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Record W4367171965 · doi:10.1186/s40168-023-01526-w

Obese-associated gut microbes and derived phenolic metabolite as mediators of excessive motivation for food reward

2023· article· en· W4367171965 on OpenAlexaff
Alice de Wouters d’Oplinter, Marko Verce, Sabrina J. P. Huwart, Jacob Lessard‐Lord, Clara Depommier, Matthias Van Hul, Yves Desjardins, Patrice D. Cani, Amandine Everard

Bibliographic record

VenueMicrobiome · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité Laval
FundersFonds De La Recherche Scientifique - FNRSUniversité Catholique de Louvain
KeywordsGut floraObesityBiologyDopaminergicMetabolomicsMetaboliteFecal bacteriotherapyMicrobiomeGut–brain axisGut microbiomeFood intakeDopamineEndocrinologyImmunologyBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Excessive hedonic consumption is one of the main drivers for weight gain. Identifying contributors of this dysregulation would help to tackle obesity. The gut microbiome is altered during obesity and regulates host metabolism including food intake. RESULTS: By using fecal material transplantation (FMT) from lean or obese mice into recipient mice, we demonstrated that gut microbes play a role in the regulation of food reward (i.e., wanting and learning processes associated with hedonic food intake) and could be responsible for excessive motivation to obtain sucrose pellets and alterations in dopaminergic and opioid markers in reward-related brain areas. Through untargeted metabolomic approach, we identified the 3-(3'-hydroxyphenyl)propanoic acid (33HPP) as highly positively correlated with the motivation. By administrating 33HPP in mice, we revealed its effects on food reward. CONCLUSIONS: Our data suggest that targeting the gut microbiota and its metabolites would be an interesting therapeutic strategy for compulsive eating, preventing inappropriate hedonic food intake. Video Abstract.

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 score0.044
Threshold uncertainty score0.744

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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