Risk factor screening and prediction modeling of gastrointestinal adverse reactions caused by GLP-1RAs

Abstract

Objective: This study aimed to explore the risk factors for gastrointestinal side effects (GISEs) in patients with type 2 diabetes mellitus (T2DM) during treatment with glucagon-like peptide-1 receptor agonists (GLP-1RAs) based on real-world data and to develop a prediction model for GLP-1RA-related GISEs.

Methods: A total of 855 patients who attended the First Affiliated Hospital of Shandong First Medical University from January 2020 to May 2023 were selected as the study participants, who were divided into the training set (598 cases) and the validation set (297 cases) using a simple random sampling method at a ratio of 7:3. The general information and biochemical indicators of the participants were collected to assess the risk factors for GLP-1RA-related GISEs, and multifactorial logistic regression analysis was used to obtain the best predictors. A nomogram prediction model was constructed. The Hosmer-Lemeshow test was used to assess the differentiation and calibration of the nomogram model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model.

Results: Age, gender, history of gastrointestinal disorders, and number of combined oral medications were found as risk factors for the occurrence of GISEs in patients with T2DM using GLP-1RAs (p < 0.05). The nomogram prediction model based on these four factors had good discriminability (AUC values of the training and validation sets of 0.855 and 0.836, respectively) and accuracy (Hosmer-Lemeshow test: p > 0.05 for the validation set). DCA showed that the prediction model curve had clinical utility in the threshold probability interval of >5%.

Conclusions: The established nomogram model has an excellent predictive effect on GISEs induced by GLP-1RAs in patients with T2DM.

Keywords: gastrointestinal side effects; glucagon-like peptide-1 receptor agonists; nomogram; risk factors; type 2 diabetes mellitus.