Genetic bases of hypertriglyceridemic phenotypes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hypertriglyceridemia (HTG) is a common diagnosis. Although secondary factors are important for clinical expression, susceptibility to HTG has a strong genetic component, which we review here. RECENT FINDINGS: Severe HTG in a few families follows Mendelian - typically autosomal recessive - inheritance of rare loss-of-function mutations in genes such as LPL, APOC2, APOA5, LMF1, and GPIHBP1. In contrast, common complex HTG results from the cumulative influence of small-effect variants (single nucleotide polymorphisms) in genes such as APOA5, GCKR, LPL, and APOB. Intensive resequencing of these four genes has also shown accumulated heterozygous rare variants in HTG patients. Together, more than 20% of the susceptibility to HTG is now accounted for by common and rare variants. Further, classical Fredrickson HTG phenotypes, which were once considered to be distinct based on biochemical features, have a shared genetic architecture. SUMMARY: Compared to other complex traits, genetic variants account for a high proportion of HTG diagnoses. By tallying the number of HTG risk alleles, it is possible to discriminate between individuals with HTG and normolipidemia, particularly in those with extreme scores. Future directions include finding the missing genetic component and determining whether genetic profiling can help with diagnosis or personalized treatment advice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".