Toward One Health: a spatial indicator system to model the facilitation of the spread of zoonotic diseases.

Recurrent outbreaks of zoonotic infectious diseases highlight the importance of considering the interconnections between human, animal, and environmental health in disease prevention and control. This has given rise to the concept of One Health, which recognizes the interconnectedness of between human and animal health within their ecosystems. As a contribution to the One Health approach, this study aims to develop an indicator system to model the facilitation of the spread of zoonotic diseases. Initially, a literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify relevant indicators related to One Health. The selected indicators focused on demographics, socioeconomic aspects, interactions between animal and human populations and water bodies, as well as environmental conditions related to air quality and climate. These indicators were characterized using values obtained from the literature or calculated through distance analysis, geoprocessing tasks, and other methods. Subsequently, Multi-Criteria Decision-Making (MCDM) techniques, specifically the Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, were utilized to combine the indicators and create a composite metric for assessing the spread of zoonotic diseases. The final indicators selected were then tested against recorded zoonoses in the Valencian Community (Spain) for 2021, and a strong positive correlation was identified. Therefore, the proposed indicator system can be valuable in guiding the development of planning strategies that align with the One Health principles. Based on the results achieved, such strategies may prioritize the preservation of natural landscape features to mitigate habitat encroachment, protect land and water resources, and attenuate extreme atmospheric conditions.

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.

Spatial statistical analysis of the relationship between self-reported mental health during the COVID-19 lockdown and closeness to green infrastructure.

The COVID-19 pandemic has produced alterations in the behaviour and psychological health of people, who have had to learn living under uncertain circumstances escaping their control. This situation has been aggravated in those countries applying strict home confinement rules to try bending their epidemic curve. This is the case of Spain, where the stringent lockdown period was extended over three months. This study aimed at proving a research hypothesis whereby living close to Green Infrastructure (GI) during the confinement period was beneficial for mental health. To this end, La Palma (Canary Islands) and Zaragoza (Peninsular Spain) were taken as case studies, since both locations distributed a questionnaire to address citizenry's self-reported mental health under strict lockdown conditions. A spatial statistical analysis of the responses collected by these questionnaires revealed that variables such as stress, anger, medication use, alcohol consumption or visits to the doctor significantly decreased if citizens were close to GI, whereas people having very high expectations of enjoying the city after the confinement were positively correlated to proximity of green areas. Although these outcomes are limited by the inferential capacity of correlation analysis, they point out to a sense of relief derived from having visual contact with vegetated landscapes and feeling stimulated about using them for recreation, aesthetical or sporting purposes. The joint consideration of these psychological gains with the social and environmental benefits provided by GI emphasizes the importance of approaching urban regeneration through the design and implementation of interconnected green spaces.

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.

Assessment of Glucose Control Metrics by Discriminant Ratio.

Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics. Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials. Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes.

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Glycemic Variability and Hypoglycemic Excursions With Continuous Glucose Monitoring Compared to Intermittently Scanned Continuous Glucose Monitoring in Adults With Highest Risk Type 1 Diabetes.

BACKGROUND: The I-HART CGM study has shown that real-time continuous glucose monitoring (rtCGM) has greater beneficial impact on hypoglycemia than intermittently scanned continuous glucose monitoring (iscCGM) in adults with type 1 diabetes at high risk (Gold score ≥4 or recent severe hypoglycemia using insulin injections). In this subanalysis, we present the impact of rtCGM and iscCGM on glycemic variability (GV). METHODS: Forty participants were recruited to this parallel group study. Following two weeks of blinded rtCGM (DexcomG4), participants were randomized to rtCGM (Dexcom G5; n = 20) or iscCGM (Freestyle Libre; n = 20) for eight weeks. An open-extension phase enabled participants on rtCGM to continue for a further eight weeks and those on iscCGM to switch to rtCGM over this period. Glycemic variability measures at baseline, 8- and 16-week endpoints were compared between groups. RESULTS: At the eight-week endpoint, between-group differences demonstrated significant reduction in several GV measures with rtCGM compared to iscCGM (GRADE%hypoglycemia, index of glycemic control [IGC], and average daily risk range [ADRR]; P < .05). Intermittently scanned continuous glucose monitoring reduced mean average glucose and glycemic variability percentage and GRADE%hyperglycemia compared with rtCGM (P < .05). At 16 weeks, the iscCGM group switching to rtCGM showed significant improvement in GRADE%hypoglycemia, personal glycemic status, IGC, and ADRR. CONCLUSION: Our data suggest most, but not all, GV measures improve with rtCGM compared with iscCGM, particularly those measures associated with the risk of hypoglycemia. Selecting appropriate glucose monitoring technology to address GV in this high-risk cohort is important to minimize the risk of glucose extremes and severe hypoglycemia. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT03028220.

Continuous Glucose Monitoring in People With Type 1 Diabetes on Multiple-Dose Injection Therapy: The Relationship Between Glycemic Control and Hypoglycemia.

OBJECTIVE: The inverse relationship between overall glucose control and hypoglycemia risk is weakened by the use of real-time continuous glucose monitoring (rtCGM). We assess the relationship between glucose control and hypoglycemia in people with type 1 diabetes using multiple-dose injection (MDI) regimens, including those at highest risk of hypoglycemia. RESEARCH DESIGN AND METHODS: CGM data from the intervention (rtCGM) and control (self-monitored blood glucose [SMBG]) phases of the Multiple Daily Injections and Continuous Glucose Monitoring in Diabetes (DIAMOND) and HypoDE studies were analyzed. The relationship between glucose control (HbA1c and mean rtCGM glucose levels) and percentage time spent in hypoglycemia was explored for thresholds of 3.9 mmol/L (70 mg/dL) and 3.0 mmol/L (54 mg/dL), and ANOVA across the range of HbA1c and mean glucose was performed. RESULTS: A nonlinear relationship between mean glucose and hypoglycemia was identified at baseline, with the steepest relationship seen at lower values of mean glucose. The use of rtCGM reduces the exposure to hypoglycemia at all thresholds and flattens the relationship between overall glucose and hypoglycemia, with the most marked impact at lower values of mean glucose and HbA1c. Exposure to hypoglycemia varied at all thresholds across the range of overall glucose at baseline, in the SMBG group, and with rtCGM, but the relationships were weaker in the rtCGM group. CONCLUSIONS: Use of rtCGM can flatten and attenuate the relationship between overall glucose control and hypoglycemia, exerting its greatest impact at lower values of HbA1c and mean glucose in people with type 1 diabetes using MDI regimens and at highest risk of hypoglycemia.

Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions.

BACKGROUND: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. METHODS: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. RESULTS: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). CONCLUSIONS: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.

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.

Intraperitoneal insulin therapy in patients with type 1 diabetes. Does it fit into the current therapeutic arsenal? / Infusión continua de insulina intraperitoneal en pacientes con diabetes tipo 1. ¿Encaja en el arsenal terapéutico actual?

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Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Data Sets.

OBJECTIVE: The Diabetes Control and Complications Trial (DCCT) identified an inverse relationship between HbA1c and severe hypoglycemia. We investigated the relationship between hypoglycemia and HbA1c in a large type 1 diabetes cohort on multiple daily injection or insulin pump therapy using blinded continuous glucose monitoring (CGM) data. The impact of real-time CGM on these relationships and how these relationships differ with biochemical definitions of hypoglycemia have also been assessed. RESEARCH DESIGN AND METHODS: CGM data were obtained from the JDRF CGM randomized control trial. Baseline blinded CGM data were used to assess time in hypoglycemia in all individuals. End point data from the CGM intervention group were used to assess the impact of CGM. Percentage of time spent below 3.9, 3.3, 3.0, and 2.8 mmol/L were calculated and quadratic regression plots drawn. Relationships were analyzed visually, and ANOVA was used to assess relationships between glycemia and time below threshold. RESULTS: J-shaped relationships were observed for all biochemical hypoglycemia thresholds, with the lowest hypoglycemia risk occurring at HbA1c values between 8.1 and 8.6% (65-70 mmol/mol). The use of an average of 5 days/week of CGM flattened the relationships for 3.3, 3.0, and 2.8 mmol/L, and ANOVA confirmed the loss of relationship for the 3.3 mmol/L threshold using CGM. CONCLUSIONS: The relationship between hypoglycemia and HbA1c in a population with type 1 diabetes is J-shaped. Lower HbA1c values are still associated with increased hypoglycemia risk, although the magnitude of risk depends on biochemical threshold. Real-time CGM may reduce the percentage time spent in hypoglycemia, changing the relationship between HbA1c and hypoglycemia.

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.