Effect of olezarsen targeting APOC-III on lipoprotein size and particle number measured by NMR in patients with hypertriglyceridemia
Bibliographic record
Abstract
BACKGROUND: Olezarsen is a hepatocyte-targeted, GalNAc-modified antisense oligonucleotide that decreases plasma levels of apolipoprotein C-III (apoC-III) and triglyceride-rich lipoproteins (TRLs). OBJECTIVE: To define the effect of olezarsen on NMR-derived lipoprotein particle size and concentration. METHODS: Patients (n=114) with or at risk for atherosclerotic cardiovascular disease and fasting triglycerides ≥200 and <500 mg/dL received olezarsen (10 or 50 mg every 4 weeks, 15 mg every 2 weeks, or 10 mg every week) or saline placebo subcutaneously for 6-12 months. NMR LipoProfile® analysis was performed in frozen EDTA plasma samples collected at baseline and at the primary analysis timepoint (PAT) at 6 months. RESULTS: A dose-dependent relationship was generally noted with increasing cumulative doses of olezarsen in TRL particle (TRLP), LDL particle (LDL-P) and HDL (HDL-P) particle concentrations. In the 50 mg every 4 weeks dose, compared to placebo, olezarsen resulted in a significant reduction in total TRL-P (51%, P<0.0001) with largest reductions in large-size (68%, P<0.0001) and medium-size (63%, P<0.0001) TRL-P. Total LDL-P concentration was not changed, but large LDL-P increased by 186% (p=0.0034), and small LDL-P decreased by 39% (p=0.0713). Total HDL-P concentration increased by 15% (P=0.0006), driven primarily by a 32% increase in small HDL subspecies (diameters <8.3 nm) (P=0.0008). CONCLUSION: Olezarsen results in favorable changes in lipoprotein concentration and particle size, primarily manifested by reduction in TRLs, remodeling to larger LDL particles, and increase in small HDL-P. These findings suggest that apoC-III inhibition improves the overall atherogenic risk profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".