TBC1D1 Ser<sup>237</sup> phosphorylation, but not insulin sensitivity, is higher following a bout of high-intensity interval exercise in healthy males as compared with females
Bibliographic record
Abstract
Interval training has been found to lower glucose concentrations and increase insulin sensitivity in males but not in females, which may be due to inherent sex-based differences in metabolism. Twenty-four (12/sex) participants completed a bout of high-intensity interval exercise (HIIE, 10 × 1 min at 90% HR max ) to evaluate whether sex influenced the physiological effects of HIIE on postexercise glycemic control during an oral glucose tolerance test (OGTT). Given that body anthropometrics influence postprandial glucose, data were also expressed as a function of the normalized glucose dose. In addition, we examined whether sex differences in postexercise glycemic control were related to sex differences in muscle metabolism and/or insulin signaling proteins. HIIE increased insulin sensitivity in both sexes as characterized by the Matsuda ( P = 0.03, η p 2 = 0.20) and HOMA-IR ( P = 0.047, η p 2 = 0.17) indices. HIIE also lowered insulin concentration during the OGTT ( P = 0.04, η p 2 = 0.18) as compared with control. When normalized for glucose dose relative to lean mass, glucose area under the curve (AUC) was lower in females than in males ( P ≤ 0.001, η p 2 = 0.47). TBC1D1 Ser 237 phosphorylation increased in males, but not in females, postexercise ( P = 0.03, η p 2 = 0.19). There was no difference in total insulin signaling protein content, muscle glycogen utilization, or AMPK activation during exercise between the sexes. These findings indicate that when the glucose dose is normalized for differences in body composition glycemic handling is better in females and that an acute bout of HIIE improves insulin sensitivity equally in healthy males and females.
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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) | 0.003 | 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".