Socioeconomic Status May Influence Brain Aging

Lower socioeconomic status (SES) could be connected to premature age-related brain changes according to research published in Neurobiology of Aging.

Premature brain aging is usually understood as excessive brain atrophy that is not in line with chronological age, suggesting early structural compromise. This phenomenon, known as premature brain age, is emerging as a significant factor linked to lower cognitive function in healthy older adults. Research has consistently shown a strong association between premature brain age and cardiovascular risk factors, as well as other negative health conditions. However, the impact of environmental factors on brain aging, particularly SES, remains less understood. 

In order to investigate the association between SES and brain aging, brain age was estimated from T1-weighted images using BrainAgeR in 217 participants aged 20 to 79 from the Aging Brain Cohort at the University of South Carolina (ABC@UofSC) Repository in an ongoing original cross-sectional cohort. Individuals with a history of stroke, neurodegenerative disease, conditions that limit their participation, severe illnesses, psychiatric diagnosis, or BMI over 42 kg/m2 are excluded from ABC@UofSC, which primarily focuses on healthy aging. 

The T1-weighted images were segmented and normalized using SPM12's DARTEL toolbox, followed by visual inspection of probabilistic tissue maps by a neurologist to ensure accurate segmentation. A machine learning algorithm processed matter tissue mass to estimate brain age based on a pretrained Gaussian regression model. BrainGAP, the difference between premature brain age and chronological age, was determined by subtracting an individual's chronological age from their estimated brain age. 

SES was determined by factors such as total household income, number of individuals in the household, and cost of living. The income tier was calculated using the Pew Research Center's income calculator with data from the 2018 US Census. The questionnaire provided to participants during the magnetic resonance imaging scanning addressed medical history variables related to brain health. This included variables such as diabetes and hypertension, which were corroborated with medical records when available. Additionally, body mass index (BMI) was calculated based on height and weight.

“Multiple regression models were used to predict BrainGAP with age, SES, body mass index, diabetes, hypertension, sex, race, and education as predictors,” said researchers. 

The mean age of participants was 47.44 and their estimated brain ages ranged from 16.95 to 80.22 (mean age = 43.72). A significant positive correlation was found between chronological age and predicted brain age (r = 0.936, P <0.001). The average BrainGAP was -3.72 and 55 participants (24.35%) showed premature brain aging. There was a significant negative correlation between chronological age and BrainGAP, a significant positive correlation was found between age and socioeconomic status, and the multiple regression analysis showed that chronological age, sex at birth, and socioeconomic status were statistically significant predictors of BrainGAP. Those with the highest BrainGAP scores were younger, male, and had lower SES.

“This study demonstrates that lower SES is an independent contributor to premature brain aging,” said researchers. “This may provide additional insight into the mechanisms associated with brain health, cognition, and resilience to neurological injury.”

Reference:
Busby N, Newman-Norlund S, Sayers S, et all. Lower socioeconomic status is associated with premature brain aging. Neurobiology of Aging. 2023(130);135-140. doi.org/10.1016/j.neurobiolaging.2023.06.012