Heterozygous familial hypercholesterolemia: an underrecognized cause of early cardiovascular disease
Bibliographic record
Abstract
Heterozygous familial hypercholesterolemia (HeFH) is a monogenic disorder that affects about 1 in 500 people, with a higher prevalence in certain subpopulations such as people of Quebecois, Christian Lebanese and Dutch South Afrikaner extraction. HeFH is characterized by cholesterol deposits affecting the corneas, eyelids and extensor tendons; elevated plasma concentrations of low-density lipoprotein (LDL) cholesterol; and accelerated vascular disease, especially coronary artery disease (CAD). Although HeFH is genetically heterogeneous, it is most often caused by heterozygous mutations in the LDLR gene encoding the LDL receptor. We describe a man who was diagnosed with HeFH after he had a myocardial infarction at 33 years of age. By DNA sequence analysis, he was found to have a heterozygous splicing mutation in his LDLR gene. This discovery expanded the growing mutational spectrum in patients with HeFH in Ontario. Given that HeFH is a treatable cause of early vascular disease, it is important that this condition be recognized, diagnosed and treated in affected patients; but as yet, there is no consensus on the best approach. Diagnostic criteria based on family history and clinical presentation have been proposed for patients with suspected HeFH. Biochemical or molecular screening might be considered to detect new cases of HeFH in populations with a relatively high HeFH prevalence and a relatively small number of possible causative mutations. So far, however, the most cost-effective and efficient systematic strategy to detect previously undiagnosed cases of HeFH is still cascade testing: clinical and biochemical screening of close relatives of the proband patient diagnosed with HeFH. Pharmacologic treatment of HeFH is cost-effective.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".