Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass.

ABSTRACT:

Objectives

Our aim was to investigate in a real-life setting the use of machine learning for modelling the postprandial glucose concentrations in morbidly obese patients undergoing Roux-en-Y gastric bypass (RYGB) or one-anastomosis gastric bypass (OAGB).

Methods

As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m²) non-diabetic adult participants were included. Glucose concentrations, measured with FreeStyle Libre, were recorded over 14 preoperative and 14 postoperative days. During these periods, 3-day food intake was self-reported. A machine learning model was applied to estimate glycaemic responses to the reported carbohydrate intakes before and after the bariatric surgeries.

Results

Altogether, 10 participants un

SUBMITTER: Ashrafi RA 

PROVIDER: S-EPMC8567939 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature