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 and accelerated atherosclerosis. Accurate identification of patients with HoFH is essential as they may be eligible for specialized treatments. We hypothesized that a subset of patients with clinically diagnosed heterozygous FH (HeFH) may in fact have HoFH, and this could be identified by genetic diagnosis. Methods: We recruited patients with a clinical diagnosis of HeFH based on a Dutch Lipid Clinic Network score ≥6 and no secondary cause of hypercholesterolemia. We performed targeted next-generation sequencing of the low-density lipoprotein receptor ( LDLR ), apolipoprotein B ( APOB ), proprotein convertase subtilisin/kexin type 9 ( PCSK9 ), and low-density lipoprotein receptor adapter protein 1 ( LDLRAP1 ) genes, followed by long-read sequencing of the LDLR gene in patients with >1 pathogenic LDLR variant. We examined lipid levels and cardiovascular events. Results: Among 705 patients with clinically diagnosed HeFH, we identified a single pathogenic variant in 300 (42.6%) and >1 pathogenic variant in the LDLR gene in 11 patients (1.6%). We established a genetic diagnosis of HoFH in 6 (0.9%) patients (3 true homozygotes and 3 compound heterozygotes). The mean baseline low-density lipoprotein cholesterol and prevalence of premature cardiovascular disease of patients with genetically identified HoFH was significantly higher than patients with HeFH. Conclusions: In a cohort of patients with clinically diagnosed HeFH, genetic testing including long-read sequencing revealed that 0.9% had HoFH. These patients tended to have a more severe clinical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".