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Association of relative fat mass with asthma: inflammatory markers as potential mediators

Lipids in Health and Disease volume 24, Article number: 13 (2025) Cite this article

Abstract

Background

This study aimed to investigate the association between relative fat mass (RFM) and asthma, as well as to explore the mediating role of Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI).

Methods

This cross-sectional study utilized data from the National Health and Nutrition Examination Survey from 2007 to 2018. Associations between RFM and asthma were tested using multivariable logistic regressions, restricted cubic splines, subgroup analyses, and interaction tests, with mediation analysis for SII and SIRI. The inflection point was determined by the two-piecewise linear regression. Sensitivity analysis and propensity score matching (PSM) was applied to validate the stability of the associations.

Results

Higher RFM was positively associated with asthma, with an inflection point at 34.08. Below this threshold, each unit increase in RFM was positively associated with a 2% increase in the odds of asthma (Odds ratio (OR) 1.02, 95% Confidence interval (CI): 1.00—1.03), while above it, the association strengthened, with a 5% increase in the odds per unit (OR 1.05, 95% CI: 1.04—1.07). The association was consistent across subgroups. The association between RFM and asthma is stronger in current asthma patients than in ever had asthma ones. Mediation analyses showed that SII and SIRI partially mediated 7.48% and 3.88% of the RFM-asthma association, respectively. The findings remained robust after sensitivity analyses and adjusting for confounding bias using PSM.

Conclusions

RFM is positively associated with the prevalence of asthma in the U.S., particularly among individuals with current asthma, with systemic inflammation partially mediating this relationship.

Introduction

Asthma is a major global health issue, affecting approximately 339 million people of all ages. In 2017, it accounts for an estimated 297.92 disability-adjusted life years per 100,000 individuals. Furthermore, asthma imposes a substantial economic burden on healthcare systems, due to both direct medical costs and indirect expenses related to lost productivity [1]. Obesity is a significant, modifiable risk factor for asthma, with studies showing a higher prevalence of asthma among those with increased body fat [2,3,4,5]. Obesity and increased adiposity are also significant risk factors for the development of asthma [6]. Moreover, obesity and increased body fat are associated with worse asthma outcomes [6, 7], including more frequent and severe symptoms [8], reduced response to asthma medications and a reduced quality of life. This relationship may differ across different asthma statuses, such as between individuals who have a history of asthma and those who are currently living with asthma. In particular, individuals with a higher BMI may experience more difficulty in achieving adequate symptom control, even when treatment guidelines are followed [2]. Conversely, significant improvements in both asthma control and lung function can be observed with sufficient weight reduction [9, 10].

Despite the well-established link between obesity and asthma, there remains ongoing debate about the optimal method for assessing adiposity in asthma research. Body Mass Index (BMI), the most commonly used anthropometric measure, has limitations due to its inability to accurately reflect body fat distribution, particularly visceral fat, which is strongly linked to metabolic syndrome [11] and pulmonary dysfunction [12]. BMI can erroneously classify individuals with elevated body fat as either non-obese or metabolically healthy [13, 14]. Additionally, studies using dual-energy X-ray absorptiometry (DXA) have shown that trunk or central obesity is strongly associated with an increased risk of asthma [15]. Relative Fat Mass (RFM), which incorporates both height and waist circumference (WC), has been found to yield results consistent with DXA and magnetic resonance imaging. Given these advantages, we considered RFM as a more accurate measure of body fat distribution. Its health implications and simplicity make it a convenient tool for large-scale studies.

Recent studies have demonstrated that RFM is not only a reliable marker of obesity in the general population but also strongly associated with metabolic and cardiovascular risk factors, including diabetes [16], non-alcoholic fatty liver disease [17], heart failure, and coronary artery disease [18, 19]. In addition, RFM has shown superior predictive ability compared to BMI or WC, with higher area under curve values [20, 21] for conditions like diabetes, arthritis, high blood pressure and cardiovascular diseases. Notably, RFM has been identified as a strong predictor of cardiorespiratory fitness in school-aged children, surpassing other common indices [22]. Although direct studies linking RFM to respiratory diseases are limited, its strong correlation with other chronic diseases implies that RFM may serve as a valuable tool for assessing respiratory health, particularly in relation to obesity- related lung function impairment.

The mechanism by which obesity influences asthma is complex, involving both mechanical and biological factors. Obesity directly affects lung volume and increases pulmonary blood volume, further impairing pulmonary function. Beyond these mechanical limitations, obesity exacerbates asthma through chronic low-grade systemic inflammation driven by adipose tissue. This systemic inflammation not only promotes airway hyperresponsiveness but also contributes to airway remodeling [23]. Further research has shown that obese individuals with current asthma often experience more severe inflammation compared to those in remission [24]. Low-grade systemic inflammation is considered a hallmark of chronic asthma, extending beyond the airways to affect other systemic functions [25]. Inflammation plays a critical role in the onset and progression of asthma. Various immune cells, including neutrophils, monocytes, and lymphocytes, contribute to the inflammatory process [26, 27]. In this context, complete blood cell count-derived inflammatory biomarkers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), have been widely used as prognostic markers in respiratory diseases [28,29,30]. Compared to traditional markers like NLR and PLR, SII and SIRI are newer indices that consider multiple blood cell, providing more comprehensive clinical insights. Notably, one study demonstrated that, among these markers, only SII and SIRI exhibited significant differences between asthma and non-asthma patients, highlighting their particular clinical value [31].

Given the limitations of traditional adiposity measures, there has been no research investigating the link between RFM and asthma. We aim to explore the association between RFM and asthma in both children and adults, while examining its predictive value across different asthma statuses in a large U.S. population sample. Additionally, we further investigated the mediating role of SII and SIRI in this relationship, exploring their unique contributions to the pathogenesis of asthma. Finally, we used sensitivity analyses and propensity score methods to test the robustness of our results.

Methods

Study design and population

The data for our study was derived from the National Health and Nutrition Examination Survey (NHANES), which is a continuous, population-based, cross-sectional survey designed to assess the health and nutritional status of the non-institutionalized U.S. civilian population. Conducted by the Centers for Disease Control and Prevention, NHANES collects data biennially through structured interviews, physical examinations and laboratory tests. The survey utilizes a stratified, multi-stage probability sampling design to ensure representative sampling. All survey protocols are reviewed and approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent.

Participants in this study were enrolled from the NHANES cycle spanning 2007 to 2018, which encompassed data on both asthma and RFM for children and adults. Figure 1 illustrates the participant selection process employed in this research. This study initially recruited 59,842 participants from the NHANES 2007–2018 dataset. After excluding 9,679 participants with incomplete asthma questionnaire data and RFM definition, and 4,313 participants missing key blood cell data, 45,850 remained. An additional 31,451 participants with missing multivariable data were excluded. Finally, a total of 14,399 individuals were included in our analysis, among whom 2,228 were diagnosed with asthma.

Fig. 1
figure 1

Flow chart of patient selection

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Asthma classification

The participants' medical conditions were assessed through a self-administered questionnaire. Asthma was diagnosed based on the question: “Has a doctor or other health professional ever told you that you have asthma?”. Participants who answered negatively were categorized as never having asthma (the “never” group), whereas those responding affirmatively were regarded as asthma patients. Additionally, asthma status was further classified according to two subsequent questions:1) “Do you still have asthma?” 2) “During the past 12 months, have you had an episode of asthma or an asthma attack?” Those who responded positively to both questions were classified as having current asthma (the “current asthma” group), while all other participants were classified as ever having asthma (the “ever” group).

RFM

Height and WC were measured by a qualified health professional at the Mobile Examination Center. RFM [32, 33] was calculated based on participants’ age and gender using the following formulas: 1) For participants aged 8 to 14 years: RFM = 74 − (22 × (height/waist)) + (5 × sex). 2) For participants aged over 14: RFM = 64 − (20 × height/waist) + (12 × sex), where sex is coded as 1 for females and 0 for males. Furthermore, RFM values were stratified into three tertiles according to their distribution, with cut-off values defined as follows: Q1 score less than or equal to 30.14, Q2 score between 30.14 and 38.93, and Q3 score greater than 38.93.

Measurement of inflammatory biomarkers

Complete blood cell count measurements on blood specimens were performed using the MECs automated analytical instrument (Beckman Coulter MAXM; Beckman Coulter Inc.). Lymphocyte, neutrophil, monocyte, and platelet counts were reported as × 103 cells/μL. The following formulas were employed to calculate SII [34] and SIRI [35]:

$$\text{SII }= \left(\text{platelet count }\times \text{ neutrophil count}\right) / \text{lymphocyte count};\text{ SIRI }= \left(\text{neutrophil count }\times \text{ monocyte count}\right) / \text{lymphocyte count}.$$

Covariables

In this research, covariables comprised demographic characteristics (age, gender, race and family poverty index ratio (PIR)), family history of asthma among relatives (family history of asthma), cotinine, inflammatory biomarkers (SII and SIRI) and biochemical parameters (Eosinophils, HDL-cholesterol and LDL-cholesterol).

Demographic data were systematically collected using a standardized questionnaire. Age was further classified as minors (age ≤ 18 years), adults (age > 18 to ≤ 60), and elderly people (age > 60 years). Race was classified as Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, or other. PIR [36] was classified as low-income (PIR ≤ 1.30), middle-income (PIR > 1.30 to ≤ 3.50) and high-income (PIR > 3.50). Family history of asthma was determined by the question: “Including living and deceased, were any of your close biological that is, blood relatives including father, mother, sisters or brothers, ever told by a health professional that they had asthma?”. We evaluated the exposure to tobacco smoke by determining the concentration of cotinine in serum, which was determined by isotope dilution-high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry [37]. Cotinine was classified as [38]: cotinine < 0.05 ng/ml, cotinine 0.05—2.99 ng/ml, and cotinine ≥ 3 ng/ml. Biochemical parameters were evaluated following the stringent protocols delineated in the NHANES Laboratory/Medical Technician Procedures Manual.

Statistical analysis

R software (version 4.2.3) was used for data analysis and differences were considered statistically significant at P < 0.05. To enhance the representativeness of the research results, we followed the NHANES official recommended weighted procedures to process the data in this study. Continuous variables adhering to a normal distribution were expressed as mean ± standard deviation (SD), whereas those with a non-normal distribution were described as median and interquartile range [M (Q1, Q3)]; and the comparisons between groups were analyzed by t-test or Kruskal–Wallis H test. Qualitative variables were expressed as number and percentage [n (%)], and the chi-square test (χ2) or Fisher's exact test was employed for the comparisons between groups. To avoid multicollinearity, the variance inflation factor (VIF) was used to assess the covariables. The VIF of all covariables included was less than 5, indicating the absence of severe multicollinearity [39].

The relationship between asthma and RFM was assessed using logistic regression model. For the three tertiles, RFM Q1 was used as the reference group. Three logistic models were constructed for the analyses: Model 1 was a univariable analysis without adjustment for any confounding variables. Model 2 accounted for the main demographic variables (gender, age, race and PIR). Model 3 incorporated all covariables, further integrating family history of asthma, cotinine, SII, SIRI, Eosinophils, HDL-cholesterol and LDL-cholesterol. Additionally, we further explored the relationship between asthma statuses and RFM in the multivariable regression model, employing the same methods as we mentioned above. To visualize more closely the linear or non-linear association between asthma and RFM, curves were fitted using restricted cubic spline (RCS). If it was nonlinear, a threshold effect model was employed to investigate the association and identify the inflection point between RFM and asthma. Stratified analysis was performed in minors and adults.

Subgroup analyses and interaction term tests were performed to explore the consistency and interactions of the prognostic value of RFM across various subgroups. These subgroups were defined based on gender, age, race, PIR, family history of asthma and cotinine. We employed the “mediation” package to conduct mediation analyses, and the Bootstrap method was utilized to evaluate the proportion of the mediating effect attributed to SII and SIRI.

To improve the reliability of our findings, we performed three sensitivity analyses and propensity score matching (PSM). First of all, to address missing data and reduce potential bias, multiple imputation was employed for covariables with less than 20% missing values (family history of asthma, PIR, cotinine, and HDL). For the covariable LDL, which had 61.86% missing data, it was excluded from the analysis due to the high proportion of missing values. The imputation process was conducted using the Multiple Imputation by Chained Equations algorithm with the full conditional specification approach. These data were finally used to assess the association between RFM and asthma. Secondly, considering the predominance of adults in our study population, many of whom present multiple underlying conditions such as hypertension, diabetes, drinking status, cardiovascular diseases [40] or require controller asthma medication use [41], we used 3 models to evaluate the robustness of the findings. Model 4 extended Model 3 by including additional covariates: drinking status, hypertension, diabetes, and cardiovascular diseases. Model 5 further adjusted for controller asthma medication use based on Model 3. Model 6 combined all these variables to comprehensively account for potential confounders within the adult population. Lastly, to address the potential confounding effects of other respiratory diseases, a sensitivity analysis was performed by excluding participants diagnosed with chronic bronchitis and emphysema. Models 4–6 were then re-estimated to validate the consistency and robustness of our findings. Based on a 1:1 nearest neighbor matching algorithm, we used PSM to align the distribution of observed baseline covariables between never-asthma and asthma groups, thereby facilitating an efficient comparison of potential outcomes to assess sensitivity. Absolute Standardized Mean Difference (ASMD) was utilized to assess the balance of covariables before and after PSM. In the matched sample, we employed multivariable logistic regression analyses to evaluate the impact of RFM on asthma. Additionally, mediation analyses were conducted to assess the role of SII and SIRI in the relationship between RFM and asthma.

Results

Demographic characteristics of the study

A total of 14,399 participants were included in the study, with a median age of 42 years (Q1: 27, Q3: 58). The gender distribution was 7,003 males (48%) and 7,396 females (52%). Of the participants, 2,228 had been diagnosed with asthma, while 12,171 had never had asthma. The comparative analysis of participants with and without asthma demonstrated that, with the exception of HDL and LDL, significant differences were observed across all characteristics between the two groups (P < 0.05, Table 1). RFM was significantly higher in the asthma group compared to the non-asthma group (36 [29, 44] vs. 34 [28, 41], P < 0.001). When stratified by RFM tertiles, a significant trend was observed, with higher RFM tertiles being more prevalent among participants with asthma (P < 0.001).

Table 1 The weighted demographic characteristics of study participants by asthma
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Specifically, the proportion of participants in the highest tertile (Q3) was considerably greater in the asthma group (40%) than in the non-asthma group (32%). Additionally, the baseline characteristics of the three groups (never, ever and current asthma group) were outlined in appendix Table 1. RFM was significantly higher in the current asthma group compared to those who never had asthma (P < 0.001). The RFM tertiles showed distinct distributions across asthma status categories. Among participants without asthma, the distribution of RFM tertiles was 34% in Q1, 34% in Q2, and 32% in Q3. For individuals with a history of asthma, 33% were in Q1, 32% in Q2, and 35% in Q3. Notably, in the current asthma group, 19% of participants were in Q1, 29% in Q2, and 52% in Q3, indicating a higher proportion of participants with elevated RFM in the current asthma group (P < 0.001).

Relationship between RFM and asthma

RFM was stratified into three tertiles, and in the logistic regression analyses, a significant association between RFM and asthma was identified (Table 2). Compared to the first tertile of RFM group, OR for asthma in Q2 was 1.08 (95% CI: 0.93–1.25, P = 0.300) in the crude model, 1.25 (95% CI: 1.07–1.46, P = 0.006) in Model 2, and 1.26 (95% CI: 1.07–1.47, P = 0.005) in Model 3. In the Q3, the OR for asthma was 1.46 (95% CI: 1.25–1.69, P < 0.001) in the crude model, 1.88 (95% CI: 1.52–2.33, P < 0.001) in Model 2, and 1.81 (95% CI: 1.43–2.30, P < 0.001) in Model 3. The P value for trend tests across all three models were statistically significant, indicating a trend between RFM and asthma prevalence (P for trend < 0.001).

Table 2 Relationship between RFM and asthma
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For RCS analysis, we used 5 knots to model the relationship between RFM and asthma. The knots were placed at the 5th, 27.5th, 50th, 72.5th, and 95th percentiles of the variable. RCS analysis indicated a non-linear relationship between RFM and asthma (P non-linear = 0.0013; Fig. 2). A threshold effect analysis of RFM on asthma was further conducted using two-piecewise linear regression. As shown in Fig. 2 and Table 3, the inflection point was 34.08. Below this threshold, each unit increase in RFM was positively associated with a 2% rise in asthma prevalence (OR 1.02, 95% CI: 1.00 −1.03, P = 0.035), while above 34.08, the odds increased by 5% per unit increase (OR 1.05, 95% CI: 1.04–1.07, P < 0.001). P for the log-likelihood ratio test < 0.001. In minors, the relationship between RFM and asthma was observed to be linear (P non-linear = 0.6960; Appendix Fig. 1A). The overall association was significant increase in asthma with higher RFM levels (P = 0.0163). In adults, a non-linear association was evident (P non-linear = 0.0071; Appendix Fig. 1B). A threshold effect analysis of RFM on asthma was conducted using two-piecewise linear regression. The inflection point was identified at 35.18 (Appendix Table 2). Below this threshold, each unit increase in RFM was associated with a 2% increase in asthma (OR 1.02, 95% CI: 1.00–1.04, P = 0.033). While above it, the odds of asthma increased by 5% per unit increase in RFM (OR 1.05, 95% CI: 1.03–1.08, P < 0.001).

Fig. 2
figure 2

Restricted cubic spline curve fit between relative fat mass and asthma

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Table 3 Threshold effect analysis of the relationship of RFM with asthma by the two-piecewise linear regression
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We further analyzed the relationship between RFM and asthma across different asthma statuses (Fig. 3). Compared to participants in the never group, those in the ever group showed no significant difference in the crude model. However, after adjusting for confounding factors, there was a significant increase in asthma prevalence in model 2 (OR = 1.03, 95% CI:1.02–1.04, P < 0.001) and model 3 (OR = 1.03, 95% CI:1.02–1.04, P < 0.001). For participants in the current group, all three models showed significant differences; specifically in model 3, each unit increase in RFM corresponded to an 8% increase in the odds of asthma (OR = 1.08, 95% CI:1.07–1.09, P < 0.001). Furthermore, in model 3, compared to the first tertile of RFM, participants in the ever group showed an increased prevalence of asthma in the third tertile (OR = 1.59, 95% CI:1.29–1.96, P < 0.001). In contrast, those in the current group demonstrated an elevated odds of asthma both in the second tertile (OR = 1.41, 95% CI:1.11–1.81, P = 0.006) and in the third tertile (OR = 3.00, 95% CI:2.26–3.98, P < 0.001).

Fig. 3
figure 3

Relationship between RFM and asthma status. A Model 1 was adjusted for none. B Model 2 was adjusted for gender, age, race and PIR. C Model 3 was adjusted for all covariables. OR, odds ratio; CI, confidence interval

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Subgroup analyses

We performed a subgroup analysis of RFM (Fig. 4), categorized by race, gender, age, PIR, family history of asthma and cotinine. RFM was significantly associated with asthma across various subgroups. Specifically, compared to the first tertile of RFM, the possitive association was observed among non-Hispanic black individuals (OR = 1.60, 95% CI:1.16–2.21) and non-Hispanic white individuals (OR = 1.89, 95% CI:1.36–2.62) in the third tertile. Furthermore, RFM was significantly associated with an increased odds of asthma across different genders (Male: OR = 2.13, 95% CI:1.26–3.62; Female: OR = 1.87, 95% CI:1.28–2.71), ages (≤ 18 years: OR = 2.08, 95% CI:1.30–3.33; 18–60 years: OR = 1.82, 95% CI:1.32–2.51), PIR (≤ 1.30: OR = 1.63, 95% CI:1.14–2.34; 1.30–3.50: OR = 1.93, 95% CI:1.26–2.97 and > 3.50: OR = 1.89, 95% CI:1.25–2.85), family history of asthma (No: OR = 1.76, 95% CI:1.31–2.37; Yes: OR = 1.84, 95% CI:1.26–2.68), and cotinine (< 0.05 ng/ml: OR = 1.95, 95% CI:1.36–2.81; 0.05–2.99 ng/ml: OR = 1.84, 95% CI:1.12–3.03) in the third tertile. No significant interactions between RFM and asthma were observed in the subgroups (all P for interaction > 0.05).

Fig. 4
figure 4

Forest plots of relative fat mass for asthma in different subgroups. PIR: Family poverty index ratio, OR: Odds ratio, CI (confidence interval)

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Mediation analyses

SII and SIRI partially mediated the association between RFM and asthma, accounting for approximately 7.48% of the total effect in SII and 3.88% in SIRI (Fig. 5 A, D; P < 0.05). Additionally, we further explored the mediating effects across different populations. The mediating effects remained significant in adults (Fig. 5 C, F; SII: 6.94%, P < 0.001; SIRI: 2.86%, P = 0.003), while it was not significant in minors (Fig. 5 B, E; SII: P = 0.875; SIRI: P = 0.922).

Fig. 5
figure 5

Path diagram of the mediation analysis of inflammatory biomarkers (SII and SIRI) on the relationship between relative fat mass and asthma. A the mediating role of SII in the whole population. B the mediating role of SII in minors. C the mediating role of SII in adults. D the mediating role of SIRI in the whole population. E the mediating role of SIRI in minors. F the mediating role of SIRI in adults

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Sensitivity analyses and PSM

After applying multiple imputation to handle missing data, we analyzed the dataset using three logistic regression models. RFM remained significantly associated with asthma (Appendix Table 3). Meanwhile, RFM showed a consistent association with asthma statuses (Appendix Fig. 2), with a stronger association observed in individuals with current asthma compared to those with a history of asthma. For adults, we further adjusted for drinking status, chronic conditions (hypertension, diabetes, and cardiovascular diseases), and controller asthma medication use. The results consistently demonstrated a significant association between RFM and asthma (Appendix Table 4). Additionally, in sensitivity analyses excluding adults diagnosed with chronic bronchitis and emphysema, the adjusted models yielded similar findings, further supporting the robustness of the association between RFM and asthma (Appendix Table 5). PSM was performed with all covariables included, which effectively rectified the imbalance in the matched sample groups, and the distribution of propensity scores became more balanced between the asthma and non-asthma cohorts. Absolute standardized differences are illustrated in appendix Fig. 3, and the effect size indice for all covariables remained at 0.1. In the matched sample, apart from the exposed variable RFM, no significant intergroup differences were observed in any other variables that had previously presented differences (Appendix Table 6). The relationship between RFM and asthma (Appendix Table 7), along with the mediating effects of SII and SIRI (Appendix Fig. 4) in the association between RFM and asthma, yielded consistent results. After adjusting for all covariables and compared to individuals in the first tertile of RFM, those in the second and third tertiles exhibited higher odds of asthma, with an OR of 1.32 (95% CI:1.06–1.66, P = 0.015) for the second tertile and 2.00 (95% CI:1.45–2.74, P < 0.001). The trend across all three models was statistically significant (P for trend < 0.05). The mediating effects remained significant (Appendix Fig. 4A, D; SII: 7.48%, P = 0.012; SIRI: 3.88%, P < 0.001).

Discussion

In this study, we observed a significant association between RFM and asthma, with higher RFM was consistently linked to an increased prevalence of asthma. This relationship was evident both in continuous and categorical analyses, with an inflection point identified at a RFM of 34.08. Subgroup analyses further validated the robustness of this association across various factors, including sex, age, race, PIR, family history of asthma and smoking exposure. Stronger associations were notably observed in current asthma patients than in ever had asthma ones. Furthermore, mediation analyses indicated that SII and SIRI partially mediated the relationship between RFM and asthma. These findings remained robust in sensitivity analyses and adjusting for confounding bias through PSM. To our knowledge, this is the first study to explore the relationship between RFM and asthma in a representative US population.

Previous research has consistently shown that obesity is a risk factor for asthma, but the metric for assessing obesity in asthma research remains under debate. Traditional measures such as BMI have limitations, as they fail to account for fat distribution. Emerging researches suggests that fat distribution in specific body regions, particularly the upper body, may have a more significant effect on lung function and asthma outcomes compared to lower body fat [42,43,44,45]. Therefore, new indicators, waist-to-hip ratio and WC, have demonstrated a more closely associated with asthma [46, 47]. RFM may also be a reliable indicator of the relationship between obesity and asthma. Firstly, it incorporates both height and WC, allowing for a more accurate assessment of health risks in individuals with the same weight but different body proportions. For clinical practitioners, RFM can identify individuals with lower stature but significant abdominal fat accumulation, who are often overlooked when relying solely on BMI. Secondly, RFM as a simple, non-invasive, and cost-effective measurement approach that can be effortlessly incorporated into conventional clinical practice for the identification of individuals with higher odds of asthma, and is easy to understand by the public. Our research indicates that RFM is not merely associated with asthma but also demonstrates a pronounced threshold effect. The threshold of RFM furnishes clinicians with a distinct intervention target, enabling the implementation of targeted intervention measures to achieve optimal physical condition. This is particularly important for individuals exceeding this threshold, given that asthma prevalence escalates significantly with each unit increase in RFM. Finally, RFM has demonstrated excellent suitability across diverse populations. Research indicates that, apart from the adult population, RFM provides superior accuracy in estimating the percentage of total body fat. In adolescents, its error rate in classifying overweight and obesity is significantly lower than BMI [32]. Our analyses also provided further insight into the complexity of the obesity-asthma relationship. The observed differences in asthma prevalence based on asthma status suggested that obesity were associated with a poorer control of asthma symptoms [48, 49], underscoring the necessity for personalized strategies in asthma management tailored to disease status.

In our study, the mediating role of systemic inflammatory markers such as SII and SIRI provided further support for the hypothesis that inflammation is a critical pathway linking obesity and asthma, which was consistent with growing evidences [50]. For asthmatic patients with obesity, the level of neutrophils in sputum increases significantly, thereby resulting in more severe airway inflammation [51]. Additionally, the level of IL-17A in the sputum of these patients is also higher than that of lean patients [52]. This cytokine, IL-17A, plays a crucial role in the recruitment of neutrophils, and airway hyperresponsiveness was not observed in the absence of this factor [53]. As we delved deeper into our research, we observed that SII and SIRI significantly mediate the obesity-asthma relationship in adults but not in younger populations. This disparity may arise from differences in immune system maturity and inflammatory response mechanisms between adults and children. In younger individuals, the immune system is still maturing, making it less responsive to obesity-induced chronic inflammation [54, 55]. As a result, children may exhibit a different inflammatory profile compared to adults, with potential variations in the types of immune cells involved. While adults typically show a stronger inflammatory response characterized by increased neutrophil accumulation in the airways, children are more likely to have eosinophil-driven airway inflammation, which could explain why SII and SIRI do not mediate the obesity-asthma relationship in the pediatric population [56]. Furthermore, the response to treatment varies across age groups, suggesting that asthma's underlying mechanisms differ between adults and children [57]. In younger populations, asthma may be more influenced by local allergic responses rather than the systemic inflammation marker. This distinction underscores the importance of age-specific asthma management strategies. A systematic survey of obesity-related biomarkers showed that IL-6 and adiponectin are potential biological mediators linking obesity and asthma in children [58]. These biomarkers, along with others, could provide insights into how obesity affects asthma in the pediatric population, where immune system maturation and inflammatory mechanisms differ from those seen in adults. This age-related difference highlights the need for further research to better understand the specific inflammatory pathways in children with asthma and obesity. Given the unique physiological characteristics of the pediatric population, more targeted interventions focusing on childhood obesity-related biomarkers and localized airway inflammation are necessary to optimize asthma treatment in this group.

Based on these findings, we proposed the following practical significance: Early identification of individuals with elevated RFM provides a scientifically sound basis for targeted weight management and anti-inflammatory strategies, particularly in high-risk groups. The identified threshold of RFM (34.08) marks a critical point where the relationship between RFM and asthma significantly intensifies. The sharp increase underscores the importance of this threshold as a clinical marker for identifying individuals at heightened odds of asthma exacerbations and more severe disease progression. For those exceeding the RFM threshold, personalized interventions such as dietary modifications and increased physical activity can be particularly beneficial, as these strategies not only address obesity but also help reduce inflammation, which is closely linked to asthma exacerbations. Additionally, incorporating RFM into community screening programs can serve as an effective tool for early detection of high-risk individuals. By using RFM as an accessible and simple measure, healthcare professionals can identify those who may benefit from early interventions, raising public awareness about the connection between obesity and asthma, and ultimately improving health outcomes. Furthermore, the threshold could be considered a key marker in preventive asthma care, potentially guiding clinical decision-making on when to initiate more aggressive interventions. Future research should explore RFM’s applicability across different populations and investigate its long-term impact on asthma prevention. As well as its combined effect with other biomarkers of inflammation, to refine personalized management strategies.

Our study has several strengths. Firstly, we used RFM as an indicator of obesity, compared to traditional measures (such as BMI), which do not fully consider the important factor of fat distribution in asthma. By incorporating both height and WC, RFM provides a more accurate measure of obesity-related asthma prevalence, particularly in identifying individuals with central obesity. Secondly, we used a large-scale and representative sample of the US population and employs sensitivity analyses and robust statistical methods PSM, to adjust for potential confounders. This enhances the generalizability and reliability of the findings. Thirdly, identifying an RFM threshold associated with higher asthma odds provides clinicians with a clear, actionable target for intervention, aiding in the development of personalized asthma management strategies. Additionally, the comprehensive subgroup analyses shed light on the stronger associations in specific populations, offering valuable insights into population-specific asthma prevalence.

Meanwhile, there are limitations to consider. As the study is cross-sectional in design, it is limited in establishing causality between RFM and asthma, and longitudinal cohort studies are required to confirm this relationship in future. Additionally, while systemic inflammatory markers such as SII and SIRI were examined as mediators, they only captured part of the complex inflammatory processes involved in asthma. Other important mechanisms may be involved. Future research should consider investigating additional factors, such as metabolic syndrome or localized lung inflammation, to further explore the potential mechanisms linking RFM to asthma. Furthermore, as the study sample was primarily based on the U.S. population, the findings may not be fully generalizable to other ethnic groups or countries. Future research should consider including more diverse populations to assess whether the observed associations between RFM and asthma hold across different ethnicities and healthcare settings. Finally, further research is needed to explore the relationship between RFM and asthma in children and adolescents, as the underlying mechanisms may differ across age groups.

Conclusion

RFM was positively associated with asthma, with an inflection point identified at 34.08. Stronger associations were observed in current asthma patients. SII and SIRI were identified as partial mediators of this relationship, emphasizing the role of inflammation in asthma for obese individuals. Clinically, RFM as a more precise and actionable metric, providing a clear target for early intervention strategies like weight management and anti-inflammatory treatments. These findings highlight RFM's potential for broader clinical use in routine practice, helping healthcare providers identify individuals with higher asthma odds and tailor personalized management, especially in high-risk populations.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

RFM:

Relative Fat Mass

SII:

Systemic Immune-Inflammation Index

SIRI:

Systemic Inflammation Response Index

PSM:

Propensity score matching

BMI:

Body Mass Index

DXA:

Dual-energy X-ray absorptiometry

WC:

Waist Circumference

NLR:

Neutrophil to lymphocyte ratio

PLR:

Platelet to lymphocyte ratio (PLR)

NHANES:

National Health and Nutrition Examination Survey

PIR:

Family poverty index ratio

 VIF:

Variance Inflation Factor

RCS:

Restricted cubic spline

ASMD:

Absolute Standardized Mean Difference

OR:

Odds ratio

CI:

Confidence interval

References

  1. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med 2020, 8:585–596.

  2. Baffi CW, Winnica DE, Holguin F. Asthma and obesity: mechanisms and clinical implications. Asthma Res Pract. 2015;1:1.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mebrahtu TF, Feltbower RG, Greenwood DC, Parslow RC. Childhood body mass index and wheezing disorders: a systematic review and meta-analysis. Pediatr Allergy Immunol. 2015;26:62–72.

    Article  PubMed  Google Scholar 

  4. Ho WC, Lin YS, Caffrey JL, Lin MH, Hsu HT, Myers L, Chen PC, Lin RS. Higher body mass index may induce asthma among adolescents with pre-asthmatic symptoms: a prospective cohort study. BMC Public Health. 2011;11:542.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Flaherman V, Rutherford GW. A meta-analysis of the effect of high weight on asthma. Arch Dis Child. 2006;91:334–9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Peters U, Dixon AE, Forno E. Obesity and asthma. J Allergy Clin Immunol. 2018;141:1169–79.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Vortmann M, Eisner MD. BMI and health status among adults with asthma. Obesity (Silver Spring). 2008;16:146–52.

    Article  PubMed  Google Scholar 

  8. Dixon AE, Holguin F, Sood A, Salome CM, Pratley RE, Beuther DA, Celedón JC, Shore SA. An official American Thoracic Society Workshop report: obesity and asthma. Proc Am Thorac Soc. 2010;7:325–35.

    Article  PubMed  Google Scholar 

  9. Ma J, Strub P, Xiao L, Lavori PW, Camargo CA Jr, Wilson SR, Gardner CD, Buist AS, Haskell WL, Lv N. Behavioral weight loss and physical activity intervention in obese adults with asthma. A randomized trial. Ann Am Thorac Soc. 2015;12:1–11.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Willeboordse M, van de Kant KDG, Tan FE, Mulkens S, Schellings J, Crijns Y, Ploeg L, van Schayck CP, Dompeling E. A multifactorial weight reduction programme for children with overweight and asthma: a randomized controlled trial. PLoS ONE. 2016;11:e0157158.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444:881–7.

    Article  PubMed  Google Scholar 

  12. Wehrmeister FC, Menezes AM, Muniz LC, Martínez-Mesa J, Domingues MR, Horta BL. Waist circumference and pulmonary function: a systematic review and meta-analysis. Syst Rev. 2012;1:55.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, Lopez-Jimenez F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes (Lond). 2010;34:791–9.

    Article  PubMed  Google Scholar 

  14. Javed A, Jumean M, Murad MH, Okorodudu D, Kumar S, Somers VK, Sochor O, Lopez-Jimenez F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10:234–44.

    Article  PubMed  Google Scholar 

  15. Forno E, Han YY, Libman IM, Muzumdar RH, Celedón JC. Adiposity and asthma in a nationwide study of children and adults in the United States. Ann Am Thorac Soc. 2018;15:322–30.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Suthahar N, Wang K, Zwartkruis VW, Bakker SJL, Inzucchi SE, Meems LMG, Eijgenraam TR, Ahmadizar F, Sijbrands EG, Gansevoort RT, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. Eur J Intern Med. 2023;109:73–8.

    Article  PubMed  Google Scholar 

  17. Shen W, Cai L, Wang B, Wang Y, Wang N, Lu Y. Associations of relative fat mass, a novel adiposity indicator, with non-alcoholic fatty liver disease and cardiovascular disease: data from SPECT-China. Diabetes Metab Syndr Obes. 2023;16:2377–87.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zwartkruis VW, Suthahar N, Idema DL, Mahmoud B, van Deutekom C, Rutten FH, van der Schouw YT, Rienstra M, de Boer RA. Relative fat mass and prediction of incident atrial fibrillation, heart failure and coronary artery disease in the general population. Int J Obes (Lond). 2023;47:1256–62.

    Article  PubMed  Google Scholar 

  19. Cacciatore S, Calvani R, Marzetti E, Coelho-Júnior HJ, Picca A, Fratta AE, Esposito I, Tosato M, Landi F. Predictive values of relative fat mass and body mass index on cardiovascular health in community-dwelling older adults: Results from the Longevity Check-up (Lookup) 7. Maturitas. 2024;185:108011.

    Article  PubMed  Google Scholar 

  20. Cichosz SL, Rasmussen NH, Vestergaard P, Hejlesen O. Is predicted body-composition and relative fat mass an alternative to body-mass index and waist circumference for disease risk estimation? Diabetes Metab Syndr. 2022;16:102590.

    Article  PubMed  Google Scholar 

  21. Macek P, Biskup M, Terek-Derszniak M, Krol H, Smok-Kalwat J, Gozdz S, Zak M. Optimal cut-off values for anthropometric measures of obesity in screening for cardiometabolic disorders in adults. Sci Rep. 2020;10:11253.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zadarko-Domaradzka M, Sobolewski M, Zadarko E. Comparison of several anthropometric indices related to body fat in predicting cardiorespiratory fitness in school-aged children-a single-center cross-sectional study. J Clin Med. 2023;12:6226.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Jiang D, Wang L, Bai C, Chen O. Association between abdominal obesity and asthma: a meta-analysis. Allergy Asthma Clin Immunol. 2019;15:16.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ruck L, Wiegand S, Kühnen P. Relevance and consequence of chronic inflammation for obesity development. Mol Cell Pediatr. 2023;10:16.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cheng W, Bu X, Xu C, Wen G, Kong F, Pan H, Yang S, Chen S. Higher systemic immune-inflammation index and systemic inflammation response index levels are associated with stroke prevalence in the asthmatic population: a cross-sectional analysis of the NHANES 1999–2018. Front Immunol. 2023;14:1191130.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zheng J, Zhang X, Zhang L, Zhang HP, Wang L, Wang G. Interactive effects between obesity and atopy on inflammation: a pilot study for asthma phenotypic overlap. Ann Allergy Asthma Immunol. 2016;117:716–7.

    Article  PubMed  Google Scholar 

  27. Burke LA, Wilkinson JR, Howell CJ, Lee TH. Interactions of macrophages and monocytes with granulocytes in asthma. Eur Respir J Suppl. 1991;13:85s–90s.

    PubMed  Google Scholar 

  28. Benz E, Wijnant SRA, Trajanoska K, Arinze JT, de Roos EW, de Ridder M, Williams R, van Rooij F, Verhamme KMC, Ikram MA, et al. Sarcopenia, systemic immune-inflammation index and all-cause mortality in middle-aged and older people with COPD and asthma: a population-based study. ERJ Open Res. 2022;8:00628–2021.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ghobadi H, Mohammadshahi J, Javaheri N, Fouladi N, Mirzazadeh Y, Aslani MR. Role of leukocytes and systemic inflammation indexes (NLR, PLR, MLP, dNLR, NLPR, AISI, SIR-I, and SII) on admission predicts in-hospital mortality in non-elderly and elderly COVID-19 patients. Front Med (Lausanne). 2022;9:916453.

    Article  PubMed  Google Scholar 

  30. Shen Q, Mu X, Bao Y, Xu F, Zhang D, Luo A, Liu L, Huang H, Xu Y. An S-like curve relationship between systemic inflammation response index (SIRI) and respiratory failure in GBS patients. Neurol Sci. 2023;44:3279–85.

    Article  PubMed  Google Scholar 

  31. Ke J, Qiu F, Fan W, Wei S. Associations of complete blood cell count-derived inflammatory biomarkers with asthma and mortality in adults: a population-based study. Front Immunol. 2023;14:1205687.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Woolcott OO, Bergman RN. Relative Fat Mass as an estimator of whole-body fat percentage among children and adolescents: a cross-sectional study using NHANES. Sci Rep. 2019;9:15279.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage ─ A cross-sectional study in American adult individuals. Sci Rep. 2018;8:10980.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, Zhang X, Wang WM, Qiu SJ, Zhou J, Fan J. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20:6212–22.

    Article  PubMed  Google Scholar 

  35. Qi Q, Zhuang L, Shen Y, Geng Y, Yu S, Chen H, Liu L, Meng Z, Wang P, Chen Z. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122:2158–67.

    Article  PubMed  Google Scholar 

  36. Zheng D, Zhao C, Ma K, Ruan Z, Zhou H, Wu H, Lu F. Association between visceral adiposity index and risk of diabetes and prediabetes: results from the NHANES (1999–2018). PLoS ONE. 2024;19:e0299285.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bernert JT Jr, Turner WE, Pirkle JL, Sosnoff CS, Akins JR, Waldrep MK, Ann Q, Covey TR, Whitfield WE, Gunter EW, et al. Development and validation of sensitive method for determination of serum cotinine in smokers and nonsmokers by liquid chromatography/atmospheric pressure ionization tandem mass spectrometry. Clin Chem. 1997;43:2281–91.

    Article  PubMed  Google Scholar 

  38. Hou W, Chen S, Zhu C, Gu Y, Zhu L, Zhou Z. Associations between smoke exposure and osteoporosis or osteopenia in a US NHANES population of elderly individuals. Front Endocrinol (Lausanne). 2023;14:1074574.

    Article  PubMed  Google Scholar 

  39. Liu M, Zhang Z, Zhou C, Ye Z, He P, Zhang Y, Li H, Liu C, Qin X. Predicted fat mass and lean mass in relation to all-cause and cause-specific mortality. J Cachexia Sarcopenia Muscle. 2022;13:1064–75.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, Liu L, Ming Z, Tao X, Li Y. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23:8.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Uong SP, Hussain H, Thanik E, Lovinsky-Desir S, Stingone JA. Urinary metabolites of polycyclic aromatic hydrocarbons and short-acting beta agonist or systemic corticosteroid asthma medication use within NHANES. Environ Res. 2023;220:115150.

    Article  PubMed  Google Scholar 

  42. Lindgren M, Toska T, Alex C, Lundberg CE, Cronie O, Rosengren A, Adiels M, Sjöland H. BMI, sex and outcomes in hospitalised patients in western Sweden during the COVID-19 pandemic. Sci Rep. 2022;12:4918.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hagenburg J, Bertin E, Salmon JH, Thierry A, Perotin JM, Dormoy V, Dury S, Gaubil I, Bolko L, Lebargy F, et al. Association between obesity-related dyspnea in daily living, lung function and body composition analyzed by DXA: a prospective study of 130 patients. BMC Pulm Med. 2022;22:103.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Vangeepuram N, Teitelbaum SL, Galvez MP, Brenner B, Doucette J, Wolff MS. Measures of obesity associated with asthma diagnosis in ethnic minority children. J Obes. 2011;2011:517417.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Leone N, Courbon D, Thomas F, Bean K, Jégo B, Leynaert B, Guize L, Zureik M. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med. 2009;179:509–16.

    Article  PubMed  Google Scholar 

  46. Forno E, Acosta-Pérez E, Brehm JM, Han YY, Alvarez M, Colón-Semidey A, Canino G, Celedón JC. Obesity and adiposity indicators, asthma, and atopy in Puerto Rican children. J Allergy Clin Immunol. 2014;133(1308–1314):1314.e1301-1305.

    Google Scholar 

  47. Alwadeai KS, Alhammad SA. Asthma and its relationship with anthropometric markers among adults. PLoS ONE. 2023;18:e0281923.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Maalej S, Yaacoub Z, Fakhfekh R, Yaalaoui S, Kheder AB, Drira I. Association of obesity with asthma severity, control and quality of life. Tanaffos. 2012;11:38–43.

    PubMed  PubMed Central  Google Scholar 

  49. Reyes-Angel J, Kaviany P, Rastogi D, Forno E. Obesity-related asthma in children and adolescents. Lancet Child Adolesc Health. 2022;6:713–24.

    Article  PubMed  Google Scholar 

  50. Shiina S, Maruyama H, Tobari M, Yamashita T. Obesity and non-alcoholic steatohepatitis in immunotherapy for hepatocellular carcinoma. Hepatol Int. 2023;17:827–9.

    Article  PubMed  Google Scholar 

  51. Scott HA, Gibson PG, Garg ML, Wood LG. Airway inflammation is augmented by obesity and fatty acids in asthma. Eur Respir J. 2011;38:594–602.

    Article  PubMed  Google Scholar 

  52. Marijsse GS, Seys SF, Schelpe AS, Dilissen E, Goeminne P, Dupont LJ, Ceuppens JL, Bullens DM. Obese individuals with asthma preferentially have a high IL-5/IL-17A/IL-25 sputum inflammatory pattern. Am J Respir Crit Care Med. 2014;189:1284–5.

    Article  PubMed  Google Scholar 

  53. Kim HY, Lee HJ, Chang YJ, Pichavant M, Shore SA, Fitzgerald KA, Iwakura Y, Israel E, Bolger K, Faul J, et al. Interleukin-17-producing innate lymphoid cells and the NLRP3 inflammasome facilitate obesity-associated airway hyperreactivity. Nat Med. 2014;20:54–61.

    Article  PubMed  Google Scholar 

  54. Polinski KJ, Bell GA, Trinh MH, Sundaram R, Mendola P, Robinson SL, Bell EM, Adeyeye T, Lin TC, Yeung EH. Maternal obesity, gestational weight gain, and offspring asthma and atopy. Ann Allergy Asthma Immunol. 2022;129:199-204.e193.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Guo J, Zhu W, Wang H, Holt PG, Zhang G, Liu C. Risk factors and prognosis of recurrent wheezing in Chinese young children: a prospective cohort study. Allergy Asthma Clin Immunol. 2019;15:38.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Licari A, Manti S, Castagnoli R, Leonardi S, Marseglia GL. Measuring inflammation in paediatric severe asthma: biomarkers in clinical practice. Breathe (Sheff). 2020;16:190301.

    Article  PubMed  Google Scholar 

  57. Djukanovic R. Airway inflammation in asthma and its consequences: implications for treatment in children and adults. J Allergy Clin Immunol. 2002;109:S539-548.

    Article  PubMed  Google Scholar 

  58. Han X, Zhu Z, Xiao Q, Li J, Hong X, Wang X, Hasegawa K, Camargo CA Jr, Liang L. Obesity-related biomarkers underlie a shared genetic architecture between childhood body mass index and childhood asthma. Commun Biol. 2022;5:1098.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the department of Science and Technology of Sichuan Province (2023ZYD0122 and 2023NSFSC0579), and the National Natural Science Foundation of China (82100590 and 82241036).

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Authors and Affiliations

  1. Department of Pediatrics, West China Second University Hospital, Sichuan University, No. 20, Section 3, South Renmin Road, Chengdu, Sichuan Province, 610041, China

    Meicen Zhou, Ting Zhang, Ziyi Zeng, Shuqin Zeng, Shaopu Wang & Hua Wang

  2. Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Sichuan University, Chengdu, Sichuan Province, 610041, China

    Meicen Zhou, Ting Zhang, Ziyi Zeng, Shuqin Zeng, Shaopu Wang & Hua Wang

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Contributions

The authors contributed to the paper as follows: Conception and design: MC Zhou, SP Wang, H Wang. Methodology: MC Zhou, T Zhang, ZY Zeng, SQ Zeng. Data curation: MC Zhou. Formal analysis: MC Zhou, T Zhang, ZY Zeng, SQ Zeng. Original draft: MC Zhou. Critical revision of manuscript: MC Zhou, T Zhang, ZY Zeng, SQ Zeng, SP Wang, H Wang. All authors reviewed the results and approved the final version of the manuscript..

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Supplementary Information

12944_2024_2428_MOESM1_ESM.docx

Supplementary Material 1: Appendix Figure 1. Restricted cubic spline curve fit between relative fat mass and asthma among different population. (A) For participants≤18. (B) For participants>18. Appendix Figure 2. Relationship between RFM and asthma status after multiple imputation for sensitivity analysis. (A) Model 1 was adjusted for none. (B) Model 2 was adjusted for gender, age, race and PIR. (C) Model 3 was adjusted for all covariables. OR, odds ratio; CI, confidence interval. Appendix Figure 3. Absolute standardized differences for baseline covariables comparing treated to untreated subjects in the original and the matched sample. Appendix Figure 4. Path diagram of the mediation analysis of inflammatory biomarkers (SII and SIRI) on the relationship between relative fat mass and asthma after Propensity score matching. (A) the mediating role of SII in the whole population. (B) the mediating role of SII in minors. (C) the mediating role of SII in adults. (D) the mediating role of SIRI in the whole population. (E) the mediating role of SIRI in minors. (F) the mediating role of SIRI in adults. Appendix Table 1. The weighted demographic characteristics of study participants by asthma status. Appendix Table 2. Threshold effect analysis of the relationship of RFM with asthma over 18 years old by the two-piecewise linear regression. Appendix Table 3. Relationship between RFM and asthma after multiple imputation for sensitivity analysis. Appendix Table 4. Relationship between RFM and asthma incorporating drinking status, chronic conditions, and medication use in adults. Appendix Table 5. Relationship between RFM and asthma after excluding participants with chronic bronchitis and emphysema in adults. Appendix Table 6. The weighted demographic characteristics of study participants by asthma after propensity score matching. Appendix Table 7. Relationship between RFM and asthma after propensity score matching

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Zhou, M., Zhang, T., Zeng, Z. et al. Association of relative fat mass with asthma: inflammatory markers as potential mediators. Lipids Health Dis 24, 13 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02428-y

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Lipids in Health and Disease

ISSN: 1476-511X