Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.

BBACKGROUND: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and HIIT in individuals with type 1 diabetes (T1D). METHODS: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. RESULTS: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference (MARD) of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. CONCLUSIONS: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise, but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6 and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons.

DegraderTCM: A Computationally Sparing Approach for Predicting Ternary Degradation Complexes.

Proteolysis targeting chimeras (PROTACs or degraders) represent a novel therapeutic modality that has raised interest thanks to promising results and currently undergoing clinical testing. PROTACs induce the selective proteasomal degradation of undesired proteins by the formation of ternary complexes (TCs). Having knowledge of the 3D structure of TCs is crucial for the design of PROTAC drugs. Here, we describe DegraderTCM, a new computational method for modeling PROTAC-mediated TCs that requires low computational power and provides sound results in a short time span. We validated DegraderTCM against a selected set of experimentally determined structures and defined a method to predict the PROTAC degradation activity based on the computed TC structure. Finally, we modeled TCs of known degraders holding significance for defining the method's applicability domain. A retrospective analysis of structure-activity relationships unveiled possibilities for utilizing DegraderTCM in the initial stages of designing novel PROTAC drugs.

Chamelogk: A Chromatographic Chameleonicity Quantifier to Design Orally Bioavailable Beyond-Rule-of-5 Drugs.

New chemical modalities in drug discovery include molecules belonging to the bRo5 chemical space. Because of their complex and flexible structure, bRo5 compounds often suffer from a poor solubility/permeability profile. Chameleonicity describes the capacity of a molecule to adapt to the environment through conformational changes; the design of molecular chameleons is a medicinal chemistry strategy simultaneously optimizing solubility and permeability. A default method to quantify chameleonicity in early drug discovery is still missing. Here we introduce Chamelogk, an automated, fast, and cheap chromatographic descriptor of chameleonicity. Moreover, we report measurements for 55 Ro5 and bRo5 compounds and validate our method with literature data. Then, selected case studies (macrocycles, nonmacrocyclic compounds, and PROTACs) are used to illustrate the application of Chamelogk in combination with lipophilicity (BRlogD) and polarity (Δ log kwIAM) descriptors. Overall, we show how Chamelogk deserves being included in property-based drug discovery strategies to design oral bioavailable bRo5 compounds.

In Silico Tools to Extract the Drug Design Information Content of Degradation Data: The Case of PROTACs Targeting the Androgen Receptor.

Proteolysis-Targeting Chimeras (PROTACs) have recently emerged as a promising technology in the drug discovery landscape. Large interest in the degradation of the androgen receptor (AR) as a new anti-prostatic cancer strategy has resulted in several papers focusing on PROTACs against AR. This study explores the potential of a few in silico tools to extract drug design information from AR degradation data in the format often reported in the literature. After setting up a dataset of 92 PROTACs with consistent AR degradation values, we employed the Bemis-Murcko method for their classification. The resulting clusters were not informative in terms of structure-degradation relationship. Subsequently, we performed Degradation Cliff analysis and identified some key aspects conferring a positive contribution to activity, as well as some methodological limits when applying this approach to PROTACs. Linker structure degradation relationships were also investigated. Then, we built and characterized ternary complexes to validate previous results. Finally, we implemented machine learning classification models and showed that AR degradation for VHL-based but not CRBN-based PROTACs can be predicted from simple permeability-related 2D molecular descriptors.

Effect of Ethanol Consumption on the Accuracy of a Glucose Oxidase-Based Subcutaneous Glucose Sensor in Subjects with Type 1 Diabetes.

Continuous glucose monitors (CGM) have improved the management of patients with type 1 diabetes (T1D), with glucose oxidase (GOx)-based sensors being the most used. However, they are potentially subject to both electrochemical and enzymatic interferences, including those related to changes of pH. The objective of this study is to investigate the effect of ethanol, given as beer along with a mixed meal, on the accuracy of a commercial GOx-CGM. Data from 12 T1D participants in a randomized crossover trial to evaluate the effect of meal composition and alcohol consumption on postprandial glucose concentration were used. Absolute error (AE) and mean absolute relative difference (MARD) were calculated. The differences between the alcohol and nonalcohol scenarios were assessed using the Mann−Whitney U and Wilcoxon signed-rank tests. The AE in the alcohol study was low, but significantly greater as compared to the study without alcohol (p-value = 0.0418). The MARD was numerically but not significantly greater. However, both variables were greater at pH < 7.36 and significantly affected by time only in the alcohol arm. In T1D, alcohol consumption affects the accuracy of a GOx-CGM. This effect could be at least partially related to the ethanol-induced changes in pH.

Effect of meal composition and alcohol consumption on postprandial glucose concentration in subjects with type 1 diabetes: a randomized crossover trial.

INTRODUCTION: Meal composition is known to affect glycemic variability and glucose control in type 1 diabetes. The objective of this work was to evaluate the effect of high carbohydrate meals of different nutritional composition and alcohol on the postprandial glucose response in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS: Twelve participants were recruited to this randomized crossover trial. Following a 4-week run-in period, participants received a mixed meal on three occasions with the same carbohydrate content but different macronutrient composition: high protein-high fat with alcohol (0.7g/kg body weight, beer), high protein-high fat without alcohol, and low protein-low fat without alcohol at 2-week intervals. Plasma and interstitial glucose, insulin, glucagon, growth hormone, cortisol, alcohol, free fatty acids, lactate, and pH concentrations were measured during 6 hours. A statistical analysis was then carried out to determine significant differences between studies. RESULTS: Significantly higher late postprandial glucose was observed in studies with higher content of fats and proteins (p=0.0088). This was associated with lower time in hypoglycemia as compared with the low protein and fat study (p=0.0179), at least partially due to greater glucagon concentration in the same period (p=0.04). Alcohol significantly increased lactate, decreased pH and growth hormone, and maintained free fatty acids suppressed during the late postprandial phase (p<0.001), without significant changes in plasma glucose. CONCLUSIONS: Our data suggest that the addition of proteins and fats to carbohydrates increases late postprandial blood glucose. Moreover, alcohol consumption together with a mixed meal has relevant metabolic effects without any increase in the risk of hypoglycemia, at least 6 hours postprandially. TRIAL REGISTRATION NUMBER: NCT03320993.

Estudio de receptor anómalo suprarrenal en sujetos con síndrome de Cushing ACTH-independiente e hiperplasia nodular suprarrenal / Study of abnormal adrenal receptors in subjects with ACTH-independent Cushing's syndrome and nodular adrenal hyperplasia

INTRODUCCIÓN: El síndrome de Cushing ACTH-independiente (SCAI) supone el 15-20% de los casos de síndrome de Cushing, de los cuales <1% son debidos a receptor anómalo. Nuestro objetivo es estudiar la presencia de receptor anómalo en los sujetos diagnosticados de SCAI con hiperplasia nodular suprarrenal en un período de 14 años (2002-2016), así como sus características clínico-biológicas y evolutivas. MATERIAL Y MÉTODOS: Estudio descriptivo multicéntrico de una serie de 15 casos de SCAI con hiperplasia nodular suprarrenal (período de estudio: 2002-2016). En ellos se hizo el despistaje de receptor anómalo, mediante pruebas de estimulación, considerando patológico un aumento de cortisol plasmático ≥ del 25% respecto al valor basal. RESULTADOS: De los 15 casos, 13 fueron mujeres, con una edad media al diagnóstico de 56,8 años. En 12 de los 15 casos estudiados se detectó positividad de las pruebas de estimulación. De ellos, fueron positivos para comida de prueba el 25%, para test postural de deambulación el 58,3%, para desmopresina el 33,3%, para terlipresina el 25%, para GnRH el 33,3%, para LH el 25% y para metoclopramida el 50%. En cuanto al tratamiento, se llevó a cabo suprarrenalectomía bilateral en el 16,7% y unilateral en el 41,7%. El resto continúan en observación con revisiones periódicas (41,7%). CONCLUSIONES: En la mayor parte de los casos estudiados con SCAI e hiperplasia nodular suprarrenal (80%) se detecta una respuesta de cortisol anormal debida a la presencia de receptor anómalo. La prueba con mayor porcentaje de positividad fue el test postural de deambulación (58,3%)

INTRODUCTION: ACTH-independent Cushing's Syndrome (AICS) accounts for 15-20% of cases of Cushing's syndrome, with < 1% due to abnormal receptors. Our aim is to study the presence of abnormal receptors in subjects diagnosed with AICS with nodular adrenal hyperplasia in a 14-year period (2002-2016), as well as its clinical-biological and evolutive characteristics. MATERIAL AND METHODS: A multicentre descriptive study of a 15-case series of AICS with nodular adrenal hyperplasia (study period: 2002-2016). In these cases, abnormal receptor screening was performed by means of stimulation tests, with a plasma cortisol increase of ≥ 25% from baseline being considered pathologic. RESULTS: Of the 15 cases, 13 were female, with a mean age at diagnosis of 56.8 years. In 12 of the 15 cases studied, positivity was detected with stimulation tests, and, of them, 25% were positive for the meal test, 58.3% for posture walking test, 33.3% for desmopressin; 25% for terlipressin; 33.3% for GnRH; 25% for LH and 50% for metoclopramide. Regarding treatment, bilateral adrenalectomy was performed in 16.7% and unilateral adrenalectomy in 41.7%. The rest continue under observation with periodic follow-up (41.7%). CONCLUSIONS: In most of the cases studied with AICS and nodular adrenal hyperplasia (80%), an abnormal cortisol response is detected due to the presence of abnormal receptors. The test with the highest percentage of positivity was the postural walking test (58.3%)

Study of abnormal adrenal receptors in subjects with ACTH-independent Cushing's syndrome and nodular adrenal hyperplasia. / Estudio de receptor anómalo suprarrenal en sujetos con síndrome de Cushing ACTH-independiente e hiperplasia nodular suprarrenal.

INTRODUCTION: ACTH-independent Cushing's Syndrome (AICS) accounts for 15-20% of cases of Cushing's syndrome, with <1% due to abnormal receptors. Our aim is to study the presence of abnormal receptors in subjects diagnosed with AICS with nodular adrenal hyperplasia in a 14-year period (2002-2016), as well as its clinical-biological and evolutive characteristics. MATERIAL AND METHODS: A multicentre descriptive study of a 15-case series of AICS with nodular adrenal hyperplasia (study period: 2002-2016). In these cases, abnormal receptor screening was performed by means of stimulation tests, with a plasma cortisol increase of ≥ 25% from baseline being considered pathologic. RESULTS: Of the 15 cases, 13 were female, with a mean age at diagnosis of 56.8 years. In 12 of the 15 cases studied, positivity was detected with stimulation tests, and, of them, 25% were positive for the meal test, 58.3% for posture walking test, 33.3% for desmopressin; 25% for terlipressin; 33.3% for GnRH; 25% for LH and 50% for metoclopramide. Regarding treatment, bilateral adrenalectomy was performed in 16.7% and unilateral adrenalectomy in 41.7%. The rest continue under observation with periodic follow-up (41.7%). CONCLUSIONS: In most of the cases studied with AICS and nodular adrenal hyperplasia (80%), an abnormal cortisol response is detected due to the presence of abnormal receptors. The test with the highest percentage of positivity was the postural walking test (58.3%).

Seasonal Local Models for Glucose Prediction in Type 1 Diabetes.

Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.

Statements and Recommendations for Guided Bone Regeneration: Consensus Report of the Guided Bone Regeneration Symposium Held in Bologna, October 15 to 16, 2016.

INTRODUCTION: In the past 10 years, long-term studies have demonstrated that guided bone regeneration (GBR) is a successful and reliable technique for vertical and horizontal ridge augmentation, but strict and rigorous protocols must be adopted. MATERIAL AND METHODS: Because no reports have yet been published with statements and clinical recommendations for GBR, a closed meeting of all authors was organized to discuss this matter during a GBR symposium held in Bologna (Italy) in October 2016. The authors focused on the findings of systematic and narrative reviews, prepared before the meeting, covering aspects of the clinical management of GBR techniques. Successively, a discussion based on the scientific evidence and on the experts' opinions led to the formulation of statements, clinical recommendations, and implications for future research. RESULTS: To avoid complications and to optimize outcomes, the following factors should be considered by clinicians: patient selection; analysis of defect type; blood supply; antibiotic treatment; flap passivation; delayed implant placement; combination of autogenous bone and xenograft or allograft; rigorous fixation of membranes; removal after 6 to 9 months; analysis of complications; soft-tissue management; and high care in scarred sites and in esthetic areas. CONCLUSIONS: The present consensus report reviewed the scientific evidence and provided specific guidelines and recommendations for clinical practice and the different approaches to GBR techniques to ensure surgical success and predictable outcomes.

Plasma Insulin Levels and Hypoglycemia Affect Subcutaneous Interstitial Glucose Concentration.

BACKGROUND: Continuous glucose monitoring (CGM) accuracy during hypoglycemia is suboptimal. This might be partly explained by insulin or hypoglycemia-induced changes in the plasma interstitial subcutaneous (SC) fluid glucose gradient. The aim of the present study was to assess the role of plasma insulin (PI) and hypoglycemia itself in the plasma and interstitial SC fluid glucose concentration in patients with type 1 diabetes mellitus. METHODS: Eleven subjects with type 1 diabetes (age 36.5 ± 9.1 years, HbA1c 7.9 ± 0.4% [62.8 ± 2.02 mmol/mol]; mean ± standard deviation) were evaluated under hyperinsulinemic euglycemia and hypoglycemia. Each subject underwent two randomized crossover clamps with either a primed 0.3 (low insulin) or 1 mU/(kg·min) (high insulin) insulin infusion. The raw CGM signal was normalized with median preclamp values to obtain a standardized measure of the interstitial glucose (IG) concentration before statistical analysis. RESULTS: The mean PI concentration was greater in high insulin studies (HISs) versus low insulin studies (LISs) (412.89 ± 13.63 vs. 177.22 ± 10.05 pmol/L). During hypoglycemia, glucagon, adrenaline, free fatty acids, glycerol, and beta-OH-butyrate were higher in the LIS (P < 0.0001). Likewise, the IG concentration was significantly different (P < 0.0001). This was due to lower IG concentration than plasma glucose (PG) concentration during the euglycemic hyperinsulinemic phases in the HIS. In contrast, no difference was observed during hypoglycemia. This was the result of an unchanged PG/IG gradient during the entire LIS, while in the HIS, this gradient increased during the hyperinsulinemic euglycemia phase. CONCLUSION: Both PI levels and hypoglycemia affect the relationship between IG and PG concentration. ClinicalTrials.gov Identifier: NCT01714895.

Extensive Assessment of Blood Glucose Monitoring During Postprandial Period and Its Impact on Closed-Loop Performance.

BACKGROUND: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. METHODS: We performed an extensive analysis of sensor's performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. RESULTS: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). CONCLUSIONS: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target.

BACKGROUND: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. OBJECTIVE: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. METHODS: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (Cmax), and time to Cmax glucose concentrations and time in range (<70, 70-180, >180 mg/dL). RESULTS: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). CONCLUSIONS: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements.

Continuous glucose monitors can measure interstitial glucose concentration in real time for closed-loop glucose control systems, known as artificial pancreas. These control systems use an insulin feedback to maintain plasma glucose concentration within a narrow and safe range, and thus to avoid health complications. As it is not possible to measure plasma insulin concentration in real time, insulin models have been used in literature to estimate them. Nevertheless, the significant inter- and intra-patient variability of insulin absorption jeopardizes the accuracy of these estimations. In order to reduce these limitations, our objective is to perform a real-time estimation of plasma insulin concentration from continuous glucose monitoring (CGM). Hovorka's glucose-insulin model has been incorporated in an extended Kalman filter in which different selected time-variant model parameters have been considered as extended states. The observability of the original Hovorka's model and of several extended models has been evaluated by their Lie derivatives. We have evaluated this methodology with an in-silico study with 100 patients with Type 1 diabetes during 25 h. Furthermore, it has been also validated using clinical data from 12 insulin pump patients with Type 1 diabetes who underwent four mixed meal studies. Real-time insulin estimations have been compared to plasma insulin measurements to assess performance showing the validity of the methodology here used in comparison with that formerly used for insulin models. Hence, real-time estimations for plasma insulin concentration based on subcutaneous glucose monitoring can be beneficial for increasing the efficiency of control algorithms for the artificial pancreas.

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring.

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.

Metabolism of insulin glargine after repeated daily subcutaneous injections in subjects with type 2 diabetes.

OBJECTIVE: To investigate concentration of plasma insulin glargine after its subcutaneous dosing compared with concentration of its metabolites 1 (M1) and 2 (M2) in subjects with type 2 diabetes. RESEARCH DESIGN AND METHODS: Nine subjects underwent a 32-h euglycemic glucose clamp study (0.4 units/kg glargine after 1 week of daily glargine administration). Glargine, M1, and M2 were measured by a specific liquid chromatography-tandem mass spectrometry assay. RESULTS: Glargine was detected in only five of the nine subjects, at few time points, and at negligible concentrations. M1 was detected in all subjects and exhibited the same pattern as traditional radioimmunoassay-measured plasma insulin. M2 was not detected at all. CONCLUSIONS: After subcutaneous injection, glargine was minimally detectable in blood, whereas its metabolite M1 accounted for most (>90%) of the plasma insulin concentration and metabolic action of the injected glargine.

Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing--the iBolus--in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial.

OBJECTIVE: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. SUBJECTS AND METHODS: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t(0)) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG(0-5h)) and variability as the individual coefficient of variation (CV) of AUC-PG(0-5h). The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t(0) (PG(t0)), and insulin dose to AUC-PG(0-5h) and its CV was also investigated. RESULTS: AUC-PG(0-5h) was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG(t0) being the best predictor of AUC-PG(0-5h) explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied. CONCLUSIONS: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Commentary on "Performance of a glucose meter with a built-in automated bolus calculator versus manual bolus calculation in insulin-using subjects".

Since the early 2000s, there has been an exponentially increasing development of new diabetes-applied technology, such as continuous glucose monitoring, bolus calculators, and "smart" pumps, with the expectation of partially overcoming clinical inertia and low patient compliance. However, its long-term efficacy in glucose control has not been unequivocally proven. In this issue of Journal of Diabetes Science and Technology, Sussman and colleagues evaluated a tool for the calculation of the prandial insulin dose. A total of 205 insulin-treated patients were asked to compute a bolus dose in two simulated conditions either manually or with the bolus calculator built into the FreeStyle InsuLinx meter, revealing the high frequency of wrong calculations when performed manually. Although the clinical impact of this study is limited, it highlights the potential implications of low diabetesrelated numeracy in poor glycemic control. Educational programs aiming to increase patients' empowerment and caregivers' knowledge are needed in order to get full benefit of the technology.

A multiple local models approach to accuracy improvement in continuous glucose monitoring.

BACKGROUND: Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance. METHODS: CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations G, at different time instants [I(k), I(k)(-1), G(k)(-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm. RESULTS: Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD)=10.8±1.5% versus 14.7±5.4%, respectively (P=0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8±2.6%, P<0.05). CONCLUSIONS: The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.

Differential effects of adiposity on pharmacodynamics of basal insulins NPH, glargine, and detemir in type 2 diabetes mellitus.

OBJECTIVE: To assess the role of adiposity on the pharmacodynamics of basal insulins NPH, detemir, and glargine in type 2 diabetes mellitus (T2DM), as estimated by glucose infusion rate (GIR) and endogenous glucose production (EGP) rate in the euglycemic clamp. RESEARCH DESIGN AND METHODS: We examined the variables that best predicted GIR and EGP in 32-h clamp studies after treatment with subcutaneous injection of 0.4 units/kg NPH, detemir, and glargine in 18 T2DM subjects (crossover). RESULTS: A multiple regression analysis revealed that BMI best predicted GIR variation during the clamp. BMI was inversely correlated with GIR in all three insulin treatments, but was statistically significant in detemir treatment only. BMI correlated positively with residual suppression of EGP in detemir, but not with glargine and NPH treatments. CONCLUSIONS: Adiposity blunts the pharmacodynamics of all basal insulins in T2DM. However, as adiposity increases, the effect of detemir is lower versus NPH and glargine.