ArticleGut2021
Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects.
Article in Gut, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 58 citations in OpenAlex.
- Arsenic exposure, arsenic metabolism, and plasma lipidome in relation to type 2 diabetes and glycemic traits: a lipidome-wide association study.Journal of advanced research · 2026Article
- A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Application of untargeted plasma metabolomics and machine learning to construct a diagnostic model for hypertrophic cardiomyopathy: a case-control study.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Plasma metabolomic signatures of all and cause-specific cancers: a multi-platform population-based study.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Shifts in amino acids profile in chronic pancreatitis.PloS one · 2026Article
- Advancing Gene Therapy for Pancreatitis: From Genetic Insights to Clinical Translation.Research (Washington, D.C.) · 2026Review
- RNF187 neddylation in pancreatic cancer activates malignancy via IQGAP1-dependent actin cytoskeleton rearrangement.Oncogene · 2026Article
- Advancing Clinical and Pathophysiological Insights into Pancreatitis Using Lipidomics and Metabolomics.Metabolites · 2025Review
- Metabolic and proteomic signatures differentiate inflammatory phenotypes from cancer and predict treatment response in patient sera.Bioengineering & translational medicine · 2025Article
- Survival Modelling Using Machine Learning and Immune-Nutritional Profiles in Advanced Gastric Cancer on Home Parenteral Nutrition.Nutrients · 2025Article
- Associations of prenatal metal exposure with child neurodevelopment and mediation by perturbation of metabolic pathways.Nature communications · 2025Article
- Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study.Hereditas · 2025Article
- A Reproducibility Crisis for Clinical Metabolomics Studies.Trends in analytical chemistry : TRAC · 2024Article
- Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study.Scientific reports · 2024Article
- Review
- Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.Nature communications · 2024Article
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.PLoS computational biology · 2023Article
- Small molecule metabolites: discovery of biomarkers and therapeutic targets.Signal transduction and targeted therapy · 2023Review
- A systems approach points to a therapeutic role for retinoids in asparaginase-associated pancreatitis.Science translational medicine · 2023Article
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Authors and funding
16 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChronic pancreatitis (CP) is a fibroinflammatory syndrome leading to organ dysfunction, chronic pain, an increased risk for pancreatic cancer and considerable morbidity. Due to a lack of specific biomarkers, diagnosis is based on symptoms and specific but insensitive imaging features, preventing an early diagnosis and appropriate management.
designWe conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A signature to distinguish CP from controls (n=160) was identified using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma and validated in independent cohorts.
resultsA Naive Bayes algorithm identified eight metabolites of six ontology classes. After algorithm training and computation of optimal cut-offs, classification according to the metabolic signature detected CP with an area under the curve (AUC) of 0.85 ((95% CI 0.79 to 0.91). External validation in two independent cohorts (total n=502) resulted in similar accuracy for detection of CP compared with non-pancreatic controls in EDTA-plasma (AUC 0.85 (95% CI 0.81 to 0.89)) and serum (AUC 0.87 (95% CI 0.81 to 0.95)).
conclusionsThis is the first study that identifies and independently validates a metabolomic signature in plasma and serum for the diagnosis of CP in large, prospective cohorts. The results could provide the basis for the development of the first routine laboratory test for CP.
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