Human Milk Nutrient Composition Data is Critically Lacking in the United States and Canada: Results from a Systematic Scoping Review of 2017–2022
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».