High hsCRP Concentration Is Associated With Acute Pancreatitis in Multifactorial Chylomicronemia Syndrome
Bibliographic record
Abstract
BACKGROUND: Multifactorial chylomicronemia syndrome (MCS) is a severe form of hypertriglyceridemia (hyperTG) associated with an increased risk of acute pancreatitis. However, the risk of acute pancreatitis is very heterogenous in MCS. Previous studies suggested that inflammation might promote disease progression in hyperTG-induced acute pancreatitis. OBJECTIVE: To determine if low-grade inflammation is associated with acute pancreatitis in MCS. METHODS: This study included 102 subjects with MCS for whom high-sensitivity C-reactive protein (hsCRP) concentration was measured at their first visit at the Montreal Clinical Research Institute. RESULTS: Patients with MCS who had a previous history of acute pancreatitis had a significant higher hsCRP concentration (4.62 mg/L vs 2.61 mg/L; P = .003), and high hsCRP concentration (≥ 3 mg/L) was independently associated with acute pancreatitis prevalence (P < .05). Up to 64% of the variability in acute pancreatitis prevalence was explained by the maximal triglycerides (TG) concentration, hsCRP concentration, the presence of rare variants in TG-related genes, and fructose intake, based on a stepwise multivariate regression model (P < .0001). CONCLUSION: This retrospective study showed for the first time that hsCRP concentration is strongly associated with acute pancreatitis prevalence in MCS. It also suggests that low-grade inflammation may be a driver of acute pancreatitis in severe hypertriglyceridemia. Prospective studies could help determine the causality of this association and assess whether medication known to reduce low-grade inflammation could help prevent acute pancreatitis in individuals with severe hypertriglyceridemia.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".