High-Density Lipoprotein and Long-Term Incidence and Progression of Aortic Valve Calcification: The Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: Aortic valve calcification (AVC) shares pathological features with atherosclerosis. Lipoprotein components have been detected in aortic valve tissue, including HDL (high-density lipoprotein). HDL measures have inverse associations with cardiovascular disease, but relationships with long-term AVC progression are unclear. We investigated associations of HDL cholesterol, HDL-particle number and size, apoC3-defined HDL subtypes, and, secondarily, CETP (cholesteryl ester transfer protein) mass and activity, with long-term incidence and progression of AVC. METHODS: We used linear mixed-effects models to evaluate the associations of baseline HDL indices with AVC. AVC was quantified by Agatston scoring of up to 3 serial computed tomography scans over a median of 8.9 (maximum 11.2) years of follow-up in the Multi-Ethnic Study of Atherosclerosis (n=6784). RESULTS: After adjustment, higher concentrations of HDL-C (high-density lipoprotein cholesterol), HDL-P (HDL particles), large HDL-P, and apoC3-lacking HDL-C were significantly associated with lower incidence/progression of AVC. Neither small or medium HDL-P nor apoC3-containing HDL-C was significantly associated with AVC incidence/progression. When included together, a significant association was observed only for HDL-C, but not for HDL-P. Secondary analyses showed an inverse relationship between CETP mass, but not activity, and AVC incidence/progression. In exploratory assessments, inverse associations for HDL-C, HDL-P, large HDL-P, and apoC3-lacking HDL with AVC incidence/progression were more pronounced for older, male, and White participants. ApoC3-containing HDL-C only showed a positive association with AVC in these subgroups. CONCLUSIONS: In a multiethnic population, HDL-C, HDL-P, large HDL-P, and apoC3-lacking HDL-C were inversely associated with long-term incidence and progression of AVC. Further investigation of HDL composition and mechanisms could be useful in understanding pathways that slow AVC.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".