Genetic testing for familial hypercholesterolemia: Impact on diagnosis, treatment and cardiovascular risk
Bibliographic record
Abstract
Aims Familial hypercholesterolemia (FH) is the most common genetic disorder in medicine, with a prevalence of 1/250. Affected individuals have elevated low-density lipoprotein cholesterol (LDL-C) and an increased lifetime risk of atherosclerotic cardiovascular disease (ASCVD). The diagnosis of FH is based on algorithms that include LDL-C levels, physical manifestations, family history of high LDL-C and premature ASCVD, and, more recently, genetic testing. We sought to determine the impact of genetic testing on the: 1) diagnosis of ‘definite familial hypercholesterolemia’, 2) initiation and adherence of lipid-lowering therapy and 3) risk of ASCVD. Methods We performed a systematic review and meta-analysis, pooling odds ratios and 95% confidence intervals for ASCVD from studies comparing risk estimates in individuals harboring FH-causing variants and unaffected individuals. Results After screening 3304 unique publications, 56 studies were included in the analysis. 1) Genetic testing provided confirmation of FH in 28–80%, over clinical criteria alone, depending on the diagnostic algorithm and the method of analysis. In two large population-based studies comprising 76,751 individuals, an FH-causing variant was identified in only 1.7–2.5% of subjects with an LDL-C > 4.9 mmol/L (190 mg/dL). 2) A confirmed molecular diagnosis increased lipid-lowering therapy adherence (five studies, n = 4181 definite FH). 3) Loss-of-function variant of the LDLR were at a markedly increased risk of myocardial infarction (odds ratio 6.77, 95% confidence interval 4.75–9.66), and patients with a milder (hypomorphic) pathogenic LDLR change had a 4.4-fold increase in risk (odds ratio 4.4, 95% confidence interval 2.34–8.26), compared with controls. Conclusion DNA sequencing confirms the diagnosis of FH but has a poor yield in unselected patients whose sole criterion is an elevated LDL-C. Initiation and adherence to treatment is improved. The risk of ASCVD is 4.4- to 6.8-fold increased in patients with an FH-causing variant compared with controls, depending on the severity of the DNA change.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".