Associations of Quadriceps Torque Properties with Muscle Size, Attenuation, and Intramuscular Adipose Tissue in Older Adults
Bibliographic record
Abstract
Background: Atrophy and fatty infiltration of muscle with aging are associated with fractures and falls, however, their direct associations with muscle function are not well described. It was hypothesized that participants with lower quadriceps muscle attenuation, area, and greater intramuscular adipose tissue (IMAT) will exhibit slower rates of torque development (RTD) and lower peak knee extension torques. Methods: Data from 4,842 participants (2,041 men, 2,801 women) from the Age Gene/Environment Susceptibility Reykjavik Study (mean age 76 ± 0.1 years) with complete thigh computed tomography and isometric knee testing. Regression models were adjusted for health, behavior, and comorbidities. Muscle attenuation was further adjusted for muscle area and IMAT; muscle area adjusted for IMAT and attenuation; and IMAT adjusted for muscle area and attenuation. Standardized betas (β) indicate association effect sizes. Results: In the fully-adjusted models, attenuation (men β = 0.06, 95% CI: 0.01, 0.11; women β = 0.07, 95% CI: 0.03, 0.11) and muscle area (men β = 0.13, 95% CI: 0.07, 0.19; women β = 0.10, 95% CI: 0.06, 0.15) were associated with knee RTD. Attenuation (men β = 0.12, 95% CI: 0.08, 0.16; women β = 0.12, 95% CI: 0.09, 0.16) and muscle area (men β = 0.38, 95% CI: 0.33, 0.43; women β = 0.33, 95% CI: 0.29, 0.37) were associated with peak torque. Conclusions: These data suggest that muscle attenuation and area are independently associated with RTD and peak torque; and that area and attenuation demonstrate similar contributions to RTD.
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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 | 0.003 |
| 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) | 0.001 | 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".