Longitudinal multi-trajectory phenotypes of severe eosinophilic asthma on type 2 biologics treatment

Abstract

Background: Limited understanding exists regarding the progression trajectory of severe eosinophilic asthma (SEA) patients on type 2 biologics therapies.

Objective: We aim to explore distinct longitudinal phenotypes of these patients based on crucial asthma biomarkers.

Methods: We enrolled 101 adult patients with SEA. Of these, 51 were treated with anti-IL5/IL5Rα or anti-IL5/IL5RαR antibody, and 50 with anti-IL-4Rα antibody. Multi-trajectory analysis, an extension of univariate group-based trajectory modeling, was used to categorize patients based on their trajectories of forced expiratory volume in 1 s (FEV₁), blood eosinophil counts (BEC), and fractional exhaled nitric oxide (FeNO) levels at baseline, and after 1, 6, and 12 months of treatment. Associations between trajectory-based clusters and clinical parameters were examined.

Results: Among anti-IL5/IL5Rα antibody-treated patients, 2 clusters were identified. The cluster characterized by higher baseline BEC and lower FEV₁ showed a better response, with improvements in FEV₁ and reductions in BEC over time. Among anti-IL-4Rα antibody-treated, 3 clusters were identified. Clusters with moderate BEC and FeNO at baseline demonstrated better improvements in FEV₁ and reductions in FeNO, despite increased BEC during follow-up. Conversely, individuals with extremely low FeNO and high BEC at baseline were more likely to experience poorer progression, demonstrating an increase in FeNO and a reduction in FEV₁.

Conclusion: To optimally monitor treatment response in SEA patients on type 2 biologics, integrating longitudinal biomarker features is essential.

Keywords: Multi-trajectory analysis; Severe eosinophilic asthma; Type 2 biologics.

Fig. 1

Study flow

Fig. 2

Clinical parameters during 12-month follow-up across trajectory-based clusters among anti-IL5/IL5Rα antibody-treated patients. (A) Predicted FEV₁ (%), (B) blood eosinophil counts, (C) FeNO, (D) budesonide equivalent (mcg per day), (E) prednisolone equivalent dose (mg per month), and (F) asthma control test score. The data are presented as mean with 95% coefficient intervals estimated by linear mix-effect regression, adjusted for age and sex. P_base and P_slope are p-values for group differences in baseline levels and the slope of change, respectively. Significant P-values are indicated in red. (G) Mean exacerbation events during the 12-month follow-up period. P-values were estimated using the rank-sum test. (H) The exacerbation rate ratio was estimated using negative binomial regression, adjusted for age and sex, with Cluster 1 as the reference. (I) Proportion of high-dose ICS users. (J) Proportion of OCS maintenance. P-values were estimated using the Fisher exact test. ∗, P-value <0.05

Fig. 3

Clinical parameters during 12-month follow-up across trajectory-based clusters among anti-IL-4Rα antibody-treated patients. (A) Predicted FEV₁ (%), (B) blood eosinophil counts, (C) FeNO, (D) budesonide equivalent (mcg per day), (E) prednisolone equivalent dose (mg per month), and (F) asthma control test score. The data are presented as mean with 95% coefficient intervals estimated by linear mix-effect regression, adjusted for age and sex. P_base and P_slope are p-values for group differences in baseline levels and the slope of change, respectively. Significant P-values are indicated in red. (G) Mean exacerbation events during the 12-month follow-up period. P-values were estimated using the rank-sum test. (H) The exacerbation rate ratio was estimated using negative binominal regression, adjusted for age and sex, with Cluster 2 as the reference. (I) Proportion of high-dose ICS users. (J) Proportion of OCS maintenance. P-values were estimated using the Fisher exact test. ∗, P-value <0.05