Association of insulin sensitivity and muscle strength in overweight and obese sedentary postmenopausal women
Bibliographic record
Abstract
The objective of this study was to examine the relationship between insulin sensitivity and lower body muscle strength in overweight and obese sedentary postmenopausal women. The design of the study was cross-sectional. The study population consisted of 82 non-diabetic overweight and obese sedentary postmenopausal women (age: 58.2 +/- 5.1 y; body mass index (BMI): 32.4 +/- 4.6 kg.m-2). Subjects were classified by dividing the entire cohort into quartiles based on relative insulin sensitivity expressed per kilograms of lean body mass (LBM) (Q1, < 10.3, vs. Q2, 10.3-12.4, vs. Q3, 12.5-14.0, vs. Q4, >14.0 mg.min-1.kg LBM-1). We measured insulin sensitivity (using the hyperinsulinemic-euglycemic clamp technique), body composition (using dual-energy X-ray absorptiometry), visceral fat and muscle attenuation (using computed tomography), and a lower-body muscle strength index expressed as weight lifted in kilograms per kilogram of LBM (kg.kg LBM-1) (using weight-training equipment). A positive and significant relationship was observed between insulin sensitivity and the muscle strength index (r = 0.37; p < 0.001). Moreover, a moderate but significant correlation was observed between the muscle strength index and muscle attenuation (r = 0.22; p < 0.05). Finally, the muscle strength index was significantly higher in the Q4 group compared with the Q2 and Q1 groups, respectively (3.78 +/- 1.13 vs. 2.99 +/- 0.77 and 2.93 +/- 0.91 kg.kg LBM-1; p < 0.05). Insulin sensitivity is positively associated with lower-body muscle strength in overweight and obese sedentary postmenopausal women.
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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 | 0.002 |
| 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".