Effects of High Density Lipoprotein Raising Therapies on Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus, with or without Renal Impairment: The Action to Control Cardiovascular Risk in Diabetes Study
Bibliographic record
Abstract
BACKGROUND: The role of high density lipoprotein-raising interventions in addition to statin therapy in patients with diabetes remains controversial. Chronic kidney disease (CKD) is a strong modifier of cardiovascular (CV) outcomes. We therefore investigated the impact of CKD status at baseline on outcomes in patients with diabetes randomized to standard statin or statin plus fenofibrate treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) lipid trial. METHODS: Among 5,464 participants in the ACCORD lipid trial, 3,554 (65%) were free of CKD at baseline, while 1,910 (35%) had mild to moderate CKD. Differences in CV outcomes during follow-up between CKD and non-CKD subgroups were examined. In addition, the effect of fenofibrate as compared to placebo on CV outcomes was examined for both subgroups. RESULTS: All CV outcomes were 1.4-3 times higher among patients with CKD as compared to non-CKD patients. In patients with CKD, the addition of fenofibrate had no effect on any of the primary or secondary outcomes. In patients without CKD, however, the addition of fenofibrate was associated with a significant 36% reduction of CV mortality (hazards ratio [HR] 0.64; 95% CI 0.42-0.97; p value for treatment interaction <0.05) and 44% lower rate of fatal or non-fatal congestive heart failure (CHF; HR 0.56; 95% CI 0.37-0.84; p value treatment interaction <0.03). CONCLUSIONS: For patients with type 2 diabetes at high CV risk but no CKD, fenofibrate therapy added to statin reduced the CV mortality and the rate of fatal and non-fatal CHF.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".