Polygenic Contribution to Low-Density Lipoprotein Cholesterol Levels and Cardiovascular Risk in Monogenic Familial Hypercholesterolemia
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it