Haptoglobin Phenotype and Intensive Glycemic Control for Coronary Artery Disease Risk Reduction in People With Type 2 Diabetes: The ADVANCE Study
Bibliographic record
Abstract
OBJECTIVE: Intensive glycemic control reduced coronary artery disease (CAD) events among the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study participants with the haptoglobin (Hp)2-2 phenotype but not in participants without the Hp2-2 phenotype. It is unknown whether and how these results translate across different demographic/clinical characteristics and treatment strategies. RESEARCH DESIGN AND METHODS: Haptoglobin phenotype was measured in available samples from the Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) biomarker case-cohort study. Weighted multivariable-adjusted Cox regression models were used to evaluate the association between intensive glycemic control (HbA1c target of ≤6.5%) versus standard therapy (based on local guidelines) and major CAD events among participants with (n = 1,327) and without (n = 2,077) the Hp2-2 phenotype separately and within prespecified stratifications by sex, race, previous cardiovascular disease (CVD), diabetes duration, and HDL-cholesterol. RESULTS: While the hazard ratios (HRs) were in the hypothesized differing directions, compared with standard therapy, intensive glycemic control was not significantly associated with risk of CAD events among participants without (1.04, 95% CI 0.82-1.32) or with (0.84, 0.63-1.14, Pinteraction = 0.27) the Hp2-2 phenotype overall. Intensive therapy was associated with lower CAD risk among participants with the Hp2-2 phenotype who had no previous CVD (0.47, 0.29-0.76, Pinteraction = 0.01). CONCLUSIONS: Our findings suggest that intensive glycemic control contributes to the prevention of major CAD events among ADVANCE participants with the Hp2-2 phenotype and no previous CVD and are in alignment with our hypothesis that intensive glycemic control may be beneficial in a subset of people with the Hp2-2 phenotype.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".