Bibliographic record
Abstract
Hypertriglyceridemia, a commonly encountered phenotype in cardiovascular and metabolic clinics, is surprisingly complex. A range of genetic variants, from single-nucleotide variants to large-scale copy number variants, can lead to either the severe or mild-to-moderate forms of the disease. At the genetic level, severely elevated triglyceride levels resulting from familial chylomicronemia syndrome (FCS) are caused by homozygous or biallelic loss-of-function variants in LPL, APOC2, APOA5, LMF1 and GPIHBP1 genes. In contrast, susceptibility to multifactorial chylomicronemia (MCM), which has an estimated prevalence of ~1 in 600 and is at least 50- to 100-times more common than FCS, results from two different types of genetic variants: 1) rare heterozygous variants (minor allele frequency 5%) whose individually small phenotypic effects are quantified using a polygenic score. There is indirect evidence of similar complex genetic predisposition in other clinical phenotypes that have a component of hypertriglyceridemia, such as combined hyperlipidemia and dysbetalipoproteinemia. Future considerations include: 1) evaluation of whether the specific type of genetic predisposition to hypertriglyceridemia affects medical decisions or long-term outcomes; and 2) searching for other genetic contributors, including the role of genome-wide polygenic scores, novel genes, nonlinear gene-gene or gene-environment interactions, and non-genomic mechanisms including epigenetics and mitochondrial DNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".