Correlation of muscle mass and bone mineral density in the NHANES US general population, 2017–2018
Bibliographic record
Abstract
The appendicular skeletal muscle mass index (ASMI) is commonly used to evaluate human skeletal muscle mass. Muscle, an adjacent tissue of bone, is closely related to bone growth and development. The purpose of this study was to explore the association between the ASMI and lumbar bone mineral density (BMD) to identify potential risk factors for osteoporosis. We analyzed the data collected by the NHANES from 2017 to 2018, and finally included 948 participants aged 40 to 59 years. We evaluated the correlation between the ASMI and lumbar spine BMD using univariate and multiple linear regression models. The ASMI was calculated from height and appendicular skeletal muscle mass obtained by dual energy X-ray absorptiometry. Lumbar spine BMD was obtained by dual energy X-ray absorptiometry and used as an observation in our study. In all the models, ASMI was significantly associated with lumbar spine BMD (model 1: β = 0.013, P < .001; model 2: β = 0.013, P < .001). In the subgroup analysis stratified by sex, this positive correlation was present in both sexes (male: β = 0.023, P < .001, β = 0.022, < 0.001; female: β = 0.030, P < .001, β = 0.031, P < .001). This study showed that the ASMI was positively associated with lumbar BMD, and that this correlation is present in both men and women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".