Incidence of familial hypercholesterolemia in patients with early manifestations of coronary artery disease: data from a Russian multicenter study and meta-analysis
Bibliographic record
Abstract
Aim. To assess the possibility of familial hypercholesterolemia (FH) detection among patients with early coronary artery disease (CAD) in practice in comparison with data from different populations. Patients with early manifestations of CAD are a promising group for identifying a proband with FH and subsequent cascade screening. The question remains open about the sufficiency of clinical criteria for diagnosing this disease. Material and methods. We examined 651 patients with CAD manifestations aged £55 years in men and £60 years in women. FH was diagnosed according to the Dutch Lipid Clinic Network (DLCN) criteria, and cardiovascular risk was assessed using the Montreal-FH-SCOR E. In 35 phenotype-positive patients with FH, as well as 5 with lowdensity lipoprotein cholesterol levels ³5,5 mmol/l and 23 with age of manifestation of coronary artery disease £35 years, the coding sequence of the genes for apolipoprotein B (APOB), low-density lipoprotein receptor (LDLR), low-density lipoprotein receptor adapter protein 1 (LDLRAP1), proprotein convertase subtilisin/kexin type 9 (PCSK9). Results. Definite FH was in 8 (1,2%), probable in 27 (4.2%), possible in 339 (52,1%) patients, while 277 (42,5%) patients had DLCN score of <3 points; 31 (88,6%), of 35 phenotype-positive patients had a high Montreal-FH-SCORE risk. Six carriers of pathogenic variants were identified, 2 of which were among phenotype-negative patients. A meta-analysis of 16 studies with 13065 patients (2012-2023) showed that the incidence of FH is 5,22 (4,848-5,619)% (fixed model) and 5,93 (4,528-7,515)% (random model). Conclusion. The use of existing diagnostic scales does not provide guaranteed detection of FH among patients with early CAD. It is likely that DLCN modification by additional gradation of the criterion for the age of CAD manifestation will help increase its diagnostic value.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".