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Record W4392784133 · doi:10.1002/mnfr.202300641

Assessing the Gut Microbiota's Ability to Metabolize Oligomeric and Polymeric Flavan‐3‐ols from Aronia and Cranberry

2024· article· en· W4392784133 on OpenAlexafffund
Jacob Lessard‐Lord, Charlène Roussel, Valérie Guay, Yves Desjardins

Bibliographic record

VenueMolecular Nutrition & Food Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGut floraFlavanCatechinProanthocyanidinFood scienceMetabolismChemistryPopulationBiologyBiochemistryPolyphenolMedicineAntioxidantStereochemistry

Abstract

fetched live from OpenAlex

Clinical trials investigating the health effects of flavan-3-ols yield heterogeneous results due to interindividual variability in the gut microbiota metabolism. In fact, different groups in the population have similar metabolic profiles following (-)-epicatechin and (+)-catechin gut microbial metabolism and can be regrouped into so-called metabotypes. In this study, the capacity of 34 donors to metabolize polymeric B-type flavan-3-ols from aronia and oligomeric A-type flavan-3-ols from cranberry is investigated by in vitro fecal batch fermentations. Less than 1% of the flavan-3-ols from both sources are converted into microbial metabolites, such as phenyl-γ-valerolactones (PVLs). To further confirm this result, gut microbial metabolites from flavan-3-ols are quantified in urine samples collected from participants, before and after a 4-day supplementation of cranberry extract providing 82.3 mg of flavan-3-ols per day. No significant difference is observed in the urinary excretion of flavan-3-ols microbial metabolites. Hence, it demonstrates by both in vitro and in vivo approaches that flavan-3-ols from aronia and cranberry are poorly degraded by the gut microbiota. The beneficial health impacts of these molecules likely stem from their capacity to affect gut microbiota and their interactions with the gut epithelium, rather than from their breakdown into smaller metabolites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.407
Teacher spread0.359 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations11
Published2024
Admission routes2
Has abstractyes

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