Complementary asthma attacks predictors: Salivary microbiome, inflammatory mediators, and attacks history

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Background: Identifying children at risk of asthma attacks is critical for optimizing treatment. This study aimed to develop a predictive model by integrating salivary microbiome, serum inflammatory mediator profiles, and asthma attack history. Methods: The study had a discovery (SysPharmPediA) and replication phase (U-BIOPRED). School-aged children with asthma were categorized as at-risk or no-risk based on ≥1 severe attack during one-year follow-up. Prediction models were built using random forest on training data (70%) from past attacks, microbiome, and inflammatory mediators, tested on the remaining 30%. Outcomes were replicated in a subset of children with severe asthma from U-BIOPRED. Results: Complete data were available for 154 children (SysPharmPediA=121, U-BIOPRED=33). In discovery, model based on past attacks resulted in an area under the receiver operating characteristic curve (AUROCC) ~ 0.7. Models including six salivary bacteria or six inflammatory mediators achieved similar results. Combined model using seven features—past asthma attacks, Capnocytophaga, Corynebacterium and Cardiobacterium, vascular endothelial growth factor (VEGF), tissue inhibitor of metalloproteinase 4, monocyte inflammatory protein 3b achieved highest accuracy (AUROCC ~ 0.87). The combined model in the U-BIOPRED limited to available inflammatory mediators (VEGF), and combining past asthma attacks, Capnocytophaga, Corynebacterium, Cardiobacterium, resulted in AUROCC of 0.84. Conclusion: Salivary microbiome and serum inflammatory mediators complement asthma attacks history, enhancing prediction accuracy. This highlights the need for further exploration of oral microbiota and immune interactions.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.