Prevalence and risk factors of sarcopenia in Asian adults with type 2 diabetes: A systematic review and meta‐analysis
Bibliographic record
Abstract
ABSTRACT Aims/Introduction Middle‐aged and elderly individuals in Asia with type 2 diabetes mellitus have an increased risk of developing sarcopenia. This systematic review and meta‐analysis aimed to estimate sarcopenia prevalence and identify key risk factors among middle‐aged and elderly type 2 diabetes mellitus persons in Asia. Materials and Methods We searched PubMed , Embase, Cochrane Library, Web of Science, and CINAHL up to August 2025. Observational studies on persons aged ≥45 with type 2 diabetes mellitus reporting sarcopenia prevalence and risk factors were included. Quality was assessed using the Newcastle–Ottawa Scale, and random‐effects models calculated pooled estimates. Results A total of 43 studies with sarcopenia prevalence in type 2 diabetes mellitus ranging from 4.0% to 50.0% were included. The pooled prevalence among individuals with type 2 diabetes mellitus was 17 (14–21%), 13 (8–18%), and 17% (14–21%) for overall, middle‐aged, and elderly individuals, respectively, with no significant difference between the middle‐aged and elderly individuals. Sarcopenia was higher in Southeast Asia (28%). Key risk factors included advanced age ( OR = 1.12), elevated HbA 1c ( OR = 1.11), diabetes‐related complications (nephropathy OR = 1.76 and neuropathy OR = 3.28), and a higher body fat percentage in men ( OR = 1.26) and women ( OR = 1.27). In contrast, protective factors included a higher BMI ( OR = 0.66), a better Mini Nutritional Assessment score ( OR = 0.37), and the use of metformin ( OR = 0.26). Conclusions Sarcopenia is common in middle‐aged and older Asian adults with type 2 diabetes mellitus. Interventions targeting glycemic control, complications, body composition, nutrition, and metformin may help reduce risk. This study highlights the importance of early sarcopenia screening and prevention, especially in middle‐aged individuals with type 2 diabetes mellitus. Further studies are needed to establish causal relationships for prevention.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".