Dose-dependent relationship between social drinking and brain aging

Abstract

Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n = 240, mean age 35.1 (±10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age ("brain age gap"). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap (β = 0.014, p = 0.023). We replicated these results in an independent cohort [n = 231 adults, mean age 34.3 (±11.1) years, 55% male, 28% African American: β = 0.014, p = 0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.

Keywords: Alcohol; Brain age prediction; Community sample; accelerated aging.

Fig. 1.. Correlation between chronological age and predicted brain age

Fig. 1. Significant correlation between participants’ chronological ages and brain age predictions (n = 240, r = 0.70, R² = 0.49, p < 0.0001).

Fig. 2.. Association between total number of drinks consumed over 90 days and the brain age gap in the discovery sample

Fig. 2. Normalized brain age gap and its association with the number of drinks consumed in the previous 90-day assessment period in the discovery sample (n = 240). The normalized brain age gap was calculated by subtracting the mean brain age gap from each individual brain age gap. Therefore, a normalized brain age gap equal to zero indicates no difference from the mean brain age gap. The linear regression model predicting brain age gap showed a significant, positive association between the total number of drinks consumed in the 90-day period and the brain age gap (β = 0.014, p = 0.023, partial R² = 0.015). The variability in the brain age gap indicates that alcohol consumption plays a small, but significant, role in accelerated brain age predictions.

Fig. 3.. Association between total number of drinks consumed over 90 days and the brain age gap in the replication cohort

Fig. 3. Normalized brain age gap and its association with the number of drinks consumed in the previous 90-day assessment period in the replication cohort. The normalized brain age gap was calculated by subtracting the mean brain age gap from each individual brain age gap. Therefore, a normalized brain age gap equal to zero indicates no difference from the mean brain age gap. Similar to the discovery sample, the linear regression model predicting brain age gap showed a significant, positive association between the total number of drinks consumed in the previous 90-day period and the brain age gap (β = 0.014, p = 0.002, partial R² = 0.034). The variability in the brain age gap indicates that alcohol consumption plays a small, but significant, role in accelerated brain age predictions.