Concentric and eccentric exercise, glycemic responses to a postexercise meal, and inflammation in women with high versus low waist circumference
Bibliographic record
Abstract
Carbohydrate ingestion and level of concentric versus eccentric muscle activity may alter exercise-induced health benefits for individuals who have high waist circumference as a metabolic risk factor. The purpose of this study was to determine whether metabolic and inflammation responses to an exercise recovery meal differ between women with lower (Lo-WC, <80 cm) compared with higher (Hi-WC) waist circumference when the exercise is primarily concentric (uphill walking; UPHILL) versus primarily eccentric (downhill walking; DOWNHILL). Recreationally active women (age, 18–39 years; body mass index, 19–35.4 m·kg−2; Lo-WC, n = 13; Hi-WC, n = 10) completed UPHILL, DOWNHILL, and resting (CONTROL) conditions followed 30 min later by a mixed meal tolerance test (MMTT) with carbohydrates to protein ratio of 4:1, and blood glucose, insulin, and inflammation markers were compared across conditions. Compared with Lo-WC, the Hi-WC group had higher (p < 0.05) (i) insulin during the MMTT in CONTROL (mean ± SE; 48.5 ± 8.2 vs 22.9 ± 2.8 pmol·L−1), (ii) baseline (0.7 ± 0.4 vs 2.0 ± 1.7 pg·mL−1) interleukin-6 (IL-6), and (iii) IL-6 responses 8 h after UPHILL and CONTROL. Both groups had (i) increases in IL-6 at 0 h after UPHILL and at 8 h after DOWNHILL, and (ii) lower glycemic responses in UPHILL. Women with Hi-WC had higher IL-6 at rest and delayed increases in IL-6 after a high-carbohydrate meal in all conditions. This is consistent with an inflammation response to the meal and or uphill walking exercise. However, both concentrically and eccentrically biased exercises offered benefits to insulin responses to a high carbohydrate meal for Hi-WC.
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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 | 0.001 |
| 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) | 0.001 | 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".