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

doi:10.1007/s11357-022-00588-2

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

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12 Scopus citations

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 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.

Original language	English
Pages (from-to)	2319-2336
Number of pages	18
Journal	GeroScience
Volume	44
Issue number	4
DOIs	https://doi.org/10.1007/s11357-022-00588-2
State	Published - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to American Aging Association.

Funding

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. 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).

Funders	Funder number
Eisai
Alzheimer's Association
GE Healthcare
H. Lundbeck A/S
Merck
BioClinica Inc.
Fujirebio Europe
Euroimmun
Lumosity
Janssen Alzheimer Immunotherapy Research and Development
CereSpir, Inc.
National Institute of Biomedical Imaging and Bioengineering
AbbVie
Biogen IDEC
Eli Lilly and Company
IXICO plc
Elan Pharmaceuticals, Inc.
Johnson & Johnson Pharmaceutical Research &Development LLC.
Bristol-Myers Squibb
DoD Alzheimer's Disease Neuroimaging Initiative
Araclon Biotech
National Institute on Aging
Genentech Incorporated
Alzheimer's Drug Discovery Foundation
U.S. Department of Defense
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
National Key Basic Research and Development Program of China	2018YFC1312000, 2018YFC1707704, 2016YFC1306300
National Institutes of Health (NIH)	U01 AG024904
UK Industrial Decarbonization Research and Innovation Centre	53706
Beijing Municipal Commission of Health and Family Planning	PXM2020_026283_000002
Science and Technology Commission of Shanghai Municipality	2017SHZDZX01
Higher Education Discipline Innovation Project	D20031
National Natural Science Foundation of China (NSFC)	81801052, 61633018, 61603236, 82020108013, 81830059

Keywords

Brain ageing
Glucose metabolism
Pattern
Positron emission tomography

ASJC Scopus subject areas

Aging
Veterinary (miscellaneous)
Complementary and alternative medicine
Geriatrics and Gerontology
Cardiology and Cardiovascular Medicine

Access to Document

10.1007/s11357-022-00588-2

Cite this

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

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 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer{\textquoteright}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 \textasciitilde{} 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.",

keywords = "Brain ageing, Glucose metabolism, Pattern, Positron emission tomography",

author = "Jiehui Jiang and Can Sheng and Guanqun Chen and Chunhua Liu and Shichen Jin and Lanlan Li and Xueyan Jiang and Ying Han and Weiner, \{Michael W.\} and Paul Aisen and Ronald Petersen and Jack, \{Clifford R.\} and William Jagust and Trojanowski, \{John Q.\} and Toga, \{Arthur W.\} and Laurel Beckett and Green, \{Robert C.\} and Saykin, \{Andrew J.\} and John Morris and Shaw, \{Leslie M.\} and Zaven Khachaturian and Greg Sorensen and Lew Kuller and Marcus Raichle and Steven Paul and Peter Davies and Howard Fillit and Franz Hefti and David Holtzman and Mesulam, \{Marek M.\} and William Potter and Peter Snyder and Adam Schwartz and Tom Montine and Thomas, \{Ronald G.\} and Michael Donohue and Sarah Walter and Devon Gessert and Tamie Sather and Gus Jiminez and Danielle Harvey and Matthew Bernstein and Paul Thompson and Norbert Schuff and Bret Borowski and Jeff Gunter and Matt Senjem and Greg Jicha and Elizabeth Oates and Richard King",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to American Aging Association.",

year = "2022",

month = aug,

doi = "10.1007/s11357-022-00588-2",

language = "English",

volume = "44",

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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 - Jicha, Greg

AU - Oates, Elizabeth

AU - King, Richard

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

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ER -

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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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