Intestinal glucose absorption but not endogenous glucose production differs between colostrum- and formula-fed neonatal calves

Glucose supply markedly changes during the transition to extrauterine life. In this study, we investigated diet effects on glucose metabolism in neonatal calves. Calves were fed colostrum (C; n = 7) or milk-based formula (F; n = 7) with similar nutrient content up to d 4 of life. Blood plasma samples were taken daily before feeding and 2 h after feeding on d 4 to measure glucose, lactate, nonesterified fatty acids, protein, urea, insulin, glucagon, and cortisol concentrations. On d 2, additional blood samples were taken to measure glucose first-pass uptake (FPU) and turnover by oral [U-(13)C]-glucose and i.v. [6,6-(2)H(2)]-glucose infusion. On d 3, endogenous glucose production and gluconeogenesis were determined by i.v. [U-(13)C]-glucose and oral deuterated water administration after overnight feed deprivation. Liver tissue was obtained 2 h after feeding on d 4 and glycogen concentration and activities and mRNA abundance of gluconeogenic enzymes were measured. Plasma glucose and protein concentrations and hepatic glycogen concentration were higher (P < 0.05), whereas plasma urea, glucagon, and cortisol (d 2) concentrations as well as hepatic pyruvate carboxylase mRNA level and activity were lower (P < 0.05) in group C than in group F. Orally administered [U-(13)C]-glucose in blood was higher (P < 0.05) but FPU tended to be lower (P < 0.1) in group C than in group F. The improved glucose status in group C resulted from enhanced oral glucose absorption. Metabolic and endocrine changes pointed to elevated amino acid degradation in group F, presumably to provide substrates to meet energy requirements and to compensate for impaired oral glucose uptake.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Clinical Validation of a Peritoneal Dialysis Prescription Model in the PatientOnLine Software

Peritoneal transport characteristics and residual renal function require regular control and subsequent adjustment of the peritoneal dialysis (PD) prescription. Prescription models shall facilitate the prediction of the outcome of such adaptations for a given patient. In the present study, the prescription model implemented in the PatientOnLine software was validated in patients requiring a prescription change. This multicenter, international prospective cohort study with the aim to validate a PD prescription model included patients treated with continuous ambulatory peritoneal dialysis. Patients were examined with the peritoneal function test (PFT) to determine the outcome of their current prescription and the necessity for a prescription change. For these patients, a new prescription was modeled using the PatientOnLine software (Fresenius Medical Care, Bad Homburg, Germany). Two to four weeks after implementation of the new PD regimen, a second PFT was performed. The validation of the prescription model included 54 patients. Predicted and measured peritoneal Kt/V were 1.52 ± 0.31 and 1.66 ± 0.35, and total (peritoneal + renal) Kt/V values were 1.96 ± 0.48 and 2.06 ± 0.44, respectively. Predicted and measured peritoneal creatinine clearances were 42.9 ± 8.6 and 43.0 ± 8.8 L/1.73 m2/week and total creatinine clearances were 65.3 ± 26.0 and 63.3 ± 21.8 L/1.73 m2/week, respectively. The analysis revealed a Pearson's correlation coefficient for peritoneal Kt/V of 0.911 and Lin's concordance coefficient of 0.829. The value of both coefficients was 0.853 for peritoneal creatinine clearance. Predicted and measured daily net ultrafiltration was 0.77 ± 0.49 and 1.16 ± 0.63 L/24 h, respectively. Pearson's correlation and Lin's concordance coefficient were 0.518 and 0.402, respectively. Predicted and measured peritoneal glucose absorption was 125.8 ± 38.8 and 79.9 ± 30.7 g/24 h, respectively, and Pearson's correlation and Lin's concordance coefficient were 0.914 and 0.477, respectively. With good predictability of peritoneal Kt/V and creatinine clearance, the present model provides support for individual dialysis prescription in clinical practice. Peritoneal glucose absorption and ultrafiltration are less predictable and are likely to be influenced by additional clinical factors to be taken into consideration.