Genetic determinants of pancreatitis: relevance in severe hypertriglyceridemia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Not all patients with severe hypertriglyceridemia develop acute pancreatitis. We surveyed recent literature on inter-individual genetic variation in susceptibility to pancreatitis. RECENT FINDINGS: Genetic determinants of pancreatitis include: rare Mendelian disorders caused by highly penetrant pathogenic variants in genes involved in trypsinogen activation; uncommon susceptibility variants in genes involved in trypsinogen activation, protein misfolding as well as calcium metabolism and cystic fibrosis, that have variable penetrance and show a range of odds ratios for pancreatitis; and common polymorphisms in many of the same genes that have only a small effect on risk. The role of these genetic variants in modulating pancreatitis risk in hypertriglyceridemia is unclear. However, among genetic determinants of plasma triglycerides, those predisposing to more severe hypertriglyceridemia associated with chylomicronemia appear to have higher pancreatitis risk. SUMMARY: Currently, among patients with severe hypertriglyceridemia, the most consistent predictor of pancreatitis risk is the triglyceride level. Furthermore, pancreatitis risk appears to be modulated by a higher genetic burden of factors associated with greater magnitude of triglyceride elevation. The role of common and rare genetic determinants of pancreatitis itself in this metabolic context is unclear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".