Associations of <i>CYP24A1</i> copy number variation with vitamin D deficiency and insulin secretion
Bibliographic record
Abstract
Vitamin D plays an important role in insulin secretion. As the enzyme that initiates degradation of the active metabolite of vitamin D (1,25-(OH)2 vitamin D), 24-hydroxylase encoded by CYP24A1 may be associated with insulin secretion. In this study, we aimed at investigating the association between copy number of CYP24A1 and the concentration of insulin. Included in the study were 1528 rural people from Henan Province of China. The copy number of CYP24A1 and the concentrations of serum 25(OH) vitamin D3 and insulin were determined. Association between copy number of CYP24A1 and vitamin D deficiency was investigated with logistic regression model. Correlation between copy number of CYP24A1 and serum insulin was observed by Spearman correlation. The results suggested that copy number variation of CYP24A1 was associated with vitamin D deficiency. Higher copy number of CYP24A1 was a risk factor for vitamin D deficiency (adjusted odds ratio: 1.199; 95% confidence interval: 1.028–1.397; P = 0.021). Furthermore, copy number of CYP24A1 was positive correlated with the concentration of serum insulin (r = 0.115; P < 0.001), regardless of vitamin D status, age, and body mass index (BMI). Increased copy number of CYP24A1 is associated with not only vitamin D deficiency but also increased serum insulin. Vitamin D supplement may be beneficial to individuals with high copy number of CYP24A1. Novelty Increased copy number of CYP24A1 was a risk factor of vitamin D deficiency. Increased copy number of CYP24A1 was associated with increased serum concentration of insulin independent of age, BMI, and vitamin D status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".