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Record W4380538330 · doi:10.1016/j.advnut.2023.06.005

Human Milk Micronutrients and Child Growth and Body Composition in the First 2 years: A Systematic Review

2023· review· en· W4380538330 on OpenAlexafffund
Sarah M. Reyes, Meredith Brockway, Joann M. McDermid, Deborah Chan, Matthew Granger, Rebecca Refvik, Karanbir K. Sidhu, Suad Musse, 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
TopicChild Nutrition and Water Access
Canadian institutionsMcGill UniversityUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersCanadian Nurses FoundationMedelaCanada Research ChairsCanadian Institute for Advanced ResearchNational Health and Medical Research CouncilBill and Melinda Gates Foundation
KeywordsMicronutrientComposition (language)MedicinePediatricsFood sciencePhysiologyBiologyPathologyArt

Abstract

fetched live from OpenAlex

Human milk (HM) provides a plethora of nutritional and non-nutritional compounds that support infant development. For many compounds, concentrations vary substantially among mothers and across lactation, and their impact on infant growth is poorly 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 infants. Outcomes included weight-for-length, length-for-age, weight-for-age, body mass index (in kg/m2)–for–age, and growth velocity. From 9992 abstracts screened, 144 articles were included and categorized based on their reporting of HM micronutrients, macronutrients, or bioactive components. Micronutrients (vitamins and minerals) are reported here, based on 28 articles involving 2526 mother-infant dyads. Studies varied markedly in their designs, sampling times, geographic and socioeconomic settings, reporting practices, and the HM analytes and infant anthropometrics measured. Meta-analysis was not possible because data were sparse for most micronutrients. The most-studied minerals were zinc (15 articles, 1423 dyads) and calcium (7 articles, 714 dyads). HM iodine, manganese, calcium, and zinc concentrations were positively associated with several outcomes (each in ≥2 studies), whereas magnesium (in a single study) was negatively associated with linear growth during early lactation. However, few studies measured HM intake, adjusted for confounders, provided adequate information about complementary and formula feeding, or adequately described HM collection protocols. Only 4 studies (17%) had high overall quality scores. The biological functions of individual HM micronutrients are likely influenced by other HM components; yet, only 1 study analyzed data from multiple micronutrients simultaneously, and few addressed other HM components. Thus, available evidence on this topic is largely inconclusive and fails to address the complex composition of HM. High-quality research employing chronobiology and systems biology approaches is required to understand how HM components work independently and together to influence infant growth and to identify new avenues for future maternal, newborn, or infant nutritional interventions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.332
Teacher spread0.312 · 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 teacher head, not a consensus.

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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