Muscle Mass and Respiratory Quotient as Predictors of Insulin Resistance in Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: Aging is related with a decrease in muscle mass, which causes physical functioning impairments and reductions in quality of life. Although it is largely suggested that muscle mass is a main determinant of glucose metabolism, clinical studies do not support this assumption. On the other hand, the use of substrate to produce energy and its relationship with glucose metabolism has never been studied. METHODS: Our study examined the relationship between muscle mass (DXA) and RQ (indirect calorimetry) with resting energy expenditure and glucose metabolism (HOMA2) in 164 postmenopausal women (mean age 62 years old; mean BMI 29.1 kg/m2). To verify our findings, we performed a stepwise regression analyses to predict HOMA2. RESULTS: Our results demonstrated that muscle mass and RQ were significantly associated with RMR. RQ was also associated with HOMA2. The results indicated that total MM and RQ were both independent predictors of HOMA2 CONCLUSIONS: The main finding of our study is that RQ is an independent predictor of glucose metabolism and a lower RQ is associated with a higher glucose metabolism. Further studies should include substrate utilisation in addition to 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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".