290-OR: High-Intensity Interval Training and Moderate-Intensity Continuous Training Induce Similar Modifications to Factors Regulating Skeletal Muscle Lipolysis
Bibliographic record
Abstract
Abnormalities in muscle lipid metabolism in obesity have been linked to insulin resistance. Exercise training alters skeletal muscle lipid abundance and localization, but the direct effects of exercise training (independent of weight loss) on skeletal muscle lipolytic proteins are not clearly understood, and data are lacking regarding influence of training intensity on factors regulating muscle lipid metabolism in obesity. Our aim was to examine the effects of high-intensity interval training (HIIT) vs. moderate-intensity continuous training (MICT) on skeletal muscle lipolytic proteins in obese humans. Eighteen sedentary, obese adults completed 12 weeks (4 sessions weekly) of either HIIT (10 x 1 min at 90% HRmax, 1 min recovery; n=8) or MICT (45 min at 70% HRmax; n=10). Muscle biopsies (vastus lateralis) were collected before and after training. Subjects maintained body weight and fat mass and the post-training biopsy occurred 3 days after the final exercise session. Both exercise training programs increased aerobic capacity (VO2max) by ~10% (p=0.003), with no significant difference between HIIT and MICT. In muscle samples, hormone-sensitive lipase (HSL) protein abundance increased ~2-fold after training in HIIT (P<0.05), but not MICT. Training did not affect muscle abundance of adipose triglyceride lipase (ATGL) in either group but interestingly, the abundance of both CGI-58 (a positive regulator of ATGL lipolytic activity) and G0S2 (a negative regulator of ATGL lipolytic activity) increased ~15-20% after training (p<0.02), with no difference between HIIT and MICT. In summary, HIIT and MICT result in similar modifications to lipolytic proteins in skeletal muscle, although our findings suggest that HIIT may be a more potent stimulus for increasing HSL abundance. Future work is needed to determine if the concomitant increases in protein abundance of CGI-58 and G0S2 with training may improve lipid handling in obesity. Disclosure B.J. Ryan: None. M.W. Schleh: None. P. Varshney: None. A. Ludzki: None. J.B. Gillen: None. K. Foug: None. B.D. Carr: None. J.F. Horowitz: Research Support; Self; American Diabetes Association. Funding National Institutes of Health (R01DK077966, P30DK089503, T32DK007245); Canadian Institutes of Health Research (DFS146190)
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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) | 0.000 | 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) | 0.009 | 0.001 |
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".