Prior Exercise Training Protects Against Short‐Term High Fat Feeding Induced Weight Gain and Glucose Intolerance
Bibliographic record
Abstract
It has recently been shown that high fat diet induced metabolic changes such as impaired glucose tolerance and adipose tissue inflammation occur in as few as three days. We sought to determine whether these changes could be mitigated with prior exercise training. Male C57BL/6J mice (8 weeks old) were fed control diet (10% kcal from lard) and either treadmill trained or kept sedentary for four weeks. Twenty‐four hours after the final bout of exercise, mice were provided with high fat diet (60% kcal from lard), ad libitum , for four days, with no further exercise. Prior training resulted in 68% less weight gain on the high fat diet and 20% (epididymal) and 33% (subcutaneous) smaller fat pad mass. This occurred despite similar food intake. Compared to control fed mice, high fat feeding resulted in increased total AUC following an intraperitoneal glucose tolerance test, which was significantly blunted with prior training. The improvement in high fat feeding induced impaired glucose tolerance following training was mirrored by greater insulin‐induced increases in Akt phosphorylation in skeletal muscle and liver, but not adipose tissue. The blunted impairment in glucose homeostasis was not explained by differences in indices of adipose tissue inflammation and was lost after 6 days of high fat feeding. Further, when compared to sedentary high fat fed mice that were calorie restricted (‐30%) to match the weight gain of the trained high fat fed mice, the same attenuated impairments in glucose tolerance were observed. Taken together, our data suggest that prior exercise protects against high fat diet induced weight gain and glucose intolerance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".