Self-monitoring of blood glucose in non-insulin treated patients with type 2 diabetes: a systematic review and meta-analysis

To assess the effect of self-monitoring of blood glucose (SMBG) on glycaemic control in non-insulin treated patients with type 2 diabetes by means of a systematic review and meta-analysis.

Intensive insulin treatment

Hyperglycaemia is common in acute illness and more severe hyperglycaemia is associated with worse outcomes in critically ill patients in general and after acute myocardial infarction, stroke, and trauma. Normalization of blood glucose by intensive insulin therapy has been shown to reduce morbidity and mortality in one study in surgical intensive care patients; a subsequent study in medical intensive care patients resulted in reduced morbidity but not a reduction in mortality. Multicentre studies and current meta-analyses in the critically ill have not demonstrated improved outcomes when normalization of blood glucose was targeted; furthermore all studies to date have detected an increased risk of hypoglycaemia in patients subjected to intensive insulin therapy. At present, universal treatment guidelines or recommendations to target strict normoglycaemia must be considered premature. Further data will be available after the completion of the NICE-SUGAR study which has recruited 6103 patients; the NICE SUGAR study will add significant power to future meta-analyses and may help define the role of intensive insulin therapy in critically ill patients.

[Insulin and type 2 diabetes mellitus]

Worldwide an increasing number of persons suffers from type 2 diabetes. Often treatment with oral hypoglycemic agents is not sufficient for adequate glycemic control and additional insulin treatment is necessary. Treatment with insulin is recommended if HbA1c levels below 7% cannot be achieved despite lifestyle measures and the proper use of oral hypoglycemic agents. In addition, pregnancy, periods pre and post major operations, treatment in intensive care units, glucocorticoid medication, severe peripheral neuropathy as well as contraindications of oral hypoglycaemic agents may be indications for insulin therapy irrespective of the actual glycemic control. The choice of the appropriate insulin regimen depends on the daily blood glucose profiles and patient needs.

A reduction in severe hypoglycaemia in type 1 diabetes in a randomized crossover study of continuous intraperitoneal compared with subcutaneous insulin infusion

Continuous intraperitoneal insulin infusion (CIPII) with the DiaPort system using regular insulin was compared to continuous subcutaneous insulin infusion (CSII) using insulin Lispro, to investigate the frequency of hypoglycemia, blood glucose control, quality of life, and safety.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

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.

A neural network approach for insulin regime and dose adjustment in type 1 diabetes

A decision support system based on a neural network approach is proposed to advise on insulin regime and dose adjustment for type 1 diabetes patients.

Aerobic exercise but not resistance exercise reduces intrahepatic lipid content and visceral fat and improves insulin sensitivity in obese adolescent girls: a randomized controlled trial

It is unclear whether regular exercise alone (no caloric restriction) is a useful strategy to reduce adiposity and obesity-related metabolic risk factors in obese girls. We examined the effects of aerobic (AE) vs. resistance exercise (RE) alone on visceral adipose tissue (VAT), intrahepatic lipid, and insulin sensitivity in obese girls. Forty-four obese adolescent girls (BMI ≥95th percentile, 12-18 yr) with abdominal obesity (waist circumference 106.5 ± 11.1 cm) were randomized to 3 mo of 180 min/wk AE (n = 16) or RE (n = 16) or a nonexercising control group (n = 12). Total fat and VAT were assessed by MRI and intrahepatic lipid by proton magnetic resonance spectroscopy. Intermuscular AT (IMAT) was measured by CT. Insulin sensitivity was evaluated by a 3-h hyperinsulinemic (80 mU·m(2)·min(-1)) euglycemic clamp. Compared with controls (0.13 ± 1.10 kg), body weight did not change (P > 0.1) in the AE (-1.31 ± 1.43 kg) and RE (-0.31 ± 1.38 kg) groups. Despite the absence of weight loss, total body fat (%) and IMAT decreased (P < 0.05) in both exercise groups compared with control. Compared with control, significant (P < 0.05) reductions in VAT (Δ-15.68 ± 7.64 cm(2)) and intrahepatic lipid (Δ-1.70 ± 0.74%) and improvement in insulin sensitivity (Δ0.92 ± 0.27 mg·kg(-1)·min(-1) per μU/ml) were observed in the AE group but not the RE group. Improvements in insulin sensitivity in the AE group were associated with the reductions in total AT mass (r = -0.65, P = 0.02). In obese adolescent girls, AE but not RE is effective in reducing liver fat and visceral adiposity and improving insulin sensitivity independent of weight loss or calorie restriction.