The Canine <i>POMC</i> Gene, Obesity in Labrador Retrievers and Susceptibility to Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) in dogs is a common endocrinopathy with a complex genetic architecture. Disease susceptibility in several breeds is associated with polymorphisms in immune response genes, but in the Labrador retriever breed, no genetic associations with DM have been identified. A deletion in the pro-opiomelanocortin (POMC) gene in Labrador retrievers is associated with increased appetite and risk of obesity. HYPOTHESIS/OBJECTIVES: To characterize the POMC deletion in Labrador retrievers, to develop a simple genetic test for this mutation, and to test the hypothesis that the POMC gene deletion is associated with an increased risk of DM in this breed. ANIMALS: Sixty-one non-diabetic Labrador retrievers aged >6 years and 57 Labrador retrievers with DM. METHODS: Case-control genotyping study to compare the frequency of the POMC deletion in dogs with and without DM. After polymerase chain reaction (PCR) and sequencing to characterize the mutation, a PCR-based test was developed and validated using 2 different restriction fragment length polymorphism assays. RESULTS: A 14-base-pair deletion was confirmed and localized to exon 3 of the canine POMC gene. A PCR-based test for the deletion was successfully developed. There was no association between the presence of the POMC deletion mutation and DM in this population of Labrador retriever dogs (P = .31). CONCLUSIONS AND CLINICAL IMPORTANCE: This study adds to the existing scientific literature indicating that there is little evidence for a direct link between obesity and DM in dogs.
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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) | 0.002 | 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".