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Big Data and asthma

Millions of people suffer from asthma which is now recognised as a syndrome rather than a single disease.  However, we lack the high-quality data sets to understand the ‘bigger picture’ of this common lung condition and will separate out the different mechanisms linked to distinct asthma types. Yes, we can look at NHS data and patient records, but this information is not collected in a coherent, standardised way and it is difficult to draw conclusions on a wider scale particularly about what may cause disease or what might be the best treatment for each patient – so-called ‘personalised medicine’.

We set about changing that by gathering together a cohort of 1000 adults and children with severe but stable asthma throughout Europe and collecting data from them in a consistent manner. We wanted to focus on people with the more severe form of the disease because these are the people most at risk of poor quality of life due to their disease. They also have the highest mortality rate of asthma sufferers.

We did a range of standardised tests on our subjects, including lung function, blood tests, questionnaires about their quality of life and what medications they took to manage their condition. We also collected sputum, blood and urine samples and, in some cases, took bronchial biopsies and brushings as well as nasal brushing to collect cell samples.

Using the comprehensive clinical and physiological data we gathered, we were able to group severe asthmatics in different subcategories with more surety than ever before. These categories included well-controlled asthmatics on medium to high dose corticosteroids, and late onset asthmatics who had developed the disease in their 30s and 40s who were often ex-smokers.  Our data confirmed findings already published by a group of US researchers but also gave us new information about types of asthmatics not studied before.

However, this approach did not tell us anything about what drives disease. To get a greater insight into these driver mechanisms, we used the various tissue samples gathered to obtain a range of different types of data that represent the many different processes that occur in cells.  This data has enabled us to begin to determine the molecular pathways that underpinned the disease in each patient and to examine the optimal treatments that should be used.

This was the first step in collecting data that will help us understand asthma and other lung diseases. We have already followed up our cohort at 18 months and intend to follow them for three to five years to see if we can learn how symptoms progress and establish patterns. The DNA we collected has been biobanked so researchers will be able to tap into this resource well into the future. We are linking academia and industry too so that our findings can quickly be transferred into therapies for chronic inflammatory conditions.

Ian Adcock, Professor of Respiratory Cell & Molecular Biology, Imperial College London, recently presented his work on using big data to understand asthma at the first Imperial College AHSC seminar.

Professor Ian Adcock
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