Systematic Review of the Relationship Between Handgrip Strength and Blood Glucose Levels in Young Adults and the Elderly
Bibliographic record
Abstract
Background: Handgrip strength (HGS) is an indicator of overall muscle health and is affected by impaired blood glucose levels. This review discusses the relationship between HGS and blood glucose levels and provides solutions to the known problems of HGS and blood glucose regulation. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. The articles were sourced from Google Scholar and PubMed. A total of 418 studies were screened, of which 19 articles were included in this study. The Newcastle–Ottawa Scale was used to assess the risk of bias. Results: A relationship was observed between low HGS and high blood glucose levels. The suggested mechanisms involve insulin resistance, Caspase-3 activation, and the mitochondrial impact. Sarcopenia emerged as an independent risk factor for impaired glucose control. Interventions including insulin administration and exercise have been proposed to preserve muscle mass. Conclusion: Resistance training and HGS exercises can be added to the rehabilitation practices for managing diabetes mellitus. HGS measurements are vital for predicting muscle mass loss in clinical practice.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".