Abstract 9838: Genetic Architecture and Clinical Outcomes of the Fredrickson-Levy-Lees Dyslipoproteinemias
Bibliographic record
Abstract
Introduction: In 1967, Fredrickson, Levy, and Lees (FLL) proposed a classification scheme for dyslipidemias based on lipoprotein particle patterns. However, the genetic basis and clinical relevance of the FLL classification has not been evaluated in general population-based cohorts. Methods: Among UK Biobank participants free of prevalent coronary artery disease (CAD), we used statin-adjusted triglycerides, total cholesterol, and apolipoprotein-B to infer FLL classes (Types I, IIa, IIb, III, IV, and V). We performed Cox proportional hazards regression to quantify risk of incident CAD comparing each FLL class to unaffected individuals, adjusting for age, sex, statin use, BMI, smoking status, hypertension, diabetes mellitus, principal components 1-5, and genotyping array. Genome-wide association studies (GWAS) were performed for each of the FLL classes. Results: Of 412,502 participants without prevalent CAD, 237,570 (57.6%) had a FLL dyslipoproteinemia: Type I 30 (<0.01%); Type IIa 36,629 (8.9%); Type IIb 86,682 (21%); Type III 12,957 (3.1%); Type IV 101,226 (24.5%); Type V 46 (<0.01%). Over a median of 7.0 years of follow-up, Type IIb individuals were at highest risk of incident CAD (HR 1.97, 95% CI 1.83-2.13) followed by IIa (HR 1.68, 95% CI 1.52-1.86) and IV (HR 1.25, 95% CI 1.16-1.36). Across strata of non-HDL cholesterol, Type IIb pattern was still associated with greater incident CAD risk compared with other dyslipoproteinemias ( Figure ). GWAS revealed 316 total loci contributing to any of the FLL phenotypes, of which 12 were shared; 145 loci were unique to Type IIb. SNP heritability ranged from 23.5% to 53.9% across FLL types. Conclusions: Type IIb dyslipoproteinemia pattern is associated with incident CAD independent of non-HDL cholesterol. GWAS revealed the distinct genetic signatures for atherogenic dyslipoproteinemias.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".