The Fast-Glycator Phenotype, Skin Advanced Glycation End Products, and Complication Burden Among People With Type 1 Diabetes.

OBJECTIVE: Existence of a fast-glycator phenotype among people with type 1 diabetes (T1D) is debated. Routine use of glucose sensors allows the comparison of long-term average glucose levels with laboratory HbA1c values. We herein evaluated whether participants with T1D and HbA1c values higher than their glucose management indicator (GMI) had greater accumulation of advanced glycation end products (AGEs) and chronic complications. RESEARCH DESIGN AND METHODS: We included participants with T1D using the intermittently scanned continuous glucose monitoring system consecutively for at least 90 days and having a laboratory-determined HbA1c at the end of observation. Skin AGEs were estimated using the skin autofluorescence (SAF) method. The complication burden was assessed by a standardized screening. The fast-glycator phenotype was defined as having a GMI to HbA1c ratio <0.9. RESULTS: We included 135 individuals with T1D (58% men; mean age, 44.4 years) with a mean diabetes duration of 21 years and a mean HbA1c value of 7.7%. Thirty (22.2%) were defined as having the fast-glycator phenotype. As expected, fast glycators had higher HbA1c (8.6% vs. 7.5%; P < 0.001) with similar 90-day mean glucose level (172 vs. 168 mg/dL; P = 0.52). Fast glycators had higher SAF than did other participants (2.5 vs. 2.1 arbitrary units; P = 0.005) and had a significantly higher prevalence of dyslipidemia (73% vs. 44%; P = 0.005), macroangiopathy (38% vs. 9%; P = 0.001), albuminuria (25% vs. 7%; P = 0.038), and retinopathy (61% vs. 38%; P = 0.022). After adjusting for age and dyslipidemia, the fast-glycator phenotype remained significantly associated with macroangiopathy (odds ratio 3.72; 95% CI 1.22-11.4). CONCLUSIONS: In T1D, a fast-glycator phenotype defined by the GMI to HbA1c ratio is characterized by elevated skin AGEs and is associated with the complication burden.

Newly-diagnosed diabetes and admission hyperglycemia predict COVID-19 severity by aggravating respiratory deterioration.

AIMS: We investigated whether pre-existing diabetes, newly-diagnosed diabetes, and admission hyperglycemia were associated with COVID-19 severity independently from confounders. METHODS: We retrospectively analyzed data on patients with COVID-19 hospitalized between February and April 2020 in an outbreak hospital in North-East Italy. Pre-existing diabetes was defined by self-reported history, electronic medical records, or ongoing medications. Newly-diagnosed diabetes was defined by HbA1c and fasting glucose. The primary outcome was a composite of ICU admission or death. RESULTS: 413 subjects were included, 107 of whom (25.6%) had diabetes, including 21 newly-diagnosed. Patients with diabetes were older and had greater comorbidity burden. The primary outcome occurred in 37.4% of patients with diabetes compared to 20.3% in those without (RR 1.85; 95%C.I. 1.33-2.57; p < 0.001). The association was stronger for newly-diagnosed compared to pre-existing diabetes (RR 3.06 vs 1.55; p = 0.004). Higher glucose level at admission was associated with COVID-19 severity, with a stronger association among patients without as compared to those with pre-existing diabetes (interaction p < 0.001). Admission glucose was correlated with most clinical severity indexes and its association with adverse outcome was mostly mediated by a worse respiratory function. CONCLUSION: Newly-diagnosed diabetes and admission hyperglycemia are powerful predictors of COVID-19 severity due to rapid respiratory deterioration.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. METHODS: We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. RESULTS: The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). CONCLUSION: The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Impaired cognitive processing speed in type 1 diabetic patients who had severe/recurrent hypoglycaemia.

AIMS: To detect whether adults with type 1 diabetes mellitus (T1DM) have lower cognitive performance than healthy individuals and to detect risk factors for low cognitive performance. METHODS: Twenty-six adults with T1DM and twenty-six healthy subjects matched for age, gender and educational level were compared for cognitive performance by a chronometric computerized test measuring visuo-spatial working memory (N-Back) and by two validated neuropsychological tests (Mini Mental State Examination, Animal Naming Test). Clinical data about diabetes duration, average daily insulin dosage, glycated haemoglobin, retinopathy, urine albumin-creatinine ratio, previous hypoglycaemic coma and awareness of hypoglycaemia were obtained from medical records. Basal pre-test glycemia and blood pressure were measured for each patient. RESULTS: No differences were found between patients (n = 26) and healthy controls (n = 26) in neuropsychological tests. Within diabetic patients, those with impaired awareness of hypoglycaemia (n = 7) or history of coma in the recent 1-3 years (n = 5) had psychomotor slowing at the N-Back test (592 ±â€¯35 vs. 452 ±â€¯21 ms and 619 ±â€¯40 vs. 462 ±â€¯19 ms, respectively; both p < 0.01). The variables related to diabetic severity did not show a relationship with reaction times of the N-Back test. CONCLUSION: Psychomotor speed slowing is detectable in patients with T1DM who have a history of previous hypoglycaemic episodes or coma.

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data.

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters-age, sex, BMI, and waist circumference-with an accuracy of 87.1%.

Cognitive, neurophysiologic and metabolic sequelae of previous hypoglycemic coma revealed by hyperinsulinemic-hypoglycemic clamp in type 1 diabetic patients.

To examine the relationship between electroencephalographic (EEG) activity and hypoglycemia unawareness, we investigated early parameters of vigilance and awareness of various symptom categories in response to hypoglycemia in intensively treated type 1 diabetic (T1DM) patients with different degrees of hypoglycemia unawareness. Hypoglycemia was induced with a hyperinsulinemic-hypoglycemic clamp in six T1DM patients with a history of hypoglycemia unawareness previous severe hypoglycemic coma (SH) and in six T1DM patients without (C) history of hypoglycemia unawareness previous severe hypoglycemic coma. Cognitive function tests (four choice reaction time), counterregulatory responses (adrenaline), and symptomatic responses were evaluated at euglycemia (90 mg/dl) and during step-wise plasma glucose reduction (68, 58 and 49 mg/dl). EEG activity was recorded continuously throughout the study and analyzed by spectral analysis. Cognitive function deteriorated significantly at a glucose threshold of 55 ± 1 mg/dl in both groups (p = ns) during hypoglycemia, while the glucose threshold for autonomic symptoms was significantly lower in SH patients than in C patients (49 ± 1 vs. 54 ± 1 mg/dl, p < 0.05, respectively). In SH patients, eye-closed resting EEG showed a correlation between the mean dominance frequency and plasma glucose (r = 0.62, p < 0.001). Theta relative power increased during controlled hypoglycemia compared to euglycemia (21.6 ± 6 vs. 15.5 ± 3% Hz p < 0.05) and was higher than in the C group (21.6 ± 6 vs. 13.8 ± 3%, p < 0.03). The cognitive task beta activity was lower in the SH group than in the C group (14.8 ± 3 Hz, vs. 22.6 ± 4 vs. p < 0.03). Controlled hypoglycemia elicits cognitive dysfunction in both C and SH patients; however, significant EEG alterations during hypoglycemia were detected mainly in patients with a history of hypoglycemia unawareness and previous severe hypoglycemic coma. These data suggest that prior episodes of hypoglycemic coma modulate brain electric activity.

The GesTIO protocol experience: safety of a standardized order set for subcutaneous insulin regimen in elderly hospitalized patients.

BACKGROUNDS: In non-critical hospitalized patients with diabetes mellitus, guidelines suggest subcutaneous insulin therapy with basal-bolus regimen, even in old and vulnerable inpatients. AIM: To evaluate safety, efficacy, and benefit on clinical management of the GesTIO protocol, a set of subcutaneous insulin administration rules, in old and vulnerable non-ICU inpatients. METHODS: Retrospective, observational study. Patients admitted to Geriatric Clinic of Padua were studied. 88 patients matched the inclusion criteria: type 2 diabetes or hospital-related hyperglycemia, ≥65 years, regular measurements of capillary glycemia, and basal-bolus subcutaneous insulin regimen managed by "GesTIO protocol" for five consecutive days. MAIN OUTCOME MEASURES: ratio of patients with blood glucose (BG) <3.9 mmol/l; number of BG per patient in target range (5-11.1 mmol/l); daily mean BG; and calls to physicians for adjusting insulin therapy. RESULTS: Mean age was 82 ± 7 years. 9.1% patients experienced mild hypoglycaemia, and no severe hypoglycaemia was reported. The median number of BG per patients in target range increased from 2.0 ± 2 to 3.0 ± 2 (p < 0.001). The daily mean BG decreased from 11.06 ± 3.03 to 9.64 ± 2.58 mmol/l (-12.8%, p < 0.005). The mean number of calls to physicians per patient decreased from 0.83 to 0.45 (p < 0.05). CONCLUSIONS: Treatment with GesTIO protocol allows a safe and effective treatment even in very old and vulnerable inpatients with a faster management insulin therapy.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis.

BACKGROUND: Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets. MATERIALS AND METHODS: A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them. RESULTS: SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets. CONCLUSIONS: The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Use of an integrated strip-free blood glucose monitoring system increases frequency of self-monitoring and improves glycemic control: Results from the ExAct study.

AIMS: We investigated the impact of using an integrated, strip-free system compared to the use of single-strip systems on testing frequency and glycemic control in individuals with insulin-treated diabetes. METHODS: This multinational, comparative, cluster-randomized, observational study included 311 patients with type 1 and insulin-treated type 2 diabetes who were performing SMBG at suboptimal frequencies. Sites were cluster-randomized to "integrated strip-free" system (EXP group) or any "single-strip" system (CNL group). Testing frequency and HbA1c were measured at baseline, 12 weeks and 24 weeks. RESULTS: At week 24, the EXP group showed an increase in SMBG frequency from baseline of 4.17 tests/week (95% CI 2.76, 5.58) compared with an increase of 0.53 tests/week (95% CI -0.73, 1.79) among CNL patients, resulting in a between-group difference of 3.63 tests/week (p < 0.0002). Mixed-effects models for repeated measurements (MMRM) controlling for baseline frequency of testing, country and clinical site confirmed a higher SMBG testing frequency in the EXP group compared to the CNL group, with a between-group difference of 2.70 tests/week (p < 0.01). Univariate analysis showed greater HbA1c reductions in the EXP group than CNL group: -0.44% (95% CI -0.59, -0.29) vs. -0.13% (95% CI -0.27, 0.01), respectively, p < 0.0002. MMRM analyses confirmed these HbA1c reductions. A greater percentage of EXP than CNL patients achieved HbA1c reductions of ≥0.5%: 45.1% vs. 29.1%, respectively, p < 0.01. CONCLUSIONS: The use of an integrated, strip-free SMBG system improved testing adherence and was associated with improvements in glycemic control.

Italian contributions to the development of continuous glucose monitoring sensors for diabetes management.

Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community.

Hypoglycemic syndrome in a patient with proinsulin-only secreting pancreatic adenoma (proinsulinoma).

We describe an unusual case of hypoglycemic syndrome in a 69-year old woman with a proinsulin-only secreting pancreatic endocrine adenoma. The clinical history was highly suggestive of an organic hypoglycemia, with normal or relatively low insulin concentrations and elevated proinsulin levels. Magnetic resonance and computed tomography of the abdomen showed a 1 cm pancreatic nodule and multiple accessory spleens. The diagnosis was confirmed by selective angiography, showing location and vascularization of the nodule, despite no response to intra-arterial calcium. After resection, the hypoglycemic syndrome resolved. The surgical specimen was comprised of a neuroendocrine adenomatous tissue with high proinsulin immunoreactivity. Study of this unusual case of proinsulinoma underlines (i) the need to assay proinsulin in patients with hypoglycemia and normal immunoreactive insulin, (ii) the differential diagnosis in the presence of accessory spleens, (iii) the unresponsiveness to intra-arterial calcium stimulation, and (iv) the extensive evaluation needed to reach a final diagnosis.

Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: summary of the results.

BACKGROUND: In 2008-2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). METHODS: The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N=300 synthetic "subjects" with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3-4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. RESULTS: In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p<.01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p=.03). CONCLUSIONS: In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.

Continuous glucose monitoring reveals delayed nocturnal hypoglycemia after intermittent high-intensity exercise in nontrained patients with type 1 diabetes.

OBJECTIVE: Exercise is a cornerstone of diabetes therapy in type 1 diabetes mellitus (DMT1) patients. The type of exercise is important in determining the propensity to hypoglycemia. We assessed, by continuous glucose monitoring (CGM), the glucose profiles during and in the following 20h after a session of two different types of exercise. RESEARCH DESIGN AND METHODS: Eight male volunteers with well-controlled DMT1 were studied. They underwent 30min of both intermittent high-intensity exercise (IHE) and moderate-intensity exercise (MOD) in random order. Expired air was recorded during exercise, while metabolic and hormonal determinations were performed before and for 120 min after exercises. The CGM system and activity monitor were applied for the subsequent 20h. RESULTS: Blood glucose level declined during both type of exercise. At 150 min following the start of exercise, plasma glucose content was slightly higher after IHE. No changes were observed in plasma insulin concentration. A significant increase of norepinephrine concentration was noticed during IHE. Between midnight and 6:00 a.m. the glucose levels were significantly lower after IHE than those observed after MOD (area under the curve, 23.3 ± 3 vs. 16 ± 3 mg/dL/420 min [P = 0.04]; mean glycemia at 3 a.m., 225 ± 31 vs. 147 ± 17 mg/dL [P<0.05]). The number of hypoglycemic episodes after IHE was higher than that observed after MOD (seven vs. two [P<0.05]). CONCLUSIONS: We demonstrate that (1) CGM is a useful approach in DMT1 patients who undergo an exercise program and (2) IHE is associated with delayed nocturnal hypoglycemia.

Practical guidance to insulin management.

The practical guidance to insulin management is a simple tool for health care providers, particularly primary care physicians (PCPs). Developed by experts in diabetes care at an international meeting, it aims to help physicians make key decisions to optimize insulin management and decrease long-term morbidity risk. With a growing role for PCPs in type 2 diabetes, the practical guidance focuses on confident, appropriate and timely insulin initiation. Using the acronym 'TIME' (Targets, Insulin, Managing weight, Encouragement and support) the practical guidance aims, in a visually appealing format, to help physicians address the challenges of insulin management with their patients, from diagnosis through insulin initiation to follow-up.

The transcriptional response in human umbilical vein endothelial cells exposed to insulin: a dynamic gene expression approach.

BACKGROUND: In diabetes chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance. METHODOLOGY AND PRINCIPAL FINDINGS: The transcriptional response of endothelial cells in the 440 minutes following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway-based enrichment analysis revealed "Electron Transport Chain" significantly enriched. Results were validated on genes belonging to "Electron Transport Chain" pathway, using quantitative RT-PCR. CONCLUSIONS: As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction.

Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: preliminary studies in Padova and Montpellier.

New effort has been made to develop closed-loop glucose control, using subcutaneous (SC) glucose sensing and continuous subcutaneous insulin infusion (CSII) from a pump, and a control algorithm. An approach based on a model predictive control (MPC) algorithm has been utilized during closed-loop control in type 1 diabetes patients. Here we describe the preliminary clinical experience with this approach. Six type 1 diabetes patients (three in each of two clinical investigation centers in Padova and Montpellier), using CSII, aged 36 +/- 8 and 48 +/- 6 years, duration of diabetes 12 +/- 8 and 29 +/- 4 years, hemoglobin A1c 7.4% +/- 0.1% and 7.3% +/- 0.3%, body mass index 23.2 +/- 0.3 and 28.4 +/- 2.2 kg/m(2), respectively, were studied on two occasions during 22 h overnight hospital admissions 2-4 weeks apart. A Freestyle Navigator(R) continuous glucose monitor and an OmniPod insulin pump were applied in each trial. Admission 1 used open-loop control, while admission 2 employed closed-loop control using our MPC algorithm. In Padova, two out of three subjects showed better performance with the closed-loop system compared to open loop. Altogether, mean overnight plasma glucose (PG) levels were 134 versus 111 mg/dl during open loop versus closed loop, respectively. The percentage of time spent at PG > 140 mg/dl was 45% versus 12%, while postbreakfast mean PG was 165 versus 156 mg/dl during open loop versus closed loop, respectively. Also, in Montpellier, two patients out of three showed a better glucose control during closed-loop trials. Avoidance of nocturnal hypoglycemic excursions was a clear benefit during algorithm-guided insulin delivery in all cases. This preliminary set of studies demonstrates that closed-loop control based entirely on SC glucose sensing and insulin delivery is feasible and can be applied to improve glucose control in patients with type 1 diabetes, although the algorithm needs to be further improved to achieve better glycemic control.

Continuous glucose monitoring time series and hypo/hyperglycemia prevention: requirements, methods, open problems.

A clinically important task in diabetes management is the prevention of hypo/hyperglycemic events. The availability of continuous glucose monitoring (CGM) devices allow to develop new strategies, but new problems have also emerged. In this contribution, we discuss three major challenges which, in practical real time CGM applications, should be dealt with: filtering to enhance the signal-to-noise ratio, ahead-of-time prediction of glucose concentration, and generation of hypo/hyper-alerts. For all these challenges, some techniques, with a different degree of sophistication, have been proposed recently in the literature, but several issues remain open.

Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series.

A clinically important task in diabetes management is the prevention of hypo/hyperglycemic events. In this proof-of-concept paper, we assess the feasibility of approaching the problem with continuous glucose monitoring (CGM) devices. In particular, we study the possibility to predict ahead in time glucose levels by exploiting their recent history monitored every 3 min by a minimally invasive CGM system, the Glucoday, in 28 type 1 diabetic volunteers for 48 h. Simple prediction strategies, based on the description of past glucose data by either a first-order polynomial or a first-order autoregressive (AR) model, both with time-varying parameters determined by weighted least squares, are considered. Results demonstrate that, even by using these simple methods, glucose can be predicted ahead in time, e.g., with a prediction horizon of 30 min crossing of the hypoglycemic threshold can be predicted 20-25 min ahead in time, a sufficient margin to mitigate the event by sugar ingestion.