Induction of cytochrome P450 1A by cow milk‐based formula: a comparative study between human milk and formula
Bibliographic record
Abstract
During the treatment of neonatal apnea, formula-fed infants, compared to breastfed infants, show nearly three-fold increase in clearance of caffeine, a substrate of cytochrome P450 1A (CYP1A) and in part CYP3A4. However, human milk is known to contain higher concentrations of environmental pollutants than infant formula, which are potent CYP1A inducers. To gain insight into the mechanism underlying this apparent contradiction, we characterized CYP1A and CYP3A4 induction by human milk and cow milk-based infant formula. The mRNA and protein expression of CYP1A1/1A2 were significantly induced by cow milk-based formula, but not by human milk, in HepG2 cells. Luciferase reporter assay demonstrated that cow milk-based formula but not human milk activated aryl hydrocarbon receptor (AhR) significantly. The cotreatment of 3,4-dimethoxyflavone, an AhR antagonist, abolished the formula-induced CYP1A expression. In addition, AhR activation by dibenzo[a,h]anthracene, a potent AhR agonist, was significantly suppressed by infant formula and even more by human milk. In contrast, CYP3A4 mRNA expression was only mildly induced by formula and human milk. Consistently, neither formula nor human milk substantially activated pregnane X receptor (PXR). Effects of whey and soy protein-based formulas on the AhR-CYP1A and the PXR-CYP3A4 pathways were similar to those of cow milk-based formula. In conclusion, infant formula, but not human milk, enhances in vitro CYP1A expression via an AhR-mediated pathway, providing a potential mechanistic basis for the increased caffeine elimination in formula-fed infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".