Impact of C-reactive protein levels on lipoprotein(a)-associated aortic stenosis incidence and progression
Bibliographic record
Abstract
Aims: Elevated lipoprotein(a) [Lp(a)] levels are associated with the risk of coronary artery disease (CAD) and calcific aortic valve stenosis (CAVS). Observational studies revealed that Lp(a) and C-reactive protein (CRP) levels, a biomarker of systemic inflammation, may jointly predict CAD risk. Whether Lp(a) and CRP levels also jointly predict CAVS incidence and progression is unknown. Methods and results: = 220), which assessed the haemodynamic progression rate of pre-existing mild-to-moderate aortic stenosis. In EPIC-Norfolk, in comparison to individuals with low Lp(a) levels (<50 mg/dL) and low CRP levels (<2.0 mg/L), those with elevated Lp(a) (>50 mg/dL) and low CRP levels (<2.0 mg/L) and those with elevated Lp(a) (>50 mg/dL) and elevated CRP levels (>2.0 mg/L) had a higher CAVS risk [hazard ratio (HR) = 1.86 (95% confidence intervals, 1.30-2.67) and 2.08 (1.44-2.99), respectively]. A comparable predictive value of Lp(a) in patients with vs. without elevated CRP levels was also noted in the UK Biobank. In ASTRONOMER, CAVS progression was comparable in patients with elevated Lp(a) levels with or without elevated CRP levels. Conclusion: Lp(a) predicts the incidence and possibly progression of CAVS regardless of plasma CRP levels. Lowering Lp(a) levels may warrant further investigation in the prevention and treatment of CAVS, regardless of systemic inflammation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".