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. 2025 Aug 18.
doi: 10.1111/all.70004. Online ahead of print.

Complementary Predictors for Asthma Attack Prediction in Children: Salivary Microbiome, Serum Inflammatory Mediators, and Past Attack History

Shahriyar Shahbazi Khamas  1   2   3 , Paul Brinkman  1   2   3 , Anne H Neerincx  1   2   3 , Susanne J H Vijverberg  1   2   3 , Simone Hashimoto  1   2   3   4 , Jelle M Blankestijn  1   2   3 , Jan Willem Duitman  1   2   5 , Tamara Dekker  2   5 , Barbara S Smids  2   5 , Suzanne W J Terheggen-Lagro  4 , René Lutter  1   2   5 , Nariman K A Metwally  1 , Fleur Sondaal  1 , Eric G Haarman  4 , Peter J Sterk  1 , Ian M Adcock  6   7 , Charles Auffray  8 , Corinna Bang  9 , Aruna T Bansal  10 , Heike Buntrock-Döpke  11 , Klaus Bønnelykke  12 , Andrew Bush  6   7 , Bo Lund Chawes  12 , Kian Fan Chung  6 , Paula Corcuera-Elosegui  13 , Sven-Erik Dahlén  14   15   16 , Ratko Djukanovic  17 , Louise J Fleming  6   7 , Stephen J Fowler  18   19 , Andre Franke  9 , Urs Frey  20 , Mario Gorenjak  21 , Susanne Brandstetter  22 , Susanne Harner  22 , Gunilla Hedlin  23   24 , Michael Kabesch  11   22 , Nazanin Zounemat-Kermani  6   7 , Parastoo Kheirolldein  22 , Alexander Kiefer  22 , Jon R Konradsen  23   24 , Aletta D Kraneveld  25 , Leyre López-Fernández  13 , Clare S Murray  18   19 , Björn Nordlund  23   24 , Maria Pino-Yanes  26   27   28 , Uroš Potočnik  21   29   30 , Graham Roberts  17 , Jakob Stokholm  12   31 , Søren Johannes Sørensen  32 , Olaia Sardón-Prado  13   33 , Dominick E Shaw  34 , Florian Singer  35   36 , Ana R Sousa  37 , Jonathan Thorsen  12   38 , Antoaneta A Toncheva  22 , Nadja H Vissing  12 , Christine Wolff  11 , Mahmoud I Abdel-Aziz  1   2   3 , Anke H Maitland-van der Zee  1   2   3   4 ; SysPharmPediA and U‐BIOPRED Consortia
Affiliations

Complementary Predictors for Asthma Attack Prediction in Children: Salivary Microbiome, Serum Inflammatory Mediators, and Past Attack History

Shahriyar Shahbazi Khamas et al. Allergy. .

Abstract

Background: Early identification of children at risk of asthma attacks is important for optimizing treatment strategies. We aimed to integrate salivary microbiome and serum inflammatory mediator profiles with asthma attacks history to develop a comprehensive predictive model for future attacks.

Methods: This study contained a discovery (SysPharmPediA) and a replication phase (U-BIOPRED). School-aged children with asthma were classified into at risk and no-risk groups, based on the presence or absence of one or more severe attacks during one-year follow-up. Prediction models were developed using random forest on the training set (70%) with data on past asthma attacks, microbiome composition, serum inflammatory mediator levels, and their combinations and then tested on the rest of the population (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, the model based on past attacks resulted in an area under the receiving characteristic curve (AUROCC) ~ 0.7. Models including six salivary bacteria or six inflammatory mediators achieved similar results. The combined model incorporating seven features, past asthma attacks, Capnocytophaga, Corynebacterium, and Cardiobacterium, TIMP-4, VEGF, and MIP-3β achieved the highest accuracy with AUROCC ~0.87. The combined model in the U-BIOPRED limited to available inflammatory mediators (VEGF), and incorporating past asthma attacks, Capnocytophaga, Corynebacterium, and Cardiobacterium, resulted in an AUROCC of 0.84.

Conclusion: Serum inflammatory mediators and salivary microbiome complement asthma attacks history for predicting future attacks. These results highlight the imperative for continued investigation into oral microbiota and its interaction with the immune system.

Keywords: 16S rRNA; asthma; biomarker; exacerbations; precision medicine; saliva.

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