Excerpts (no abstract)
The search for robust, reliable and valid biomarkers of the ageing process is a key goal for gerontological science. Such tools should enable the quantification of individual differences in underlying biological ageing. This could have great utility for mapping personalised ageing trajectories, for predicting risk of future age-related deterioration and disease and for evaluating potential treatments aimed at improving healthspan or even slowing ageing itself. Given the multi-faceted nature of biological ageing, numerous potential candidate biomarkers have been proposed. These can be anthropometric, physiological or blood-based; indexing immune function, epigenetic signatures, gene expression profiles, physical capacity or body composition [1]. To improve on individual predictors of biological age, panels combining multiple markers have also been proposed [2]. While many of these approaches are highly promising, the results have yet to be translated into clinical practice.
It is well-known that ageing affects the brain, both in terms of outward behavioural changes and cognitive decline, alongside alterations to the brain's biophysical structure and cellular and molecular functioning. Using measures of brain volume derived from T1-weighted structural MRI, assumed to reflect grey and white matter atrophy, high levels of age prediction accuracy have been consistently achieved. For example, our work found a mean/median absolute error of age prediction of 4.2/3.4 years, with a correlation between age and brain-predicted age of r = 0.96, R2 = 0.92 [3]. This is comparable to or better than leading biological age prediction models, for example using DNA methylation status (r = 0.96, median absolute error = 3.6 years) [4] or a panel of blood chemistry markers (r = 0.91, mean absolute error = 5.6 years) [2].