High dose vitamin D exacerbates central nervous system autoimmunity by raising T-cell excitatory calcium
Bibliographic record
Abstract
Poor vitamin D status is associated with a higher relapse rate and earlier disability in multiple sclerosis. Based on these associations, patients with multiple sclerosis are frequently supplemented with the vitamin D precursor cholecalciferol, although it is unclear whether this regimen is of therapeutic benefit. To model consequences of this common practice, mice were fed for more than 3 months with a low, medium or high dose of cholecalciferol, representative of vitamin D deficiency, modest and disproportionally high supplementation, respectively, in patients with multiple sclerosis. Compared to vitamin D-deprived mice, its moderate supplementation reduced the severity of subsequent experimental autoimmune encephalomyelitis, which was associated with an expansion of regulatory T cells. Direct exposure of murine or human T cells to vitamin D metabolites inhibited their activation. In contrast, mice with 25-(OH) vitamin D levels above 200 nmol/l developed fulminant experimental autoimmune encephalomyelitis with massive CNS infiltration of activated myeloid cells, Th1 and Th17 cells. When dissecting this unexpected outcome, we observed that high, but not medium dose vitamin D had caused mild hypercalcaemia, which rendered T cells more prone to pro-inflammatory activation. Exposing murine or human T cells to equivalent calcium concentrations in vitro enhanced its influx, triggering activation, upregulation of pro-inflammatory gene products and enhanced transmigration across a blood-brain barrier model. These findings suggest that vitamin D at moderate levels may exert a direct regulatory effect, while continuous high dose vitamin D treatment could trigger multiple sclerosis disease activity by raising mean levels of T-cell excitatory calcium.
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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) | 0.002 | 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".