Relationship between sarcopenia and type 2 diabetes mellitus among adults with prediabetes: evidence from a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The relationship between sarcopenia and type 2 diabetes mellitus (T2DM) among those with prediabetes is largely inconclusive given the heterogeneous findings of previous studies. We aimed to clarify the association between sarcopenia and prediabetes progression to T2DM in adult participants. METHODS: Participants with baseline prediabetes from the UK Biobank cohort were included in this study. Sarcopenia was classified as present or absent based on the criteria from the 2019 European Working Group of Sarcopenia in Older People (EWGSOP2 criteria) including handgrip strength, muscle mass and walking pace. The primary outcome was incident T2DM during follow-up. Multivariable Cox regression model adjusting for sociodemographic factors, lifestyles, comorbidities, and laboratory measures, was used to assess the association between sarcopenia and risk of T2DM. RESULTS: We included 60,325 participants (mean age: 59.6 years, 54.5% females) with baseline prediabetes for analysis, among whom 9,305 (15.4%) had incident T2DM during a mean follow up of 11.4 years. There were 7,139 (11.8%) participants categorized as having sarcopenia. Those with sarcopenia had a higher cumulative incidence of T2DM than those without (19.3% vs. 14.9%, P < 0.001). Sarcopenia was associated with a 22% increased risk of T2DM when compared with no sarcopenia (hazard ratio [HR] = 1.22, 95% confidence interval [CI]: 1.15-1.30) in the fully adjusted model. This association was more evident in participants with a waist to hip ratio < 0.9 (P-value for interaction < 0.05). CONCLUSIONS: Sarcopenia was associated with increased risk of T2DM in adults with prediabetes. Sarcopenia appears to be a marker for risk of T2DM in people with prediabetes and interventions targeted at preserving and improving muscle health might be another novel potential approach to effectively decelerating prediabetes progression to T2DM.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".