The prevalence and risk factors of sarcopenia in patients with type 2 diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Sarcopenia was a frequent chronic complication in patients with type 2 diabetes mellitus (T2DM), and previous evidence showed conflicting results regarding the prevalence and risk factors of sarcopenia in T2DM. In the current study, we aimed at systematically exploring the prevalence and risk factors of sarcopenia in patients with T2DM. Methods PubMed, Embase, and Cochrane Central Register of Controlled Trials were systematically searched to identify observational studies which investigated the prevalence and risk factors of sarcopenia in patients with T2DM. The quality of individual included studies was evaluated using The Newcastle–Ottawa scale. Pooled effects regarding prevalence and associated factors were calculated using random-effects models. The potential publication bias was assessed via funnel plot and Egger test. Results Twenty-eight studies involving 16,800 patients were included in our meta-analysis. The pooled prevalence of sarcopenia in patients with T2DM was 18% (95% CI 0.15–0.22; I 2 = 97.4%). The pooled results showed that elder age (OR 4.73; 95% CI 4.30–5.19; I 2 = 85.6%), male gender, chronic hyperglycemia (higher HbA1c) (OR 1.16; 95% CI 1.05–2.47; I 2 = 99.2%) and osteoporosis (OR 1.16; 95% CI 1.05–2.47; I 2 = 99.2%) was predictors for sarcopenia, whereas patients with lower BMI (OR 1.16; 95% CI 1.05–2.47; I 2 = 99.2%) and metformin administrations (OR 1.16; 95% CI 1.05–2.47; I 2 = 99.2%) were not prone to get sarcopenia. The funnel plot and statistical tests showed no obvious publication bias. Conclusions Sarcopenia was frequent in T2DM patients. Elder age, male gender and chronic hyperglycemia, Osteoporosis were significant risk factors for Sarcopenia. Lower BMI and metformin administrations were associated with lower risk of sarcopenia.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".