Haptoglobin phenotype and intensive glycemic control for coronary artery disease risk reduction in people with type two diabetes: The Veterans Affairs Diabetes Trial
Bibliographic record
Abstract
Intensive glycemic control reduced the risk of coronary artery disease (CAD) events among White ACCORD study participants with the haptoglobin (Hp)2-2 phenotype, and not among participants without the Hp2-2 phenotype. It is unknown whether these results persist in a population with more severe diabetes. Haptoglobin phenotype was measured in 1746 (97%) samples from the Veterans Affairs Diabetes Trial (VADT) randomized controlled trial. Multivariable-adjusted Cox regression models assessed the effect of intensive therapy on CAD risk among participants with and without the Hp2-2 phenotype separately and when stratified within pre-specified race/ethnicity-based subgroups. Time-varying (achieved) HbA1c data (<7.0% or ≥8.0% compared to 7.0-7.9, updated every 3 months) were also analyzed in relation to CAD risk within each phenotype. 567 (32.5%) participants had the Hp2-2 phenotype. Compared to standard therapy, intensive glycemic control was not associated with risk of CAD among participants with the non-Hp2-2 or the Hp2-2 phenotype or for any race/ethnicity-based group. Compared to HbA1c of 7.0-7.9%, having HbA1c <7.0% was not associated with CAD risk for either phenotype or among any race/ethnicity-based group. Having HbA1c ≥8.0% was associated with an increased risk of CAD among Hispanic participants without the Hp2-2 phenotype (HR= 3.61, 95% CI: 1.54-8.41, p-interaction=0.53). The effect of intensive glycemic therapy on CAD events was not dependent on Hp phenotype in the VADT study of veterans with severe diabetes who may represent a population where Hp phenotype information would not be useful for personalizing diabetes management. However, further research is needed to determine if these results are conclusive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".