Modifiable Contributors to Socioeconomic Inequality in Brain Aging
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Importance
Socioeconomic disadvantage is associated with accelerated brain aging. However, the modifiable factors accounting for this association, and whether they differ across socioeconomic indicators, remains unclear.
Objective
To determine whether modifiable risk factors account for socioeconomic differences in the brain age gap, and whether the contributions of these risk factors differ between individual-level and area-level socioeconomic indicators.
Design, Setting, and Participants
This cohort study used data from participants in the UK Biobank, a population-based cohort recruited at ages 40 to 70 years from 2006 to 2010. Participants with T1-weighted and T2-FLAIR brain magnetic resonance imaging at first imaging visit were eligible; 7700 participants used for model development in previous work were excluded, yielding 36 878. Data were analyzed from April to July 2026.
Exposures
Household income, highest educational attainment, and area-level deprivation (Townsend Deprivation Index).
Main Outcomes and Measures
Brain Age Gap (predicted minus chronological age, years) from T1-weighted (primary) and T2-FLAIR (secondary) magnetic resonance imaging, derived with a deep learning model. Eleven risk factors and the Life’s Essential 8 composite cardiovascular health score were modeled as mediators; indirect effects were estimated in single-mediator and parallel mediation models with 95% confidence intervals from 5000 bootstrap resamples.
Results
Among 36 878 participants (mean [SD] age, 65.1 [7.7] years; 20 360 [55.2%] female), lower income and greater area deprivation were associated with a larger brain age gap: lowest vs highest income group, 0.35 years (95% CI, 0.22-0.47); most vs least deprived quartile, 0.33 years (95% CI, 0.23-0.43). Education showed weaker associations that differed in direction between imaging contrasts. Life’s Essential 8 score mediated 36% of the income (indirect effect, 0.031 [95% CI, 0.025-0.036]) and 17.9% of the area-deprivation (0.024 [95% CI, 0.019-0.029]) associations. In parallel models, where all risk factors were entered simultaneously, smoking was the largest mediator for both income (0.022 [95% CI, 0.016-0.028]) and area-deprivation (0.030 [95% CI, 0.023-0.037]). Higher income was associated with higher alcohol intake, offsetting part of the income association.
Conclusions and Relevance
Socioeconomic differences in brain age gap were partly accounted for by modifiable cardiovascular and lifestyle risk factors, with smoking being the single largest contributor. These findings identify modifiable cardiovascular risk factors as a substantial component of socioeconomic inequalities in brain aging.
Key Points
Question
Do modifiable risk factors statistically mediate socioeconomic differences in the brain age gap, and do the contributing factors differ across socioeconomic indicators?
Findings
In this cohort study of 36 878 UK Biobank participants, lower household income and greater area-level deprivation were associated with an older-appearing brain, whereas educational attainment was not consistently associated. Modifiable risk factors, particularly smoking, mediated part of these associations, and the mediating factors differed by socioeconomic indicator.
Meaning
Modifiable risk factors are candidate targets for reducing socioeconomic inequalities in brain aging, where individual and area-level disadvantage are associated with divergent risk profiles.
