Journal article
Misconceptions of Traits to Predict Response to Inhaled Corticosteroid and Bronchodilator Therapies in Asthma: A Narrative Review
- Abstract:
- The “treatable traits” approach to asthma management has helped revolutionize severe asthma treatment with biologic therapy and includes using biomarkers to identify patients most likely to benefit from a specific treatment. The ability to understand which characteristics predict response to inhaled corticosteroid (ICS) or bronchodilator therapy in mild and moderate-to-severe asthma is also vital for physicians to provide treatment tailored to an individual’s phenotype/endotype. Here, we identified studies of inhaled treatments in asthma exploring treatment outcomes based upon subgroups of baseline characteristics, including type 2 biomarkers, asthma attack history, baseline lung function, bronchodilator reversibility, patient age and age at asthma onset, body mass index, smoking status, sex, and ethnicity. We assessed the available evidence regarding the influence of each characteristic on lung function, asthma attacks or asthma control in patients with asthma following treatment with either ICS, ICS/long-acting β2-agonist (LABA) therapy, or ICS/LABA/long-acting muscarinic antagonist therapy. Of all the characteristics examined, only type 2 biomarkers (blood eosinophil levels and fractional exhaled nitric oxide) appear to consistently predict treatment response, particularly regarding ICS. For all other characteristics, we found either evidence that baseline values are not predictive of response to inhaled treatment or mixed and inconclusive evidence requiring further investigation.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 579.3KB, Terms of use)
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- Publisher copy:
- 10.1007/s41030-025-00323-0
Authors
- Publisher:
- Springer
- Journal:
- Pulmonary Therapy More from this journal
- Pages:
- 1-22
- Publication date:
- 2025-12-01
- Acceptance date:
- 2025-09-19
- DOI:
- EISSN:
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2364-1746
- ISSN:
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2364-1746
- Language:
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English
- Pubs id:
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2344799
- UUID:
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uuid_37b4e934-f0b2-44d3-8368-fc262d365be7
- Local pid:
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pubs:2344799
- Source identifiers:
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W4416850965
- Deposit date:
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2026-01-23
- ARK identifier:
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- Copyright date:
- 2025
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