Concentrations and predictors of select nutrients in Canadian human milk samples from the Maternal-Infant Research on Environmental Chemicals pregnancy cohort
Bibliographic record
Abstract
Human milk (HM) composition data are widely used in clinical, regulatory, and public health initiatives. The existing HM profiles in United States and Canadian nutrient databanks are outdated and now considered inappropriate to estimate current nutrient intakes. Recent reviews have underscored the limited North American data available to generate a new profile. To describe concentrations and sources of variability of nutrients in HM from a large cohort collected in Canada. The Maternal-Infant Research on Environmental Chemicals (MIREC) study recruited participants in the first trimester of pregnancy from 10 Canadian cities between 2008 and 2011. HM samples ( n = 559–835, depending on nutrient) were collected 3–10 wk postpartum and analyzed for minerals (calcium, magnesium, phosphorus, potassium, sodium, manganese, molybdenum, zinc, copper, iodine, selenium), vitamin D [vitamin D 3 , 25-hydroxyvitamin D 3 ], folate vitamers (folic acid, 5-methyltetrahydrofolate, total folates), and fatty acids (panel). We examined associations between participant characteristics and log-transformed nutrient concentrations using linear regression. Concentrations of HM components in MIREC samples were within the range observed in literature except for manganese, which was >100-fold lower than the value in the existing Canadian nutrient databank profile [2.43 (standard deviation 2.84) compared with 260 ng/g]. In multivariable models, concentrations of folate vitamers, vitamin D, and fatty acids demonstrated greater variability with maternal and sample characteristics than minerals. Factors such as relevant supplement use, body mass index, and for vitamin D, skin color and season, had a larger impact on nutrient concentrations than characteristics typically standardized in HM research, such as maternal or infant health, and method of collection. HM mineral concentrations from this study meet the methodological inclusion criteria for updating nutrient databank values and dietary reference intakes. Consideration of factors such as diet, skin color, and BMI will be important for selecting studies for developing representative reference values based on HM.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".