Gut microbiota predict development of postdischarge diabetes mellitus in acute pancreatitis
Bibliographic record
Abstract
BACKGROUND: Postdischarge morbidity and mortality is high in acute pancreatitis (AP) and pathophysiological mechanisms remain poorly understood. OBJECTIVES: We aim to investigate the composition of gut microbiota and clinical long-term outcomes of prospectively enrolled patients with AP to predict postdischarge complications. DESIGN: In this long-term follow-up study, we analysed clinical and microbiome data of 277 patients from the prospective multicentre Pancreatitis-Microbiome As Predictor of Severity trial. The primary endpoint was the association of the microbial composition with postdischarge mortality, recurrent AP (RAP), progression to chronic pancreatitis, pancreatic exocrine insufficiency, diabetes mellitus (DM) and pancreatic ductal adenocarcinoma. RESULTS: Buccal (n=238) and rectal (n=249) swabs were analysed by 16S rRNA and metagenomics sequencing using Oxford Nanopore Technologies. Median follow-up was 2.8 years. Distance-based redundancy analysis with canonical analysis of principal coordinates showed significant differences for β-diversity (Bray-Curtis) for postdischarge mortality (p=0.04), RAP (p=0.02) and DM (p=0.03). A ridge regression model including 11 differentially abundant species predicted postdischarge DM with an area under the receiving operating characteristic of 94.8% and 86.2% in the matched and entire cohort, respectively. Using this classifier, a positive predictive value of 66.6%, a negative predictive value of 96% and an accuracy of 95% was achieved. CONCLUSION: Our data indicate that the admission microbiome of patients with AP correlates with postdischarge complications independent from multiple risk factors such as AP severity, smoking or alcohol. Microbiota at admission show excellent capacity to predict postdischarge DM and may thus open new stratification tools for a tailored risk assessment in the future. TRIAL REGISTRATION NUMBER: NCT04777812.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".