Publication Date

1-1-2023

Journal

Diabetes Care

DOI

10.2337/dc22-1414

PMID

36382801

PMCID

PMC9797648

PubMedCentral® Posted Date

11-16-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Male, Diabetes Mellitus, Type 2, Acute Disease, Cross-Sectional Studies, Models, Statistical, Prognosis, Pancreatitis, Chronic, Risk Factors, Obesity

Abstract

OBJECTIVE: Diabetes that arises from chronic pancreatitis (CP) is associated with increased morbidity and mortality. Methods to predict which patients with CP are at greatest risk for diabetes are urgently needed. We aimed to examine independent risk factors for diabetes in a large cohort of patients with CP.

RESEARCH DESIGN AND METHODS: This cross-sectional study comprised 645 individuals with CP enrolled in the PROCEED study, of whom 276 had diabetes. We conducted univariable and multivariable regression analyses of potential risk factors for diabetes. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) analysis, and accuracy was evaluated by cross validation. Exploratory analyses were stratified according to the timing of development of diabetes relative to the diagnosis of pancreatitis.

RESULTS: Independent correlates of diabetes in CP included risk factors for type 2 diabetes (older age, overweight/obese status, male sex, non-White race, tobacco use) as well as pancreatic disease-related factors (history of acute pancreatitis complications, nonalcoholic etiology of CP, exocrine pancreatic dysfunction, pancreatic calcification, pancreatic atrophy) (AUROC 0.745). Type 2 diabetes risk factors were predominant for diabetes occurring before pancreatitis, and pancreatic disease-related factors were predominant for diabetes occurring after pancreatitis.

CONCLUSIONS: Multiple factors are associated with diabetes in CP, including canonical risk factors for type 2 diabetes and features associated with pancreatitis severity. This study lays the groundwork for the future development of models integrating clinical and nonclinical data to identify patients with CP at risk for diabetes and identifies modifiable risk factors (obesity, smoking) on which to focus for diabetes prevention.

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