Niacin-mediated rejuvenation of macrophage/microglia enhances remyelination of the aging central nervous system
Bibliographic record
Abstract
Remyelination following CNS demyelination restores rapid signal propagation and protects axons; however, its efficiency declines with increasing age. Both intrinsic changes in the oligodendrocyte progenitor cell population and extrinsic factors in the lesion microenvironment of older subjects contribute to this decline. Microglia and monocyte-derived macrophages are critical for successful remyelination, releasing growth factors and clearing inhibitory myelin debris. Several studies have implicated delayed recruitment of macrophages/microglia into lesions as a key contributor to the decline in remyelination observed in older subjects. Here we show that the decreased expression of the scavenger receptor CD36 of aging mouse microglia and human microglia in culture underlies their reduced phagocytic activity. Overexpression of CD36 in cultured microglia rescues the deficit in phagocytosis of myelin debris. By screening for clinically approved agents that stimulate macrophages/microglia, we have found that niacin (vitamin B3) upregulates CD36 expression and enhances myelin phagocytosis by microglia in culture. This increase in myelin phagocytosis is mediated through the niacin receptor (hydroxycarboxylic acid receptor 2). Genetic fate mapping and multiphoton live imaging show that systemic treatment of 9-12-month-old demyelinated mice with therapeutically relevant doses of niacin promotes myelin debris clearance in lesions by both peripherally derived macrophages and microglia. This is accompanied by enhancement of oligodendrocyte progenitor cell numbers and by improved remyelination in the treated mice. Niacin represents a safe and translationally amenable regenerative therapy for chronic demyelinating diseases such as multiple sclerosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".