Interactions between bone density and muscle mass in predicting all-cause mortality: a 10-year prospective cohort study of 1388 older men (aged 77–101 years)
Bibliographic record
Abstract
BACKGROUND: Low bone density and low muscle mass are both independent risk factors for mortality in older men. However, it is unknown if these tissues interact to increase mortality risk. Elucidating this information is important as bone and muscle are modifiable across the life cycle. OBJECTIVE: To examine whether there is an interconnection between bone density and muscle mass on all-cause mortality in older men. DESIGN: Prospective cohort study. SETTING: The Osteoporotic Fractures in Men study, an multicenter longitudinal study across six US sites. PARTICIPANTS: Exposures measured at baseline visit (2014-2016) included bone density by dual-energy X-ray absorptiometry (hip, g/cm2); muscle mass by creatine dilution stable isotope (whole body, kg); bone strength by high-resolution computed tomography (tibia, newtons); and muscle volume by high-resolution computed tomography (calf, mm3). Covariates measured at baseline visit (2014-2016) included demographics and lifestyle factors as well as medical conditions. MAIN OUTCOME MEASURE: All-cause mortality by death certificates and International Classification of Diseases-Ninth Revision codes measured from 2014 to 2016 through August 2024. Data analysis was performed during December 2024. Cox hazards models were used to model the relationship between the exposures and outcomes, unadjusted and adjusted for covariates. RESULTS: A total of 1388 men with a mean age of 84.2 ± 4.1 years (77-101 years, 91.6% white) were followed for 6.58 ± 2.61 years. A total of 663 (47.8%) men died. In unadjusted analyses using continuous exposures, interaction terms were significant between bone and muscle variables for all-cause mortality (P < 0.001 to 0.039). In men with low muscle mass or low muscle volume (≤50th percentile), each SD decrease in bone density increased all-cause mortality by a respective 19% (HR = 1.19 95% CI 1.07-1.34) and 29% (HR = 1.29 95% CI 1.11-1.49) in multivariable-adjusted models. Likewise, in men with low muscle mass or low muscle volume (≤50th percentile), each SD decrease in bone strength increased all-cause mortality by a respective 19% (HR = 1.19 95% CI 1.06-1.33) and 29% (HR = 1.29 95% CI 1.12-1.48) in multivariable-adjusted models. CONCLUSIONS: We found consistent evidence for a combined association of bone and muscle health on all-cause mortality. Randomised controlled trials are now needed to confirm if increasing or preserving bone and muscle mass in old age reduces mortality risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".