Predicting responsiveness to GLP-1 pathway drugs using real-world data

Abstract

Background: Medications targeting the glucagon-like peptide-1 (GLP-1) pathway are an important therapeutic class currently used for the treatment of Type 2 diabetes (T2D). However, there is not enough known about which subgroups of patients would receive the most benefit from these medications.

Objective: The goal of this study was to develop a predictive model for patient responsiveness to medications, here collectively called GLP-1 M, that include GLP-1 receptor agonists and dipeptidyl peptidase-4 (DPP4) inhibitors (that normally degrade endogenously-produced GLP-1). Such a model could guide clinicians to consider certain patient characteristics when prescribing second line medications for T2D.

Methods: We analyzed de-identified electronic health records of 7856 subjects with T2D treated with GLP-1 M drugs at Vanderbilt University Medical Center from 2003-2019. Using common clinical features (including commonly ordered lab tests, demographic information, other T2D medications, and diabetes-associated complications), we compared four different models: logistic regression, LightGBM, artificial neural network (ANN), and support vector classifier (SVC).

Results: Our analysis revealed that the traditional logistic regression model outperforms the other machine learning models, with an area under the Receiver Operating Characteristic curve (auROC) of 0.77.Our model showed that higher pre-treatment HbA1C is a dominant feature for predicting better response to GLP-1 M, while features such as use of thiazolidinediones or sulfonylureas is correlated with poorer response to GLP-1 M, as assessed by lowering of hemoglobin A1C (HbA1C), a standard marker of glycated hemoglobin used for assessing glycemic control in individuals with diabetes. Among female subjects under 40 taking GLP-1 M, the simultaneous use of non-steroidal anti-inflammatory drugs (NSAIDs) was associated with a greater reduction in HbA1C (0.82 ± 1.72% vs 0.28 ± 1.70%, p = 0.008).

Conclusion: These findings indicate a thorough analysis of real-world electronic health records could reveal new information to improve treatment decisions for the treatment of T2D. The predictive model developed in this study highlights the importance of considering individual patient characteristics and medication interactions when prescribing GLP-1 M drugs.

Keywords: Electronic health record; GLP-1; HbA1c; Predictive model; Type 2 diabetes.

Fig. 1

Cohort inclusion and exclusion strategy and response to GLP-1 M. A. Overview of the cohort building procedure. B. The number of subjects treated with GLP-1 M per year during the defined period (see Methods) in the VUMC Synthetic Derivative database. The years indicated are synthetic time (see Methods). C. Subject responsiveness to GLP-1 M is heterogeneous. Each ‘ + ’ indicates one subject. The blue line indicates an unchanged HbA1C level after GLP-1 M treatment. The blue shading contains subjects whose HbA1C levels were changed by less than ± 0.5% in the treatment period. The green shading contains subjects whose HbA1C decreased by more than 0.5% in the period after treatment. White area contains subjects whose HbA1C increased by 0.5% or more in the time period after treatment

Fig. 2

Histograms of training and testing data. Training data (blue) and testing data (orange) are similar for (A) treatment year and (B) HbA1C before treatment. (C) Cohort BMI. (D) Cohort age

Fig. 3

Distribution of responders and non-responders for several key features. A. Histogram showing range of HbA1C values of the study cohort pre-GLP-1 M treatment. Blue shading: HbA1C values for subjects that did not respond to GLP-1 M treatment. Orange shading: HbA1C values for subjects that responded positively to GLP-1 M treatment. Compared to the non-responder group, the curve for the responder group is right-shifted (toward higher pretreatment HbA1C values). B. Histogram showing BMI values for the study cohort pre-GLP-1 M treatment. Blue shading: BMI values for subjects that did not respond to GLP-1 M treatment. Orange shading: BMI values for subjects that responded positively to GLP-1 M treatment. Compared to the non-responder group, the curve for the responder group has fewer people with BMI lower than 40. C. Mosaic plot showing patient response to GLP-1 M treatment stratified by race. The numbers in the plots indicate the number of the subjects in each group. The top tiles indicate the number of the responders. The bottom tiles indicate the number of non-responders. Tiles from left to right are White, African American and Asian, respectively. D. Mosaic plots showing patient response stratified by NSAIDs usage, sex and age. The numbers in the plots indicate the number of the subjects in each group. Left plot: female subjects; Right plot: male subjects. In both plots, purple tiles indicate subjects treated with both GLP-1 M and NSAIDs, pink tiles indicate subject only treated with GLP-1 M. Top tiles: subjects who started GLP-1 M before or equal to 40 years old. Bottom tiles: subjects who started GLP-1 M after 40. E. Violin plots showing subjects age of the first EHR records of NSAIDs stratified by year and sex. Blue line: median age for Female, Red line, median age for Male

Fig. 4

Assessment of model performance. A. Confusion matrices for: (A1) logistic regression and (A2) LightGBM. A3. ROC (Receiver Operating Characteristic) curve for logistic regression (green) and LightGBM (orange). The blue dotted line indicates where the false positive rate equals the true positive rate. B. Model learning curves for (B1) Logistic regression and (B2) LightGBM. Area under the ROC (auROC) curve was used to evaluate the model performance and is shown in the y-axis. C. Logisitic regression coefficients were plotted to show feature importance