Influence of Subcutaneous Adipose Tissue and Skeletal Muscle Thickness on Rectus Femoris Echo Intensity in Younger and Older Males and Females
Bibliographic record
Abstract
OBJECTIVES: Ultrasound measurements of muscle echo intensity are commonly used surrogates of muscle composition (eg, intramuscular adipose tissue). However, given that soundwaves are increasingly attenuated with tissue depth, the interpretation of echo intensity may be confounded by adipose and skeletal muscle thickness. Our objectives are to compare the associations between adipose or muscle tissue thickness and rectus femoris echo intensity in younger and older males and females. METHODS: Participants included in this analysis were derived from 3 previously published cohorts of younger (<45 years) and older (≥60 years) males and females. Ultrasound images of the rectus femoris were evaluated for muscle thickness, echo intensity, and subcutaneous adipose tissue thickness. RESULTS: Older adults (n: 49 males, 19 females) had a higher body mass index (P = .001) compared with younger adults (n: 37 males, 49 females). Muscle thickness was negatively associated with echo intensity in older males (r = -0.59) and females (r = -0.53), whereas no associations were observed in younger males (r = 0.00) or females (r = -0.11). Subcutaneous adipose tissue thickness displayed no associations with echo intensity in any group. CONCLUSIONS: Despite the known influence of subcutaneous adipose tissue thickness on beam attenuation, we observed no association with muscle echo intensity, indicating that adipose tissue correction may be required to better understand muscle echo intensity across differences in adiposity. The negative associations between muscle thickness and echo intensity in older, but not younger adults, suggests these associations may be related to the co-occurrence of skeletal muscle atrophy and intramuscular adipose tissue infiltration with advancing age.
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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) | 0.003 | 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".