Effective Exercise Modality to Reduce Insulin Resistance in Women With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate whether a combined resistance and aerobic training program would improve insulin sensitivity compared with aerobic training alone in postmenopausal women with type 2 diabetes. A second objective was to relate the improved insulin sensitivity to changes in abdominal adipose tissue (AT) and thigh muscle density. RESEARCH DESIGN AND METHODS: A total of 28 obese postmenopausal women with type 2 diabetes were randomly assigned to one of three 16-week treatments: control, aerobic only training (Ae only), or aerobic plus resistance training (Ae+RT). Pre- and posttreatment outcome measures included glucose disposal by hyperinsulinemic-euglycemic clamp and computed tomography scans of abdominal AT and mid-thigh skeletal muscle. RESULTS: Glucose infusion rates increased significantly (P < 0.05) in the Ae+RT group. Both exercise groups had reduced abdominal subcutaneous and visceral AT and increased muscle density. The Ae+RT training group exhibited a significantly greater increase in muscle density than the Ae only group. Improved glucose disposal was independently associated with changes in subcutaneous AT, visceral AT, and muscle density. Muscle density retained a relationship with glucose disposal after controlling for abdominal AT. CONCLUSIONS: Adding resistance training to aerobic training enhanced glucose disposal in postmenopausal women with type 2 diabetes. The improved insulin sensitivity is related to loss of abdominal subcutaneous and visceral AT and to increased muscle density.
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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) | 0.003 | 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".