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Published on: August 7, 2017
Complementary Predictors for Asthma Attack Prediction in Children: Salivary Microbiome, Serum Inflammatory Mediators,
Shahriyar Shahbazi Khamas1,2,3, Paul Brinkman1,2,3, Anne H Neerincx1,2,3
1Amsterdam UMC Location University of Amsterdam, Department of Pulmonary Medicine, Amsterdam, the Netherlands.
Insights
Predicting future asthma attacks in children is improved by combining medical history with salivary microbiome and inflammatory markers. This integrated approach enhances accuracy for early risk identification and optimized treatment strategies.
Area of Science:
- Pediatric Pulmonology
- Microbiome Research
- Immunology
Background:
- Early identification of children at risk for asthma attacks is crucial for effective treatment.
- Integrating diverse biological data can enhance predictive capabilities for pediatric asthma exacerbations.
Purpose of the Study:
- To develop a comprehensive predictive model for future asthma attacks in children.
- To assess the combined utility of salivary microbiome, serum inflammatory mediators, and asthma history.
Main Methods:
- A two-phase study (discovery and replication) involving school-aged children with asthma.
- Random forest models were trained using historical asthma attacks, microbiome composition, and inflammatory mediator levels.
- Models were validated on independent datasets, including a subset from U-BIOPRED.
Main Results:
- A combined model integrating past asthma attacks, specific salivary bacteria (Capnocytophaga, Corynebacterium, Cardiobacterium), and inflammatory mediators (TIMP-4, VEGF, MIP-3β) achieved the highest predictive accuracy (AUROCC ~0.87).
- Models based solely on past attacks, salivary bacteria, or inflammatory mediators showed moderate predictive power (AUROCC ~0.7).
- The combined model demonstrated strong performance in the replication phase (AUROCC 0.84).
Conclusions:
- Serum inflammatory mediators and salivary microbiome data significantly complement asthma attack history in predicting future events.
- These findings underscore the importance of investigating the oral microbiota and its interplay with the immune system for asthma prediction.
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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