Dysbetalipoproteinemia Is Associated With Increased Risk of Coronary and Peripheral Vascular Disease
Bibliographic record
Abstract
CONTEXT: Dysbetalipoproteinemia (DBL) is a disorder in which remnant lipoproteins accumulate in the plasma due to a genetic apolipoprotein E dysfunction in conjunction with the presence of secondary metabolic factors. An increased risk of both coronary and peripheral vascular disease (PVD) has been observed in these patients in retrospective studies. OBJECTIVE: The primary objective was to compare the incidence of atherosclerotic cardiovascular disease (ASCVD) and PVD in a cohort of patients with DBL compared with normolipidemic controls. As a secondary objective, the incidence of ASCVD and PVD was compared between patients with DBL and patients with familial hypercholesterolemia (FH). METHODS: A total of 221 patients with DBL, 725 patients with FH, and 1481 normolipidemic controls were included in the study. The data were obtained by review of medical records. RESULTS: In patients with DBL, there was an overall excess risk of PVD (hazard ratio [HR] 13.58, 95% CI 4.76-38.75) and ASCVD (HR 3.55, 95% CI 2.17-5.83) (P < .0001) when compared with normolipidemic controls. When compared with patients with FH, an increased risk of PVD (HR 3.89, 95% CI 1.20-12.55, P = .02) was observed in patients with DBL. CONCLUSION: We demonstrated that the risks of ASCVD and PVD in DBL are >3-fold and >13-fold higher, respectively, than normolipidemic controls. Furthermore, the risk of PVD is ∼4-fold higher in DBL than in FH. Adequate screening of DBL is imperative to improve the clinical care of these patients by preventing the development of ASCVD.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".