1359-P: Effect of Fat Content on Postprandial Gastric Retention and Glucose Absorption in Subjects with Type 1 Diabetes during Daily Life Conditions: Assessment through a Computational Model

• Published in: Diabetes (New York, N.Y.) Vol. 71; no. Supplement_1
• Main Authors: FAGGIONATO, EDOARDO, SCHIAVON, MICHELE, BUCKINGHAM, BRUCE A., EKHLASPOUR, LAYA, MAN, CHIARA DALLA
• Format: Journal Article
• Language: English
• Published: New York American Diabetes Association 01.06.2022
• Subjects: Computer applications; Diabetes; Diabetes mellitus (insulin dependent); Glucose; Insulin; Meals; Sensors
• ISSN: 0012-1797; 1939-327X
• DOI: 10.2337/db22-1359-P

Understanding the impact of dietary nutrients on postprandial gastric retention (GR) and glucose absorption (GA) is crucial for optimizing both open- and closed-loop therapy in type 1 diabetes (T1D) . Here, we aim to investigate the effect of fat (F) content of a meal on both GR and GA in real life conditions, using a recently developed minimally invasive oral minimal model (MI-OMM) . Data was collected during an outpatient 5-days clinical trial involving 1patients (age = 3-61 y; M/F = 60/50) wearing a sensor augmented insulin pump. At each meal, the medical staff recorded carbohydrate (C) and F intake. A total of 159 meals were analyzed that included >75% sensor data, C and F contents, no exercise sessions, or other meals/snacks within 2h before and 3h after the meal. Meals were grouped based on F content: high fat (HF, F≥30%) , and Low Fat (LF, F≤15%) . The model well fitted glucose sensor data and provided estimated GR and GA curves. Fig. 1 shows that model-derived GR was significantly lower from 0.5h onward, and GA was significantly faster from 0.5h to 2.3h after a LF meal compared to a HF. These findings proved that MI-OMM can be used to estimate GR and GA, using sensor data, in real life meals with different F content and, therefore, it is potentially applicable to optimize insulin therapy in T1D.