Abstract 427: Plasma PCSK9 Levels Are Positively Correlated With LDL-Cholesterol Concentrations in Familial Hypercholesterolemia
Bibliographic record
Abstract
Autosomal dominant familial hypercholesterolemia (FH) is caused by mutations in the LDL receptor (LDLR), its ligand apoB or proprotein convertase subtilisin/kexin type 9 (PCSK9) genes. PCSK9 regulates LDL-C levels by binding to LDLR, thereby enhancing its intracellular degradation. Although PCSK9 levels have been shown to be elevated in FH subjects, the extent to which PCSK9 levels correlate with LDL-C concentrations in FH has not been examined. Therefore, the objective of the present study was to assess the relationship between PCSK9 and LDL-C levels in a large cohort of genetically defined French-Canadian (FC) FH subjects. A total of 292 FH heterozygotes (heFH) carrying one of the nine FC mutations in the LDLR gene were recruited. 226 subjects were carriers of a negative receptor (NR) mutation in the LDLR gene, while the other 66 were carriers of a defective (DR) LDLR gene mutation. 56 control subjects matched for gender and body mass index (BMI) were also recruited. Fasting blood samples were collected after a 6-week period without lipid lowering medication. PCSK9 levels were significantly higher in the heFH group than in the control group (317.9 ng/ml vs 203.3 ng/ml; P<0.001). In the heFH group, PCSK9 levels were positively and independently correlated with LDL-C concentrations and predicted 8.1% (P<0.001) of the variability in LDL-C levels. Age, the type of LDLR mutation (NR vs DR), and BMI were also significantly associated with the variability in LDL-C levels, predicting 13.6% (P<0.001), 3.9% (P<0.001), and 1.8% (P=0.02) of the LDL-C variance, respectively. Interestingly, PCSK9 levels were also positively correlated with lipoprotein(a) levels in heFH (r=0.20; P<0.001). These results indicate that PCSK9 levels are positively correlated with LDL-C and lipoprotein(a) concentrations in heFH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".