Cell-free, high-density lipoprotein–specific phospholipid efflux assay predicts incident cardiovascular disease
Bibliographic record
Abstract
BACKGROUNDCellular cholesterol efflux capacity (CEC) is a better predictor of cardiovascular disease (CVD) events than HDL-cholesterol (HDL-C) but is not suitable as a routine clinical assay.METHODSWe developed an HDL-specific phospholipid efflux (HDL-SPE) assay to assess HDL functionality based on whole plasma HDL apolipoprotein-mediated solubilization of fluorescent phosphatidylethanolamine from artificial lipid donor particles. We first assessed the association of HDL-SPE with prevalent coronary artery disease (CAD): study I included NIH severe-CAD (n = 50) and non-CAD (n = 50) participants, who were frequency matched for sex, BMI, type 2 diabetes mellitus, and smoking; study II included Japanese CAD (n = 70) and non-CAD (n = 154) participants. We also examined the association of HDL-SPE with incident CVD events in the Prevention of Renal and Vascular End-stage Disease (PREVEND) study comparing 340 patients with 340 controls individually matched for age, sex, smoking, and HDL-C levels.RESULTSReceiver operating characteristic curves revealed stronger associations of HDL-SPE with prevalent CAD. The AUCs in study I were as follows: HDL-SPE, 0.68; apolipoprotein A-I (apoA-I), 0.62; HDL-C, 0.63; and CEC, 0.52. The AUCs in study II were as follows: HDL-SPE, 0.83; apoA-I, 0.64; and HDL-C, 0.53. Also longitudinally, HDL-SPE was significantly associated with incident CVD events independent of traditional risk factors with ORs below 0.2 per SD increment in the PREVEND study (P < 0.001).CONCLUSIONHDL-SPE could serve as a routine clinical assay for improving CVD risk assessment and drug discovery.TRIAL REGISTRATIONClinicalTrials.gov NCT01621594.FUNDINGNHLBI Intramural Research Program, NIH (HL006095-06).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".