Low-volume high-intensity interval training reduces hyperglycemia and increases muscle mitochondrial capacity in patients with type 2 diabetes
Bibliographic record
Abstract
Low-volume high-intensity interval training (HIT) is emerging as a time-efficient exercise strategy for improving health and fitness. This form of exercise has not been tested in type 2 diabetes and thus we examined the effects of low-volume HIT on glucose regulation and skeletal muscle metabolic capacity in patients with type 2 diabetes. Eight patients with type 2 diabetes (63 ± 8 yr, body mass index 32 ± 6 kg/m(2), Hb(A1C) 6.9 ± 0.7%) volunteered to participate in this study. Participants performed six sessions of HIT (10 × 60-s cycling bouts eliciting ∼90% maximal heart rate, interspersed with 60 s rest) over 2 wk. Before training and from ∼48 to 72 h after the last training bout, glucose regulation was assessed using 24-h continuous glucose monitoring under standardized dietary conditions. Markers of skeletal muscle metabolic capacity were measured in biopsy samples (vastus lateralis) before and after (72 h) training. Average 24-h blood glucose concentration was reduced after training (7.6 ± 1.0 vs. 6.6 ± 0.7 mmol/l) as was the sum of the 3-h postprandial areas under the glucose curve for breakfast, lunch, and dinner (both P < 0.05). Training increased muscle mitochondrial capacity as evidenced by higher citrate synthase maximal activity (∼20%) and protein content of Complex II 70 kDa subunit (∼37%), Complex III Core 2 protein (∼51%), and Complex IV subunit IV (∼68%, all P < 0.05). Mitofusin 2 (∼71%) and GLUT4 (∼369%) protein content were also higher after training (both P < 0.05). Our findings indicate that low-volume HIT can rapidly improve glucose control and induce adaptations in skeletal muscle that are linked to improved metabolic health in patients with type 2 diabetes.
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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) | 0.001 | 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".