Low lean mass and all-cause mortality risk in the middle-aged and older population: a dose-response meta-analysis of prospective cohort studies
Bibliographic record
Abstract
Objective The accelerated aging process has raised substantial public health concerns regarding the health of the middle-aged and older population. The aim of our study was to investigate the association between low lean mass and the risk of all-cause mortality in older people, with the goal of promoting a long lifespan and reducing public health burdens. Methods Three databases (PubMed, Web of Science, and Scopus) were searched for articles before May 22, 2025. The quality of the included articles was assessed using the Newcastle-Ottawa Scale (NOS). A meta-analysis was conducted using a random effects model. Subgroup analysis and meta-regression analysis were performed based on research characteristics. A dose-response analysis was performed to assess the specific association between lean mass and the risk of all-cause mortality. Sensitivity analysis was conducted using a leave-one-out meta-analysis. Publication bias analysis was conducted using Begg’s and Egger’s tests, as well as a funnel plot. Results In total, 11 studies involving 130,079 participants were included in the meta-analysis of the association between low lean mass and the risk of all-cause mortality in the middle-aged and older population, all of which the included studies were of high quality. The average overall study quality score was 8 points. The random effects model analysis results showed that the pooled RR of all-cause mortality risk in the middle-aged and older population was 1.30 (95% CI, 1.16–1.47, P < 0.001) across the lowest to normal lean mass category. There was an inverse non-linear dose-response relationship between lean mass and the risk of all-cause mortality (P < 0.001). Conclusion Low lean mass was significantly associated with 30% higher risk of all-cause mortality in the middle-aged and older population. These findings highlighted low lean mass as an important risk factor for mortality in middle-aged and older population, warranting its integration into clinical assessments. Future research should establish causality through longitudinal studies and randomized trials, while refining diagnostic cutoffs for diverse populations. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/#myprospero, Identifier CRD42023445297.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.069 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".