MétaCan
Menu
Back to cohort
Record W4406138904 · doi:10.1093/brain/awae394

Zooming in on brain inflammation in Alzheimer’s disease

2025· article· en· W4406138904 on OpenAlexaff
Wiesje M. van der Flier, Michael T. Heneka

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsDiseaseInflammationNeuroscienceAlzheimer's diseaseBrain diseaseMedicinePsychologyPathologyImmunology

Abstract

fetched live from OpenAlex

The brain’s immune reaction in neurodegenerative disease has historically been viewed as a bystander reaction. Evidence from genetic, pathological, clinical and experimental studies, however, points to a pathogenetic role of neuroinflammation for several neurodegenerative diseases including Alzheimer’s disease (AD).1 In this disease, this reaction is driven by the intracerebral accumulation of beta sheet structured amyloids which induce a strong inflammatory response of microglial cells and associated macrophages, representing the major part of the brain’s innate immune system. An increasing body of research in AD focuses on the role of immune-mediated mechanisms.2 AD develops over the course of 20 to 30 years, with the longest part of the disease taking place before onset of dementia. Fluid and imaging biomarkers can be used to study immune related processes across the entire disease spectrum—from cognitively normal through mild cognitive impairment (MCI) to dementia—and may help to identify the time and site where modification of immune processes can be developed into therapeutic intervention. In recognition of both the important role of inflammatory processes in the brain of AD patients, as well as the development of methods to measure brain inflammation in vivo, glial fibrillary acidic protein (GFAP) has now been added as a measure for ‘I’ of inflammation to the recently published and updated NIA-AA criteria for diagnosis of AD.3 Further, a considerable portion of trials in the AD drug pipeline now focus on inflammatory targets. Of 164 trials being actively conducted at the beginning of 2024, 25 target inflammation/immune related processes.4 Nonetheless, many questions remain as to how brain inflammation is related to other AD mechanisms leading to amyloid and tau pathology, where in the cascade of events it has greatest impact, and how targeting brain inflammation would be most beneficial for patients. In the December issue of Brain, Perretti and colleagues5 used plasma GFAP to study in detail how it is related to distribution of amyloid load (as assessed by amyloid PET), tau deposition (indexed by tau PET), and rate of cognitive decline. Elevated plasma GFAP concentrations were associated with increased tau deposition particularly in temporal and frontal regions, and with steeper subsequent cognitive decline. Moreover, brain inflammation as indexed by GFAP had mediating effects both on the association with amyloid and tau, and on the association between tau and rate of cognitive decline. Plasma biomarkers have the advantage of being affordable and accessible. Yet the disadvantage is that they do not allow insight in the precise spatial distribution of the inflammatory process. Translocator 11 protein (TSPO) PET allows for the visualization of inflammation in the brain by quantification of regional density of microglia and migrated macrophages, both of which are important aspects of the brain inflammatory response. In this months’ Brain, Appleton and colleagues6 used a novel tracer 11C-ER176, which—in contrast to other TSPO tracers—allows measurement of individuals with any TSPO rs6971 genotype. They studied distribution of inflammation in MCI caused by early-onset AD. They found increased inflammation, particularly in the precuneus and lateral temporal and parietal cortices. Inflammation co-localized most strongly with tau, rather than amyloid or atrophy. In addition, inflammation in AD-related regions was associated with impaired cognitive performance. Both of these studies show, with different methods to measure inflammation and in different samples, that brain inflammation has close connections to tau deposition. Such a relationship has also been suggested by experimental work, which showed that amyloid-β exposure can stimulate immune processes which in turn lead to the spread of tau pathology in rodent models.7 Similarly, astro- and microglial senescence has been reported to cause tau pathology through the release of a senescence associated secretory profile that usually contains several immune mediators.8 Further mechanisms by which immune factors can contribute to tau pathology may exist.9 Together this experimental work suggests that inflammation plays an important and mediating role. It may be induced by the deposition of amyloid-β and ultimately cause intraneuronal tau accumulation and neuronal death. A successful inflammation-targeting approach would require the identification of abnormal inflammation ideally through a biomarker that also provides information about target engagement. While GFAP and TSPO-PET may well serve as global markers of inflammation, such pathway and target-tailored biomarkers have yet to be identified and validated for their use in human studies. It seems likely that along the long, pre-dementia trajectory of AD, several opportunities for anti-inflammatory therapies exist. The ideal time point for targeting those pathways, e.g. the NLRP3 inflammasome or TREM2 may have to be determined on an individual level and depend on several factors including lifestyle and genetic background. One may speculate that, assuming neuroinflammation drives tau pathology and cognitive decline, an anti-inflammatory intervention may give best results when initiated prior to the rise of tau biomarkers and PET signal. Whilst anti-inflammatory interventions could be a therapeutic strategy in their own right, it is also conceivable that they would be a useful combination with anti-amyloid treatment. The first generation of anti-amyloid therapy modifies the disease course, yet does not halt the disease. Given that inflammatory processes may fuel the cascade initiated by amyloid deposition, combination therapy of anti-amyloid treatment with anti-inflammatory treatment to stop further disease progression is an attractive direction of study. In addition, the identification of molecular subtypes of AD suggests that while some subgroups of patients might benefit from anti-inflammatory strategies, for others such a strategy would not be helpful, or even prove to be detrimental.10 In conclusion, increasing evidence suggests that inflammatory processes in AD are closely related to deposition of tau and predispose for subsequent steeper rate of cognitive decline. This highlights the importance of inflammation as a putative target for personalized treatment in AD, either alone, or in combination with other therapeutic strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.303
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207