Osteosarcopenia, bone-muscle interactions, and frailty risk: A prospective cohort study of community-dwelling older adults
Bibliographic record
Abstract
BACKGROUND: Osteosarcopenia, defined as the co-existence of osteopenia/osteoporosis and sarcopenia, may influence frailty risk in older adults. However, the longitudinal association between osteosarcopenia or its components and frailty remain unclear. This study aimed to address this. METHODS: Data from a prospective cohort study of community-dwelling adults in Australia. Frailty was defined by the presence of ≥ 3 components based on Fried criteria: exhaustion, slow gait speed, low grip strength, unintentional weight loss, and low physical activity. Osteosarcopenia was defined by osteopenia/osteoporosis (WHO criteria) and sarcopenia (European Working Group on Sarcopenia in Older People [EWGSOP2] and Sarcopenia Definition and Outcome consortium [SDOC]). Multivariable logistic regression models evaluated the associations between osteosarcopenia, its components and frailty. RESULTS: Of 300 enrolled, 151 (mean age: 65.1years, 59.6%women) completed follow-up (median: 4.8 years). Among 143 non-frails at baseline, 13 (9.1%) transitioned to frailty. Osteosarcopenia (versus non-osteosarcopenia) was associated with frailty irrespective of the definition used: EWGSOP2: OR = 9.53, 95%CI 2.53-35.92; SDOC: OR = 9.19, 95%CI 2.19-38.56). Grip strength reduction by 1 kg (OR = 0.92, 95%CI 0.84-1.00) and gait speed reduction by 0.1 m/sec (OR = 0.67, 95%CI 0.53-0.85) were associated with 8% and 33% lower odds of frailty, respectively, whereas bone mineral density or lean mass was not. An interaction between bone-muscle function and frailty risk was observed (p < 0.011), whereby higher values of grip strength were associated with lower odds of frailty when bone density was lower. CONCLUSION: In this prospective cohort study, osteosarcopenia increased the risk of frailty. Our interaction analysis suggests therapies targeting bone density and grip strength may mitigate frailty.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".