ArticleFrontiers in allergy2025
Machine learning-derived genetic risk scores identify IL21 as a predictor of response to omalizumab and dupilumab in asthma.
Article in Frontiers in allergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Contemporary Concise Review 2025: Asthma.Respirology (Carlton, Vic.) · 2026Review
- Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.Current allergy and asthma reports · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rationale: Genetic risk scores (GRS) of Th1/2/17-related loci may be associated with response to biologics. We leveraged previously published machine learning-derived GRSs associated with plasma proteins from the INTERVAL/UK-Biobank study. Methods: We assessed 42 Th1/2/17-related GRSs and SNPs for association with response (≥50% reduction in exacerbations) to biologics in 172 White patients with moderate-to-severe asthma in the Mass General Brigham Biobank (MGBB: 92 omalizumab, 38 mepolizumab, 42 dupilumab). Replication was sought in 243 individuals in the All of Us (AoU) cohort (111 omalizumab, 58 mepolizumab, 74 dupilumab). Models adjusted for age, sex, BMI, baseline exacerbations, and principal components 1-10. AUROC was used to evaluate top predictors; type I error was assessed using random GRS sets (target FDR ≤20%). Results: Females comprised a large proportion; mean BMI was 28-35 kg/m Conclusions: Using ML-based GRS applied to an independent cohort of asthma patients, we found that IL-21-related GRSs were predictors of response to omalizumab and dupilumab.
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