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Record W4388798101 · doi:10.1016/j.advnut.2023.100149

Human Milk Macronutrients and Child Growth and Body Composition in the First Two Years: A Systematic Review

2023· review· en· W4388798101 on OpenAlexafffund
Meredith Brockway, Allison I Daniel, Sarah M. Reyes, Matthew Granger, Joann M. McDermid, Deborah Chan, Rebecca Refvik, Karanbir K. Sidhu, Suad Musse, Pooja P. Patel, Caroline Monnin, Larisa Lotoski, Donna T. Geddes, Fyezah Jehan, Patrick Kolsteren, Lindsay H. Allen, Daniela Hampel, Kamilla G. Eriksen, Natalie Rodriguez, Meghan B. Azad

Bibliographic record

VenueAdvances in Nutrition · 2023
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMcGill University Health CentreHospital for Sick ChildrenSickKids FoundationCentre for Global Health ResearchUniversity of TorontoUniversity of ManitobaChildren's Hospital Research Institute of ManitobaMcGill UniversityUniversity of Calgary
FundersNational Health and Medical Research CouncilMedelaCanada Research ChairsCanadian Nurses FoundationBill and Melinda Gates Foundation
KeywordsBreastfeedingAnthropometryCochrane LibraryInfant formulaMedicineMEDLINEBreast milkMeta-analysisPediatricsBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Among exclusively breastfed infants, human milk (HM) provides complete nutrition in the first mo of life and remains an important energy source as long as breastfeeding continues. Consisting of digestible carbohydrates, proteins, and amino acids, as well as fats and fatty acids, macronutrients in human milk have been well studied; however, many aspects related to their relationship to growth in early life are still not well understood. We systematically searched Medline, EMBASE, the Cochrane Library, Scopus, and Web of Science to synthesize evidence published between 1980 and 2022 on HM components and anthropometry through 2 y of age among term-born healthy infants. From 9992 abstracts screened, 57 articles reporting observations from 5979 dyads were included and categorized based on their reporting of HM macronutrients and infant growth. There was substantial heterogeneity in anthropometric outcome measurement, milk collection timelines, and HM sampling strategies; thus, meta-analysis was not possible. In general, digestible carbohydrates were positively associated with infant weight outcomes. Protein was positively associated with infant length, but no associations were reported for infant weight. Finally, HM fat was not consistently associated with any infant growth metrics, though various associations were reported in single studies. Fatty acid intakes were generally positively associated with head circumference, except for docosahexaenoic acid. Our synthesis of the literature was limited by differences in milk collection strategies, heterogeneity in anthropometric outcomes and analytical methodologies, and by insufficient reporting of results. Moving forward, HM researchers should accurately record and account for breastfeeding exclusivity, use consistent sampling protocols that account for the temporal variation in HM macronutrients, and use reliable, sensitive, and accurate techniques for HM macronutrient analysis.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0070.009
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.025
GPT teacher head0.375
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations37
Published2023
Admission routes2
Has abstractyes

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