Identifying early predictors of cognitive decline and at-risk individuals is essential for timely intervention and prevention of dementia. This study aimed to detect neurobiological changes and factors related to cognitive performance in the Metropolit 1953 Danish male birth cohort. We analyzed data from 582 participants, aged 57-68 years, using machine learning techniques to group cognitive trajectories into four clusters differentiating high- and low-performing groups. These clusters were then evaluated with MRI, EEG, and lifestyle/familial risk factors to identify predictors of cognitive decline. Low education and occupation, alcohol consumption, and type 2 diabetes were associated with lower cognitive performance. Declines in neocortical volume and increases in frontotemporal alpha and temporoparietal gamma activity preceded clinical symptoms of cognitive decline. Neocortical atrophy and disruptions in network activity were prominent in lower-performing groups, with higher education and IQ scores and a lower prevalence of lifestyle factors moderating cognitive decline.
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