Abstract P594: High-Density Lipoprotein Particle Concentrations and Long-Term Atherosclerotic Disease Risk in Young Adults
Bibliographic record
Abstract
Introduction: HDL particles vary in size and concentration. Indices of overall HDL particle concentration (HDL-P) and the concentrations of different HDL size subspecies (small: H1-H3, medium: H4, H5, and large: H6, H7) have differential associations with near-term CVD events in middle-aged adults. It is unclear if measures of HDL particle concentration predict long-term ASCVD risk in young adults. Methods: Among CARDIA participants (ppts), NMR was used to measure HDL-P and HDL particle size subgroup H1-H7 concentrations. HDL cholesterol (HDL-C) was measured using standard assays. We stratified the ppts into 2 age windows: 20-30y (n= 1645) and 30-40y (n=2922). We used adjusted Cox proportional hazards models to assess the associations between a 1SD higher HDL-C, HDL-P, and HDL1-7 subgroups with incident ASCVD events. We added HDL-P, HDL H1-H7, and HDL-C separately to a modified Pooled Cohort Equation (PCE) model; model performance (discrimination and reclassification) was evaluated. Results: 81 and 163 ASCVD events occurred over (median (IQR)) 31.8y (31.1-32.0y) for the 20-30y age window and over 26.8y (19.1-27.1y) for the 30-40y age window, respectively. In ppts age 20-30y, a higher HDL-P and HDL-C were not associated with ASCVD events, however a higher HDL H6 subgroup level was associated with lower risk for ASCVD in demographic adjusted models. In the age 30-40y group, higher HDL-P, HDL-C, and H6 subgroup were significantly associated with lower ASCVD risks in all models. There were no significant differences in c-statistics across PCE models. However, there were improvements in reclassification for all HDL measures when added to the PCE model in the 20-30y age window, and significant improvements in reclassification when HDL H1-7 were added to the PCE for the 30-40y age window. Conclusion: At younger ages (<40y) differences in HDL particle abundance, in particular large particles, may help reclassify long-term risk for ASCVD in some.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.006 | 0.001 |
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".