Utility of Genetically Predicted Lp(a) (Lipoprotein [a]) and ApoB Levels for Cardiovascular Risk Assessment
Bibliographic record
Abstract
BACKGROUND: Current lipid guidelines suggest measurement of Lp(a) (lipoprotein[a]) and ApoB (apolipoprotein B) for atherosclerotic cardiovascular disease risk assessment. Polygenic risk scores (PRSs) for Lp(a) and ApoB may identify individuals unlikely to have elevated Lp(a) or ApoB and thus reduce such suggested testing. METHODS: PRSs were developed using least absolute shrinkage and selection operator regression among 273 222 and 356 958 UK Biobank participants of white British ancestry for Lp(a) and ApoB, respectively, and validated in separate sets of 60 771 UK Biobank and 15 050 European Prospective Investigation into Cancer and Nutrition-Norfolk participants. We then assessed the proportion of participants who, based on these PRSs, were unlikely to benefit from Lp(a) or ApoB measurements, according to current lipid guidelines. RESULTS: In the UK Biobank and European Prospective Investigation into Cancer and Nutrition-Norfolk cohorts, the area under the receiver operating curve for the PRS-predicted Lp(a) and ApoB to identify individuals with elevated Lp(a) and ApoB was at least 0.91 (95% CI, 0.90-0.92) and 0.74 (95% CI, 0.73-0.75), respectively. The Lp(a) PRS and measured Lp(a) showed comparable association with atherosclerotic cardiovascular disease incidence, whereas the ApoB PRS was in general less predictive of atherosclerotic cardiovascular disease risk than measured ApoB. In the context of the European Society of Cardiology/European Atherosclerosis Society lipid guidelines, at a 95% sensitivity to identify individuals with elevated Lp(a) and ApoB levels, at least 54% of Lp(a) and 24% of ApoB testing could be reduced by prescreening with a PRS while maintaining a low false-negative rate. CONCLUSIONS: A substantial proportion of suggested testing for elevated Lp(a) and a modest proportion of testing for elevated ApoB could potentially be reduced by prescreening individuals with PRSs.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".