A Federated Database for Obesity Research: An IMI-SOPHIA Study.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Gut Microbiota Composition and Functionality Are Associated With REM Sleep Duration and Continuous Glucose Levels.

CONTEXT: Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models. OBJECTIVE: We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition. METHODS: This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality. RESULTS: Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (ß = -.339; P < .001) and glucose variability (SD, ß = -.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations. CONCLUSION: Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

Design and Usability of an Avatar-Based Learning Program to Support Diabetes Education: Quality Improvement Study in Colombia.

BACKGROUND: This quality improvement study, entitled Avatar-Based LEarning for Diabetes Optimal Control (ABLEDOC), explored the feasibility of delivering an educational program to people with diabetes in Colombia. The aim was to discover how this approach could be used to improve awareness and understanding of the condition, the effects of treatment, and strategies for effective management of blood-glucose control. METHODS: Individuals with diabetes were recruited by Colombian endocrinologists to a human-centered study to codesign the educational program, using the Double Diamond model. Participants contributed to two phases. The first phase focused on gathering unmet educational needs and choice of curriculum. Three prototypes were developed as a result. During phase 2, a different group of participants engaged with the program for several weeks, before reporting back. RESULTS: Thirty-six participants completed a Web survey during phase 1, and five were also interviewed by telephone. The majority (33 of 36; 91%) were receptive to the prospect of educational interventions and ranked the chosen topic of hypoglycemia highly. In phase 2, the three prototypes were tested by 17 participants, 10 of whom also gave feedback in focus groups. The response was overwhelmingly positive, with 16 of 17 (94%) stating they would use a program like this again. The 3D version was the most highly rated. CONCLUSIONS: Immersive, avatar-based programs, delivered through smartphone, have the potential to deliver educational information that is trusted, engaging, and useful. Future work includes expansion of the curriculum, evaluation with a larger group, and exploration of the prospective role of artificial intelligence in personalizing this form of educational intervention.

Is a cloud-based platform useful for diabetes management in Colombia? The Tidepool experience.

BACKGROUND: There are several medical devices used in Colombia for diabetes management, most of which have an associated telemedicine platform to access the data. In this work, we present the results of a pilot study evaluating the use of the Tidepool telemedicine platform for providing remote diabetes health services in Colombia across multiple devices. METHOD: Individuals with Type 1 and Type 2 diabetes using multiple diabetes devices were recruited to evaluate the user experience with Tidepool over three months. Two endocrinologists used the Tidepool software to maintain a weekly communication with participants reviewing the devices data remotely. Demographic, clinical, psychological and usability data were collected at several stages of the study. RESULTS: Six participants, from ten at the baseline (five MDI and five CSII), completed this pilot study. Three different diabetes devices were employed by the participants: a glucose meter (Abbot), an intermittently-scanned glucose monitor (Abbot), and an insulin pump (Medtronic). A score of 81.3 in the system usability scale revealed that overall, most participants found the system easy to use, especially the web interface. The system also compared highly favourably against the proprietary platforms. The ability to upload and share data and communicate remotely with the clinicians was raised consistently by participants. Clinicians cited the lockdown imposed by the Covid-19 pandemic as a valuable test for this platform. Inability to upload data from mobile devices was identified as one of the main limitations. CONCLUSION: Tidepool has the potential to be used as a tool to facilitate remote diabetes care in Colombia. Users, both participants and clinicians, agreed to recommend the use of platforms like Tidepool to achieve better disease management and communication with the health care team. Some improvements were identified to enhance the user experience.

Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study.

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.

The effect of commercial functional food with probiotics on microorganisms from early carious lesions.

Caries rates in school-age children are still high enough to be the cause of serious concern for health systems in different countries. The biotechnology strategies studied to decrease these rates include the consumption of probiotics-available via a variety of functional foods obtainable on the market-that are able to inhibit bacteria associated with this disease. In this vein, the purpose of this study was to determine the effect of these foods on the growth of microorganisms in early carious lesions in children aged between 6 and 12. In the first phase, an agar well diffusion method was applied to selected foods, available in supermarkets, which contain probiotics that have already been shown to inhibit Streptococcus mutans (ATCC 25175), and to lower the pH in liquid culture media. In a second phase, these foods (n = 4) were examined in terms of their ability to inhibit the microorganisms in contact with early carious lesions in children and to reduce the pH of mixed cultures combined with the food. The results revealed that, of the foods tested, three inhibit the growth of microorganisms in carious lesions and, at the same time, lower the pH of the culture by more than 2.5 units. The food with the highest inhibitory capacity (14 mm, IQR 13-14) showed a similar effect among patients (P > 0.05), which together with the fact that its sugar concentration is less than 10%, makes it an ideal candidate for clinical study.

A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study.

BACKGROUND: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. METHODS: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. RESULTS: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint (P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) (P = .03). There was also a reduction in number of carbohydrate recommendations. CONCLUSION: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Overnight Closed-Loop Control Improves Glycemic Control in a Multicenter Study of Adults With Type 1 Diabetes.

Context: Closed-loop control (CLC) for the management of type 1 diabetes (T1D) is a novel method for optimizing glucose control, and strategies for individualized implementation are being developed. Objective: To analyze glycemic control in an overnight CLC system designed to "reset" the patient to near-normal glycemic targets every morning. Design: Randomized, crossover, multicenter clinical trial. Participants: Forty-four subjects with T1D requiring insulin pump therapy. Intervention: Sensor-augmented pump therapy (SAP) at home vs 5 nights of CLC (active from 23:00 to 07:00) in a supervised outpatient setting (research house or hotel), with a substudy of 5 nights of CLC subsequently at home. Main Outcome Measure: The percentage of time spent in the target range (70 to 180 mg/dL measured using a continuous glucose monitor). Results: Forty subjects (age, 45.5 ± 9.5 years; hemoglobin A1c, 7.4% ± 0.8%) completed the study. The time in the target range (70 to 180 mg/dL) significantly improved in CLC vs SAP over 24 hours (78.3% vs 71.4%; P = 0.003) and overnight (85.7% vs 67.6%; P < 0.001). The time spent in a hypoglycemic range (<70 mg/dL) decreased significantly in the CLC vs SAP group over 24 hours (2.5% vs 4.3%; P = 0.002) and overnight (0.9% vs 3.2%; P < 0.001). The mean glucose level at 07:00 was lower with CLC than with SAP (123.7 vs 145.3 mg/dL; P < 0.001). The substudy at home, involving 10 T1D subjects, showed similar trends with an increased time in target (70 to 180 mg/dL) overnight (75.2% vs 62.2%; P = 0.07) and decreased time spent in the hypoglycemic range (<70 mg/dL) overnight in CLC vs SAP (0.6% vs 3.7%; P = 0.03). Conclusion: Overnight-only CLC increased the time in the target range over 24 hours and decreased the time in hypoglycemic range over 24 hours in a supervised outpatient setting. A pilot extension study at home showed a similar nonsignificant trend.

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor.

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.

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

OBJECTIVE: To explore the experiences of children with type 1 diabetes and their parents taking part in an artificial pancreas (AP) clinical trial during a 7-day summer camp. RESEARCH DESIGN AND METHODS: A semistructured interview, composed of 14 questions based on the Technology Acceptance Model, was conducted at the end of the clinical trial. Participants also completed the Diabetes Treatment Satisfaction Questionnaire (DTSQ, parent version) and the AP Acceptance Questionnaire. RESULTS: Thirty children, aged 5-9 years, and their parents completed the study. A content analysis of the interviews showed that parents were focused on understanding the mechanisms, risks, and benefits of the new device, whereas the children were focused on the novelty of the new system. The parents' main concerns about adopting the new system seemed related to the quality of glucose control. The mean scores of DTSQ subscales indicated general parents' satisfaction (44.24 ± 5.99, range 32-53) and trustful views of diabetes control provided by the new system (7.8 ± 2.2, range 3-12). The AP Acceptance Questionnaire revealed that most parents considered the AP easy to use (70.5%), intended to use it long term (94.0%), and felt that it was apt to improve glucose control (67.0%). CONCLUSIONS: Participants manifested a positive attitude toward the AP. Further studies are required to explore participants' perceptions early in the AP development to individualize the new treatment as much as possible, and to tailor it to respond to their needs and values.

Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

Real-time continuous glucose monitoring in an intensive care unit: better accuracy in patients with septic shock.

OBJECTIVE: This study assessed the accuracy of real-time continuous glucose monitoring system (RTCGMS) devices in an intensive care unit (ICU) to determine whether the septic status of the patient has any influence on the accuracy of the RTCGMS. SUBJECTS AND METHODS: In total, 41 patients on insulin therapy were included. Patients were monitored for 72 h using RTCGMS. Arterial blood glucose (ABG) samples were obtained following the protocol established in the ICU. The results were evaluated using paired values (excluding those used for calibration) with the performance assessed using numerical accuracy. Nonparametric tests were used to determine statistically significant differences in accuracy. RESULTS: In total, 956 ABG/RTCGMS pairs were analyzed. The overall median relative absolute difference (RAD) was 13.5%, and the International Organization for Standardization (ISO) criteria were 68.1%. The median RADs reported for patients with septic shock, with sepsis, and without sepsis were 11.2%, 14.3%, and 16.3%, respectively (P<0.05). Measurements meeting the ISO criteria were 74.5%, 65.6%, and 63.7% for patients with septic shock, with sepsis, and without sepsis, respectively (P<0.05). CONCLUSIONS: The results showed that the septic status of patients influenced the accuracy of the RTCGMS in the ICU. Accuracy was significantly better in patients with septic shock in comparison with the other patient cohorts.

Enhanced algorithm for glucose estimation using the continuous glucose monitoring system.

BACKGROUND: The CGMS Gold continuous glucose monitor presents a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false alarms. The new algorithm aims to improve the measurement accuracy and hypoglycemia detection. MATERIAL/METHODS: Twenty-one patients with type 1 diabetes were monitored for 3 days (1 day at the hospital and 2 at home) using the CGMS Gold. For these patients, blood glucose samples were taken every 15 minutes for 2 hours after meals and every half hour otherwise during the first day. A new calibration algorithm was developed and implemented using CGMS Gold intensity readings and capillary glucose. RESULTS: After 1 day, a comparison of results from either the CGMS Gold algorithm and the proposed algorithm, compared with results from blood (2450 points), showed an increase of data in zone A with the proposed algorithm (4.4% in the Clarke error grid analysis (EGA) and 5.0% in the Consensus EGA). After comparing for 3 days, a reduction of 24.7%, p<0.05, in the overall median relative absolute difference (RAD) was also obtained. In the hypoglycemic range, a significant decrease in median RAD was observed (64.4%, p<0.05). Furthermore, the undetected hypoglycemia events in capillary samples by the proposed algorithm were reduced by 59.8% compared to the CGMS Gold algorithm. CONCLUSIONS: The performance as measured with clinical and numerical accuracy criteria illustrates the improved accuracy of the proposed algorithm in comparison with the CGMS Gold algorithm. A significant improvement in hypoglycemia detection was observed.

Real-time glucose estimation algorithm for continuous glucose monitoring using autoregressive models.

BACKGROUND: Continuous glucose monitors (CGMs) present a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false hypoglycemia. A new algorithm is presented here aimed at improving the measurement accuracy and hypoglycemia detection. Its core is the estimation of blood glucose (BG) in real time (RT) from CGM intensity readings using autoregressive (AR) models. METHODS: Eighteen patients with type 1 diabetes were monitored for three days (one at the hospital and two at home) using the CGMS Gold. For these patients, BG samples were taken every 15 min for 2 h after meals and every half hour otherwise during the first day. The relationship between the current measured by the CGMS Gold and BG was learned by an AR model, allowing its RT estimation. New capillary glucose measurements were used to correct the model BG estimations. RESULTS: A total of 563 paired points were obtained from BG and monitor readings to validate the new algorithm. 98.5% of paired points fell in zones A+B of the Clarke error grid analysis with the proposed algorithm. The overall mean and median relative absolute differences (RADs) were 9.6% and 6.7%. Measurements meeting International Organization for Standardization (ISO) criteria were 88.7%. In the hypoglycemic range, the mean and median RADs were 8.1% and 6.0%, and measurements meeting ISO criteria were 86.7%. The sensitivity and specificity with respect to hypoglycemia detection were 91.5% and 95.0%. CONCLUSIONS: The performance measured with both clinical and numerical accuracy metrics illustrates the improved accuracy of the proposed algorithm compared with values presented in the literature. A significant improvement in hypoglycemia detection was also observed.