Familial combined hyperlipidemia is a polygenic trait
Bibliographic record
Abstract
PURPOSE OF REVIEW: : Familial combined hyperlipidemia (FCH), defined by concurrently elevated plasma triglyceride (TG) and low-density lipoprotein (LDL) cholesterol, has long been investigated to characterize its genetic basis. Despite almost half a century of searching, a single gene cause for the phenotype has not yet been identified. RECENT FINDINGS: : Recent studies using next-generation genetic analytic methods confirm that FCH has a polygenic basis, with a clear large contribution from the accumulation of small-to-moderate effect common single nucleotide polymorphisms (SNPs) throughout the genome that is associated with raising TG, and probably also those raising LDL cholesterol. On the other hand, rare monogenic variants, such as those causing familial hypercholesterolemia, play a negligible role, if any. Genetic profiling suggests that patients with FCH and hypertriglyceridemia share a strong polygenic basis and show a similar profile of multiple TG-raising common SNPs. SUMMARY: : Recent progress in genomics has shown that most if not all of the genetic susceptibility to FCH is polygenic in nature. Future research should include larger cohort studies, with wider ancestral diversity, ancestry-specific polygenic scores, and investigation of epigenetic and lifestyle factors to help further elucidate the causative agents at play in cases where the genetic etiology remains to be defined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".