Abstract 4144876: Genetic evaluation of metabolic signatures associated with lipid-lowering medications
Bibliographic record
Abstract
Introduction: Human genetic studies have identified hundreds of risk loci for circulating lipoproteins, motivating the development of new lipid-lowering medications. Although these targets influence lipids and metabolites through unique mechanisms, the extent to which they exert shared pleiotropic effects on other effectors of atherosclerotic cardiovascular disease remains uncertain. This study explored the lipid subfraction, cytokine, and metabolic signatures associated with lipid-lowering targets using Mendelian Randomization (MR). Methods: The study focused on protein targets of novel lipid-lowering medications: PCSK9, APOB, LPA, LPL, and ANGPTL3. Instruments for PCSK9, LPL, and ANGPTL3 were derived from the UK Biobank Pharma Proteomics Project (54,219 participants), and for LPA and APOB from the Pan-UK Biobank (335,796 and 418,505 participants). These exposures were tested against outcomes including 40 cytokines (meta-analysis of 74,783 individuals), 249 lipid-subfraction measures (118,461 UK Biobank participants), and 1,400 metabolite measures (8,299 participants in the Canadian Longitudinal Study on Aging). Univariable MR was performed using the inverse-variance weighted (IVW) method to test associations. The Jaccard index quantified the similarity of effects of each target across these outcomes, focusing on significant results after adjusting for multiple testing (FDR q < 0.05). Results: The lipid-lowering targets were each significantly associated with between 1-17 cytokines, 184-220 lipid subfractions, and 307-396 metabolites (FDR q < 0.05). ANGPTL3 and PCSK9 showed the highest similarity of effects on lipid subfractions (Jaccard Index: 0.83, p < 0.001). LPA, APOB, and ANGPTL3 were the only targets with significant cytokine effects, with low similarity scores potentially suggesting their inflammatory signatures are unique. ANGPTL3 and LPA had the highest similarity of effects on metabolites (Jaccard Index: 0.19, p < 0.05). Conclusion: Our analyses indicate that lipid-lowering targets influence metabolic signatures through shared and unique mechanisms. Further exploration of how cytokine, lipid, and metabolic profiles are impacted may help in the development of more effective medications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".