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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".