Serum level of periostin and interleukin-38 in atopic asthma and their association with eosinophilic airway inflammatory response

Background Bronchial asthma is a prevalent long-term respiratory disorder that impacts the airways. Periostin and interleukin-38 (IL-38) are crucial in the pathogenesis of atopic asthma, serving as important biomarkers of the eosinophilic inflammatory pathway. Aim To determine serum periostin and IL-38 levels in atopic asthma patients and find their correlation with different asthma severity categories, asthma control, and eosinophilic inflammation. Patients and methods This case–control study involved 41 atopic asthmatic patients and 41 healthy controls matched by age and sex. Serum levels of total immunoglobulin E, periostin, and IL-38 were determined for both case and control groups using an enzyme-linked immunosorbent assay. Results Patients with atopic asthma had statistically significantly higher serum levels of periostin and IL-38 than healthy controls ( P <0.001). Serum periostin and IL-38 levels varied statistically significantly ( P <0.001) among the various asthma severity categories. In patients with atopic asthma, peripheral blood eosinophil absolute count and serum periostin levels had a positive correlation (R=0.44, P =0.004), although it had a negative correlation with IL-38 levels (R=−0.86, P <0.001). With areas under the curve of 0.77 and 0.88, the ideal cutoff values for serum periostin and IL-38 as diagnostic biomarkers for asthma with eosinophilia were greater than 101.2 ng/ml and less than 408.3 pg/ml, respectively. Conclusion Periostin could be used as a marker for diagnosis of asthma with eosinophilia as well as patient selection for new asthma treatment options that target T helper 2 inflammation. IL-38 might be a candidate biomarker as well as a target of biological therapy of severe asthma with eosinophilia. This study had some limitations including the single center nature without expanded geographical residence of the study population, the presence of some confounding factors such as medication use which might have some influence on the study results, as well as lack of longitudinal data and changes in biomarker levels over time or in response to treatment.