Influence of Known Genetic Risk Factors for Atherosclerosis on People with Different Racial Backgrounds
Bibliographic record
Abstract
Atherosclerosis, a key contributor to cardiovascular diseases (CVD), presents significant health disparities among different racial groups due to variations in genetic risk factors. This review investigates the influence of Apolipoprotein E (ApoE) and Paraoxonase 1 (PON1) genes on atherosclerosis susceptibility across various racial backgrounds. ApoE polymorphisms, particularly the ε4 allele, are associated with elevated LDL cholesterol levels, contributing to higher atherosclerosis risk. PON1 activity, crucial for protecting lipoproteins from oxidative stress, also varies with racial background, influencing disease risk. A comprehensive literature search identified relevant studies, focusing on these genetic factors' prevalence and impact in different populations. The findings highlight significant racial differences in ApoE and PON1 distributions, underscoring the need for tailored prevention and treatment strategies. Understanding these genetic disparities is vital for developing precision medicine approaches to mitigate CVD risk and improve health outcomes across diverse racial groups.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".