The clinical utility of polygenic risk scores for combined hyperlipidemia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Combined hyperlipidemia is the most common lipid disorder and is strongly polygenic. Given its prevalence and associated risk for atherosclerotic cardiovascular disease, this review describes the potential for utilizing polygenic risk scores for risk prediction and management of combined hyperlipidemia. RECENT FINDINGS: Different diagnostic criteria have led to inconsistent prevalence estimates and missed diagnoses. Given that individuals with combined hyperlipidemia have risk estimates for incident coronary artery disease similar to individuals with familial hypercholesterolemia, early identification and therapeutic management of those affected is crucial. With diagnostic criteria including traits such apolipoprotein B, low-density lipoprotein cholesterol, and triglyceride, polygenic risk scores for these traits strongly associate with combined hyperlipidemia and could be used in combination for clinical risk prediction models and developing specific treatment plans for patients. SUMMARY: Polygenic risk scores are effective tools in risk prediction of combined hyperlipidemia, can provide insight into disease pathophysiology, and may be useful in managing and guiding treatment plans for patients. However, efforts to ensure equitable polygenic risk score performance across different genetic ancestry groups is necessary before clinical implementation in order to prevent the exacerbation of racial disparities in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".