Substitution of parts of aerobic training by resistance training lowers fasting hyperglycemia in individuals with metabolic syndrome
Bibliographic record
Abstract
We sought to determine the effects of substituting parts of aerobic training (AT) by resistance training (RT) on metabolic syndrome (MetS) factors. MetS patients (aged 56 ± 7 years; body mass index 33 ± 5 kg·m−2 and 3.9 ± 0.8 MetS factors) were randomized to undergo 1 of the following isocaloric, 16-week long exercise programs: (i) cycling 4 bouts of 4-min at 90% of maximal heart rate (HRmax) followed by 3 sets of 12 repetitions of 3 lower limb free-weight exercises (high-intensity interval training (HIIT)+RT group; n = 33), (ii) cycling 5 bouts of 4 min at 90% of HRmax (HIIT+HIIT group; n = 33), or (iii) no exercise control group (n = 21). We measured the evolution of all 5 MetS components (z score), cardiorespiratory fitness (maximal oxygen uptake), leg strength and power (leg press 1-repetition maximum (1RM) and countermovement jump (CMJ)), fasting blood glucose (FG), fasting insulin, and insulin resistance (homeostasis model assessment 2). Both training groups improved maximal oxygen uptake similarly (170 ± 310 and 190 ± 210 mL O2·min−1; P < 0.001) and z score (−0.12 ± 0.29 and −0.12 ± 0.31 for HIIT+RT and HIIT+HIIT, respectively; P < 0.02). However, only HIIT+RT improved CMJ (P = 0.002) and leg press 1RM above the HIIT+HIIT group (21% vs 6%; P < 0.001). Furthermore, FG only decreased in the HIIT+RT group (5%; P = 0.026, time × group). Our findings suggest that substitution of part of HIIT by leg RT improves glucose control in MetS individuals. Novelty Most studies addressing the efficacy of endurance versus resistance training are not matched by energy expenditure. We found that substituting 20% of AT with RT reduces hyperglycemia in MetS individuals. Training recommendations to regain glycemic control in MetS individuals should include resistance training.
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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".