Modern prevalence of dysbetalipoproteinemia (Fredrickson-Levy-Lees type III hyperlipoproteinemia)
Bibliographic record
Abstract
Introduction: Dysbetalipoproteinaemia (HLP3) is a disorder characterized by excess cholesterol-enriched, triglyceride-rich lipoprotein remnants in genetically predisposed individuals that powerfully promote premature cardiovascular disease if untreated. The current prevalence of HLP3 is largely unknown. Material and methods: We performed cross-sectional analysis of 128,485 U.S. adults from the Very Large Database of Lipids (VLDbL), using four algorithms to diagnose HLP3 employing three Vertical Auto Profile ultracentrifugation (UC) criteria and a previously described apolipoprotein B (apoB) method. We evaluated 4,926 participants from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) with the apoB method. We examined demographic and lipid characteristics stratified by presence of HLP3 and evaluated lipid characteristics in those with HLP3 phenotype discordance and concordance as determined by apoB and originally defined UC criteria 1. Results: In U.S. adults in VLDbL and NHANES, a 1.7-2.0% prevalence is observed for HLP3 with the novel apoB method as compared to 0.2-0.8% prevalence in VLDbL via UC criteria 1-3. Participants who were both apoB and UC criteria HLP3 positive had higher remnant particles as well as more elevated triglyceride/apoB and total cholesterol/apoB ratios (all p < 0.001) than those who were apoB method positive and UC criteria 1 negative. Conclusions: HLP3 may be more prevalent than historically and clinically appreciated. The apoB method increases HLP3 identification via inclusion of milder phenotypes. Further work should evaluate the clinical implications of HLP3 diagnosis at various lipid algorithm cut-points to evaluate the ideal standard in the modern era.
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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 | 0.001 |
| 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) | 0.003 | 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".