Influence of Dietary Cholesterol on Vitamin D Metabolism in Formula‐Fed Preterm Neonates
Bibliographic record
Abstract
ABSTRACT Objectives Supplementation of preterm formulas with cholesterol could help to mimic the fat composition of human milk. However, this could possibly influence vitamin D 25‐hydroxylation because this reaction is catalyzed in part by the mitochondrial cytochrome P‐450, the enzyme responsible for the 27‐hydroxylation of cholesterol. The purpose of this study was to verify whether the addition of cholesterol to preterm formulas could interfere with vitamin D metabolism in preterm neonates. Methods In a prospective study, 30 preterm neonates were randomly assigned to a low (< 0.03 g/L), medium (0.15 g/L), or high (0.30 g/L) cholesterol‐content preterm formula until theoretical term (i.e., 40 weeks post‐conceptional age). Anthropometric data and serum hydroxy‐vitamin D and 1,25 dihydroxy‐vitamin D concentrations were measured at study entry and theoretical term. In a subgroup of 14 subjects, serum cholesterol and lymphocyte 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase mRNA were also assessed. Results (median [25th, 75 th centiles]): At theoretical term, there were no significant differences in serum hydroxy‐vitamin D concentrations among the three groups, even after adjustment for confounding variables (65 [50, 78] nmol/L, 79 [59, 86] nmol/L, and 67 [43, 103] nmol/L, respectively, P = 0.65) or 1,25 dihydroxy‐vitamin D (P = 0.88). Furthermore, there were no significant differences in 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase mRNA copy numbers. Conclusions In preterm neonates fed formulas with a cholesterol content similar to or higher than that of human milk, we did not observe deleterious effects on vitamin D metabolism. However, long‐term effects of cholesterol supplementation require further studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".