AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles

Background Aspirin-exacerbated respiratory disease (AERD) is a distinct asthma endotype marked by asthma, nasal polyposis, and respiratory reactions to COX-1 inhibitors. Early and accurate identification of AERD remains clinically challenging.Objective We sought to develop and externally validate an artificial intelligence (AI)-based diagnostic model that uses nasal epithelial mRNA expression profiles to accurately identify AERD.Methods mRNA gene expression profiles were obtained from nasal epithelial brushing in 71 subjects with AERD and 57 without AERD. AI models were trained to predict an AERD diagnosis in a training cohort using gene expression alone, which was then validated on an independent validation cohort.Results The clinical data analysis revealed noteworthy findings of AERD: 29% reported cutaneous manifestations during nonsteroidal anti-inflammatory drug reactions, 50% experienced symptoms related to alcohol consumption, and 59% required 2 or more sinus surgeries. AERD was predicted with an accuracy of 93% in the training cohort and 83% in the independent validation cohort. The top AERD-predicting genes included IL1RL1 (IL-33 receptor) and CLC (Charcot-Leyden crystal protein), which are known to be important to AERD pathogenesis.Conclusions Nasal transcriptomics can predict AERD diagnosis accurately and may improve disease understanding, enabling earlier and more precise endotype-based diagnosis and management.