No difference in insulin sensitivity between healthy postmenopausal women with or without sarcopenia: a pilot study
Bibliographic record
Abstract
Insulin plays a pivotal role in skeletal muscle protein metabolism and its action decreases with age. A loss of muscle mass, termed sarcopenia, also occurs with age. The age-associated decline in insulin sensitivity (IS) may negatively alter muscle protein metabolism and, therefore, be implicated in the aetiology of sarcopenia. However, no studies have yet compared the level of IS between older individuals with or without sarcopenia. Thus, in this study, we compared the IS of 20 class I sarcopenics (CIS), 8 class II sarcopeniscs (CIIS), and 16 non-sarcopenics (NS), among a group of otherwise healthy, non-obese, postmenopausal women. IS was estimated with the quantitative IS check index (QUICKI). Muscle mass index (MMI), which was used to determine sarcopenia, was calculated as follows: (appendicular muscle massx1.19)-1.01/h2, where h=height. Fat-free mass (FFM), fat mass (FM), and trunk FM (TFM) were measured by dual-energy X-ray absorptiometry. Accelerometry and indirect calorimetry were used to estimate resting (REE), daily (DEE), and physical activity (PAEE) energy expenditure. A 3 d food record was used to determine total energy, protein (animal and vegetal), and carbohydrate intakes. As expected, MMI and FFM differed significantly among groups. However, no significant differences were found among groups for IS, FM, TFM, REE, DEE, PAEE, or total energy, protein (both animal and vegetable), and carbohydrate intakes. Using QUICKI, a surrogate measure of IS, the present results suggest that the action of insulin does not play an important role in the development and maintenance of sarcopenia in healthy, non-obese, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".