Dietary energy source and physical conditioning affect insulin sensitivity and skeletal muscle glucose metabolism in horses
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Starch rich (S) feeds reduce insulin sensitivity in untrained horses when compared to high fat (F) feeds, but insulin sensitivity is not affected when S or F are fed during exercise training. The effects of S vs. F on training-associated alterations in skeletal muscle glucose metabolism are unknown. OBJECTIVES: To determine the effects of dietary energy source on training-associated changes in insulin sensitivity, skeletal muscle GLUT4 protein and hexokinase (HK) and glycogen synthase (GS) activities in horses. METHODS: After a baseline period on an all forage diet (Phase 1), horses were adapted to high starch (S) or high fat (F) diets (n = 7/group) for 6 weeks (Phase 2) and then completed 7 weeks of exercise training (Phase 3) on the same diets. To measure insulin sensitivity (SI), minimal model analysis of a frequently-sampled i.v. glucose tolerance test was performed at the end of each phase. Middle gluteal muscle biopsies to measure GLUT-4 protein content, muscle glycogen and HK and GS activities were taken before and after euglycaemic-hyperinsulinaemic clamps administered after each phase. Data were analysed by repeated measures ANOVA. RESULTS: In S, SI was 36% lower (P < 0.05) after Phase 2 when compared to Phase 1 but was unchanged in F. After Phase 3, SI was increased (P < 0.01) in S and F compared to Phase 2 and did not differ (P > 0.05) between diets. Middle gluteal muscle GLUT-4 protein and post clamp HK activity were increased (P < 0.05) in S after Phase 3, with higher (P < 0.01) GLUT4 in S than in F. GS activities were unchanged in both diets. CONCLUSIONS: Adaptation to S resulted in decreased SI mitigated by moderate physical conditioning. Increased GLUT-4 protein content and HK activity in S may have contributed to higher SI after training.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".