Higher milk fat content is associated with higher 25-hydroxyvitamin D concentration in early childhood
Bibliographic record
Abstract
Current guidelines for cow's milk consumption in children older than age 2 years suggest 1% or 2% milk to reduce the risk of obesity. Given that milk is the main dietary source of vitamin D for North American children and that vitamin D is fat soluble, we hypothesized 25-hydroxyvitamin D (25(OH)D) concentration to be positively associated with the fat content of milk. The objective was to determine the relationship between the fat content of milk consumed and the serum 25(OH)D concentration; our secondary objective was to explore the role that the volume of milk consumed played in this relationship. We completed a cross-sectional study of children aged 12-72 months in the TARGetKids! research network. Multivariable linear regression was used to test the association between milk fat content and child 25(OH)D, adjusted for clinically relevant covariates. The interaction between volume of milk and fat content was examined. Two thousand eight hundred fifty-seven children were included in the analysis. The fat content of milk was positively associated with 25(OH)D (p = 0.03), and the interaction between the volume of milk consumed and the milk fat content was statistically significant (p = 0.005). Children who drank 1% milk needed 2.46 cups (95% confidence interval (CI) 2.38-2.54) of milk to have a 25(OH)D concentration similar to that of children who drank 1 cup of homogenized milk (3.25% fat). Children who consumed 1% milk had 2.05 (95% CI 1.73-2.42) times higher odds of having a 25(OH)D concentration <50 nmol/L compared with children who consumed homogenized milk. In conclusion, recommendations for children to drink lower-fat milk (1% or 2%) may compromise serum 25(OH)D levels and may require study to ensure optimal childhood health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".