Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects

• Published in: Frontiers in aging neuroscience Vol. 6; p. 94
• Main Authors: Franke, Katja, Ristow, Michael, Gaser, Christian
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 23.05.2014; Frontiers Media S.A
• Subjects: Age; Age determination; Aging; Alcohol; Alzheimer's disease; Atrophy; Biomarkers; Body mass index; BrainAGE; Cognitive ability; Dementia; Diabetes; Functional anatomy; Geriatrics; Homocysteine; Kidneys; lifestyle factors; Lifestyles; Liver; Magnetic Resonance Imaging; Males; Medical imaging; Memory; Metabolic syndrome; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Nutrition research; Parameter estimation; Personal health; Psychiatry; Risk factors; Structure-function relationships; Vitamin B12; Voxel-based morphometry (VBM); Womens health; Germany; magnetic resonance imaging (MRI); voxel-based morphometry (VBM); Alzheimer's disease (AD); aging; BrainAGE; lifestyle
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2014.00094

Aging alters brain structure and function. Personal health markers and modifiable lifestyle factors are related to individual brain aging as well as to the risk of developing Alzheimer's disease (AD). This study used a novel magnetic resonance imaging (MRI)-based biomarker to assess the effects of 17 health markers on individual brain aging in cognitively unimpaired elderly subjects. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for developing AD. Within this cross-sectional, multi-center study 228 cognitively unimpaired elderly subjects (118 males) completed an MRI at 1.5Tesla, physiological and blood parameter assessments. The multivariate regression model combining all measured parameters was capable of explaining 39% of BrainAGE variance in males (p < 0.001) and 32% in females (p < 0.01). Furthermore, markers of the metabolic syndrome as well as markers of liver and kidney functions were profoundly related to BrainAGE scores in males (p < 0.05). In females, markers of liver and kidney functions as well as supply of vitamin B12 were significantly related to BrainAGE (p < 0.05). In conclusion, in cognitively unimpaired elderly subjects several clinical markers of poor health were associated with subtle structural changes in the brain that reflect accelerated aging, whereas protective effects on brain aging were observed for markers of good health. Additionally, the relations between individual brain aging and miscellaneous health markers show gender-specific patterns. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of subtly abnormal patterns of brain aging probably preceding cognitive decline and development of AD.