Effects of volume-matched resistance training with different loads on glycemic control, inflammation, and body composition in prediabetic older adults
Bibliographic record
Abstract
The purpose of the investigation was to examine the influence of resistance training (RT) with equal volume and varying load on glycemic control, inflammation, and body composition in non-obese prediabetic older adults. Non-obese older adults with prediabetes were randomized into 2 groups, high-load (80% of 1RM) and low-load (40% of 1RM) RT (n = 12/group), both with the same training volume. Oral glucose tolerance test (OGTT) and blood samples were collected at baseline and again after 10 weeks of RT. Fasting blood glucose (103.8 vs. 99.9 mg/dL) and the area under the curve (AUC) of OGTT (0–30 min) decreased significantly in older adults with prediabetes after 10 weeks of volume-matched RT (p < 0.05). Serum levels of MCP-1 (138.7 vs. 98.5 pg/mL) and TNF-α (1.8 vs. 1.3 pg/mL) showed significant decrease after 10 weeks of high-load RT (p < 0.05). There were no changes in IL-10, IL-6, and CRP levels in both groups. Leptin showed significant decrease after 10 weeks of low-load RT (p < 0.05). Changes in fasting glucose and AUC of OGTT (0–120 min) were positively correlated with changes in MCP-1 and TNF-α (p < 0.05). Lean body mass (39.6 vs. 40.3 kg) increased significantly after 10 weeks of volume-matched RT (p < 0.05). Results indicate that equal-volume RT at different loads is beneficial to glycemic control and muscle growth, and high-load RT shows more prominent anti-inflammatory effects. Novelty: Short-term high-load resistance training can help older adults bring their blood sugar level back to normal. High-load resistance training attenuates aging-associated chronic inflammation.
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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 | 0.001 | 0.000 |
| 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".