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Association between platelet/high-density lipoprotein cholesterol ratio and blood eosinophil counts in American adults with asthma: a population-based study

Lipids in Health and Disease volume 24, Article number: 67 (2025) Cite this article

Abstract

Objective

This study aims to evaluate the relationship between the platelet-to-high-density lipoprotein cholesterol ratio (PHR) and blood eosinophil counts (BEOC) in asthmatic patients, using data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018.

Methods

This research explored the link between PHR and BEOC among adults with asthma, drawing on data from a representative U.S. population sample (n = 3034; NHANES 2011–2018). To assess this relationship, multivariable linear models were employed, alongside subgroup and interaction analyses to identify any potential variations across different groups. Additionally, generalized additive models, smooth curve fitting, and threshold effect analysis were employed to explore the relationships in greater detail. Sensitivity tests were performed to ensure the robustness of the findings.

Results

The weighted multivariable linear regression analysis showed that after adjusting for all covariables, each one-unit rise in PHR was linked to an increase of 41.61 in BEOC (β: 41.61, 95% CI: 25.25–57.97). Subgroup analyses demonstrated consistency across various categories, reinforcing the significant positive association between PHR and BEOC. Interaction tests indicated that this positive association remained stable regardless of factors such as body mass index, smoking, hypertension, or diabetes, with all interaction P-values greater than 0.05. Additionally, the application of generalized additive models and two-piece linear regression models further confirmed the linear association between PHR and BEOC.

Conclusions

Our study indicates that a higher PHR may be associated with an increased risk of elevated BEOC in American adults with asthma. Thus, PHR might be considered a potential marker for predicting elevated BEOC levels in this population.

Introduction

Asthma is a chronic inflammatory respiratory disease characterized by reversible airflow obstruction [1]. Over the past few decades, its prevalence has significantly increased, with the proportion of asthma patients in the general population ranging from 1 to 18% [2]. For example, the estimated prevalence of adult asthma in the United States is approximately 7–8% [3]. Data from 2017 indicate that there were about 1.6 million emergency department visits and around 183,000 hospitalizations due to asthma in the United States [3]. The World Health Organization predicts that by 2025, the global number of asthma patients will increase by an additional 100 million [4]. The recurrent exacerbations and high medical costs associated with asthma have imposed a heavy economic burden worldwide, making it a critical public health issue that demands urgent attention [5].

The development of asthma involves various inflammatory mechanisms, with airway eosinophilia being the most common [6]. Eosinophils enhance immune responses by secreting cytokines such as IL-4, IL-5, and IL-9, playing a crucial role in pathological processes like airflow obstruction and chronic airway remodeling, which include epithelial damage and repair, neuronal plasticity, and airway smooth muscle hypertrophy [7,8,9]. Consequently, asthma is generally classified into eosinophilic and non-eosinophilic phenotypes [10]. Over 80% of patients with severe asthma worldwide may exhibit an eosinophilic phenotype [11]. Numerous studies have shown that blood eosinophil counts (BEOC) levels are closely associated with asthma severity [12, 13]. As BEOC are easily accessible and widely recognized as biomarkers, they provide significant clinical value in the management and treatment of asthma [14].

Lipid metabolism is closely linked to inflammation, and dyslipidemia can exacerbate the inflammatory response in asthma patients [15, 16]. Wen et al. found a negative association between serum high-density lipoprotein cholesterol (HDL-C) and BEOC in American adults with asthma [17]. A cross-sectional study involving 417,132 participants further confirmed that higher BEOC are strongly associated with lower HDL-C levels [18]. Platelets, as small anucleate cells, play a crucial role in hemostasis [19]. Additionally, both clinical and experimental studies indicate that platelets are involved in the pathogenesis of asthma, contributing to airway inflammation and bronchial hyperresponsiveness [20]. Platelets can bind to eosinophils, further activating them, increasing their count, and thereby promoting the onset and progression of asthma [21].

The platelet-to-high-density lipoprotein cholesterol ratio (PHR), proposed by Jialal et al., is a novel marker of inflammation and hypercoagulability that can be used to reflect the severity of metabolic syndrome [22]. Studies have shown that metabolic syndrome (Mets) can promote the development of a pro-inflammatory state in asthma [23]. Meanwhile, multiple studies have revealed that PHR is closely associated with various diseases [24,25,26]. Specifically, PHR is considered an effective indicator of non-alcoholic fatty liver disease and liver fibrosis [24], and a study by Ni et al. found a positive nonlinear relationship between PHR and kidney stones [25]. Additionally, there is a threshold effect between PHR and the occurrence of stroke, with a turning point of 223.684 [26]. Therefore, PHR demonstrates promising clinical utility in disease evaluation [22]. Given that both platelets and HDL-C are crucial factors in asthma development and that Mets, as represented by PHR, is also closely linked to the inflammatory state of asthma, further exploration of the relationship between PHR and BEOC levels is of significant clinical value.

This study systematically analyzed the relationship between the PHR and BEOC in the U.S. asthma population based on data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018, providing new perspectives and insights into a deeper understanding of the inflammatory characteristics in asthma patients.

Methods

Database and study subjects

The NHANES is an ongoing nationwide cross-sectional survey that began in the 1960s to evaluate individuals' health and nutritional status in the U.S. Utilizing sophisticated multistage sampling methodology, NHANES continuously selects nationally representative samples, releasing data in two-year cycles under the auspices of the CDC. All research protocols are subject to approval by the Ethics Review Board at the National Center for Health Statistics and require informed consent from all participants [27].

The study included data from four cycles between 2011 and 2018, encompassing a total of 39,156 participants. First, we excluded participants under the age of 20 (n = 16,539). Next, we excluded participants with missing asthma data and non-asthmatic individuals (n = 19,251), as well as those with missing PHR and BEOC data (n = 332). Ultimately, 3,034 eligible participants were included in the study (Fig. 1).

Fig. 1
figure 1

Flow chart of the study participant selection process

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Assessment of BEOC and PHR

Blood cell differential counts were performed using the Beckman Coulter HMX (Beckman Coulter, Fullerton, CA). This device is a quantitative and automated hematology analyzer and leukocyte differential counter specifically designed for in vitro diagnostics in clinical laboratories. Detailed information on laboratory testing methods can be found on the NHANES official website.

In this study, lipid testing was conducted following standard clinical laboratory procedures. The concentration of HDL-C was measured using an enzyme-linked immunosorbent assay (ELISA) on serum samples. The procedure involved the following steps: first, fasting venous blood was collected and centrifuged to separate the serum. Then, using a specific enzyme reagent system, the HDL-C concentration was determined through a chemical reaction, and the results were reported in millimoles per liter (mmol/L). PHR is defined as the platelet-to-HDL-C ratio. Given that PHR is right-skewed, we applied a log2 transformation.

Covariables and asthma assessment

Based on previous research [28], this study considered the possible effects of a variety of covariables on the association between PHR and BEOC in the U.S. asthma population. These covariables included age, gender, race, marital status, PIR, education level, BMI, waist circumference, smoking status, alcohol consumption, as well as health conditions (e.g., diabetes and hypertension). Supplementary Material 1 contained detailed definitions of the covariables. To assess asthma status, participants were asked, “Has a doctor or other health professional ever told you that you have asthma?” If the response was “yes,” the participant was considered to have asthma.

Statistical analyses

Because NHANES employed a sophisticated multistage probability sampling method to choose representative subjects, all analyses in our study incorporated sample weights to approximate national statistics. Continuous variables were reported as means with standard errors (SE), while categorical variables were presented as survey-weighted percentages. To evaluate differences between groups, Student's t-tests were utilized for continuous variables, while chi-square tests were employed for categorical variables. To evaluate the relationship between PHR and BEOC, weighted multivariable logistic regression models were employed. We employed three models to adjust for potential confounding factors that could influence the relationship between PHR and BEOC. Model 1 was the baseline model without adjusting for any covariables, providing a crude association between PHR and BEOC. Model 2 adjusted for demographic factors such as age, sex, and race. Model 3 further built upon Model 2 by adjusting for additional potential confounders, including sociodemographic variables (marital status, poverty-to-income ratio, education level), anthropometric measures (body mass index, waist circumference), lifestyle factors (smoking status, alcohol consumption), health conditions (diabetes, hypertension), as well as chronic obstructive pulmonary disease (COPD), lipid-lowering drugs, and corticosteroids. Additionally, we divided PHR into four quartiles as a continuous variable for a more detailed analysis. We calculated the effect sizes odds ratios (OR) and their corresponding 95% confidence intervals (CI). To further explore the nonlinear relationship between PHR and BEOC, we applied generalized additive models (GAM), smooth curve fitting, and threshold effect analysis.

To identify sub-populations that may be especially susceptible, we conducted stratified analyses based on age (grouped into 20–40 years, 41–59 years, and 60–85 years) and gender (male and female). Furthermore, subgroup analyses were conducted based on BMI (underweight, normal, overweight, or obese), diabetes (presence or absence), and hypertension (presence or absence) to investigate the relationship between PHR and BEOC. Tests for interaction were used to explore the variation in the relationship between PHR and BEOC among different subgroups.

Finally, to assess the robustness of our results and gain a broader understanding of the potential role of PHR in inflammation, we conducted the following sensitivity analyses: First, we included additional biochemical data as covariables alongside the original ones, aiming to more comprehensively examine the relationship between PHR and BEOC. Second, we explored the association between PHR and neutrophils In the asthma population to further understand its potential role in inflammation. Third, we expanded the analysis to the general population, investigating the relationship between PHR, neutrophils, and BEOC.

Given the skewed distribution of PHR, we applied a log2 transformation. We applied multiple imputation using chained equations (MICE) with five iterations, implemented through the R MI package, to address missing covariables. The criterion for statistical significance was set at a threshold where the two-sided P-value is smaller than 0.05. All data analyses were performed utilizing R software, specifically version 4.3.2.

Results

Baseline characteristics

This study involved a total of 3,034 qualified participants, consisting of 1,266 men and 1,768 women. Table 1 outlines the participants' baseline characteristics according to PHR quartiles. Participants in the highest PHR quartile demonstrated notable differences in several health indicators compared to those in the lowest quartile. Specifically, those in the highest quartile were younger and had a higher body mass index (BMI). In addition to health metrics, socioeconomic factors such as lower educational attainment and a majority of non-Hispanic white participants also varied in the highest quartile. Moreover, significant statistical differences (P-value < 0.05) were observed in the distribution of age, sex, race, BMI, PIR, smoking status and diabetes across the BEOC quartiles.

Table 1 Weighted baseline characteristics of participants
Full size table

Association between the PHR and BEOC

Table 2 shows a statistically significant positive association between log2-transformed PHR and BEOC across all three models. In the unadjusted model (Model 1: β = 43.08; 95% CI, 27.05–59.10), the model adjusted for gender, age, and race (Model 2: β = 44.87; 95% CI, 28.92–60.81), and the fully adjusted model (Model 3: β = 41.61; 95% CI, 25.25–57.97), this positive relationship remained stable. To explore the PHR and BEOC relationship further, PHR was categorized into three levels. In Model 3, a one-unit rise in PHR corresponded to an increase of 61.06 (β = 61.06; 95% CI, 34.37–87.75) in the highest quartile compared to the first quartile.

Table 2 Weighted Multivariate linear regression models of log2-transformed PHR with blood eosinophil counts
Full size table

Stratified and interaction analyses

The results of the stratified analysis revealed that, except for women aged 20–40, PHR was significantly positively associated with BEOC across various gender and age groups (Supplementary Materials 2–4). Furthermore, as outlined in Supplementary Material 5, after adjusting for potential confounders including BMI, hypertension, diabetes, and smoking, no statistically significant interactions were observed between these covariables and the impact of PHR on BEOC. These results suggest that these factors did not have a significant effect on the PHR-BEOC relationship.

Linear Relationships between PHR and BEOC

Figure 2 displays the results of the generalized additive model (GAM) along with its fitted smoothing curve, showing a linear association between PHR and BEOC. A log-likelihood ratio test was used to compare the goodness-of-fit between the linear regression model and the segmented linear regression model, revealing no statistically significant difference (P = 0.224). Therefore, the linear regression model is more suitable for describing the PHR-BEOC relationship. Together with the results from Table 3, this supports the existence of a positive linear association between PHR and BEOC.

Fig. 2
figure 2

Dose–response relationship between serum PHR levels and blood eosinophil counts in adult asthma patients

Full size image
Table 3 Threshold effect analysis of log2-transformed PHR on blood eosinophil counts using two-piecewise linear regression model
Full size table

Sensitivity analyses

We conducted the following sensitivity analyses: First, in addition to adjusting for the original covariables, we further adjusted for biochemical markers, including low-density lipoprotein, total cholesterol, uric acid, creatinine, and blood urea nitrogen. The results still indicated a positive association between PHR and BEOC (Supplementary Material 6). Second, we explored the association between PHR and neutrophils in the asthma population. After fully adjusting for covariables, PHR remained positively correlated with neutrophil counts, as shown in Supplementary Material 7 (Model: β = 0.54; 95% CI, 0.38–0.69). Finally, we examined the relationship between PHR and both neutrophils and BEOC in the general population, and found that the positive association persisted (Supplementary Material 8).

Discussion

C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are traditional inflammatory markers [29]. CRP reflects acute inflammation, while ESR measures the rate of red blood cell sedimentation and indicates chronic inflammation [30,31,32]. CRP is highly responsive to inflammation but lacks specificity and is influenced by various factors, such as infections or trauma [33]. Therefore, it is often used in conjunction with other markers in clinical practice [33]. In contrast, ESR can be affected by red blood cell characteristics and plasma components [32]. Compared to these traditional markers, PHR combines two relatively stable laboratory parameters—platelet count and high-density lipoprotein cholesterol—reducing the impact of single-variable fluctuations [22]. Wang et al.'s research suggests that PHR not only reflects inflammation but also provides insight into cardiovascular health [34]. Thus, PHR holds greater clinical potential compared to traditional inflammatory markers. Our findings indicate that PHR is significantly associated with BEOC in asthma patients, suggesting it could serve as a potential biomarker for assessing their inflammatory status. This discovery offers a new reference for clinical practice and could improve asthma management by enhancing precision and comprehensiveness. Incorporating PHR into routine monitoring provides clinicians with a low-cost, easy-to-operate tool to assess inflammation, particularly in patients with frequent disease fluctuations [35]. By dynamically tracking PHR levels, physicians can gain a better understanding of the patient's inflammatory state, enabling more targeted and individualized treatment strategies that improve asthma management's specificity and effectiveness [17, 36]. Moreover, regular PHR monitoring can optimize asthma care by detecting early signs of disease deterioration, allowing for timely intervention to prevent acute exacerbations and reduce hospitalization risk [17, 36].

Previous studies have explored the relationships between HDL-C, asthma, and eosinophils. For example, a study conducted in Korea found a negative correlation between serum HDL-C levels and BEOC [37]. Similarly, research involving 24,853 Taiwanese participants demonstrated a causal relationship between lower HDL-C levels and an increased risk of asthma [38]. In another study, Barochia et al. discovered that HDL-C was negatively associated with eosinophil counts in patients with atopic asthma [39]. Furthermore, an animal experiment showed that elevating HDL-C levels by inactivating endothelial lipase could reduce eosinophil infiltration in the lungs and exert a protective effect against pulmonary allergic inflammation [40]. Sun et al. noted that mean platelet volume (MPV), an early marker of platelet activation, was lower in patients with stable asthma and even further reduced in those experiencing acute asthma exacerbations [41]. A U.S. study reported that P-selectin, a marker expressed on platelets, can mediate eosinophil activation, thereby increasing eosinophil counts in both mild and severe asthma patients [42]. In an allergic guinea pig model, Lellouch-Tubiana and colleagues found that reducing platelet counts using anti-platelet antiserum (APAS) significantly decreased eosinophil infiltration in lung tissues [43]. Another study further demonstrated that APAS treatment could reduce eosinophil counts and alleviate pulmonary hyperreactivity in allergic rabbits and mice [44, 45]. These findings suggest a potential link between the PHR and blood eosinophil counts, as both HDL-C and platelets are associated with eosinophil regulation. Additionally, previous research has shown that PHR may reflect the severity of MetS [46, 47]. A cohort study involving 23,191 adults reported that over a follow-up period of up to 57 years, patients with MetS had an 11% higher risk of developing asthma compared to those without MetS [46]. Similarly, another study involving 4,060 elderly individuals found a significant association between MetS and asthma incidence, suggesting that MetS may influence the onset and progression of asthma through insulin resistance and inflammatory mechanisms [47]. Collectively, these studies support the findings of the present research and underscore a close relationship between PHR and BEOC.

The potential mechanisms by which the PHR influences BEOC can be elucidated from several perspectives. First, as an indicator of systemic inflammation, elevated PHR levels may signify an overall pro-inflammatory state characterized by platelet activation and reduced HDL-C levels [48]. This inflammatory milieu could contribute to an increase in BEOC [49]. Upon activation, platelets release various inflammatory mediators, such as platelet factor 4 and β-thromboglobulin, which can attract and activate eosinophils, thereby amplifying the inflammatory response [50]. Supporting evidence from an ovalbumin (OVA) antigen-induced mouse asthma model suggests that platelet depletion significantly alleviates OVA-induced leukocyte (including eosinophil) infiltration in the lungs and airway remodeling [51]. Furthermore, platelets are known to interact directly with leukocytes, including eosinophils, through cell-cell contact and the secretion of pro-inflammatory factors, which can enhance eosinophil chemotaxis and activation [52]. These interactions may further contribute to elevated BEOC [52]. In the context of asthma, platelets are often persistently activated due to increased vulnerability of airway vasculature to inflammatory insults, potentially leading to higher eosinophil levels in the airways compared to healthy individuals. In contrast, a reduction in HDL-C levels may also elevate BEOC in asthma patients [21]. HDL-C plays a critical role in reverse cholesterol transport, preservation of cell membrane stability, and modulation of inflammatory responses at the membrane level [53]. It carries several anti-inflammatory components, such as apolipoprotein A1 (ApoA1), which can inhibit T-cell activation by antigen-presenting cells through disruption of membrane lipid rafts, thereby neutralizing inflammatory signals and limiting eosinophil recruitment and activation [17]. Notably, research has shown that apolipoprotein E or apolipoprotein A-I mimetic peptides can significantly attenuate eosinophilic infiltration in experimental asthma models, highlighting the potential of HDL-C in modulating eosinophilic responses [54, 55].

Strengths and limitations

This study has several notable strengths. First, it is the initial attempt to investigate the association between PHR and BEOC among asthma patients, and it identifies susceptible populations through stratified analyses. Second, a complex multistage probability sampling method was employed, selecting 3034 adult participants from the nationally representative NHANES database, ensuring an adequate sample size. Finally, a wide range of variables were analyzed, and potential confounders were adjusted, thereby enhancing the robustness and reliability of the findings.

This study has several limitations. First, due to its cross-sectional design, it is not possible to establish a causal association between PHR and BEOC in individuals with asthma. The cross-sectional nature of the study also limits our ability to rule out a potential reverse association between PHR and BEOC. Although a significant association between PHR and BEOC was observed, the clinical value of PHR as a biomarker is limited, as BEOC are already widely measured and serve as a direct marker of inflammation. Moreover, PHR does not predict BEOC levels over time, which further reduces its added value. Despite adjusting for potential confounders, it remains challenging to fully eliminate all biases. Additionally, since this study is based on a U.S. population, caution should be exercised when generalizing the findings to other populations. Furthermore, the inclusion of asthma patients was based on questionnaire data rather than lung function tests, which may introduce selection bias, as questionnaire data may not fully reflect the patients’ lung function status. This study also did not consider the impact of biologic agents, which could influence BEOC and potentially affect the results. Future prospective studies are needed to explore the role of blood lipids in asthma control, progression, and treatment, and to further elucidate the underlying mechanisms of asthma.

Conclusions

This study identifies a significant association between PHR and BEOC in individuals with asthma, suggesting a possible link between lipid metabolism, metabolic syndrome, and asthma. However, it is important to note that this association does not imply causation or a direct relationship with disease progression or exacerbations. While BEOC are commonly used as markers of inflammation, the added value of PHR as a biomarker remains unclear, particularly since it does not predict BEOC levels in a temporally preceding manner. Future studies should focus on clarifying the mechanisms underlying these associations and evaluating whether PHR could serve as a useful biomarker for asthma management.

Data availability

The datasets for this study can be found in the NHANES database (https:// www.cdc.gov/nchs/nhanes/index.htm).

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Author notes

  1. Qian Zhao and Peng Yang contributed equally to this work.

Authors and Affiliations

  1. Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, Sichuan, China

    Qian Zhao, Jing-Pan Li, Lei Du, Wei Wang & Yun-Feng Chen

  2. Chengdu Second People’s Hospital, Chengdu, Sichuan, China

    Peng Yang

  3. Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China

    Si-Xuan Zhu & Shan Wu

Authors
  1. Qian Zhao
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  2. Peng Yang
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Contributions

Authors’ contributions Qian Zhao, Peng Yang, Wei Wang and Yun-Feng Chen conceived and designed the study. Qian Zhao, Jing-Pan Li, Lei Du and Si-Xuan Zhu conducted data collection, data analysis, and data interpretation. Qian Zhao, Jing-Pan Li, Lei Du and Si-Xuan Zhu and Shan Wu contributed to literature checks and data visualization. Yun-Feng Chen drafted the initial manuscript and all authors made critical revisions of the manuscript. Qian Zhao, Peng Yang, Wei Wang and Yun-Feng Chen verified the underlying study data. All authors read the manuscript and approved the final draft. All authors confirm that they had full access to all the data in the study and accept responsibility to submit for publication.

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Correspondence to Yun-Feng Chen.

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The NCHS Institutional Review Board has approved NHANES’s investigation, and all participants have provided written informed consent.

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Supplementary Information

Supplementary Material 1. Detailed information on covariables.

Supplementary Material 2. Association between log2-transformed PHR with BEOC in the 20-40 age group.

Supplementary Material 3. Association between log2-transformed PHR with BEOC in the 41-59 age group.

Supplementary Material 4. Association between log2-transformed PHR with BEOC in the 60-82 age group.

Supplementary Material 5. Subgroup analysis for the association between log2-transformed PHR with BEOC.

12944_2025_2479_MOESM6_ESM.docx

Supplementary Material 6. Sensitivity analysis: weighted multivariate linear regression Models of Log2-transformed PHR with blood eosinophil counts after adjusting for biochemical data.

12944_2025_2479_MOESM7_ESM.docx

Supplementary Material 7. Sensitivity analysis: weighted multivariate linear regression Models of Log2-transformed PHR with neutrophils in the asthma population.

12944_2025_2479_MOESM8_ESM.docx

Supplementary Material 8. Sensitivity analysis: weighted multivariate linear regression Models of Log2-transformed PHR with neutrophils and BEOC in the general population.

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Zhao, Q., Yang, P., Li, JP. et al. Association between platelet/high-density lipoprotein cholesterol ratio and blood eosinophil counts in American adults with asthma: a population-based study. Lipids Health Dis 24, 67 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02479-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02479-9

Keywords

Lipids in Health and Disease

ISSN: 1476-511X