Clinical Research Regional Distribution and Metabolic Effect of PCSK9 insLEU and R46L Gene Mutations and apoE Genotype
Bibliographic record
Abstract
Background: Natural loss-of-function mutations in the proprotein convertase subtilisin/kexin type-9 gene (PCSK9) are associated with lower cholesterol and cardiovascular risk. Because a founder effect exists in French Canadians for many lipid-related genes, we sought to investigate PCSK9 mutations and associated variables in this population. We also investigated the combined effect of PCSK9 mutations and the apolipoprotein E (apoE) polymorphism on metabolic variables. Methods: Gene sequencing and screening was carried out in 1745 healthy individuals ages 9, 13, and 16 years from a provincially representative population sample. In parallel, we measured related metabolic markers and used appropriate statistical methods. Results: We report herein that the carrier rates of the R46L singlenucleotide polymorphism were higher in the French Canadian population (4.8%) than previously seen in Caucasian individuals (2.4%). This is second to the most common variant, insertion of leucine, at a carrier rate of 24%, making it the most common PCSK9 loss-offunction mutation in French Canadian individuals. In R46L carriers, the contribution of the apoE genotype better explains the cholesterol phenotype than the R46L mutation alone. Patients, with both the
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".