Biobanking Science: Disease Patterns in UK Biobank Data

Researchers use UK Biobank data to study which chronic diseases most commonly occur together.

UK Biobank data used to study chronic diseases.
Researchers have mined UK Biobank data to find out which chronic diseases most commonly occur together.

The UK Biobank is one of the biggest biobanking projects in the world with samples and data from over 500,000 participants aged 40 to 69 years. UK Biobank participants allow doctors to take repeat physiological measurements such as blood pressure, donate biological samples including blood and urine, and provide medical history and lifestyle information. All information is de-identified to protect participant privacy. Biological samples and associated information from this biobanking project are available to biomedical researchers around the world. It is an unparalleled resource for scientists researching chronic and age-related diseases.

The global population is becoming older and therefore, chronic diseases are becoming an increasing healthcare burden. Better prevention and treatment options are needed for age-related and chronic diseases such as cardiovascular and neurodegenerative diseases. The goal of the UK Biobank is to provide longitudinal data from a large cohort of aging individuals.

Up to 45% of UK patients with a chronic disease have more than one condition, which is known as multimorbidity (1). Patients with multiple medical conditions often have complicated treatment plans, poorer outcomes and poorer quality of life than patients with one disease. A better understanding of multimorbidity patterns and knowledge of how patients with multiple conditions respond to treatment will allow improved management of patients with more than one chronic condition.

Researchers from the University of Birmingham and the University of Leicester used UK Biobank data to assess multimorbidity patterns in middle-aged and older adults. This study was recently published in Mayo Clinic Proceedings (2). The authors collected data on 36 chronic conditions. They analyzed this data using cluster analysis and association rule mining to search for any patterns of chronic diseases occurring at the same time. Association rule mining is often used in market research but is not often used to analyze medical data.

Almost half (44%) of the 500,000 UK Biobank participants had no chronic condition, 37% had one chronic disease and 19% had two or more multimorbid conditions. Hypertension was the most prevalent condition, present in 26% of the cohort. This was followed by asthma in 11% and cancer in 8.3% of participants.

Results showed associations between up to 26 diseases. Unsurprisingly, the strongest associations were between heart failure and atrial fibrillation, and angina and myocardial infarction. UK Biobank patients with heart failure were 23 times more likely to have atrial fibrillation than the general cohort. Similarly patients with angina were 13 times more likely to have myocardial infarction, and vice versa. Heart failure was part of the largest disease cluster which contained 26 diseases including diabetes, chronic kidney disease, liver failure and stroke. Diabetes was at the center of this large disease association cluster. Diabetes was directly associated with 14 other diseases including liver failure, stroke, heart failure, chronic kidney disease, arthritis and schizophrenia. In a separate disease cluster, cancer was associated with hypertension, asthma and depression.

Conclusions

This study used an unbiased analysis method to assess the patterns of chronic diseases in the UK Biobank cohort. UK Biobank participants were aged between 40 and 69 years and lived in the UK. Multimorbidity patterns may be different in other countries or in other age groups. However, this data is valuable because it shows which diseases are most likely to occur together. This information may help healthcare professionals diagnose and treat patients with multimorbid conditions.

This research shows the power of large datasets and underscores the value that biobanks can provide to the global research community.

 

References

1. Long Term Conditions Compendium of Information. 3rd Edition.Department of Health. London, UK. 2012

2. Zemedikun et al. Patterns of Multimorbidity in Middle-Aged and Older Adults: An Analysis of the UK Biobank Data. Mayo Clin Proc. 2018