Transient Effects of Snow Cover Duration on Primary Growth and Leaf Traits in a Tundra Shrub

With the recent climate warming, tundra ecotones are facing a progressive acceleration of spring snowpack melting and extension of the growing season, with evident consequences to vegetation. Along with summer temperature, winter precipitation has been recently recognised as a crucial factor for tundra shrub growth and physiology. However, gaps of knowledge still exist on long-living plant responses to different snowpack duration, especially on how intra-specific and year-to-year variability together with multiple functional trait adjustments could influence the long-term responses. To fill this gap, we conducted a 3 years snow manipulation experiment above the Alpine treeline on the typical tundra species Juniperus communis, the conifer with the widest distributional range in the north emisphere. We tested shoot elongation, leaf area, stomatal density, leaf dry weight and leaf non-structural carbohydrate content of plants subjected to anticipated, natural and postponed snowpack duration. Anticipated snowpack melting enhanced new shoot elongation and increased stomatal density. However, plants under prolonged snow cover seemed to compensate for the shorter growing period, likely increasing carbon allocation to growth. In fact, these latter showed larger needles and low starch content at the beginning of the growing season. Variability between treatments slightly decreased over time, suggesting a progressive acclimation of juniper to new conditions. In the context of future warming scenarios, our results support the hypothesis of shrub biomass increase within the tundra biome. Yet, the picture is still far from being complete and further research should focus on transient and fading effects of changing conditions in the long term

Nutritional strategies for correcting low glucose values in patients with postbariatric hypoglycaemia: A randomized controlled three-arm crossover trial.

AIM To evaluate the efficacy of nutritional hypoglycaemia correction strategies in postbariatric hypoglycaemia (PBH) after Roux-en-Y gastric bypass (RYGB). MATERIALS AND METHODS In a randomized, controlled, three-arm crossover trial, eight post-RYGB adults (mean [SD] 7.0 [1.4] years since surgery) with PBH ingested a solid mixed meal (584 kcal, 85 g carbohydrates, 21 g fat, 12 g protein) to induce hypoglycaemia on three separate days. Upon reaching plasma glucose of less than 3.0 mmol/L, hypoglycaemia was corrected with 15 g of glucose (G15), 5 g of glucose (G5) or a protein bar (P10, 10 g of protein) in random order. The primary outcome was percentage of time spent in the target plasma glucose range (3.9-5.5 mmol/L) during 40 minutes after correction. RESULTS Postcorrection time spent in the target glucose range did not differ significantly between the interventions (P = .161). However, postcorrection time with glucose less than 3.9 mmol/L was lower after G15 than P10 (P = .007), whereas time spent with glucose more than 5.5 mmol/L, peak glucose and insulin 15 minutes postcorrection were higher after G15 than G5 and P10 (P < .001). Glucagon 15 minutes postcorrection was higher after P10 than after G15 and G5 (P = .002 and P = .003, respectively). G15 resulted in rebound hypoglycaemia (< 3.0 mmol/L) in three of eight cases (38%), while no rebound hypoglycaemia occurred with G5 and P10. CONCLUSIONS Correcting hypoglycaemia with 15 g of glucose should be reconsidered in post-RYGB PBH. A lower dose appears to sufficiently increase glucose levels outside the critical range in most cases, and complementary nutrients (e.g. proteins) may provide glycaemia-stabilizing benefits. REGISTRATION NUMBER OF CLINICAL TRIAL NTC05250271 (ClinicalTrials.gov)

Nutritional strategies for correcting low glucose values in patients with postbariatric hypoglycaemia: A randomized controlled three‐arm crossover trial

AimTo evaluate the efficacy of nutritional hypoglycaemia correction strategies in postbariatric hypoglycaemia (PBH) after Roux‐en‐Y gastric bypass (RYGB).Materials and methodsIn a randomized, controlled, three‐arm crossover trial, eight post‐RYGB adults (mean [SD] 7.0 [1.4] years since surgery) with PBH ingested a solid mixed meal (584 kcal, 85 g carbohydrates, 21 g fat, 12 g protein) to induce hypoglycaemia on three separate days. Upon reaching plasma glucose of less than 3.0 mmol/L, hypoglycaemia was corrected with 15 g of glucose (G15), 5 g of glucose (G5) or a protein bar (P10, 10 g of protein) in random order. The primary outcome was percentage of time spent in the target plasma glucose range (3.9‐5.5 mmol/L) during 40 minutes after correction.ResultsPostcorrection time spent in the target glucose range did not differ significantly between the interventions (P = .161). However, postcorrection time with glucose less than 3.9 mmol/L was lower after G15 than P10 (P = .007), whereas time spent with glucose more than 5.5 mmol/L, peak glucose and insulin 15 minutes postcorrection were higher after G15 than G5 and P10 (P < .001). Glucagon 15 minutes postcorrection was higher after P10 than after G15 and G5 (P = .002 and P = .003, respectively). G15 resulted in rebound hypoglycaemia (< 3.0 mmol/L) in three of eight cases (38%), while no rebound hypoglycaemia occurred with G5 and P10.ConclusionsCorrecting hypoglycaemia with 15 g of glucose should be reconsidered in post‐RYGB PBH. A lower dose appears to sufficiently increase glucose levels outside the critical range in most cases, and complementary nutrients (e.g. proteins) may provide glycaemia‐stabilizing benefits.Registration number of clinical trialNTC05250271 (ClinicalTrials.gov)

Prediction of blood glucose concentrations and hypoglycemic events in Type 1 Diabetes by linear and nonlinear algorithms

Il diabete di tipo 1 (T1D) è una malattia metabolica caratterizzata da una mancanza di produzione di insulina che provoca un’alterazione dei livelli di glucosio nel sangue (BG). Di conseguenza, per mantenere la glicemia in un adeguato range fisiologico (generalmente [70-180] mg/dL) durante la giornata, i soggetti diabetici devono somministrarsi insulina esogena, assumere carboidrati ad azione rapida, seguire una dieta equilibrata ed eseguire attività fisica. Infatti, limitare le escursioni della glicemia consente di ridurre il rischio di mortalità e le conseguenze, a lungo e breve termine, causate da eventi iperglicemici (BG > 180 mg/dL) e ipoglicemici (BG 180 mg/dL) and hypoglycemia (i.e., BG<70 mg/dL). Minimally invasive continuous glucose monitoring (CGM) sensors have become a widely used tool by T1D individuals to keep track, and eventually correct, their BG levels. These devices provide frequent BG measurements (commonly one every 5 minutes) for several days, and embed visual and acoustic alerts when the hypo-/hyperglycemic thresholds are crossed, thus helping patients in taking corrective actions like hypotreatments and corrective insulin boluses. However, timely preventive alerts coupled with targeted corrective strategies would be even more helpful to avoid or mitigate the onset of impending, adverse events. For this reason, the real-time forecasting of BG levels has a key role in the development of i) advanced decision support systems (DSS), which are software for helping patients in the decision-making process, and ii) artificial pancreas systems (APS), which are devices for automatizing insulin delivery. The large plethora of data provided by CGM devices (but also insulin pumps, wearable devices, electronic diaries and dedicated mobile applications), coupled with the technological advancements in artificial intelligence, have driven the diabetes technology community to intensively focus on developing glucose predictive algorithms, exploiting methodologies already employed in the fields of time series forecasting, system identification, machine and deep learning. Among the possible approaches for glucose prediction, two main categories can be identified: algorithms fed only by the past history of the CGM signal or fed by CGM data plus additional information such as insulin, carbohydrates or physical exercise. One main open issue is that none of the literature studies have systematically investigated how and/or how much different input information as well as complex algorithms contribute to improve glucose prediction on datasets recorded in daily-life conditions. To address this gap, this PhD thesis presents the development and application of several linear and nonlinear algorithms for the forecasting of BG levels and hypoglycemic events, and investigates how and how much different input information and model complexity play a role in the prediction

A Predictive Algorithm for the Administration of Corrective Insulin Bolus for Decision Support Systems in Type 1 Diabetes

Type 1 diabetes management can be improved by leveraging decision support systems (DSSs), specific tools that, by suggesting therapeutic actions, can assist patients during decision-making process, reducing their daily burden routine. This work proposes a predictive algorithm for DSSs aimed at generating preventive corrective insulin boluses (CIBs) to reduce the duration of hyperglycemic events. Our approach is compared with a recent heuristic-based methodology proposed by Aleppo et al. and it is retrospectively assessed on a dataset recorded in free-living conditions. Preliminary results indicate that the proposed predictive CIB strategy decreases the time above range, largely increases the percentage of time spent in eglycemia without increasing the time spent in hypoglycemia

A Correction Insulin Bolus Delivery Strategy for Decision Support Systems in Type 1 Diabetes

: Management of type 1 diabetes (T1D) requires affected individuals to perform multiple daily actions to keep their blood glucose levels within the safe rage and avoid adverse hypo-/hyperglycemic episodes. Decision support systems (DSS) for T1D are composite tools that implement multiple software modules aiming to ease such a burden and to improve glucose control. At the University of Padova, we are developing a new DSS that currently integrate a smart insulin bolus calculator for optimal insulin dosing and a rescue carbohydrate intake advisor to tackle hypoglycemia. However, a module specifically targeting hyperglycemia, that suggests the administration of corrective insulin boluses (CIB), is still missing. For such a scope, this work aims to assess a recent literature methodology, proposed by Aleppo et al., which provides a simple strategy for dealing with hyperglycemia. The methodology is tested retrospectively on clinical data of individuals with T1D. In particular, here we leveraged a novel in silico tool that first identifies a non-linear model of glucose-insulin dynamics on data, then uses such model to simulate and compare the glucose trace obtained by "replaying" the recorded scenario and the glucose trace obtained using the CIB delivery strategy under evaluation. Results show that the CIB delivery strategy significantly reduce the percentage of time spent in hyperglycemia (-15.63%) without inducing any hypoglycemic episode, demonstrating both safety and efficacy of its use. These preliminary results suggest that the CIB delivery strategy proposed by Aleppo et al. is a promising candidate to be included in our system to counteract hyperglycemia. Future work will extensively evaluate the methodology and will compare it against other competing approaches

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-box Approaches To a Physiological White-box One

Unlabelled:   Objective: Accurate blood glucose (BG) prediction are key in next-generation tools for type 1 diabetes (T1D) management, such as improved decision support systems and advanced closed-loop control. Glucose prediction algorithms commonly rely on black-box models. Large physiological models, successfully adopted for simulation, were little explored for glucose prediction, mostly because their parameters are hard to individualize. In this work, we develop a BG prediction algorithm based on a personalized physiological model inspired by the UVA/Padova T1D Simulator. Then we compare white-box and advanced black-box personalized prediction techniques. Methods: A personalized nonlinear physiological model is identified from patient data through a Bayesian approach based on Markov Chain Monte Carlo technique. The individualized model was integrated within a particle filter (PF) to predict future BG concentrations. The black-box methodologies considered are non-parametric models estimated via gaussian regression (NP), three deep learning methods: long-short-term-memory (LSTM), gated recurrent unit (GRU), temporal convolutional networks (TCN), and a recursive autoregressive with exogenous input model (rARX). BG forecasting performances are assessed for several prediction horizons (PH) on 12 individuals with T1D, monitored in free-living conditions under open-loop therapy for 10 weeks. Results: NP models provide the most effective BG predictions by achieving a root mean square error (RMSE), RMSE = 18.99 mg/dL, RMSE = 25.72 mg/dL and RMSE = 31.60 mg/dL, significantly outperforming: LSTM, GRU (for PH=30 minutes), TCN, rARX, and the proposed physiological model for PH=30, 45 and 60 minutes. Conclusions: Black-box strategies remain preferable for glucose prediction even when compared to a white-box model with sound physiological structure and individualized parameters