Abstract
Background
The hemoglobin-to-red cell distribution width ratio (HRR) is a newly identified inflammatory biomarker associated with various inflammatory diseases. Asthma is a major global health burden and an inflammatory airway disease significantly affected by inflammation. This study primarily aims to evaluate the relationship between HRR and asthma, as well as asthma-related outcomes, including stable asthma and asthma exacerbations in the past year.
Methods
Data from 2007 to 2023 were collected from the NHANES database. Multivariable logit and restricted cubic spline (RCS) analyses were conducted to elucidate the impact of HRR on asthma and its nonlinear relationship. Subgroup and interaction analyses were performed as part of the study’s sensitivity analysis.
Results
This study was based on an analysis of 13,343 individuals. According to logistic regression analysis, a higher HRR was associated with a lower likelihood of asthma (OR = 0.37, 95% CI: 0.23–0.6). Compared to the lowest group, the higher HRR group exhibited a 34% reduction in prevalence (OR = 0.66; 95% CI: 0.54–0.81, p < 0.01). According to RCS analysis, a linear relationship existed between HRR and asthmaprevalence. We further distinguished between stable asthma and acute exacerbations, and this association remained statistically significant.
Conclusion
This study observed an inverse association between HRR and asthma. Specifically, an increase in HRR was significantly associated with a lower prevalence of stable asthma and acute exacerbation, providing potential evidence for the use of HRR in evaluating asthma status.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12890-025-03852-1.
Keywords: Asthma, Asthma exacerbation, NHANES, HRR, Red cell distribution width
Introduction
Asthma is a common chronic respiratory disease worldwide, characterized by reversible airway constriction and hyperresponsiveness. Analysis of the Global Burden of Disease (GBD) database indicates that in 2019, approximately 262 million people were affected by asthma, with its incidence continuing to rise [1, 2]. One of the most severe consequences of asthma is exacerbation, also known as an asthma attack, which results in a substantial number of emergency department visits. This imposes a significant burden on global healthcare resources and the economy [3, 4]. Therefore, identifying accessible, cost-effective, and highly sensitive biomarkers for asthma and its exacerbations holds significant clinical value.
Complete blood count (CBC) parameters have become valuable tools in asthma research in recent years due to their non-invasive nature, affordability, and accessibility. Among them, eosinophil count is recognized as a key biomarker of asthma-related inflammation, with elevated levels often associated with disease exacerbation and chronic inflammation [5, 6]. However, eosinophil count exhibits limited sensitivity at low levels, restricting its predictive utility across all asthma patients. Additionally, RDW an indicator of erythrocyte volume heterogeneity, has been linked to systemic inflammation and hypoxic conditions [7]. Recent studies have demonstrated its association with the prevalence and mortality of several inflammatory diseases, including chronic obstructive pulmonary disease (COPD) [8, 9], rheumatic diseases [10], sepsis [11], and heart failure [12]. Similarly, hemoglobin (HGB) primarily functions as an oxygen transport molecule and is widely used as a key biochemical marker for diagnosing anemia [13].
The HRR is an innovative composite biomarker that integrates HGB and RDW, providing comprehensive information on oxygen transport and inflammation. HRR was initially introduced in cancer research [14, 15], and has since been demonstrated to be associated with various diseases, including cardiovascular and cerebrovascular diseases [16–19], offering greater comprehensiveness and reliability compared to HGB or RDW alone. HRR is influenced by both its components: hemoglobin levels can be altered by anemia, chronic disease, nutritional deficiencies, or hypoxia, while RDW tends to increase under conditions of systemic inflammation, oxidative stress, or disrupted erythropoiesis. These factors make HRR a composite marker that captures both oxygen transport capacity and systemic inflammatory burden. Compared to RDW or HGB alone, HRR may offer improved stability and interpretability in reflecting chronic inflammatory conditions. Therefore, HRR may serve as an integrative marker reflecting both oxygen delivery and inflammatory burden.
Given the complex pathological characteristics of asthma and the interplay between systemic inflammation and hypoxia, we hypothesize that HRR may serve as a potential biomarker for assessing disease status in asthma patients. This study aims to analyze the relationship between HRR, asthma, and asthma exacerbations.
Materials and methods
Study population
This study utilized data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cross-sectional survey that collects information on population nutrition, health, disease prevalence, and risk factors through various methods, including demographic assessments, laboratory tests, physical examinations, and questionnaires. The study protocol was approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) (Protocol No. 2005–06, Protocol No. 2011–17, Protocol No. 2018–01, Protocol No. 2021–05. available at: https://www.cdc.gov/nchs/nhanes/about/erb.html#print), and informed consent was obtained from all participants. All procedures were conducted in accordance with relevant guidelines and regulations [20].
The study included data from seven survey cycles spanning 2007 to 2023, incorporating a total of 42,755 participants aged 20 years and older. A total of 645 participants were excluded due to incomplete asthma data. Subsequently, 2,099 participants were excluded due to missing red cell distribution width and hemoglobin data, and 391 pregnant individuals were also excluded. To ensure methodological transparency and study clarity, prior literature was referenced to obtain detailed variable definitions [21, 22], leading to the exclusion of an additional 26,277 participants due to missing relevant covariates (Supplement Figure 1). Ultimately, a total of 13,343 participants met the inclusion criteria. Figure 1 illustrates the complete inclusion and exclusion process of this study.
Fig. 1.
Flowchart showing the number of NHANES participants included in the current analyses. Abbreviations: NHANES, National Health and Nutrition Examination Survey
Definition of asthma
Asthma was classified as either stable asthma or asthma exacerbation based on whether an acute worsening episode or an emergency hospitalization occurred within the past year. Diagnosis was determined using responses to the following four asthma-related questions from the NHANES medical conditions questionnaire [23]:
Has a doctor or other health professional ever told you that you have asthma?
Do you still have asthma?
Have you had an asthma attack in the past year?
During the past 12 months, had to visit an emergency room or urgent care center because of asthma?
Only participants who answered"yes"to questions 1 and 2 were included in the final analysis. Stable asthma was defined as a negative response to both questions 3 and 4, whereas unstable asthma was defined as a positive response to either question 3 or 4 [24, 25].
Evaluation of the HRR
Blood samples from NHANES participants were collected at standardized mobile examination centers [26]. HRR was calculated by dividing HGB (measured in grams per deciliter, g/dL) by RDW, with the result rounded to two decimal places. In our analysis, HRR was treated as a continuous variable, and participants were subsequently divided into tertiles for further examination. HRR was identified as the exposure variable in our study. Additionally, HRR was categorized into tertiles: Low (< 1.06), Medium (1.06 to 1.18), and High (> 1.18), with Low serving as the reference group.
Covariates
In this study, we included relevant covariates that are considered potential confounding factors for asthma. These covariates included self-reported age, sex (men or women), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Race), educational level (≤ high school vs. > high school), poverty index, and alcohol consumption status: never (lifetime consumption < 12 drinks), former (≥ 12 drinks in a lifetime but no alcohol intake in the past year, or ≥ 12 drinks per year but none in the past year), and current (at least one drink in the past year). Smoking status was categorized as never smokers, former smokers, and current smokers. Body mass index (BMI) was calculated as weight (in kilograms) divided by height squared (in meters). Medical history was determined through a questionnaire, asking participants whether they had ever been diagnosed with hypertension, diabetes, stroke, chronic obstructive pulmonary disease (COPD), or cardiovascular disease (CVD). Hypertension was identified based on self-reported diagnosis, medication use, and/or an average systolic blood pressure of at least 130 mmHg or a diastolic blood pressure of at least 80 mmHg. Diabetes was defined based on self-reported diagnosis, insulin or medication use, and/or a glycated hemoglobin (HbA1c) level ≥ 6.5%. The Healthy Eating Index-2015 (HEI-2015) is a comprehensive dietary quality measure used to assess adherence to the Dietary Guidelines for Americans [22]. Comprehensive data on all study variables are available on the NHANES website. The selection of covariates was based on prior literature using NHANES data to assess asthma-related outcomes [27–29], as well as clinical relevance to asthma and systemic inflammation.
Statistical analysis
All analyses were conducted using appropriate NHANES survey weights to account for complex sampling design. The weights were applied in R using the “survey” package, including strata and PSU adjustments to obtain nationally representative estimates. All statistical analyses were performed using R Studio software (version 4.2.2), with a two-sided P-value < 0.05 considered statistically significant.
Continuous variables were presented as weighted mean (SE) and compared using the Wilcoxon rank-sum test, while categorical variables were expressed as unweighted counts (N) and weighted percentages (%) and compared using the chi-square test. In this analysis, HRR was treated as a continuous variable. Three logistic regression models were developed to assess the association between HRR and asthma, estimating odds ratios (OR) and 95% confidence intervals (95% CI) to enhance statistical power and minimize potential bias in this observational study: Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was adjusted for age, sex, race/ethnicity, education levels, family poverty, smoking status, and alcohol consumption. Model 3 was adjusted for the variables in model 2 plus Hypertension, Diabetes, Stroke, COPD, CVD, Hyperlipidemia, BMI, HEI-2015, PA, Eosinophils percent.
Additionally, a restricted cubic spline (RCS) analysis was conducted to explore the dose–response relationship between HRR, asthma, and asthma exacerbations, with all covariates from Model 3 included in the RCS adjustment. Subgroup analyses were also performed based on age, sex, race, education level, family poverty, alcohol consumption, smoking status, hypertension, diabetes, COPD and hyperlipidemia. To assess the impact of missing data on study outcomes, a sensitivity analysis was performed by excluding variables with excessive missing values. Subgroup analysis was conducted using the fully adjusted Model 3.
Results
A total of 13,343 participants were included in this study, with 51.75% being men and 48.25% women. Table 1 summarizes the clinical and biochemical characteristics of participants categorized by HRR tertiles. Significant differences were observed between HRR and asthma history, asthma presence, and asthma exacerbations (P < 0.05). Supplementary Table 1 presents the clinical and biochemical characteristics of participants with asthma and asthma exacerbations. Among individuals with stable asthma and those experiencing asthma exacerbations, significant differences were observed in HRR, hemoglobin, red cell distribution width, race, COPD, CVD and eosinophil percentage (P < 0.05).
Table 1.
Baseline characteristics of participants stratified by HRR tertiles
| N (%) | HRR | ||||
|---|---|---|---|---|---|
| Characteristics | Total | Low | Medium | High | P value |
| (< 1.06) | (1.06 to 1.18) | (> 1.18) | |||
| Age (years), mean (SE) | 45.65(0.32) | 48.54(0.41) | 46.27(0.36) | 42.36(0.46) | < 0.01 |
| Sex | < 0.01 | ||||
| Men | 7039(51.75) | 1371(22.77) | 2179(47.51) | 3489(82.74) | |
| Women | 6304(48.25) | 3587(77.23) | 2028(52.49) | 689(17.26) | |
| Race/ethnicity | < 0.01 | ||||
| Non-Hispanic White | 6265(71.80) | 1811(62.97) | 2141(75.39) | 2313(76.40) | |
| Non-Hispanic Black | 2468(9.20) | 1496(17.52) | 605(6.62) | 367(4.06) | |
| Mexican American | 1918(7.42) | 614(6.93) | 590(6.58) | 714(8.72) | |
| Other Race | 2692(11.58) | 1037(12.59) | 871(11.41) | 784(10.82) | |
| Education level | 0.04 | ||||
| Less than high school | 1145(4.21) | 453(4.81) | 349(3.96) | 343(3.90) | |
| More high school or than | 12,198(95.79) | 4505(95.19) | 3858(96.04) | 3835(96.10) | |
| Family poverty | < 0.01 | ||||
| 0–1.3 | 3866(18.62) | 1554(21.71) | 1109(16.25) | 1203(18.10) | |
| 1.3–3.5 | 4756(32.99) | 1844(35.33) | 1439(30.65) | 1473(33.15) | |
| > 3.5 | 4721(48.39) | 1560(42.95) | 1659(53.10) | 1502(48.75) | |
| Smoking status | < 0.01 | ||||
| Never | 7527(57.16) | 3033(60.97) | 2385(58.15) | 2109(52.65) | |
| Former | 3177(24.45) | 1127(24.59) | 1035(24.22) | 1015(24.54) | |
| Now | 2639(18.39) | 798(14.43) | 787(17.63) | 1054(22.80) | |
| Alcohol drinker | < 0.01 | ||||
| Never | 1628(9.30) | 813(12.85) | 477(8.64) | 338(6.67) | |
| Former | 2004(12.30) | 871(14.89) | 601(11.71) | 532(10.48) | |
| Now | 9711(78.41) | 3274(72.26) | 3129(79.65) | 3308(82.86) | |
| Medical history | |||||
| Hypertension | 6574(45.44) | 2630(46.80) | 1963(43.73) | 1981(45.87) | 0.12 |
| Diabetes | 1462(7.85) | 770(11.54) | 399(6.92) | 293(5.35) | < 0.01 |
| Stroke | 345(1.94) | 184(3.02) | 90(1.47) | 71(1.42) | < 0.01 |
| COPD | 570(4.21) | 203(3.87) | 156(3.57) | 211(5.17) | 0.03 |
| CVD | 1082(6.38) | 526(8.76) | 318(5.84) | 238(4.72) | < 0.01 |
| Hyperlipidemia | 9277(68.80) | 3457(68.19) | 2873(67.58) | 2947(70.55) | 0.05 |
| Biochemical indexes, mean (SE) | |||||
| Hemoglobin (g/dL) | 14.33(0.03) | 12.94(0.02) | 14.31(0.02) | 15.64(0.03) | < 0.01 |
| Red cell distribution width (%) | 13.02(0.02) | 13.90(0.03) | 12.82(0.02) | 12.39(0.02) | < 0.01 |
| Eosinophils percent (%) | 2.81(0.02) | 2.72(0.04) | 2.83(0.03) | 2.87(0.04) | 0.01 |
| BMI (kg/m2) | < 0.01 | ||||
| < 25 | 4145(32.64) | 1472(32.66) | 1445(36.22) | 1228(29.09) | |
| ≥ 25 | 9198(67.36) | 3486(67.34) | 2762(63.78) | 2950(70.91) | |
| Physical activity,MET/week | < 0.01 | ||||
| 0–600 | 2640(18.20) | 1219(22.28) | 799(17.76) | 622(14.86) | |
| 600–1200 | 2083(15.25) | 858(17.57) | 679(15.86) | 546(12.49) | |
| > 1200 | 8620(66.55) | 2881(60.15) | 2729(66.38) | 3010(72.66) | |
| HEI-2015 (score) | < 0.01 | ||||
| < 60 | 8736(65.22) | 3117(62.31) | 2668(62.62) | 2951(70.47) | |
| ≥ 60 | 4607(34.78) | 1841(37.69) | 1539(37.38) | 1227(29.53) | |
| Asthma questionnaire | |||||
| Asthma History | 1692(13.27) | 705(15.64) | 540(13.68) | 447(10.67) | < 0.01 |
| Stable asthma | 297(2.44) | 144(3.21) | 100(2.71) | 53(1.45) | < 0.01 |
| Asthma exacerbation | 515(3.98) | 258(5.76) | 151(3.69) | 106(2.62) | < 0.01 |
Percentages or mean (95% CI) were estimated using US population weights
Abbreviations: N number, HRR hemoglobin-to-red cell distribution width ratio, COPD chronic obstructive pulmonary disease, CVD cardiovascular disease, BMI body mass index, Met metabolic equivalent, HEI Healthy Eating Index, NHANES National Health and Nutrition Examination Survey
*Numbers (N) in the table were unweighted
Association between HRR and asthma
This analysis included 1,692 asthma patients (13.27%). Our results indicate that higher HRR levels were associated with a lower likelihood of asthma (Table 2). After adjusting for all covariates, the negative association between HRR and asthma incidence remained significant (Model 3: OR = 0.37, 95% CI: 0.23–0.6). When HRR was categorized into tertiles, a consistent inverse association with asthma prevalence was observed. In Model 3, compared to participants in the lowest tertile, those in the highest HRR tertile had a 34% lower likelihood of developing asthma (OR = 0.66; 95% CI: 0.54–0.81, p < 0.01). Further differentiation between stable asthma and asthma exacerbations revealed that this association remained statistically significant. HRR was still inversely associated with both stable asthma and asthma exacerbations. In the dose–response analysis, the fully adjusted RCS regression model demonstrated a significant nonlinear association between HRR and asthma (Fig. 2). This association remained statistically significant in both stable asthma and asthma exacerbation groups.
Table 2.
Multivariate logit analysis of HRR and prevalence of asthma
| Model 1 | Model 2 | Model 3 | |||||
|---|---|---|---|---|---|---|---|
| HRR | Event (%) | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
| Asthma history | |||||||
| Low (< 1.06) | 705(15.64) | 1(Ref) | 1(Ref) | 1(Ref) | |||
| Medium (1.06 to 1.18) | 540(13.68) | 0.86(0.73,1.01) | 0.06 | 0.87(0.74,1.02) | 0.08 | 0.9(0.76,1.05) | 0.18 |
| High (> 1.18) | 447(10.67) | 0.66(0.54,0.81) | < 0.01 | 0.66(0.54,0.8) | < 0.01 | 0.66(0.54,0.81) | < 0.01 |
| P for trend | < 0.01 | < 0.01 | < 0.01 | ||||
| Continuity | 0.34(0.21,0.54) | < 0.01 | 0.34(0.21,0.54) | < 0.01 | 0.37(0.23,0.6) | < 0.01 | |
| Stable asthma | |||||||
| Low (< 1.06) | 144(3.21) | 1(Ref) | 1(Ref) | 1(Ref) | |||
| Medium (1.06 to 1.18) | 100(2.71) | 0.8(0.56,1.14) | 0.21 | 0.81(0.57,1.16) | 0.24 | 0.84(0.59,1.19) | 0.32 |
| High (> 1.18) | 53(1.45) | 0.41(0.25,0.69) | < 0.01 | 0.42(0.25,0.71) | < 0.01 | 0.44(0.26,0.74) | < 0.01 |
| P for trend | < 0.01 | < 0.01 | < 0.01 | ||||
| Continuity | 0.11(0.05,0.25) | < 0.01 | 0.12(0.05,0.26) | < 0.01 | 0.13(0.06,0.29) | < 0.01 | |
| Asthma exacerbation | |||||||
| Low (< 1.06) | 258(5.76) | 1(Ref) | 1(Ref) | 1(Ref) | |||
| Medium (1.06 to 1.18) | 151(3.69) | 0.72(0.55,0.94) | 0.02 | 0.73(0.56,0.95) | 0.02 | 0.63(0.4,0.98) | 0.04 |
| High (> 1.18) | 106(2.62) | 0.63(0.41,0.99) | 0.04 | 0.63(0.4,0.98) | 0.04 | 0.59(0.37,0.95) | 0.03 |
| P for trend | 0.03 | 0.03 | 0.02 | ||||
| Continuity | 0.33(0.12,0.92) | 0.03 | 0.33(0.12,0.94) | 0.03 | 0.44(0.14,1.33) | 0.03 | |
Model 1 was adjusted for age, sex, and race/ethnicity
Model 2 was adjusted for age, sex, race/ethnicity, education levels, family poverty, smoking status, and alcohol consumption
Model 3 was adjusted for the variables in model 2 plus Hypertension, Diabetes, Stroke, COPD, CVD, Hyperlipidemia, BMI, HEI-2015, Physical activity, Eosinophils percent
Abbreviations: OR odds ratio, CI confidence interval, HRR hemoglobin-to-red cell distribution width ratio, COPD chronic obstructive pulmonary disease, CVD cardiovascular disease, BMI body mass index, Met metabolic equivalent, HEI Healthy Eating Index
Fig. 2.
Adjusted spline curve analysis of the association between HRR and asthma. Solid lines were hazard ratios with 95%CI in shaded areas. Knot locations were the 10th, 50th, and 90th tertiles of HRR. All models were adjusted for age, sex, race/ethnicity, education levels, family poverty, smoking status, and alcohol consumption, Hypertension, Diabetes, Stroke, COPD, CVD, Hyperlipidemia, BMI, HEI-2015, Physical activity, Eosinophils percent. Abbreviations: OR, odds ratio; CI, confidence interval; HRR, hemoglobin-to-red cell distribution width ratio; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; BMI, body mass index; Met, metabolic equivalent; HEI, Healthy Eating Index
Subgroup and sensitivity analyses
To further investigate the relationship between HRR and asthma across different populations, a series of subgroup analyses were conducted. As shown in Table 3, a negative association between HRR and asthmawas observed across most subgroups, including age, sex, education level, poverty index, alcohol consumption status, smoking status, hypertension, diabetes, COPD and hyperlipidemia. To assess the impact of missing data on study outcomes, a sensitivity analysis was performed by excluding variables with excessive missing values (Healthy Eating Index, Physical Activity, COPD). The results remained consistent with the primary analysis, further supporting the robustness of our findings (Supplemental Tables S2).
Table 3.
Stratified analyses of the relationships between HRR and asthma prevalence
| Characteristic | Stable Asthma | P value | P for interaction | Asthma Exacerbation | P value | P for interaction |
|---|---|---|---|---|---|---|
| Age group | 0.79 | 0.73 | ||||
| ≤ 65 years | 0.12(0.05,0.31) | < 0.01 | 0.34(0.10, 1.12) | 0.08 | ||
| > 65 years | 0.47(0.10, 2.15) | 0.32 | 2.23(0.23,21.53) | 0.48 | ||
| Gender | 0.36 | 0.21 | ||||
| Men | 0.07(0.01, 0.38) | < 0.01 | 0.12(0.01, 1.72) | 0.12 | ||
| Women | 0.18(0.06,0.55) | < 0.01 | 0.83(0.32, 2.19) | 0.7 | ||
| Races/ethnicity | 0.12 | 0.45 | ||||
| Non-Hispanic White | 0.07(0.03,0.22) | < 0.01 | 0.28(0.05, 1.47) | 0.13 | ||
| Non-Hispanic Black | 0.66(0.13,3.35) | 0.61 | 0.82(0.21, 3.26) | 0.78 | ||
| Mexican American | 1.24(0.11,13.81) | 0.86 | 0.54(0.11, 2.63) | 0.43 | ||
| Other Race | 0.60(0.07, 4.93) | 0.63 | 1.94(0.38, 9.85) | 0.42 | ||
| Educations Levels | 0.32 | 0.84 | ||||
| Less than high school | 0.10(0.01, 1.73) | 0.11 | 2.05(0.18,23.83) | 0.56 | ||
| More high school or than | 0.14(0.06,0.33) | < 0.01 | 0.42(0.13, 1.33) | 0.14 | ||
| Family poverty | 0.49 | 0.68 | ||||
| 0–1.3 | 0.20(0.06, 0.67) | 0.01 | 0.78(0.29, 2.11) | 0.61 | ||
| 1.3–3.5 | 0.17(0.05,0.57) | < 0.01 | 0.23(0.04, 1.46) | 0.12 | ||
| > 3.5 | 0.10(0.02, 0.39) | < 0.01 | 0.43(0.04, 4.41) | 0.47 | ||
| Smoking status | 0.02 | 0.61 | ||||
| Never | 0.16(0.06,0.41) | < 0.01 | 0.50(0.10, 2.37) | 0.37 | ||
| Former | 0.32(0.06,1.87) | 0.20 | 0.57(0.07, 4.89) | 0.60 | ||
| Now | 0.03(0.01, 0.13) | < 0.01 | 0.20(0.03, 1.37) | 0.10 | ||
| Alcohol drinker | 0.79 | 0.99 | ||||
| Never | 3.00(0.27, 33.29) | 0.36 | 0.06(0.00, 1.39) | 0.08 | ||
| Former | 0.34(0.04,2.72) | 0.30 | 0.65(0.10, 4.41) | 0.66 | ||
| Now | 0.11(0.04,0.27) | < 0.01 | 0.45(0.13, 1.55) | 0.20 | ||
| COPD | 0.28 | 0.76 | ||||
| NO | 0.16(0.07,0.37) | < 0.01 | 0.36(0.10,1.28) | 0.11 | ||
| Yes | 0.08(0.01, 1.03) | 0.05 | 0.75(0.14, 3.90) | 0.73 | ||
| Diabetes | 0.40 | 0.96 | ||||
| NO | 0.11(0.05,0.27) | < 0.01 | 0.41(0.12, 1.46) | 0.17 | ||
| Yes | 0.57(0.05, 6.47) | 0.64 | 1.04(0.12, 9.06) | 0.97 | ||
| Hypertension | 0.60 | 0.25 | ||||
| NO | 0.16(0.05, 0.53) | < 0.01 | 0.52(0.13, 2.03) | 0.34 | ||
| Yes | 0.13(0.04,0.41) | < 0.01 | 0.36(0.08, 1.72) | 0.20 | ||
| Hyperlipidemia | 0.94 | 0.09 | ||||
| NO | 0.06(0.01, 0.28) | < 0.01 | 1.27(0.37, 4.40) | 0.70 | ||
| Yes | 0.19(0.07,0.52) | < 0.01 | 0.26(0.07, 1.05) | 0.06 |
All models were adjusted for age, sex, race/ethnicity, education levels, family poverty, smoking status, and alcohol consumption, Hypertension, Diabetes, Stroke, COPD, CVD, Hyperlipidemia, BMI, HEI-2015, Physical activity, Eosinophils percent
Abbreviations: OR odds ratio, CI confidence interval, HRR hemoglobin-to-red cell distribution width ratio, COPD chronic obstructive pulmonary disease, CVD cardiovascular disease, BMI body mass index, Met metabolic equivalent, HEI Healthy Eating Index
Discussion
This study is a cross-sectional analysis based on NHANES data from 2007 to 2023, examining the relationship between HRR and both asthma and asthma exacerbations. The results indicate a significant negative correlation between HRR and asthma presence: higher HRR levels were associated with a lower prevalence of asthma, and this association remained significant in the subgroup of asthma exacerbations. These findings suggest that higher HRR levels may be linked to a reduced likelihood of asthma and its acute exacerbations.
This is the first study to investigate the association between HRR and asthma prevalence. HRR is a novel biomarker proposed in recent years, with its biological basis rooted in the multifaceted effects of inflammation and oxidative stress on erythropoiesis, maturation, and function [30, 31]. Among these, RDW reflects erythrocyte volume heterogeneity, and both inflammation and oxidative stress disrupt iron metabolism, suppress erythropoiesis, and lead to increased RDW, thereby indicating an elevated systemic inflammatory burden [32]. Previous studies have demonstrated an association between RDW and various diseases and clinical outcomes, with elevated RDW serving as a marker of poor prognosis in COPD patients [9]. Preliminary findings have also provided promising data on the role of RDW in asthma [33]. Compared to RDW alone, the introduction of HRR integrates hemoglobin's oxygen transport capacity, offering a more comprehensive reflection of the interplay between oxygen supply–demand balance and inflammation. Higher HRR levels may indicate enhanced oxygen-carrying capacity and reduced inflammatory burden, providing theoretical support for its application in systemic diseases. In studies on cardiovascular disease and stroke, HRR has been identified as a sensitive marker of inflammation and oxidative stress, with low HRR associated with increased all-cause mortality [19, 34, 35]. Furthermore, studies on systemic diseases such as depression have also suggested that low HRR may reflect systemic inflammation and oxidative stress [36]. By validating the association between HRR and asthma, this study further supports HRR's potential as a biomarker of inflammation and oxidative stress, expanding its application in chronic inflammatory diseases.
The results of this study demonstrate a significant negative association between higher HRR levels and a lower prevalence of asthma, which aligns with the crucial roles of inflammation and hypoxia in asthma pathogenesis. Asthma is characterized by chronic airway inflammation and airway remodeling, both of which are driven by inflammatory responses and hypoxic conditions [37, 38]. Elevated RDW is generally associated with increased systemic inflammation and oxidative stress, while decreased Hb levels may indicate impaired oxygen-carrying capacity. Therefore, a higher HRR may reflect a lower systemic inflammatory burden and greater oxygen supply capacity, thereby decreasing the likelihood of asthma presence. Interestingly, this study not only revealed a significant negative correlation between HRR and asthma but also identified a protective role of HRR in asthma exacerbations. Acute exacerbations are typically triggered by acute inflammatory activation or airway hypoxia [39, 40]. Individuals with higher HRR levels may be better equipped to cope with these triggers due to a lower inflammatory burden and enhanced oxygen-carrying capacity. This study is the first to integrate these parameters into HRR, providing a more comprehensive and sensitive tool for asthma assessment. Incorporating HRR into asthma screening and management protocols, particularly in resource-limited healthcare settings, may improve the early identification of high-risk patients, optimize personalized intervention strategies, and ultimately reduce asthma-related disease burden and healthcare costs. This study has several strengths. First, it features a large, representative dataset that is easily accessible, with standardized questionnaire assessments, making the findings relatively robust. Second, confounding factors were minimized through various statistical approaches, including multivariable, subgroup, and interaction analyses. Finally, the components of HRR are readily available, cost-effective, and widely used in clinical practice, supporting their broader application in healthcare settings.
Limitations
This study has several limitations. First, Due to its observational nature and cross-sectional design, one limitation is that the causal relationship between HRR and asthmacannot be definitively established. We recommend future prospective studies to clarify this causal relationship. Second, data on glucocorticoid use-an important factor in asthma control-were not consistently available in NHANES and thus could not be included in the analysis. This remains a limitation of our study. Third, asthma diagnosis in NHANES is based on self-reported physician diagnosis and symptom history, without objective clinical testing such as spirometry or medication records. This introduces the possibility of misclassification, particularly given the variability in how asthma is diagnosed across populations. These limitations highlight the need for future studies with validated clinical endpoints to confirm our findings. Finally, asthma diagnosis was based on self-reported data from a single U.S. cohort, which may have influenced the results. Future multicenter, prospective studies are necessary to validate these findings.
Conclusion
This study is the first to reveal a negative association between HRR and both asthma and asthma exacerbations, suggesting that higher HRR levels may reduce thelikelihood of asthma by alleviating systemic inflammation and improving oxygen transport capacity. As an affordable and readily accessible biomarker, HRR holds significant potential for clinical application in asthmastatus stratification and management. Future research should further explore the underlying mechanisms of HRR and validate its utility across diverse populations to advance precision medicine in asthma management.
Supplementary Information
Supplementary Material 1: Supplemental Tables S1-S2. Supplemental Figure S1.
Acknowledgements
We express our gratitude to the National Center for Health Statistics at the CDC for their responsibilities in designing, collecting, and administering the NHANES data and for making it accessible to the public.
Authors’ contributions
CK, LH, SL contributed to the conception and design of the study. CK and QZ contributed to the data analysis, while the statistical analysis was performed by QZ, LH. CK and SL were responsible for drafting the manuscript, while QZ and SL contributed to its review and modification. All authors have approved the submitted version of the manuscript and agreed to be accountable for all aspects of the work.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The data described in this article can be freely and openly accessed at NHANES data (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).
Declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the NCHS Institutional Review Board. Written informed consent was obtained from all subjects/patients.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chunling Kuang and Liuliu He contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Supplemental Tables S1-S2. Supplemental Figure S1.
Data Availability Statement
The data described in this article can be freely and openly accessed at NHANES data (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).