Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank

This study investigates the link between chronic stress—quantified as allostatic load (AL)—and structural brain aging. Using data from 22,951 participants in the UK Biobank, the researchers examined whether the "wear and tear" on physiological systems (inflammatory, anthropometric, respiratory, lipid, and glucose markers) predicts a discrepancy between chronological age and biological brain age, known as the white matter brain age gap (BAG).

To determine the effect of chronic stress on brain health, the authors employed advanced neuroimaging derived from diffusion tensor imaging to calculate a single, scalar metric of brain age. They trained a machine learning model (random forest regression) to predict chronological age based on fractional anisotropy values across 39 white matter tracts. The difference between this predicted age and actual age yielded the BAG. The researchers utilized multiple statistical approaches, including linear regression, Mendelian randomization (to assess potential causality), inverse probability weighting, and doubly robust methods, all while controlling for socioeconomic and lifestyle factors.

The study found that for every unit increase in the allostatic load score, the white matter brain age gap increased by approximately 0.29 to 0.33 years. This relationship remained consistent across different analytical models. Furthermore, the association appeared strongest among individuals aged 45–64. These results suggest that chronic stress contributes to the premature degeneration of white matter integrity, potentially acting as a modifiable risk factor for neurodegenerative conditions.

Understanding the biological toll of chronic stress on the brain is essential for public health, particularly as global rates of dementia and cognitive decline rise. By establishing a potential causal link between allostatic load and accelerated brain aging, this research underscores the urgency of stress management and systemic health monitoring as strategies to preserve long-term cognitive function and brain health.