Polygenic Contribution to Low-Density Lipoprotein Cholesterol Levels and Cardiovascular Risk in Monogenic Familial Hypercholesterolemia
Bibliographic record
Abstract
Background: Familial hypercholesterolemia (FH) is a common autosomal codominant genetic disorder, which causes elevated levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of premature atherosclerotic cardiovascular disease (ASCVD). Even among individuals with monogenic FH, there is substantial interindividual variability in LDL-C levels and risk of ASCVD. We assessed the influence of an LDL-C polygenic score on levels of LDL-C and risk of ASCVD for individuals with monogenic FH. Methods: We constructed a weighted LDL-C polygenic score, composed of 28 single-nucleotide variants, for individuals with monogenic FH from the British Columbia FH (n=262); Nutrition, Metabolism and Atherosclerosis Clinic (n=552); and UK Biobank cohorts (n=306). We assessed the association between LDL-C polygenic score with LDL-C levels and ASCVD risk using linear regression and Cox-proportional hazard models, respectively. ASCVD was defined as myocardial infarction, coronary or carotid revascularization, transient ischemic attack, or stroke. The results from individual cohorts were combined in fixed-effect meta-analyses. Results: Levels of LDL-C were significantly associated with LDL-C polygenic score in the Nutrition, Metabolism and Atherosclerosis Clinic cohort, UK Biobank cohort, and in the meta-analysis (β [95% CI]=0.13 [0.072–0.19] per a 20% increase in LDL-C polygenic score percentile, P <0.0001). Additionally, an elevated LDL-C polygenic score (≥80th percentile) was associated with a trend towards increased ASCVD risk in all 3 cohorts individually. This association was statistically significant in the meta-analysis (hazard ratio [95% CI]=1.48 [1.02–2.14], P =0.04). Conclusions: Polygenic contributions to LDL-C explain some of the heterogeneity in clinical presentation and ASCVD risk for individuals with FH.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".