A comparative study on the effects of high-fat diet and endurance training on the PGC-1α-FNDC5/irisin pathway in obese and nonobese male C57BL/6 mice
Bibliographic record
Abstract
The present study was performed to clarify how a combined exercise/diet treatment could affect the expression level of the muscle fibronectin type III domain containing 5 (Fndc5) with respect to body fat mass. Male C57BL/6 mice were divided into 2 groups including low-fat (LF) and high-fat (HF) diets for 12 weeks. Then, LF fed (nonobese) and HF fed mice (obese) were divided into the following 4 groups: HF-Exercise, HF-Sedentary, LF-Exercise, and LF-Sedentary. The exercise group exercised on a motor-driven treadmill for 45 min/day, 5 days/week for 8 weeks. Mice were sacrificed 24 h after the final exercise session. Gastrocnemius muscle and the visceral adipose tissue were excised and frozen for the assessment of proliferator-activated receptor gamma coactivator 1 alpha (Pgc-1α) and Fndc5 messenger RNA (mRNA) and protein levels. Data indicated that protein level of muscle PGC-1α was decreased in HF versus LF groups and in obese versus nonobese mice. Moreover, Fndc5 mRNA levels were increased in the muscle tissue of HF versus LF groups and in obese versus nonobese mice. Also, in the gastrocnemius skeletal muscle, protein levels of FNDC5 were significantly higher in the HF fed mice, as compared with their low-fat fed counterparts, similar to what was observed for exercised versus sedentary mice. Overall, we found that the HF diet increased Fndc5 transcript levels in the skeletal muscle, but exercise had a minimal effect on the transcript level of Fndc5, whereas endurance training increased the protein content of FNDC5 in the skeletal muscle.
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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 | 0.001 |
| 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 | 0.001 |
| 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".