Temporal optimization of exercise to lower fasting glucose levels.

Exercise stimulates glucose uptake and increases insulin sensitivity acutely. Temporally optimizing exercise timing may minimize the nocturnal rise in glucose levels. This study examined the effect of exercise timing on evening and overnight glucose concentrations in individuals who were non-obese with normal fasting glucose levels (Non-Ob; n = 18) and individuals with obesity (OB) with impaired fasting glucose levels (OB+IFG) and without (n = 16 and n = 18, respectively). Subjects were studied on three occasions (no exercise (NOEX)), morning exercise (AMEX; 0700 h) and evening exercise (PMEX; 2000 h). The evening meal was provided (1800 h) and blood samples were taken from 1740 to 0700 h and morning endogenous glucose production (EGP) was measured. Glucose and insulin concentrations increased with the dinner meal with peak concentrations being higher in OB+IFG than in OB and Non-Ob (P = 0.04). In OB+IFG, evening glucose concentrations rose above baseline levels at about 2300 h, with the glucose concentrations staying somewhat lower with AMEX and PMEX until ∼0500 h than with NOEX. In OB+IFG, insulin concentrations decreased following the dinner meal and waned throughout the night, despite the rising glucose concentrations. In the OB and Non-Ob individuals following the dinner meal, no increase in glucose concentrations occurred in the evening period and insulin levels mirrored this. No difference was observed in the morning fasting glucose levels between study days or between groups. Regardless of time of day, exercise delays the evening rise in glucose concentrations in adults with OB+IFG but does not lower morning fasting glucose levels or improve the synchrony between glucose and insulin concentrations. KEY POINTS: Insulin resistance and type 2 diabetes have been linked to disturbances of the core clock, and glucose tolerance demonstrates a diurnal rhythm in healthy humans with better glucose tolerance in the morning than in the afternoon and evening. Skeletal muscle is a primary site for insulin resistance in people with impaired glucose tolerance. In individuals with obesity and impaired fasting glucose levels (OB+IFG), following a dinner meal, glucose concentrations started to rise and continues throughout the night, resulting in elevated glucose levels, while concomitantly, insulin levels are waning. Exercise, regardless of the time of day, suppressed the rise in glucose levels in OB+IFG for many hours during the night but did not lower morning fasting glucose levels. Morning exercise was not quite as effective as evening exercise.

A new oral model of free fatty acid kinetics to assess lipolysis in subjects with and without type 2 diabetes.

Assessing free fatty acids (FFAs) kinetics and the role of insulin and glucose on FFA lipolysis and disposal may improve our understanding of the pathogenesis of type 2 diabetes (T2D). Some models have been proposed to describe FFA kinetics during an intravenous glucose tolerance test and only one during an oral glucose tolerance test. Here, we propose a model of FFA kinetics during a meal tolerance test and use it to assess possible differences in postprandial lipolysis in individuals with type 2 diabetes (T2D) and individuals with obesity without type 2 diabetes (ND). We studied 18 obese ND and 16 T2D undergoing three meal tolerance tests (MTT) on three occasions (breakfast, lunch, and dinner). We used plasma glucose, insulin, and FFA concentrations collected at breakfast to test a battery of models and selected the best one based on physiological plausibility, ability to fit the data, precision of parameter estimates, and the Akaike parsimony criterion. The best model assumes that the postprandial suppression of FFA lipolysis is proportional to the above basal insulin, while FFA disposal is proportional to FFA concentration. It was used to compare FFA kinetics in ND and T2D along the day. The maximum lipolysis suppression occurred significantly earlier in ND than T2D (39 ± 6 min vs. 102 ± 13 min, 36 ± 4 min vs. 78 ± 11 min, and 38 ± 6 min vs. 84 ± 13 min, P < 0.01, at breakfast, lunch, and dinner, respectively), making lipolysis significantly lower in ND than T2D. This is mainly attributable to the lower insulin concentration in the second group. This novel FFA model allows to assess lipolysis and insulin antilipolytic effect in postprandial conditions.NEW & NOTEWORTHY In this study, we propose a new mathematical model able to quantify postprandial FFA kinetics and adipose tissue insulin sensitivity in both subjects with obesity without type 2 diabetes (ND) and subjects with type 2 diabetes (T2D). Results show that the slower postprandial suppression of lipolysis in T2D contributes to the higher free fatty acid (FFA) concentration that, in turn, may contribute to hyperglycemia.

Tracking the time course of reproduction number and lockdown's effect on human behaviour during SARS-CoV-2 epidemic: nonparametric estimation.

Understanding the SARS-CoV-2 dynamics has been subject of intense research in the last months. In particular, accurate modeling of lockdown effects on human behaviour and epidemic evolution is a key issue in order e.g. to inform health-care decisions on emergency management. In this regard, the compartmental and spatial models so far proposed use parametric descriptions of the contact rate, often assuming a time-invariant effect of the lockdown. In this paper we show that these assumptions may lead to erroneous evaluations on the ongoing pandemic. Thus, we develop a new class of nonparametric compartmental models able to describe how the impact of the lockdown varies in time. Our estimation strategy does not require significant Bayes prior information and exploits regularization theory. Hospitalized data are mapped into an infinite-dimensional space, hence obtaining a function which takes into account also how social distancing measures and people's growing awareness of infection's risk evolves as time progresses. This also permits to reconstruct a continuous-time profile of SARS-CoV-2 reproduction number with a resolution never reached before in the literature. When applied to data collected in Lombardy, the most affected Italian region, our model illustrates how people behaviour changed during the restrictions and its importance to contain the epidemic. Results also indicate that, at the end of the lockdown, around [Formula: see text] of people in Lombardy and [Formula: see text] in Italy was affected by SARS-CoV-2, with the fatality rate being 1.14%. Then, we discuss how the situation evolved after the end of the lockdown showing that the reproduction number dangerously increased in the summer, due to holiday relax, reaching values larger than one on August 1, 2020. Finally, we also document how Italy faced the second wave of infection in the last part of 2020. Since several countries still observe a growing epidemic and others could be subject to other waves, the proposed reproduction number tracking methodology can be of great help to health care authorities to prevent SARS-CoV-2 diffusion or to assess the impact of lockdown restrictions on human behaviour to contain the spread.

Inducing remission of Type 2 diabetes in the Caribbean: findings from a mixed methods feasibility study of a low-calorie liquid diet-based intervention in Barbados.

AIM: In a high proportion of people with recently diagnosed Type 2 diabetes, a short (2-3-month) low-calorie diet is able to restore normal glucose and insulin metabolism. The aim of this study was to determine the feasibility of this approach in Barbados. METHODS: Twenty-five individuals with Type 2 diabetes diagnosed within past 6 years, not on insulin, BMI ≥ 27 kg/m2 were recruited. Hypoglycaemic medication was stopped on commencement of the 8-week liquid (760 calorie) diet. Insulin response was assessed in meal tests at baseline, 8 weeks and 8 months. Semi-structured interviews, analysed thematically, explored participants' experiences. 'Responders' were those with fasting plasma glucose (FPG) < 7 mmol/l at 8 weeks. RESULTS: Ten men and 15 women (mean age 48, range 26-68 years) participated. Mean (sd) BMI was 34.2 kg/m2 (6.0); FPG 9.2 mmol/l (2.2). Mean weight loss at 8 weeks and 8 months was 10.1 kg [95% confidence interval (CI) 8.1, 12.0] and 8.2 kg (95% CI 5.8, 10.6); FPG was lower by 2.2 mmol/l (95% CI 1.2, 3.2) and 1.7 mmol/l (95% CI 0.8, 2.7) respectively. Nine of 11 (82%) of those who lost ≥ 10 kg were 'responders' compared with 6 of 14 (43%) who lost < 10 kg (P = 0.048). The 30-min insulin increment was higher in responders at baseline and follow-up (P ≤ 0.01). A food culture based on starchy foods and pressures to eat large amounts at social events were among the challenges identified by participants. CONCLUSIONS: The feasibility of this approach to weight loss and diabetes remission in a predominantly black population in Barbados was demonstrated.

Parental evaluation of a telemonitoring service for children with Type 1 Diabetes.

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. The service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents' perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents' perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children's diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents' suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0-100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 ( p-value = 0.0001). The analysis of free text comments highlighted an improvement in mood, and parents' commitment was also proved by their willingness to pay for the service (median = 200 euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family's own peace of mind.

Validation of plantar pressure simulations using finite and discrete element modelling in healthy and diabetic subjects.

Plantar pressure simulation driven by integrated 3D motion capture data, using both a finite element and a discrete element model, is compared for ten healthy and ten diabetic neuropathic subjects. The simulated peak pressure deviated on average between 16.7 and 34.2% from the measured peak pressure. The error in the position of the peak pressure was on average smaller than 4.2 cm. No method was more accurate than the other although statistical differences were found between them. Both techniques are thus complementary and useful tools to better understand the alteration of diabetic foot biomechanics during gait.

Psychological outcomes of evening and night closed-loop insulin delivery under free living conditions in people with Type 1 diabetes: a 2-month randomized crossover trial.

AIM: To assess the impact on fear of hypoglycaemia and treatment satisfaction with an artificial pancreas system used for 2 consecutive months, as well as participant acceptance of the artificial pancreas system. METHODS: In a randomized crossover trial patient-related outcomes associated with an evening-and-night artificial pancreas and sensor-augmented pump therapy were compared. Both intervention periods lasted 8 weeks. The artificial pancreas acceptance questionnaire (range 0-90, higher scores better), Hypoglycaemia Fear Survey II (range 0-72, higher scores worse) and Diabetes Treatment Satisfaction Questionnaire (range 0-36, higher scores better) were completed by 32 participants. Semi-structured interviews were conducted after study completion in a subset of six participants. Outcomes were compared using a repeated-measures anova model or paired t-test when appropriate. RESULTS: The total artificial pancreas acceptance questionnaire score at the end of the artificial pancreas period was 69.1 (sd 14.7; 95% CI 63.5, 74.7), indicating a positive attitude towards the artificial pancreas. No significant differences were found among the scores at baseline, end of sensor-augmented pump therapy period or end of the artificial pancreas period with regard to fear of hypoglycaemia [28.2 (sd 17.5), 23.5 (sd 16.6) and 23.5 (sd 16.7), respectively; P = 0.099] or diabetes treatment satisfaction [29.0 (sd 3.9), 28.2 (sd 5.2) and 28.0 (sd 7.1), respectively; P = 0.43]. Themes frequently mentioned in the interviews were 'positive effects at work', 'improved blood glucose', 'fewer worries about blood glucose', but also 'frequent alarms', 'technological issues' and 'demand for an all-in-one device'. CONCLUSIONS: The psychological outcomes of artificial pancreas and sensor-augmented pump therapy were similar. Current artificial pancreas technology is promising but user concerns should be taken into account to ensure utility of these systems.

Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes.

AIMS: To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement. METHODS: 13 patients affected by type 1 diabetes participated to a non-randomized outpatient 42-h experiment that included two evening meals and overnight periods (in short, dinner & night periods). CSII was patient-driven during dinner & night period 1 and MMPC-driven during dinner&night period 2. The study was conducted in hotels, where patients could move around freely. A CGM system (G4 Platinum; Dexcom Inc., San Diego, CA, USA) and insulin pump (AccuChek Combo; Roche Diagnostics, Mannheim, Germany) were connected wirelessly to a smartphone-based platform (DiAs, Diabetes Assistant; University of Virginia, Charlottesville, VA, USA) during both periods. RESULTS: A significantly lower percentage of time spent with glucose levels <3.9 mmol/l was achieved in period 2 compared with period 1: 1.96 ± 4.56% vs 12.76 ± 15.84% (mean ± standard deviation, p < 0.01), together with a greater percentage of time spent in the 3.9-10 mmol/l target range: 83.56 ± 14.02% vs 62.43 ± 29.03% (p = 0.04). In addition, restricting the analysis to the overnight phases, a lower percentage of time spent with glucose levels <3.9 mmol/l (1.92 ± 4.89% vs 12.7 ± 19.75%; p = 0.03) was combined with a greater percentage of time spent in 3.9-10 mmol/l target range in period 2 compared with period 1 (92.16 ± 8.03% vs 63.97 ± 2.73%; p = 0.01). Average glucose levels were similar during both periods. CONCLUSIONS: The results suggest that MMPC managed by a wearable system is safe and effective during evening meal and overnight. Its sustained use during this period is currently being tested in an ongoing randomized 2-month study.

Accuracy of devices for self-monitoring of blood glucose: A stochastic error model.

Self-monitoring of blood glucose (SMBG) devices are portable systems that allow measuring glucose concentration in a small drop of blood obtained via finger-prick. SMBG measurements are key in type 1 diabetes (T1D) management, e.g. for tuning insulin dosing. A reliable model of SMBG accuracy would be important in several applications, e.g. in in silico design and optimization of insulin therapy. In the literature, the most used model to describe SMBG error is the Gaussian distribution, which however is simplistic to properly account for the observed variability. Here, a methodology to derive a stochastic model of SMBG accuracy is presented. The method consists in dividing the glucose range into zones in which absolute/relative error presents constant standard deviation (SD) and, then, fitting by maximum-likelihood a skew-normal distribution model to absolute/relative error distribution in each zone. The method was tested on a database of SMBG measurements collected by the One Touch Ultra 2 (Lifescan Inc., Milpitas, CA). In particular, two zones were identified: zone 1 (BG≤75 mg/dl) with constant-SD absolute error and zone 2 (BG>75mg/dl) with constant-SD relative error. Mean and SD of the identified skew-normal distributions are, respectively, 2.03 and 6.51 in zone 1, 4.78% and 10.09% in zone 2. Visual predictive check validation showed that the derived two-zone model accurately reproduces SMBG measurement error distribution, performing significantly better than the single-zone Gaussian model used previously in the literature. This stochastic model allows a more realistic SMBG scenario for in silico design and optimization of T1D insulin therapy.

Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment.

In type 1 diabetes (T1D) therapy, continuous glucose monitoring (CGM) sensors, which provide glucose concentration in the subcutis every 1-5 min for 7 consecutive days, should allow in principle a more efficient insulin dosing than that based on the conventional 3-4 self-monitoring of blood glucose (SMBG) measurements per day. However, CGM, at variance with SMBG, is still not approved for insulin dosing in T1D management because regulatory agencies, e.g. FDA, are looking for more factual evidence on its safety. An in silico assessment of SMBG- vs CGM-driven insulin therapy can be a first step. Here we present a simulation model of T1D patient decision-making obtained by interconnecting models of glucose-insulin dynamics, SMBG and CGM measurement errors, carbohydrates-counting errors, insulin boluses time variability and forgetfulness, and subcutaneous insulin pump delivery. Inter- and intra- patient variability of model parameters are considered. The T1D patient decision-making model allows to run realistic multi-day simulations scenarios in a population of virtual subjects. We present the first results of simulations run in 20 virtual subjects over a 7-day period, which demonstrates that additional information brought by CGM (trend and hypo/hyperglycemic warnings) with respect to SMBG produces a statistically significant increment (about of 9%) of time spent by the patient in the euglycemic range (70-180 mg/dl).

Decrease of EEG Coherence during hypoglycemia in type 1 diabetic subjects.

Hypoglycemic events have been proven to be associated with measurable EEG changes. Several works in the literature have evaluated these changes by considering approaches at the single EEG channel level, but multivariate analyses have been scarcely investigated in Type 1 diabetes (T1D) subjects. The aim of the present work is to assess if and how hypoglycemia affects EEG coherence in a subset of EEG channels acquired in a hospital setting where eye- and muscle activation-induced artifacts are virtually absent. In particular, EEG multichannel data, acquired in 19 T1D hospitalized subjects undertaken to an insulin-induced hypoglycemia experiment, are considered. Computation of Partial Directed Coherence (PDC) through multivariate autoregressive models of P3-A1A2, P4-A1A2, C3-A1A2 and C4-A1A2 EEG channels shows that a decrease in the value of coherence, most likely related to the progressive loss of cognitive function and altered cerebral activity, occurs when passing from eu- to hypoglycemia, in both theta ([4, 8] Hz) and alpha ([8, 13] Hz) bands.

rTMS on left prefrontal cortex contributes to memories for positive emotional cues: a comparison between pictures and words.

The present research explored the cortical correlates of emotional memories in response to words and pictures. Subjects' performance (Accuracy Index, AI; response times, RTs; RTs/AI) was considered when a repetitive Transcranial Magnetic Stimulation (rTMS) was applied on the left dorsolateral prefrontal cortex (LDLPFC). Specifically, the role of LDLPFC was tested by performing a memory task, in which old (previously encoded targets) and new (previously not encoded distractors) emotional pictures/words had to be recognized. Valence (positive vs. negative) and arousing power (high vs. low) of stimuli were also modulated. Moreover, subjective evaluation of emotional stimuli in terms of valence/arousal was explored. We found significant performance improving (higher AI, reduced RTs, improved general performance) in response to rTMS. This "better recognition effect" was only related to specific emotional features, that is positive high arousal pictures or words. Moreover no significant differences were found between stimulus categories. A direct relationship was also observed between subjective evaluation of emotional cues and memory performance when rTMS was applied to LDLPFC. Supported by valence and approach model of emotions, we supposed that a left lateralized prefrontal system may induce a better recognition of positive high arousal words, and that evaluation of emotional cue is related to prefrontal activation, affecting the recognition memories of emotions.

A two-phase model of plantar tissue: a step toward prediction of diabetic foot ulceration.

A new computational model, based on the thermodynamically constrained averaging theory, has been recently proposed to predict tumor initiation and proliferation. A similar mathematical approach is proposed here as an aid in diabetic ulcer prevention. The common aspects at the continuum level are the macroscopic balance equations governing the flow of the fluid phase, diffusion of chemical species, tissue mechanics, and some of the constitutive equations. The soft plantar tissue is modeled as a two-phase system: a solid phase consisting of the tissue cells and their extracellular matrix, and a fluid one (interstitial fluid and dissolved chemical species). The solid phase may become necrotic depending on the stress level and on the oxygen availability in the tissue. Actually, in diabetic patients, peripheral vascular disease impacts tissue necrosis; this is considered in the model via the introduction of an effective diffusion coefficient that governs transport of nutrients within the microvasculature. The governing equations of the mathematical model are discretized in space by the finite element method and in time domain using the θ-Wilson Method. While the full mathematical model is developed in this paper, the example is limited to the simulation of several gait cycles of a healthy foot.

Sparse Principal Component Analysis for the parsimonious description of glucose variability in diabetes.

Abnormal glucose variability (GV) is considered to be a risk factor for the development of diabetes complications. For its quantification from continuous glucose monitoring (CGM) data, tens of different indices have been proposed in the literature, but the information carried by them is highly redundant. In the present work, the Sparse Principal Component Analysis (SPCA) technique is used to select, from a wide pool of GV metrics, a smaller subset of indices that preserves the majority of the total original variance, providing a parsimonious but still comprehensive description of GV. In detail, SPCA is applied to a set of 25 literature GV indices evaluated on CGM time-series collected in 17 type 1 (T1D) and 13 type 2 (T2D) diabetic subjects. Results show that the 10 GV indices selected by SPCA preserve more than the 75% of the variance of the original set of 25 indices, both in T1D and T2D. Moreover, 6 indices of the parsimonious set are shared by T1D and T2D.

Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information.

Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.

Use of a simple liquid meal test to evaluate insulin sensitivity and beta-cell function in children.

Insulin sensitivity and ß-cell function are useful indices of metabolic disease risk but are difficult to assess in young children because of the invasive nature of commonly used methodology. A meal-based method for assessing insulin sensitivity and ß-cell function may at least partially alleviate concerns. The objectives of this study were to: (i) determine the association of insulin sensitivity assessed by liquid meal test with that determined by an insulin-modified frequently sampled intravenous glucose tolerance test (FSIGT); (ii) examine the association of insulin sensitivity derived from each test with measures of body composition, fat distribution and metabolic health (lipids, fasting insulin and glucose, and surrogate indices of insulin sensitivity); and (iii) examine the associations of indices of ß-cell function derived from each test with total and regional adiposity. Forty-seven children (7-12 years) underwent both a liquid meal test and an FSIGT. The insulin sensitivity index derived from the meal test (SI-meal) was positively associated with that from the FSIGT (SI-FSIGT; r = 0.63; P < 0.001), and inversely with all measures of insulin secretion derived from the meal test. Both SI-meal and SI-FSIGT were associated with measures of total and regional adiposity. SI-meal, but not SI-FSIGT, was associated with triglycerides and fasting insulin, after adjusting for ethnicity, gender, pubertal stage and fat mass. Basal insulin secretion measured during the meal test was positively associated with all measures of adiposity, independent of insulin sensitivity. In conclusion, a liquid meal offers a valid and sensitive means of assessing insulin sensitivity and ß-cell responsivity in young children.

Assessment of insulin action on carbohydrate metabolism: physiological and non-physiological methods.

Carbohydrate metabolism in humans is regulated by insulin secretion from pancreatic ß-cells and glucose disposal by insulin-sensitive tissues. Insulin facilitates glucose utilization in peripheral tissues and suppresses hepatic glucose production. Any defects in insulin action predispose an individual to glucose intolerance and Type 2 diabetes mellitus. Early detection of defects in insulin action could provide opportunities to prevent or delay progression of the disease state. There are different approaches to assess insulin action. Initial methods, such as peripheral insulin concentration and simple indices, have several limitations. Subsequently, researchers developed methodologies using intravenous glucose infusion to determine glucose fluxes. However, these methodologies are limited by being non-physiological. Newer, innovative techniques that have been developed are more sophisticated and physiological. By modelling glucose kinetics using isotope dilution techniques, several robust parameters can be obtained that are physiologically relevant and sound. This brief review summarizes most of the non-physiological and physiological methodologies used to measure the variables of insulin action.

Mechanism of insulin resistance in normal pregnancy.

Normal pregnancy is associated with insulin resistance although the mechanism is not understood. Increased intramyocellular lipid is closely associated with the insulin resistance of type 2 diabetes and obesity, and the aim of this study was to determine whether this was so for the physiological insulin resistance of pregnancy. Eleven primiparous healthy pregnant women (age: 27-39 years, body mass index 24.0±3.1 kg/m2) and no personal or family history of diabetes underwent magnetic resonance studies to quantify intramyocellular lipid, plasma lipid fractions, and insulin sensitivity. The meal-related insulin sensitivity index was considerably lower in pregnancy (45.6±9.9 vs. 193.0±26.1; 10(-4) dl/kg/min per pmol/l, p=0.0002). Fasting plasma triglyceride levels were elevated 3-fold during pregnancy (2.3±0.2 vs. 0.8±0.1 mmol/l, p<0.01) and the low-density density lipoprotein fraction, responsible for fatty acid delivery to muscle and other tissues, was 6-fold elevated (0.75±0.43 vs. 0.12±0.09 mmol/l; p=0.001). However, mean intramyocellular lipid concentrations of the soleus muscle were not different during pregnancy (20.0±2.3 vs. 19.1±3.2 mmol/l, p=0.64). The pregnancy effect on muscle insulin resistance is distinct from that underlying type 2 diabetes.

Modular closed-loop control of diabetes.

Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.

A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information.

In the last decade, improvements in diabetes daily management have become possible thanks to the development of minimally-invasive portable sensors which allow continuous glucose monitoring (CGM) for several days. In particular, hypo and hyperglycemia can be promptly detected when glucose exceeds the normal range thresholds, and even avoided through the use of on-line glucose prediction algorithms. Several algorithms with prediction horizon (PH) of 15-30-45 min have been proposed in the literature, e.g. including AR/ARMA time-series modeling and neural networks. Most of them are fed by CGM signals only. The purpose of this work is to develop a new short-term glucose prediction algorithm based on a neural network that, in addition to past CGM readings, also exploits information on carbohydrates intakes quantitatively described through a physiological model. Results on simulated data quantitatively show that the new method outperforms other published algorithms. Qualitative preliminary results on a real diabetic subject confirm the potentialities of the new approach.