Journal
SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-82459-y
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Categories
Funding
- Wellcome Trust [WT 206274/Z/17/Z, MR/K006584/1, WT 110284/Z/15/Z]
- Rosetrees and Stoneygate Trust
- Wellcome Trust Strategic Award
- Max Planck Society
- UK Medical Research Council [MR/N013867/1]
- National Institute for Health Research University College London Hospitals Biomedical Research Centre
- British Heart Foundation Accelerator Award [AA/18/6/24223]
- Medical Research Council
- Arthritis Research UK
- British Heart Foundation
- Cancer Research UK
- Chief Scientist Office
- Economic and Social Research Council
- Engineering and Physical Sciences Research Council
- National Institute for Health Research
- National Institute for Social Care and Health Research
- UK Medical Research Council
- Department of Health and Social Care (England)
- Chief Scientist Office of the Scottish Government Health and Social Care Directorates
- Health and Social Care Research and Development Division (Welsh Government)
- Public Health Agency (Northern Ireland)
- Wellcome Trust
- UKRI Innovation/Rutherford Fellowship
- Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust [WT 201375/Z/16/Z]
- Alan Turing Fellowship
- MRC [MR/S003754/1, G0902393, MR/M501633/2, 1764958] Funding Source: UKRI
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Reducing the burden of late-life morbidity requires understanding the mechanisms of ageing-related diseases. The study proposed a framework using machine learning and actuarial techniques to identify and cluster ageing-related diseases, resulting in 207 diseases being categorized into four clusters based on different age ranges.
Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82-83) for Cluster 1, 77 years (IQR 75-77) for Cluster 2, 69 years (IQR 66-71) for Cluster 3 and 57 years (IQR 54-59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.
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