Risk factor profiling of age-associated multi-morbidity and longitudinal deterioration of metabolic health
Approved Research ID: 72486
Approval date: July 20th 2021
The aim of this project is to determine 1) how metabolism changes throughout human life course, 2) how genetics affects these changes, 3) how the environment modulates metabolism, and 4) how early life course predicts late-life diseases such as type 2 diabetes, heart disease, cancers and dementia. Ageing is a fundamental aspect of an individual's life and the ongoing global demographic shift towards older age distribution requires adaptation at the population level as well. Simultaneously, the public health impact of dysfunctional metabolism is evident from the soaring rates of obesity and diabetes that increase the risk of multiple other diseases and secondary complications. Accurate understanding of what is happening across population segments over long perids of time and why is essential for developing evidence-based strategies to improve the situation. Yet reliable longitudinal information on the metabolic health of populations is surprisingly sparse: it is challenging and costly to follow thousands of individuals across decades to determine how their metabolism changes and what genetic and environmental factors underlie these changes. Multi-morbidity is another area where more information is needed: aged individuals often live with multiple chronic diseases that complicates treatment, thus information about the specific risk factors that promote multi-morbidity later in life will be highly valuable to public health programs that aim to postpone and reduce the burden of age-associated diseases.
We are in a unique position to address the aforementioned gaps in knowledge. To get around the first challenge of long-term follow-up, we will leverage several datasets and employ sophisticated statistical techniques to tease out decades of longitudinal trends. Furthermore, we have developed new analysis tools that allow us to investigate the diversity of populations from a new perspective and improve understanding of the human metabolic life course. To meet the second challenge of multi-morbidity, we will rely on the extensive diagnostic codes and health monitoring of the UK Biobank participants. We have also developed specific analytical tools to identify patterns of disease co-occurrence from population-based data. The scale and scope of these analyses is unprecedented, but we have strong results from pilot studies that demonstrate the feasibility of the proposed work. We expect the project to run for three years.