Melatonin and vitamin D, two sides of the same coin, better to land on its edge to improve multiple sclerosis
Bibliographic record
Abstract
Previous studies revealed a latitudinal gradient of multiple sclerosis (MS) prevalence, increasing by moving from the equator to the poles. The duration and quality of an individual's exposure to sunlight vary with latitude. Skin exposure to sunlight activates vitamin D synthesis, while light absence, as perceived by the eyes, activates melatonin synthesis in the pineal gland. Vitamin D or melatonin deficiency/insufficiency or overdose can occur at any latitude due to specific lifestyles and diets. Moving away from the equator, especially beyond 37°, decreases vitamin D while raising melatonin. Furthermore, melatonin synthesis increases in cold habitats like northern countries. Since melatonin's beneficial role was shown in MS, it is expected that northern countries whose individuals have higher endogenous melatonin should show a lower MS prevalence; however, these are ranked with the highest scores. In addition, countries like the United States and Canada have uncontrolled over-the-counter usage. In high latitudes, vitamin D deficiency and a higher MS prevalence persist even though vitamin D is typically compensated for by supplementation and not sunlight. Recently, we found that prolonged darkness increased MS melatonin levels, mimicking the long-term increase in northern countries. This caused a reduction in cortisol and increased infiltration, inflammation, and demyelination, which were all rescued by constant light therapy. In this review, we explain melatonin and vitamin D's possible roles in MS prevalence. The possible causes in northern countries are then discussed. Finally, we suggest strategies to treat MS by manipulating vitamin D and melatonin, preferably with sunlight or darkness, not supplements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".