SARCOPENIA: REVISITING CRITERIA DEFINITION AND ASSOCIATION WITH PROTEIN INTAKE AND INSULIN RESISTANCE
Bibliographic record
Abstract
Sarcopenia is an important component of frailty and its diagnosis could lead to specific intervention to improve functional capacity in older adults. There are several clinical oriented criteria definitions of sarcopenia available in literature for case finding based on low gait speed and handgrip strength. These criteria may be too sensitive, mandating unnecessary body composition measures. Total protein intake is important for muscle mass maintenance but we will present evidence from the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) Study that distribution across meals may also prevent losses. Although muscle is considered the principal site of glucose uptake and its loss to contribute to the development of insulin resistance, these assumptions have not always been verified. The method used to present muscle mass index correcting absolute mass by either height squared or weight could account for the discrepancies in establishing the relationship with insulin resistance. Data from the NuAge Study will illustrate this concept and arguments will be brought forth to propose the most appropriate method. Furthermore, several hormonal and inflammatory factors associated with insulin resistance are also responsible for loss of muscle mass, therefore creating a false relationship between insulin resistance and low muscle mass. Using data from the NuAge Study, we will show how the application of logistic regression analysis of these factors along with muscle mass index will disentangle this spurious association.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".