Glycemic control, HbA1c variability, and major cardiovascular adverse outcomes in type 2 diabetes patients with elevated cardiovascular risk: insights from the ACCORD study
Bibliographic record
Abstract
BACKGROUND: Although recent guidelines advocate for HbA1c target individualization, a comprehensive criterion for patient categorization remains absent. This study aimed to categorize HbA1c variability levels and explore the relationship between glycemic control, cardiovascular outcomes, and mortality across different degrees of variability. METHODS: Action to Control Cardiovascular Risk in Diabetes study data were used. HbA1c variability was measured using the HbA1c variability score (HVS) and standard deviation (SD). K-means and K-medians clustering were used to combine the HVS and SD. RESULTS: K-means clustering was the most stable algorithm with the lowest clustering similarities. In the low variability group, intensive glucose-lowering treatment significantly reduced the risk of adverse cardiovascular outcomes (HR: 0·78 [95% CI: 0·63, 0·97]) without increasing mortality risk (HR: 1·07 [0.81, 1·42]); the risk of adverse cardiovascular events (HR: 1·33 [1·14, 1·56]) and all-cause mortality (HR: 1·23 [1·01,1·51]) increased with increasing mean HbA1c. In the high variability group, treatment increased the risk of cardiovascular events (HR: 2.00 [1·54, 2·60]) and mortality (HR: 2·20 [1·66, 2·92]); a higher mean HbA1c (7·86%, [7·66%, 8·06%]) had the lowest mortality risk, when the mean HbA1c was < 7·86%, a higher mean HbA1c was associated with a lower mortality risk (HR: 0·63 [0·42, 0·95]). In the medium variability group, a mean HbA1c around 7·5% was associated with the lowest risk. CONCLUSIONS: HbA1c variability can guide glycemic control targets for patients with type 2 diabetes. For patients with low variability, the lower the HbA1c, the lower the risk. For those with medium variability, controlling HbA1c at 7·5% provides the maximum benefit. For patients with high variability, a mean HbA1c of around 7·8% presents the lowest risk of all-cause mortality, a lower HbA1c did not provide cardiovascular benefits but instead increased the mortality risk. Further studies, especially those with patients that reflect the general population with type 2 diabetes undergoing the latest therapeutic approaches, are essential to validate the conclusions of this study.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".