Effect of the LDL receptor mutation type on incident major adverse cardiovascular events in familial hypercholesterolaemia
Bibliographic record
Abstract
AIMS: Patients with familial hypercholesterolaemia (FH) are at increased risk of cardiovascular disease (CVD) due to extremely high circulating LDL cholesterol (LDL-C) concentrations. Our objective was to study the effect of the type of LDL receptor (LDLR) mutation on the incidence of major adverse cardiovascular events (MACEs). METHODS AND RESULTS: This was a multinational prospective cohort study, which included patients with heterozygous FH aged 18-65 years, without a prior history of CVD, and carrying a pathogenic or likely pathogenic variant in the LDLR gene. A total of 2131 patients (20 535person-years of follow-up) were included in the study, including 1234 subjects carrying a defective mutation in the LDLR and 897 subjects carrying a null mutation. During the follow-up, a first MACE occurred in 79 cases (6%) in the defective group and in 111 cases (12%) in the null group. The mean baseline LDL-C concentration was 17% higher in the null group than in the defective group (7.90 vs. 6.73 mmoL/L, P < 0.0001). In a Cox regression model corrected for traditional cardiovascular risk factors, the presence of a null mutation was associated with a hazard ratio of 2.09 (1.44-3.05), P = 0.0001. CONCLUSION: Carriers of a null mutation have an independent ∼2-fold increased risk of incident MACE compared with patients carrying a defective mutation. This study highlights the importance of genetic screening in FH in order to improve patient care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".