Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

Ray, Anuradha; Das, Jishnu; Wenzel, Sally E.

doi:10.1016/j.xcrm.2022.100857

Cited by 21 publications

(22 citation statements)

References 120 publications

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“…With regards to the limited number of patients in this study, these results need to be con rmed on another independent cohort, with integration of other omic-dataset such as transcriptomic data that could indeed provided interesting information to re ne asthma endotypes into intra-SA speci c new groups [9].…”

Section: Discussionmentioning

confidence: 89%

“…To date, biomarkers indicative of T2-high asthma include mainly fractional exhaled nitric oxide (FeNO), serum levels of periostin, and blood or sputum eosinophils [4,5,9]. Recently, we also showed that a large set of immune variables in blood and bronchoalveolar lavages (BALs) may differentiate children with SA form control subjects [6] and, among children with SA, frequent exacerbators from non-frequent exacerbators [10].…”

Section: Introductionmentioning

confidence: 99%

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Cytokines and metabolites are the main descriptors of severe asthma in children when performing multi-omics analysis of bronchoaleveolar lavages

Briard

Guillon

Venot

et al. 2023

Preprint

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Background. Severe asthma (SA) is a heterogeneous condition with multiple phenotypes. There is still an unmet need to characterize and understand underlying mechanisms taking place in the lungs in order to propose the most suitable therapeutic strategies for SA. For this purpose, we aimed to identify a local signature of severe asthma by conducting comprehensive multi-omics analysis of bronchoalveolar lavages fluids (BALs) from children with SA versus non-asthmatic (NA) controls. Method. BALs were collected from twenty children with SA and from ten age-matched NA. We previously analyzed soluble and cellular immune components in those samples, and now propose to perform comprehensive analysis of their microbiota and their metabolome. Briefly, DNA from BALs was extracted and 16S rRNA gene (V3-V4 region) was amplified by PCR and sequenced. In parallel, untargeted metabolomics was performed using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) following an established workflow for sample preparation, data acquisition and treatment. Each microbiome and metabolome dataset was first analysed independently by unsupervised multivariate analyses (Principal component analyses, PCA). Differences between groups for microbiota diversity indices, the relative distribution of each phyla and genera were then analysed. Metabolite set enrichment analysis (MSEA) and univariate supervised analysis were also performed. To identify a local signature of severe asthma, microbiota and metabolome data were further integrated, together with immune and with clinical data, using unsupervised Multi-Omics Factor Analysis (MOFA). Results. Microbiota diversity was higher in children with SA versus NA, with higher relative abundances of Streptococcus, Corynebacterium, Tropheryma whipplei, Dolosigranulum pigrum and Moraxella nonliquefaciens. We identified 88 metabolites in BALs, but unsupervised PCA of corresponding data did not differentiate children with SA from NA. However, MSEA evidenced that biotin and carnitine synthesis, lysine degradation, methionine metabolism and spermidine and spermine biosynthesis pathways were significantly enriched in children with SA. Finally, multiblocks data integration identified a signature of SA, mainly described by metabolites and cytokines. Conclusion. By integrating metabolome, microbiome and cytokines data obtained on BALs from children with severe asthma versus NA, our study uniquely described a local signature of SA.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Cytokines and metabolites are the main descriptors of severe asthma in children when performing multi-omics analysis of bronchoaleveolar lavages

Briard

Guillon

Venot

et al. 2023

Preprint

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“…Within the field of allergy, AI investigation and application have most frequently been applied to asthma due to the large population affected as well as healthcare utilization and costs associated with poorly controlled or severe asthma. Proposed benefits of AI for asthma include the prediction of future development of asthma, more accurate diagnosis, prediction of asthma exacerbations, evaluation of clinician adherence to asthma guidelines, and classifying asthma endotypes for optimal therapeutic intervention [ 11 •, 14 – 23 ].…”

Section: Key Clinical Applications Of Ai In Allergymentioning

confidence: 99%

“…For optimal treatment of asthma, ML approaches to multi-omics datasets have been proposed to better understand asthma endotypes using predictive biomarkers [ 23 ]. The heterogeneity of asthma belies even standard biomarker approaches with patients exhibiting differential responses to the same monoclonal agent despite similar biomarker profiles.…”

Section: Key Clinical Applications Of Ai In Allergymentioning

confidence: 99%

“…The heterogeneity of asthma belies even standard biomarker approaches with patients exhibiting differential responses to the same monoclonal agent despite similar biomarker profiles. ML integration of high-dimensional “omics” data paired with clinical characteristics or “non-omics” represents an opportunity to increase precision and personalization in asthma care in the future [ 23 ]. The significant cost of asthma therapeutics, including biologic agents and inhalers, represents an opportunity for AI to reduce healthcare costs with quicker and more precise identification of responders to specific drugs.…”

Section: Key Clinical Applications Of Ai In Allergymentioning

confidence: 99%

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Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

MacMath

Chen

Khoury

2023

Curr Allergy Asthma Rep

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Purpose of Review Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research. Recent Findings In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. Summary These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

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Machine learning for brain-stroke prediction: comparative analysis and evaluation

Bhowmick,

Mishra,

Tiwary

et al. 2024

Multimed Tools Appl

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Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

Cited by 21 publications

References 120 publications

Cytokines and metabolites are the main descriptors of severe asthma in children when performing multi-omics analysis of bronchoaleveolar lavages

Cytokines and metabolites are the main descriptors of severe asthma in children when performing multi-omics analysis of bronchoaleveolar lavages

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

Machine learning for brain-stroke prediction: comparative analysis and evaluation

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