Bacterial fecal microbiota is only minimally affected by a standardized weight loss plan in obese cats
Bibliographic record
Abstract
BACKGROUND: Research in humans and mice suggests that obesity influences the abundance and diversity of gastrointestinal (GI) microbiota, and that an "obese microbiome" influences energy metabolism and fat storage in the host. Microbiota membership and composition have been previously assessed in healthy cats. However, research investigating the effects of obesity and weight loss on the cat's fecal microbiota is limited. Therefore, this study's objective was to evaluate differences in fecal microbial abundance and biodiversity, as well as serum cobalamin and folate concentrations in obese cats, before and after weight loss, and compare to lean cats. Fourteen lean and 17 obese healthy client-owned cats were fed a veterinary therapeutic weight loss food at maintenance energy requirement for 4 weeks. At the end of week 4, lean cats finished the study, whereas obese cats continued with a 10-week weight loss period on the same food, fed at individually-tailored weight loss energy requirements. Body weight and body condition score were recorded every 2 weeks throughout the study. At the end of each period, a fecal sample and food-consumption records were obtained from the owners, and serum cobalamin and folate concentrations were analysed. DNA was extracted from fecal samples, polymerase chain reaction (PCR) was performed, and products were sequenced using next-generation sequencing (Illumina MiSeq). RESULTS: No significant differences in the relative abundance of taxa and in biodiversity indices were observed between cats in either group (P > 0.05 for all tests). Nevertheless, some significantly enriched taxa, mainly belonging to Firmicutes, were noted in linear discriminant analysis effect size test in obese cats before weight loss compared to lean cats. Serum cobalamin concentrations were significantly higher in lean compared to obese cats both before and after weight loss. Serum folate concentrations were higher in obese cats before weight loss compared to after. CONCLUSIONS: The association between feline obesity and the fecal bacterial microbiota was demonstrated in enriched taxa in obese cats compared to lean cats, which may be related to enhanced efficiency of energy-harvesting. However, in obese cats, the fecal microbial abundance and biodiversity were only minimally affected during the early phase of a standardized weight loss plan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".