In September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G "hybrid" closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on ...continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system.
This was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic.
Of the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9-61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A
(HbA
) and Auto Mode utilization.
While Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.
Continuous glucose monitoring (CGM) provides real-time assessment of glucose levels and may be beneficial in reducing hypoglycemia in older adults with type 1 diabetes.
To determine whether CGM is ...effective in reducing hypoglycemia compared with standard blood glucose monitoring (BGM) in older adults with type 1 diabetes.
Randomized clinical trial conducted at 22 endocrinology practices in the United States among 203 adults at least 60 years of age with type 1 diabetes.
Participants were randomly assigned in a 1:1 ratio to use CGM (n = 103) or standard BGM (n = 100).
The primary outcome was CGM-measured percentage of time that sensor glucose values were less than 70 mg/dL during 6 months of follow-up. There were 31 prespecified secondary outcomes, including additional CGM metrics for hypoglycemia, hyperglycemia, and glucose control; hemoglobin A1c (HbA1c); and cognition and patient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate.
Of the 203 participants (median age, 68 interquartile range {IQR}, 65-71 years; median type 1 diabetes duration, 36 IQR, 25-48 years; 52% female; 53% insulin pump use; mean HbA1c, 7.5% SD, 0.9%), 83% used CGM at least 6 days per week during month 6. Median time with glucose levels less than 70 mg/dL was 5.1% (73 minutes per day) at baseline and 2.7% (39 minutes per day) during follow-up in the CGM group vs 4.7% (68 minutes per day) and 4.9% (70 minutes per day), respectively, in the standard BGM group (adjusted treatment difference, -1.9% (-27 minutes per day); 95% CI, -2.8% to -1.1% -40 to -16 minutes per day; P <.001). Of the 31 prespecified secondary end points, there were statistically significant differences for all 9 CGM metrics, 6 of 7 HbA1c outcomes, and none of the 15 cognitive and patient-reported outcomes. Mean HbA1c decreased in the CGM group compared with the standard BGM group (adjusted group difference, -0.3%; 95% CI, -0.4% to -0.1%; P <.001). The most commonly reported adverse events using CGM and standard BGM, respectively, were severe hypoglycemia (1 and 10), fractures (5 and 1), falls (4 and 3), and emergency department visits (6 and 8).
Among adults aged 60 years or older with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in hypoglycemia over 6 months. Further research is needed to understand the long-term clinical benefit.
ClinicalTrials.gov Identifier: NCT03240432.
To determine whether self-monitoring of blood glucose (SMBG), either alone or with additional instruction in incorporating the results into self-care, is more effective than usual care in improving ...glycaemic control in non-insulin-treated diabetes.
An open, parallel group randomised controlled trial.
24 general practices in Oxfordshire and 24 in South Yorkshire, UK.
Patients with non-insulin-treated type 2 diabetes, aged > or = 25 years and with glycosylated haemoglobin (HbA1c) > or = 6.2%.
A total of 453 patients were individually randomised to one of: (1) standardised usual care with 3-monthly HbA1c (control, n = 152); (2) blood glucose self-testing with patient training focused on clinician interpretation of results in addition to usual care (less intensive self-monitoring, n = 150); (3) SMBG with additional training of patients in interpretation and application of the results to enhance motivation and maintain adherence to a healthy lifestyle (more intensive self-monitoring, n = 151).
The primary outcome was HBA1c at 12 months, and an intention-to-treat analysis, including all patients, was undertaken. Blood pressure, lipids, episodes of hypoglycaemia and quality of life, measured with the EuroQol 5 dimensions (EQ-5D), were secondary measures. An economic analysis was also carried out, and questionnaires were used to measure well-being, beliefs about use of SMBG and self-reports of medication taking, dietary and physical activities, and health-care resource use.
The differences in 12-month HbA1c between the three groups (adjusted for baseline HbA1c) were not statistically significant (p = 0.12). The difference in unadjusted mean change in HbA1c from baseline to 12 months between the control and less intensive self-monitoring groups was -0.14% 95% confidence interval (CI) -0.35 to 0.07 and between the control and more intensive self-monitoring groups was -0.17% (95% CI -0.37 to 0.03). There was no evidence of a significantly different impact of self-monitoring on glycaemic control when comparing subgroups of patients defined by duration of diabetes, therapy, diabetes-related complications and EQ-5D score. The economic analysis suggested that SMBG resulted in extra health-care costs and was unlikely to be cost-effective if used routinely. There appeared to be an initial negative impact of SMBG on quality of life measured on the EQ-5D, and the potential additional lifetime gains in quality-adjusted life-years, resulting from the lower levels of risk factors achieved at the end of trial follow-up, were outweighed by these initial impacts for both SMBG groups compared with control. Some patients felt that SMBG was helpful, and there was evidence that those using more intensive self-monitoring perceived diabetes as having more serious consequences. Patients using SMBG were often not clear about the relationship between their behaviour and the test results.
While the data do not exclude the possibility of a clinically important benefit for specific subgroups of patients in initiating good glycaemic control, SMBG by non-insulin-treated patients, with or without instruction in incorporating findings into self-care, did not lead to a significant improvement in glycaemic control compared with usual care monitored by HbA1c levels. There was no convincing evidence to support a recommendation for routine self-monitoring of all patients and no evidence of improved glycaemic control in predefined subgroups of patients.
Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals
. This could be related to a lack of ...expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10-21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n = 54) or by physicians (physician arm, n = 54). The results for the primary efficacy measure-the percentage of time spent within the target glucose range (70-180 mg dl
(3.9-10.0 mmol l
))-in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P < 1 × 10
). The percentage of readings below 54 mg dl
(<3.0 mmol l
) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers.
Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and ...prolonged exercise in adolescents and children. We present the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control.
In a randomized controlled trial, 24 adolescents (ages 13-18 years) and 24 school-aged children (6-12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor-augmented pump (RM-SAP), and the experimental group used the t: slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff.
The Control-IQ system improved percent time within range (70-180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events.
The use of the Control-IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM-SAP in pediatric patients with T1D during prolonged intensive winter sport activities.
Diabetes mellitus is a chronic metabolic disorder lasting for the lifetime of a person. Glucose and insulin are the main indicators in the monitoring and control of this disease. Most often, various ...laboratory tests are used in the diagnosis and control of diabetes. Among them, the estimation of blood glucose concentration is one of the main diagnostic criteria. Proper control of the blood glucose level can delay, and to a greater extent, prevent complications. Thus, blood glucose monitoring is a requisite tool in the management of diabetes mellitus. Insulin plays a major role in glucose metabolism and its determination is of great value in the diagnosis and control of diabetes. An uncountable number of biosensors have been developed based on various mechanisms which will make sure a continuous glucose as well as insulin monitoring. Biosensors became the most sophisticated tool for the detection of glucose and insulin and they are of different types. Enzymatic, non-enzymatic, electrochemical, optical, non-invasive, and continuous monitoring biosensors are discussed in this review. In recent years, there is progress towards the development of nanobiosensors using various nanomaterials. Here, we have reviewed the fabrication, modification, and recent approaches associated with insulin and glucose biosensors for the treatment of diabetes.
•Different types of advanced biosensors for glucose and insulin are reviewed.•Enzymatic, non-enzymatic, electrochemical, optical, non-invasive, and continuous monitoring biosensors are available.•Nanobiosensors have grabbed attention in glucose detection.•Label-free insulin detection and aptamer-based electrochemical insulin biosensors are very promising.
Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system.
This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare ...mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA
<10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L.
Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (
= 65) versus SAP (
= 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% 95% CI -2.4, -1.0;
< 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% 95% CI -6.1, 0.1;
< 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device.
In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.
The expanded views of Abbott's
FreeStyle Navigator sensor chip for blood glucose monitoring
80.
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► In this review article, we provide critical views on the technology behind commercial ...glucose sensor. ► We express opinion regarding the relative merits of designed sensors for monitoring serum glucose. ► We also comment as on the pros and cons of recently reported glucose biosensors in the literature.
The blood glucose monitoring devices (BGMDs) are an integral part of diabetes management now-a-days. They have evolved tremendously within the last four decades in terms of miniaturization, rapid response, greater specificity, simplicity, minute sample requirement, painless sample uptake, sophisticated software and data management. This article aims to review the developments in the technologies behind commercial BGMD, especially those in the areas of chemistries, mediators and other components. The technology concerns, on-going developments and future trends in blood glucose monitoring (BGM) are also discussed.
To evaluate the respective contributions of short-term glycemic variability and mean daily glucose (MDG) concentration to the risk of hypoglycemia in type 1 diabetes.
People with type 1 diabetes (
= ...100) investigated at the University Hospital of Montpellier (France) underwent continuous glucose monitoring (CGM) on two consecutive days, providing a total of 200 24-h glycemic profiles. The following parameters were computed: MDG concentration, within-day glycemic variability (coefficient of variation for glucose %CV), and risk of hypoglycemia (presented as the percentage of time spent below three glycemic thresholds: 3.9, 3.45, and 3.0 mmol/L).
MDG was significantly higher, and %CV significantly lower (both
< 0.001), when comparing the 24-h glycemic profiles according to whether no time or a certain duration of time was spent below the thresholds. Univariate regression analyses showed that MDG and %CV were the two explanatory variables that entered the model with the outcome variable (time spent below the thresholds). The classification and regression tree procedure indicated that the predominant predictor for hypoglycemia was %CV when the threshold was 3.0 mmol/L. In people with mean glucose ≤7.8 mmol/L, the time spent below 3.0 mmol/L was shortest (
< 0.001) when %CV was below 34%.
In type 1 diabetes, short-term glycemic variability relative to mean glucose (i.e., %CV) explains more hypoglycemia than does mean glucose alone when the glucose threshold is 3.0 mmol/L. Minimizing the risk of hypoglycemia requires a %CV below 34%.
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