Prevalence of FH-Causing Variants and Impact on LDL-C Concentration in European, South Asian, and African Ancestry Groups of the UK Biobank—Brief Report
Bibliographic record
Abstract
Background: Familial hypercholesterolemia (FH) is a monogenic disease that causes high low-density lipoprotein cholesterol (LDL-C) and higher risk of premature coronary heart disease. The prevalence of FH-causing variants and their association with LDL-C in non-European populations remains largely unknown. Using DNA diagnosis in a population-based cohort, we aimed to estimate the prevalence of FH across 3 major ancestry groups in the United Kingdom. Methods: Principal component analysis was used to distinguish genetic ancestry in UK Biobank participants. Whole exome sequencing data were analyzed to provide a genetic diagnosis of FH. LDL-C concentrations were adjusted for statin use. Results: Principal component analysis distinguished 140 439 European, 4067 South Asian, and 3906 African participants with lipid and whole exome sequencing data. There were significant differences between the 3 groups, including total and LDL-C concentrations, and prevalence and incidence of coronary heart disease. We identified 488, 18, and 15 participants of European, South Asian, and African ancestry carrying a likely pathogenic or pathogenic FH-variant. No statistical difference in the prevalence of an FH-causing variant was observed: 1 out of 288 (95% CI, 1/316–1/264) in European, 1 out of 260 (95% CI, 1/526–1/173) in African, and 1 out of 226 (95% CI, 1/419–1/155) in South Asian. Carriers of an FH-causing variant had significantly higher LDL-C concentration than noncarriers in every ancestry group. There was no difference in median (statin-use adjusted) LDL-C concentration in FH-variant carriers depending on their ancestry background. Self-reported statin use was nonsignificantly highest in FH-variant carriers of South Asian ancestry (55.6%), followed by African (40.0%) and European (33.8%; P =0.15). Conclusions: The prevalence of FH-causing variants in the UK Biobank is similar across the ancestry groups analyzed. Despite overall differences in lipid concentrations, FH-variant carriers across the 3 ancestry groups had similar LDL-C levels. In all ancestry groups, the proportion of FH-variant carriers treated with lipid-lowering therapy should be improved to reduce future risk of premature coronary heart disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".