Remnant cholesterol concentrations best explain the cardiovascular benefit of APOC3 genetic inhibition: A drug target Mendelian Randomization study
Bibliographic record
Abstract
Abstract Background Apolipoprotein C-III (APOC3) inhibitors are approved for hypertriglyceridemia. Genetic evidence suggests that APOC3 inhibition may also prevent coronary artery disease (CAD), but mechanisms remain unclear. Methods To clarify how APOC3 inhibition could prevent CAD, we performed two-step cis Mendelian randomization using genetic variants in the APOC3 gene region associated with plasma levels of APOC3. For comparison, we investigated proprotein convertase subtilisin/kexin type 9 (PCSK9). Potential mediators included apolipoprotein B, triglycerides, LDL-cholesterol, and remnant cholesterol measured by nuclear magnetic resonance spectroscopy in mostly fasting samples from Karjalainen et al., and in non-fasting samples from the UK Biobank. CAD data were from CARDIoGRAMplusC4D. Results APOC3 associations with apolipoprotein B and remnant cholesterol levels were two-fold larger in the study by Karjalainen et al., (55% fasted individuals) when compared with the UK Biobank study (non-fasted individuals). Genetically predicted lower APOC3 and PCSK9 levels were similarly associated with reduced CAD risk (OR = 0.83, 95% CI=0.75 to 0.92, p=4.6e-04 and 0.76, 95% CI=0.73 to 0.80, p=1.6e-31, respectively). In the two-step cis-Mendelian randomization analysis, the association between genetically predicted APOC3 and CAD was attenuated to null when adjusting for apolipoprotein B, triglycerides, or remnant cholesterol. Multivariable Mendelian randomization using genome-wide variants showed that remnant cholesterol, not triglycerides, was conditionally associated with CAD risk. Conclusions Remnant cholesterol best explains the mechanism through which APOC3 inhibition could prevent CAD. APOC3 inhibition may influence fasting remnant cholesterol to a greater extent than non-fasting remnant cholesterol. People with high levels of remnant cholesterol could benefit from APOC3 inhibition
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".