Exercise Responsiveness in Obese Adults
Bibliographic record
Abstract
Obesity is associated with several skeletal muscle impairments which can be improved through an aerobic exercise prescription. The possibility that exercise responsiveness is diminished in people with obesity has been suggested but not well‐studied. PURPOSE The purpose of this study was to investigate how obesity influences acute exercise responsiveness within skeletal muscle. METHODS Non‐obese (NO; n=19; 10F/9M; BMI=25.1 ± 2.8 kg/m 2 ) and Obese (O; n=21; 14F/7M; BMI=37.3 ± 4.6 kg/m 2 ) adults performed 30 minutes of single‐leg cycling at 70% of VO 2 peak. Serial muscle biopsies (vastus lateralis) were collected before exercise and 3 and 6 hours post‐exercise to measure protein synthesis and gene expression. RESULTS The exercise‐induced fold change in mixed muscle protein synthesis trended (p=0.058) higher in NO (1.28 ± 0.54‐fold) compared to O (0.95 ± 0.42‐fold) and was inversely related to BMI (r=‐.374, p=0.027). RNA sequencing revealed 331 and 280 genes that were up or downregulated after exercise in NO and O, respectively. Gene set enrichment analysis showed O had blunted post‐exercise pathways related to MAPK signaling, chemokine‐mediated signaling, FC receptor mediated stimulation signaling, cell proliferation, apoptosis, and RNA polymerase II promotion. Quantitative polymerase chain reaction of select metabolic ( PGC1a, PGC1b, TFAM, PDK4, IRS, MAPK, and PRKAA1 ) and muscle growth ( SLC, TBC, MSTN, MyoD, TRIM32, FOX03, and FBXO3) genes were similar between groups 3 and 6 hours after exercise. Conclusion These data highlight several unique pathways in individuals with obesity that result in a blunted exercise response.
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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) | 0.002 | 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".