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Distinguishing asthma and allergy endotypes

Rebecca Howard, PhD student 

Title

Distinguishing asthma and allergy endotypes using longitudinal probabilistic modelling

Project Overview

  • Aiming to find out whether the diagnostics for asthma and allergy in children and young adults can be improved using a new test that relies on immunoglobulin response levels, and whether the development trajectories and severity of these conditions can be predicted from a young age

Start: September 2014

End: September 2017

Funded by: Medical Research Council

Disease Area

Asthma and allergy

Data Source

  • Manchester Asthma and Allergy Study (MAAS) mainly immunoglobulin levels. Results shall be related to the corresponding patient, and clinician reported data and other laboratory measurements

Methodology

  • Bayesian focus, have performed Markov Chain Monte Carlo (MCMC) to derive the groupings of the study participants and of the allergen components that the immunoglobulin levels are tested against

Benefits and Outcomes

This research could benefit asthma and allergy sufferers. In the UK it’s estimated that 1.1 million children have asthma, and that up to 8% of children have an allergy, so the scope for impact is huge.

The aim is to find out whether testing immunoglobulin levels can be an effective tool in predicting the likelihood of a person developing asthma and/or allergies, and the severity of them. Knowing this information could influence treatment from a young age.

Researchers Involved

Prof. Magnus Rattray

Prof. Adnan Custovic

Publications

Distinguishing Asthma Phenotypes Using Machine Learning Approaches (Howard et al., 2015) in Current Allergy and Asthma Reports (doi: 10.1007/s11882-­‐015-­‐0542-­‐0).