Suchergebnisse

Funding Information: The study was funded by the Academy of Finland [grant numbers 335443, 314383, 266286] and the Academy of Finland, Centre of Excellence in Research on Mitochondria, Metabolism and Disease (FinMIT), [grant number 272376]; Finnish Medical Foundation; Gyllenberg Foundation; Novo Nordisk Foundation, [grant numbers NNF20OC0060547, NNF17OC0027232, NNF10OC1013354]; Finnish Diabetes Research Foundation; Orion Research Foundation; Finnish Foundation for Cardiovascular Research; University of Helsinki and Helsinki University Hospital; Government Research Funds. TS was funded by Martti I Turunen Foundation; Finnish Medical Foundation; and Mary and Georg C. Ehrnrooth Foundation. AJ was funded by the Academy of Finland [grant numbers 335447 and 341157]. Publisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. ; 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 underwent RYGB and 7 participants OAGB surgeries. The glucose concentrations and carbohydrate intakes were reduced postoperatively in both groups. The relative time spent in hypoglycaemia increased regardless of the operation (RYGB, from 9.2 to 28.2%; OAGB, from 1.8 to 37.7%). Postoperatively, we observed an increase in the height of the fitted response curve and a reduction in its width, suggesting that the same amount of carbohydrates caused a larger increase in the postprandial glucose response and that the clearance of the meal-derived blood glucose was faster, with no clinically meaningful differences between the surgeries. Conclusions: A detailed analysis of the glycaemic responses using food diaries has previously been difficult because of the noisy meal data. The utilized machine learning model resolved this by modelling the uncertainty in meal times. Such an approach is likely also applicable in other applications involving dietary data. A marked reduction in overall glycaemia, increase in postprandial glucose response, and rapid glucose clearance from the circulation immediately after surgery are evident after both RYGB and OAGB. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to preventthem by dietary means should be investigated.KEY MESSAGES The use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study. Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated. ; Peer reviewed