Influences Of Adipose And Bone Tissues On Lean Soft Tissues Estimated By BIA
Bibliographic record
Abstract
Skeletal muscle size is closely related to its mechanical usage, and so optimization of muscle mass and function is crucial for assurance of a high quality of life. Bioelectrical impedance analysis (BIA) is accepted as a useful method for assessing accurately both favorable and unfavorable changes in limb muscle size and/or lean soft tissue mass (LST) with exercise, or alternatively, early detection of muscle wasting with ageing, in humans. However, the influences of concurrent exercise/ageing related changes in adipose tissue mass (ATM) and bone mineral density (BMD) on estimates of muscle and/or LST mass by BIA have been ignored. PURPOSE The purpose of this study is to investigate the influences of BMD and ATM on the accuracy of BIA estimates of LST in the forearm. METHODS Fifteen healthy females (age: 34.5 ± 4yrs, height: 163.6 ± 7.2cm, body mass: 71.6 ± 18.9 kg, mean ± SD) were recruited as subjects. The bio-electrical impedance (BI) of the whole forearm (elbow to wrist in the right arm) was measured. The LST of the whole forearm was obtained by DXA (LSTDXA) and used as reference data. A simple regression equation for the relationship between the LST index (forearm length2/BI) and (LSTDXA) was used to estimate LST (LSTBIA). BMD and ATM of the whole forearm were also obtained by DXA. RESULTS The LSTBIA was strongly correlated with the LSTDXA (r=0.857, P < 0.05, SEE=50.2g, 7.2%). Bland and Altman plot did not show a significant systematic error. There was no significant correlation between ATM and BMD. However, the residual (LSTBIA - LSTDXA) of the estimate was negatively and positively related to BMD (r=−0.536, p < 0.05) and ATM (r=0.635, p < 0.05), respectively. CONCLUSION The findings obtained here indicate that bio-electrical impedance analysis is applicable to estimate limb lean soft tissue mass, but the error of estimation is affected by both adipose tissue mass and bone mineral density.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".