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Record W4313252103 · doi:10.1016/j.tjnut.2022.12.016

The Relationship Between Diet, Gut Microbiota, and Serum Metabolome of South Asian Infants at 1 Year

2022· article· en· W4313252103 on OpenAlexafffundabout
Colin Y. Bruce, Meera Shanmuganathan, Sandi M. Azab, Elinor Simons, Piush J. Mandhane, Stuart E. Turvey, Padmaja Subbarao, Meghan B. Azad, Philip Britz‐McKibbin, Sonia S. Anand, Russell J. de Souza, Jennifer C. Stearns

Bibliographic record

VenueJournal of Nutrition · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsImpactChildren's Hospital Research Institute of ManitobaHospital for Sick ChildrenUniversity of British ColumbiaPopulation Health Research InstituteUniversity of AlbertaUniversity of ManitobaSickKids FoundationUniversity of TorontoBC Children's HospitalMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMetabolomeGut floraBiologyPhysiologyBifidobacteriumMetaboliteBreast feedingFecesOperational taxonomic unitRuminococcusLactobacillusZoologyEndocrinologyMicrobiologyImmunologyFood scienceMedicineBacteriaGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Diet is known to affect the gut microbiota and the serum metabolome in adults, but this has not been fully explored in infants. Infancy is an important developmental period that may influence a person's long-term health. Infant development can be affected by diet, which also interacts with the developing gut microbiota. OBJECTIVES: This study aimed to explore the associations between diet, the gut microbiota, and the serum metabolome of 1-y-old infants with the overarching goal of identifying serum biomarkers of diet and/or the gut microbiota. METHODS: We derived dietary patterns of 1-y-old infants (n = 182) participating in the Canadian South Asian Birth Cohort (START) study. We compared gut microbiota α-diversity and β-diversity and taxa relative abundance from 16S rRNA gene profiles with dietary patterns (PERMANOVA, Envfit) and investigated diet-serum metabolite associations using a multivariate analysis (partial least squares-discriminant analysis) and univariate analysis (t test). We explored the effect of nondietary factors on diet-serum metabolite relationships by incorporating diet, the gut microbiota, and maternal, perinatal, and infant characteristics in a multivariable forward stepwise regression. We replicated this analysis in White European infants, from the CHILD Cohort Study (n = 81). RESULTS: = 0.547). Breastfed participants showed higher abundance of microbes from the genera Bifidobacterium (3.29 log2-fold) and Lactobacillus (7.93 log2-fold) and higher median concentrations of the metabolites S-methylcysteine (1.38 μM) and tryptophan betaine (0.43 μM) than nonbreastfed participants. Formula consuming infants showed higher median concentrations of branched-chain/aromatic amino acids (average 48.3 μM) than non-formula-consuming infants. CONCLUSIONS: Formula consumption and breastfeeding most strongly predicted the serum metabolites of 1-y-old infants, even when the gut microbiota, solid food consumption, and other covariates were considered.

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 score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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