Developing an Updated and Comprehensive Nutrient Profile for Human Milk Composition in the U.S. and Canada
Bibliographic record
Abstract
Objectives: Human milk is recommended as the sole source of nutrition for infants up to about six months of age and recommended in combination with solid foods for at least the first year. The current nutrient profile for human milk shared by Canada and the United States (U.S.) has been moved to legacy status, meaning it can no longer be used to estimate current nutrient exposures. The objective of this study is to develop an interim human milk nutrient profile (iHMNut) that incorporates more recently published data on human milk composition in the U.S. and Canada to serve as a bridge until new analytical values can be established. Methods: Data from two literature reviews initiated by U.S. agencies of the Human Milk Composition Initiative (HMCI) and one large Canadian biomonitoring study were used to develop the iHMNut. Human milk collection and analytical methods were assessed against inclusion criteria of a review published by the National Academies of Sciences Engineering and Medicine (NASEM). Nutrient data from individual studies were extracted, quality control was performed, and weighted mean and standard deviation were calculated for each nutrient. These values were compared to the retired U.S. Department of Agriculture (USDA) Standard Reference (SR) Legacy profile for human milk. Results: A proposed iHMNut was generated for 22 nutrients, energy, and 22 fatty acids. Preliminary data analyses indicated that the largest percent differences in mean concentrations between USDA’s SR Legacy profile and iHMNut were for thiamin (-87%), manganese (99%), and iron (-102%). The smallest percent differences were for phosphorus (-1%), calcium (1%) and lactose (2%). The mean concentrations of iodine and chloride, previously unreported in the SR, were calculated as 21.21 μg/100g and 43.72 mg/100g, respectively. Conclusions: These preliminary findings provide insight into the potential to develop an iHMNut profile estimating the nutritional composition of human milk to inform public health professionals and researchers. Furthermore, this process identifies nutrients with inadequate North American data, which may inform future research. The next step of this project will be subject matter expert review of these findings to gain additional insights to develop an iHMNut and move toward inclusion in the USDA Food Data Central Database. Funding Sources: N/A.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".