Haptoglobin Phenotype Modifies the Effect of Fenofibrate on Risk of Coronary Event: ACCORD Lipid Trial
Bibliographic record
Abstract
OBJECTIVE: The haptoglobin (Hp)2-2 phenotype (∼35-40% of people) is associated with increased oxidation and dysfunctional HDL in hyperglycemia and may explain why drugs designed to pharmacologically raise HDL cholesterol and lower triglycerides have not reliably prevented cardiovascular disease in diabetes. We aimed to determine whether the effect of adding fenofibrate versus placebo to simvastatin on the risk of coronary artery disease (CAD) events depends on Hp phenotype in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) lipid trial. RESEARCH DESIGN AND METHODS: Cox proportional hazards regression models quantified the relationship between fenofibrate therapy and CAD events in the ACCORD lipid trial in participants with the Hp2-2 phenotype (n = 1,795) separately from those without (n = 3,201). RESULTS: Fenofibrate therapy successfully lowered the risk of CAD events in participants without the Hp2-2 phenotype (multivariable adjusted hazard ratio 0.74 [95% CI 0.60-0.90] compared with no fenofibrate therapy) but not in participants with the Hp2-2 phenotype (1.16 [0.87-1.56]; P interaction = 0.009). Subgroup analyses revealed that this protective effect of fenofibrate against CAD events among the non-Hp2-2 phenotype group was pronounced in participants with severe dyslipidemia (P interaction = 0.01) and in males (P interaction = 0.02) with an increased CAD risk from fenofibrate treatment observed in females with the Hp2-2 phenotype (P interaction = 0.002). CONCLUSIONS: The effect of fenofibrate added to simvastatin on risk of CAD events depends on Hp phenotype in the ACCORD lipid trial.
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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 | 0.002 |
| 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 | 0.000 | 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".