Association of Muscle Strength to Body Composition Measures using DXA, D <sub>3</sub> Cr, and BIA in Collegiate Athletes
Bibliographic record
Abstract
Abstract Background Measurements of body composition are helpful indicators of health outcomes, but muscle strength has a greater correlation with disease risk and long-term health outcomes, particularly among older adults. Whole-body DXA scans uniquely parse out total and regional lean soft tissue (LST) and appendicular (ALST), primarily composed of skeletal muscle and often used to diagnose sarcopenia and frailty. An alternative approach measures the enrichment of deuterated Creatinine (D 3 Crn) in urine after ingesting a tracer dose of deuterated creatine (D 3 Cr) to determine creatine pool size and estimate whole-body muscle mass. The utility of D 3 Cr relationships between strength and body composition in young athletes has yet to be established. In this study, we investigated the association of muscle strength and body composition using multiple methods including DXA, D 3 Cr, and bioelectrical impedance (BIA), in a collegiate athletic population. Methods The Da Kine Study enrolled 80 multi-sport collegiate athletes. Each subject consumed a 60 mg dose of D 3 Cr and completed whole-body DXA, BIA, and strength tests of the leg and trunk using an isokinetic dynamometer. The analysis was stratified by sex. Pearson’s correlations, forward stepwise linear regression and quartile p trend significance were used to show the associations of body composition measures to muscle strength. Results The mean (SD) age of the 80 (40M/40F) athletes was 21.8 (4.2) years. Raw whole-body values had higher correlations with muscle strength in both sexes compared to the normalized values by height, body mass (BM), and BMI. DXA LST had the highest leg ( R 2 =0.36, 0.37) and trunk ( R 2 =0.53, 0.61) strength in both males and females. Trunk strength was more highly associated with body composition measures than leg strength in both sexes and all measurement techniques. One or more DXA LST measures (total, leg, and ALST) were consistently more highly associated with leg and trunk strengths for both sexes than BIA and D 3 Cr measures. Adjusting all body composition values by age, BMI, and BIA variables did not improve the associations. A significant p trend across quartiles was observed for DXA LST and ALST for all measures of strength in both sexes. Conclusion Although statistical significance was not reached between devices, DXA body composition output variables, especially LST, showed the highest associations with both sexes’ leg and trunk muscle strength. Furthermore, without adjustment for demographic information or BIA variables, whole-body values show stronger associations with muscle strength. Future research should investigate the impact of muscle mass changes on LST and functional measures.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".