Microglial activation states drive glucose uptake and FDG-PET alterations in neurodegenerative diseases.
Abstract
2-Deoxy-2-[ 18 F]fluoro-d-glucose positron emission tomography (FDG-PET) is widely used to study cerebral glucose metabolism. Here, we investigated whether the FDG-PET signal is directly influenced by microglial glucose uptake in mouse models and patients with neurodegenerative diseases. Using a recently developed approach for cell sorting after FDG injection, we found that, at cellular resolution, microglia displayed higher glucose uptake than neurons and astrocytes. Alterations in microglial glucose uptake were responsible for both the FDG-PET signal decrease in Trem2 -deficient mice and the FDG-PET signal increase in mouse models for amyloidosis. Thus, opposite microglial activation states determine the differential FDG uptake. Consistently, 12 patients with Alzheimer’s disease and 21 patients with four-repeat tauopathies also exhibited a positive association between glucose uptake and microglial activity as determined by 18 F-GE-180 18-kDa translocator protein PET (TSPO-PET) in preserved brain regions, indicating that the cerebral glucose uptake in humans is also strongly influenced by microglial activity. Our findings suggest that microglia activation states are responsible for FDG-PET signal alterations in patients with neurodegenerative diseases and mouse models for amyloidosis. Microglial activation states should therefore be considered when performing FDG-PET.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fasting blood glucose levels only exceeded 240 mg/dL in mice fed with LOAD1 HFD and LOAD1.Plcg2M28L HFD.
“Fasting blood glucose levels only exceeded 240 mg/dL (a level that previous studies consider B6 mice to be diabetic (Xiang et al., 2021) in mice fed with LOAD1 HFD and LOAD1.Plcg2M28L HFD.”
From Plcg2M28L Interacts With High Fat/High Sugar Diet to Accelerate Alzheimer’s Disease-Relevant Phenotypes in Mice (2022) · cites this paper as Xiang et al., 2021
In patients with AD, increases in glucose uptake were observed with an increase in microglial activity using TSPO-PET.
“In patients with AD, glucose uptake was measured using FDG-PET, and increases in glucose uptake were observed with an increase in the microglial activity (utilizing TSPO-PET) (Xiang et al., 2021).”
From Plcg2M28L Interacts With High Fat/High Sugar Diet to Accelerate Alzheimer’s Disease-Relevant Phenotypes in Mice (2022) · cites this paper as Xiang et al., 2021
Recent in vivo imaging studies have suggested that microglia displayed higher glucose uptake than neurons and astrocytes.
“Recent in vivo imaging studies have suggested that microglia displayed higher glucose uptake than neurons and astrocytes (Xiang et al., 2021).”
From Plcg2M28L Interacts With High Fat/High Sugar Diet to Accelerate Alzheimer’s Disease-Relevant Phenotypes in Mice (2022) · cites this paper as Xiang et al., 2021
Cites
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
Sokoloff, L., Reivich, M., Kennedy, C., Rosiers, M. H. D., Patlak, C. S., Pettigrew, K. D., Sakurada, O., & Shinohara, M. (1977) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.1977.tb10649.x · Cited by 6,000 · see citing works
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493 · see citing works
Krasemann, S., Madore, C., Cialic, R., Baufeld, C., Calcagno, N., El Fatimy, R., Beckers, L., O’Loughlin, E., Xu, Y., Fanek, Z., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.08.008 · Cited by 2,979 · see citing works
Self-citationPellerin, 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× · see citing works
“It is tempting to speculate that microglia might metabolically support neurons similar to the well-known astrocyte-neuron lactate shuttle (45) and oligodendrocyteneuron lactate shuttle (46, 47).”
BackgroundBuckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862 · see citing works
Microglial <scp>M1/M2</scp> polarization and metabolic states
Orihuela, R., McPherson, C. A., & Harry, G. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13139 · Cited by 2,070 · see citing works
The human brain in numbers: a linearly scaled-up primate brain
Herculano-Houzel, S. (2009) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.031.2009 · Cited by 1,778 · cited 1× · see citing works
“We note that the human brain comprises a higher neuron to nonneuronal cell ratio when compared to a weight normalized brain of rodents (56).”
Oligodendroglia metabolically support axons and contribute to neurodegeneration
Lee, Y., Morrison, B. M., Li, Y., Lengacher, S., Farah, M. H., Hoffman, P. N., Liu, Y., Tsingalia, A., Jin, L., Zhang, P.-W., et al. (2012) · Nature · doi:10.1038/nature11314 · Cited by 1,664 · see citing works
Glycolytic oligodendrocytes maintain myelin and long-term axonal integrity
Fünfschilling, U., Supplie, L. M., Mahad, D., Boretius, S., Saab, A. S., Edgar, J., Brinkmann, B. G., Kassmann, C. M., Tzvetanova, I. D., Möbius, W., et al. (2012) · Nature · doi:10.1038/nature11007 · Cited by 1,523 · see citing works
Single App knock-in mouse models of Alzheimer’s disease
Saito, T., Matsuba, Y., Mihira, N., Takano, J., Nilsson, P., Itohara, S., Iwata, N., & Saido, T. C. (2014) · Nature Neuroscience · doi:10.1038/nn.3697 · Cited by 1,447 · see citing works
TREM2 Maintains Microglial Metabolic Fitness in Alzheimer’s Disease
Ulland, T. K., Song, W. M., Huang, S. C.-C., Ulrich, J. D., Sergushichev, A., Beatty, W. L., Loboda, A. A., Zhou, Y., Cairns, N. J., Kambal, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.023 · Cited by 1,263 · cited 1× · see citing works
“Trem2 deficiency reduces metabolic fitness in microglia (10), and cerebral glucose uptake is reduced in a Trem2 loss-of-function mutant (12) and in Trem2−/− mice (Fig.”
BackgroundClusters of Hyperactive Neurons Near Amyloid Plaques in a Mouse Model of Alzheimer’s Disease
Busche, M. A., Eichhoff, G., Adelsberger, H., Abramowski, D., Wiederhold, K.-H., Haass, C., Staufenbiel, M., Konnerth, A., & Garaschuk, O. (2008) · Science · doi:10.1126/science.1162844 · Cited by 1,125 · see citing works
Self-citationSpangenberg, E., Severson, P. L., Hohsfield, L. A., Crapser, J., Zhang, J., Burton, E. A., Zhang, Y., Spevak, W., Lin, J., Phan, N. Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11674-z · Cited by 939 · see citing works
Brain metabolism in health, aging, and neurodegeneration
Camandola, S., & Mattson, M. P. (2017) · The EMBO Journal · doi:10.15252/embj.201695810 · Cited by 752 · see citing works
Microglia, neuroinflammation, and beta-amyloid protein in Alzheimer’s disease
Cai, Z., Hussain, M. D., & Yan, L.-J. (2014) · International Journal of Neuroscience · doi:10.3109/00207454.2013.833510 · Cited by 583 · see citing works
Microbiota-derived short chain fatty acids modulate microglia and promote Aβ plaque deposition
Colombo, A. V., Sadler, R. K., Llovera, G., Singh, V., Roth, S., Heindl, S., Sebastian Monasor, L., Verhoeven, A., Peters, F., Parhizkar, S., et al. (2021) · eLife · doi:10.7554/elife.59826 · Cited by 354 · see citing works
Self-citationTREM2 deficiency impairs chemotaxis and microglial responses to neuronal injury
Mazaheri, F., Snaidero, N., Kleinberger, G., Madore, C., Daria, A., Werner, G., Krasemann, S., Capell, A., Trümbach, D., Wurst, W., et al. (2017) · The EMBO Reports · doi:10.15252/embr.201743922 · Cited by 351 · see citing works
Self-citationXiang, X., Piers, T. M., Wefers, B., Zhu, K., Mallach, A., Brunner, B., Kleinberger, G., Song, W., Colonna, M., Herms, J., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0280-6 · Cited by 170 · see citing works
Self-citationIn Vivo Assessment of Neuroinflammation in <scp>4‐Repeat</scp> Tauopathies
Palleis, C., Sauerbeck, J., Beyer, L., Harris, S., Schmitt, J., Morenas‐Rodriguez, E., Finze, A., Nitschmann, A., Ruch‐Rubinstein, F., Eckenweber, F., et al. (2021) · Movement Disorders · doi:10.1002/mds.28395 · Cited by 62 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Emerging diagnostics and therapeutics for Alzheimer disease
Self, W. K., & Holtzman, D. M. (2023) · Nature Medicine · doi:10.1038/s41591-023-02505-2 · Cited by 381 · see citing works
Lee, S., Devanney, N. A., Golden, L. R., Smith, C. T., Schwartz, J. L., Walsh, A. E., Clarke, H. A., Goulding, D. S., Allenger, E. J., Morillo-Segovia, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112196 · Cited by 203 · see citing works
Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease
Johnson, E. C. B., Bian, S., Haque, R. U., Carter, E. K., Watson, C. M., Gordon, B. A., Ping, L., Duong, D. M., Epstein, M. P., McDade, E., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02476-4 · Cited by 170 · see citing works
Self-citationReifschneider, A., Robinson, S., van Lengerich, B., Gnörich, J., Logan, T., Heindl, S., Vogt, M. A., Weidinger, E., Riedl, L., Wind, K., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021109108 · Cited by 89 · see citing works
Self-citationHuang, Q., Wang, Y., Chen, S., & Liang, F. (2023) · Aging and disease · doi:10.14336/ad.2023.0807 · Cited by 62 · see citing works
Microglial Activation and Connectivity in Alzheimer Disease and Aging
Rauchmann, B., Brendel, M., Franzmeier, N., Trappmann, L., Zaganjori, M., Ersoezlue, E., Morenas‐Rodriguez, E., Guersel, S., Burow, L., Kurz, C., et al. (2022) · Annals of Neurology · doi:10.1002/ana.26465 · Cited by 61 · see citing works
“Limited blood–brain barrier passing of [(18)F]GE-180 needs to be acknowledged, but our previous data indicated strong agreement with immunohistochemistry in AD models(58) and patterns matching known topology of microglial activation in neurodegenerative diseases.(10,59) The low background signal of [(18)F]GE-180 appea…”
ResultSelf-citationFeiten, A. F., Dahm, K., Schlepckow, K., van Lengerich, B., Suh, J. H., Reifschneider, A., Wefers, B., Bartos, L. M., Wind-Mark, K., de Weerd, L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68706-8 · Cited by 39 · see citing works
Self-citationINPP5D modulates TREM2 loss-of-function phenotypes in a β-amyloidosis mouse model
Iguchi, A., Takatori, S., Kimura, S., Muneto, H., Wang, K., Etani, H., Ito, G., Sato, H., Hori, Y., Sasaki, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106375 · Cited by 37 · see citing works
Oblak, A. L., Kotredes, K. P., Pandey, R. S., Reagan, A. M., Ingraham, C., Perkins, B., Lloyd, C., Baker, D., Lin, P. B., Soni, D. M., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.886575 · Cited by 23 · see citing works
“Fasting blood glucose levels only exceeded 240 mg/dL (a level that previous studies consider B6 mice to be diabetic (Xiang et al., 2021) in mice fed with LOAD1 HFD and LOAD1.”
BackgroundMethodInfluentialDuan, J., Lv, A., Guo, Z., Liu, Q., Tian, C., Yang, Y., Bi, J., Yu, X., Peng, G., Luo, B., et al. (2024) · Journal of Neuroinflammation · doi:10.1186/s12974-024-03243-z · Cited by 22 · see citing works
Zimmer, E. R., Pascoal, T. A., Rosa-Neto, P., Nordberg, A., & Pellerin, L. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abm8302 · Cited by 17 · see citing works
Chongtham, A., Sharma, A., Nath, B., Murtha, K., Gorbachev, K., Ramakrishnan, A., Schmidt, E. F., Shen, L., & Pereira, A. C. (2024) · Experimental Neurology · doi:10.1016/j.expneurol.2024.114967 · Cited by 10 · see citing works
- Authors
- Xiang, X., Wind, K., Wiedemann, T., Blume, T., Shi, Y., Briel, N., Beyer, L., Biechele, G., Eckenweber, F., Zatcepin, A., Lammich, S., Ribicic, S., Tahirovic, S., Willem, M., Deussing, M., Palleis, C., Rauchmann, B.-S., Gildehaus, F.-J., Lindner, S., Spitz, C., Franzmeier, N., Baumann, K., Rominger, A., Bartenstein, P., Ziegler, S., Drzezga, A., Respondek, G., Buerger, K., Perneczky, R., Levin, J., Höglinger, G. U., Herms, J., Haass, C., & Brendel, M.
- Affiliations
- Chinese Academy of Sciences (CN)
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, 81377 Munich, Germany.
- Ludwig-Maximilians-Universität München (DE)
- German Center for Neurodegenerative Diseases (DE)
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany.
- LMU Klinikum (DE)
- University of Augsburg (DE)
- F. Hoffmann-La Roche AG (CH)
- University of Bern (CH)
- Munich Cluster for Systems Neurology (DE)
- Forschungszentrum Jülich (DE)
- Medizinische Hochschule Hannover (DE)
- Corresponding author
- Haass, Christian
- Brendel, Matthias
- ORCID
- Xiang, Xianyuan
- Shi, Yuan
- Briel, Nils
- Beyer, Leonie
- Biechele, Gloria
- Zatcepin, Artem
- Ribicic, Sara
- Tahirovic, Sabina
- Willem, Michael
- Deussing, Maximilian
- Palleis, Carla
- Rauchmann, Boris-Stephan
- Lindner, Simon
- Franzmeier, Nicolai
- Rominger, Axel
- Bartenstein, Peter
- Ziegler, Sibylle
- Drzezga, Alexander
- Respondek, Gesine
- Perneczky, Robert
- Levin, Johannes
- Höglinger, Günter U.
- Herms, Jochen
- Haass, Christian
- Brendel, Matthias
- Venue
- Science Translational Medicine
- Published
- 2021-10-13
- Volume
- 13
- Issue
- 615
- Pages
- eabe5640-eabe5640
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W3205542324
- Cited by
- 268
- References
- 20
- Citations by year
- 2026: 50 · 2025: 53 · 2024: 64 · 2023: 56 · 2022: 41 · 2021: 4
- FWCI
- 16.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- DZNE Pub (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Keywords
- microglial activation, glucose uptake, FDG-PET, neurodegenerative diseases, amyloidosis, translocator protein (TSPO), brain glucose metabolism
- MeSH terms
- Animals, Glucose, Humans, Microglia, Neurodegenerative Diseases, Fluorodeoxyglucose F18, Positron-Emission Tomography, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
