An algorithm to simulate missing data for mixed meal tolerance test response curves

Background: Response curves formed by analyte concentrations measured at sampled time points after consuming a mixed meal are increasingly being used to characterize responses to differing diets. Unfortunately, owing to a variety of reasons, analyte concentrations for some of the time points may be missing. Objectives: This study aimed to develop an algorithm to estimate the missing values at sampled time points in the analyte response curve to a mixed meal tolerance test (MMTT). Methods: We developed an algorithm to simulate the missing postprandial concentration values for an MMTT. The algorithm was developed to handle any number of missing values for 2 or less consecutive missing values. The algorithm was tested on MMTT response curve data for glucose and triglyceride measurements in data from 3 different studies with 2119 postprandial MMTT response curves. The algorithm was validated by removing concentration values that were not missing and replacing them with the algorithm simulated values. The AUC error between the actual curve and simulated curves were also calculated. A web -based application was developed to automatically simulate missing values for an uploaded MMTT data set. Results: The algorithm was programmed in Python and the resulting web -based application and a video tutorial were provided. The validation indicated good agreement between actual and simulated values with error increasing for less frequently sampled time points. The study with the mean minimum error of glucose concentrations was 6.2 +/- 2.1 mg/dL and study with the mean maximum error of glucose concentrations was 11.3 +/- 4.7 mg/dL. Triglycerides had 16.1 +/- 6.2 mg/dL mean error. The AUC error was small ranging between 0.01% and 0.28%. Conclusions: The presented algorithm reconstructs postprandial response curves with estimations of values that are missing.