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Enregistrement W4406138904 · doi:10.1093/brain/awae394

Zooming in on brain inflammation in Alzheimer’s disease

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

Notice bibliographique

RevueBrain · 2025
Typearticle
Langueen
DomaineNeuroscience
ThématiqueNeuroinflammation and Neurodegeneration Mechanisms
Établissements canadiensBell (Canada)
Organismes subventionnairesnon disponible
Mots-clésDiseaseInflammationNeuroscienceAlzheimer's diseaseBrain diseaseMedicinePsychologyPathologyImmunology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,147
Score d'incertitude au seuil0,582

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,303
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2025
Routes d'admission1
Résumé présentoui

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