Phenotype of definite familial hypercholesterolemia with negative genetic study in Argentina
Bibliographic record
Abstract
Objective: Familial hypercholesterolemia (FH) is a monogenic disease, associated with variants in the LDLR, APOB and PCSK9 genes. The initial diagnosis is based on clinical criteria like the DLCN criteria. A score > 8 points qualifies the patient as "definite" for FH diagnosis. The detection of the presence of a variant in these genes allows carrying out familial cascade screening and better characterizes the patient in terms of prognosis and treatment. Methods: In the context of the FH detection program in Argentina (Da Vinci Study) 246 hypercholesterolemic patients were evaluated, 21 with DLCN score > 8 (definite diagnosis).These patients were studied with next generation sequencing to detect genetic variants, with an extended panel of 23 genes; also they were adding the large rearrangements analysis and a polygenic score of 10 SNP (single nucleotide polymorphism) related to the increase in LDL-c. Results: Of the 21 patients, 10 had variants in LDLR, 1 in APOB with APOE, 1 in LIPC plus elevated polygenic score, and 2 patients showed one deletion and one duplication in LDLR, the later with a variation in LIPA. It is highlighted that 6 of the 21 patients with a score > 8 did not show any genetic alteration. Conclusions: We can conclude that 28% of the patients with definite clinical diagnosis of FH did not show genetic alteration. The possible explanations for this result would be the presence of mutations in new genes, confusing effects of the environment over the genes, the gene-gene interactions, and finally the impossibility of detecting variants with the current available methods.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".