Metabolomic and omic assessment of biological ageing across the life-course (METAGE)
Lead Research Organisation:
Imperial College London
Department Name: School of Public Health
Abstract
Both genetic and environmental factors affect the ageing process, leading to differences in ageing rates. Therefore, a person's "biological age", or overall physiological state, may differ from what is expected given their actual (chronological) age. Differences in biological aging rates mean some people die earlier and have more health problems in later life. In the first part of my project, I used metabolomics (whole sets of metabolites or small molecules) data from multiple population-based cohort studies in the UK, Europe, and the USA, to develop blood tests of biological age using advanced statistics. We found that the metabolomic-based biological age tests provided improved prediction, over chronological age itself, of age-related ill health and mortality. Furthermore, We found that that biological ageing was slower over two years among people who were supported to follow a healthier lifestyle, compared to those who did not receive this support in the FINGER study.
For the next part of my project, I will examine if the metabolomic-based biological age test can predict cognitive and physical aging among two cohort studies of older people (the CHARIOT PRO and TILDA studies). I will further refine the metabolomic-based biological age test, so it is less expensive and easier to measure in clinical laboratories. I will also test the use of proteins, another biologically important class of molecules, to develop biological age models in over 20,000 people in the EPIC and CHARIOT PRO studies. Since we know which organs in the body produce specific proteins, it is possible to test "organ-specific" ageing. This is useful as different biological systems in the body may age at different rates, and I will test organ-specific ageing against many types of disease. Furthermore, I will use genetic data and a technique called Medelian Randomization to test if proteins and metabolites that are correlated with age, may cause unhealthy ageing. I will use this information to further improve the biological age tests. Finally, I will analysis the pathways that connect proteins, metabolites and genetic factors involved in ageing to more deeply understand the biological processes that change with age.
For the next part of my project, I will examine if the metabolomic-based biological age test can predict cognitive and physical aging among two cohort studies of older people (the CHARIOT PRO and TILDA studies). I will further refine the metabolomic-based biological age test, so it is less expensive and easier to measure in clinical laboratories. I will also test the use of proteins, another biologically important class of molecules, to develop biological age models in over 20,000 people in the EPIC and CHARIOT PRO studies. Since we know which organs in the body produce specific proteins, it is possible to test "organ-specific" ageing. This is useful as different biological systems in the body may age at different rates, and I will test organ-specific ageing against many types of disease. Furthermore, I will use genetic data and a technique called Medelian Randomization to test if proteins and metabolites that are correlated with age, may cause unhealthy ageing. I will use this information to further improve the biological age tests. Finally, I will analysis the pathways that connect proteins, metabolites and genetic factors involved in ageing to more deeply understand the biological processes that change with age.
