Association between estimated glucose disposal rate and major adverse cardiovascular events in patients with type 2 diabetes.
Bibliographic record
Abstract
BACKGROUND: Estimated glucose disposal rate (eGDR) is associated with risk of adverse outcomes in patients with type 2 diabetes. However, whether eGDR affects long-term glycemic management in patients with type 2 diabetes remains unclear. This study aimed to investigate the relationships between eGDR, long-term visit-to-visit HbA1c variability, and adverse outcomes in patients with type 2 diabetes. METHODS: This post-hoc analysis of the Action to Control Cardiovascular Risk in Diabetes study (ACCORD) included 10,129 type 2 diabetes patients with high cardiovascular risk. Participants were divided into tertiles based on eGDR values. HbA1c variability was assessed using the HbA1c Variability Score (HVS). Primary outcome was major adverse cardiovascular events (MACEs), composite of nonfatal myocardial infarction, nonfatal stroke, and death from cardiovascular causes. Weibull accelerated failure-time models and causal mediation analyses were employed to evaluate relationships between variables. RESULTS: During a median follow-up of 4.75 years, compared to T1, T3 showed significantly higher risks of MACEs (HR 1.56, 95% CI: 1.27-1.91) and all-cause mortality (HR 1.81, 95% CI: 1.40-2.32) in fully adjusted models. Restricted cubic spline analysis revealed nonlinear relationships between eGDR and outcomes, with risk declining rapidly as eGDR increased until approximately 5 mg/kg/min, after which the curve flattened. A significant negative correlation was observed between HVS and eGDR (β -2.30; 95% CI: -2.65 to -1.95), following a similar nonlinear pattern. In mediation analysis, HVS explained 37.8% of eGDR's effect on MACEs risk and 21.53% of its effect on all-cause mortality. CONCLUSION: In type 2 diabetes patients with high cardiovascular risk, lower eGDR was associated with higher risk of MACEs and all-cause mortality, with HbA1c variability playing an important mediating role in this relationship.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".