Prior exercise training blunts short-term high-fat diet-induced weight gain
Bibliographic record
Abstract
High-fat diets rapidly cause weight gain and glucose intolerance. We sought to determine whether these changes could be mitigated with prior exercise training. Male C57BL/6J mice were exercise-trained by treadmill running (1 h/day, 5 days/wk) for 4 wk. Twenty-four hours after the final bout of exercise, mice were provided with a high-fat diet (HFD; 60% kcal from lard) for 4 days, with no further exercise. In mice fed the HFD prior to exercise training, the results were blunted weight gain, reduced fat mass, and a slight attenuation in glucose intolerance that was mirrored by greater insulin-induced Akt phosphorylation in skeletal muscle compared with sedentary mice fed the HFD. When ad libitum-fed sedentary mice were compared with sedentary high-fat fed mice that were calorie restricted (-30%) to match the weight gain of the previously trained high-fat fed mice, the same attenuated impairments in glucose tolerance were found. Blunted weight gain was associated with a greater capacity to increase energy expenditure in trained compared with sedentary mice when challenged with a HFD. Although mitochondrial enzymes in white adipose tissue and UCP-1 protein content in brown adipose tissue were increased in previously exercised compared with sedentary mice fed a HFD, ex vivo mitochondrial respiration was not increased in either tissue. Our data suggest that prior exercise training attenuates high-fat diet-induced weight gain and glucose intolerance and is associated with a greater ability to increase energy expenditure in response to a high-fat diet.
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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) | 0.000 | 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 | 0.001 |
| 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".