Dysbetalipoproteinemia: Differentiating Multifactorial Remnant Cholesterol Disease From Genetic ApoE Deficiency
Bibliographic record
Abstract
CONTEXT: Dysbetalipoproteinemia (DBL) is characterized by the accumulation of remnant lipoprotein particles and associated with an increased risk of cardiovascular and peripheral vascular disease (PVD). DBL is thought to be mainly caused by the presence of an E2/E2 genotype of the apolipoprotein E (APOE) gene, in addition to environmental factors. However, there exists considerable phenotypic variability among DBL patients. OBJECTIVE: The objectives were to verify the proportion of DBL subjects, diagnosed using the gold standard Fredrickson criteria, who did not carry E2/E2 and to compare the clinical characteristics of DBL patients with and without E2/E2. METHODS: A total of 12 432 patients with lipoprotein ultracentrifugation as well as APOE genotype or apoE phenotype data were included in this retrospective study. RESULTS: Among the 12 432 patients, 4% (n = 524) were positive for Fredrickson criteria (F+), and only 38% (n = 197) of the F+ individuals were E2/E2. The F+ E2/E2 group had significantly higher remnant cholesterol concentration (3.44 vs 1.89 mmol/L) and had higher frequency of DBL-related xanthomas (24% vs 2%) and floating beta (95% vs 11%) than the F+ non-E2/E2 group (P < 0.0001). The F+ E2/E2 group had an independent higher risk of PVD (OR 11.12 [95% CI 1.87-66.05]; P = 0.008) events compared with the F+ non-E2/E2 group. CONCLUSION: In the largest cohort of DBL worldwide, we demonstrated that the presence of E2/E2 was associated with a more severe DBL phenotype. We suggest that 2 DBL phenotypes should be distinguished: the multifactorial remnant cholesterol disease and the genetic apoE deficiency disease.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".