Exploring relationships between body condition score, body fat, activity level and inflammatory biomarkers
Bibliographic record
Abstract
Summary Obesity is associated with inflammatory disorders in humans, including degenerative joint disease. While obesity is endemic in horses, its relationship to equine degenerative joint disease has not been explored. The current study sought to describe relationships between: body weight (BW), body condition score (BCS), lameness grade (AAEP), total body fat mass (kg; FM) and fat per cent (FP) [multifrequency bioelectrical impedance analysis (mfBIA)], age, gender, activity level (AL), synovial fluid (SF) and plasma (PL) PGE2 and glycosaminoglycan (GAG) in horses. During this field investigation, the BCS (of nine) of 54 horses at multiple farms in southern Ontario, Canada, was determined. Horses were categorized as thin (BCS=3/9; n = 6), moderate (BCS=4 or 5/9; n = 18), overweight (BCS=6 or 7/9; n = 19) or obese (BCS=8 or 9/9; n = 11). Total fat mass (kg) and body fat% was measured using mfBIA, lameness was assessed (AAEP lameness scale) and synovial fluid was collected via aseptic arthrocentesis from the left intercarpal joint for assessment of inflammatory biomarkers (PGE2, GAG). Means were compared with a one‐way ANOVA; correlation coefficients were calculated using a Spearman Rank Order Correlation to reveal correlations between variables. BCS was positively correlated with BW, FM, FP, AL and PL‐PGE2. BW was also significantly positively correlated with PL‐PGE2. It is concluded that BCS is significantly correlated with PL‐PGE2, due in part to the combined effect of AL and body condition. Net inflammatory effects of body fat on risk for joint disease require further study.
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 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".