ABSTRACT
Background and Aims
To explore the association between high‐density lipoprotein cholesterol ratio (NHR) and the prevalence of heart failure (HF).
Methods
Data from National Health and Nutrition Examination Survey (1999–2018) were downloaded. Unweighted multivariate logistic regression model evaluated whether NHR levels were independently related to HF, adjusting for demographics and clinical variables. Subgroup analyses examined consistency across populations. The nonlinear relation between NHR and HF was appraised by restricted cubic spline (RCS), with inflection point identified by two‐piece linear regression.
Results
The study included 41,449 participants, of whom 1322 (3.19%) were diagnosed with HF. Analyzing NHR as a continuous variable, multivariable‐adjusted logistic regression analysis demonstrated a significant independent relationship between elevated NHR levels and HF risk (OR = 1.08; 95% CI: 1.04–1.12; p < 0.001). When NHR was stratified by quartiles, the risk of HF was 1.94 times higher (95% CI: 1.56–2.41; p < 0.001) for individuals in the highest NHR quartile compared to those in the lowest quartile. RCS analysis indicated a notable nonlinear relationship (p‐nonlinear < 0.001), with a threshold effect identified at an NHR of 6.39. A 21% higher risk of HF was associated with each unit increase in NHR below this turning point. In subgroup analyses, it was shown that the connection between NHR and HF was noticeable across both males and females, in participants with and without hypertension, in those with and without diabetes, and certain racial groups (Non‐Hispanic Whites, Non‐Hispanic Blacks, and other races).
Conclusion
The increased NHR was correlated with a heightened prevalence of HF.
Keywords: heart failure, high‐density lipoprotein cholesterol, National Health and Nutrition Examination Survey, neutrophil
1. Introduction
Heart failure (HF), a clinical syndrome marked by decreased cardiac output, is the ultimate stage for most chronic cardiovascular diseases [1]. As the global population ages and cardiovascular conditions become more prevalent, the number of HF cases is projected to rise significantly in the upcoming years [2]. It is predicted that, by 2027, over 64 million individuals worldwide will have HF [3]. Despite the fact that medical treatment advancements have greatly enhanced the prognosis for HF patients, its high morbidity and mortality rates still cause significant clinical and public health worry. Consequently, it is crucial to find accessible and dependable biomarkers for early risk stratification in order to enhance the prevention, diagnosis, and management of HF.
Neutrophils are the main cells responsible for the body's inflammatory response. Elevated circulating neutrophil levels reflect a state of chronic systemic inflammation, which can contribute to cardiovascular damage through releasing pro‐inflammatory cytokines and is strongly associated with adverse cardiovascular outcomes [4, 5]. High‐density lipoprotein cholesterol (HDL‐C), in contrast, is considered a protective factor against cardiovascular diseases because of its well‐known anti‐inflammatory and antioxidant properties [6, 7, 8].
The ratio of neutrophil to HDL‐C (NHR), which is obtained by dividing the neutrophil count by the HDL‐C level, is regarded as a comprehensive marker indicating both systemic inflammation and lipid metabolism disorders [9]. NHR has been shown to be a useful predictor for a variety of situations in several observational studies, such as peripheral arterial disease in patients with type 2 diabetes [9], coronary heart disease [10], metabolic syndrome [11], and cardiovascular mortality in the general population [12]. However, the predictive value of NHR for HF, which is the final stage of cardiovascular and metabolic diseases, has not been determined yet.
Consequently, the current study aimed to comprehensively assess the predictive capacity of the NHR for HF risk within a representative cross‐sectional study.
2. Methods
2.1. Participants and Definition of HF
Data from NHANES 1999–2018 were used in this study. Participants aged ≥ 20 years were initially eligible for inclusion. Individuals were excluded if they had missing information on HF status, HDL‐C levels, neutrophil counts, or any covariates included in the multivariable analyses. After applying these exclusion criteria, a total of 41,449 participants were included in the final analysis (Figure 1). All participants provided written informed consent, and the NCHS Research Ethics Review Board gave ethical approval.
Figure 1.
The flowchart of the participant selection.
The diagnosis of HF is based on the MCQ160b questionnaire, which asks, “Has a doctor or other health professional ever told you had congestive heart failure?” Those who responded “yes” were put into the HF group, and the others were assigned to the non‐HF group. Because NHANES does not provide echocardiographic parameters or left ventricular ejection fraction data, HF could not be further classified into heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF).
2.2. Evaluation of NHR
Participants were instructed to provide venous blood samples for laboratory testing after a 9‐h fast. A Beckman Coulter DxH 800 automated hematology analyzer was used to perform neutrophil level tests, and the results were reported in 103 cells/µl. HDL‐C concentrations were determined via Roche Cobas 6000 chemistry analyzers. The neutrophil count was divided by the HDL‐C level to calculate the NHR.
2.3. Covariates
We gathered extensive data on demographics and laboratory that could potentially influence the relationship between HF and NHR, treating them as covariates. Age, sex, race, marital status, education level, smoking status, drinking status, body mass index (BMI), hypertension, diabetes, white blood cell count, monocyte, platelet, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, uric acid, and blood urea nitrogen were among the factors considered. Covariates were selected based on their established associations with HF, systemic inflammation, lipid metabolism, or their potential role as confounding factors according to previous literature and clinical relevance.
2.4. Statistical Analysis
Continuous variables were presented as mean ± standard deviation (SD), and comparisons between groups were performed using unpaired t‐tests. Categorical variables were expressed as counts and percentages, and differences between groups were assessed using the chi‐square test. The independent association between NHR and HF was examined by unweighted multivariate logistic regression across three models. The crude model was without adjustment for any confounding factors. Model 1 made adjustments for main demographic variables like age, sex, race, education level, marital status, and BMI. Model 2 included additional modifications from Model 1, considering lifestyle factors like smoking and drinking status, clinical comorbidities including hypertension and diabetes, and a broad array of laboratory parameters.
Restricted cubic spline (RCS) regression analysis was performed to evaluate the potential nonlinear relationship between NHR and HF. A two‐piecewise linear regression model was subsequently applied to identify threshold effects and estimate the association between NHR and HF across different NHR ranges. Subgroup analyses were conducted according to sex, race, hypertension status, and diabetes status to assess the consistency of the association between NHR and HF across clinically relevant populations. Interaction tests were performed to evaluate potential effect modification. In addition, a sensitivity analysis restricted to participants without diabetes was conducted to examine the robustness of the primary findings. The R software (version 4.4.1) packages (nhanesR, nortest, tableone, rcssci, forestploter, jstable) were utilized for statistical analyses. If not otherwise specified, all statistical tests were bilateral, and statistical significance was defined as p < 0.05.
3. Results
3.1. Baseline Characteristics and Laboratory Data of the Study Population
Figure 1 indicated that 41,449 participants were involved in the analysis, of whom 1,322 were diagnosed with HF. All the participants' baseline demographic, clinical characteristics, and laboratory data were comprehensively summarized in Table 1. Compared with the non‐HF group, individuals with HF were older (67.50 ± 12.45 vs. 49.44 ± 17.77 years, p < 0.001), had a greater percentage of males (57.56% vs. 50.02%, p < 0.001), and exhibited a significantly different racial composition, with a higher percentage of non‐Hispanic whites and non‐Hispanic blacks. Moreover, those in the HF group tended to be unmarried, had lower levels of education level, and present with a higher prevalence of hypertension (82.38% vs. 41.23%, p < 0.001) and diabetes (46.82% vs. 16.43%, p < 0.001), as well as a higher BMI (31.43 ± 7.92 vs. 28.91 ± 6.70 kg/m2, p < 0.001) than those without HF. Laboratory assessments showed that individuals with HF had significantly higher levels of neutrophils (4.69 ± 1.82 vs. 4.23 ± 1.73 × 103 cells/μl), monocytes (0.63 ± 0.22 vs. 0.56 ± 0.20 × 103 cells/μl), serum creatinine (113.65 ± 94.11 vs. 78.76 ± 36.54 μmol/L), uric acid (384.45 ± 107.09 vs. 323.78 ± 84.14 μmol/L), and blood urea nitrogen (7.20 ± 4.20 vs. 4.86 ± 1.97 μmol/L), compared with the non‐HF group. In contrast, platelet counts (228.82 ± 73.54 vs. 252.95 ± 66.75 × 103 cells/μl), total cholesterol (4.68 ± 1.16 vs. 5.07 ± 1.06 mmol/L), and HDL‐C levels (1.26 ± 0.41 vs. 1.37 ± 0.41 mmol/L) were significantly lower in the HF group. Notably, the NHR was markedly higher in individuals with HF (4.13 ± 2.11) than in those without HF (3.43 ± 2.03).
Table 1.
Baseline characteristics of the study population.
| Variables | Total (n = 41449) | Non‐HF (n = 40,127) | HF (n = 1322) | p |
|---|---|---|---|---|
| Age, years | 50.01 ± 17.91 | 49.44 ± 17.77 | 67.50 ± 12.45 | < 0.001 |
| Sex | < 0.001 | |||
| Female | 20,615 (49.74) | 20,054 (49.98) | 561 (42.44) | |
| Male | 20,834 (50.26) | 20,073 (50.02) | 761 (57.56) | |
| Race | < 0.001 | |||
| Non‐Hispanic White | 18,978 (45.79) | 18,236 (45.45) | 742 (56.13) | |
| Non‐Hispanic Black | 8261 (19.93) | 7964 (19.85) | 297 (22.47) | |
| Mexican‐American | 7271 (17.54) | 7129 (17.77) | 142 (10.74) | |
| Other | 6939 (16.74) | 6798 (16.94) | 141 (10.67) | |
| Marital status | < 0.001 | |||
| Married | 22,034 (53.16) | 21,385 (53.29) | 649 (49.09) | |
| Other | 19,415 (46.84) | 18,742 (46.71) | 673 (50.91) | |
| Education level | < 0.001 | |||
| High school below | 10,797 (26.05) | 10,285 (25.63) | 512 (38.73) | |
| High school or equivalent | 9610 (23.19) | 9286 (23.14) | 324 (24.51) | |
| High school above | 21,042 (50.77) | 20,556 (51.23) | 486 (36.76) | |
| Smoking status | < 0.001 | |||
| Never | 22 223 (53.62) | 21 717 (54.12) | 506 (38.28) | |
| Former | 10 449 (25.21) | 9897 (24.66) | 552 (41.75) | |
| Current | 8777 (21.18) | 8513 (21.22) | 264 (19.97) | |
| Drinking status | < 0.001 | |||
| Never | 5910 (14.26) | 5705(14.22) | 205 (15.51) | |
| Former | 7233 (17.45) | 6739 (16.79) | 494 (37.37) | |
| Current | 28 306 (68.29) | 27 683 (68.99) | 623 (47.13) | |
| BMI, kg/m2 | 28.99 ± 6.76 | 28.91 ± 6.70 | 31.43 ± 7.92 | < 0.001 |
| Hypertension | < 0.001 | |||
| No | 23817 (57.46) | 23,584 (58.77) | 233 (17.62) | |
| Yes | 17,632 (42.54) | 16,543 (41.23) | 1089 (82.38) | |
| Diabetes | < 0.001 | |||
| No | 34,237 (82.60) | 33,534 (83.57) | 703 (53.18) | |
| Yes | 7212 (17.40) | 6593 (16.43) | 619 (46.82) | |
| White blood cell count (1000 cells/μl) | 7.22 ± 3.13 | 7.21 ± 3.13 | 7.64 ± 2.93 | < 0.001 |
| Neutrophil (1000 cells/μl) | 4.24 ± 1.73 | 4.23 ± 1.73 | 4.69 ± 1.82 | < 0.001 |
| Monocyte (1000 cells/μl) | 0.56 ± 0.20 | 0.56 ± 0.20 | 0.63 ± 0.22 | < 0.001 |
| Platelet (1000 cells/μl) | 252.18 ± 67.11 | 252.95 ± 66.75 | 228.82 ± 73.54 | < 0.001 |
| ALT, U/L | 25.56 ± 24.83 | 25.61 ± 24.13 | 24.10 ± 40.65 | 0.181 |
| AST, U/L | 25.57 ± 18.49 | 25.53 ± 17.94 | 26.75 ± 30.76 | 0.152 |
| Creatinine (μmol/L) | 79.88 ± 40.16 | 78.76 ± 36.54 | 113.65 ± 94.11 | < 0.001 |
| Uric acid (μmol/L) | 325.72 ± 85.67 | 323.78 ± 84.18 | 384.45 ± 107.09 | < 0.001 |
| Blood urea nitrogen (μmol/L) | 4.93 ± 2.12 | 4.86 ± 1.97 | 7.20 ± 4.20 | < 0.001 |
| Total cholesterol (mmol/L) | 5.06 ± 1.07 | 5.07 ± 1.06 | 4.68 ± 1.16 | < 0.001 |
| HDL‐C (mmol/L) | 1.36 ± 0.41 | 1.37 ± 0.41 | 1.26 ± 0.41 | < 0.001 |
| NHR | 3.45 ± 2.03 | 3.43 ± 2.03 | 4.13 ± 2.11 | < 0.001 |
| NHR | < 0.001 | |||
| Q1 | 10,297 (24.84) | 10,095 (25.16) | 202 (15.28) | |
| Q2 | 10,491 (25.31) | 10,221 (25.47) | 270 (20.42) | |
| Q3 | 10,308 (24.87) | 9976 (24.86) | 332 (25.11) | |
| Q4 | 10,353 (24.98) | 9835 (24.51) | 518 (39.18) |
Note: Values are shown as number (%) unless otherwise indicated.
Abbreviations: ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; BMI, body mass index; HDL‐C, high‐density lipoprotein cholesterol; NHR, the neutrophil to HDL‐C ratio; NHR quartile rang: Q1: 0.08, 2.15; Q2: 2.16, 3.07; Q3: 3.08, 4.29; Q4: 4.29, 153.89.
3.2. The Association of NHR With the Potential Risk of HF
The analysis using multivariate logistic regression, as shown in Table 2, uncovered a strong positive relationship between NHR and the likelihood of HF. When NHR was analyzed as a continuous variable, each additional unit was linked to a 13% increased risk of HF in the unadjusted crude model (OR = 1.13; 95% CI: 1.10–1.16; p < 0.001). The link persisted in Model 1 (OR = 1.13; 95% CI: 1.10–1.16; p < 0.001) and even after adjusting for all variables in Model 2, where each one‐unit rise in NHR was associated with an 8% increased risk of HF (OR = 1.08; 95% CI: 1.04–1.12; p < 0.001). When categorizing NHR into quartiles (Q1–Q4), where Q1 is the lowest and Q4 is the highest, multivariate logistic regression analysis indicated that participants in Q3 and Q4 group had a notably increased risk of HF in the fully adjusted Model 2 compared to those in Q1 (Q3: OR = 1.30, 95% CI: 1.06–1.59, p = 0.010; Q4: OR = 1.94, 95% CI: 1.56–2.41, p < 0.001). Although the risk was also elevated in Q2 (OR = 1.16, 95% CI: 0.95–1.41) compared with Q1, this link was not statistically significant (p = 0.147).
Table 2.
Logistic regression analysis on the association between NHR and the risk of HF.
| Crude model | p | Model 1a | p | Model 2b | p | |
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
| NHR (continuous) | 1.13 (1.10–1.16) | < 0.001 | 1.13 (1.10–1.16) | < 0.001 | 1.08 (1.04–1.12) | < 0.001 |
| NHR (categories) | ||||||
| Q1 | Ref. | Ref. | Ref. | |||
| Q2 | 1.32 (1.10–1.59) | 0.003 | 1.26 (1.04–1.53) | 0.017 | 1.16 (0.95–1.41) | 0.147 |
| Q3 | 1.66 (1.39–1.99) | < 0.001 | 1.56 (1.29–1.88) | < 0.001 | 1.30 (1.06–1.59) | 0.010 |
| Q4 | 2.63 (2.23–3.10) | < 0.001 | 2.61 (2.17–3.13) | < 0.001 | 1.94 (1.56–2.41) | < 0.001 |
| p for trend | < 0.001 | < 0.001 | < 0.001 |
Abbreviations: CI, confidence interval; OR, odds ratio.
Model 1: adjusted for age, sex, race, marital status, education, and BMI.
Model 2: model 1 + smoking status, drinking status, hypertension, diabetes, white blood cell, monocyte, platelet, ALT, AST, creatinine, uric acid, and blood urea nitrogen.
As depicted in Figure 2, the RCS analysis demonstrated a meaningful nonlinear connection between NHR and the risk of HF (p‐nonlinear < 0.001). This nonlinear association was further supported by the two‐piecewise linear regression model (Table 3), which identified a threshold effect at an NHR value of 6.39. Below this inflection point, each unit increase in NHR was associated with a 21% higher risk of HF (OR = 1.21, 95% CI: 1.14–1.28, p < 0.001), whereas above this threshold, the association (OR = 1.05, 95% CI: 0.97–1.13, p = 0.206) was not statistically significant, suggesting a plateau in the risk of HF at higher NHR levels.
Figure 2.
Restricted cubic spline analysis between NHR and the risk of HF.
Table 3.
Threshold effect analysis of NHR on risk of HF using a two‐piecewise linear regression model.
| Adjusted OR (95% CI) | p | |
|---|---|---|
| Fitting by standard linear model | 1.08 (1.04–1.12) | < 0.001 |
| Fitting by two‐piecewise linear model | ||
| Inflection point | 6.39 | |
| < 6.39 | 1.21 (1.14–1.28) | < 0.001 |
| ≥ 6.39 | 1.05 (0.97–1.13) | 0.206 |
| Log‐likelihood ratio | < 0.001 |
Abbreviations: CI, confidence interval; OR, odds ratio.
3.3. Subgroup Analyses
Interaction tests and subgroup analyses were carried out to examine if the connection between NHR and the HF risk differed in clinical subpopulations categorized by sex, race, hypertension, and diabetes (Table 4). It was found that race and diabetes could notably influence the connection between NHR and HF, and the interaction p‐values were 0.001 and 0.004, respectively.
Table 4.
Subgroup analyses for the association between NHR and the risk of HF stratified by sex, race, hypertension, and diabetes.
| Variables | OR | 95% CI | p | p for interaction |
|---|---|---|---|---|
| Sex | 0.095 | |||
| Male | 1.05 | 1.01–1.11 | 0.039 | |
| Female | 1.25 | 1.13–1.38 | < 0.001 | |
| Race | 0.001 | |||
| Non‐Hispanic White | 1.18 | 1.10–1.26 | < 0.001 | |
| Non‐Hispanic Black | 1.23 | 1.07– 1.40 | 0.002 | |
| Mexican‐American | 0.96 | 0.83– 1.09 | 0.496 | |
| Other | 1.28 | 1.07– 1.51 | 0.004 | |
| Hypertension | 0.673 | |||
| No | 1.22 | 1.04–1.43 | 0.012 | |
| Yes | 1.10 | 1.04–1.16 | 0.001 | |
| Diabetes | 0.004 | |||
| No | 1.05 | 1.02–1.09 | 0.005 | |
| Yes | 1.23 | 1.13–1.35 | < 0.001 |
Note: Adjusted for age, sex, race, marital status, education, smoking status, drinking status, hypertension, diabetes, white blood cell, monocyte, platelet, ALT, AST, creatinine, uric acid, and blood urea nitrogen.
Sex‐stratified analysis showed a stronger link between NHR and HF was observed in females (OR = 1.25, 95% CI: 1.13–1.38, p < 0.001), while the connection in males was weaker (OR = 1.05, 95% CI: 1.01–1.11, p = 0.039). In terms of race, a significantly increased risk of HF in relation to higher NHR was found in Non‐Hispanic Whites (OR = 1.18, 95% CI: 1.10–1.26, p < 0.001), Non‐Hispanic Blacks (OR = 1.23, 95% CI: 1.07–1.40, p = 0.002), and other races (OR = 1.28, 95% CI: 1.07–1.51, p = 0.004), but not in Mexican‐Americans (OR = 0.96, 95% CI: 0.83–1.09, p = 0.496). Stratification by hypertension revealed that higher NHR was associated with an increased risk of HF in both hypertensive (OR = 1.10, 95% CI: 1.04–1.16, p = 0.001) and non‐hypertensive participants (OR = 1.22, 95% CI: 1.04–1.43, p = 0.012). Likewise, the association persisted in both diabetic (OR = 1.23, 95% CI: 1.13–1.35, p < 0.001) and non‐diabetic individuals (OR = 1.05, 95% CI: 1.02–1.09, p = 0.005).
In particularly, we examined the association between NHR and HF in participants without diabetes. The results indicated that higher NHR was significantly associated with an increased risk of HF (Model 2: OR = 1.05, 95% CI: 1.02–1.09). Participants in the highest NHR quartile had nearly twice the risk of HF compared with those in the lowest quartile (OR = 1.91, 95% CI: 1.45–2.53) (Table 5).
Table 5.
Sensitivity analysis on the association between NHR and the risk of HF.
| Crude model | p | Model 1a | p | Model 2b | p | |
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
| NHR (continuous) | 1.06 (1.03–1.09) | < 0.001 | 1.06 (1.02–1.10) | 0.001 | 1.05 (1.02–1.09) | 0.005 |
| NHR (categories) | ||||||
| Q1 | Ref. | Ref. | Ref. | |||
| Q2 | 1.40 (1.11–1.76) | 0.004 | 1.40 (1.10–1.77) | 0.005 | 1.33 (1.05–1.70) | 0.020 |
| Q3 | 1.44 (1.14–1.81) | 0.002 | 1.46 (1.14–1.86) | 0.003 | 1.28 (0.99–1.65) | 0.064 |
| Q4 | 2.10 (1.69–2.61) | < 0.001 | 2.41 (2.90–3.07) | < 0.001 | 1.91 (1.45–2.53) | < 0.001 |
| p for trend | < 0.001 | < 0.001 | < 0.001 |
Abbreviations: CI, confidence interval; OR, odds ratio.
Model 1: adjusted for age, sex, race, marital status, education, and BMI.
Model 2: model 1 + smoking status, drinking status, hypertension, white blood cell, monocyte, platelet, ALT, AST, creatinine, uric acid, and blood urea nitrogen.
4. Discussion
As far as we know, this study was the first one to comprehensively investigate the association between NHR and the risk of HF within a large population that was nationally representative. The results demonstrated that there was a strong positive link between increased NHR values and an elevated chance of HF development. Subgroup analyses showed that the connection was noticeable across both males and females, in participants with and without hypertension, in those with and without diabetes, and certain racial groups (Non‐Hispanic Whites, Non‐Hispanic Blacks, and other races). A nonlinear pattern characterized the association between NHR and HF, and an inflection point was found at 6.39. When the value was below this point, NHR independently contributed to an increased risk of HF.
NHR serves as a composite biomarker integrating inflammatory and lipid metabolic pathways, offering a comprehensive, cost‐effective, and non‐invasive biomarker. Previous studies have associated NHR with metabolic syndrome and cardiovascular disease across diverse populations [11]. For example, a cross‐sectional study identified a nonlinear saturation effect between NHR and atherosclerosis in individuals undergoing health check‐ups [13]. Elevated NHR has also been independently associated with negative cardiovascular events, including stroke and myocardial infarction, among both normoglycemic and prediabetic individuals [14]. Moreover, in patients with T2DM, it has been identified as a predictor of acute coronary syndrome [15]. In the context of stable coronary heart disease, NHR served as a reliable predictor of coronary artery disease severity [10]. Furthermore, elevated NHR has been recognized as a robust indicator of cardiovascular mortality and all‐cause in both acute myocardial infarction patients and the general population [12, 16, 17]. Given the poor prognosis of HF, identifying reliable prognostic markers is critical for early risk stratification and preventive strategies. Building on its established prognostic value in cardiovascular disease, this study extended the application of NHR to HF, highlighting its potential as an easy and effective means for early identification of high‐risk individuals.
Given the close relationship between diabetes, systemic inflammation, and HF, the inclusion of diabetic participants may raise concerns regarding potential confounding. However, diabetes is a highly prevalent comorbidity and an established risk factor for HF in real‐world populations. Excluding individuals with diabetes would substantially reduce the representativeness of the study population, limit the external validity of the findings, and potentially introduce selection bias by removing a clinically important subgroup. Therefore, diabetic participants were retained in the primary analysis. To address the potential influence of diabetes, we further conducted stratified analyses according to diabetes status and a sensitivity analysis restricted to non‐diabetic individuals. Notably, the positive association between NHR and HF remained significant in both diabetic and non‐diabetic participants, supporting the robustness of our findings.
Although the exact pathophysiological links between NHR and HF require further investigation, multiple plausible mechanisms may explain this association. Inflammation served as a critical mediator in both the pathogenesis and progression of HF [18, 19]. As key effector cells in innate immunity, neutrophils contributed significantly to inflammatory processes through phagocytosis and pathogen clearance [20]. In HF, activated neutrophils exacerbated myocardial injury via multiple mechanisms: (1) releasing pro‐inflammatory cytokines like TNF‐α, IL‐6, and CRP; (2) secreting proteolytic enzymes; and (3) inducing oxidative stress [21]. These inflammatory mediators collectively contributed to cardiomyocyte apoptosis, myocardial fibrosis, and ventricular remodeling, which were central to HF pathogenesis [22, 23]. In addition, increasing data emphasized the pathogenic impact of neutrophil extracellular traps (NETs) and the process of NETosis in HF. NETs, created by the release of DNA and granular proteins, enhanced sterile inflammation, compromised mitochondrial function in cardiomyocytes, and contributed to negative changes in the ventricles. Clinical and experimental research has revealed that greater myocardial NET deposition was connected to a reduced ejection fraction, elevated cardiac dysfunction biomarkers, and more adverse clinical outcomes, while preventing NET formation or degrading NETs mitigated myocardial injury and preserved cardiac function [24, 25].
In contrast, HDL‐C was shown to confer cardioprotective effects through its anti‐inflammatory and antioxidant effects. It neutralized free radicals to reduce oxidative stress and bound to lipopolysaccharides, thereby lowering systemic pro‐inflammatory cytokine levels. HDL‐C also modulated inflammasome activity, inhibiting the maturation of key cytokines such as IL‐1β and IL‐12 [26, 27]. Beyond systemic effects, HDL‐C directly suppressed neutrophil‐mediated inflammation by inhibiting neutrophil activation, adhesion, proliferation, and migration [28]. Additionally, HDL‐C improved endothelial function by promoting angiogenesis and reducing endothelial activation and inflammatory mediator expression. It also protected cardiomyocytes from apoptosis, further supporting its cardiovascular benefits [29, 30]. Additionally, recent research highlighted that the importance of HDL‐c lies more in its quality than in its quantity. For patients suffering from HFpEF, HDL functionality was diminished, as oxidative alterations caused a decline in its antioxidant abilities. This impaired HDL was found to be an independent predictor of HFpEF, emphasizing the importance of HDL quality rather than quantity [31]. Unfortunately, the NHANES database only provided quantitative HDL‐C measurements and lacked information on its functional properties, which requires further investigation.
This research has multiple important strengths. First, using a nationally representative NHANES sample ensured broad generalizability to the diverse US adult population. Second, the robustness of the findings was reinforced by comprehensive adjustments for potential confounders and detailed subgroup analyses. Moreover, the investigation of non‐linear associations and threshold effects between NHR and HF provided a deeper understanding of their complex interplay, offering novel insights beyond simple linear correlations. However, the following constraints need to be addressed. First, the cross‐sectional design prevented us from establishing a causal link between NHR and HF. Second, NHR was measured solely at baseline, potentially overlooking its time‐dependent correlation with long‐term outcomes. Moreover, the NHANES database relied on self‐reported physician‐diagnosed HF and lacked echocardiographic parameters (e.g., ejection fraction), etiological classification, and clinical severity data, thereby limiting our ability to distinguish HF subtypes, including HFrEF and HFpEF, or to assess disease severity. Given that these HF phenotypes differ substantially in their underlying pathophysiology, inflammatory profiles, and lipid metabolism, the observed association between NHR and HF may vary across subtypes. Therefore, the inability to differentiate HF phenotypes may have introduced clinical heterogeneity and potentially attenuated or obscured subtype‐specific associations. Future studies with more detailed clinical and echocardiographic data are warranted to validate these findings and clarify the role of NHR across different HF phenotypes.
5. Conclusion
Our study demonstrates that NHR is independently related to HF risk in the general population, indicating that NHR may function as a viable predictor for HF.
Author Contributions
The manuscript was drafted, and data were collected, analyzed, and interpreted by Yi Lin. Dan Li designed the study and revised the article. All authors have read and approved the final version of the manuscript. Dr. Dan Li, the corresponding author, had full access to the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
The authors have nothing to report.
Ethics Statement
The NCHS Ethics Review Board (https://www.cdc.gov/nchs/nhanes/irba98.htm) gave its approval to the study protocol. All NHANES participants provided their written informed consent, and the study was carried out in line with the Declaration of Helsinki.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Transparency Statement
The Dan Li affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Acknowledgments
We appreciate the staff and participants of the NHANES.
Data Availability Statement
Publicly available data sets were analyzed in this study. Data can be found below: https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Publicly available data sets were analyzed in this study. Data can be found below: https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm.