Association Between Bone Ultrasonometry and Cardiovascular Morbimortality: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Context Quantitative ultrasound (QUS) can estimate bone mineral density and predict fracture risk, but its association with cardiovascular outcomes remains unclear. Objective We aimed to assess the associations between bone QUS parameters and cardiovascular event risk, cardiovascular mortality (CVM) and all-cause mortality (ACM). Data Sources Pubmed, Embase, Cochrane Library databases, and grey literature were searched. Study Selection We considered studies including people aged >40 years who reported associations between bone QUS parameters (any bone site) and our outcomes. Data Extraction Two reviewers selected eligible studies, extracted and analyzed data, and assessed risk of bias with the Risk of Bias in Non-randomized Studies of Exposure tool. Adjusted hazard ratios (HR) with 95% confidence intervals (CIs), estimated for 1 SD reduction of QUS parameters, were pooled using random effects meta-analyses. Data Synthesis We included 9 studies with 275 to 477 683 (median = 3244) participants (follow-up duration range 2.8-12.8 years). All studies presented associations based on calcaneal QUS parameters; only 2 reported associations with cardiovascular events with discordant results. Seven studies reported associations with CVM and 7 with ACM. Meta-analyses based on 3 studies showed that broadband ultrasound attenuation (BUA) was inversely associated with CVM (HR = 1.22, 95% CI: 1.11-1.34, I2 = 0%) and ACM (HR = 1.16, 95% CI: 1.10-1.23, I2 = 0%). Meta-analyses, based on 4 and 3 studies, respectively, showed that speed of sound (SOS) was also inversely associated with CVM (HR = 1.19, 95% CI: 1.11-1.27, I2 = 29%) and ACM (HR = 1.15, 95% CI: 1.07-1.23, I2 = 0%). Conclusion In a cohort of middle-aged individuals, a decrease in calcaneal BUA and SOS were both independently associated with higher cardiovascular and ACM.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".