The correlation between sarcopenia and osteoporosis in the elderly: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Sarcopenia and osteoporosis, as two prevalent geriatric syndromes, synergistically elevate risks of falls, fractures, and disability in older adults. Despite shared pathophysiological mechanisms-including hormonal dysregulation, chronic inflammation, and attenuated mechanical loading. Existing studies have yet to establish consensus regarding the epidemiological association strength and interaction dynamics between sarcopenia and osteoporosis, particularly as heterogeneous characteristics-including sex, geographic region, and population subgroups-remain insufficiently characterized. This study aimed to quantitatively evaluate the sarcopenia-osteoporosis association in older adults through systematic review and meta-analysis of global observational studies, while analyzing the moderating effects of geographic location, sex, population characteristics, and diagnostic criteria on outcomes. Methods: We searched PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases until September 2024. Fourteen observational studies quantifying muscle mass/function and bone mineral density were included. Two investigators independently performed literature screening and data extraction. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Studies were meta-analyzed by Review Manager 5.4 and Stata 17.0. Results: < 0.001). Subgroup analyses demonstrated an OR of 4.74 [3.19, 7.06] for osteoporosis in the male sarcopenia group compared to females (OR = 3.46; 95% CI, 2.50-4.78). Geographically, European populations exhibited the highest risk (OR = 4.37; 95% CI, 3.72-5.13), surpassing Asian (OR = 2.66; 95% CI, 1.74-4.07) and American cohorts (OR = 2.32; 95% CI, 1.54-3.49). Community-dwelling individuals showed greater susceptibility (OR = 3.70; 95% CI, 3.24-4.23) compared to inpatient and outpatient populations. Conclusion: Our meta-analysis demonstrates that sarcopenia significantly elevates osteoporosis susceptibility, with heterogeneous risk profiles across geographic regions and population subgroups. However, limitations inherent to the methodological quality and sample size of included studies necessitate validation through large-scale prospective cohort investigations.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".