Association of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) With Cardiovascular Risk in Primary Prevention
Bibliographic record
Abstract
OBJECTIVE: Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays an important role in the modulation of low-density lipoprotein metabolism. This study was conducted to evaluate the relationship between serum PCSK9 concentrations and measures of vascular health, subclinical atherosclerosis, and adverse cardiovascular events. The relationship between traditional risk factors and PCSK9 concentrations was also examined. APPROACH AND RESULTS: The cohort consisted of 1527 middle-aged men enrolled in the Firefighters and Their Endothelium (FATE) study, who were free of vascular disease and followed up over a mean period of 7.2±1.7 years. Baseline evaluation included assessment of traditional cardiovascular risk factors and measurements of flow-mediated dilation, reactive hyperemic velocity time integral, and carotid intima-media thickness. Biochemical parameters, including serum PCSK9 concentrations, were analyzed to determine predictors of vascular measures and to evaluate the role of PCSK9 in the occurrence of adverse cardiovascular events. Multivariate linear regression analyses indicated that body mass index, insulin, low-density lipoprotein-cholesterol, and triglycerides were independent predictors of PCSK9. Further modeling revealed no correlation between PCSK9 concentration and carotid intima media thickness, flow-mediated dilation, or reactive hyperemic velocity time integral. Analyses indicated no significant association between PCSK9 concentrations and cardiovascular event occurrences. CONCLUSIONS: Although correlated with low-density lipoprotein-cholesterol, insulin, and triglycerides, PCSK9 was not associated with measures of vascular function or structure. There was also no significant relationship between PCSK9 concentrations and cardiovascular events. Thus, although PCSK9 is an important therapeutic target to reduce circulating low-density lipoprotein-cholesterol concentrations, it is unlikely to be a biomarker of atherosclerotic risk or vascular health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".