Abstract 12552: Causal Effect of Exposure to Elevated Lipoprotein(a) Levels on the Blood Transcriptome : A Mendelian Randomization Study
Bibliographic record
Abstract
INTRODUCTION: Lipoprotein(a) [Lp(a)] is one of the strongest genetic risk factors for atherosclerotic cardiovascular diseases (ASCVD). Several ex vivo studies have revealed a pro-inflammatory and pro-atherogenic role of Lp(a) in the etiology of ASCVD, mainly through induction of inflammation, metabolic processes, and apoptosis. However, whether Lp(a) causally influences the human transcriptome is unknown. Here, we aimed at identifying whether genetically-predicted Lp(a) levels could also affect the whole blood expression of genes involved in metabolic- and inflammatory-related pathways. Methods: We performed a series of inverse-variance weighted mendelian randomization analyses using single-nucleotide polymorphisms located in the LPA locus (±500kb; p<5x10 -8 ) from the UK Biobank GWAS on Lipoprotein(a) quantiles as exposure (n=377,590 and whole blood expression levels of 14,288 coding gene from eQTLGen as outcomes (n=31,684 from 37 studies). We then used the Functional Mapping and Annotation of Genome-Wide Association Studies tool (FUMA) to identify gene sets for cardiometabolic- and inflammatory-related pathways from gene ontology biological processes. Results: The MR analyses identified 1363 genes that may be causally influenced by exposure to high Lp(a) levels (with p<0.05). The 10 most strongly associated genes with genetically-predicted Lp(a) levels are FSTL3 , PRKC1 , PDE6C , PLXNB3 , SESN1 , SASH1 , CD1D , CCDC102A , SS18L1 and TCF12 . These genes are also differentially expressed in the heart. We identified 16 cardiometabolic-related pathways, mostly related to immune response, signaling, apoptosis, cardiovascular and vasculature development, cell proliferation, metabolic process, and catalytic activity. The 5 most significant pathways are regulation of cell population proliferation, regulation of intracellular signal transduction, positive regulation of cell population proliferation, cardiovascular system development and TOR signaling. Conclusions: Results of this study highlight a potentially causal effect of exposure to elevated Lp(a) levels on several key genes involved in metabolic processes and inflammation and provide mechanistic insight on the pathobiological role of Lp(a) in the etiology of ASCVD.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".