Acute exercise rescues high fat diet induced reductions in PGC‐1alpha protein content in mouse cortex
Bibliographic record
Abstract
High fat diet induced obesity and insulin resistance have been directly implicated in the neuropathology of Alzheimer's disease. Specifically, previous work has demonstrated that a HFD suppresses PGC‐1alpha in male mice, leading to a down‐regulation of estrogen receptor alpha and increased inflammation. Exercise can improve neuroinflammation but the underlying mechanisms remain unknown. The purpose of this study was to determine the effects of a single bout of exercise on PGC‐1alpha, ER‐alpha, and markers of inflammation in brains from mice fed a high fat diet. Male C57BL/6 mice were fed a low (LFD, 10% kcals from lard) or a high fat diet (HFD, 60% kcals from lard) for 7wks. HFD mice underwent an acute bout of exercise (treadmill running: 15m/min, 5% incline, 120min) followed by a recovery period of 2h. The HFD increased body mass and glucose intolerance (both p<0.05). The HFD resulted in a decline in both PGC‐1alpha and ER‐alpha protein content (p<0.05). There were no diet‐induced alterations in either PGC‐1alpha or ER‐alpha mRNA expression. Similarly, the HFD did not alter the mRNA expression of inflammatory markers (IL‐6, TNF‐alpha, or IL‐1beta). Acute exercise rescued PGC‐1alpha protein content (p>0.05) and increased ER‐alpha protein content. Exercise did not affect gene expression of PGC‐1alpha, ER‐alpha, or inflammatory markers, 2 hours post‐exercise. This indicates post‐translational modification of PCG‐alpha and ER‐alpha proteins post‐exercise. Our findings demonstrate for the first time that an acute bout of exercise can increase PCG‐1alpha and ER‐alpha protein content in obese male mice. Support or Funding Information Rebecca EK MacPherson is supported by a Postdoctoral Fellowship from the Alzheimer's Society of Canada
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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 | 0.000 | 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".