The proliferation of brain-based aging biomarkers has generated optimism that MRI could be useful in gauging individual risk for aging-related functional decline and chronic diseases. However, it is unclear how existing brain-based biomarkers compare to one another in predicting decline and disease. To be nominated for clinical adoption within the precision medicine model, we need to better understand how different brain-based biomarkers map onto different types of chronic aging-related disease risk and how they relate to known risk factors for aging-related disease and decline.
Ideally, MRI-derived brain-based biomarkers of accelerated aging are first identified in one dataset to predict a health-related outcome and then generated in an independent dataset to establish predictive utility. Here, brain-based biomarkers of accelerated aging identified in other studies will be derived in the UK Biobank dataset to test each biomarkers’ sensitivity to disease and decline. We will also compare brain-based biomarkers with non-brain-based biomarkers of accelerated aging including various -omic clocks. Furthermore, we will map the differential associations of these biomarkers onto established risk factors for aging-related decline and disease including genetics, experience, environment, socioeconomics, lifestyle, and health.
Identifying which specific brain-based biomarkers or combination of biomarkers is most effective at predicting specific outcomes will help guide the prioritization of specific biomarkers for clinical translation.