Plasma PCSK9 Levels Are Elevated with Acute Myocardial Infarction in Two Independent Retrospective Angiographic Studies
Bibliographic record
Abstract
OBJECTIVE: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a circulating protein that promotes degradation of the low density lipoprotein (LDL) receptor. Mutations that block PCSK9 secretion reduce LDL-cholesterol and the incidence of myocardial infarction (MI). However, it remains unclear whether elevated plasma PCSK9 associates with coronary atherosclerosis (CAD) or more directly with rupture of the plaque causing MI. METHODS AND RESULTS: Plasma PCSK9 was measured by ELISA in 645 angiographically defined controls (<30% coronary stenosis) and 3,273 cases of CAD (>50% stenosis in a major coronary artery) from the Ottawa Heart Genomics Study. Because lipid lowering medications elevated plasma PCSK9, confounding association with disease, only individuals not taking a lipid lowering medication were considered (279 controls and 492 with CAD). Replication was sought in 357 controls and 465 with CAD from the Emory Cardiology Biobank study. PCSK9 levels were not associated with CAD in Ottawa, but were elevated with CAD in Emory. Plasma PCSK9 levels were elevated in 45 cases with acute MI (363.5±140.0 ng/ml) compared to 398 CAD cases without MI (302.0±91.3 ng/ml, p = 0.004) in Ottawa. This finding was replicated in the Emory study in 74 cases of acute MI (445.0±171.7 ng/ml) compared to 273 CAD cases without MI (369.9±139.1 ng/ml, p = 3.7×10(-4)). Since PCSK9 levels were similar in CAD patients with or without a prior (non-acute) MI, our finding suggests that plasma PCSK9 is elevated either immediately prior to or at the time of MI. CONCLUSION: Plasma PCSK9 levels are increased with acute MI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".