Rosuvastatin, Proprotein Convertase Subtilisin/Kexin Type 9 Concentrations, and LDL Cholesterol Response: the JUPITER Trial
Bibliographic record
Abstract
BACKGROUND: Although statin therapy is known to increase concentrations of PCSK9, whether this effect is related to the magnitude of LDL reduction is uncertain. This study was undertaken to understand the extent of this effect and examine the relationship between PCSK9 and LDL cholesterol (LDL-C) reduction. METHODS: We measured plasma PCSK9 concentrations by ELISA at baseline and at 1 year in 500 men and 500 women participating in the Justification for Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial that randomly allocated participants to rosuvastatin 20 mg daily or placebo. We also evaluated rs11591147, a single nucleotide polymorphism known to have an impact on plasma PCSK9 concentrations. RESULTS: At baseline, median (interquartile range) PCSK9 concentrations were higher in women [73 (62-90)] ng/mL than in men [69 (57-81) ng/mL] (P<0.005). During 1 year, there was no change in PCSK9 concentrations in the placebo arm, suggesting stability in time. In contrast, the rosuvastatin increased PCSK9 by 35% in women [101 (82-117) ng/mL] and 28% in men [89 (71-109) ng/mL] (P<0.0001). Among those allocated to rosuvastatin, greater reductions in LDL-C were associated with greater increases in PCSK9 on both absolute and relative scales (r=-0.15, P<0.0005). Furthermore PCSK9 (rs11591147) did not alter the magnitude of LDL-C reduction associated with rosuvastatin use. CONCLUSIONS: In this randomized trial, rosuvastatin increased plasma concentration of PCSK9 in proportion to the magnitude of LDL-C reduction; the LDL-C response to statin could not be inferred by PCSK9 concentrations.
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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) | 0.002 | 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".