Genetic variation in the vitamin D receptor (VDR) and the plasma proteome
Bibliographic record
Abstract
Vitamin D deficiency has been linked to numerous diseases. 1,25(OH) 2 D, the active vitamin D form, binds to the nuclear vitamin D receptor (VDR) and affects target gene expression in multiple pathways. However, it is not known if genetic variants in VDR affect plasma protein levels. The objective was to explore associations between VDR variants and 54 plasma proteins belonging to disease‐associated pathways in healthy young adults (n=488). 16 genetic variants across VDR were extracted from genome‐wide data available for the study cohort. Protein concentrations were measured by a multiple reaction monitoring HPLC‐MS/MS assay. Associations between proteins and variants were explored by linear regression with an additive inheritance mode. We found 32 significant associations. The strongest association ( p =5.06×10 −5 ) was observed between rs2283342 and inter‐α‐trypsin inhibitor HC (IT) ( r 2 =0.03, β= −0.03±0.01), a serine protease inhibitor involved in inflammation. We then examined whether serum 25(OH)D, a marker of vitamin D status, affected any associations. 25(OH)D modified the association between rs2283342 and IT, as well as associations between rs2228570 ( Fok1 ) and several apolipoproteins. These results suggest a role for vitamin D in disease associated pathways such as inflammation and lipid metabolism. Research support from the Advanced Foods and Materials Network. Grant Funding Source : Advanced Foods and Materials Network
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".