Longitudinal multi-trajectory phenotypes of severe eosinophilic asthma on type 2 biologics treatmentopen access
- Authors
- Pham,, Duong Duc; Lee,, Ji-Hyang; Kwon,, Hyouk-Soo; Song,, Woo-Jung; Cho,, You Sook; Kim,, Hyunkyoung; Kwon,, Jae-Woo; Park,, So-Young; Kim,, Sujeong; Hur,, Gyu Young; Kim,, Byung Keun; Nam,, Young-Hee; Yang,, Min-Suk; Kim,, Mi-Yeong; Kim,, Sae-Hoon; Lee,, Byung-Jae; Lee,, Taehoon; Park,, So Young; Kim,, Min-Hye; Cho,, Young-Joo; Park,, ChanSun; Jung,, Jae-Woo; Park,, Han Ki; Kim,, Joo-Hee; Moon,, Ji-Yong; Bhavsar, Pankaj; Adcock,, Ian M.; Chung,, Kian Fan; Kim,, Tae-Bum
- Issue Date
- Dec-2024
- Publisher
- Elsevier Inc.
- Keywords
- Multi-trajectory analysis; Severe eosinophilic asthma; Type 2 biologics
- Citation
- World Allergy Organization Journal, v.17, no.12, pp 1 - 13
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- World Allergy Organization Journal
- Volume
- 17
- Number
- 12
- Start Page
- 1
- End Page
- 13
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211689
- DOI
- 10.1016/j.waojou.2024.101000
- ISSN
- 1939-4551
- 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.
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