Recent Insights into the Genetics of Plasma Triglycerides and Possible Causal Mechanisms in Cardiovascular Disease
Bibliographic record
Abstract
Abstract Plasma triglyceride (TG) concentration is an integrated measurement of circulating TG‐rich lipoproteins. The specific lipoprotein fractions and subfractions that contribute to this measurement differ between the fasting and nonfasting states. Although the association between fasting plasma TG concentration and cardiovascular disease (CVD) has been controversial, recent studies of nonfasting plasma TG and related biomarkers have rekindled interest in a possible direct causative relationship. Here, we review current understanding of the phenotypic and genetic spectrum of plasma TG concentrations, focusing on recent evidence from Mendelian randomisation studies that seem to implicate nonfasting TG and remnant cholesterol in CVD susceptibility. The totality of evidence suggests that nonfasting TG concentration, perhaps because of its relationship with remnant cholesterol, is causally associated with CVD outcomes. Key Concepts: Susceptibility to clinical hypertriglyceridaemia is determined by a burden of both common and rare variants, on which are superimposed secondary nongenetic factors. The allelic and phenotypic spectrum of plasma triglyceride (TG) concentrations explains a variety of TG‐related phenotypes and their phenotypic heterogeneity. Monogenic hypertriglyceridemias are associated with increased pancreatitis risk and result from rare mutations on both alleles of 6 different genes. Nonfasting plasma TG concentration is closely associated with elevated remnant cholesterol concentrations; this may explain the relationship with cardiovascular risk. Mendelian randomisation studies appear to implicate a causal relationship between both nonfasting plasma TG, and more recently remnant cholesterol levels as determinants of CVD risk. The spectrum of genes newly implicated as being involved in plasma triglyceride metabolism has expanded the range of pathways and potential drug targets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".