Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment

dc.contributor.authorJiang, Jiehui
dc.contributor.authorSheng, Can
dc.contributor.authorChen, Guanqun
dc.contributor.authorLiu, Chunhua
dc.contributor.authorJin, Shichen
dc.contributor.authorLi, Lanlan
dc.contributor.authorJiang, Xueyan
dc.contributor.authorHan, Ying
dc.contributor.authorAlzheimer’s Disease Neuroimaging Initiative
dc.contributor.departmentMedical and Molecular Genetics, School of Medicine
dc.date.accessioned2024-10-25T09:49:05Z
dc.date.available2024-10-25T09:49:05Z
dc.date.issued2022
dc.description.abstractExploring 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 18F-fluorodeoxyglucose (18F-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.
dc.eprint.versionFinal published version
dc.identifier.citationJiang J, Sheng C, Chen G, et al. Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment. Geroscience. 2022;44(4):2319-2336. doi:10.1007/s11357-022-00588-2
dc.identifier.urihttps://hdl.handle.net/1805/44222
dc.language.isoen_US
dc.publisherSpringer
dc.relation.isversionof10.1007/s11357-022-00588-2
dc.relation.journalGeroScience
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectPattern
dc.subjectBrain ageing
dc.subjectPositron emission tomography
dc.subjectGlucose metabolism
dc.titleGlucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment
dc.typeArticle
ul.alternative.fulltexthttps://pmc.ncbi.nlm.nih.gov/articles/PMC9616982/
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