Dataset Information

Study of blood glucose and insulin infusion rate in real-time in diabetic rats using an artificial pancreas system.

ABSTRACT: Artificial pancreas system (APS) is an emerging new treatment for type 1 diabetes mellitus. The aim of this study was to develop a rat APS as a research tool and demonstrate its application. We established a rat APS using Medtronic Minimed Pump 722, Medtronic Enlite sensor, and the open artificial pancreas system as a controller. We tested different dilutions of Humalog (100 units/ml) in saline ranged from 1:3 to 1:20 and determined that 1:7 dilution works well for rats with ~500g bodyweight. Blood glucose levels (BGL) of diabetic rats fed with chow diet (58% carbohydrate) whose BGL was managed by the closed-loop APS for the total duration of 207h were in euglycemic range (70-180 mg/dl) for 94.5% of the time with 2.1% and 3.4% for hyperglycemia (>180mg/dl) and hypoglycemia (<70 mg/dl), respectively. Diabetic rats fed with Sucrose pellets (94.8% carbohydrate) for the experimental duration of 175h were in euglycemic range for 61% of the time with 35% and 4% for hyperglycemia and hypoglycemia, respectively. Heathy rats fed with chow diet showed almost a straight line of BGL ~ 95 mg/dl (average 94.8 mg/dl) during the entire experimental period (281h), which was minimally altered by food intake. In the healthy rats, feeding sucrose pellets caused greater range of BGL in high and low levels but still within euglycemic range (99.9%). Next, to study how healthy and diabetic rats handle supra-physiological concentrations of glucose, we intraperitoneally injected various amounts of 50% dextrose (2, 3, 4g/kg) and monitored BGL. Duration of hyperglycemia after injection of 50% dextrose at all three different concentrations was significantly greater for healthy rats than diabetic rats, suggesting that insulin infusion by APS was superior in reducing BGL as compared to natural insulin released from pancreatic β-cells. Ex vivo studies showed that islets isolated from diabetic rats were almost completely devoid of pancreatic β-cells but with intact α-cells as expected. Lipid droplet deposition in the liver of diabetic rats was significantly lower with higher levels of triacylglyceride in the blood as compared to those of healthy rats, suggesting lipid metabolism was altered in diabetic rats. However, glycogen storage in the liver determined by Periodic acid-Schiff staining was not altered in diabetic rats as compared to healthy rats. A rat APS may be used as a powerful tool not only to study alterations of glucose and insulin homeostasis in real-time caused by diet, exercise, hormones, or antidiabetic agents, but also to test mathematical and engineering models of blood glucose prediction or new algorithms for closed-loop APS.

SUBMITTER: Kirilmaz OB

PROVIDER: S-EPMC8284668 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundDiet acts on the human body through digestion in the stomach and absorption in the intestines. Thus, the emptying of the stomach should be the focus of the research mechanism of the combined medicine and food treatment of diabetes. The emptying function of the stomach and the secretion of related hormones may be the key points of traditional Chinese medicine. In the clinic, Yunvjian is a famous traditional Chinese formula for preventing and curing diabetes. However, the pharmacological action and mechanism of Yunvjian are also need to be probe.ObjectiveTo assess the effect of Yunvjian on glucose, insulin level and gastric emptying function and related hormones on high-fat diet combined with STZ-induced diabetic rats.MethodsHigh-fat diet combined with STZ was used to construct type 2 diabetes mellitus (T2DM) rats model and received a 4-week Yunvjian administration. The animals were divided into 6 groups, respectively, as the Control group, the DM group, the DM + Acarbose group, the DM + YNH group, and the DM + YNL group. Radionuclide single-photon emission computed tomography (SPECT) technology was used to observe the gastric emptying rate and half-empty time; blood was took to test fasting insulin, and then the insulin resistance index (HOMA-IR) was calculated; HE staining was performed to detect islets and gastric antrum, immunohistochemical staining was performed to detect the number and morphology of pancreatic β cells and gastric antrum Cajal cells, and the average optical density was calculated; the expression of ghrelin hormone in gastric antrum and serum was detected by ELISA and immunofluorescence; the expression of GHRS mRNA in gastric antrum was detected by RT-PCR method.ResultsYunvjian could significantly improve the glucose level and insulin function of rats. Compared with the DM group, Yunvjian was beneficial to low fasting blood glucose (FBG) (P < 0.01), increased glucose tolerance, and improved islet function at the same time (P < 0.05). At the same time, compared with the DM group (25.02 ± 0.05, 44 ± 12.33), the emptying rate of the DM + YNH group was significantly faster (64.98 ± 0.12), and the half row time was shortened (26 ± 8.29, P < 0.05). The gastric ghrelin levels in each group of Yunvjian increased with different degrees compared with the DM group (616.2 ± 26.23), especially in the DM + YNH group (863.51 ± 23.76, P < 0.01). Correspondingly, the expression of gastric GHSR mRNA in the DM + YNH and DM + YNL groups increased significantly compared with the DM group (P < 0.01).ConclusionsYunvjian can effectively control glucose and improve islet function, which may be closely related to its influence on gastric emptying function and related hormone secretion regulation.

Project description:Erratic blood glucose levels can be a cause and consequence of delayed gastric emptying in patients with diabetes. It is unknown if better glycemic control increases risks of hypoglycemia or improves hemoglobin A1c levels and gastrointestinal symptoms in diabetic gastroparesis. This study investigated the safety and potential efficacy of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) in poorly controlled diabetes with gastroparesis. Forty-five type 1 or 2 patients with diabetes and gastroparesis and hemoglobin A1c >8% from the NIDDK Gastroparesis Consortium enrolled in a 24 week open-label pilot prospective study of CSII plus CGM. The primary safety outcome was combined numbers of mild, moderate, and severe hypoglycemic events at screening and 24 weeks treatment. Secondary outcomes included glycemic excursions on CGM, hemoglobin A1c, gastroparesis symptoms, quality-of-life, and liquid meal tolerance. Combined mild, moderate, and severe hypoglycemic events occurred similarly during the screening/run-in (1.9/week) versus treatment (2.2/week) phases with a relative risk of 1.18 (95% CI 0.85-1.64, P = 0.33). CGM time in hypoglycemia (<70 mg/dL) decreased from 3.9% to 1.8% (P<0.0001), time in euglycemia (70-180 mg/dL) increased from 44.0% to 52.0% (P = 0.02), time in severe hyperglycemia (>300 mg/dL) decreased from 14.2% to 7.0% (P = 0.005), and hemoglobin A1c decreased from 9.4±1.4% to 8.3±1.3% (P = 0.001) on CSII plus CGM. Symptom scores decreased from 29.3±7.1 to 21.9±10.2 with lower nausea/vomiting, fullness/early satiety, and bloating/distention scores (P?0.001). Quality-of-life scores improved from 2.4±1.1 to 3.1±1.1 (P<0.0001) and volumes of liquid nutrient meals tolerated increased from 420±258 to 487±312 mL (P = 0.05) at 24 weeks. In conclusion, CSII plus CGM appeared to be safe with minimal risks of hypoglycemic events and associated improvements in glycemic control, gastroparesis symptoms, quality-of-life, and meal tolerance in patients with poorly controlled diabetes and gastroparesis. This study supports the safety, feasibility, and potential benefits of improving glycemic control in diabetic gastroparesis.

Project description:BackgroundPeople with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals' different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions.MethodsA new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient's insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control.ResultsIncorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%.ConclusionAdapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.

Dataset Information

Study of blood glucose and insulin infusion rate in real-time in diabetic rats using an artificial pancreas system.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets