ArticleJCI insight2021
Machine learning implicates the IL-18 signaling axis in severe asthma.
Article in JCI insight, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Genetic and environmental risk factors of asthma: a narrative review.Frontiers in immunology · 2026Pooled it
- Aberrant mucin expression and keratinization distinguishing severe from mild asthma revealed by interpretable machine learning.JCI insight · 2026Article
- Contemporary Concise Review 2025: Asthma.Respirology (Carlton, Vic.) · 2026Review
- Identification and validation of cilia-associated molecular candidates deregulated in severe asthma.Respiratory research · 2026Article
- Differences in Type 2 Phenotype and Clinical Characteristics of Adult Asthma Patients with Different Severity, Across GINA Step 1 to 5 Asthma.Journal of asthma and allergy · 2026Article
- CPA3 drives the development of asthma by promoting the activation of the JAK/STAT6 pathway.Respiratory research · 2025Article
- CD8+ TEMRAs in severe asthma associate with asthma symptom duration and escape proliferation arrest.JCI insight · 2025Article
- T1-T2 Interplay in the Complex Immune Landscape of Severe Asthma.Immunological reviews · 2025Review
- Exploring the molecular mechanisms of asthma across multiple datasets.Annals of medicine · 2024Article
- Asthma Pathogenesis: Phenotypes, Therapies, and Gaps: Summary of the Aspen Lung Conference 2023.American journal of respiratory cell and molecular biology · 2024Review
- Low Serum IL-18 Levels May Predict the Effectiveness of Dupilumab in Severe Asthma.Internal medicine (Tokyo, Japan) · 2024Article
- Genetic biomarker prediction based on gender disparity in asthma throughout machine learning.Frontiers in medicine · 2024Article
- IL-18 biology in severe asthma.Frontiers in medicine · 2024Review
- Increased epithelial mTORC1 activity in chronic rhinosinusitis with nasal polyps.bioRxiv : the preprint server for biology · 2023Article
- IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis.Journal of proteome research · 2023Article
- Distinct Epithelial-Innate Immune Cell Transcriptional Circuits Underlie Airway Hyperresponsiveness in Asthma.American journal of respiratory and critical care medicine · 2023Article
- Bronchial epithelial cell transcriptional responses to inhaled corticosteroids dictate severe asthmatic outcomes.The Journal of allergy and clinical immunology · 2023Article
- Selective Inhibition of Extracellular Signal-regulated Kinases 1 and 2: When Less Is More.American journal of respiratory cell and molecular biology · 2023Article
- Advancing Lung Immunology Research: An Official American Thoracic Society Workshop Report.American journal of respiratory cell and molecular biology · 2022Article
- Differences in Inflammatory Cytokine Profile in Obesity-Associated Asthma: Effects of Weight Loss.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has focused on identifying subgroups of patients with similar pathobiological underpinnings. Previous studies of the Severe Asthma Research Program (SARP) cohort linked gene expression changes to specific clinical and physiologic characteristics. While invaluable for hypothesis generation, these data include extensive candidate gene lists that complicate target identification and validation. In this analysis, we performed unsupervised clustering of the SARP cohort using bronchial epithelial cell gene expression data, identifying a transcriptional signature for participants suffering exacerbation-prone asthma with impaired lung function. Clinically, participants in this asthma cluster exhibited a mixed inflammatory process and bore transcriptional hallmarks of NF-κB and activator protein 1 (AP-1) activation, despite high corticosteroid exposure. Using supervised machine learning, we found a set of 31 genes that classified patients with high accuracy and could reconstitute clinical and transcriptional hallmarks of our patient clustering in an external cohort. Of these genes, IL18R1 (IL-18 Receptor 1) negatively associated with lung function and was highly expressed in the most severe patient cluster. We validated IL18R1 protein expression in lung tissue and identified downstream NF-κB and AP-1 activity, supporting IL-18 signaling in severe asthma pathogenesis and highlighting this approach for gene and pathway discovery.
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Registered trials
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.