Abstract
With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman’s rs= −0.102, p = 0.005) and resilience (rs= −0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a ‘younger’ brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.
Highlights
The human brain changes with age, and aging is known to be associated with alterations of brain function and sometimes neurodegenerative diseases
Considering the positive effect of mental well-being on healthy aging, we hypothesized that life satisfaction and/or other mental factors may affect the brain’s aging process independently beyond the common lifestyle-related metabolic factors, and that the clarification of such relationships could provide key insights for better health in the elderly. In this cross-sectional observational study, we investigated the relationships between brain aging and relevant mental factors as well as lifestyle-related metabolic diseases in a cognitively unimpaired population of older participants living in their community in Tokyo
Alcohol use, smoking, diabetes, and hypertension were present more frequently in the males, and dyslipidemia was more prevalent in the females
Summary
The human brain changes with age, and aging is known to be associated with alterations of brain function and sometimes neurodegenerative diseases. The advances in machine learning and its applications have been remarkable, and it is currently possible to predict an individual’s brain age using structural and/or functional brain images [1, 2]. A neuroimaging-derived brain-age prediction model learns the patterns of image data of many healthy people and their actual ages, and when new image data are input into the system, it can predict a given brain’s age based on the learning model. The application of brain-age prediction models has been spreading rapidly in recent years to explore the relationship between brain aging and neuropsychiatric disorders, including psychosis, dementia, and epilepsy [3,4,5,6,7]
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