Human Milk Nutrient Composition Data is Critically Lacking in the United States and Canada: Results from a Systematic Scoping Review of 2017–2022
Bibliographic record
Abstract
Characterization of the nutrients in human milk is important to understand the dietary and developmental requirements of infants. The objective of this review was to summarize the state-of-the-science on the nutrient composition of human milk in the United States and Canada published from 2017 to 2022. Four databases were searched for randomized controlled studies and others given the scoping nature of this review. We limited type to mature milk collected 21 days postpartum and beyond from lactating individuals in the United States and Canada who gave birth at 37 weeks’ gestation or later (full-term). Outcomes of interest included traditional macro- and micronutrients including human milk oligosaccharides (HMOs), and milk volume. The publication date range was selected as January 1, 2017, to the day the literature search was performed. A total of 32 articles were included in the scoping review from primarily longitudinal cohort or cross-sectional designs. The most prevalent sample collection method was full breast expression (n=20) with most studies (n=26) collecting samples from a single timepoint. Carbohydrates (HMOs [n=12], glucose [n=8], and lactose [n=6]) and protein (n=5) were the most frequently assessed nutrients in this body of work, with consensus among studies that glucose is present in limited concentrations compared to lactose (24-64 mg/dL vs 6-7 g/dL), and that HMOs are influenced by temporality and secretor status. Included studies displayed an overall level of heterogeneity and sparsity paralleling previous reports and nutrient data in the United States Department of Agriculture (USDA) FoodData Central system. Much of the data extracted from retained articles generally provided analysis of a specific nutrient or group of nutrients. Moreover, many studies did not use the preferred analytical methods as outlined by the Human Milk Composition Initiative to increase measurement confidence. Up-to-date nutrient composition data of human milk is still greatly needed as it is paramount for the management of infant feeding, assessment of infant and maternal nutritional and health needs, and as a reference for infant formula development. The current body of research profiling the nutrient content of human milk cannot be used to update the USDA’s FoodData Central based on the scarcity of reliable data derived from preferred analytical and sampling methods. There is a critical need for nutrient composition data and priorities must be placed on addressing the methodological deficiencies described in this review to enable the development of evidence-based reference values for human milk composition across the course of lactation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".