Abstract 10476: Impact of C-Reactive Protein Levels on Lipoprotein(a)-Associated Coronary Artery Disease and Aortic Stenosis Risk in Apparently Healthy Individuals
Bibliographic record
Abstract
Introduction: Elevated Lipoprotein(a) (Lp[a]) levels are strongly and linearly associated with coronary artery disease (CAD) and calcific aortic valve stenosis (CAVS). Observational studies in primary and secondary prevention settings revealed that Lp(a) and other CAD risk factors such as 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 risk is unknown. Methods: We investigated the long-term associations of Lp(a) with CAD and CAVS according to CRP levels in the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Norfolk study. Lp(a) and CRP levels were measured in 18,459 participants who were followed for 20 years. During the follow-up, 3915 and 418 participants had incident CAD and CAVS, respectively. Results: As presented in Figure 1, in comparison to individuals with low Lp(a) levels (<30 mg/dL) and low CRP levels (<2.0 mg/dL), those with elevated Lp(a) (>30 mg/dL) and low CRP levels (<2.0 mg/dL) and those with elevated Lp(a) (>30 mg/dL) and elevated CRP levels (>2.0 mg/dL) had a higher CAD risk (hazard ratio [HR] = 1.38 [95% CI, 1.28-1.49] and 1.93 [95% CI, 1.74-2.13] , respectively), and a higher CAVS risk (HR = 1.46 [95% CI, 1.08-1.98] and 1.87 [95% CI, 1.40-2.49], respectively). Conclusions: Results of this study confirm the joint association of high Lp(a) and CRP levels on incident CAD and extend these joint associations to valvular diseases such as CAVS. Lowering Lp(a) levels may warrant further investigation in the prevention of CAD and CAVS, especially in the context 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".