Journal article
Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment
GeroScience, Vol.44(4), pp.2319-2336
05/18/2022
DOI: 10.1007/s11357-022-00588-2
PMCID: PMC9616982
PMID: 35581512
Abstract
Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected
F-fluorodeoxyglucose (
F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.
Details
- Title: Subtitle
- Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment
- Creators
- Jiehui Jiang - Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China. jiangjiehui@shu.edu.cn.Can Sheng - Capital Medical UniversityGuanqun Chen - Capital Medical UniversityChunhua Liu - Shanghai UniversityShichen Jin - Shanghai UniversityLanlan Li - Shanghai UniversityXueyan Jiang - Hainan UniversityYing Han - Capital Medical UniversityAlzheimer’s Disease Neuroimaging Initiative
- Contributors
- HyungSub Shim (Contributor) - University of Iowa, Neurology
- Resource Type
- Journal article
- Publication Details
- GeroScience, Vol.44(4), pp.2319-2336
- DOI
- 10.1007/s11357-022-00588-2
- PMID
- 35581512
- PMCID
- PMC9616982
- NLM abbreviation
- Geroscience
- ISSN
- 2509-2715
- eISSN
- 2509-2723
- Grant note
- 2018YFC1312000 / National Key Research and Development Program of China 61633018 / National Natural Science Foundation of China 2018YFC1707704 / National Key Research and Development Program of China 2017SHZDZX01 / Shanghai Municipal Science and Technology Major Project 82020108013 / National Natural Science Foundation of China D20031 / the 111 Project PXM2020_026283_000002 / Beijing Municipal Commission of Health and Family Planning 81801052 / National Natural Science Foundation of China 61603236 / National Natural Science Foundation of China 81830059 / National Natural Science Foundation of China 2016YFC1306300 / National Key Research and Development Program of China
- Language
- English
- Date published
- 05/18/2022
- Academic Unit
- Neurology; Psychiatry
- Record Identifier
- 9984302201802771
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