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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".