Childhood asthma in the new omics era: challenges and perspectives.
Anti-Asthmatic Agents
/ pharmacology
Asthma
/ diagnosis
Biological Products
/ pharmacology
Biomarkers
/ analysis
Child
Disease Progression
Epigenomics
/ methods
Gene Expression Profiling
/ trends
Genetic Predisposition to Disease
Humans
Metabolomics
/ methods
Pharmacogenomic Testing
/ methods
Precision Medicine
/ methods
Proteomics
/ methods
Severity of Illness Index
Treatment Outcome
Journal
Current opinion in allergy and clinical immunology
ISSN: 1473-6322
Titre abrégé: Curr Opin Allergy Clin Immunol
Pays: United States
ID NLM: 100936359
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
pubmed:
28
1
2020
medline:
16
6
2021
entrez:
28
1
2020
Statut:
ppublish
Résumé
Childhood asthma is a heterogeneous inflammatory disease comprising different phenotypes and endotypes and, particularly in its severe forms, has a large impact on the quality-of-life of patients and caregivers. The application of advanced omics technologies provides useful insights into underlying asthma endotypes and may provide potential clinical biomarkers to guide treatment and move towards a precision medicine approach. The current article addresses how novel omics approaches have shaped our current understanding of childhood asthma and highlights recent findings from (pharmaco)genomics, epigenomics, transcriptomics, and metabolomics studies on childhood asthma and their potential clinical implications to guide treatment in severe asthmatics. Until now, omics studies have largely expanded our view on asthma heterogeneity, helped understand cellular processes underlying asthma, and brought us closer towards identifying (bio)markers that will allow the prediction of treatment responsiveness and disease progression. There is a clinical need for biomarkers that will guide treatment at the individual level, particularly in the field of biologicals. The integration of multiomics data together with clinical data could be the next promising step towards development individual risk prediction models to guide treatment. However, this requires large-scale collaboration in a multidisciplinary setting.
Identifiants
pubmed: 31985545
doi: 10.1097/ACI.0000000000000626
pmc: PMC7060050
pii: 00130832-202004000-00012
doi:
Substances chimiques
Anti-Asthmatic Agents
0
Biological Products
0
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
155-161Commentaires et corrections
Type : ErratumIn
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