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. 2023 Sep 4:14:1201160.
doi: 10.3389/fimmu.2023.1201160. eCollection 2023.

Relationship between serum iron and blood eosinophil counts in asthmatic adults: data from NHANES 2011-2018

Jun Wen  1 , Changfen Wang  2 , Jing Xia  3 , Mohan Giri  1 , Shuliang Guo  1
Affiliations

Relationship between serum iron and blood eosinophil counts in asthmatic adults: data from NHANES 2011-2018

Jun Wen et al. Front Immunol. .

Abstract

Background: So far, quite a few studies have revealed that systemic iron levels are related to asthmatic inflammatory reactions. And most studies have focused on the correlation between systemic iron levels and asthma, with inconsistent findings. Yet, few studies have investigated the connection between serum iron and blood eosinophil counts. Hence, we have explored the connection between serum iron and blood eosinophil counts in asthmatics by utilizing data from NHANES.

Methods: A total of 2549 individuals were included in our study after screening NHANES participants from 2011 to 2018. The linear regression model and XGBoost model were used to discuss the potential connection. Linear or nonlinear association was further confirmed by the generalized additive model and the piecewise linear regression model. And we also performed stratified analyses to figure out specific populations.

Results: In the multivariable linear regression models, we discovered that serum iron levels were inversely related to blood eosinophil counts in asthmatic adults. Simultaneously, we found that for every unit increase in serum iron (umol/L), blood eosinophil counts reduced by 1.41/uL in model 3, which adjusted for all variables excluding the analyzed variables. Furthermore, the XGBoost model of machine learning was applied to assess the relative importance of chosen variables, and it was determined that vitamin C intake, age, vitamin B12 intake, iron intake, and serum iron were the five most important variables on blood eosinophil counts. And the generalized additive model and piecewise linear regression model further verify this linear and inverse association.

Conclusion: Our investigation discovered that the linear and inverse association of serum iron with blood eosinophil counts in asthmatic adults, indicating that serum iron might be related to changes in the immunological state of asthmatics. Our work offers some new thoughts for next research on asthma management and therapy. Ultimately, we hope that more individuals become aware of the role of iron in the onset, development, and treatment of asthma.

Keywords: National Health And Nutrition Examination Survey (NHANES); XGBoost; asthma; eosinophil; machine learning; serum iron.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart for choosing asthmatic subjects.
Figure 2
Figure 2
Stratified correlation of serum iron with blood eosinophil counts. In any of subgroups, the model adjusted sex, age, race, educational background, marital state, poverty to income ratio, body mass index, smoking state, alcohol intake, vitamin A intake, vitamin B12 intake, vitamin C intake, folate intake, iron intake, hypertension history, diabetes history, and steroid drugs use except for the stratification variable.
Figure 3
Figure 3
The XGBoost algorithm determines the relative importance of each variable on blood eosinophil counts and assigns a variable importance score to each variable. The X-axis represents the importance score, which is the relative importance of variables used to distribute the data; the Y-axis represents the variables chosen. PIR, poverty to income ratio; Vit, Vitamin.
Figure 4
Figure 4
Dose-response connection of serum iron levels with blood eosinophil counts based on model 3. (A) The red solid line is the smooth fitting curve between serum iron levels and blood eosinophil counts, while the blue dashed line reflects the 95% confidence intervals of the fitting. (B) Each red dot represents a separate sample, and the blue dots above and below the red dot represent 95% confidence intervals.
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