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Background <p>Clinical features influence cytokine profiles and can inform biomarker studies.</p> Objectives <p>We assessed the impact of 13 preselected patient characteristics on the circulating levels of 15 Th-1/2/17 cytokines in moderate-to-severe asthma patients on omalizumab, anti-IL-5 (mepolizumab, benralizumab), or dupilumab (<i>n</i> = 76) versus controls (<i>n</i> = 162) not yet on biologics but meeting eligibility criteria for a T2-biologic.</p> Methods <p>Plasma cytokines (Olink) were analyzed for associations with these clinical/lifestyle factors using LASSO regression and observed variance explained estimated using generalized linear models. Differential expression analysis was conducted using limma.</p> Results <p>In controls, IL-6 had the highest variance explained by clinical/lifestyle factors (50% in non-allergic rhinitis patients, 22% in allergic rhinitis), with BMI and exacerbations contributing most to this. In T2-biologics users, eotaxin-1 had the highest explained variance (26.0%) and smoking was the most linked to Th1/17 cytokines. In omalizumab users: IFN-γ (51%) was most explained (exacerbations, smoking, age). In anti-IL-5 users, eotaxin-1 (58%; BMI, sex) and in dupilumab users, IL-4 (83%) was most explained (exacerbations, sex, BMI). The association between patient characteristics and cytokine levels differed by the season of sample collection. In non-biologic users, IL-6 was the cytokine with the most explained variance in the Winter (asthma admissions accounted for most of this variance) and IL-18 in the Spring/Summer/Fall. In T2-biologic users, TNF-α was the top cytokine in the Winter (smoking accounted for most of this variance); IL-4 (allergic rhinitis), IL-33 (IgE and eosinophil), and CXCL10 (allergic rhinitis and IgE) were the top cytokines in the Spring/Summer/Fall. In differential expression analyses, IL-1β was lower in biologics users than non-biologics users.</p> Conclusions <p>In moderate-to-severe asthma, multiple clinical features and season are associated with cytokine levels and might impact inference from proteomics studies. Smoking and BMI are the key proinflammatory factors in biologics-treated and untreated patients.</p>

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Impact of clinical factors and season on inflammatory cytokines in biologic-treated and untreated asthma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Tanawin Nopsopon,
  • Javier Cabrera-Perez,
  • Pui Y. Lee,
  • Kailey E. Brodeur,
  • Njira L. Lugogo,
  • Evan E. Hsu,
  • Courtney LeSon,
  • Georg Hahn,
  • Steven A. Carr,
  • Scott T. Weiss,
  • Ayobami Akenroye

摘要

Background

Clinical features influence cytokine profiles and can inform biomarker studies.

Objectives

We assessed the impact of 13 preselected patient characteristics on the circulating levels of 15 Th-1/2/17 cytokines in moderate-to-severe asthma patients on omalizumab, anti-IL-5 (mepolizumab, benralizumab), or dupilumab (n = 76) versus controls (n = 162) not yet on biologics but meeting eligibility criteria for a T2-biologic.

Methods

Plasma cytokines (Olink) were analyzed for associations with these clinical/lifestyle factors using LASSO regression and observed variance explained estimated using generalized linear models. Differential expression analysis was conducted using limma.

Results

In controls, IL-6 had the highest variance explained by clinical/lifestyle factors (50% in non-allergic rhinitis patients, 22% in allergic rhinitis), with BMI and exacerbations contributing most to this. In T2-biologics users, eotaxin-1 had the highest explained variance (26.0%) and smoking was the most linked to Th1/17 cytokines. In omalizumab users: IFN-γ (51%) was most explained (exacerbations, smoking, age). In anti-IL-5 users, eotaxin-1 (58%; BMI, sex) and in dupilumab users, IL-4 (83%) was most explained (exacerbations, sex, BMI). The association between patient characteristics and cytokine levels differed by the season of sample collection. In non-biologic users, IL-6 was the cytokine with the most explained variance in the Winter (asthma admissions accounted for most of this variance) and IL-18 in the Spring/Summer/Fall. In T2-biologic users, TNF-α was the top cytokine in the Winter (smoking accounted for most of this variance); IL-4 (allergic rhinitis), IL-33 (IgE and eosinophil), and CXCL10 (allergic rhinitis and IgE) were the top cytokines in the Spring/Summer/Fall. In differential expression analyses, IL-1β was lower in biologics users than non-biologics users.

Conclusions

In moderate-to-severe asthma, multiple clinical features and season are associated with cytokine levels and might impact inference from proteomics studies. Smoking and BMI are the key proinflammatory factors in biologics-treated and untreated patients.