative stress in the body [44, 45]. Meanwhile, numerous studies have shown that oxidative stress plays a key role in the onset and progression of cognitive impairment. It can damage the structure and function of neuronal cells, leading to pathological changes such as neuronal death, neurofibrillary tangles, and amyloid plaque deposition [46, 47]. Therefore, we infer that the combination of high NHR and cognitive impairment may exacerbate brain neurodegeneration through inflammation-induced oxidative stress, thereby increasing the risk of mortality. However, the specific mechanisms still need further research to be confirmed in the future.

Several limitations of this study should be acknowledged. First, although the study includes a relatively large sample size, the generalizability of our findings to other populations, particularly those outside the United States, is constrained. Second, the use of the DSST to assess cognitive function may not fully encompass the range of cognitive impairment. More comprehensive neuropsychological assessments would yield a clearer understanding of the relationship between cognitive decline and mortality. Third, while we adjusted for numerous confounders, residual confounding may still persist, especially concerning lifestyle factors and unmeasured variables such as physical activity and medication use. Fourth, the study did not account for potential variations in NHR levels over time, which may fluctuate due to acute infections, inflammatory conditions, or other transient factors. Finally, due to the limited sample size, we were unable to analyze the associations between NHR, DSST, and cause-specific mortality, which restricts our ability to determine whether the observed relationships are driven by specific disease processes, such as cardiovascular events or cancer.

Future research should address these limitations by employing longitudinal study designs that track individuals over extended periods. This approach would allow for a more accurate assessment of the temporal relationships between NHR, cognitive function, and mortality, thereby strengthening the evidence for causal inferences. Additionally, incorporating a broader range of neuropsychological tests into future studies would provide a more comprehensive evaluation of cognitive function, potentially revealing more detailed insights into how different cognitive domains interact with NHR to influence mortality. Conducting similar studies in diverse populations would enhance the generalizability of the findings. Finally, future research with larger sample sizes should investigate the associations between NHR, DSST, and cause-specific mortality to better understand the disease processes driving the observed relationships and to develop more precise risk stratification tools.

Conclusion

In conclusion, this study found that both high NHR and cognitive impairment are independently associated with an increased risk of all-cause mortality in older adults, with their combination further amplifying this risk. This underscores the importance of considering both cognitive function and NHR when assessing mortality risk in this population. The findings suggest that addressing both high NHR and cognitive impairment together is crucial for public health, and further studies are needed to confirm these results and explore effective prevention strategies.

Data availability

The datasets generated and analyzed during the current study are available in the National Health and Nutrition Examination Survey (NHANES), https://www.cdc.gov/nchs/nhanes/; Public-useLinked Mortality Files (https://www.cdc.gov/nchs/data-linkage/mortality.htm).

References

  1. Yaqub A, Darweesh SKL, Dommershuijsen LJ, Vernooij MW, Ikram MK, Wolters FJ, Ikram MA. Risk factors, neuroimaging correlates and prognosis of the motoric cognitive risk syndrome: A population-based comparison with mild cognitive impairment. Eur J Neurol. 2022;29:1587–99.

    Article  PubMed  Google Scholar 

  2. Mishra A, Chauhan G, Violleau MH, Vojinovic D, Jian X, Bis JC, Li S, Saba Y, Grenier-Boley B, Yang Q, et al. Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjects. Brain. 2019;142:1009–23.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Liu-Seifert H, Siemers E, Price K, Han B, Selzler KJ, Henley D, Sundell K, Aisen P, Cummings J, Raskin J, Mohs R. Cognitive impairment precedes and predicts functional impairment in mild Alzheimer’s disease. J Alzheimers Dis. 2015;47:205–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chen B, Wang M, He Q, Wang Y, Lai X, Chen H, Li M. Impact of frailty, mild cognitive impairment and cognitive frailty on adverse health outcomes among community-dwelling older adults: A systematic review and meta-analysis. Front Med (Lausanne). 2022;9:1009794.

    Article  PubMed  Google Scholar 

  5. Kim J, Park E, An M. The cognitive impact of chronic diseases on functional capacity in Community-Dwelling adults. J Nurs Res. 2019;27:1–8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15:505–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pan X, Zhang X, Ban J, Yue L, Ren L, Chen S. Association of neutrophil to High-Density lipoprotein cholesterol ratio with cardiac ultrasound parameters and cardiovascular risk: A Cross-Sectional study based on healthy populations. J Inflamm Res. 2023;16:1853–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kou T, Luo H, Yin L. Relationship between neutrophils to HDL-C ratio and severity of coronary stenosis. BMC Cardiovasc Disord. 2021;21:127.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chen T, Chen H, Xiao H, Tang H, Xiang Z, Wang X, Wang X, Zou H. Comparison of the value of neutrophil to High-Density lipoprotein cholesterol ratio and lymphocyte to High-Density lipoprotein cholesterol ratio for predicting metabolic syndrome among a population in the Southern Coast of China. Diabetes Metab Syndr Obes. 2020;13:597–605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhu B, Cao C, Liu W, Liu Y, Luo Y, Peng D. The predictive value of estimated glucose disposal rate for all-cause and cardiovascular mortality in the US non-diabetic population aged ≥ 60 years: A population-based cohort study. Diabetes Metab Syndr. 2024;19:103182.

    Article  PubMed  Google Scholar 

  11. Shi K, Hou J, Zhang Q, Bi Y, Zeng X, Wang X. Neutrophil-to-high-density-lipoprotein-cholesterol ratio and mortality among patients with hepatocellular carcinoma. Front Nutr. 2023;10:1127913.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Jiang M, Sun J, Zou H, Li M, Su Z, Sun W, Kong X. Prognostic role of neutrophil to High-Density lipoprotein cholesterol ratio for All-Cause and cardiovascular mortality in the general population. Front Cardiovasc Med. 2022;9:807339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Xu JP, Zeng RX, Lu HN, Zhang YZ, Mai XY, Mao S, Zhang MZ. Analysis of the Dose-Response effects of physical activity on cardiocerebrovascular and All-Cause mortality in hypertension. Front Cardiovasc Med. 2022;9:844680.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Egan BM, Li J, Qanungo S, Wolfman TE. Blood pressure and cholesterol control in hypertensive hypercholesterolemic patients: National health and nutrition examination surveys 1988–2010. Circulation. 2013;128:29–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Huang JB, Chen YS, Ji HY, Xie WM, Jiang J, Ran LS, Zhang CT, Quan XQ. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids Health Dis. 2020;19:59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. He J, Bian X, Song C, Zhang R, Yuan S, Yin D, Dou K. High neutrophil to lymphocyte ratio with type 2 diabetes mellitus predicts poor prognosis in patients undergoing percutaneous coronary intervention: a large-scale cohort study. Cardiovasc Diabetol. 2022;21:156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. He R, Zheng R, Li J, Cao Q, Hou T, Zhao Z, Xu M, Chen Y, Lu J, Wang T, et al. Individual and combined associations of glucose metabolic components with cognitive function modified by obesity. Front Endocrinol (Lausanne). 2021;12:769120.

    Article  PubMed  Google Scholar 

  19. Kim SM, Zhao D, Schneider ALC, Korada SK, Lutsey PL, Guallar E, Alonso A, Windham BG, Gottesman RF, Michos ED. Association of parathyroid hormone with 20-year cognitive decline: the ARIC study. Neurology. 2017;89:918–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bancks M, Alonso A, Allen N, Yaffe K, Carnethon M. Temporal trends in cognitive function of older US adults associated with population changes in demographic and cardiovascular profiles. J Epidemiol Community Health. 2019;73:612–8.

    Article  PubMed  Google Scholar 

  21. Dong X, Li S, Sun J, Li Y, Zhang D. Association of coffee, decaffeinated coffee and caffeine intake from coffee with cognitive performance in older adults: National health and nutrition examination survey (NHANES) 2011–2014. Nutrients 2020, 12.

  22. Peeri NC, Egan KM, Chai W, Tao MH. Association of magnesium intake and vitamin D status with cognitive function in older adults: an analysis of US National health and nutrition examination survey (NHANES) 2011 to 2014. Eur J Nutr. 2021;60:465–74.

    Article  CAS  PubMed  Google Scholar 

  23. Alam MT, Vásquez E, Etnier JL, Echeverria S. Dietary adherence and cognitive performance in older adults by nativity status: results from the National health and nutrition examination survey (NHANES), 2011–2014. Geriatr (Basel) 2024, 9.

  24. Liu ZY, Wang C, Zhang YJ, Zhu HL. Combined lifestyle, mental health, and mortality in US cancer survivors: a National cohort study. J Transl Med. 2022;20:376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Weiss JW, Peters D, Yang X, Petrik A, Smith DH, Johnson ES, Thorp ML, Morris C, O’Hare AM. Systolic BP and mortality in older adults with CKD. Clin J Am Soc Nephrol. 2015;10:1553–9.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Liu SL, Feng BY, Song QR, Zhang YM, Wu SL, Cai J. Neutrophil to high-density lipoprotein cholesterol ratio predicts adverse cardiovascular outcomes in subjects with pre-diabetes: a large cohort study from China. Lipids Health Dis. 2022;21:86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhou R, Peng N, Li W. Identification of ISCA1 as novel immunological and prognostic biomarker for bladder cancer. Front Immunol. 2022;13:975503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Alboukadel Kassambara M, Kosinski M, Fabian S, Biecek P. survminer: Drawing Survival Curves using ggplot2. R package version 0.4. 9. URLhttps://rpkgs.datanovia.com/survminer/index.html 2021.

  29. Ma D, Ma H, Li Y, Yang L. Association between Neutrophil-to-high-density lipoprotein-cholesterol ratio and gallstones: insights from the National health and nutrition examination survey (2017–2020). Lipids Health Dis 2024, 23.

  30. Lu Y, Xu X, Wu J, Ji L, Huang H, Chen M. Association between neutrophil-to-high-density lipoprotein cholesterol ratio and metabolic dysfunction-associated steatotic liver disease and liver fibrosis in the US population: a nationally representative cross-sectional study using NHANES data from 2017 to 2020. BMC Gastroenterol. 2024;24:300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chuang SM, Liu SC, Chien MN, Lee CC, Lee YT, Chien KL. Neutrophil-to-High-Density lipoprotein ratio (NHR) and Neutrophil-to-Lymphocyte ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study. Am J Prev Cardiol. 2024;20:100869.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wu C-C, Wu C-H, Lee C-H, Chen T-Y, Cheng C-I. Prognostic value of neutrophil, monocyte, lymphocyte, and Platelet/High-Density lipoprotein ratios in ischemic heart disease: an NHANES analysis. Medicina. 2024;60:2084.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chen Y, Jiang D, Tao H, Ge P, Duan Q. Neutrophils to high-density lipoprotein cholesterol ratio as a new prognostic marker in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: a retrospective study. BMC Cardiovasc Disord. 2022;22:434.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ren H, Zhu B, Zhao Z, Li Y, Deng G, Wang Z, Ma B, Feng Y, Zhang Z, Zhao X, et al. Neutrophil to high-density lipoprotein cholesterol ratio as the risk mark in patients with type 2 diabetes combined with acute coronary syndrome: a cross-sectional study. Sci Rep. 2023;13:7836.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hashemi SM, Kheirandish M, Rafati S, Ghazalgoo A, Amini-Salehi E, Keivanlou MH, Abbaszadeh S, Saberian P, Rahimi A. The association between neutrophil and lymphocyte to high-density lipoprotein cholesterol ratio and metabolic syndrome among Iranian population, finding from Bandare Kong cohort study. Lipids Health Dis. 2024;23:393.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Adjoian Mezzaca T, Dodds LV, Rundek T, Zeki Al Hazzouri A, Caunca MR, Gomes-Osman J, Loewenstein DA, Schneiderman N, Elfassy T. Associations between cognitive functioning and mortality in a Population-Based sample of older united States adults: differences by sex and education. J Aging Health. 2022;34:905–15.

    Article  PubMed  Google Scholar 

  37. Pavlik VN, de Moraes SA, Szklo M, Knopman DS, Mosley TH Jr., Hyman DJ. Relation between cognitive function and mortality in middle-aged adults: the atherosclerosis risk in communities study. Am J Epidemiol. 2003;157:327–34.

    Article  PubMed  Google Scholar 

  38. O’Connell MDL, Marron MM, Boudreau RM, Canney M, Sanders JL, Kenny RA, Kritchevsky SB, Harris TB, Newman AB. Mortality in relation to changes in a healthy aging index: the health, aging, and body composition study. J Gerontol Biol Sci Med Sci. 2019;74:726–32.

    Article  Google Scholar 

  39. Cárdenas-Peña D, Collazos-Huertas D, Castellanos-Dominguez G. Enhanced data representation by kernel metric learning for dementia diagnosis. Front Neurosci. 2017;11:413.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Duan J, Lv YB, Gao X, Zhou JH, Kraus VB, Zeng Y, Su H, Shi XM. Association of cognitive impairment and elderly mortality: differences between two cohorts ascertained 6-years apart in China. BMC Geriatr. 2020;20:29.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Li S, He X, Wu L, Tang X, Ouyang Y, Jing W, Yang Y, Yang J, Che K, Pan C, et al. The association of cognitive function and its changes with all-cause mortality among community-dwelling older adults. Front Aging Neurosci. 2024;16:1419235.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhao S, Yang H, Zhao H, Miao M, Wang Q, Wang Y, Yin Y, Wang X. Role of cognitive impairment in predicting the long-term risk of all-cause mortality: a 20-year prospective cohort study in China. Archives Public Health. 2025;83:27.

    Article  Google Scholar 

  43. Georgakis MK, Papadopoulos FC, Protogerou AD, Pagonari I, Sarigianni F, Biniaris-Georgallis S-I, Kalogirou EΙ, Thomopoulos TP, Kapaki E, Papageorgiou C, et al. Comorbidity of cognitive impairment and Late-Life depression increase mortality:results from a cohort of Community-Dwelling elderly individuals in rural Greece. J Geriatr Psychiatr Neurol. 2016;29:195–204.

    Article  Google Scholar 

  44. Liu Z, Fan Q, Wu S, Wan Y, Lei Y. Compared with the monocyte to high-density lipoprotein ratio (MHR) and the neutrophil to lymphocyte ratio (NLR), the neutrophil to high-density lipoprotein ratio (NHR) is more valuable for assessing the inflammatory process in Parkinson’s disease. Lipids Health Dis. 2021;20:35.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Qing G, Bao C, Yang Y, Wei B. Association between neutrophil to high-density lipoprotein cholesterol ratio (NHR) and depression symptoms among the united States adults: a cross-sectional study. Lipids Health Dis. 2024;23:215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Theofilas P, Ehrenberg AJ, Nguy A, Thackrey JM, Dunlop S, Mejia MB, Alho AT, Paraizo Leite RE, Rodriguez RD, Suemoto CK, et al. Probing the correlation of neuronal loss, neurofibrillary tangles, and cell death markers across the Alzheimer’s disease Braak stages: a quantitative study in humans. Neurobiol Aging. 2018;61:1–12.

    Article  PubMed  Google Scholar 

  47. Ohm DT, Fought AJ, Martersteck A, Coventry C, Sridhar J, Gefen T, Weintraub S, Bigio E, Mesulam MM, Rogalski E, Geula C. Accumulation of neurofibrillary tangles and activated microglia is associated with lower neuron densities in the aphasic variant of Alzheimer’s disease. Brain Pathol. 2021;31:189–204.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to express our sincere gratitude to the participants of the National Health and Nutrition Examination Survey (NHANES) for their valuable contribution to this research.

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 82160327 for T.L. and Grant No. 82271977 for F.C.; the Key Science and Technology Project of Hainan Province under Grant No. ZDYF2024SHFZ058 for F.C. and Grant No. ZDYF2023SHFZ096 for T.L.; the Joint Program on Health Science & Technology Innovation of Hainan Province under Grant No. WSJK2024QN075 for A.H.; the Hainan Academician Innovation Platform Fund; and the Hainan Province Clinical Medical Center.

Author information

Author notes

  1. Anquan Hu and Kun Zhang contributed equally to this work.

Authors and Affiliations

  1. Geriatric Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China

    Anquan Hu & Kun Zhang

  2. Department of Neurology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China

    Wei Sun, Xian Li, Lianwan Zhou, Xi Li & Tao Liu

  3. Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China

    Feng Chen

Authors
  1. Anquan Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kun Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wei Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xian Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Lianwan Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Feng Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Tao Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

A.H. wrote the main manuscript text and performed the statistical analysis. K.Z. wrote the main manuscript text and prepared Figs. 1, 2 and 3. W.S. and X.L. collected the data. L.Z. and X.L. prepared Tables 1, 2, and 3. T.L. and F.C. designed the study, interpretation of the results, and critical revision of the manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Feng Chen or Tao Liu.

Ethics declarations

Ethics approval and consent to participate

The survey protocol was approved by NCHS Ethics Review Board (https://www.cdc.gov/nchs/nhanes/irba98.htm), and all participants have written informed consent.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, A., Zhang, K., Sun, W. et al. Combined impact of neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) and cognitive function on all-cause mortality in older adults: a population-based study. Lipids Health Dis 24, 83 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02501-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02501-0

Keywords

Lipids in Health and Disease

ISSN: 1476-511X