The association of sarcopenia, possible sarcopenia and cognitive impairment: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The study aimed to investigate the relationship between sarcopenia, possible sarcopenia, and cognitive impairment, and to analyze the impact of potential moderating factors. METHODS: A comprehensive search was conducted on PubMed, EmBase, Web of Science, Cochrane Library, CNKI, Wanfang Databases, VIP and SinoMed from inception until March 2025. The quality of cross-sectional studies was assessed using the Agency for Healthcare Research and Quality Scale, while the Newcastle-Ottawa scale was used to assess the quality of included case-control and cohort studies. Subgroup analyses and meta-regression were employed to explore potential moderating variables and heterogeneity. RESULTS: A total of 31 studies were included in this systematic review, of which 27 studies were quantitatively analyzed. The meta-analysis revealed that both sarcopenia and possible sarcopenia significantly associated with cognitive impairment (OR=1.88, 95%CI = 1.71-2.08), (OR=1.96, 95%CI = 1.50-2.58). Subgroup analyses revealed a stronger association between sarcopenia and cognitive impairment in specific demographics: among females with sarcopenia (OR=3.22, 95%CI = 1.23-8.40), in Asian populations (OR=1.96, 95%CI = 1.76-2.18), and within hospital settings (OR=3.12, 95%CI = 2.18-4.48). These findings underscore the influence of gender, ethnicity, and healthcare environment on the relationship between sarcopenia and cognitive impairment. An assessment of publication bias within studies providing adjusted odds ratios indicated potential bias. However, sensitivity analyses and trim-and-filling analyses confirmed the robustness of our findings, suggesting that the observed associations remain reliable despite the presence of publication bias. CONCLUSIONS: Individuals with sarcopenia or possible sarcopenia have approximately twice the odds of developing cognitive impairment compared to those without sarcopenia. Implementing systematic screening and targeted interventions for possible sarcopenia patients is essential to prevent from cognitive decline. Specifically, healthcare professionals should focus on women and inpatients with sarcopenia, employing proactive measures to avert cognitive impairment.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".