Sarcopenia as a predictor of negative health outcomes in patients with type 2 diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Sarcopenia represents a considerable public health issue and is associated with increased mortality and complication rates in patients with type 2 diabetes mellitus (T2DM). Existing evidence regarding adverse outcomes in patients with T2DM and sarcopenia is currently scattered and limited, and comprehensive evidence is lacking. METHODS: A comprehensive search of Embase, PubMed, Scopus, and Web of Science was conducted to identify relevant studies that assessed the impact of sarcopenia on mortality, cardiovascular disease (CVD), and complications in individuals with T2DM. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale and the Joanna Briggs Institute Critical Appraisal tool. The pooled hazard ratios and odds ratios, along with their corresponding 95% confidence intervals for mortality, CVD, and complication estimates, were analyzed. RESULTS: Fifteen studies were included in the meta-analysis. The overall risk of bias across the studies was low. Patients with T2DM and sarcopenia had a considerably elevated risk of mortality, with a pooled hazard ratio of 1.72 (95% CI = 1.28-2.32). Similarly, sarcopenia was associated with an increased hazard ratio for CVD of 1.94 (95% CI = 1.67-2.25). Furthermore, sarcopenia was associated with an elevated risk of developing diabetic complications, as evidenced by a hazard ratio of 1.12 (95% CI = 1.09-1.15) and an odds ratio of 2.49 (95% CI = 1.53-4.05). CONCLUSIONS: Sarcopenia is predictive of adverse outcomes, including mortality, CVD, and diabetic complications, in patients with T2DM. Our findings underscore the clinical imperative for integrating sarcopenia assessment into routine T2DM management to facilitate early risk stratification.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".