Dietary factors may be associated with measures of ultrasound-derived skeletal muscle echo intensity
Bibliographic record
Abstract
Skeletal muscle echo intensity (EI) is affected by ageing and physical activity; however, the effects of nutrition are less understood. The aim of this study was to explore whether habitual nutrient intake may be associated with ultrasound-derived EI. Partial least squares regression (PLSR) models were trained on an initial sample ( n = 100, M = 45; F = 55; 38 ± 15 years) to predict EI of two quadriceps muscles from 19 variables, using the “ jack-knife” function within the “ pls” package (RStudio), which was then tested in an additional dataset ( n = 30, M = 13; F = 17; 38 ± 16 years). EI was determined using B-mode ultrasonography of the rectus femoris (RF) and vastus lateralis (VL) and nutritional intake determined via 3-day weighed food diaries. Mean daily intake of specific nutrients were included as predictor variables with age, sex, and self-reported physical activity. PLSR training model 1 explained ∼52% and model 2 ∼46% of the variance in RF and VL EI, respectively. Model 1 also explained ∼35% and model 2 ∼30% of the variance in RF and VL EI in the additional testing dataset. Age and biological sex were associated with EI in both models ( P < 0.025). Dietary protein (RF: β = −7.617, VL: β = −7.480), and selenium (RF: β = −7.144, VL: β = −4.775) were associated with EI in both muscles ( P < 0.05), whereas fibre intake (RF: β = −5.215) was associated with RF EI only and omega-3 fatty acids (n-3/ω-3 FAs, RF: β = 3.145) with VL EI only ( P < 0.05). Therefore, absolute protein, selenium, fibre, and n-3 FAs may be associated with skeletal muscle EI, although further mechanistic work is required before claiming causal inference.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".