Abstract 9691: Lipoprotein Subclasses Are Associated With Hepatic Steatosis: Insights from the Prospective Multicenter Imaging Study for the Evaluation of Chest Pain (PROMISE) Clinical Trial
Bibliographic record
Abstract
Introduction: Hepatic steatosis (HS) is associated with coronary artery disease (CAD) and cardiovascular (CV) events. Previously, we have demonstrated that granular measures of lipids (lipoprotein particle number/size) are associated with CAD and CV events and incremental to traditional lipid measures. Hypothesis: Granular measures of lipids are associated with HS detected by cardiac computed tomography (CT) and with HS detected by histopathology. Methods: We included 1524 subjects from the PROMISE trial. HS was defined as CT attenuation of the liver <40HU or liver Results: Subjects with HS (n=413) were slightly younger (59±8 vs 61±8 yrs) and more likely men (53 vs 44%) as compared to controls (n=1111). Three lipoprotein factors were associated with HS: LDL/LDL particle size (OR 1.36, 95%CI 1.21-1.53, p<0.001); HDL/HDL size (OR 1.75, 95%CI, 1.53-2.02, p<0.001), and TG-rich-lipoprotein particles (OR 0.74, 95% CI 0.65-0.84, p<0.002). Individual lipoproteins heavily loaded in these factors were also significant in multivariable analysis (Figure). These lipoproteins were also associated with HS in the validation cohort: small LDL (OR 6.36, p<0.05), large HDL (OR 0.29, p<0.05), and large TG particles (OR 13.83, p<0.05). Conclusions: We found association of small LDL, large HDL and large TG particles, previously associated with CV event risk, with HS phenotyped by CT and histopathology. These results suggest that use of lipoprotein subclasses may improve CV risk assessment in patients with HS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".