Icosapent ethyl reduces CVD risk in cardiovascular-kidney-metabolic syndrome: REDUCE-IT CKM
Bibliographic record
Abstract
Background/Introduction: Cardiovascular-kidney-metabolic (CKM) syndrome was recently identified as a cardiometabolic disorder that incorporates chronic kidney disease with the metabolic syndrome (MetS). REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial) was an international, double-blind, placebo-controlled trial that randomized hypertriglyceridemic (TG, 150-499 mg/dL) statin-treated patients with established cardiovascular disease (CVD) or diabetes and multiple CVD risk factors to icosapent ethyl (IPE) or placebo (4 grams/day). It is unknown if renal insufficiency added to MetS confers incremental CVD risk in secondary prevention patients without diabetes and if IPE lowers that risk. Methods: (n=609). Event rates of the primary and secondary trial endpoints were compared in placebo subjects with higher vs lower baseline eGFR, and the effect of IPE on these endpoints was also compared within each of the three subgroups. Results: ). Treatment with IPE was associated with an absolute risk reduction of 11.2% and number needed to treat of 9 patients to prevent an initial primary composite endpoint event over the study period. Conclusions: In this REDUCE-IT analysis of secondary prevention patients without diabetes at baseline, the recently defined CKM syndrome was associated with incremental CVD risk compared with MetS and normal renal function. Treatment with IPE substantially reduced CVD risk in MetS patients with renal insufficiency (i.e., CKM) and CVD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".