Approved Research

The impact of lifestyle risk factors on brain health across aging

Lay summary

Our project aims to understand the impact of lifestyle risk factors on cognitive decline, such as that experienced in Alzheimer's disease and dementia. Twelve potentially modifiable risk factors have been ascertained for dementia, including lower educational achievement, hypertension, smoking, obesity, and diabetes; we also know that sex and one particular gene (APOE4) increase risk. Our project will use novel data-driven methodologies to ascertain the impact of these risk factors on brain health, as we can measure it via magnetic resonance imaging (MRI). We will derive a model able to compute the elevation in risk of cognitive decline related to each individual factor as well as combinations of multiple factors.  We will create this model using the UK Biobank data, then test it in other datasets to which we have access locally. Given that the number of new cases of dementia has fallen in many countries, probably because of improvements in education, nutrition, health care, and lifestyle changes, the potential for prevention is therefore high. However, to be effective, one needs to assess risk in the first place, which is what our project will attempt to do. To have any validity, it requires a lot of examples to learn from; and therefore, the UK Biobank is essential to its success.

Scope extension:

A growing body of evidence points to twelve potentially modifiable risk factors for dementia: less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, low social contact, alcohol consumption, traumatic brain injury, and air pollution. These lifestyle risk factors further interact with genetic risk factors, the most well-established APOE4 gene, and putatively as well with gene-editing viral infections that tax the immune system. Meanwhile, cortical neurodegeneration and hypometabolism are factors strongly related to aging and recognized signs of incipient dementia. Cortical thickness and metabolism, turned into brain age as a derived metric, can be measured from MRI and quantify the degree of neurodegeneration. In our project we intend to first assess the impact of specific risk factors on brain age, then devise an algorithm able to assess the risk of cognitive decline based on these risk factors. We aim to test the hypothesis that the brain signature of specific risk factors can be predictive of future decline and severity, modulated by systemic factors such as genetics and lifelong infection load.

Additional scope:

We want to increase the scope of our brain health model by including cerebral blood perfusion as well as small vessel disease biomarkers. First, Cerebral blood perfusion will be measured from ASL MRI. To obtain flow from images collected in the UK Biobank, a synthetic ASL algorithm based on deep learning and taking as input T1w, T2w, and resting state functional MRI will be developed and validated using the UK Biobank data. Secondly, we will measure small vessel disease such as white matter hyperintensities using T2w-MRI. Then, using both these cerebrovascular measures we will generate a cerebrovascular brain age model like we plan to conduct for the other brain measures (cortical thickness and metabolism) that will be able to assess the risk of cognitive decline based on these risk factors.