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Record W4319659223 · doi:10.1016/j.ajcnut.2023.02.002

Maternal nutritional status and milk volume and composition in India: an observational study

2023· article· en· W4319659223 on OpenAlexaff
Melissa Young, Emily Faerber, Rukshan Mehta, Samriddhi Ranjan, Sweekruthi A. Shetty, Usha Ramakrishnan, Kannan Rangiah, Beena Bose, Sarita Devi, Pratibha Dwarkanath, Anura V. Kurpad, Sunita Taneja, Reynaldo Martorell

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2023
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsObservational studyComposition (language)Food scienceMedicineEnvironmental healthBiologyInternal medicineArt

Abstract

fetched live from OpenAlex

BACKGROUND: Human milk provides essential nutrition for infants, and its benefits are well established. We lack data on the influence of maternal nutritional status on milk volume and composition in low-middle income countries. OBJECTIVE: We aimed to 1) assess lactation performance (human milk volume, macronutrient composition, and infant energy intake) in Indian females and 2) examine the associations between maternal anthropometry (BMI, percentage body fat) and lactation performance. METHODS: We conducted an observational study among 232 mother-infant dyads, 2 to 4 mo postpartum in Haryana, India. We used deuterium oxide dose-to-mother technique to measure milk volume and maternal percentage body fat and collected human milk samples to determine macronutrient and energy concentrations. Adjusted multiple linear regression models were used to examine the associations between maternal anthropometry and lactation performance. RESULTS: and 29.5 ± 7.7, respectively. Milk volume and macronutrient composition were similar to the reference values (means ± standard deviations: milk volume, 724 ± 184 mL/d; median (25th, 75th percentile); protein, 9.9 (8.3, 11.7) g/L; fat, 41.0 ± 15.2 g/L; energy density, 0.71 ± 0.14 kcal/g; lactose, 65.5 (55.3, 71.3) g/L). Maternal BMI and percentage body fat were not significantly associated with macronutrient composition. Both maternal BMI and percentage body fat were negatively associated with milk volume (-7.0, 95% CI: -12.4, -1.6 mL/d; -3.5, 95% CI: -6.0, -1.1mL/d, respectively) but there were no effects on the total energy intake of infants after adjusting for covariates. CONCLUSION: Most mothers had a normal BMI and milk of similar composition and volume to reference values. Future work in populations with a greater burden of underweight and/or obesity are needed to examine the underlying mechanisms between maternal body composition and milk volume. This trial was registered at The Clinical Trials Registry- India as CTRI/2017/01/007636.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.120
GPT teacher head0.447
Teacher spread0.328 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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