Using machine learning to analyze brain-imaging data, researchers developed a model that determines 'brain age' and identified key proteins linked to brain aging.
Studying biomarkers related to brain age could lead to the identification of therapeutic targets for treating age-related brain disorders, providing hope for future interventions.
The study found that, on average, participants' brain ages differed from their chronological ages by about three years, indicating varying brain health among individuals.
The research identified 13 proteins in blood that predict brain aging speed, with eight linked to faster aging and five to slower aging, showing potential treatment markers.
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