Combination of <scp>DXA</scp> and <scp>BIS</scp> Predicts Jump Power Better Than Traditional Measures of Sarcopenia
Bibliographic record
Abstract
ABSTRACT Traditional diagnostic criteria for sarcopenia use dual‐energy X‐ray absorptiometry (DXA)‐measured appendicular lean mass (ALM), normalized to height (ALM/ht 2 ) or body mass index (ALM/BMI) to define low muscle mass. However, muscle function declines with aging before the loss of muscle mass is detected by ALM. This is likely due, in part, to qualitative muscle changes such as extracellular and intracellular fluid compartment shifts uncaptured by DXA. We propose combining bioimpedance spectroscopy (BIS), which estimates extracellular and intracellular compartment volume, with DXA to more accurately predict muscle function. This combination may help incorporate muscle quality, thereby improving sarcopenia diagnosis. We cross‐sectionally analyzed data from 248 Black and White participants aged 25 to 75 years from the Midlife in the United States Refresher Cohort. We proposed two novel muscle measures: ALM corrected by the BIS‐derived whole‐body extracellular to intracellular fluid ratio (E/I) and leg lean mass (LLM) corrected by leg‐specific E/I, creating (ALM/(E/I) W ) and (LLM/(E/I) L ), respectively. We compared the associations of traditional muscle measures, ALM/(E/I) W , and LLM/(E/I) L , with grip strength and lower limb power using jumping mechanography. LLM/(E/I) L explained jump power best at R 2 = 0.803 compared with ALM/(E/I) W ( p < 0.0001) and all other measures. ALM/(E/I) W explained jump power second best ( R 2 = 0.759) but not significantly better than traditional muscle measures. No muscle measure performed better than covariates when predicting handgrip strength. LLM/(E/I) L outperformed ALM/ht 2 and ALM/BMI when predicting jump power. We propose LLM/(E/I) L is a powerful and clinically relevant method that accounts for muscle quality. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".