Microglial activation and tau burden predict cognitive decline in Alzheimer’s disease
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Abstract
Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer’s dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s disease.
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Tau pathology is linked to cognitive decline, may progress more rapidly than Aβ accumulation, and correlates with neuroinflammation, making it a valuable prognostic marker.
“Numerous studies have linked tau pathology to cognitive decline, with some evidence suggesting it progresses more rapidly than Aβ accumulation and correlates with neuroinflammation, making it a valuable prognostic marker [20, 25–28].”
From Targeted Nanoparticles for Drug Delivery Across the Blood–Brain Barrier in Early and Late Stages of Alzheimer's Disease: A Review (2026) · cites this paper as [28]
The Discussion (page unavailable) reports that baseline tau burden predicted cognitive decline and supports tau PET as a prognostic marker. It does not establish that tau accumulates faster than amyloid-β. It also says tau pathology and neuroinflammation did not correlate in this cohort, although it reports correlations found in some other studies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBaseline PK-PET in AD and PSP was able to predict clinical progression.
“while baseline PK-PET in AD and PSP was able to predict clinical progression [221, 222]”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [222]
Variance in distribution and burden of tauopathy in AD is closely associated with clinical phenotype, cognitive deficit, and rate of cognitive decline.
“Variance in the distribution and burden of tauopathy in AD is closely associated with the clinical phenotype, 13 cognitive deficit, 14 and rate of cognitive decline. 15”
From Alzheimer's disease and its progression reduce pyramidal cell gain and connectivity (2025) · cites this paper as [15]
Neuroinflammation and microglial activation are consistent features across neurodegenerative diseases, including AD, PD, HD, FTD, and ALS.
“Indeed, neuroinflammation and microglial activation are consistent features across neurodegenerative diseases, including AD,126–128 PD,86,129 HD,130,131 FTD,91,132 and ALS.89,133–135”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [127]
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Neuroinflammation in neurodegenerative disorders: the roles of microglia and astrocytes
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“…activation with a positive correlation with amyloid PET deposition and inverse correlation with FDG-PET [220], while baseline PK-PET in AD and PSP was able to predict clinical progression [221, 222] and was associated with small vessel disease, particularly hypertensive arteriopathy in AD [223].”
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Self-citation<i>APOE</i> ε4 associates with microglial activation independently of Aβ plaques and tau tangles
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Alzheimer's disease and its progression reduce pyramidal cell gain and connectivity
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“15 The effect of aggregated tau on cognition in part results from synaptic toxicity and dendritic de-arborization of pyramidal cells.”
Self-citation
- Authors
- Malpetti, M., Kievit, R. A., Passamonti, L., Jones, P. S., Tsvetanov, K. A., Rittman, T., Mak, E., Nicastro, N., Bevan-Jones, W. R., Su, L., Hong, Y. T., Fryer, T. D., Aigbirhio, F. I., O’Brien, J. T., & Rowe, J. B.
- Affiliations
- University of Cambridge (GB)
- MRC Cognition and Brain Sciences Unit (GB)
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Corresponding author
- Rowe, James B
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-01
- Volume
- 143
- Issue
- 5
- Pages
- 1588-1602
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC7241955
- OpenAlex
- W3020903493
- Cited by
- 194
- References
- 92
- Citations by year
- 2026: 17 · 2025: 37 · 2024: 32 · 2023: 35 · 2022: 31 · 2021: 33 · 2020: 8 · 2018: 1
- FWCI
- 19.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- NIHR
- Cambridge Biomedical Research Centre Dementia and Neurodegeneration
- Wellcome Trust (103838)
- Cambridge Trust & Sidney Sussex College Scholarship
- Medical Research Council (SUAG/04/G101400, MR/P01271X/1)
- Cambridge Centre for Parkinson Plus, and British Academy (PF160048)
- Department of Health and Social Care
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, cognitive decline, tau pathology, microglial activation, neuroinflammation, [11C]PK11195 PET, gray matter atrophy, anterior temporal lobe, prognostic factors, mild cognitive impairment, Alzheimer's dementia, longitudinal cognitive change
- MeSH terms
- Aged, Alzheimer Disease, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, tau Proteins, Microglia, Positron-Emission Tomography, Neuroimaging, Cognitive Dysfunction
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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.
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