Association between body composition indices and vascular health: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This systematic review explores the intricate relationship between body composition, with a specific focus on skeletal muscle mass, and vascular health indices, including measures of arterial stiffness-pulse wave velocity (PWV) and cardio-ankle vascular index (CAVI)-as well as arterial structure, specifically carotid artery intima-media thickness (cIMT). METHODS: An extensive literature search, encompassing PubMed, Scopus, EMBASE, Web of Science, and Google Scholar, was conducted until January 2024. Inclusion criteria involved original observational studies, with cross-sectional or longitudinal designs, reporting body composition parameters and vascular health measures. The Newcastle-Ottawa Scale (NOS) assessed study quality. Statistical analyses utilized Stata 17.0, employing random-effects meta-analysis, sensitivity analysis, and evaluation of publication bias. RESULTS: Fifteen observational studies (n = 21,215) met the inclusion criteria. Pooled analyses revealed a positive association between fat-free mass (FFM) and carotid intima-media thickness (IMT) (effect size [ES]: 1.79, 95% CI 1.68-1.91), highlighting a relationship with arterial structure. Similarly, body fat percentage (BFP) was positively associated with PWV (ES: 1.45, 95% CI 1.15-1.82), and FFM showed a positive association with CAVI (ES: 1.46, 95% CI 0.78-2.71), both measures of arterial stiffness. Subgroup analyses revealed a non-significant association between appendicular skeletal muscle (ASM) and IMT (ES: 1.01, 95% CI 0.76-1.35). CONCLUSION: This meta-analysis highlights the complex relationship between body composition and vascular health. Subgroup analyses suggest the need for further research into specific body composition indices and their clinical implications. LEVEL OF EVIDENCE: III evidence obtained from well-designed cohort and cross-sectional studies.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".