Pancreatitis polygenic risk score is independently associated with all‐cause acute pancreatitis risk in the UK Biobank
Bibliographic record
Abstract
Abstract Background and Aim Acute pancreatitis (AP) is a complex disease most commonly caused by gallstones, alcohol intake, or hypertriglyceridemia. Even in subjects with hypertriglyceridemia, the risk of AP is heterogeneous. Identifying individuals with a high genetic susceptibility to AP could contribute to a better risk stratification in the clinic. This study aimed to determine if a weighted polygenic risk score (PRS) of common variants in pancreatitis susceptibility genes can independently predict all‐cause AP incidence in the general population. Methods A weighted PRS was calculated for 484 932 individuals from the UK Biobank, including 3346 individuals who developed AP during follow‐up. The PRS included eight single nucleotide polymorphisms in known pancreatitis susceptibility genes. Results Individuals with a pancreatitis PRS above the 90th percentile had a 1.21‐fold (1.03–1.43; P = 0.02) increased risk of AP compared with those with a pancreatitis PRS below the 90th percentile. When comparing individuals in the third tertile versus the first tertile, the risk of AP was 1.13‐fold (1.00–1.28; P = 0.06) higher. Individuals with both a high triglyceride (TG) level and a high pancreatitis PRS (third tertile) had a 2.31‐fold (1.83–2.93; P = 3.4 × 10 −12 ) increased risk of AP compared with those with a low pancreatitis PRS and a low TG level (first tertile). Overall, the association between pancreatitis PRS and incident AP was independent of baseline TG level. Conclusions Results of this study suggest that the accumulation of common variants in pancreatitis susceptibility genes is associated with all‐cause AP incidence. Pancreatitis PRS could help clinicians identify patients who may be at higher risk of AP and who may benefit from more aggressive treatment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".