Disclosures: The study was supported by Deutsche Krebshilfe/Dr. Mildred-Scheel-Stiftung (109102), Deutsche Forschungsgemeinschaft (DFG MA 4115/1-2/3, SFB1321: Project-ID 329628492, BE 6395/1-1), the Federal Ministry of Education and Research (BMBF GANI-MED 03IS2061A and BMBF 0314107, 01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012), Wilhelm Sander Stiftung (2009.039.2) and EFRE-State Ministry of Economics MV (V-630-S150-2012/132/133), ESF/14-BM-A55-0045/16 PePPP and ESF/14-BM-A55-0010/18 EnErGie).
February 05, 2021
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Study validates prediction model to differentiate chronic pancreatitis vs healthy cohort

Disclosures: The study was supported by Deutsche Krebshilfe/Dr. Mildred-Scheel-Stiftung (109102), Deutsche Forschungsgemeinschaft (DFG MA 4115/1-2/3, SFB1321: Project-ID 329628492, BE 6395/1-1), the Federal Ministry of Education and Research (BMBF GANI-MED 03IS2061A and BMBF 0314107, 01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012), Wilhelm Sander Stiftung (2009.039.2) and EFRE-State Ministry of Economics MV (V-630-S150-2012/132/133), ESF/14-BM-A55-0045/16 PePPP and ESF/14-BM-A55-0010/18 EnErGie).
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Investigators identified and validated a human blood-metabolome prediction model that distinguished between patients with chronic pancreatitis and healthy individuals.

“Whether this biomarker has clinical value for diagnosing early stages of [chronic pancreatitis (CP)] or can be used to monitor disease progression needs further prospective studies,” M. Gordian Adam, MD, from the Metanomics Health GmbH, Berlin, Germany, and colleagues wrote in Gut.

Adam and colleagues performed a type 3 study to predict individual prognosis of chronic pancreatitis in 670 patients based on transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. They used gas chromatography-mass spectrometry and liquid chromatography‐tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma to distinguish chronic pancreatitis patients from controls (n = 160).

Researchers identified eight metabolites from six ontology classes with the Naive Bayes algorithm.

“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, 0.79-0.91),” Adam and colleagues wrote.

According to study results, the two groups demonstrated an overlap in principal component analysis. However, samples from CP patients trended toward lower scores in principal component 1.

=“[Which] was remarkable for a heterogeneous cohort with high interindividual variability due to diverse lifestyles, medications and comorbidities,” the investigators wrote.

According to researchers, external validation in two independent cohorts (n = 502) demonstrated similar accuracy in the detection of chronic pancreatitis vs. non-pancreatic controls regarding EDTA-plasma (AUC 0.85; 95% CI, 0.81-0.89) and serum (AUC 0.87; 95% CI, 0.81-0.95).

“A potential clinical use of this metabolic signature is the identification of CP patients early in the disease course (early CP), of patients with unexplained abdominal symptoms and a history of pancreatic disease, but (yet) no definitive morphological signs of CP (probable CP), or of patients with [recurrent acute pancreatitis] at risk for developing CP,” the investigators wrote.