The niacin receptor HCAR2 modulates microglial response and limits disease progression in a mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Increased dietary intake of niacin has been correlated with reduced risk of Alzheimer’s disease (AD). Niacin serves as a high-affinity ligand for the receptor HCAR2 (GPR109A). In the brain, HCAR2 is expressed selectively by microglia and is robustly induced by amyloid pathology in AD. The genetic inactivation of Hcar2 in 5xFAD mice, a model of AD, results in impairment of the microglial response to amyloid deposition, including deficits in gene expression, proliferation, envelopment of amyloid plaques, and uptake of amyloid-β (Aβ), ultimately leading to exacerbation of amyloid burden, neuronal loss, and cognitive deficits. In contrast, activation of HCAR2 with an FDA-approved formulation of niacin (Niaspan) in 5xFAD mice leads to reduced plaque burden and neuronal dystrophy, attenuation of neuronal loss, and rescue of working memory deficits. These data provide direct evidence that HCAR2 is required for an efficient and neuroprotective response of microglia to amyloid pathology. Administration of Niaspan potentiates the HCAR2-mediated microglial protective response and consequently attenuates amyloid-induced pathology, suggesting that its use may be a promising therapeutic approach to AD that specifically targets the neuroimmune response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".