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Record W2729972353 · doi:10.1371/journal.pone.0180086

The urine metabolome differs between lean and overweight Labrador Retriever dogs during a feed-challenge

2017· article· en· W2729972353 on OpenAlexaboutno aff
Josefin Söder, Ragnvi Hagman, Johan Dicksved, Sanna Lindåse, Kjell Malmlöf, Peter Agback, Ali A. Moazzami, Katja Höglund, Sara Wernersson

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersSveriges LantbruksuniversitetVetenskapsrådetThure F och Karin Forsbergs StiftelseSvenska Forskningsrådet Formas
KeywordsPostprandialUrineOverweightMetaboliteMetabolomeEndocrinologyInternal medicineObesityChemistryMedicineDiabetes mellitus

Abstract

fetched live from OpenAlex

Obesity in dogs is an increasing problem and better knowledge of the metabolism of overweight dogs is needed. Identification of molecular changes related to overweight may lead to new methods to improve obesity prevention and treatment. The aim of the study was firstly to investigate whether Nuclear Magnetic Resonance (NMR) based metabolomics could be used to differentiate postprandial from fasting urine in dogs, and secondly to investigate whether metabolite profiles differ between lean and overweight dogs in fasting and postprandial urine, respectively. Twenty-eight healthy intact male Labrador Retrievers were included, 12 of which were classified as lean (body condition score (BCS) 4-5 on a 9-point scale) and 16 as overweight (BCS 6-8). After overnight fasting, a voided morning urine sample was collected. Dogs were then fed a high-fat mixed meal and postprandial urine was collected after 3 hours. Metabolic profiles were generated using NMR and 45 metabolites identified from the spectral data were evaluated using multivariate data analysis. The results revealed that fasting and postprandial urine differed in relative metabolite concentration (partial least-squares discriminant analysis (PLS-DA) 1 comp: R2Y = 0.4, Q2Y = 0.32; cross-validated ANOVA: P = 0.00006). Univariate analyses of discriminant metabolites showed that taurine and citrate concentrations were elevated in postprandial urine, while allantoin concentration had decreased. Interestingly, lean and overweight dogs differed in terms of relative metabolite concentrations in postprandial urine (PLS-DA 1 comp: R2Y = 0.5, Q2Y = 0.36, cross-validated ANOVA: P = 0.005) but not in fasting urine. Overweight dogs had lower postprandial taurine and a trend of higher allantoin concentrations compared with lean dogs. These findings demonstrate that metabolomics can differentiate 3-hour postprandial urine from fasting urine in dogs, and that postprandial urine metabolites may be more useful than fasting metabolites for identification of metabolic alterations linked to overweight. The lowered urinary taurine concentration in overweight dogs could indicate alterations in lipid metabolism and merits further investigation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.257
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2017
Admission routes1
Has abstractyes

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