Genetic Architecture And Clinical Outcomes Of The Fredrickson-Levy-Lees Dyslipoproteinemias
Bibliographic record
Abstract
Abstract Background and Aims The genetic basis and clinical relevance of the classical Fredrickson-Levy-Lees (FLL) dyslipoproteinemia classifications has not been studied in general population-based cohorts. We aimed to evaluate the phenotypic and genetic characteristics of FLL disorders. Methods Among UK Biobank participants free of prevalent coronary artery disease (CAD), we used blood lipids and apolipoprotein B concentrations to infer FLL classes (Types I, IIa, IIb, III, IV, and V). For each FLL class, Cox proportional hazards regression estimated risk of incident CAD. Phenome-wide association testing was performed. GWAS were performed, followed by in silico causal gene prioritization and heritability analyses. Prevalence of disruptive Mendelian lipid variants was assessed from whole exome sequencing. Results Of 450,636 individuals, 259,289 (57.5%) met criteria for a FLL dyslipoproteinemia: 63 (0.01%) type I; 40,005 (8.9%) type IIa; 94,785 (21.0%) type IIb; 13,998 (3.1%) type III; 110,389 (24.5%) type IV; and 49 (0.01%) type V. Over median 11.1 years follow-up, compared to normolipidemics the type IIb pattern conferred the highest hazard of incident CAD overall (HR 1.92, 95% CI 1.84-2.01, P <0.001) and in meta-analysis across matched non-HDL-C strata (HR 1.45, 95% CI 1.30-1.60). GWAS revealed 250 loci associated with FLL, of which 13 were shared across all classes; compared to GWAS of isolated lipid traits, 72 additional loci were detected. Mendelian lipid variants were rare (2%), but polygenic heritability was high, ranging from 23% (type III) to 54% (type IIb). Conclusions FLL classes have distinct genetic architectures yielding new insights for cardiometabolic disease beyond single lipid analyses.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".