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

Your competent? doctor determined your brain age 2 years ago, right? And then created PRECISE PROTOCOLS TO REDUCE YOUR BRAIN AGING! Oh no, NOTHING OCCURRED!

AI Uncovers Secrets of Brain Aging October 2023 

The latest here;

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

• Ruixia Zhang, 
• Fan Yi, 
• Hongjing Mao, 
• Zhengxing Huang, 
• Kai Wang & 
• Junhang Zhang 

Communications Medicine volume 5, Article number: 441 (2025) Cite this article

• 6178 Accesses

• 9 Altmetric

• Metricsdetails

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Plain language summary

This study examines whether the brain age gap (BAG)—the difference between a person’s estimated brain age and their chronological age—can predict risks of cognitive decline, mental health disorders, and early death. We analyzed MRI scans from over 40,000 participants across three large-scale cohort studies and used a deep-learning model to estimate brain age. We found that a larger BAG was linked to higher risks of cognitive decline, dementia, multiple sclerosis, and reduced survival. Importantly, lifestyle changes such as quitting smoking, moderate alcohol use, and regular exercise significantly reduce brain aging, especially in high-risk individuals. Measuring BAG could enable early detection of at-risk individuals and guide targeted lifestyle interventions and public health strategies to preserve brain health.