"Learning" Can Improve the Blood Glucose Control Performance for Type 1 Diabetes Mellitus

BACKGROUND

A learning-type artificial pancreas has been proposed to exploit the repetitive nature in the blood glucose dynamics. We clinically evaluated the efficacy of the learning-type artificial pancreas.

METHODS

We conducted a pilot clinical study in 10 participants of mean age 36.1 years (standard deviation [SD] 12.7; range 16-58) with type 1 diabetes. Each trial was conducted for eight consecutive mornings. The first two mornings were open-loop to obtain the individualized parameters. Then, the following six mornings were closed-loop, during which a learning-type model predictive control algorithm was employed to calculate the insulin infusion rate. To evaluate the algorithm's robustness, each participant took exercise or consumed alcohol on the fourth or sixth closed-loop day and the order was determined randomly. The primary outcome was the percentage of time spent in the target glucose range of 3.9-8.0 mmol/L between 0900 and 1200 h.

RESULTS

The percentage of time with glucose spent in target range was significantly improved from 51.6% on day 1 to 71.6% on day 3 (mean difference between groups 17.9%, confidence interval [95% CI] 3.6-32.1; P = 0.020). There were no hypoglycemic episodes developed on day 3 compared with two episodes on day 1. There was no difference in the percentage of time with glucose spent in target range between exercise day versus day 5 and alcohol day versus day 5.

CONCLUSIONS

The learning-type artificial pancreas system achieved good glycemic regulation and provided increased effectiveness over time. It showed a satisfactory performance even when the blood glucose was challenged by exercise or alcohol.

Feasibility of Long-Term Closed-Loop Control: A Multicenter 6-Month Trial of 24/7 Automated Insulin Delivery

BACKGROUND

In the past few years, the artificial pancreas-the commonly accepted term for closed-loop control (CLC) of blood glucose in diabetes-has become a hot topic in research and technology development. In the summer of 2014, we initiated a 6-month trial evaluating the safety of 24/7 CLC during free-living conditions.

RESEARCH DESIGN AND METHODS

Following an initial 1-month Phase 1, 14 individuals (10 males/4 females) with type 1 diabetes at three clinical centers in the United States and one in Italy continued with a 5-month Phase 2, which included 24/7 CLC using the wireless portable Diabetes Assistant (DiAs) developed at the University of Virginia Center for Diabetes Technology. Median subject characteristics were age 45 years, duration of diabetes 27 years, total daily insulin 0.53 U/kg/day, and baseline HbA1c 7.2% (55 mmol/mol).

RESULTS

Compared with the baseline observation period, the frequency of hypoglycemia below 3.9 mmol/L during the last 3 months of CLC was lower: 4.1% versus 1.3%, P < 0.001. This was accompanied by a downward trend in HbA1c from 7.2% (55 mmol/mol) to 7.0% (53 mmol/mol) at 6 months. HbA1c improvement was correlated with system use (Spearman r = 0.55). The user experience was favorable with identified benefit particularly at night and overall trust in the system. There were no serious adverse events, severe hypoglycemia, or diabetic ketoacidosis.

CONCLUSION

We conclude that CLC technology has matured and is safe for prolonged use in patients' natural environment. Based on these promising results, a large randomized trial is warranted to assess long-term CLC efficacy and safety.

Evaluating the Experience of Children With Type 1 Diabetes and Their Parents Taking Part in an Artificial Pancreas Clinical Trial Over Multiple Days in a Diabetes Camp Setting

OBJECTIVE

To explore the experiences of children with type 1 diabetes and their parents taking part in an artificial pancreas (AP) clinical trial during a 7-day summer camp.

RESEARCH DESIGN AND METHODS

A semistructured interview, composed of 14 questions based on the Technology Acceptance Model, was conducted at the end of the clinical trial. Participants also completed the Diabetes Treatment Satisfaction Questionnaire (DTSQ, parent version) and the AP Acceptance Questionnaire.

RESULTS

Thirty children, aged 5-9 years, and their parents completed the study. A content analysis of the interviews showed that parents were focused on understanding the mechanisms, risks, and benefits of the new device, whereas the children were focused on the novelty of the new system. The parents' main concerns about adopting the new system seemed related to the quality of glucose control. The mean scores of DTSQ subscales indicated general parents' satisfaction (44.24 ± 5.99, range 32-53) and trustful views of diabetes control provided by the new system (7.8 ± 2.2, range 3-12). The AP Acceptance Questionnaire revealed that most parents considered the AP easy to use (70.5%), intended to use it long term (94.0%), and felt that it was apt to improve glucose control (67.0%).

CONCLUSIONS

Participants manifested a positive attitude toward the AP. Further studies are required to explore participants' perceptions early in the AP development to individualize the new treatment as much as possible, and to tailor it to respond to their needs and values.

Mechanisms Underlying the Pathogenesis of Isolated Impaired Glucose Tolerance in Humans

CONTEXT

Prediabetes is a heterogeneous disorder classified on the basis of fasting glucose concentrations and 2-hour glucose tolerance.

OBJECTIVE

We sought to determine the relative contributions of insulin secretion and action to the pathogenesis of isolated impaired glucose tolerance (IGT).

DESIGN

The study consisted of an oral glucose tolerance test and a euglycemic clamp performed in two cohorts matched for anthropometric characteristics and fasting glucose but discordant for glucose tolerance.

SETTING

An inpatient clinical research unit at an academic medical center.

PATIENTS OR OTHER PARTICIPANTS

Twenty-five subjects who had normal fasting glucose (NFG) and normal glucose tolerance (NGT) and 19 NFG/IGT subjects participated in this study.

INTERVENTION(S)

Subjects underwent a seven-sample oral glucose tolerance test and a 4-hour euglycemic, hyperinsulinemic clamp on separate occasions. Glucose turnover during the clamp was measured using tracers, and endogenous hormone secretion was inhibited by somatostatin.

MAIN OUTCOME MEASURES

We sought to determine whether hepatic glucose metabolism, specifically the contribution of gluconeogenesis to endogenous glucose production, differed between subjects with NFG/NGT and those with NFG/IGT.

RESULTS

Endogenous glucose production did not differ between groups before or during the clamp. Insulin-stimulated glucose disappearance was lower in NFG/IGT (24.6 ± 2.2 vs 35.0 ± 3.6 μmol/kg/min; P = .03). The disposition index was decreased in NFG/IGT (681 ± 102 vs 2231 ± 413 × 10^-14 dL/kg/min² per pmol/L; P < .001).

CONCLUSIONS

We conclude that innate defects in the regulation of glycogenolysis and gluconeogenesis do not contribute to NFG/IGT. However, insulin-stimulated glucose disposal is impaired, exacerbating defects in β-cell function.

Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity

BACKGROUND

A simulation methodology based on the net effect, a signal estimated from continuous glucose monitoring (CGM) and insulin data accounting for sources of glucose variability, for example, meals and exercise, has been proposed. This method has been recently used to "replay" real-life treatment scenarios and determine the minimal level of CGM sensor accuracy required for nonadjunctive use. Given the potential of the net effect method, it is important to assess its domain of validity.

METHODS

The UVA/Padova type 1 diabetes simulator is used to generate glucose and insulin data. The net effect signal is estimated and used to predict the glucose profiles resulting from the following therapy modifications: (1) basal insulin increase/decrease, (2) bolus reduction to prevent hypoglycemia, (3) bolus addition after CGM hyperalarms, (4) hypotreatment addition after CGM hypoalarms. Results of the net effect method are compared with the reference provided by the UVA/Padova simulator.

RESULTS

The net effect method (1) well predicts the effect of small basal insulin adjustments (±10%), but overestimates time in hypo/hyperglycemia for larger adjustments (±50%); (2) underestimates the bolus reduction required to prevent hypoglycemia; (3) underestimates time in hyperglycemia when introducing correction boluses; and (4) overestimates time in hypoglycemia when introducing hypotreatments.

CONCLUSIONS

The net effect method is reliable for small adjustments of basal insulin, while outside this domain of validity it can provide inaccurate results.

Incorporation of inhaled insulin into the FDA accepted University of Virginia/Padova Type 1 Diabetes Simulator

The University of Virginia/Padova Type 1 Diabetes (T1DM) Simulator has been extensively used in artificial pancreas research mostly for testing and design of control algorithms. However, it also offers the possibility of testing new insulin analogs and alternative routes of delivery given that subcutaneous insulin administration present significant delays & variability. Inhaled insulin appears an important candidate to improve post-prandial glucose control given its rapid appearance in plasma. In this contribution, we present the results of incorporating a pharmacokinetic model of inhaled Technosphere(®) Insulin (TI) into the T1DM simulator. In particular, we successfully reproduced in silico the post-prandial glucose control observed in T1DM subjects treated with TI given at meal time, and the post-prandial glucose dynamics in response to different timing of TI dose.

Long-term continuous monitoring of the preterm brain with diffuse optical tomography and electroencephalography: a technical note on cap manufacturing

Diffuse optical tomography (DOT) has recently proved useful for detecting whole-brain oxygenation changes in preterm and term newborns' brains. The data recording phase in prior explorations was limited up to a maximum of a couple of hours, a time dictated by the need to minimize skin damage caused by the protracted contact with optode holders and interference with concomitant clinical/nursing procedures. In an attempt to extend the data recording phase, we developed a new custom-made cap for multimodal DOT and electroencephalography acquisitions for the neonatal population. The cap was tested on a preterm neonate (28 weeks gestation) for a 7-day continuous monitoring period. The cap was well tolerated by the neonate, who did not suffer any evident discomfort and/or skin damage. Montage and data acquisition using our cap was operated by an attending nurse with no difficulty. DOT data quality was remarkable, with an average of 92% of reliable channels, characterized by the clear presence of the heartbeat in most of them.

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).

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.

A Bayesian Network analysis of the probabilistic relations between risk factors in the predisposition to type 2 diabetes

In order to better understand the relations between different risk factors in the predisposition to type 2 diabetes, we present a Bayesian Network analysis of a large dataset, composed of three European population studies. Our results show, together with a key role of metabolic syndrome and of glucose after 2 hours of an Oral Glucose Tolerance Test, the importance of education, measured as the number of years of study, in the predisposition to type 2 diabetes.

The Genetic Landscape of Renal Complications in Type 1 Diabetes

Diabetes is the leading cause of ESRD. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (P=6.4×10^-3). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole-exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (P=2.2×10^-5) and the risk of type 2 diabetes (P=6.1×10^-4) associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (P=1.1×10^-4). Pathway analysis implicated ascorbate and aldarate metabolism (P=9.0×10^-6), and pentose and glucuronate interconversions (P=3.0×10^-6) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

BACKGROUND

In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study.

METHODS

We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis.

RESULTS

For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin.

CONCLUSIONS

In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

Insulin Infusion Set Use: European Perspectives and Recommendations

Insulin pump users worldwide depend on insulin infusion sets (IISs) for predictable delivery of insulin to the subcutaneous tissue. Yet emerging data indicates that IISs are associated with many pump-related adverse events and may contribute to potentially life-threatening problem of unexplained hyperglycemia. The relative scarcity of published research on IISs to date, the heterogeneity of regional IIS practices, and the increasing demand for international standards guiding their use prompted convening of a panel of diabetologists and diabetes nurse educators last February, in Milan, Italy, to discuss a framework for optimizing IIS practice in Europe. The multinational panel was tasked, first, with identifying the often-overlooked IIS issues that can affect patients' experience of pump therapy-e.g., partial or complete blockage of the cannula, skin pathologies, unpredictable variations in insulin absorption, dislodgment, and the demands of site rotation and set changes-and, second, with establishing direction for developing cohesive protocols to assure long-term success. As reported in this article, the panel examined IIS-related complications of pump therapy encountered in clinical practice, considered country-wide policies to prevent and mitigate such complications, and updated priorities for improving IIS education on issues of device selection, skin care, and troubleshooting unexplained hyperglycemia. These recommendations may be more relevant with the possibility of closed-loop systems available in the near future.

Standardized Mixed-Meal Tolerance and Arginine Stimulation Tests Provide Reproducible and Complementary Measures of β-Cell Function: Results From the Foundation for the National Institutes of Health Biomarkers Consortium Investigative Series

OBJECTIVE

Standardized, reproducible, and feasible quantification of β-cell function (BCF) is necessary for the evaluation of interventions to improve insulin secretion and important for comparison across studies. We therefore characterized the responses to, and reproducibility of, standardized methods of in vivo BCF across different glucose tolerance states.

RESEARCH DESIGN AND METHODS

Participants classified as having normal glucose tolerance (NGT; n = 23), prediabetes (PDM; n = 17), and type 2 diabetes mellitus (T2DM; n = 22) underwent two standardized mixed-meal tolerance tests (MMTT) and two standardized arginine stimulation tests (AST) in a test-retest paradigm and one frequently sampled intravenous glucose tolerance test (FSIGT).

RESULTS

From the MMTT, insulin secretion in T2DM was >86% lower compared with NGT or PDM (P < 0.001). Insulin sensitivity (Si) decreased from NGT to PDM (∼50%) to T2DM (93% lower [P < 0.001]). In the AST, insulin secretory response to arginine at basal glucose and during hyperglycemia was lower in T2DM compared with NGT and PDM (>58%; all P < 0.001). FSIGT showed decreases in both insulin secretion and Si across populations (P < 0.001), although Si did not differ significantly between PDM and T2DM populations. Reproducibility was generally good for the MMTT, with intraclass correlation coefficients (ICCs) ranging from ∼0.3 to ∼0.8 depending on population and variable. Reproducibility for the AST was very good, with ICC values >0.8 across all variables and populations.

CONCLUSIONS

Standardized MMTT and AST provide reproducible and complementary measures of BCF with characteristics favorable for longitudinal interventional trials use.

Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator

BACKGROUND

Technosphere(®) insulin (TI), an inhaled human insulin with a fast onset of action, provides a novel option for the control of prandial glucose. We used the University of Virginia (UVA)/Padova simulator to explore in-silico the potential benefit of different dosing regimens on postprandial glucose (PPG) control to support the design of further clinical trials. Tested dosing regimens included at-meal or postmeal dosing, or dosing before and after a meal (split dosing).

METHODS

Various dosing regimens of TI were compared among one another and to insulin lispro in 100 virtual type-1 patients. Individual doses were identified for each regimen following different titration rules. The resulting postprandial glucose profiles were analyzed to quantify efficacy and the risk for hypoglycemic events.

RESULTS

This approach allowed us to assess the benefit/risk for each TI dosing regimen and to compare results with simulations of insulin lispro. We identified a new titration rule for TI that could significantly improve the efficacy of treatment with TI.

CONCLUSION

In-silico clinical trials comparing the treatment effect of different dosing regimens with TI and of insulin lispro suggest that postmeal dosing or split dosing of TI, in combination with an appropriate titration rule, can achieve a superior postprandial glucose control while providing a lower risk for hypoglycemic events than conventional treatment with subcutaneously administered rapid-acting insulin products.

Effect of Slow Wave Sleep Disruption on Metabolic Parameters in Adolescents

STUDY OBJECTIVES

Cross-sectional studies report a correlation between slow wave sleep (SWS) duration and insulin sensitivity (SI) in children and adults. Suppression of SWS causes insulin resistance in adults but effects in children are unknown. This study was designed to determine the effect of SWS fragmentation on SI in children.

METHODS

Fourteen pubertal children (11.3-14.1 y, body mass index 29(th) to 97(th) percentile) were randomized to sleep studies and mixed meal (MM) tolerance tests with and without SWS disruption. Beta-cell responsiveness (Φ) and SI were determined using oral minimal modeling.

RESULTS

During the disruption night, auditory stimuli (68.1 ± 10.7/night; mean ± standard error) decreased SWS by 40.0 ± 8.0%. SWS fragmentation did not affect fasting glucose (non-disrupted 76.9 ± 2.3 versus disrupted 80.6 ± 2.1 mg/dL), insulin (9.2 ± 1.6 versus 10.4 ± 2.0 μIU/mL), or C-peptide (1.9 ± 0.2 versus 1.9 ± 0.1 ng/mL) levels and did not impair SI (12.9 ± 2.3 versus 10.1 ± 1.6 10(-4) dL/kg/min per μIU/mL) or Φ (73.4 ± 7.8 versus 74.4 ± 8.4 10(-9) min(-1)) to a MM challenge. Only the subjects in the most insulin-sensitive tertile demonstrated a consistent decrease in SI after SWS disruption.

CONCLUSION

Pubertal children across a range of body mass indices may be resistant to the adverse metabolic effects of acute SWS disruption. Only those subjects with high SI (i.e., having the greatest "metabolic reserve") demonstrated a consistent decrease in SI. These results suggest that adolescents may have a unique ability to adapt to metabolic stressors, such as acute SWS disruption, to maintain euglycemia. Additional studies are necessary to confirm that this resiliency is maintained in settings of chronic SWS disruption.

From Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying Day-Specific Bayesian Prior

BACKGROUND

In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors.

METHODS

The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid.

RESULTS

The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD).

CONCLUSIONS

A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day.

Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study

BACKGROUND

Glucose sensors measure glucose concentration in the interstitial fluid (ISF), remote from blood. ISF glucose is well known to be "delayed" with respect to blood glucose (BG). However, ISF glucose is not simply a shifted-in-time version of BG but exhibits a more complex pattern.

METHODS

To gain insight into this problem, one can use linear systems theory. However, this may lose a more clinical readership, thus we use simulation and two case studies to convey our thinking in an easier way. In particular, we consider BG concentration measured after meal and exercise in 12 healthy volunteers, whereas ISF glucose is simulated using a well-accepted model of blood-ISF glucose kinetics, which permits calculation of the equilibration time, a parameter characterizing the system. Two metrics are defined: blood and ISF glucose difference at each time point and time to reach the same glucose value in blood and ISF.

RESULTS

The simulation performed and the two metrics show that the relationship between blood-ISF glucose profiles is more complex than a pure shift in time and that the pattern depends on both equilibration time and BG.

CONCLUSIONS

In this in silico study, we have illustrated, with simple case studies, the meaning of the of ISF glucose with respect to BG. Understanding that ISF glucose is not just a shifted-in-time version but a distorted mirror of BG is important for a correct use of continuous glucose monitoring for diabetes management.

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe

OBJECTIVE

The Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5- to 9-year-old children during a camp.

RESEARCH DESIGN AND METHODS

A total of 30 children, 5- to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensor-augmented pump (SAP).

RESULTS

Overnight time-in-hypoglycemia was reduced with the AP versus SAP, median (25(th)-75(th) percentiles): 0.0% (0.0-2.2) vs. 2.2% (0.0-12.3) (P = 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5) vs. 59.7% (21.2) (P = 0.430), but with increased mean glucose 173 mg/dL (36) vs. 150 mg/dL (39) (P = 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (13.5) vs. 63.1% (11.0) (P = 0.022) and increased mean glucose 169 mg/dL (23) vs. 147 mg/dL (23) (P < 0.001).

CONCLUSIONS

This trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.

Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home

OBJECTIVE

After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP during day and night (D/N-AP) for 1 month.

RESEARCH DESIGN AND METHODS

Twenty adult patients with T1D who completed a previous randomized crossover study comparing 2-month E/N-AP versus 2-month sensor augmented pump (SAP) volunteered for 1-month D/N-AP nonrandomized extension. AP was executed by a model predictive control algorithm run by a modified smartphone wirelessly connected to a continuous glucose monitor (CGM) and insulin pump. CGM data were analyzed by intention-to-treat with percentage time-in-target (3.9-10 mmol/L) over 24 h as the primary end point.

RESULTS

Time-in-target (mean ± SD, %) was similar over 24 h with D/N-AP versus E/N-AP: 64.7 ± 7.6 vs. 63.6 ± 9.9 (P = 0.79), and both were higher than with SAP: 59.7 ± 9.6 (P = 0.01 and P = 0.06, respectively). Time below 3.9 mmol/L was similarly and significantly reduced by D/N-AP and E/N-AP versus SAP (both P < 0.001). SD of blood glucose concentration (mmol/L) was lower with D/N-AP versus E/N-AP during whole daytime: 3.2 ± 0.6 vs. 3.4 ± 0.7 (P = 0.003), morning: 2.7 ± 0.5 vs. 3.1 ± 0.5 (P = 0.02), and afternoon: 3.3 ± 0.6 vs. 3.5 ± 0.8 (P = 0.07), and was lower with D/N-AP versus SAP over 24 h: 3.1 ± 0.5 vs. 3.3 ± 0.6 (P = 0.049). Insulin delivery (IU) over 24 h was higher with D/N-AP and SAP than with E/N-AP: 40.6 ± 15.5 and 42.3 ± 15.5 vs. 36.6 ± 11.6 (P = 0.03 and P = 0.0004, respectively).

CONCLUSIONS

D/N-AP and E/N-AP both achieved better glucose control than SAP under free-living conditions. Although time in the different glycemic ranges was similar between D/N-AP and E/N-AP, D/N-AP further reduces glucose variability.

Multinational Home Use of Closed-Loop Control Is Safe and Effective

OBJECTIVE

To evaluate the efficacy of a portable, wearable, wireless artificial pancreas system (the Diabetes Assistant [DiAs] running the Unified Safety System) on glucose control at home in overnight-only and 24/7 closed-loop control (CLC) modes in patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

At six clinical centers in four countries, 30 participants 18-66 years old with type 1 diabetes (43% female, 96% non-Hispanic white, median type 1 diabetes duration 19 years, median A1C 7.3%) completed the study. The protocol included a 2-week baseline sensor-augmented pump (SAP) period followed by 2 weeks of overnight-only CLC and 2 weeks of 24/7 CLC at home. Glucose control during CLC was compared with the baseline SAP.

RESULTS

Glycemic control parameters for overnight-only CLC were improved during the nighttime period compared with baseline for hypoglycemia (time <70 mg/dL, primary end point median 1.1% vs. 3.0%; P < 0.001), time in target (70-180 mg/dL: 75% vs. 61%; P < 0.001), and glucose variability (coefficient of variation: 30% vs. 36%; P < 0.001). Similar improvements for day/night combined were observed with 24/7 CLC compared with baseline: 1.7% vs. 4.1%, P < 0.001; 73% vs. 65%, P < 0.001; and 34% vs. 38%, P < 0.001, respectively.

CONCLUSIONS

CLC running on a smartphone (DiAs) in the home environment was safe and effective. Overnight-only CLC reduced hypoglycemia and increased time in range overnight and increased time in range during the day; 24/7 CLC reduced hypoglycemia and increased time in range both overnight and during the day. Compared with overnight-only CLC, 24/7 CLC provided additional hypoglycemia protection during the day.

Effects of the BET-inhibitor, RVX-208 on the HDL lipidome and glucose metabolism in individuals with prediabetes: A randomized controlled trial

AIMS

High-density lipoprotein (HDL) and apolipoprotein A-I (apoA-I) can modulate glucose metabolism through multiple mechanisms. This study determined the effects of a novel bromodomain and extra-terminal (BET) inhibitor (RVX-208) and putative apoA-I inducer on lipid species contained within HDL (HDL lipidome) and glucose metabolism.

MATERIALS AND METHODS

Twenty unmedicated males with prediabetes received 100mg b.i.d. RVX-208 and placebo for 29-33days separated by a wash-out period in a randomized, cross-over design trial. Plasma HDL-cholesterol and apoA-I were assessed as well as lipoprotein particle size and distribution using NMR spectroscopy. An oral glucose tolerance test (OGTT) protocol with oral and infused stable isotope tracers was employed to assess postprandial plasma glucose, indices of insulin secretion and insulin sensitivity, glucose kinetics and lipolysis. Whole plasma and HDL lipid profiles were measured using mass spectrometry.

RESULTS

RVX-208 treatment for 4weeks increased 6 sphingolipid and 4 phospholipid classes in the HDL lipidome (p≤0.05 versus placebo), but did not change conventional clinical lipid measures. The concentration of medium-sized HDL particles increased by 11% (P=0.01) and small-sized HDL particles decreased by 10% (P=0.04) after RVX-208 treatment. In response to a glucose load, after RVX-208 treatment, plasma glucose peaked at a similar level to placebo, but 30min later with a more sustained elevation (treatment effect, P=0.003). There was a reduction and delay in total (P=0.001) and oral (P=0.003) glucose rates of appearance in plasma and suppression of endogenous glucose production (P=0.014) after RVX-208 treatment. The rate of glucose disappearance was also lower following RVX-208 (P=0.016), with no effect on glucose oxidation or total glucose disposal.

CONCLUSIONS

RVX-208 increased 10 lipid classes in the plasma HDL fraction, without altering the concentrations of either apoA-I or HDL-cholesterol (HDL-C). RVX-208 delayed and reduced oral glucose absorption and endogenous glucose production, with plasma glucose maintained via reduced peripheral glucose disposal. If sustained, these effects may protect against the development of type 2 diabetes.

EPMA-World Congress 2015

A1 Predictive and prognostic biomarker panel for targeted application of radioembolisation improving individual outcomes in hepatocellular carcinoma

Jella-Andrea Abraham, Olga Golubnitschaja

A2 Integrated market access approach amplifying value of “Rx-CDx”

Ildar Akhmetov

A3 Disaster response: an opportunity to improve global healthcare

Russell J. Andrews, Leonidas Quintana

A4 USA PPPM: proscriptive, profligate, profiteering medicine-good for 1 % wealthy, not for 99 % unhealthy

Russell J. Andrews

A5 The role of IDO in a murine model of gingivitis: predictive and therapeutic potentials

Babak Baban, Jun Yao Liu, Xu Qin, Tailing Wang, Mahmood S. Mozaffari

A6 Specific diets for personalised treatment of diabetes type 2

Viktoriia V. Bati, Tamara V. Meleshko, Olga B. Levchuk, Nadiya V. Boyko

A7 Towards personalized physiotherapeutic approach

Joanna Bauer, Ewa Boerner, Halina Podbielska

A8 Cells, animal, SHIME and in silico models for detection and verification of specific biomarkers of non-communicable chronic diseases

Alojz Bomba, Viktor O. Petrov, Volodymyr G. Drobnych, Rostyslav V. Bubnov, Oksana M. Bykova, Nadiya V. Boyko

A9 INTERACT-chronic care model: Self-treatment by patients with decision support e-Health solution

Hans-Peter Brunner-La Rocca, Lutz Fleischhacker, Olga Golubnitschaja, Frank Heemskerk, Thomas Helms, Tiny Jaarsma, Judita Kinkorova, Jan Ramaekers, Peter Ruff, Ivana Schnur, Emilio Vanoli, Jose Verdu

A10 PPPM in cardiovascular medicine in 2015

Hans-Peter Brunner-La Rocca

A11 Magnetic resonance imaging of nanoparticles in mice, potential for theranostic and contrast media development – pilot results

Rostyslav V. Bubnov, Sergiy A. Grabovetskyi, Olena M. Mykhalchenko, Natalia O. Tymoshok, Oleksandr B. Shcherbakov, Igor P. Semeniv, Mykola Y. Spivak

A12 Ultrasound diagnosis for diabetic neuropathy - comparative study

Rostyslav V. Bubnov, Tetyana V. Ostapenko

A13 Ultrasound for stratification patients with diabetic foot ulcers for prevention and personalized treatment - pilot results

Rostyslav V. Bubnov, Nazarii M. Kobyliak, Nadiya M. Zholobak, Mykola Ya. Spivak

A14 Project ImaGenX – designing and executing a questionnaire on environment and lifestyle risk of breast cancer

John Paul Cauchi

A15 Genomics – a new structural brand of predictive, preventive and personalized medicine or the new driver as well?

Dmitrii Cherepakhin, Marina Bakay, Artem Borovikov, Sergey Suchkov

A16 Survey of questionnaires for evaluation of the quality of life in various medical fields

Barbara Cieślik, Agnieszka Migasiewicz, Maria-Luiza Podbielska, Markus Pelleter, Agnieszka Giemza, Halina Podbielska

A17 Personalized molecular treatment for muscular dystrophies

Sebahattin Cirak

A18 Secondary mutations in circulating tumour DNA for acquired drug resistance in patients with advanced ALK + NSCLC

Marzia Del Re, Paola Bordi, Valentina Citi, Marta Palombi, Carmine Pinto, Marcello Tiseo, Romano Danesi

A19 Recombinant species-specific FcεRI alpha proteins for diagnosis of IgE-mediated allergies in dogs, cats and horses

Lukas Einhorn, Judit Fazekas, Martina Muhr, Alexandra Schoos, Lucia Panakova, Ina Herrmann, Krisztina Manzano-Szalai, Kumiko Oida, Edda Fiebiger, Josef Singer, Erika Jensen-Jarolim

A20 Global methodology for developmental neurotoxicity testing in humans and animals early and chronically exposed to chemical contaminants

Arpiné A. Elnar, Nadia Ouamara, Nadiya Boyko, Xavier Coumoul, Jean-Philippe Antignac, Bruno Le Bizec, Gauthier Eppe, Jenny Renaut, Torsten Bonn, Cédric Guignard, Margherita Ferrante, Maria Liusa Chiusano, Salvatore Cuzzocrea, Gerard O'Keeffe, John Cryan, Michelle Bisson, Amina Barakat, Ihsane Hmamouchi, Nasser Zawia, Anumantha Kanthasamy, Glen E. Kisby, Rui Alves, Oscar Villacañas Pérez, Kim Burgard, Peter Spencer, Norbert Bomba, Martin Haranta, Nina Zaitseva, Irina May, Stéphanie Grojean, Mathilde Body-Malapel, Florencia Harari, Raul Harari, Kristina Yeghiazaryan, Olga Golubnitschaja, Vittorio Calabrese, Christophe Nemos, Rachid Soulimani

A21 Mental indicators at young people with attributes hypertension and pre-hypertension

Maria E. Evsevyeva, Elena A. Mishenko, Zurida V. Kumukova, Evgeniy V. Chudnovsky, Tatyana A. Smirnova

A22 On the approaches to the early diagnosis of stress-induced hypertension in young employees of State law enforcement agencies

Maria E. Evsevyeva, Ludmila V. Ivanova, Michail V. Eremin, Maria V. Rostovtseva

A23 Сentral aortic pressure and indexes of augmentation in young persons in view of risk factors

Maria E. Evsevyeva, Michail V. Eremin, Vladimir I. Koshel, Oksana V. Sergeeva, Nadesgda M. Konovalova

A24 Breast cancer prediction and prevention: Are reliable biomarkers in horizon?

Shantanu Girotra, Olga Golubnitschaja

A25 Flammer Syndrome and potential formation of pre-metastatic niches: A multi-centred study on phenotyping, patient stratification, prediction and potential prevention of aggressive breast cancer and metastatic disease

Olga Golubnitschaja, Manuel Debald, Walther Kuhn, Kristina Yeghiazaryan, Rostyslav V. Bubnov, Vadym M. Goncharenko, Ulyana Lushchyk, Godfrey Grech, Katarzyna Konieczka

A26 Innovative tools for prenatal diagnostics and monitoring: improving individual pregnancy outcomes and health-economy in EU

Olga Golubnitschaja, Jan Jaap Erwich, Vincenzo Costigliola, Kristina Yeghiazaryan, Ulrich Gembruch

A27 Immunohistochemical assessment of APUD cells in endometriosis

Vadym M. Goncharenko, Vasyl O. Beniuk, Olga V. Kalenska, Rostyslav V. Bubnov

A28 Updating personalized management algorithm of endometrial hyperplasia in pre-menopause women

Vadym M. Goncharenko, Vasyl O. Beniuk, Rostyslav V. Bubnov, Olga Melnychuk

A29 The personified treatment approach of polimorbid patients with periodontal inflammatory diseases

Irina A. Gorbacheva, Lyudmila Y. Orekhova, Vadim V. Tachalov

A30 Ukrainian experience in hybrid war – the challenge to update algorithms for personalized care and early prevention of different military injuries

Olena I. Grechanyk, Rizvan Ya. Abdullaiev, Rostyslav V. Bubnov

A31 Tear fluid biomarkers: a comparison of tear fluid sampling and storage protocols

Suzanne Hagan, Eilidh Martin, Ian Pearce, Katherine Oliver

A32 The correlation of dietary habits with gingival problems during menstruation

Cenk Haytac, Fariz Salimov, Servin Yoksul, Anatoly A. Kunin, Natalia S. Moiseeva

A33 Genomic medicine in a contemporary Spanish population of prostate cancer: our experience

Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara, Antonia Angulo, Francisco Javier Machuca Santa-Cruz

A34 Challenges, opportunities and collaborations for personalized medicine applicability in uro-oncological disease

Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara

A35 Metabolic hallmarks of cancer as targets for a personalized therapy

John Ionescu

A36 Influence of genetic polymorphism as a predictor of the development of periodontal disease in patients with gastric ulcer and 12 duodenal ulcer

Alfiya Z. Isamulaeva, Anatoly A. Kunin, Shamil Sh. Magomedov, Aida I. Isamulaeva

A37 Challenges in diabetic macular edema

Tatjana Josifova

A38 Overview of the EPMA strategies in laboratory medicine relevant for PPPM

Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja, Vincenzo Costigliola

A39 EPMA initiative for effective organization of medical travel: European concepts and criteria

Vincenzo Costigliola, Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja

A40 Design and innovation in e-textiles: implications for PPPM

Anthony Kent, Tom Fisher, Tilak Dias

A41 Biobank in Pilsen as a member of national node BBMRI_CZ

Judita Kinkorová, Ondřej Topolčan

A42 Big data in personalized medicine: hype and hope

Matthias Kohl

A43 The 3P approach as the platform of the European Dentistry Department (DPPPD)

Anatoly A. Kunin, Natalia S. Moiseeva

A44 The endometrium cytokine patterns for predictive diagnosis of proliferation severity and cancer prevention

Andrii I. Kurchenko, Vasyl A. Beniuk, Vadym M. Goncharenko, Rostyslav V. Bubnov, Nadiya V. Boyko, Andriy M. Strokan

A45 A monocyte-based in-vitro system for testing individual responses to the implanted material: future for personalized implant construction

Julia Kzhyshkowska, Alexandru Gudima, Ksenia S. Stankevich, Victor D. Filimonov⁴, Harald Klüter, Evgeniya M. Mamontova, Sergei I. Tverdokhlebov

A46 Prediction and prevention of adverse health effects by meteorological factors: Biomarker patterns and creation of a device for self-monitoring and integrated care

Ulyana B. Lushchyk, Viktor V. Novytskyy, Igor P. Babii, Nadiya G. Lushchyk, Lyudmyla S. Riabets, Ivanna I. Legka

A47 Targeting "disease signatures" towards personalized healthcare

Mira Marcus-Kalish, Alexis Mitelpunkt, Tal Galili, Neta Shachar, Yoav Benjamini

A48 Influence of the skin imperfection on the personal quality of life and possible tools for objective diagnosis

Agnieszka Migasiewicz, Markus Pelleter, Joanna Bauer, Ewelina Dereń, Halina Podbielska

A49 The new direction in caries prevention based on the ultrastructure of dental hard tissues and filling materials

Natalia S. Moiseeva, Anatoly A. Kunin, Dmitry A. Kunin

A50 The use of LED radiation in prevention of dental diseases

Natalia S. Moiseeva, Yury A. Ippolitov, Dmitry A. Kunin, Alexei N. Morozov, Natalia V. Chirkova, Nakhid T. Aliev

A51 Status of endothelial progenitor cells in diabetic nephropathy: predictive and preventive potentials

Mahmood S. Mozaffari, Jun Yao Liu, Babak Baban

A52 The status of glucocorticoid-induced leucine zipper protein in salivary gland in Sjögren’s syndrome: predictive and personalized treatment potentials

Mahmood S. Mozaffari, Jun Yao Liu, Rafik Abdelsayed, Xing-Ming Shi, Babak Baban

A53 Maximal aerobic capacity - important quality marker of health

Jaroslav Novák, Milan Štork, Václav Zeman

A54 The EMPOWER project: laboratory medicine and Horizon 2020

Wytze P. Oosterhuis, Elvar Theodorsson

A55 Personality profile manifestations in patient’s attitude to oral care and adherence to doctor’s prescriptions

Lyudmila Y. Orekhova, Tatyana V. Kudryavtseva, Elena R. Isaeva, Vadim V. Tachalov, Ekaterina S. Loboda

A56 Results of an European survey on personalized medicine addressed to directions of laboratory medicine

Mario Pazzagli, Francesca Malentacchi, Irene Mancini, Ivan Brandslund, Pieter Vermeersch, Matthias Schwab, Janja Marc, Ron H.N. van Schaik, Gerard Siest, Elvar Theodorsson, Chiara Di Resta

A57 MCI or early dementia predictive speech based diagnosis techniques

Matus Pleva, Jozef Juhar

A58 Personalized speech based mobile application for eHealth

Matus Pleva, Jozef Juhar

A59 Circulating tumor cell-free DNA as the biomarker in the management of cancer patients

Jiří Polívka jr., Filip Janků, Martin Pešta, Jan Doležal, Milena Králíčková, Jiří Polívka

A60 Complex stroke care – educational programme in Stroke Centre University Hospital Plzen

Jiří Polívka, Alena Lukešová, Nina Müllerová, Petr Ševčík, Vladimír Rohan

A61 Sleep apnea and sleep fragmentation contribute to brain aging

Kneginja Richter, Lence Miloseva, Günter Niklewski

A62 Personalised approach for sleep disturbances in shift workers

Kneginja Richter, Jens Acker, Guenter Niklewski

A63 Medical travel and innovative PPPM clusters: new concept of integration

Olga Safonicheva, Vincenzo Costigliola

A64 Medical travel and women health

Olga Safonicheva

A65 Continuity of generations in the training of specialists in the field of reconstructive microsurgery

Maxim Sautin, Janna Sinelnikova, Sergey Suchkov

A66 Telemonitoring of stroke patients – empirical evidence of individual risk management results from an observational study in Germany

Songül Secer, Stephan von Bandemer

A67 Women’s increasing breast cancer risk with n-6 fatty acid intake explained by estrogen-fatty acid interactive effect on DNA damage: implications for gender-specific nutrition within personalized medicine

Niva Shapira

A68 Cytobacterioscopy of the gingival crevicular fluid as a method for preventive diagnosis of periodontal diseases

Aleksandr Shcherbakov, Anatoly A. Kunin, Natalia S. Moiseeva

A69 Use of specially treated composites in dentistry to avoid violations of aesthetics

Bogdan R. Shumilovich, Zhanna Lipkind, Yulia Vorobieva, Dmitry A. Kunin, Anastasiia V. Sudareva

A70 National eHealth system – platform for preventive, predictive and personalized diabetes care

Ivica Smokovski, Tatjana Milenkovic

A72 The common energy levels of Prof. Szent-Györgyi, the intrinsic chemistry of melanin, and the muscle physiopathology. Implications in the context of Preventive, Predictive, and Personalized Medicine

Arturo Solís-Herrera, María del Carmen Arias-Esparza, Sergey Suchkov

A73 Plurality and individuality of hepatocellular carcinoma: PPPM perspectives

Krishna Chander Sridhar, Olga Golubnitschaja

A74 Strategic aspects of higher medical education reforms to secure newer educational platforms for getting biopharma professionals matures

Maria Studneva, Sihong Song, James Creeden, Мark Мandrik, Sergey Suchkov

A75 Overview of the strategies and activities of the European Federation of Clinical Chemistry and Laboratory Medicine, (EFLM)

Elvar Theodorsson, EFLM

A76 New spectroscopic techniques for point of care label free diagnostics

Syed A. M. Tofail

A77 Tumor markers for personalized medicine and oncology - the role of Laboratory Medicine

Ondřej Topolčan, Judita Kinkorová, Ondřej Fiala, Marie Karlíková, Šárka Svobodová, Radek Kučera, Radka Fuchsová, Vladislav Třeška, Václav Šimánek, Ladislav Pecen, Jan Šoupal, Štěpán Svačina²

A78 Modern medical terminology (MMT) as a driver of the global educational reforms

Evgeniya Tretyak, Maria Studneva, Sergey Suchkov

A79 Juvenile hypertension; the relevance of novel predictive, preventive and personalized assessment of its determinants

Francesca M. Trovato, G. Fabio Martines, Daniela Brischetto, Daniela Catalano, Giuseppe Musumeci, Guglielmo M. Trovato

A80 Proteomarkers Biotech

George Th. Tsangaris, Athanasios K. Anagnostopoulos

A81 Proteomics and mass spectrometry based non-invasive prenatal testing of fetal health and pregnancy complications

George Th. Tsangaris, Athanasios K. Anagnostopoulos

A82 Integrated Ecosystem for an Integrated Care model for Heart Failure (HF) patients including related comorbidities (ZENITH)

José Verdú, German Gutiérrez, Jordi Rovira, Marta Martinez, Lutz Fleischhacker, Donna Green, Arthur Garson, Elena Tamburini, Stefano Cuomo, Juan Martinez-Leon, Teresa Abrisqueta, Hans-Peter Brunner-La Rocca, Tiny Jaarsma, Teresa Arredondo, Cecilia Vera, Giuseppe Fico, Olga Golubnitschaja, Fernando Arribas, Martina Onderco, Isabel Vara, on behalf of ZENITH consortium

A83 Predictive, preventive and personalized medicine in diabetes onset and complication (MOSAIC project)

José Verdú, Francesco Sambo, Barbara Di Camillo, Claudio Cobelli, Andrea Facchinetti, Giuseppe Fico, Riccardo Bellazzi, Lucia Sacchi, Arianna Dagliati, Daniele Segnani, Valentina Tibollo, Manuel Ottaviano, Rafael Gabriel, Leif Groop, Jacqueline Postma, Antonio Martinez, Liisa Hakaste, Tiinamaija Tuomi, Konstantia Zarkogianni, on behalf of MOSAIC consortium

A84 Possibilities for personalized therapy of diabetes using in vitro screening of insulin and oral hypoglycemic agents

Igor Volchek, Nina Pototskaya, Andrey Petrov

A85 The innovative technology for personalized therapy of human diseases based on in vitro drug screening

Igor Volchek, Nadezhda Pototskaya, Andrey Petrov

A86 Bone destruction and temporomandibular joint: predictive markers, pathogenetic aspects and quality of life

Ülle Voog-Oras, Oksana Jagur, Edvitar Leibur, Priit Niibo, Triin Jagomägi, Minh Son Nguyen, Chris Pruunsild, Dagmar Piikov, Mare Saag

A87 Sub-optimal health management – global vision for concepts in medical travel

Wei Wang

A88 Sub-optimal health management: synergic PPPM-TCAM approach

Wei Wang

A89 Innovative technologies for minimal invasive diagnostics

Andreas Weinhäusel, Walter Pulverer, Matthias Wielscher, Manuela Hofner, Christa Noehammer, Regina Soldo, Peter Hettegger, Istvan Gyurjan, Ronald Kulovics, Silvia Schönthaler, Gabriel Beikircher, Albert Kriegner, Stephan Pabinger, Klemens Vierlinger

A90 Rare disease diobanks for personalized medicine

Ayşe Yüzbaşıoğlu, Meral Özgüç, Member of EuroBioBank - European Network of DNA, Cell and Tissue Banks for Rare Diseases

Hyperglucagonemia Mitigates the Effect of Metformin on Glucose Production in Prediabetes

The therapeutic mechanism of metformin action remains incompletely understood. Whether metformin inhibits glucagon-stimulated endogenous glucose production (EGP), as in preclinical studies, is unclear in humans. To test this hypothesis, we studied nine prediabetic individuals using a randomized, placebo-controlled, double-blinded, crossover study design. Metformin increased glucose tolerance, insulin sensitivity, and plasma glucagon. Metformin did not alter average basal EGP, although individual variability in EGP correlated with plasma glucagon. Metformin increased basal EGP in individuals with severe hyperglucagonemia (>150 pg/ml). Decreased fasting glucose after metformin treatment appears to increase glucagon to stimulate EGP and prevent further declines in glucose. Similarly, intravenous glucagon infusion elevated plasma glucagon (>150 pg/ml) and stimulated a greater increase in EGP during metformin therapy. Metformin also counteracted the protein-catabolic effect of glucagon. Collectively, these data indicate that metformin does not inhibit glucagon-stimulated EGP, but hyperglucagonemia may decrease the ability of the metformin to lower EGP in prediabetic individuals.

Accurate Measurement of Postprandial Glucose Turnover: Why Is It Difficult and How Can It Be Done (Relatively) Simply?

Fasting hyperglycemia occurs when an excessive rate of endogenous glucose production (EGP) is not accompanied by an adequate compensatory increase in the rate of glucose disappearance (Rd). The situation following food ingestion is more complex as the amount of glucose that reaches the circulation for disposal is a function of the systemic rate of appearance of the ingested glucose (referred to as the rate of meal appearance [Rameal]), the pattern and degree of suppression of EGP, and the rapidity of stimulation of the Rd In an effort to measure these processes, Steele et al. proposed what has come to be referred to as the dual-tracer method in which the ingested glucose is labeled with one tracer while a second tracer is infused intravenously at a constant rate. Unfortunately, subsequent studies have shown that although this approach is technically simple, the marked changes in plasma specific activity or the tracer-to-tracee ratio, if stable tracers are used, introduce a substantial error in the calculation of Rameal, EGP, and Rd, thereby leading to incorrect and at times misleading results. This Perspective discusses the causes of these so-called "nonsteady-state" errors and how they can be avoided by the use of the triple-tracer approach.

Effect of Pramlintide on Postprandial Glucose Fluxes in Type 1 Diabetes

CONTEXT

Early postprandial hyperglycemia and delayed hypoglycemia remain major problems in current management of type 1 diabetes (T1D).

OBJECTIVE

Our objective was to investigate the effects of pramlintide, known to suppress glucagon and delay gastric emptying, on postprandial glucose fluxes in T1D.

DESIGN

This was a single-center, inpatient, randomized, crossover study.

PATIENTS

Twelve patients with T1D who completed the study were analyzed.

INTERVENTIONS

Subjects were studied on two occasions with or without pramlintide. Triple tracer mixed-meal method and oral minimal model were used to estimate postprandial glucose turnover and insulin sensitivity (SI). Integrated liver insulin sensitivity was calculated based on glucose turnover. Plasma glucagon and insulin were measured.

MAIN OUTCOME MEASURE

Glucose turnover and SI were the main outcome measures.

RESULTS

With pramlintide, 2-hour postprandial glucose, insulin, glucagon, glucose turnover, and SI indices showed: plasma glucose excursions were reduced (difference in incremental area under the curve [iAUC], 444.0 mMmin, P = .0003); plasma insulin concentrations were lower (difference in iAUC, 7642.0 pMmin; P = .0099); plasma glucagon excursions were lower (difference in iAUC, 1730.6 pg/mlmin; P = .0147); meal rate of glucose appearance was lower (difference in iAUC: 1196.2 μM/kg fat free mass [FFM]; P = .0316), endogenous glucose production was not different (difference in iAUC: -105.5 μM/kg FFM; P = .5842), rate of glucose disappearance was lower (difference in iAUC: 1494.2 μM/kg FFM; P = .0083). SI and liver insulin sensitivity were not different between study visits (P > .05).

CONCLUSIONS

Inhibition of glucagon and gastric emptying delaying reduced 2-hour prandial glucose excursions in T1D by delaying meal rate of glucose appearance.

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND

The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings.

METHODS

A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses.

RESULTS

Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices.

CONCLUSIONS

These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Towards the generation of a parametric foot model using principal component analysis: A pilot study

There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects.

Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement-we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers-frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the "artificial pancreas." Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components-amplitude and timing.

Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors

BACKGROUND

Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts.

MATERIALS AND METHODS

A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4(®) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function.

RESULTS

The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model.

CONCLUSIONS

The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.

Hypoglycemia-Induced Decrease of EEG Coherence in Patients with Type 1 Diabetes

BACKGROUND

Hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable electroencephalography (EEG) changes. Previous studies have, however, evaluated these changes on a single EEG channel level, whereas multivariate analysis of several EEG channels has been scarcely investigated. The aim of the present work is to use a coherence approach to quantitatively assess how hypoglycemia affects mutual connectivity of different brain areas.

MATERIALS AND METHODS

EEG multichannel data were obtained from 19 patients with T1D (58% males; mean age, 55 ± 2.4 years; diabetes duration, 28.5 ± 2.6 years; glycated hemoglobin, 8.0 ± 0.2%) who underwent a hyperinsulinemic-hypoglycemic clamp study. The information partial directed coherence (iPDC) function was computed through multivariate autoregressive models during eu- and hypoglycemia in the theta and alpha bands.

RESULTS

In passing from eu- to hypoglycemia, absolute values of the iPDC function tend to decrease in both bands in all combinations of the considered channels. In particular, the scalar indicator [Formula: see text], which summarizes iPDC information, significantly decreased (P < 0.01) in 17 of 19 subjects: from T5-A1A2 to C3-A1A2 from O1-A1A2 to C4-A1A2 and from O2-A1A2 to Cz-A1A2 in the theta band and from O1-A1A2 to T4-A1A2 and from O1-A1A2 to C4-A1A2 in the alpha band.

CONCLUSIONS

The coherence decrease measured by iPDC in passing from eu- to hypoglycemia is likely related to the progressive loss of cognitive function and altered cerebral activity in hypoglycemia. This result encourages further quantitative investigation of EEG changes in hypoglycemia and of how EEG acquisition and real-time processing can support hypoglycemia alert systems.

One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator

OBJECTIVE

The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution.

METHODS

The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed.

RESULTS

The model well describes glucose traces (coefficient of determination R² = 0.962 ± 0.027 ) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern.

CONCLUSION

The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic.

SIGNIFICANCE

The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.

TCF7L2 Genotype and α-Cell Function in Humans Without Diabetes

The diabetes-associated allele in TCF7L2 increases the rate of conversion to diabetes; however, the mechanism by which this occurs remains elusive. We hypothesized that the diabetes-associated allele in this locus (rs7903146) impairs insulin secretion and that this defect would be exacerbated by acute free fatty acid (FFA)-induced insulin resistance. We studied 120 individuals of whom one-half were homozygous for the diabetes-associated allele TT at rs7903146 and one-half were homozygous for the protective allele CC. After a screening examination during which glucose tolerance status was determined, subjects were studied on two occasions in random order while undergoing an oral challenge. During one study day, FFA was elevated by infusion of Intralipid plus heparin. On the other study day, subjects received the same amount of glycerol as present in the Intralipid infusion. β-Cell responsivity indices were estimated with the oral C-peptide minimal model. We report that β-cell responsivity was slightly impaired in the TT genotype group. Moreover, the hyperbolic relationship between insulin secretion and β-cell responsivity differed significantly between genotypes. Subjects also exhibited impaired suppression of glucagon after an oral challenge. These data imply that a genetic variant harbored within the TCF7L2 locus impairs glucose tolerance through effects on glucagon as well as on insulin secretion.

A Model Approach: Mathematical modeling provides an increasingly clear picture of glucose and neural systems

Mathematical modeling of physiological systems is a fundamental milestone of biomedical engineering. Models allow for the quantitative understanding of the intimate functions of a biological system, estimating parameters that are not accessible to direct measurement and performing in silico trials by simulating and tracking a physiological system in case its function has been deranged. Modeling has always been central in the Italian biomedical engineering community. Here, we review the progress in two areas: glucose and neurocomputational modeling with an emphasis on their clinical impact.

Model-Based Quantification of Glucagon-Like Peptide-1-Induced Potentiation of Insulin Secretion in Response to a Mixed Meal Challenge

BACKGROUND

Glucagon-like peptide-1 (GLP-1) is a powerful insulin secretagogue that is secreted in response to meal ingestion. The ability to quantify the effect of GLP-1 on insulin secretion could provide insights into the pathogenesis and treatment of diabetes. We used a modification of a model of GLP-1 action on insulin secretion using data from a hyperglycemic clamp with concomitant GLP-1 infusion. We tested this model using data from a mixed meal test (MMT), thereby measuring GLP-1-induced potentiation of insulin secretion in response to a meal.

MATERIALS AND METHODS

The GLP-1 model is based on the oral C-peptide minimal model and assumes that over-basal insulin secretion depends linearly on GLP-1 concentration through the parameter Π, representing the β-cell sensitivity to GLP-1. The model was tested on 62 subjects across the spectrum of glucose tolerance (age, 53 ± 1 years; body mass index, 29.7 ± 0.6 kg/m(2)) studied with an MMT and provided a precise estimate of both β-cell responsivity and Π indices. By combining Π with a measure of L-cell responsivity to glucose, one obtains a potentiation index (PI) (i.e., a measure of the L-cell's function in relation to prevailing β-cell sensitivity to GLP-1).

RESULTS

Model-based measurement of GLP-1-induced insulin secretion demonstrates that the PI is significantly reduced in people with impaired glucose tolerance, compared with those with normal glucose tolerance.

CONCLUSIONS

We describe a model that can quantitate the GLP-1-based contribution to insulin secretion in response to meal ingestion. This methodology will allow a better understanding of β-cell function at various stages of glucose tolerance.

A Model for the Estimation of Hepatic Insulin Extraction After a Meal

GOAL

Quantitative assessment of hepatic insulin extraction (HE) after an oral glucose challenge, e.g., a meal, is important to understand the regulation of carbohydrate metabolism. The aim of the current study is to develop a model of system for estimating HE.

METHODS

Nine different models, of increasing complexity, were tested on data of 204 normal subjects, who underwent a mixed meal tolerance test, with frequent measurement of plasma glucose, insulin, and C-peptide concentrations. All these models included a two-compartment model of C-peptide kinetics, an insulin secretion model, a compartmental model of insulin kinetics (with number of compartments ranging from one to three), and different HE descriptions, depending on plasma glucose and insulin. Model performances were compared on the basis of data fit, precision of parameter estimates, and parsimony criteria.

RESULTS

The three-compartment model of insulin kinetics, coupled with HE depending on glucose concentration, showed the best fit and a good ability to precisely estimate the parameters. In addition, the model calculates basal and total indices of HE ( HE_b and HE_tot, respectively), and provides an index of HE sensitivity to glucose ( S_G^HE ).

CONCLUSION

A new physiologically based HE model has been developed, which allows an improved quantitative description of glucose regulation.

SIGNIFICANCE

The use of the new model provides an in-depth description of insulin kinetics, thus enabling a better understanding of a given subject's metabolic state.

2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial

BACKGROUND

An artificial pancreas (AP) that can be worn at home from dinner to waking up in the morning might be safe and efficient for first routine use in patients with type 1 diabetes. We assessed the effect on glucose control with use of an AP during the evening and night plus patient-managed sensor-augmented pump therapy (SAP) during the day, versus 24 h use of patient-managed SAP only, in free-living conditions.

METHODS

In a crossover study done in medical centres in France, Italy, and the Netherlands, patients aged 18-69 years with type 1 diabetes who used insulin pumps for continuous subcutaneous insulin infusion were randomly assigned to 2 months of AP use from dinner to waking up plus SAP use during the day versus 2 months of SAP use only under free-living conditions. Randomisation was achieved with a computer-generated allocation sequence with random block sizes of two, four, or six, masked to the investigator. Patients and investigators were not masked to the type of intervention. The AP consisted of a continuous glucose monitor (CGM) and insulin pump connected to a modified smartphone with a model predictive control algorithm. The primary endpoint was the percentage of time spent in the target glucose concentration range (3·9-10·0 mmol/L) from 2000 to 0800 h. CGM data for weeks 3-8 of the interventions were analysed on a modified intention-to-treat basis including patients who completed at least 6 weeks of each intervention period. The 2 month study period also allowed us to asses HbA1c as one of the secondary outcomes. This trial is registered with ClinicalTrials.gov, number NCT02153190.

FINDINGS

During 2000-0800 h, the mean time spent in the target range was higher with AP than with SAP use: 66·7% versus 58·1% (paired difference 8·6% [95% CI 5·8 to 11·4], p<0·0001), through a reduction in both mean time spent in hyperglycaemia (glucose concentration >10·0 mmol/L; 31·6% vs 38·5%; -6·9% [-9·8% to -3·9], p<0·0001) and in hypoglycaemia (glucose concentration <3·9 mmol/L; 1·7% vs 3·0%; -1·6% [-2·3 to -1·0], p<0·0001). Decrease in mean HbA1c during the AP period was significantly greater than during the control period (-0·3% vs -0·2%; paired difference -0·2 [95% CI -0·4 to -0·0], p=0·047), taking a period effect into account (p=0·0034). No serious adverse events occurred during this study, and none of the mild-to-moderate adverse events was related to the study intervention.

INTERPRETATION

Our results support the use of AP at home as a safe and beneficial option for patients with type 1 diabetes. The HbA1c results are encouraging but preliminary.

FUNDING

European Commission.

MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation

BACKGROUND

Neuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software.

RESULTS

This paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS.

CONCLUSIONS

MOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice.

Modeling Plasma-to-Interstitium Glucose Kinetics from Multitracer Plasma and Microdialysis Data

BACKGROUND

Quantitative assessment of the dynamic relationship between plasma and interstitial fluid (ISF) glucose and the estimation of the plasma-to-ISF delay are of major importance to determine the accuracy of subcutaneous glucose sensors, an essential component of open- and closed-loop therapeutic systems for type 1 diabetes mellitus (T1DM). The goal of this work is to develop a model of plasma-to-ISF glucose kinetics from multitracer plasma and interstitium data, obtained by microdialysis, in healthy and T1DM subjects, under fasting conditions.

MATERIALS AND METHODS

A specific experimental design, combining administration of multiple tracers with the microdialysis technique, was used to simultaneously frequently collect plasma and ISF data. Linear time-invariant compartmental modeling was used to describe glucose kinetics from the tracer data because the system is in steady state.

RESULTS

A two-compartment model was shown accurate and was identified from both plasma and ISF data. An "equilibration time" between plasma and ISF of 9.1 and 11.0 min (median) in healthy and T1DM subjects, respectively, was calculated.

CONCLUSIONS

We have demonstrated that, in steady-state condition, the glucose plasma-to-ISF kinetics can be modeled with a linear two-compartment model and that the "equilibration time" between the two compartments can be estimated with precision. Future studies will assess plasma-to-interstitium glucose kinetics during glucose and insulin perturbations in both healthy and T1DM subjects.

Epicardial and pericardial fat in type 2 diabetes: favourable effects of biliopancreatic diversion

PURPOSE

Ectopic fat is often identified in obese subjects who are susceptible to the development of type 2 diabetes mellitus (T2DM). The ectopic fat favours the decrease in insulin sensitivity (IS) and adiponectin levels. We aimed to evaluate the effect of biliopancreatic diversion (BPD) on the accumulation of ectopic fat, adiponectin levels and IS in obese with T2DM.

MATERIALS AND METHODS

A nonrandomised controlled study was performed on sixty-eight women: 19 lean-control (23.0 ± 2.2 kg/m(2)) and 18 obese-control (35.0 ± 4.8 kg/m(2)) with normal glucose tolerance and 31 obese with T2DM (36.3 ± 3.7 kg/m(2)). Of the 31 diabetic women, 20 underwent BPD and were reassessed 1 month and 12 months after surgery. The subcutaneous adipose tissue, visceral adipose tissue, epicardial adipose tissue and pericardial adipose tissue were evaluated by ultrasonography. The IS was assessed by a hyperglycaemic clamp, applying the minimal model of glucose.

RESULTS

One month after surgery, there was a reduction in visceral and subcutaneous adipose tissues, whereas epicardial and pericardial adipose tissues exhibited significant reduction at the 12-month assessment (p < 0.01). Adiponectin levels and IS were normalised 1 month after surgery, resembling lean-control values and elevated above the obese-control values (p < 0.01). After 12 months, the improvement in IS and adiponectin was maintained, and 17 of the 20 operated patients exhibited fasting glucose and glycated haemoglobin within the normal range.

CONCLUSIONS

After BPD, positive physiological adaptations occurred in grade I and II obese patients with T2DM. These adaptations relate to the restoration of IS and decreased adiposopathy and explain the acute (1 month) and chronic (12 months) improvements in the glycaemic control.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

CONTEXT

Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort.

OBJECTIVE

The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC.

DESIGN

This study reports a multicenter, outpatient, randomized, crossover clinical trial.

PATIENTS

Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites.

INTERVENTIONS

Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session.

MAIN OUTCOMES AND MEASURES

Time in the glucose range was 80-140 mg/dL, compared between both arms.

RESULTS

Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL.

CONCLUSIONS

A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

Glucagon sensitivity and clearance in type 1 diabetes: insights from in vivo and in silico experiments

Glucagon use in artificial pancreas for type 1 diabetes (T1D) is being explored for prevention and rescue from hypoglycemia. However, the relationship between glucagon stimulation of endogenous glucose production (EGP) viz., hepatic glucagon sensitivity, and prevailing glucose concentrations has not been examined. To test the hypothesis that glucagon sensitivity is increased at hypoglycemia vs. euglycemia, we studied 29 subjects with T1D randomized to a hypoglycemia or euglycemia clamp. Each subject was studied at three glucagon doses at euglycemia or hypoglycemia, with EGP measured by isotope dilution technique. The peak EGP increments and the integrated EGP response increased with increasing glucagon dose during euglycemia and hypoglycemia. However, the difference in dose response based on glycemia was not significant despite higher catecholamine concentrations in the hypoglycemia group. Knowledge of glucagon's effects on EGP was used to develop an in silico glucagon action model. The model-derived output fitted the obtained data at both euglycemia and hypoglycemia for all glucagon doses tested. Glucagon clearance did not differ between glucagon doses studied in both groups. Therefore, the glucagon controller of a dual hormone control system may not need to adjust glucagon sensitivity, and hence glucagon dosing, based on glucose concentrations during euglycemia and hypoglycemia.

Six and 12 Weeks of Caloric Restriction Increases β Cell Function and Lowers Fasting and Postprandial Glucose Concentrations in People with Type 2 Diabetes

BACKGROUND

Caloric restriction alone has been shown to improve insulin action and fasting glucose metabolism; however, the mechanism by which this occurs remains uncertain.

OBJECTIVE

We sought to quantify the effect of caloric restriction on β cell function and glucose metabolism in people with type 2 diabetes.

METHODS

Nine subjects (2 men, 7 women) with type 2 diabetes [BMI (in kg/m(2)): 40.6 ± 1.4; age: 58 ± 3 y; glycated hemoglobin: 6.9% ± 0.2%] were studied using a triple-tracer mixed meal after withdrawal of oral diabetes therapy. The oral minimal model was used to measure β cell function. Caloric restriction limited subjects to a pureed diet (<900 kcal/d) for the 12 wk of study. The studies were repeated after 6 and 12 wk of caloric restriction.

RESULTS

Fasting glucose concentrations decreased significantly from baseline after 6 wk of caloric restriction with no further reduction after a further 6 wk of caloric restriction (9.8 ± 1.3, 5.9 ± 0.2, and 6.2 ± 0.3 mmol/L at baseline and after 6 and 12 wk of caloric restriction, respectively; P = 0.01) because of decreased fasting endogenous glucose production (EGP: 20.4 ± 1.1, 16.2 ± 0.8, and 17.4 ± 1.1 μmol · kg(-1) · min(-1) at baseline and after 6 and 12 wk of caloric restriction, respectively; P = 0.03). These changes were accompanied by an improvement in β cell function measured by the disposition index (189 ± 51, 436 ± 68, and 449 ± 67 10(-14) dL · kg(-1) · min(-2) · pmol(-1) at baseline and after 6 and 12 wk of caloric restriction, respectively; P = 0.01).

CONCLUSIONS

Six weeks of caloric restriction lowers fasting glucose and EGP with accompanying improvements in β cell function in people with type 2 diabetes. An additional 6 wk of caloric restriction maintained the improvement in glucose metabolism. This trial was registered at clinicaltrials.gov as NCT01094054.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.

Visual Predictive Check in Models with Time-Varying Input Function

The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher.