Do Muscle Mass, Muscle Density, Strength, and Physical Function Similarly Influence Risk of Hospitalization in Older Adults?
Bibliographic record
Abstract
OBJECTIVES: To examine the association between strength, function, lean mass, muscle density, and risk of hospitalization. DESIGN: Prospective cohort study. SETTING: Two U.S. clinical centers. PARTICIPANTS: Adults aged 70 to 80 (N=3,011) from the Health, Aging and Body Composition Study. MEASUREMENTS: Measurements were of grip strength, knee extension strength, lean mass, walking speed, and chair stand pace. Thigh computed tomography scans assessed muscle area and density (a proxy for muscle fat infiltration). Hospitalizations were confirmed by local review of medical records. Negative binomial regression models estimated incident rate ratios (IRRs) of hospitalization for race- and sex-specific quartiles of each muscle and function parameter separately. Multivariate models adjusted for age, body mass index, health status, and coexisting medical conditions. RESULTS: During an average 4.7 years of follow-up, 1,678 (55.7%) participants experienced one or more hospitalizations. Participants in the lowest quartile of muscle density were more likely to be subsequently hospitalized (multivariate IRR=1.47, 95% confidence interval (CI)=1.24-1.73) than those in the highest quartile. Similarly, participants with the weakest grip strength were at greater risk of hospitalization (multivariate IRR=1.52, 95% CI=1.30-1.78, Q1 vs. Q4). Comparable results were seen for knee strength, walking pace, and chair stands pace. Lean mass and muscle area were not associated with risk of hospitalization. CONCLUSION: Weak strength, poor function, and low muscle density, but not muscle size or lean mass, were associated with greater risk of hospitalization. Interventions to reduce the disease burden associated with sarcopenia should focus on increasing muscle strength and improving physical function rather than simply increasing lean mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".