Defining new asthma phenotypes using high-dimensional data

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Asthma is a complex respiratory condition that affects millions worldwide and poses significant public health challenges. Traditional models, primarily centred on allergic/atopic mechanisms, have been insufficient in fully explaining the diverse manifestations and treatment responses of asthma. This project seeks to redefine asthma phenotypes (subtypes) by conducting an integrative analysis of high-dimensional data, which includes the sputum microbiome, epigenome, inflammatory markers, and clinical variables.This study will analyse data previously collected from 998 asthmatics and 356 non-asthmatics in Brazil, Ecuador, Uganda, New Zealand, and the UK as part of the WASP study. Investigating these diverse populations across both high-income and low- and middle-income countries is key to understanding the variability in asthma phenotypes, which are influenced by different socioeconomic contexts. The use of advanced statistical methods and multi-omics analysis is crucial to dissect the complex relationships between various biological factors and asthma, providing insights into the disease's heterogeneity.The project's primary components include:a. A systematic review to identify and describe asthma phenotypes using data from multiple sources.b. A scoping review focused on methods for integrating different types of data, emphasizing multidisciplinary research.c. An analysis of sputum epigenome, microbiome, and immunological and clinical data to discern variations related to asthma's presence, severity, and control.d. An exploration of the interrelationships among these variables to develop predictive models of asthma.e. The classification of asthma phenotypes using advanced statistical methods.f. The identification of variations in asthma phenotypes among different clinical subgroups.These objectives are designed to build upon existing knowledge and to assess current health data science techniques for managing complex biological and clinical data. This aligns with the project's commitment to interdisciplinary research and quantitative skills, reflecting key areas of focus for the MRC.A notable aspect of this project is its innovative methodology in classifying asthma phenotypes, blending established definitions with advanced classification systems. This approach, employing advanced statistical methods to integrate clinical and laboratory data, highlights the MRC's emphasis on interdisciplinary skill development and effectively bridges clinical and biomedical research areas. This methodology is vital for delving into the complexities of asthma and providing valuable insights.Expected outcomes of the project include a deeper understanding of asthma's causes, potentially opening new paths for prevention, diagnosis, and treatment strategies. The findings are expected to make a meaningful contribution to the global management of asthma, particularly benefiting LMICs where resources are more limited. Overall, the project represents a robust integration of clinical and biomedical research, enhancing our global understanding of asthma phenotypes.