Effects of sarcopenia on the risks of osteoporosis and fragility fractures among community-dwelling middle and old age people: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To explore the relationship between sarcopenia and the risks of osteoporosis and fragility fractures among community-dwelling middle and old people (≥50 years). Methods OVID/Medline, Pubmed, EMBASE, Cochrane Library, Web of Science (SCI), China National Knowledge Infrastructure (CNKI), and Chinese WanFang Database were searched systematically for literatures related to the relationship between sarcopenia and osteoporosis and fragility fractures from January 1987 to July 2018. The searched literatures were screened based on the inclusion and exclusion criteria. The quality of the literatures were evaluated by using the risk assessment tool NOS (Newcastle-Ottawa Scale) and AHRQ (Agency for Healthcare Research and Quality). Meta-regression analysis was used to explore the cause of heterogeneity between studies. The subgroup analysis was used to assess the differences in the risk of osteoporosis based on the important characteristic variables, such as gender, ethnicity, age, diagnostic criteria of sarcopenia, and outcome type. Sensitivity analysis and trim and fill method were conducted to test the stability of the results of this Meta-analysis. Data collected and summarized by Stata 12.2 software. Results A total of 23 studies in line with quality requirements were included eventually, including 56, 544 subjects. The results of this Meta-analysis were relatively robust. Compared with non-sarcopenia, the relative risk (RR) for osteoporosis among subjects with sarcopenia was 1.61 (95% CI: 1.42~1.82, P<0.000 01). Especially, compared with postmenopausal women RR=1.37 (95% CI: 1.23-1.53, P<0.000 1) and yellow race RR=1.53 (95% CI: 1.34-1.75, P<0.000 1), sarcopenia had a higher impact trend on the risk of osteoporosis in older men RR=2.26 (95% CI: 1.71-2.98, P<0.000 1) and Caucasian RR=2.03 (95% CI: 1.46-2.81, P<0.000 1). Conclusion Among community-dwelling middle and old aged people(≥50 year), sarcopenia increases the risk of osteoporosis and fragility fracture by 61% and 59% -61% significantly, respectively. Middle and old aged people should be prevented and screened early for sarcopenia, which attributes to identify high risk groups of fragile fractures and reduce the risk of adverse outcomes. Key words: Sarcopenia; Osteoporosis; Fragility fractures; Meta-analysis
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".