Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function

INTRODUCTION: Neuroimaging-based ‘brain age’ can identify individuals with ‘advanced’ or ‘resilient’ brain aging. Brain-predicted age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unknown how individual health and lifestyle factors may modify the relationship between brain-PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain-PAD, and determine if membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years. METHODS: Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) trial. Participants were grouped as having resilient (n=159) or advanced (n=167) brain-PAD, and latent class analysis (LCA) was performed using a set of cognitive, lifestyle, and health measures. We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex and education. RESULTS: Subgroups of resilient and advanced brain aging were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 were characterized by low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient brain agers, class 1 was associated with a decrease in cognition, and class 2 with an increase over 5 years, though was a small effect that was equivalent to a 0.04 standard deviation difference per year. No significant class distinctions were evident with FI. For advanced brain agers, there was no evidence of an association between class membership and changes in cognition or FI. CONCLUSION: These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. In particular, people with age-resilient brains had different trajectories of cognitive change depending on their cognitive and physical health status at baseline. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple lifestyle and health indices alongside brain age.