Prevalence of Dysbetalipoproteinemia in the UK Biobank According to Different Diagnostic Criteria
Bibliographic record
Abstract
CONTEXT: Dysbetalipoproteinemia (DBL) is a multifactorial disorder that disrupts the normal metabolism of remnant lipoproteins, causing increased risk of cardiovascular disease. However, establishing a proper diagnosis is difficult, and the true prevalence of the disease in the general population remains unknown. OBJECTIVE: The objectives were to study the prevalence of the disease and to validate the performance of different clinical diagnostic criteria in a large population-based cohort. METHODS: This study included 453 437 participants from the UK Biobank. DBL was established in participants having an ε2ε2 genotype with mixed dyslipidemia or lipid-lowering therapy use (n = 964). The different diagnostic criteria for DBL were applied in individuals without lipid-lowering medication (n = 370 039, n = 534 DBL), to compare their performance. RESULTS: Overall, 0.6% of participants had an ε2ε2 genotype, of which 36% were classified as DBL, for a disease prevalence of 0.2% (1:469). The prevalence of DBL was similar between the different genetic ancestries (≤0.2%). Several diagnostic criteria showed good sensitivity for the diagnosis of DBL (>90%), but they suffered from a very low positive predictive value (0.6-15.4%). CONCLUSION: This study reported for the first time the prevalence of DBL in the UK Biobank according to genetic ancestry. Furthermore, we provided the first external validation of different diagnostic criteria for DBL in a large population-based cohort and highlighted the fact that these criteria should not be used to diagnose DBL alone but should rather be used as a first screening step to determine which individuals may benefit from genetic testing to confirm the diagnosis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".