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Record W2164308461 · doi:10.1002/oby.21017

Interactive effects of oligofructose and obesity predisposition on gut hormones and microbiota in diet‐induced obese rats

2015· article· en· W2164308461 on OpenAlexafffund
Nina L. Cluny, Lindsay K. Eller, Catherine M. Keenan, Raylene A. Reimer, Keith A. Sharkey

Bibliographic record

VenueObesity · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversity of Calgary
FundersObesity CanadaCanadian Institutes of Health ResearchAlberta InnovatesCrohn's and Colitis Foundation of CanadaCrohn's and Colitis Foundation
KeywordsObesityGut floraHormoneMedicineEndocrinologyGenetic predispositionInternal medicinePhysiologyImmunologyDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Oligofructose (OFS) is a prebiotic that reduces energy intake and fat mass via changes in gut satiety hormones and microbiota. The effects of OFS may vary depending on predisposition to obesity. The aim of this study was to examine the effect of OFS in diet-induced obese (DIO) and diet-resistant (DR) rats. METHODS: Adult, male DIO, and DR rats were randomized to: high-fat/high-sucrose (HFS) diet or HFS diet + 10% OFS for 6 weeks. Body composition, food intake, gut microbiota, plasma gut hormones, and cannabinoid CB(1) receptor expression in the nodose ganglia were measured. RESULTS: OFS reduced body weight, energy intake, and fat mass in both phenotypes (P < 0.05). Select gut microbiota differed in DIO versus DR rats (P < 0.05), the differences being eliminated by OFS. OFS did not modify plasma ghrelin or CB(1) expression in nodose ganglia, but plasma levels of GIP were reduced and PYY were elevated (P < 0.05) by OFS. CONCLUSIONS: OFS was able to reduce body weight and adiposity in both prone and resistant obese phenotypes. OFS-induced changes in gut microbiota profiles in DIO and DR rats, along with changes in gut hormone levels, likely contribute to the sustained lower body weights.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.655

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 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

Citations72
Published2015
Admission routes2
Has abstractyes

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