Combining existing epidemiological methods with new statistical approaches has enabled Dr Anthony Webster and colleagues to develop a new way of understanding and identifying patterns of common diseases.
Many people across the world have two or more diseases at once (multi-morbidity), with the numbers expected to rise. Multi-morbidity affects over half of Europe’s population over age 65. In the United Kingdom, the proportion of the population with multi-morbidity is predicted to rise from 54% in 2015 to 68% in 20351.
Traditionally, research and clinical training and treatment have tended to focus on single diseases or organ systems, rather than assessing the medical and practical impacts of multi-morbidity. Clinicians are trained in narrow medical disciplines, and statistical methods are designed to study individual diseases, without accounting for potential interactions between diseases or treatments.
Chief Medical Officer, Professor Chris Whitty, and Executive Chair of the Medical Research Council, Professor Fiona Watt, have been leading a call for researchers to redirect their efforts to study multi-morbidity to address the opportunities for tackling several diseases at once. They have urged researchers to ‘hunt for diseases that occur together because of shared risk factors – biological or environmental’. By mapping out disease clusters, researchers can work out which co-occurrences are non-random, ultimately enabling researchers and clinicians to uncover new mechanisms for disease, develop treatments, and reconfigure services to meet patients’ needs.
Dr Anthony Webster has been investigating disease clusters since 2019 as part of an NDPH fellowship to find new links between diseases. The results of his research are published today in Scientific Reports.
Dr Webster and colleagues combined the best of existing epidemiological methods with modern statistics, and applied this novel approach to data from UK Biobank, a large-scale biomedical database and research resource, containing in-depth genetic and health information from UK participants.
The new approach was used to cluster diseases with similar types and magnitudes of risk factors. Remarkably, when 156 diseases were split into 24 clusters, four out of five pairs of diseases affecting both men and women appeared together in the same cluster. Closer examination showed the disease clusters were medically plausible, with numerous intriguing associations to prompt further study.
Dr Webster said ‘The probability of this happening by chance is effectively zero – so we know the method works. The method will now be applied more widely, but is already providing new perspectives on the links between behaviour, biology, and disease.
‘Unlike most big data studies that tend to use “black box” machine learning methodologies, the approach will be more familiar to clinicians and epidemiologists, and the results easier to interpret. The diseases we studied are common causes of hospital admission and represent a substantial burden of ill health, so the potential impact on patients and health services is significant.’
The clustering of diseases suggested several plausible but unconfirmed associations between diseases. For example, a cluster of diseases linked to renal failure, is likely to be driven by common disease pathways and risk factors. The researchers also highlighted several symptoms of unknown causes that appear to be linked with more clearly diagnosed disease.
These and related results concerning the role of excess adiposity in the clustering of major diseases in UK adults, were discussed online by 80 people in a meeting hosted by the University of Oxford on 5 March 2021.
 Rijken, M. et al. Eurohealth 19, 29–31 (2013)