Complementary Predictors for Asthma Attack Prediction in Children: Salivary Microbiome, Serum Inflammatory Mediators, and Past Attack History Article Swipe
Shahriyar Shahbazi Khamas
,
Paul Brinkman
,
Anne H. Neerincx
,
Susanne J. H. Vijverberg
,
Simone Hashimoto
,
Jelle M. Blankestijn
,
JanWillem Duitman
,
Tamara Dekker
,
Barbara Smids
,
Suzanne W. J. Terheggen‐Lagro
,
René Lutter
,
Nariman K. A. Metwally
,
Fleur Sondaal
,
Eric G. Haarman
,
Peter Sterk
,
Ian M. Adcock
,
Charles Auffray
,
Corinna Bang
,
Aruna T. Bansal
,
Heike Buntrock‐Döpke
,
Klaus Bønnelykke
,
Andrew Bush
,
Bo Chawes
,
Kian Fan Chung
,
Paula Corcuera
,
Sven‐Erik Dahlén
,
Ratko Djukanović
,
Louise Fleming
,
Stephen J. Fowler
,
André Franke
,
Urs Frey
,
Mario Gorenjak
,
Susanne Brandstetter
,
Susanne Harner
,
Gunilla Hedlin
,
Michael Kabesch
,
Nazanin Zounemat Kermani
,
Parastoo Kheirolldein
,
Alexander Kiefer
,
Jon R. Konradsen
,
Aletta D. Kraneveld
,
Leyre López‐Fernández
,
Clare Murray
,
Björn Nordlund
,
Maria Pino‐Yanes
,
Uroš Potočnik
,
Graham Roberts
,
Jakob Stokholm
,
Søren J. Sørensen
,
Olaia Sardón
,
Dominick Shaw
,
Florian Singer
,
Ana R. Sousa
,
Jonathan Thorsen
,
Antoaneta A. Toncheva
,
Nadja Hawwa Vissing
,
Christine Wolff
,
Mahmoud I. Abdel‐Aziz
,
Anke H. Maitland‐van der Zee
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/all.70004
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/all.70004
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.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/all.70004
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/all.70004
- OA Status
- hybrid
- Cited By
- 1
- References
- 79
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413286314
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https://openalex.org/W4413286314Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/all.70004Digital Object Identifier
- Title
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Complementary Predictors for Asthma Attack Prediction in Children: Salivary Microbiome, Serum Inflammatory Mediators, and Past Attack HistoryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-18Full publication date if available
- Authors
-
Shahriyar Shahbazi Khamas, Paul Brinkman, Anne H. Neerincx, Susanne J. H. Vijverberg, Simone Hashimoto, Jelle M. Blankestijn, JanWillem Duitman, Tamara Dekker, Barbara Smids, Suzanne W. J. Terheggen‐Lagro, René Lutter, Nariman K. A. Metwally, Fleur Sondaal, Eric G. Haarman, Peter Sterk, Ian M. Adcock, Charles Auffray, Corinna Bang, Aruna T. Bansal, Heike Buntrock‐Döpke, Klaus Bønnelykke, Andrew Bush, Bo Chawes, Kian Fan Chung, Paula Corcuera, Sven‐Erik Dahlén, Ratko Djukanović, Louise Fleming, Stephen J. Fowler, André Franke, Urs Frey, Mario Gorenjak, Susanne Brandstetter, Susanne Harner, Gunilla Hedlin, Michael Kabesch, Nazanin Zounemat Kermani, Parastoo Kheirolldein, Alexander Kiefer, Jon R. Konradsen, Aletta D. Kraneveld, Leyre López‐Fernández, Clare Murray, Björn Nordlund, Maria Pino‐Yanes, Uroš Potočnik, Graham Roberts, Jakob Stokholm, Søren J. Sørensen, Olaia Sardón, Dominick Shaw, Florian Singer, Ana R. Sousa, Jonathan Thorsen, Antoaneta A. Toncheva, Nadja Hawwa Vissing, Christine Wolff, Mahmoud I. Abdel‐Aziz, Anke H. Maitland‐van der ZeeList of authors in order
- Landing page
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- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/all.70004Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/all.70004Direct OA link when available
- Concepts
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Asthma, Medicine, Microbiome, Population, Immunology, Bioinformatics, Biology, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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79Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.complement | 238 |
| abstract_inverted_index.discovery, | 145 |
| abstract_inverted_index.imperative | 250 |
| abstract_inverted_index.microbiome | 22, 98, 237 |
| abstract_inverted_index.microbiota | 256 |
| abstract_inverted_index.one‐year | 78 |
| abstract_inverted_index.optimizing | 14 |
| abstract_inverted_index.population | 115 |
| abstract_inverted_index.predicting | 243 |
| abstract_inverted_index.predictive | 36 |
| abstract_inverted_index.replicated | 119 |
| abstract_inverted_index.U‐BIOPRED | 141, 208 |
| abstract_inverted_index.interaction | 259 |
| abstract_inverted_index.replication | 50 |
| abstract_inverted_index.strategies. | 16 |
| abstract_inverted_index.U‐BIOPRED. | 129 |
| abstract_inverted_index.combinations | 106 |
| abstract_inverted_index.composition, | 99 |
| abstract_inverted_index.follow‐up. | 79 |
| abstract_inverted_index.inflammatory | 25, 101, 171, 212, 233 |
| abstract_inverted_index.School‐aged | 53 |
| abstract_inverted_index.comprehensive | 35 |
| abstract_inverted_index.incorporating | 179, 216 |
| abstract_inverted_index.investigation | 253 |
| abstract_inverted_index.(SysPharmPediA | 138 |
| abstract_inverted_index.(U‐BIOPRED). | 52 |
| abstract_inverted_index.Capnocytophaga | 185 |
| abstract_inverted_index.characteristic | 159 |
| abstract_inverted_index.identification | 3 |
| abstract_inverted_index.(SysPharmPediA) | 47 |
| abstract_inverted_index.Capnocytophaga, | 220 |
| abstract_inverted_index.Cardiobacterium | 190, 223 |
| abstract_inverted_index.Corynebacterium | 187 |
| abstract_inverted_index.Corynebacterium, | 221 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5018590706 |
| countries_distinct_count | 10 |
| institutions_distinct_count | 59 |
| corresponding_institution_ids | https://openalex.org/I4210096594, https://openalex.org/I4210148356, https://openalex.org/I4210151833, https://openalex.org/I887064364 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.88068156 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |