The Association Between Diabetes Mellitus and Risk of Sarcopenia: Accumulated Evidences From Observational Studies
Bibliographic record
Abstract
Aim: We performed a meta-analysis of observational studies to evaluate the association between the presence of sarcopenia and HbA1c, prediabetes, diabetes and diabetic complications. Method: The PubMed, Embase, Cochrane and Web of Science databases were searched from inception to May 2021. We included full-text English language articles that reported the prevalence of sarcopenia in patients with and without diabetes. Quality assessment was performed according to the Newcastle- Ottawa scale for observational studies. Results: Sixteen studies were included in the meta-analysis. Three studies showed that high HbA1c levels lead to loss of muscle mass, and one study involving prediabetes showed that people with prediabetes had lower muscle mass, strength, and performance than non-diabetic population. Seven studies showed that people with diabetes had a higher risk of sarcopenia than those without diabetes (combined OR: 2.09, 95% CI:1.62-2.70). The remaining five studies suggested that diabetic complications increased the risk of sarcopenia (combined OR: 2.09,95% CI:1.62-2.70). Conclusion: High HbA1c levels, prediabetes, diabetes and diabetes complications were associated with an increased risk of sarcopenia. Therapeutic strategies addressed to avoid the conversion of IGT to diabetes and to optimize glycemic control are warranted to prevent or arrest sarcopenia in the diabetic population.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".