Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass.
Adult
Anastomosis, Roux-en-Y
/ statistics & numerical data
Blood Glucose
Computer Simulation
Dietary Carbohydrates
/ administration & dosage
Female
Gastrectomy
/ statistics & numerical data
Gastric Bypass
/ statistics & numerical data
Humans
Male
Middle Aged
Obesity, Morbid
/ surgery
Prospective Studies
Self Report
Bayes’ theorem
Roux-en-Y gastric bypass
computational modelling
dietary intake
one-anastomosis gastric bypass
post-prandial glucose response
Journal
Annals of medicine
ISSN: 1365-2060
Titre abrégé: Ann Med
Pays: England
ID NLM: 8906388
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
entrez:
29
10
2021
pubmed:
30
10
2021
medline:
21
12
2021
Statut:
ppublish
Résumé
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). As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m 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. 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 prevent them by dietary means should be investigated.KEY MESSAGESThe 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.
Identifiants
pubmed: 34714211
doi: 10.1080/07853890.2021.1964035
pmc: PMC8567939
doi:
Substances chimiques
Blood Glucose
0
Dietary Carbohydrates
0
Types de publication
Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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