Lipoprotein(a) Levels Are Associated With Subclinical Calcific Aortic Valve Disease in White and Black Individuals
Bibliographic record
Abstract
OBJECTIVE: Lipoprotein(a) [Lp(a)] is a risk factor for calcific aortic valve disease (CAVD) but has not been evaluated across multiple races/ethnicities. This study aimed to determine whether Lp(a) cutoff values used in clinical laboratories to assess risk of cardiovascular disease identify subclinical CAVD and its severity and whether significant relations are observed across race/ethnicity. APPROACH AND RESULTS: Lp(a) concentrations were measured using a turbidimetric immunoassay, and subclinical CAVD was measured by quantifying aortic valve calcification (AVC) through computed tomographic scanning in 4678 participants of the Multi-Ethnic Study of Atherosclerosis. Relative risk and ordered logistic regression analysis determined cross-sectional associations of Lp(a) with AVC and its severity, respectively. The conventional 30 mg/dL Lp(a) clinical cutoff was associated with AVC in white (relative risk: 1.56; confidence interval: 1.24-1.96) and was borderline significant (P=0.059) in black study participants (relative risk: 1.55; confidence interval: 0.98-2.44). Whites with levels ≥50 mg/dL also showed higher prevalence of AVC (relative risk: 1.72; confidence interval: 1.36-2.17) than those below this level. Significant associations were observed between Lp(a) and degree of AVC in both white and black individuals. The presence of existing coronary artery calcification did not affect these associations of Lp(a) and CAVD. There were no significant findings in Hispanics or Chinese. CONCLUSIONS: Lp(a) cutoff values that are currently used to assess cardiovascular risk seem to be applicable to CAVD, but our results suggest race/ethnicity may be important in cutoff selection. Further studies are warranted to determine whether race/ethnicity influences Lp(a) and risk of CAVD incidence and its progression.
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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 | 0.001 | 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 | 0.001 |
| 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".