Age-related changes of human brain metabolism. - Qian, Shufang; Ba, Yihan; Xue, Le; Jin, Chentao; Zhou, Rui; Liao, Yi; Zhong, Yan; Xu, Yuanfan; Shi, Feng; Wang, Chengyan; Dou, Xiaofeng; Gao, Yidan; et al. (European Journal of Nuclear Medicine & Molecular Imaging, 2025)
Purpose: Glucose is the primary source of human brain energy, and is closely related to brain function. This study aims to evaluate in vivo glucose metabolic changes using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET). Methods: In this retrospective analysis, a total of 3,291 healthy adults (aged 18 to 89 years, 1816 males) who underwent 18F-FDG PET were recruited (chictr.org.cn ChiCTR2400081809). Group comparison and brain chart modeling are integrated to examine these changes. Qualitative voxel-wise group comparison among different age and gender groups are analyzed. Brain chart modeling is used to quantitatively estimate aging trajectories, generate predicted values and calculate derived percentage change. Additional analyses of aging peaks and variability are then performed on derived reference values. Results: Age-related, gender-specific patterns of glucose metabolic changes are revealed, especially in the frontal, occipital, temporal and parietal lobes. A significantly decrease in metabolism is observed in males aged 45 to 70 compared to females. Metabolic aging trajectories and centile scores demonstrate a gradual decline across the total cortical, subcortical and most brain regions. Additionally, brain regions with maximum values (not at age 18), extreme age points, and their corresponding ages were identified. Specifically, only 12 brain regions exhibited values higher than those at age 18, and only 8 regions displayed extreme points throughout the aging process. Conclusion: In summary, our large population study identifies a distinct pattern of brain glucose metabolism during aging that varies between men and women, with two critical age periods based on gender: 45–49, and 70–75. We establish benchmark trajectories for various brain regions, which could serve as references in future aging studies of healthy populations. The aging peaks, including maximum and extreme points, revealed in this study may provide insight into the molecular transformations associated with aging and the development of age-related conditions. Significance statement: A number of brain aging changes throughout the lifespan have been well documented, such as reductions in cerebral blood flow, cortical thickness, synaptic density, and neural activity. However, the spatiotemporal patterns of brain functional changes during normal aging remain poorly understood. In this study, we utilized the largest cohort of healthy individuals' FDG brain images to date. For the first time, we combined qualitative and quantitative metabolic aging analyses, and applied brain chart modeling to 18F-FDG PET imaging. By establishing benchmark trajectories for various brain regions, we identified the maximum, extrema and corresponding aging peaks, which could provide insights into the underlying molecular changes associated with the aging process and age-related diseases.