Association of muscle mass measured by D3-Creatine (D3Cr), sarcopenic obesity, and insulin-glucose homeostasis in postmenopausal women
Bibliographic record
Abstract
The D3-Creatine (D3Cr) dilution method is a direct and accurate measure of skeletal muscle mass. In this study, we examined the association of D3Cr muscle mass with measures of insulin-glucose homeostasis in community dwelling postmenopausal women. Additionally, we examined association of sarcopenic obesity, defined as low D3Cr muscle mass and high percent body fat, with fasting plasma glucose, insulin, hemoglobin A1c and insulin resistance. Insulin resistance was measured by the homeostatic measure of insulin resistance (HOMA-IR). This pilot study included 74 participants (mean age = 82.3 years) from the Women's Health Initiative-Buffalo site. The D3Cr method was initiated at a clinic visit and used to measure muscle mass via remote urine sample collection. Descriptive and graphical approaches and age-adjusted linear regression models were used to analyze study data. We examined muscle mass as an absolute value (kg) and scaled to body weight (D3Cr muscle mass/kg). There was an inverse relationship between skeletal muscle mass, and impaired insulin-glucose homeostasis. Women with low muscle mass had higher levels of insulin (uIU/mL; β = -0.40; 95% CI: -0.79, -0.01), fasting plasma glucose (mg/dL; β = -0.1; 95% CI: -0.2, 0.03), HbA1c (%; β = -2.30; 95% CI: -5.7, 1.1), and calculated homeostatic model of insulin resistance, HOMA-IR, (β = -1.49; 95% CI: -2.9, -0.1). Sarcopenic obesity was common in this population of women; 41% of participants were categorized as having low muscle mass and high percent body fat. Results demonstrate that D3Cr muscle mass is independently associated with measures of insulin-glucose homeostasis, but obesity is a stronger predictor of insulin resistance than muscle mass.
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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) | 0.000 | 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".