Abstract 15805: PCSK9 R46L, Lower LDL and Cardiovascular Disease Risk in Familial Hypercholesterolemia
Bibliographic record
Abstract
Introduction: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a down-regulator of the low density lipoprotein receptor (LDLR). Familial hypercholesterolemia (FH) is a autosomal monogenic disease associated with high LDL-C concentration and cardiovascular risk. Hypothesis: This study aimed to examine whether the PCSK9 R46L loss of function mutation found in a cohort of FH patients will be associated with lower LDL-C and cardiovascular risk. Methods: We studied FH patients attending the IRCM Lipid Clinic and whose DNA genotyping was positive for LDLR mutations. The presence of the PCSK9 loss of function R46L missense mutation was determined among a cohort of 582 FH patients by sequencing. Results: Frequency of the R46L variant was 3%. Comparison of their lipid profile showed that carriers had significantly reduced LDL-C (11%, p<0.001), TC (9%, p<0.01), ApoB (10%, p<0.01) and non-HDL (12%, p<0.001) concentrations compared to non-carriers. The analysis of physical xanthomata among both groups, showed a decreased average number of xanthoma per individual in R46L carriers (0.33 and 0.76 respectively, p<0.001). Importantly, the R46L mutation was associated with a significant 66% (p=0.05) lower risk of cardiovascular events compared to non-carriers. Conclusion: Our study showed that the presence of the PCSK9 loss of function R46L mutation in the genotype of FH patients is beneficial for the lipid homeostasis and down-regulate the effect of the LDLR mutation in terms of accumulation of LDL-C, ApoB , total cholesterol, non-HDL and thereby, lowers the coronary heart disease risk of PCSK9 LOF mutation carriers. It is therefore very likely that anti-PCSK9 therapy will be useful in reducing cardiovascular risk in FH patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".