Bibliographic record
Abstract
Dyslipidemias include both rare single gene disorders and common conditions that have a complex underlying basis. In London, ON, there is fortuitous close physical proximity between the Lipid Genetics Clinic and the London Regional Genomics Centre. For >30 years, we have applied DNA sequencing of clinical samples to help answer scientific questions. More than 2000 patients referred with dyslipidemias have participated in an ongoing translational research program. In 2013, we transitioned to next-generation sequencing; our targeted panel is designed to concurrently assess both monogenic and polygenic contributions to dyslipidemias. Patient DNA is screened for rare variants underlying 25 mendelian dyslipidemias, including familial hypercholesterolemia, hepatic lipase deficiency, abetalipoproteinemia, and familial chylomicronemia syndrome. Furthermore, polygenic scores for LDL (low-density lipoprotein) and HDL (high-density lipoprotein) cholesterol, and triglycerides are calculated for each patient. We thus simultaneously document both rare and common genetic variants, allowing for a broad view of genetic predisposition for both individual patients and cohorts. For instance, among patients referred with severe hypertriglyceridemia, defined as ≥10 mmol/L (≥885 mg/dL), <1% have a mendelian disorder (ie, autosomal recessive familial chylomicronemia syndrome), ≈15% have heterozygous rare variants (a >3-fold increase over normolipidemic individuals), and ≈35% have an extreme polygenic score (a >3-fold increase over normolipidemic individuals). Other dyslipidemias show a different mix of genetic determinants. Genetic results are discussed with patients and can support clinical decision-making. Integrating DNA testing into clinical care allows for a bidirectional flow of information, which facilitates scientific discoveries and clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".