The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Spatial correlation between brain aerobic glycolysis and amyloid-β (Aβ) deposition
Open accessAbstract
Amyloid-β (Aβ) plaque deposition can precede the clinical manifestations of dementia of the Alzheimer type (DAT) by many years and can be associated with changes in brain metabolism. Both the Aβ plaque deposition and the changes in metabolism appear to be concentrated in the brain's default-mode network. In contrast to prior studies of brain metabolism which viewed brain metabolism from a unitary perspective that equated glucose utilization with oxygen consumption, we here report on regional glucose use apart from that entering oxidative phosphorylation (so-called "aerobic glycolysis"). Using PET, we found that the spatial distribution of aerobic glycolysis in normal young adults correlates spatially with Aβ deposition in individuals with DAT and cognitively normal participants with elevated Aβ, suggesting a possible link between regional aerobic glycolysis in young adulthood and later development of Alzheimer pathology.
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In the citing paper
“Similarly, brain AG was previously found to be highest in regions most vulnerable to amyloid deposition”
From Aerobic glycolysis in the human brain is associated with development and neotenous gene expression (2014) · cites this paper as Vlassenko et al., 2010
In the citing paper
“Further, AG identifies regions most vulnerable to amyloid-beta deposition in Alzheimer’s disease”
From Aerobic glycolysis in the human brain is associated with development and neotenous gene expression (2014) · cites this paper as Vlassenko et al., 2010
In the citing paper
“Young ApoE4 carriers showed increased brain activity in default network regions, as well as in the hippocampus and the temporal lobe, possibly due to increased aerobic glycolysis.34”
From ApoE4-dependent lysosomal cholesterol accumulation impairs mitochondrial homeostasis and oxidative phosphorylation in human astrocytes (2023) · cites this paper as [34]
In the citing paper
“Whether Aβ changes precede either age-related or AD-related brain bioenergetic changes is less clear-cut, especially since abundant data from human and cell-based studies either suggest or demonstrate bioenergetic factors regulate both APP processing and Aβ production [95–100].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Whether Aβ changes precede either age-related or AD-related brain bioenergetic changes is less clear-cut, especially since abundant data from human and cell-based studies either suggest or demonstrate bioenergetic factors regulate both APP processing and Aβ production [95–100].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Brain plaque deposition anatomically corresponds to the functional magnetic resonance imaging (fMRI)-defined default mode network (DMN), a brain region characterized by relatively high glycolysis rates [69, 99, 136].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Bioenergetic metabolism certainly affects APP processing and Aβ production.”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
Citations
Cites
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Self-citationThe Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Self-citationThe Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound‐B
Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., Bergström, M., Savitcheva, I., Huang, G., Estrada, S., et al. (2004) · Annals of Neurology · doi:10.1002/ana.20009 · Cited by 4,496
Pathways towards and Away from Alzheimer’s Disease
Mattson, M. P. (2004) · Nature · doi:10.1038/nature02621 · Cited by 3,052
Pellerin, L., & Magistretti, P. J. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.22.10625 · Cited by 2,927 · cited 1×
“First, it is essential for the removal of glutamate into astrocytes from the synaptic cleft (40).”
BackgroundPhelps, M. E., Huang, S. C., Hoffman, E. J., Selin, C. E., Sokoloff, L., & Kuhl, D. E. (1979) · Annals of Neurology · doi:10.1002/ana.410060502 · Cited by 2,417
A default mode of brain function: a brief history of an evolving idea
Raichle, M. E., & Snyder, A. Z. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.041 · Cited by 2,173
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Malaquin, S., Lerchundi, R., Mougel, E., & Valette, J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403635121 · Cited by 7
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Taming Alzheimer's disease: New perspectives, newer horizons.
Sen, D., Majumder, A., Arora, V., Yadu, N., & Chakrabarti, R. (2017) · PubMed Central · Cited by 7
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Jain, K. K. (2011) · Humana Press eBooks · doi:10.1007/978-1-61779-049-2_8 · Cited by 7
Jian, Z., Wang, X., Tian, M., Liu, Y., Yao, H., Gong, L. Y., & Li, B. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/4554271 · Cited by 6
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Kandiş, S., Altıntaş, L., Kızıldağ, S., Şimşek, F., ATEŞ, M., Alaçam, H., & Uysal, N. (2021) · Ankara Üniversitesi Veteriner Fakültesi Dergisi · doi:10.33988/auvfd.847817 · Cited by 6
Wong, F. C. C., Initiative, A. D. N., Saffari, S. E., Yatawara, C., Ng, K. P., & Kandiah, N. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210333 · Cited by 6
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Sharma, Y., Gupta, J. K., Babu, M. A., Singh, S., & Sindhu, R. K. (2024) · Journal of Molecular Neuroscience · doi:10.1007/s12031-024-02269-5 · Cited by 6
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Oh, H. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.900581 · Cited by 3
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Dang, A., Wang, D., Towne, J. M., Seshadri, S. S., Habes, M., & Fox, P. T. (2025) · Human Brain Mapping · doi:10.1002/hbm.70402 · Cited by 2
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Self-citationDavargaon, R. S., Verma, N., Kotiya, D., Leibold, N. S., Velmurugan, G. V., Ly, H., Chen, K. C., Ruíz, D., Corces, V. G., Liu, H., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115157 · Cited by 2
Zaman, W., Ayaz, A., & Ullah, A. (2026) · Biochemical Pharmacology · doi:10.1016/j.bcp.2026.118275 · Cited by 2
Kang, D. W., Lim, H. K., Jung, W. S., Jeong, J., Kim, T., Han, J., Lee, C. U., & Hong, S. (2015) · Sleep Medicine Research · doi:10.17241/smr.2015.6.1.1 · Cited by 2
Ahmad, W., & Shabbiri, K. (2025) · Journal of Molecular Medicine · doi:10.1007/s00109-025-02522-3 · Cited by 2
Pasquini, L., Tonch, A., Plant, C., Zherdin, A., Ortner, M., Kurz, A., Förstl, H., Zimmer, C., Grimmer, T., Wohlschäger, A., et al. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0213 · Cited by 2
Tubi, M. A., Wheeler, K. V., Matsiyevskiy, E., Hapenney, M., Mack, W. J., Chui, H. C., King, K. S., Thompson, P. M., & Braskie, M. N. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.08.002 · Cited by 2
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Grizzanti, J., Moritz, W. R., Pait, M. C., Stanley, M., Kaye, S. D., Carroll, C. M., Constantino, N. J., Deitelzweig, L. J., Nicol, N. I., Snipes, J. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.20.481215 · Cited by 2
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Ali, S., Ayaz, A., & Zaman, W. (2026) · Biochemical Pharmacology · doi:10.1016/j.bcp.2026.118343 · Cited by 1
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Schonhaut, D. R., & Rabinovici, G. D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800105-9.00017-2 · Cited by 1
Roh, J. H., & Holtzman, D. M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809813-4.00007-7 · Cited by 1
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Cross, N. E., Gosselin, N., & Dang‐Vu, T. T. (2019) · Current Sleep Medicine Reports · doi:10.1007/s40675-019-0133-z · Cited by 1
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Singh, O. (2017) · Scholarship@Western (Western University)
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Yablonskiy, D. A., Benzinger, T. L. S., & Morris, J. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815868-5.00032-3
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Newington, J. T. (2013) · Scholarship@Western (Western University)
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Farmer, B. C., Williams, H. C., Devanney, N. A., Piron, M. A., Nation, G. K., Carter, D. J., Walsh, A. E., Khanal, R., Young, L. E. A., Kluemper, J. C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16575414
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Metadata
- Authors
- Vlassenko, A. G., Vaishnavi, S. N., Couture, L., Sacco, D., Shannon, B. J., Mach, R. H., Morris, J. C., Raichle, M. E., & Mintun, M. A.
- Affiliations
- Washington University in St. Louis (US)
- Departments of aRadiology,
- Neurology,
- Corresponding author
- Mintun, Mark A.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-09-13
- Volume
- 107
- Issue
- 41
- Pages
- 17763-17767
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2955133
- OpenAlex
- W2137243283
- Cited by
- 404
- References
- 84
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 15 · 2023: 21 · 2022: 26 · 2021: 22 · 2020: 24 · 2019: 20 · 2018: 28 · 2017: 41 · 2016: 20 · 2015: 23 · 2014: 28 · 2013: 33 · 2012: 28
- FWCI
- 20.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5p20mh077967-04, AG05681, AG003991)
- National Institute on Aging (P50AG005681, P01AG003991)
- National Institute of Mental Health (P20MH-077967)
- National Institute of Neurological Disorders and Stroke (F30NS057901, P30NS048056, P50NS006833)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- aerobic glycolysis, amyloid-beta deposition, Alzheimer's disease, default mode network, brain glucose metabolism, oxidative phosphorylation, spatial correlation, young adults, positron emission tomography
- MeSH terms
- Adult, Alzheimer Disease, Amyloid, Brain, Female, Glucose, Glycolysis, Humans, Male, Oxygen, Positron-Emission Tomography
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