Abstract 13821: Genetic Identification of Homozygous Familial Hypercholesterolemia by Long-Read Sequencing Among Patients With Clinically Diagnosed Heterozygous Familial Hypercholesterolemia
Bibliographic record
Abstract
Background: Homozygous Familial Hypercholesterolemia (HoFH) is a rare genetic disorder characterized by extremely elevated plasma low-density lipoprotein cholesterol (LDL-C) and accelerated atherosclerosis. Accurate identification of patients with HoFH is essential as they may be eligible for specialized treatments. The objective of this study was to identify and characterize patients with HoFH among patients with clinically diagnosed heterozygous FH (HeFH). Methods: We studied patients from the British Columbia FH Registry with a Dutch Lipid Clinic Network score ≥6 and no secondary cause of hypercholesterolemia. We performed targeted next-generation sequencing of the LDLR , APOB , PCSK9 and LDLRAP1 genes. Long-read sequencing of the LDLR gene was subsequently done for patients with >1 pathogenic LDLR variant to determine haplotypes. We examined lipid levels and cardiovascular events (unstable angina, myocardial infarction and coronary revascularization). Results: Among 705 patients with clinically diagnosed HeFH, we identified a single pathogenic variant in 300 (42.6%) patients and >1 pathogenic variant in the LDLR gene in 11 (1.6%) patients. We established a genetic diagnosis of HoFH in 6 (0.9%) patients (3 true homozygous and 3 compound heterozygous in trans). The mean baseline LDL-C of genetically identified HoFH patients was significantly higher than those with 1 variant. The prevalence of premature cardiovascular disease was numerically greater in the genetically identified HoFH group (29.4% vs 18.0%, p=0.2). Conclusions: In a cohort of patients with clinically diagnosed HeFH, genetic testing by long-read sequencing revealed that 0.9% had HoFH. These patients tended to have a more severe phenotype. Genetic testing of patients with clinical FH may identify patients with HoFH that had eluded clinical diagnosis.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".