Association of hypomelanotic skin disorders with autism: links to possible etiologic role of vitamin-D levels in autism?
Bibliographic record
Abstract
Vitamin D is crucial for several key physiological processes, including brain development, DNA repair, and regulation of many genes. Much evidence indicates prenatal and early postnatal vitamin-D deficiency increases autism risk, probably through multiple effects, including impaired brain development and increased de novo mutations. High autism rates in several genetically based hypomelanotic skin disorders are puzzling, because ultraviolet-B radiation (UVB) in sunlight acting on skin is a key source of vitamin-D, and lighter skin protects against vitamin-D deficiency, especially at high latitudes. We consider two hypotheses to help explain autism's co-morbidity with hypomelanosis. 1) Because genetic and epigenetic variants that produce hypomelanosis help protect against vitamin-D deficiency, they increase reproductive fitness of individuals who also have other autism risk factors. 2) Hypomelanotic children have increased autism risk because photosensitivity and skin-cancer concerns lead families to excessively reduce children's sun exposure. Hypothesis testing could involve studies comparing genomes, epigenetic markers, skin pigmentation, and vitamin-D levels in autistic individuals with and without hypomelanosis, their relatives and controls. Conducting such studies in samples from regions that differ widely in UVB availability would provide particularly valuable data. Support for either hypothesis would elucidate vitamin-D's role in autism and suggest vitamin-D enhancement may aid treatment and prevention of autism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".