| Title: | Genetic variability in people with obstructive lung disease: variation in outcome, response to treatment and disease progression. |
| Lead Institution: | Imperial College London |
| Principal investigator: | Dr Jennifer Quint |
Application 37126
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About
COPD, asthma and bronchiectasis are all common respiratory diseases, in which patients suffer from various symptoms that make breathing challenging and they are at risk of infections. Recent research suggests that these diseases often overlap, none is just a single disease but a group of diseases that have different causes despite the fact that they share similar symptoms. This makes diagnosis and treatment challenging as the current model is a "one size fits all" treatment for all patients. Using health information recorded during GP and hospital visits, this study will identify and describe subtypes of COPD, asthma and bronchiectasis using an approach called cluster analysis. This approach groups patients into groups based on individual patient clinical characteristics (such as their smoking status or the number of drugs they are taking). Patients in the same group (called a cluster) are more similar between them than with patients in other groups. The grouping is done by computer methods which group things together in more advanced ways than clinicians or scientist could by themselves. In the case of this study, the patient dataset is very large and because the data come from electronic health records, there is no limit to the amount and variety of clinical characteristics used, so the best ones will be selected by a Respiratory specialist and statistical methods. Furthermore, the study will span many years in the lifetime of patients, to better capture their journey of managing and living with respiratory disease. More effective identification and management of obstructive airways disease subtypes will enable doctors to diagnose and treat patients with these diseases in an accurate and efficient manner. This application makes use of several UK Biobank data sets, including the new primary care linkage to answer questions around common respiratory diseases that are of great public health importance and cause significant patient burden. This fits in with Biobank's aim to improve treatment of common diseases. Using statistical packages we will explore associations between the genetic information and disease outcomes and treatments for individuals with asthma and/or COPD. The recent Biobank linkage with primary care data allows us to compare self-reported diagnoses of asthma and COPD with previously validated definitions that exist for both diseases in electronic healthcare records, meaning we can make more accurate judgements about who has what disease. In addition, very detailed information about disease management and outcomes can be obtained from the primary care data.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16650 | Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population | Maria Pikoula (+4) | 2025 | BMC Pulmonary Medicine |
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