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

<bold>Background:</bold> 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. <bold>Methods:</bold> 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. <bold>Results:</bold> 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. <bold>Conclusion:</bold> 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.

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Published
2025-09-27
DOI
https://doi.org/10.1183/13993003.congress-2025.pa3799
Primary Topic
Gut microbiota and health
Type
article
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article

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

Peter Sterk, Susanne Harner, Jonathan Thorsen, Klaus Bønnelykke et al.
Gut microbiota and health
article

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

Peter Sterk, Susanne Harner, Jonathan Thorsen, Klaus Bønnelykke, Corinna Bang, Simone Hashimoto, Stephen James Fowler, Louise Fleming, Bo Chawes, Suzanne W. J. Terheggen‐Lagro, Mahmoud I. Abdel‐Aziz, Mario Gorenjak, Uroš Potočnik, Anke H. Maitland‐van der Zee, Olaia Sardón‐Prado, Anne H. Neerincx, Aruna T. Bansal, Susanne J. H. Vijverberg, Antoaneta A. Toncheva, Søren Johannes Sørensen, Dominick Shaw, Gunilla Hedlin, Jakob Stokholm, Paula Corcuera, Kian Fan Chung, Heike Buntrock‐Döpke, Paul Brinkman, Alexander Kiefer, Barbro Dahlén, Ratko Djukanović, Florian Singer, JanWillem Duitman, Aletta D. Kraneveld, Maria Pino‐Yanes, Susanne Brandstetter, Charles Auffray, Nazanin Zounemat Kermani, Barbara Smids, Urs Frey, Clare Murray, Shahriyar Shahbazi Khamas, Andrew Bush, Ian Michael Adcock, Christine Wolff, Michael Kabesch, Graham Roberts, Jelle M. Blankestijn, Jon R. Konradsen, André Franke, Nadja Hawwa Vissing, Björn Nordlund, Eric G. Haarman, Leyre López‐Fernández, René Lutter, Tamara Dekker, Nariman K. A. Metwally, Ana R. Sousa, Fleur Sondaal, Parastoo Kheirolldein
article en

Abstract

<bold>Background:</bold> 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. <bold>Methods:</bold> 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. <bold>Results:</bold> 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. <bold>Conclusion:</bold> 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.

Université Claude Bernard Lyon 1 (FR), University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), Karolinska University Hospital (SE), Inserm (FR), Universidad de La Laguna (ES), St. John's College of Nursing (US), GlaxoSmithKline (United Kingdom) (GB), Utrecht University (NL), Medical University of Graz (AT), Manchester Academic Health Science Centre (GB), University Hospital Southampton NHS Foundation Trust (GB), Karolinska Institutet (SE), University of Manchester (GB), University Hospital Regensburg (DE), Christian-Albrechts-Universität zu Kiel (DE), University of Maribor (SI), Emma Kinderziekenhuis (NL), Biogipuzkoa Health Research Institute (ES), Lung Institute (US), NIHR Southampton Biomedical Research Centre (GB), Pharmo Institute (NL), Copenhagen Prospective Studies on Asthma in Childhood (DK), University Children’s Hospital Basel (CH), Centre International de Recherche en Infectiologie (FR), NIHR Leicester Biomedical Research Centre (GB), University of Amsterdam (NL)
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Gut microbiota and health
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