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Record W2171911909 · doi:10.3390/ani5020365

Dietary Mannoheptulose Increases Fasting Serum Glucagon Like Peptide-1 and Post-Prandial Serum Ghrelin Concentrations in Adult Beagle Dogs

2015· article· en· W2171911909 on OpenAlexaff
Leslie L. McKnight, Ryan Eyre, Margaret A. Gooding, Gary M Davenport, Anna K. Shoveller

Bibliographic record

VenueAnimals · 2015
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBeagleGhrelinEndocrinologyInternal medicineGlucagonGlucagon-like peptide-1MedicineSerum concentrationChemistryHormoneType 2 diabetesDiabetes mellitus

Abstract

fetched live from OpenAlex

There is a growing interest in the use of nutraceuticals for weight management in companion animals. The purpose of this study was to determine the effects of mannoheptulose (MH), a sugar in avocados that inhibits glycolysis, on energy metabolism in adult Beagle dogs. The study was a double-blind, randomized controlled trial where dogs were allocated to a control (CON, n = 10, 10.1 ± 0.4 kg) or MH containing diet (168 mg/kg, n = 10, 10.3 ± 0.4 kg). Blood was collected after an overnight fast and 1 h post-feeding (week 12) to determine serum satiety related hormones and biochemistry. Resting and post-prandial energy expenditure and respiratory quotient were determined by indirect calorimetry (weeks 4 and 8). Physical activity was measured using an accelerometer (weeks 3, 7, 11). Body composition was assessed using dual X-ray absorptiometry (week 12). MH significantly (p < 0.05) increased fasting serum glucagon-like peptide-1 and post-prandial serum ghrelin. MH tended (p < 0.1) to increase fasting serum gastric inhibitory peptide and decrease physical activity. Together, these findings suggest that dietary MH has the ability to promote satiation and lowers daily energy expenditure.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.329
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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