A Comparison Of Methods For Quantifying Skeletal Muscle Mass In Young Women
Bibliographic record
Abstract
Magnetic resonance imaging is the gold standard for quantifying skeletal muscle mass (SMM); however, this method is expensive, time-consuming, and often inaccessible. Methods such as dual x-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA) and muscle ultrasound (US) are often used to quantify SMM. Of these, US is the only method capable of directly measuring SMM, yet it is limited in its ability to quantify whole-body (WB) SMM. US is often used to measure muscle thickness (MT) at a single site, alternatively a 5-site algorithm can be used to derive estimates of appendicular lean mass (ALM). PURPOSE: The aim of this study was to assess the agreement between various methods commonly used for quantifying SMM and evaluate the relationship between single site vastus lateralis (VL) MT and leg lean soft tissue mass (LSTM) in young, healthy women. METHODS: 21 young, healthy women (BMI: 20.0-34.4 kg/m2) arrived at the laboratory following a 12 h overnight fast. Participants underwent a series of body composition assessments, including DXA (GE-Lunar iDXA), BIA (InBody 770), and muscle US. DXA and BIA were used to assess WB and segmental LSTM and SMM, respectively. US was used to measure ALM and VL MT. The agreement between the various methods was assessed using Bland-Altman plots. Additionally, the relationship between DXA single-leg LSTM and VL MT was evaluated using Pearson correlations. RESULTS: Bland-Altman analysis revealed a large bias between BIA measures of WB SMM and DXA WB LSTM (-16.1 ± 4.3 kg). BIA measures of WB SMM were also consistently higher than DXA measures of ALM (+6.2 ± 1.9 kg). Minimal bias was detected between DXA ALM and US measures of ALM (+0.03 ± 4.5 kg). No relationship was found between DXA-measured leg LSTM and VL MT measured at 67% VL length (P = 0.13; r = 0.34). A relationship was observed between DXA-measured single-leg LSTM and VL MT measured at 50% VL length (P = 0.01; r = 0.53). CONCLUSION: Our data suggest that the 5-site US method for estimating ALM agrees well with DXA measures of ALM in young, healthy women. In contrast, BIA-measured WB SMM is consistently different from DXA for measures of WB LSTM and ALM. Finally, VL MT at 50% VL length may reflect single-leg measures of LSTM in young, healthy women.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".