DIgestive COmplications in DIabetes - the DICODI population study

Background Diabetes type 1 and type 2 may develop gastrointestinal complications e.g., gastroparesis and gastroenteropathy. Concomitant celiac disease and pancreatic exocrine insufficiency occur with high prevalence in diabetes and with symptomatic overlap. Consequently, it is a challenge to disentangle symptoms of these conditions and separate them from functional dyspepsia. We aim to develop a clinical decision-support tool to differentiate the underlying disease in a plethora of gastrointestinal symptoms. Methods An internet-based computerized survey will collect basic characteristics (diabetes type, age, gender, duration, HbA1c, treatment) and patient reported outcomes by validated questionnaires focusing on (1) gastroparesis using Gastroparesis Cardinal Symptom Index; (2) gastroenteropathy using Gastrointestinal Symptom Rating Scale; (3) celiac disease using Celiac Symptom Index and (4) pancreatic exocrine insufficiency with Pancreatic Exocrine Insufficiency Questionnaire. Logistic regression and multiple regression analyses will identify risk factors and gastrointestinal complications. Cluster analyses and machine learning will classify different symptoms and co-existing presentations, into a likely diagnosis. We seek biomarkers for autonomic neuropathy by characterizing development of retinopathy using the Visual Function Questionnaire-25 and peripheral neuropathy by the Michigan neuropathy questionnaire. Participants are re-examined yearly for disease progression over time. Results From focus group studies gastrointestinal symptoms are of major concern in diabetes. Potentially, estimates of symptom prevalence, risk factor identification and classifications of gastrointestinal complications can be unraveled for feedback to health care providers. Conclusion The web-based DICODI project will open up possibilities to detect gastrointestinal complications of diabetes in a societal setting, benefitting people living with diabetes, health care professionals, and society.

Automated summary

The study aims to develop a clinical decision-support tool to differentiate the underlying disease in gastrointestinal symptoms of diabetes patients. By collecting basic characteristics and patient reported outcomes, using statistical analysis and machine learning techniques, and searching for biomarkers for autonomic neuropathy, the study hopes to provide better healthcare for diabetes patients.