TY - JOUR
T1 - Glucose metabolism patterns
T2 - A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment
AU - For the Alzheimer’s Disease Neuroimaging Initiative
AU - Jiang, Jiehui
AU - Sheng, Can
AU - Chen, Guanqun
AU - Liu, Chunhua
AU - Jin, Shichen
AU - Li, Lanlan
AU - Jiang, Xueyan
AU - Han, Ying
AU - Weiner, Michael W.
AU - Aisen, Paul
AU - Petersen, Ronald
AU - Jack, Clifford R.
AU - Jagust, William
AU - Trojanowski, John Q.
AU - Toga, Arthur W.
AU - Beckett, Laurel
AU - Green, Robert C.
AU - Saykin, Andrew J.
AU - Morris, John
AU - Shaw, Leslie M.
AU - Khachaturian, Zaven
AU - Sorensen, Greg
AU - Kuller, Lew
AU - Raichle, Marcus
AU - Paul, Steven
AU - Davies, Peter
AU - Fillit, Howard
AU - Hefti, Franz
AU - Holtzman, David
AU - Mesulam, Marek M.
AU - Potter, William
AU - Snyder, Peter
AU - Schwartz, Adam
AU - Montine, Tom
AU - Thomas, Ronald G.
AU - Donohue, Michael
AU - Walter, Sarah
AU - Gessert, Devon
AU - Sather, Tamie
AU - Jiminez, Gus
AU - Harvey, Danielle
AU - Bernstein, Matthew
AU - Thompson, Paul
AU - Schuff, Norbert
AU - Borowski, Bret
AU - Gunter, Jeff
AU - Senjem, Matt
AU - Vemuri, Prashanthi
AU - Jones, David
AU - Grossman, Hillel
N1 - Funding Information:
Data collection and dissemination for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI): the National Institutes of Health (grant number U01 AG024904), and the Department of Defense (award numberW81XWH-12-2-0012). ADNI is funded by the National Institute of Aging and the National Institute of Biomedical Imaging and Bioengineering as well as through generous contributions from the following organizations: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica Inc., Biogen, Bristol-Myers Squibb Company, CereSpir Inc., Eisai Inc., Elan Pharmaceuticals Inc., Eli Lilly and Company, EuroImmun, F. Hoffmann-La Roche Ltd. and its affiliated company Genentech Inc., Fujirebio, GE Healthcare, IXICO Ltd., Janssen Alzheimer Immunotherapy Research & Development LLC., Johnson & Johnson Pharmaceutical Research &Development LLC., Lumosity, Lundbeck, Merck & Co. Inc., Meso Scale Diagnostics LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research are providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego, CA, USA. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California, CA, USA.
Funding Information:
This study was supported by grants received from the National Natural Science Foundation of China (grant numbers 61633018, 82020108013, 61603236, 81830059, and 81801052); the National Key Research and Development Program of China (grant numbers 2016YFC1306300, 2018YFC1312000, and 2018YFC1707704); the 111 Project (grant number D20031); the Shanghai Municipal Science and Technology Major Project (grant number 2017SHZDZX01); and the Beijing Municipal Commission of Health and Family Planning (grant number PXM2020_026283_000002).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to American Aging Association.
PY - 2022/8
Y1 - 2022/8
N2 - 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 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.
AB - 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 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.
KW - Brain ageing
KW - Glucose metabolism
KW - Pattern
KW - Positron emission tomography
UR - http://www.scopus.com/inward/record.url?scp=85130220126&partnerID=8YFLogxK
U2 - 10.1007/s11357-022-00588-2
DO - 10.1007/s11357-022-00588-2
M3 - Article
C2 - 35581512
AN - SCOPUS:85130220126
SN - 2509-2715
VL - 44
SP - 2319
EP - 2336
JO - GeroScience
JF - GeroScience
IS - 4
ER -