Gene-Environment Interactions in Vitamin D Status and Sun Exposure: A Systematic Review with Recommendations for Future Research
Bibliographic record
Abstract
Vitamin D is essential for good health. Dermal vitamin D production is dependent on environmental factors such as season and latitude, and personal factors such as time spent outdoors and genetics. Varying heritability of vitamin D status by season has been reported, suggesting that gene-environment interactions (GxE) may play a key role. Thus, understanding GxE might significantly improve our understanding of determinants of vitamin D status. The objective of this review was to survey the existing methods in GxE on vitamin D studies and report on GxE effect estimates. We searched the Embase, Medline (Ovid), and Web of Science (Core Collection) databases. We included only primary research that reported on GxE effects on vitamin D status using 25-hydroxyvitamin D as a biomarker. Sun exposure was the only environmental exposure identified in these studies. The quality assessment followed the Newcastle-Ottawa Scale for cohort studies. Seven studies were included in the final narrative synthesis. We evaluate the limitations and findings of the available GxE in vitamin D research and provide recommendations for future GxE research. The systematic review was registered on PROSPERO (CRD42021238081).
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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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".