Aging is a progressive, generalized deterioration and loss-of-function across multiple organ systems. When you age by a year, your body and certain organs may show signs of ageing that appear greater (or less than) what would be considered normal for a year, as determined relative to population norms. The apparent age of your brain is referred to as your brain age and can vary markedly from your chronological age.
Critically, organ systems age much faster in some individuals compared to others and advanced biological ageing is associated with increased risk of many age-related body and brain disorders such as cancer, coronary artery diseases and dementia and therefore, decreased life expectancy.
This project aims to develop machine learning models to predict the age of an individual's brain and other organ systems. Individuals participating in the UK Biobank (n>30,000) will be used to establish population norms and train the model.
The project also aims to investigate the extent to which environmental factors can modify the pre-determined genetic impact on ageing and whether interactions between genetic and environmental effects on ageing act differently across different organ systems.
Further research and key questions
- Identify common genetic variants that associate with biological ageing using genome-wide meta-analyses
- Subtype individuals based on their genetic variants and investigate whether individuals with certain genetic profiles are more susceptible/resilient to environmental effects on ageing. This would facilitate the development of intervention programs that could potentially delay the process of biological ageing
Ye Tian, Vanessa Cropley & Andrew Zalesky
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