Risk factors associated with canine overweightness and obesity in an owner-reported survey
Bibliographic record
Abstract
Abstract Background Overweightness and obesity in dogs are associated with negative health outcomes. A better understanding of risk factors associated with canine weight is fundamental to identifying preventative interventions and treatments. In this cross-sectional study, we used a direct to consumer approach to collect body condition scores (BCS), as well as demographic, diet, and lifestyle data on 4,446 dogs. BCS was assessed by owners using a 9-point system and categorized as ideal (BCS 4-5), overweight (BCS 6), and obese (BCS 7+). Following univariate analyses, a stepwise procedure was used to select variables which were included in multivariate logistic regression models. One model was created to compare ideal to all overweight and obese dogs, and another was created to compare ideal to obese dogs only. We then used Elastic Net selection and XGBoost variable importance measures to validate these results. Results Overall, 1,480 (33%) of dogs were reported to be overweight or obese, of which 356 (8% total) of dogs were reported to be obese. Seven factors were significantly associated with both overweightness/obesity and obesity alone in all three analyses (stepwise, Elastic Net, and XGBoost): diet composition, probiotic supplementation, treat quantity, exercise, age, food motivation level, and pet appetite. Neutering was also associated with overweightness/obesity in all analyses. Conclusions This study recapitulated established risk factors associated with BCS (age, exercise, neutering). Moreover, we elucidated associations between previously examined risk factors and BCS (diet composition, treat consumption, and temperament) and identified a novel factor (probiotic supplementation). Specifically, relative to dogs on fresh food diets, BCS was higher in dogs eating dry food both alone and in combination with other foods. Furthermore, dogs receiving probiotics, but not other forms of supplementation, were more likely to have an ideal BCS. Future studies should corroborate these findings with experimental manipulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".