Total Muscle Mass Index Is Inversely Related With Insulin Resistance In Postmenopausal Women
Bibliographic record
Abstract
Background and Aims: It remains to be determined if muscle mass index (MMI) (an index of relative muscle mass) does play a role in insulin sensitivity when age and visceral fat mass (VFM) are taken into account, and what is the direction of that relationship. METHODS: A cross-sectional study was conducted in 99 healthy postmenopausal women (mean age 63 ± 6 years) with a BMI of 28 ± 4 kg/m2. Fat mass and total fat-free mass (FFM) were obtained from DXA and fasting plasma insulin and glucose levels were also obtained. VFM was estimated by the use of the equation of Bertin. MMI was obtained using the following equations: Total FFM (kg)/height (m)2. QUICKI and HOMA were used as an insulin sensibility index. RESULTS: Total MMI and VFM were both significantly inversely correlated with QUICKI (r = -0.447 and -0.504, respectively) and positively with HOMA (r = 0.515 and 0.508, respectively). A partial correlation confirmed that Total MMI has a negative relationship with QUICKI and a positive one with HOMA and plasma insulin level. Thus, a stepwise linear regression confirmed that Total MMI and VFM were both independent predictors of HOMA (r2 = 0.25) and plasma insulin level (r2 = 0.28). CONCLUSION: In the light of our results, a lower muscle mass is not detrimental for the maintenance of insulin sensitivity as it may even be beneficial. We consider that Total MMI should be taken into account just like VFM as an important and independent predictor of insulin sensitivity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".