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Record W3034869278 · doi:10.1017/s002966512000436x

Impact of proanthocyanidin-rich apple intake on gut microbiota composition and polyphenol metabolomic activity in healthy mildly hypercholesterolemic subjects

2020· article· en· W3034869278 on OpenAlexaboutno aff
A. Koutsos, Marynka Ulaszewska, Kajetan Trôst, Jan Stanstrup, Letizia Mariani, Amanda Galvin, Tanya Braune, Francesca Fava, Elena Franciosi, Massimo Pindo, Matthias Scholz, Fulvio Mattivi, Kieran Tuohy, Julie A. Lovegrove

Bibliographic record

VenueProceedings of The Nutrition Society · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsGut floraFood sciencePolyphenolProanthocyanidinFecesMetabolomicsBiologyComposition (language)PrebioticChemistryBiochemistryMicrobiologyAntioxidant

Abstract

fetched live from OpenAlex

Abstract Apples are a rich source of polyphenols and fiber. Proanthocyanidins (PAs), the largest polyphenolic class in apples, can reach the colon almost intact where they interact with the gut microbiota producing simple phenolic acids. These metabolites have the potential to modulate gut microbiota composition and activity and impact on host physiology. A randomized, controlled, crossover, dietary intervention study was performed to determine the broad effects of whole apple intake on fecal gut microbiota composition and activity. Forty heathy mildly hypercholesterolemic volunteers (23 women, 17 men), with a mean BMI (± SD) 25.3 ± 3.7 kg/m 2 and age 51 ± 11 years, consumed 2 apples/day (Renetta Canada, rich in PAs), or a sugar matched control apple beverage, for 8 weeks separated by a 4-week washout period in a random order. Fecal and 24-h urine samples were collected before and after each treatment. The broad effects of apple intake on fecal gut microbiota composition were explored by the high throughput sequencing (HTS) of 16S rRNA gene lllumina MiSeq sequencing (V3-V4 region). Sequencing data analysis was performed using the Quantitative Insight Into Microbial Ecology (QIIME) open-source pipeline version 1.9.1. Specific bacterial groups were also enumerated using the quantitative Fluorescence In Situ Hybridization (FISH). Furthermore, the potential formation of microbial polyphenol metabolites, after apple intake, was explored in urine using Liquid Chromatography (LC) High-Resolution Mass Spectrometry (HRMS) metabolomics. Preliminary analysis showed no changes in gut microbiota abundances measured by Illumina MiSeq, after correction for multiple testing. Apple intake significantly decreased Enterobacteriaceae population ( P = 0.04) compared to the control beverage, as determined with FISH. Twenty-four polyphenol microbial metabolites were identified in higher concentrations in the apple group ( P < 0.05) compared to the control, including valerolactones, valeric and phenolic acids. In conclusion, preliminary data suggest that the daily intake of 2 Renetta Canada apples significantly decreased Enterobacteriaceae population, a family known for its pathogenic members, in healthy mildly hypercholesterolemic subjects. Moreover, several polyphenol microbial metabolites were identified, suggesting that microbial activity is crucial and a prerequisite for the absorption of apple polyphenols, producing active metabolites with potential health benefits.

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 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 score0.115
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.017
GPT teacher head0.273
Teacher spread0.255 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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