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Record W4400149003 · doi:10.1016/j.cdnut.2024.103225

Micronutrient Status and Its Effect on Infant Neurocognitive Development in Rural Cambodia

2024· article· en· W4400149003 on OpenAlexaff
Megan Oshiro, Jeffrey R. Measelle, Dare A. Baldwin, Frank T. Wieringa, Tim Green, Prak Sophonneary, Hou Kroeun, Kyly C. Whitfield

Bibliographic record

VenueCurrent Developments in Nutrition · 2024
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsMicronutrientNeurocognitiveEnvironmental healthInfant developmentPsychologyDevelopmental psychologyMedicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

Objectives: Micronutrient deficiencies can significantly impair cognitive development especially during critical periods in early infant life. It is also possible that micronutrient levels which are not explicitly deficient, but still limited, could confer developmental risk. The present study aims to assess the severity of thiamine, iron, and vitamin A deficiency in a sample of infants participating in a randomized control trial (RCT) in rural Cambodia (Whitfield et al., 2019). Methods: Data were collected in Kampong Thom, Cambodia from exclusively breastfed infants (n=335) participating in a four armed RCT; mothers were supplemented with thiamine (0, 1.2, 2.4, 10 mg/day dosage levels) across the first 6 months postpartum. Infant blood at 6 months was assayed for erythrocyte transketolase activity coefficient (ETKac) for thiamine, ferritin for iron, and retinol binding protein (RBP) for vitamin A. Neurocognition was assessed at 6 and 12 months with Mullen Scales of Early Learning (MSEL). Results: 4.8% (3.6% to 5.7%) of the sample was deficient on each of the micronutrient biomarkers, whereas upwards of 36.5% (26.3% to 52.2%) were marginally deficient. Correlational analysis further revealed that measures of infant thiamine, vitamin A, and iron were associated with infant language development at one or both of the 6- and 12-month timepoints. Additional multivariate regression models are underway to explore the joint and interactive effects of all three micronutrients on infant language and neuro-cognitive development more generally. Conclusions: Our findings underscore that infants in rural Cambodia face risk of deficiency on multiple micronutrients, and these deficiencies – even at marginal levels – are associated with negative consequences for their language development. Results will be considered in terms of the collective impact that multiple micronutrient deficiencies hold for infants’ neuro-cognitive development across the first year of life. These findings will contribute in significant ways to designing nutritional interventions to protect infants' neurocognitive outcomes. Funding Sources: New York Academy of Sciences and the Bill and Melinda Gates Foundation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.298
Teacher spread0.282 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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