Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health.

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Abstract

The brain age gap (BAG) is a neuroimaging-derived marker of accelerated brain aging. However, its clinical application faces challenges due to model inaccuracies and unclear links to disease mechanisms. This study investigates the clinical relevance of BAG across neuropsychiatric disorders, cognitive decline, mortality, and lifestyle interventions. We use data from multiple cohorts, including 38,967 participants from the UK Biobank (ages 45-82, 52.5% female), 1,402 individuals from the ADNI study (ages 55-96, 56.0% female), and 1,182 from the PPMI study (ages 45-83, 58.0% female). We develop a 3D Vision Transformer for whole-brain age estimation. Survival analysis, restricted cubic splines, and regression models assess BAG's associations with cognitive, neuropsychiatric disorders, mortality and impact of lifestyle factors. Here we show that the model achieves a mean error of 2.68 years in the UK Biobank and 2.99-3.20 years in ADNI/PPMI. Each one-year increase in BAG raises Alzheimer's risk by 16.5%, mild cognitive impairment by 4.0%, and all-cause mortality by 12%. The highest-risk group (Q4) shows a 2.8-fold increased risk of Alzheimer's disease, a 6.4-fold risk of multiple sclerosis, and a 2.4-fold higher mortality risk. Cognitive decline is most evident in Q4, particularly in reaction time and processing speed. Lifestyle interventions, especially smoking cessation, moderate alcohol consumption, and physical activity, significantly slow BAG progression in individuals with advanced neurodegeneration. BAG predicts accelerated brain aging, neuropsychiatric disorders, and mortality. Its ability to detect nonlinear cognitive thresholds and modifiability through lifestyle changes makes it useful for risk stratification and prevention.

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