Evidence map›Paper›PMID 41592537›Full record

ReviewAllergy, asthma & immunology research2026

Precision Medicine for Asthma: Tailored to its Severity and Endotype/Phenotype.

Rory Chan, Neve E Horn, Salman Siddiqui

Abstract readReview
In one paragraph

Review in Allergy, asthma & immunology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. From FEVJournal of asthma and allergy · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rory ChanUniversity of Dundee, School of Medicine, Dundee, UK.ORCID https://orcid.org/0000-0003-2805-8266
Neve E HornDepartment of Respiratory Medicine, National Heart and Lung Institute, Imperial College London, London, UK.
Salman SiddiquiDepartment of Respiratory Medicine, National Heart and Lung Institute, Imperial College London, London, UK. s.siddiqui@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asthma is a chronic respiratory disease affecting more than 300 million people globally and remains a major cause of morbidity, mortality, and healthcare burden. Traditionally, asthma has been managed using a stepwise treatment algorithm focused on inhaled corticosteroids, bronchodilators, and add-on therapies. While this approach provides a broad framework for care, it does not adequately account for the heterogeneity of the disease, which encompasses diverse clinical phenotypes, underlying endotypes, and variable treatment responses. Many patients with moderate-to-severe asthma continue to experience poor control, frequent exacerbations, and impaired quality of life despite standard therapies. Precision medicine offers an alternative strategy by identifying and targeting specific "treatable traits" across pulmonary, extrapulmonary, and behavioral domains. Advances in biomarker profiling-including blood eosinophils, fractional exhaled nitric oxide, volatile organic compounds, and transcriptomics-have enabled more accurate risk prediction and patient stratification. Imaging techniques, such as high-resolution computed tomography and hyperpolarized magnetic resonance imaging, are improving the ability to detect small airways dysfunction, mucus plugging, and other key disease mechanisms. Biologic therapies directed at type 2 inflammation pathways, including anti-immunoglobulin E, anti-interleukin (IL)5, anti-IL4Rα, and anti-thymic stromal lymphopoietin agents, have significantly reduced exacerbation rates and improved lung function, although biomarker variability, high treatment costs, and limited accessibility remain barriers to widespread use. Emerging oral, inhaled, and long-acting biologics further expand the therapeutic landscape. Looking forward, the integration of multi-omics, deep phenotyping, and artificial intelligence promises to transform asthma management by enabling personalized therapy tailored to individual patient profiles. This narrative review examines the limitations of the conventional stepwise approach, highlights recent advances in phenotyping and targeted treatment, and explores the role of novel technologies in shaping the future of asthma care. By moving beyond the one-size-fits-all model, precision medicine has the potential to improve long-term outcomes, safety, and quality of life for patients with asthma.

Indexed as

Asthmaendotypeoutcomesphenotypeprecision medicinequality of lifeseverity

Identifiers

PMID41592537
PMCPMC12865167

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.