Coping with diabetes requires frequent and even today mostly invasive blood glucose-based monitoring. Partly due to this invasive nature and the associated reduced skin wound healing and increased risk of infection, non-invasive glucose monitoring technologies would represent considerable progress. Edited keratinocytes may enable such a function.
Proteomics analysis
We identified Trisk 95 as the only protein whose expression is induced in response to high blood glucose. A luciferase reporter assay demonstrated that induction of Trisk95 expression occurs not only at the protein level but also transcriptionally. This induction was associated with a marked elevation in the Fluo-4 signal, suggesting a role for intracellular calcium changes in the signalling cascade. Strikingly, these changes lead concurrently to fragmentation of the mitochondria. As judged from the knockout findings, both the calcium flux and the mitochondrial phenotype were dependent on Trisk95 function, since the phenotypes in question were abolished.
The data demonstrate that the skin represents an organ that reacts robustly and thus mirrors changes in systemic blood glucose levels. The findings are also consistent with a channelling model of Trisk95 that serves as an insulin-independent but glucose-responsive biomarker taking part in releasing calcium from the cellular stores in the skin. The skin cells may thus provide a novel mean for glucose monitoring when analysing changes in labelled Trisk95 and calcium. By that, this study is the first proof of the concept of our registered patent (No. PCT FI2016/050917), which proposes the use of cells as biosensors for developing personalized health-monitoring devices.
Glucometers have become a fundamental tool in measuring and monitoring glucose level, both in healthcare institutions and home care. The accuracy of glucometers conditions the quality of diabetic patients’ management and is associated to the occurrence of over or under treating accidents due to inaccurate readings.This study assessed the accuracy of five commercially available glucometers by reference to laboratory venous plasma glucose measurement
A cross-sectional study conducted among diabetic patients attending King Abdulaziz Medical City laboratory. All participants underwent venipuncture regarding laboratory PG, simultaneously with capillary blood sampling, on which capillary glucose (CG) was measured using glucometers AccuCheck®, OneTouch®, Freestyle optium neo®, Contour Next®, and Contour Next One® in random order. All glucometers were adequately calibrated and verified according to American diabetes association prior to use.
A total 203 patients were included, with mean (SD) PG= 155.22 (64.88) mg/dL. The coefficient of variation (CV) of the meters ranged from 37.79% to 41.80%. Mean (SD) CGs ranged between 153.01 (57.82) and 163.00 (64.52) depending on the glucometer. Three meters showed negative bias. Mean difference was 2.20 for AccuCheck, -2.26 for One Touch, 0.90 for Freestyle, -2.08 for Contour Next and -7.78 for Contour Next One. Bias percentage ranged between -5.01 and 1.42. Bland-Altman plots showed proportional bias .
Of all glucometers, Freestyle optium neo showed the minimal mean bias, while Contour next one ®showed the highest proportional bias. However, all of them were within 5% difference. High blood glucose readings above 200 mg/dl should be confirmed by venous measurement
In September 2018 glucose flash monitoring system FreeStyle Libre was reimbursed by the public health system in Balearic Islands for selected patients with type 1 diabetes
In November 2018 we started a structured education programme in order to empower patients to use the flash monitoring system accurately.
The aim of this study is to evaluate the impact on glycemic control and quality of life at 12 months. We present the preliminary results at 6 months follow-up.
This study included type 1 diabetes patients ≥ 16 years old with high risk of hypoglycemia (hypoglycemia awareness, more than 4 hypoglycemic events at week, a severe hypoglycemic event in the last year) and/or ˃ 6 self-monitoring blood glucose daily (n=101).
The educational program is delivered by 3 diabetes nurse educators and consists of 2 group education sessions (5 patients) at the beginning whose goal is to teach how to use the flash system. Then an individual visit at 3, 6 and 12 months is done where the proper use of the device and metabolic control data are checked and the incidence of hypoglycemia and changes in quality of life are recorded.
The questionnaires show an improvement in satisfaction blood glucose monitoring method and mild improvement in quality of life related to diabetes.
In this group of T1D, many of them at high risk of hypoglycemia, the flash system associated to education programme shows a significant reduction of severe hypoglycemic events with improvement in HbA1c and time spent in the target glucose range at 6 months
The mechanism of development of insulin resistance (IR) is not clear. This makes it difficult to develop adequate ways to prevent and treat type 2 diabetes mellitus (2D).
The rats with aloxane diabetes.
There is no free glucose in the muscle tissue, upon admission it is immediately phosphorylated to glucose-6-phosphate (G-6-F), which prevents its return. With a decrease in the rate of G-6-F conversion, hexokinase is inhibited and the intake of glucose into the muscles decreases. This regulatory process can be considered as the first mechanism of development of insulin resistance. The next regulatory step is the process of assimilation of pyruvic acid or the so-called pyruvate block, since a decrease in anaerobic or aerobic conversion of pyruvate promotes inhibition of glycolysis and the development of insulin resistance. The next step in the regulation of glucose conversion is ATP or the level of utilization of the energy of its oxidation. The most volatile process in the muscle cell is protein synthesis, so the amount of glucose utilization will directly correlate with the rate of protein synthesis. With a decrease in protein synthesis with a substrate deficit or inhibition of the protein of the synthesizing apparatus, the utilization of ATP decreases and the ATP / PDP coefficient increases, which contributes to the inhibition of hexokinase and the development of insulin resistance.
Such mechanism of development of insulin resistance will allow to develop effective ways of developing the principles of prevention and treatment of patients with 2D.
We previously reported the setting up of an original educational program for the use of the flash glucose monitoring system and preliminary results in a cohort of 359 subjects with insulin-treated diabetes. We demonstrated that this ambulatory program was feasible in trained teams used to such developments. We present now the qualitative results, focus on the sessions ‘activities, the satisfactory and knowledge questionnaires.
In June 2017 we developed a five-individual or collective session (10/12 subjects) program over a maximal duration of 6 months with dual teaching. People with diabetes filled out the satisfactory questionnaire at the end of the second collective session and the knowledge questionnaire during the evaluation appointment. Data were extracted from the Sphinx system.
Eight hundred and fourteen persons with insulin-treated diabetes have achieved the program from June 2017 until the end of September 2019. Mean rate of participation to the collective sessions was 90% and 17% for the family environment. Regarding results from 287 satisfactory questionnaires, 95% have modified several points in their daily life; 94% were glad to participate in group; 12.6% of persons with diabetes have disagree with the presence of a family subject; regarding results from the knowledge questionnaire, 91% felt able to decide taking account the arrows tendencies.
The number of patients taking part illustrates the major impact of this useful technology and the need for continual evaluation in diabetic disease. Level of participation was very high; in most cases, the arrows tendencies have helped people with diabetes to manage their treatment.
The development of devices that free patients from finger piercing and from using of test-strips in diabetes control, can demonstrate us the real daily need for glycemic self-control in patients with T1D on insulin pump therapy.
58 adult patients with T1D undergoing insulin pump therapy used the FreeStyle Libre flash glucose monitoring systems (FGM) over the period of 4-15 days (the first and the last days (incomplete) were not analyzed) in both inpatient and outpatient care, followed by a retrospective assessment of the frequency of glycemic scans per day.
The average number of glycemia scans on the 1-st day was 27.5±22.4 times, on the 7-th day - 27.8±21.1 times, on the 14-th day - 25.8±18.7 times (p>0.05). The average number of scans per day during the first six days was 29.1±24.5 times, during the next seven days – 27.3±26.1 times (p>0.05). The average number of scans during 13 days – 28.2 ±25.3 times. Over all period of the study: the average glucose level – 8.8±2.1 mmol/l, time in range (TIR) – 47.6±18.6%, above TIR – 42.5±21.8%, below TIR – 9.7±10.4%, the average number of hypoglycemia events per day – 11.9±9.2, the average duration – 94.3±46.3 min. Baseline HbA1c was at 8.2±1.2%. Prognostic HbA1c by FreeStyle Libre at the end of the study – 7.2±1.5%.
The obtained results show that the recommended standard glycemia self-control frequency at least 4 times a day, is actually 7 times lower than the real need in patients with T1D for optimization of the pump insulin therapy.
The FreeStyle Libre continuous flash glucose monitoring system (FGM) provides a unique opportunity to evaluate glucose trends to optimize insulin therapy compared to the glycemic self-control by glucometer. However, working with trends requires individualization.
The main purpose of this study is the development of customized decision-making algorithms based on FGM trend arrows for T1D patients.
T1D patients on insulin pump therapy used FGM over the period of 10-14 days. At the first stage, endocrinologists used standard algorithms of action on trend arrows, evaluated their effectiveness and in case of unsatisfactory results, began to select individualized algorithm rules. At the second stage, the effectiveness of the developed individual algorithms was checked, and further corrections were made if necessary.
For 5 patients out of 15 (33.3%), the standard (developed by the company) algorithms were effective. For the rest 10 patient (66.6%), the modification of standard algorithms was required as follows: 5 patients (33.3%) needed the development of almost completely individualized algorithms (75-80% - changes), and for the other 5 patients (33.3%) some corrections (15-30% - changes) were needed. For 10 patients the insulin doses correction in the standard algorithms was 59%, and bread unit correction - was 2%. In patients trained to assess trends, the average value of TIR was 40.9%, for untrained patients - 34.6%.
Standard trending algorithms are suitable for only one-third of T1D patients who receive insulin pump therapy, indicating the actuality of the problem of diabetes treatment based on trend arrows.
Continuous glucose monitoring (CGM) to manage diabetes mellitus in children and adolescents is widely used to control the disease. Aim: to assess the impact of CGM on metabolic compensation in type 1 diabetes mellitus in children and adolescents who are currently on continuous subcutaneous insulin infusion (CSII) based on the frequency and duration of monitoring.
A comparison of HbA1 indicators in 171 children and adolescents aged 4.5 to 18 years old who had been on insulin pump therapy for the previous three or more years was made. Data on two groups of patients were compared: Group 1 included 70 patients who used CGM permanently or no less frequently than 1 week a month; Group 2 comprised 101 patients who used CGM rarely (1-2 times a year) or did not use CGM at all.
Children and adolescents of the age group who used CGM permanently or no less frequently than 1 week a month tended to have reduced levels of НbA1c compared to the children and adolescents who used no CGM or used it rarely (7.78±1.38% vs. 8.13±1.24% p>0.05) . Significantly more reduced levels of HbA1 were observed in adolescents aged 12 to 18 years old who used CGM permanently compared to adolescents who used no CGM (7.81±1.12 vs. 8.3±1.23%, p <0.05).
The use of CGM to manage diabetes mellitus in children and adolescents contributes to sustained metabolic compensation of the disease: the best estimates of glycemic control were seen in adolescents who used CGM permanently.
Increasing usage of continuous glucose monitoring (CGM) and recognition of the limitations of HbA1c lead to the adoption of new key metabolic parameters. Better understanding is needed as they are imposed as main metrics for the evaluation of patients with T1D.
Aim: To evaluate the correlation between time in range (TIR), hyperglycemia, estimated HbA1c and conventionally measured HbA1c.
A total of 43 families with child/children with T1D on pump treatment and continuous use of CGM were invited to participate in the study;31 (72.1%) families accepted.The children were followed for 6 months.Data from CGM was downloaded for 14 days 3-monthly.
The study group consists of 32 children with T1D,mean age 9.3±3.4y, 20(64.5%) boys,mean diabetes duration 4.9±1.9y; 24 (77.4%) of all are on sensor-augmented pump therapy; 5(16.1%) on OpenAPS and 2 (6.5%) are on the LOOP system.Mean duration of CGM usage is 3.0±1.3y. Mean HbA1c improved significantly at 6 months as follows: 6.9±1.0% vs. 6.8±1.0% (p =0.003).TIR strongly negatively correlated with HbA1c (baseline r=-0.77, 6th month r=-0.8) and with eHbA1c (r =-0.9, 3rd monthr =-0.8, 6th month r= -0.9).Time spent in hyperglycemia above 14 mmol/l influenced metabolic control only at the end of follow up period (r=0.8).Relationship between eHba1c and measured HbA1c is significant at the final evaluation (r=0.9, p=0.003).
There is strong correlation of eHbA1C, TIR and measured HbA1c which renders these key metrics preferred for assessing of an individual patient's glycemic control. Further studies may lead to their exclusive use also for predicting of the risk of diabetes complications.
Prior analyses of data from real-world use of the FreeStyle LibreTM flash glucose monitoring system have associated frequency of scanning with greater time in range, lower hypoglycemia, mean glucose, and glucose variability. The objective of this analysis is to assess outcomes in patients using flash glucose monitoring in Canada including more recently available data.
Anonymized data collected in Canada through uploads from FreeStyle Libre flash glucose readers was analyzed through September 2019. To understand the relationship between time in range with scanning frequency, individuals were divided into 10 equal-size groups based on scanning frequency. Average + standard error time in range (glucose 3.9 mmol/L–10.0 mmol/L) and time in very low glucose (< 3.0 mmol/L) was calculated for each group.
This analysis includes 41,049 readers, 346,624 sensors, and 393 million glucose measurements with an average of 11 scans/day per user. Patients in the lowest scanning frequency decile (3.3 scans/day) spent 13.1+0.09 hours in range (54.6% time in range) and 23.5+0.64 minutes with a glucose <3.0 mmol/L (1.6% time <3.0 mmol/L) . Patients in the highest scanning frequency decile (29.3 scans/day) spent 16.0+0.08 hours in range (66.7% time in range) and 20.4+0.59 minutes with a glucose <3.0 mmol/L (1.4% time <3.0 mmol/L).
This expanded analysis of real-world data from Canada demonstrates that FreeStyle Libre users monitor their glucose more frequently than the average rate of SMBG in Canada. Additionally, higher frequency of scanning is associated with increased time in range, decreased hypoglycemia, and is consistent with prior analyses.
Since 2016 rtCGM is covered by regular insurance in Germany. One requirement for prescription is having participated in an education program on CGM.“SPECTRUM” (Gehr,JDScT 2017) is the only structured industry- and device-independent and device-independent education program available. Its 5-6 modules, each taking about 90 min, covers all aspects of rtCGM therapy (technique, use, calibration, analysis, troubleshooting etc.).
All parents of CGM-naïve families underwent the SPECTRUM education program consecutively in the 7 participating centers by certified trainers.
A questionnaire with 40 multiple choice questions about glycaemic parameters and sensor treatment was answered after module 0 (general introduction of CGM) and after training completion.
Primary endpoint was the difference of correct answered questions.
Of 65 recruited parents who participated in visit one, 63 answered the questionnaire in visit 2.
8 children were on MDI therapy, 57 on CSII, 37 female (57%,) mean age 7.2 ± 3.7 years( range 0.9-12.0 y), diabetes duration mean 2.7 ± 2.5 years (maximum 11 years, 30 children up to 6 months after onset), last HbA1c before training 7.3± 0.8 %.
After training, the number of correct answers increased significantly (17.7±8.3 vs. 29.7±4.6; p<0.001), independent from educating center. Measured confidence and acceptance of the system was high after training.
The education program “SPECTRUM” helped to enlarge parent’s knowledge significantly and independently from educating center, as well as confidence and acceptance.
A follow up after 6 months will be performed to evaluate if there is an enduring effect in knowledge and metabolic control.
The “Eversense XL®” sensor is a subcutaneously inserted rtCGM system which lasts up to 180 days. Actual smartphones can be used as a data receiver sent by an on body transmitter with vibration alerts. As of today, it is only approved for use in adults.
Five children (6-12 years) and 10 adolescents (13-17 years) received the sensor subcutaneously. After a 30-day blinded period, the sensor was used regularly up to 180 days. Primary endpoint was superiority of Time Below Range (TBR) <70mg/dL after 90 days (assessed during 3 weeks before visit) compared to the first 30-day blinded period. Adverse events related to the device and procedure were captured.
15 patients (7 male, 12 CSII/3 MDI, diabetes duration 5.5±3.4 years) completed the study. All adolescents and one child had the insertion in the upper arm, 3 children were inserted in the hip, and one was inserted on the belly. Two patients did not continue the study after primary endpoint at 90 days and 13 patients continued up to 180 days. Although visible skin atrophy (thinning) at the sensors tip (distant from insertion site) was visible in all patients after 180 days, no change in skin depth was measured. The atrophy resolved in all cases byt 14 days after removal.
The Eversense System appears safe in the pediatric population also. Skin changes were mild and rapidly reversible.
Due to the silicone-based adhesive and the possibility of removing the transmitter daily, Eversense can be an alternative for patients with severe reaction to adhesives.
The FreeStyle Libre is a flash glucose monitoring (FSL-FGM) system. Compared to finger-prick based self-monitoring of blood glucose (SMBG), FSL-FGM may provide benefits in terms of improved glycaemic control and decreased disease burden.
Prospective nation-wide registry. Participants with diabetes mellitus (DM) used the FSL-FGM system for a period of 12 months. Endpoints included changes in HbA1c, hypoglycaemia, health-related quality of life (SF-12v2 and EQ-5D-3L), a specifically developed patient reported outcome measures (PROMs) questionnaire, diabetes related hospital admission rate and work absenteeism. Measurements were performed at baseline, after 6 and 12 months.
1365 persons (55% male) were included. Mean age was 46 (16) years, 77% of participants had 1 DM, 16% type 2 DM and 7% other forms. HbA1c decreased from 64 (95%CI 63, 65) to 60 (95%CI 60, 61) mmol/mol with a difference of -4 (95%CI -6, -3) mmol/mol. Persons with a baseline HbA1c >70 mmol/mol had the most profound HbA1c decrease: -9 (95%CI -12, -5) mmol/mol. EQ-5D tariff (0.03 (95%CI 0.01, 0.05)), EQ-VAS (4.4 (95%CI 2.1, 6.7) and SF-12v2 mental component score (3.3 (95%CI 2.1, 4.4)) improved. For most PROMs outcomes improved. Work absenteeism rate (/six months) and diabetes related hospital admission rate (/year) decreased significantly, from 18.5 to 7.7% and 13.7 to 2.3%, respectively.
Real-world data demonstrate that use of the FSL-FGM results in improved wellbeing and a decreased disease burden, as well as an improvement of glycaemic control.
Since the development of Continuous Glucose Monitoring, new metrics have emerged for assessing glycemic control. During ATTD 2019 congress, targets for glycemic control were set : time in range > 70% and time in hypoglycemia < 4% for most of diabetic patients. The aim of this study was to assess the percentage of diabetic patients achieving these objectives in our center.
All patients who received Freestyle Libre (FSL) training between June 2017 and May 2018 were included, except pregnant women. The training included an education session with a nurse and a diabetologist. Patients received a written educational document. Data from the FSL downloading three months after training were analyzed. The primary endpoint was the percentage of patients achieving the objectives set at ATTD.
581 patients were trained. 99 patients were excluded (incomplete FSL download, pregnancy). Data were analyzed for 482 patients (251 women), treated with multiple daily injections (n=222) or with pump (n=260). 81,5% had type 1 diabetes. Only 4,1% of patients with a HbA1c target <7% achieved the objectives set at ATTD, and 4,9% of patients with a HbA1c target between 7 and 8%. HbA1c decreased from 63 mmol/mol (7.87%) before FSL training to 59 mmol/mol (7.57%) three months later (p < 0.001).
In spite of the improvement in HbA1c after FSL initiation, very few of our patients achieved the targets set at ATTD. According to these results, one can wonder which means could be put in place to have more patients reaching these objectives.
Continuous glucose monitoring improves glycemic control in diabetes. This study compared the accuracy of the Dexcom G5 Mobile (Dexcom, San Diego, CA) transcutaneous sensor (DG5) and the Eversense (Senseonics, Inc., Germantown, MD) implantable sensor (EVS).
Subjects with type 1 diabetes using EVS simultaneously wore the DG5 for seven days. During day 3, patients were admitted to a clinical research center (CRC) to receive breakfast with delayed and increased insulin bolus to induce glucose excursions. At CRC, venous glucose was monitored every 15 min (or 5 min during hypoglycemia) for 6 hours by YSI 2300 STAT PLUSTM glucose and lactate analyzer. At home patients were requested to perform 4 fingerstick glucose measurements per day (Accu-Chek Aviva; Roche Diagnostics, Mannheim, Germany).
11 patients (9 M, age 47.4±11.3 years, M±SD) were enrolled. During home-stay the median [25^th-75^th percentile] absolute relative difference (ARD) over all CGM-fingerstick matched-pairs was 11.64% [5.38-20.65]% for the DG5 and 10.75% [5.15-19.74]% for the EVS (p-value=0.58). At CRC, considering all the CGM-YSI matched-pairs, the DG5 showed overall smaller median ARD than EVS, 7.91% [4.14-14.30]% vs 11.4% [5.04-18.54]% (p-value<0.001). Considering accuracy during blood glucose swings, DG5 performed better than EVS when glucose rate-of-change was -0.5 to -1.5 mg/dL/min, with median ARD of 7.34% [3.71-12.76]% vs 13.59% [4.53-20.78]% (p-value<0.001), and for rate-of-change <-1.5 mg/dl/min, with median ARD of 5.23% [2.09-15.29]% vs 12.73% [4.14-20.82]% (p-value=0.02).
Dexcom G5 Mobile was more accurate than EVS in CRC, especially when glucose decreased. No statistically significant difference was found in accuracy at home.
The aim of the study was to evaluate the accuracy of a new BGMS in development, the CONTOUR®CARE BGMS for use with CONTOUR®CARE Test Strips, against International Standards Organization (ISO) 15197:2013 Section 6.3 criteria.
The glucose concentration of fingertip capillary blood samples from 100 subjects was evaluated using each of three test strip lots. Samples were tested in duplicate: N=600. Comparator reference values were obtained by testing in parallel on a YSI 2300 STAT Plus glucose analyzer. System accuracy was evaluated per ISO 15197:2013 Section 6.3 criteria.
All three test strip lots met ISO 15197:2013 section 6.3 accuracy criteria with 100% of the results within ± 0.83 mmol/L (± 15 mg/dL) or ± 15% of the reference values. 99.2% of the results were within ± 0.56 mmol/L (± 10mg/dl) or ± 10%. For the test strip lots combined, 100% of the results were within Zone A of the Consensus Error Grid.
For values < 5.55 mmol/L (< 100 mg/dL), 100% of the results were within within ± 0.83 mmol/L (± 15 mg/dL) of the reference value and 99.4% of the results were within ± 0.56 mmol/L (± 10mg/dL). For values ≥ 5.55 mmol/L (≥ 100 mg/dL), 100% of the results were within ± 15% of the reference value and 99.1% of the results were within ± 10%. Overall, 100% of the results were within ± 12.5% or ± 0.69 mmol/L (± 12.5 mg/dL).
The new CONTOUR®CARE BGMS met ISO 15197:2013 Section 6.3 accuracy criteria.
Long-term real-world studies on the use of glucose sensors are scarce. This study evaluates the impact of real-time continuous glucose monitoring (RT-CGM) reimbursement in adults with type 1 diabetes (T1D) who use continuous subcutaneous insulin infusion (CSII) in Belgium.
Data from this 24-month, prospective, real-world study were collected between September 2014 and December 2018. Main endpoints were evolution of HbA1c, hospitalisations for hypoglycaemia and ketoacidosis, quality of life, and time in ranges. Data are mean (95% CI).
Of 515 people, 82 (16%) stopped using RT-CGM, mainly because of alarm fatigue (n=27). Baseline HbA1c decreased from 7.7% (7.5–7.8) to 7.4% (7.2–7.5) at 12 months and remained stable for 24 months (p<0.001 for both). In participants with baseline HbA1c >8.0%, HbA1c dropped from 8.8% (8.6–8.9) to 8.1% (7.9–8.2) at 24 months (p<0.001), while it increased from 6.5% (6.4–6.6) to 6.7% (6.5–6.8) (p<0.001) in participants with baseline HbA1c <7.0%. One year before reimbursement, 15% of participants were hospitalised for hypoglycaemia or ketoacidosis in contrast to 4% in year 1 and 3% in year 2 (p<0.001 for both). The worry subscale of the Hypoglycaemia Fear Survey improved (18.2 [16.8–19.5] at baseline; 14.0 [12.6–15.3] after 24 months; p<0.001). Time <54 mg/dL and <70-≥54 mg/dL significantly decreased from 1.2% (1.0–1.4) and 3.7% (3.3–4.2) in the first two weeks to respectively 0.9% (0.7–1.0) and 2.8% (2.4–3.2) after 24 months (p<0.001).
RT-CGM reimbursement for adults with T1D on CSII results in improved glycaemic control and quality of life, with fewer diabetes-related hospitalisations, which is sustained over 24 months.
To evaluate glucose metrics in a large group of children with T1D using glucose sensors in the real life.
During a planned visit, all children aged <18y with T1D for more than 1y, wearing CGM, were recruited during January-May 2019 by 11 Italian paediatric centres. According to their diabetes management, patients were divided in four groups: MDI&isCGM, MDI&rtCGM, IP&isCGM, IP&rtCGM. Predictive Low Glucose Management and Hybrid Closed-loop systems were excluded. We analysed: Time in Range (TIR), Time Below the Range (TBR), Time Above the Range (TAR), Coefficient of Variation (CV), HbA1c. Kruskal-Wallis test was used to perform comparisons and variables were summarized by median and IQR.
Seventy percent of children were using CGM. 665 cases (51% males), median age 12y, median diabetes duration 5y, were analysed. Significant differences in glucose metrics were found between groups (Figure). The lowest median CV values were found in MDI&rtCGM, 36.2% (32.8-40.8) and in IP&rtCGM, 36.8% (34.0-39.9) and significantly lower than MDI&isCGM, 39.4% (37.1-43.4) and in IP&isCGM, 40.5% (37.4-45.1), p<0.001. Regardless from CGM, groups using IP had significantly lower HbA1c values (MDI: 60 mmol/mol, 51-66; IP: 56 mmol/mol, 50-62; p<0.001).
Among a large group of children and adolescents with an average good metabolic control, the best glucose metrics are achieved with the use of rtCGM and IP.
Glucose management indicator (GMI) is an updated approach for estimating HbA1c from continuous glucose monitoring (CGM) data. GMI has been validated in patients with type 1 diabetes, however data in type 2 diabetes (T2D) is lacking.
112 paired laboratory A1c and GMI values were collected from 50 T2D patients on insulin and oral antidiabetic drugs. Patients had professional CGM (Medtronic Ipro2 with Enlite® sensor) for seven days with laboratory HbA1c determined in the same week. The agreement between laboratory HbA1c and sensor-derived GMI was examined using Bland-Altman plot with repeated subject measures.
24% of GMI values were within 0.25% of laboratory HbA1c and 40% of GMI values were within 0.5% from laboratory HbA1c values. Overall, GMI was lower than laboratory HbA1c (mean bias -0.8%, 95% limits of agreement -2.4 to 0.9%).The bias was greater at higher laboratory HbA1c values (see Fig).
Fig Bland-Altman plot of difference between Glucose Management index (GMI) minus laboratory HbA1c versus laboratory HbA1c in type 2 diabetes patient.
There are differences between sensor-derived GMI and laboratory HbA1c based on retrospective CGM in T2D patients, with GMI tending to be lower. This could be attributed to factors such as CGM duration, sensor accuracy, differences in glycation, acute change in glucose or patient behaviours on CGM. Clinicians should consider these factors when integrating both sets of values in therapeutic decision making.
Intermittently Scanned Continuous Glucose Monitoring (isCGM) provides new glucometrics as the percentage of time-in-range (TIR) and variability measures (standard deviation (SD), Coefficient of Variation (CV)) to evaluate glycemic control beyond classical HbA1c. Although TIR has been related with microvascular complication development due to its good correlation to HbA1C, this relationship could be altered under high glycemic variability (GV). The aim of the present study is to evaluate the correlation between TIR and HbA1c in this situation.
Prospective observational study in T1DM patients under isCGM and intensive insulin therapy (insulin pumps (IP) or multiple daily injections). HbA1C, TIR, SD and CV were analyzed by linear regression analysis and Pearson’s correlation coefficient. Treatment modality and demographic variables were also recorded.
146 patients were studied (age range 4-65 years,49.3% pediatric, 46% females, average HbA1c7.1±0.8% and 29.5% IP).
A strong correlation between TIR and HbA1c(R=-0.744;R2=0.544, p<0.001) was found, so, for every absolute 10% change in TIR, there was a 0.7% change in HbA1C. However, this correlation was higher in low GV patients (CV<35) (R=-0.914;R2=0.836) than in those with high variability index (R=-0.549;R2 = 0.301),p<0.001. The same correlation was found in pediatric population(<16 years old), and stratified by SD. Nonetheless, treatment modality did not show any significant differences between groups.
Although TIR has a strong correlation with HbA1c, this relationship is weakened in high variability patients defined by CV or SD. Thus, TIR should be used as the preferred metric beyond HbA1c only in low GV subpopulations.
Objective : To evaluate the impact of nocturnal hypoglycaemias on the final school performance of adolescents affected by type 1a DM by studying the periods of hypoglycemia recorded through the use of FREESTYLE.
DM1 children over 12 years in secondary school, with at least 6 months duration from the debut.Use of free FREESTYLE system> 85% of the study period..Prospective longitudinal follow-up . Rating of time hypoglycemia (blood glucose <70 md / dl) from March to June 2018; analisys schedule period 8:00 p.m. to 8 .00h am; and the average scores reached at the end of the course (1-10) in core subjects (language, mathematics, environmental knowledge, social, English )
.IBM SPSS Stastistics 19.0., Paired nonparametric samples n <30.Survey of Health Questionnaire SF-36 (Spanish and summary).
25 children ( 17♂), 1 course repeater, average age 14.5 to [12-17 .5].Needs: 1.1 IU / kg / day [ 0.88-1.56], sensitivity 58 mgr / dl / IU [25-102] H bA1c (DCA): 7.7 % [6.8-9.2 ] p: 0.38, Events of hypoglycemia / month 4, 9 [3-8] Hyperglycaemia events / month 8 [7-20]. Night study hours 38% hyper (> 180 mgr / dl) in normo 44% (70-180 mgr / dl and 18%en hipo(<70mgr/dl).18% in hipo (<70 mgr / dl)..Average grade study 7.25.If is select those patients who spend on average> 33% of the night shift in hypoglycemia, they are 8/25 cases. The distribution denotes average was for this group of 6.6 vs 8.1 p : 0.02 95%IC
As more nocturnal hypoglucemia less academic results at teenage
The benefits of continuous glucose monitoring (CGM) require ongoing use. Past studies suggest that use ≥6 days/week is optimal to obtain benefit. We examined usage of Dexcom’s G6 CGM system over the course of 12 months.
We studied anonymized data from a convenience sample of 3,000 US-based patients who first uploaded a G6 estimated glucose value (EGV) before 01-SEP-2018 and whose most recent uploaded EGV was within 1 week of the 1-year anniversary of their start date. Persistence was the percentage of observed days in which ≥1 EGV was uploaded. For each patient-week, the number of days with ≥1 EGV was calculated. Data density was the ratio of observed to possible EGVs.
At 1 year, most patients had used the system on >96% of possible days and 90% of observed patient-weeks had CGM usage on ≥6 days. Overall data density was >83%. Usage statistics were high in all self-reported age groups, but lower among teens/young adults than among those <13 or >25 years of age (Table).
Age (years) | <13 | 13-25 | >25 | Overall |
n | 500 | 700 | 1800 | 3000 |
Mean (SD) 1-year persistence | 92.04 (15.61)% | 87.14 (17.50)% | 90.93 (14.17)% | 90.22 (15.37)% |
Median (IQR) 1-year persistence | 98.36 (92.60-100.0)% | 94.25 (82.74-98.63)% | 96.71 (89.32-99.18)% | 96.44 (88.49-99.18)% |
Patient-weeks with ≥6 days CGM usage | 90.00% | 84.86% | 92.00% | 90.00% |
Data Density | 83.34% | 79.71% | 84.63% | 83.27% |
The high persistence, proportion of patient-weeks with ≥6 days of use, and data density suggest that G6 users trust, find value in, and benefit from their CGM.
This study aimed to explore the perspectives and experiences of people with type 1 diabetes who use a continuous subcutaneous insulin infusion (CSII) pump combined with a system for continuous glucose monitoring (CGM). The purpose of the study was to assess how psychological, emotional and social factors influence adequate use and optimal outcome of sensor augmented pump therapy, thereby enabling health care professionals to optimally individualize support to these patients.
This qualitative observational study used the Interpretative Phenomenological Approach (IPA) with semi-structured interviews. Participants were Caucasian adults suffering from type 1 diabetes for over 5 years , using sensor-augmented CSII for over 1 year. Three patients had an adequate HbA1c (<53mmol/mol), three had not. The research took place from January to June 2019 at a Dutchoutpatient clinic.
The study revealed themes associated positive or negative outcomes achieving or not achieving glycaemic targets with the technology. Positive outcomes were found in participants who had a more active coping style, who had good social support systems and who engaged in social activities. Negative outcomes were found in participants who were trying to manage concurrent health problems and who expressed negative emotional experiences or social stress/distress.
Strategies for supporting people using sensor augmented pump technology should consider the individual’s emotional, psychological and social capacity to optimize use and results of this therapy.
Insulin degludec (IDeg) is an ultra-long-acting insulin, with flat time-action profile, having a lower risk of hypoglycemia.The study assessed frequency, timing and severity of hypoglycemia of insulin IDeg as basal insulin in T1DM adolescence who are willing to fast.
Thirty eight patients (19 males) with T1DM (mean age 15.8 ± 3.4 years) and duration of diabetes (5.2 ± 1.7 years) were included. Patients had their IDeg doses titrated using pre- Iftar (sunset-meal) and pre-Suhur (sunrise-meal) glucose values. Participants were able to adjust their bolus doses according insulin to carbohydrates ratios. IDeg was reduced initially by 15% of pre‑Ramadan dose and administered at time of Iftar. Patients were monitored using the FreeStyle Libre® flash glucose monitoring (FGM)system.
Mean BG was 176 ± 49 mg/dl and overall time spent in hypoglycemia was 5.7%±3.0% of total monitoring period. Rate of hypoglycemia according to time intervals was 0%,3%, 8%, 15%and 64% in (19:00–24:00), (24:00–04:00), (04:00-10:00), (10:00–14:00) and (14:00–19:00) respectively. Out of all hypoglycemic flashes for patients, 74% were between60 and 69 mg/dl, 23% between 50 and 59 mg/dl, and 5% below 50mg/dl. There was no significant change (p = 0.211) in glycemic control measured by fructosamine level between pre-Ramadan ( 221.7 ± 63.8 mg/dL) and end-of-Ramadan ( 234.8 ± 71.7 mg/dL).Basal insulin was reduced by 35±18%.
Hypoglycemia was encountered in the last few hours of fasting preceding Iftar time necessitating dose reduction to minimize the severity and duration of hypoglycemia. This helps adolescents with T1DM observe Ramadan in a healthy and fulfilling manner under close supervision.
HbA1c is a parameter of glycemic control and predictor of long-term diabetic complications and plays a fundamental role in type 1 diabetes (T1D) management, nevertheless it has limitations. Continuous glucose monitoring (CGM) is a novel strategy that evaluates glycemic daily profiles and glycemic variability using the time-in-range (%TIR), glucose between 70-180 mg/d, as a new parameter. The aim of this study was to evaluate the association of %TIR with HbA1c in subjects with T1D.
Subjects of the Type 1 Diabetes National Registry in Mexico (RENACED DT1) using CGM and with complete %TIR values in the previous 2 weeks were included. The variability of HbA1c explained by the %TIR was assessed with linear regression analysis (R2). Polynomial regression was used to fit %TIR values to HbA1c.
We included 49 subjects, 32 (65.3%) female with median HbA1c 7.5% (6.8-8.1). Characteristics of the population are presented in table 1. We found a negative correlation between %TIR and HbA1c (r=-0.522, 95% CI: -0.701, -0.283; p<0.001), that increased after logarithmized HbA1c (r=-0.591, 95% CI: -0.748, -0.371; p<0.001). In linear regression analysis, %TIR explained 33.35% of the variability of HbA1c and 36.13% using a non-linear fit. There was a negative association between %TIR (B=-6.509) and HbA1c that persisted with logarithmized HbA1c (B=-0.007, p<0.001) (Figure 1).
We found a strong correlation between %TIR and HbA1c and a non-linear relation between both parameters. These findings suggest that %TIR is a good parameter for assessing glycemic control.
Insulin pump therapy (CSII) modestly reduces HbA1c compared with multiple daily insulin injections (MDI). Recent evidence suggests that continuous glucose monitoring (CGM), regardless of insulin delivery method, results in HbA1c reductions. The objective is to examine glycemic control via HbA1c in four treatment strategies: CGM+MDI, CGM+CSII, self-monitoring of blood-glucose (SMBG) +MDI and SMBG+CSII in patients of the T1D National Registry in Mexico (RENACED-DT1).
We included subjects using insulin analogues. Glycemic goal was defined as HbA1c ≤7.0%. Kruskall-Wallis and Dunn-test were used to assess differences in HbA1c between groups. A logistic-regression model evaluated the probability of achieving glycemic goal adjusting for sex, age and time since diagnosis.
603 subjects were included (SMBG+MDI: 474; SMBG+CSII: 18; CGM+MDI: 64; CGM+CSII: 47). Biochemical and anthropometric characteristics are presented in Table 1. The SMBG+CSII (7.35 [6.8-8.2], p<0.01), CGM+MDI (7.75 [7.0-8.5], p<0.001) and CGM+CSII (7.8 [6.8-8.4], p<0.001) showed lower HbA1c compared to SMBG+MDI (8.8 [7.4-10.5]) (Figure 1). There were no differences in HbA1c between CGM+MDI and CGM+CSII groups. The CGM+CSII group had increased probability (OR: 2.27, 95%CI: 1.12, 4.39, p=0.02) of achieving glycemic control compared with SMBG+MDI after adjustment for covariates (Table 2).
The use of CGM+MDI or CSII with or without CGM compared with MDI+SMBG is associated with lower HbA1c. Further research is needed to assess long-term effectiveness, impact of each component in glycemic control and in complications.
Accurate blood glucose measurements are important during normal daily activities, even more so during exercise. The accuracy of glucose sensors measuring interstitial glucose concentrations using fluorescence or glucose-oxidase techniques may be variable in different circumstances.
Prospective, observational study in twenty-five persons with diabetes during mountain biking in the Sierra Nevada for 6 days, and during normal daily activities for 6 days. We investigated the performance of two sensors, the Free Style Libre (FSL; glucose oxidase based) and the Eversense (fluorescence based), compared to the capillary based Precision NeoPro (FSLCstrip). Multiple paired measurements were performed. Data was assessed both with Parkes error grids and with direct comparisons of absolute and relative differences in point measurements.
Correlations during mountain biking (970 paired measurements): Eversense vs FSLCstrip, y=1.010x-9.88; r = 0.883 (98.3% in Parkes error grid zones A+B (73.2 + 25.1%), FSL vs FSLCstrip: y=1.21x-10.13; r = 0.917 (98.2% in Parkes error grid zones A+B(73.9 + 24.3%). During normal daily activities(896 paired measurements): Eversense vs FSLCstrip: y=0.96x-0.11; r = 0.920 (98.8% in Parkes error grid zones A+B (81.9 + 16.9%), FSL vs FSLCstrip: y=1.07x-9.57, r = 0.937 (99.9% in Parkes error grid zones A+B (87.1 + 12.8%). Differences of >15% between the measurement techniques were present during the exercise week in 35 – 65%; during normal daily activities in 25 – 58%.
During mountain biking , interstitial glucose readings with both the Eversense and the FSL were less accurate compared to measurements during normal daily activities.
Clinical trial demonstrate improved glycemic control, namely reduced time in hypoglycemia with flash glucose monitoring (FGM); however real-life conditions can modify this scenario.
Aim: To examine real world use and glycemic control following a standardized initiation process of FGM.
Individuals aged >18 years with type 1 diabetes (T1D) were prospectively recruited from diabetes outpatient clinic. Pregnancy, diabetic ketoacidosis or use of continuous glucose monitoring (CGM) in the previous 6 months were exclusion criteria. FGM metrics from baseline (first 14 days of use) and 6 months were compared, using the glycemic control targets established in the 2019 ATTD consensus.
Thirty-five individuals were included (65.7% males); median age was 48(40-60) years, diabetes duration 23(14-33) years; 94.3% were on multiple daily injections, 5,7% on pump. At baseline, median time below 70mg/dL was 4.0(2.5-9.0) % and 40% of the individuals had a time below 70mg/dL ≤4%, compared to 3(1-9)% (p=0.209) and 51.4% (p=0.206), respectively, at 6 months. Median time in hypoglycemia was 98(72.5-122.5) minutes and 75(54.0-113.0) minutes at baseline and at 6 months, respectively (p=0.022).
There was a significant decrease in the median coefficient of variation (CV) [42.8(37.3-46.4) to 38.7(34.2-45.0), p <0.001]. Median TIR (time in range) was 50(40-60)% at baseline and 47(35-55)% at 6 months (p=0.02) and mean HbA1c (chromatography method) was 7.67±1.06% and 7.74± 1.12% (p=0.509) respectively.
At 6 months, median time in hypoglycemia and CV was reduced by 23.5% and 4.1, respectively, with no deterioration on HbA1c. Our data support the benefit of FGM on glucose control in T1D.
Scuba diving is today considered a possible activity in T1D following an appropriate protocol, while the performance of most recently available rtCGM systems has not yet been tested in this setting. Aim of this study is to verify if prolonged and consecutives immersions in water and exposure to high and variable pressures can alter the subsequent performance of the sensor, in terms of accuracy.
11 T1DM subjects participating at the camp “Diabete Sommerso 2019” were enrolled (mean age 45yr, mean diabetes duration 22yr, mean A1c 7.5%). Subjects wore a rtCGM 24 hours before the first dive and used their own glucometer to check glycemia and take all therapeutic decisions.
We collected data of 88 dives (64 at depths <18mt, 24 at 18-30mt). For the assessment of rtCGM accuracy we compared 328 capillary glycaemic values to the corresponding CGM data using MARD and Pearson’s correlation and analysing first overall data, then different glucose ranges (TIR, TAR, TBR) and dive depth (<18 or 18-30mt): despite results shown an excellent Pearson correlation in overall data (R=0.85, p<0.05) and still a good one in subgroup analysis, the MARD values revealed a worsening of accuracy (overall MARD 13.4) compared with routine use.
In our experience rtCGM system used during multiple diving at depth <30mt has shown a good univariate correlation, despite a not optimal accuracy. It still could represent a valid aid to capillary controls needed to safely deal with diving in people with T1D, but it requires caution in interpreting results.
Close control of capillary glycaemia is important for treatment adjustment. This self-control provides us with static information about capillary glycemia at a specific time, and multiple daily controls are necessary to know the patient's glycemic profile. It is demonstrated that the greater number of capillary controls there is an improvement in HbA1c. Some technologies have been developed, and provide us information in real time of the patient's glycemic profile, using interstitial glycemia data, which, in times of stability, can be equivalent to capillary blood glucose values.
Performed the statistical analysis with the SPSS 19 program, comparing HbA1c means from the Levene test. The average A1C is studied in a group of patients with continuous glucose monitoring (CGM) (n = 110) and compared with the average A1C in the group of patients without monitoring (n = 72).
There is a significant HbA1c lower in the group of patients with monitoring (7.47 vs 7.88, p <0.05). In addition, among patients with monitoring, A1C is lower in patients who use continuous subcutaneous insulin infusion (7.15 vs 7.68, p <0.05).
In our group of patients we can see that the use of continuous glucose monitoring improves the metabolic control in pediatric patients, and this control is optimized when we associate a continuous glucose monitoring device with an infusion system of subcutaneous
There is only few study FGM in pregnant woman with T1DM.
Aim. Compare FGM with SMBG and CGM in pregnant woman with T1DM.
31years-old woman with T1DM, 9ws of pregnancy. HbA1c 5,4%. SMBG(AccuCheck Performa), FGM(FreeStyleLybre) and CGM(iPro) was performed. We aligned each glucose value of SMBG with the closest glucose value FGM and CGM. 109pairs of SMBG with FGM/CGM and 739pairs of FGM and CGM was obtained.
Mean glucose value of FGM was significantly lower than CGM and SMBG (4,9±2,1mmol/l; 5,8±2,2mmol/l and 6,3±2,4mmol/l accordingly, p=0,001). MAD between FGM and SMBG values was slightly more than between CGM and SMBG: 1,2±0,9mmol/l&0,9±1,4mmol/l, р=0,09. MARD between groups were significantly different, p=0,009. CGM showed better accuracy than FGM: MARD between CGM and SMBG was 13,9±13,4%, between FGM and SMBG 20,8±13,8%, p<0,001. MARD was the worst in the 1stday: 39,4±12,4% between FGM and SMBG, 33,8±16,8% between CGM and SMBG. Without 1stday MARD decreases: 20,7±13,7% in the pairs of FGM and SMBG, 11,5±9,2% in the pairs of CGM and SMBG, p<0,001. Consensus Error Grid analysis demonstrated 89,9%values of CGM and 88,1%values of FGM in zone A and B. 74,3%results of CGM and 58,7%results of FGM were in zone A, p=0,03. Most the results in zone C were on the f1stday. The Pearson correlation with SMBG was high: 0,918 for FGM and 0,911 for CGM.
FGM shows as high correlation with SMBG as CGM. Error results were often on the 1stday sensor inserting. About 90%results of FGM were in zone A and B so its clinically acceptable.
Little is known about the optimal timing of CGM initiation in youth with type 1 diabetes (T1D). This pilot randomized controlled trial examined differences in glycemic and psychosocial outcomes between youth with T1D and their caregivers who started CGM soon after diagnosis compared to those starting CGM 6 months later.
Data from 55 study participants (ages 2-17 years, mean age 11.1±3.6 years, 50% female, 69% non-Hispanic white) and their caregivers were collected at initiation of CGM and again 6 months later. Participants were randomized 2:1 to early CGM start (n=42, 24±7 days), or delayed start (n=13, 204±11 days). Caregivers completed online validated surveys about their diabetes-related distress, technology attitudes, and glucose monitoring satisfaction.
Pearson correlations showed that more time between diagnosis of T1D and CGM start was associated with higher levels of CGM discontinuation (r=0.37, p=0.005), lower CGM use over the six-month period (r=0.41, p=0.002), higher levels of diabetes distress among parents (r=0.27, p=0.05), and more time spent under 54 mg/dL (r=0.27, p=0.05). Time to CGM start was not correlated with HbA1c values following 6 months of CGM use. T-tests showed that parents of youth who discontinued CGM (n=6) had less positive technology attitudes (p=0.003) and felt more restricted by glucose monitoring (p=0.02), but experienced less sleep disturbance (p=0.002) than parents of youth using CGM over the six-month period.
Despite similar glycemic results, starting CGM closer to diagnosis of T1D in youth is correlated with higher sustained use by youth and less diabetes distress for caregivers.
Lipohypertrophy (LH) is a common diabetes problem associated with exogenous insulin exposure in the subcutaneous tissue. LH can attenuate insulin absorption, increasing glucose variability (GV). LH conveys hazards for both: excess hyperglycaemia leading to diabetes complications; and severe hypoglycaemia, when insulin is injected into an LH free area.
Aim
To assess the impact of LH nodules on glucose variability in adults with Type 1 diabetes (T1DM).
A case-crossover study of 27 participants with T1DM and LH was conducted. LH was characterised with ultrasound. GV was assessed in two conditions using blind continuous glucose monitoring (CGM): Condition 1, usual insulin injecting behaviour; and Condition 2, injecting in LH free areas.
A total of 27 participants were enrolled, of which 15 completed the study. The median age of the participants was 32 (IQR, 35) years (range 20-71 years), with a median duration of T1DM of 14 (IQR, 13) years. In terms of the impact of LH on GV, one third of the participants demonstrated improvement in variability of >10% of time in range (4-10mmol/l), while the remainder showed no improvement, or in two cases a worsening of their GV. The findings also showed reductions in the total daily insulin dose (mean±SD -4±8.5, range -25 - +4) and increases in the number of effective bolus injections by 16.1% in Condition 2.
The interaction between LH and GV is complex and mediated by patient behaviour. In addition, the study provides data on the utility of ultrasound as a method for detecting LH.
The efficacy of real-time continuous glucose monitoring (rtCGM) and intermittently-scanned continuous glucose monitoring (isCGM) in maintaining optimal glycemic control has not been well studied. We compared the effect of rtCGM and isCGM on prevention of hypoglycemia and other glycemic metrics during exercise.
In this randomized study, adults with T1D and normal awareness of hypoglycemia (GOLD score <4) participants used rtCGM (Guardian Connect Mobile, Medtronic Inc., Northridge, USA) or isCGM (Freestyle Libre, Abbott Diabetes Care, Alameda, USA) during 4 days of structured physical activity with 4 weeks of follow-up. Primary endpoints were percentage of the time spent in hypoglycemia (<3.9 mmol/L [<70 mg/dL]) and time in range (3.9-10 mmol/L [70-180 mg/dL]).
60 adults with T1D (age 38±13 years, HbA1c 62±12 mmol/mol [7.8±1.1]) were randomized to rtCGM (n=30) or isCGM (n=30). All participants completed the study. Time spent in hypoglycemia was significantly lower among rtCGM vs. isCGM participants during exercise (6.8±5.5% vs. 11.4±8.6%; p=0.0180, respectively) and throughout the post-exercise follow-up (5.3±2.5% vs. 7.3±4.4%, p=0.0353, respectively). The differences in overnight hypoglycemia (00:00-06:00) were most notable during (7.7±11.4% vs. 20.1±18.0%, p=0.0022, respectively) and post-exercise follow=up (4.9±3.3% vs. 8.9±8.3%, p=0.0192, respectively). rtCGM participants spent significantly more time in range compared with isCGM users throughout the entire study period (76.4±8.7% vs. 67.9±15.4%, p=0.0117, respectively).
Use of rtCGM was more effective than isCGM in preventing of hypoglycemia and improving time in range in T1D participants with normal awareness of hypoglycemia, demonstrating the value of rtCGM alarms during exercise and in daily diabetes self-management.
Several new CGM-derived parameters may provide additional insights into glycaemic control in T1DM, such as time in range (TIR) and parameters of glycaemic variability (GV). This study examined the interrelation and interpretation of these promising parameters.
The CGM device (seven days) provided following parameters: TIR (70-180 mg/dl), time in hypoglycaemia (total(<70);level 2(<55)) and hyperglycaemia (total(>180);level 2(>250)) and coefficient of variation (SD/mean glucose).
Patients (n=95; 45±10years; HbAc1:7.67±0.75%) were labeled as having good (HbA1c<7%;n=19), moderate (7-8%;n=46) or poor glycaemic control (HbA1c>8%;n=30). TIR was negatively associated with HbA1c (r=-0.508), MBG (r=-0.851) and time in hyperglycaemia (total:r=-0.924;level 2:r=-0.855), but not hypoglycaemia. However, TIR did associate with shorter time in level 2 hypoglycaemia in patients with good (r=-0.596) and moderate (r=-0.252) control. In contrast, COV was strongly positively associated with time in hypoglycaemia (total:r=0.750;level 2:r=0.740) but not with hyperglycaemia time. Once more, subgroup analysis showed that COV did correlate with time in hyperglycaemia in the lowest HbA1c group (total:r=0.588;level 2:r=0.662). Regarding the relationship between TIR and COV, TIR did not correlate with COV in the whole group but was negatively associated with COV in patients with good (r=-0.832) and moderate (r=-0.469) control.
This study provides arguments for the added value of CGM-derived parameters as TIR and COV in reflecting glycaemic control in T1DM, as they relate with clinical important situations such as level 2 hyper- and hypoglycaemia. It should be noted however that the interpretation depends on HbA1c level, adding less in those with poor control as it seems not to reflect hypoglycaemia or GV.
Previous studies suggest a minimum duration of ~14 days of continuous glucose monitoring (CGM) data are required for robust assessment of glucose control metrics in people with type 1 diabetes (T1D). However, this was derived from older CGM devices and the employed techniques required big datasets (>150 subjects) to achieve reliable results. In this work, we present a robust technique using data from newer generation sensors to determine if the minimum duration remains ~14 days. We also evaluate a larger set of glycaemic metrics.
CGM data from a 6-month randomised clinical trial in 25 adults with T1D was analysed. Eight glucose metrics were evaluated on different sliding time windows of durations ranging from one to 90 days. Then, the absolute percentage error was computed for each window incidence against the entire duration. Finally, the median (interquartile range) for each window length was calculated.
The figure below shows the results corresponding to the metrics: percentage time in [70,180] mg/dL, and percentage time below 70 mg/dL. Note that the latter presents a much higher error and variance.
The duration of ~14 days of CGM data remains the minimum required for most of the evaluated metrics, but not for the ones related to hypoglycaemia, which require a much longer duration. The proposed technique can be employed in a smaller dataset to attain a high level of reliability.
People with diabetes-related ulcers may benefit from hyperbaric oxygen (HBO2) therapy and from continuous glucose monitoring (CGM). Although blood glucose (BG) meters based on glucose oxidase (GO) underestimate BG at high pO2, BG meters based on glucose dehydrogenase (GD) do not. We therefore examined the performance of a GO-based continuous glucose monitoring (CGM) system in comparison to GO-based and GD-based BG systems in HBO2 and normobaric air (NBAir) environments.
Twenty-six volunteers without diabetes wore Dexcom G6 CGM systems and provided periodic blood samples while breathing NBAir (pO2 21.28 kPa) and during a standard HBO2 treatment consisting of three 30-minute intervals of HBO2 (pO2 243.18 kPa) separated by two 5-minute intervals of HBAir (pO2 51.07 kPa). Accuracy of the CGM and GO-based BG meter were assessed by comparisons with the GD-based values.
The MARD for the CGM system was 15.96% and 8.52% for the GO-based meter. Compared to NBAir, HBO2 exposure resulted in significantly higher CGM values and significantly lower GO-based meter values. Pre-HBO2 and post-HBO2 values obtained in NBAir were significantly different when measured by CGM or the GO-based meter (Table).
Device | HBO2 vs. NBAir | NBAir before vs. after HBO2 |
---|---|---|
CGM | +3.76 mg/dL (p<0.001) | +4.13 mg/dL (p=0.015) |
GO-Based | -10.38 mg/dL (p<0.001) | -9.04 mg/dL (p<0.001) |
GD-Based | +1.17 mg/dL (p=0.076) | -5.00 mg/dL (p=0.039) |
HBO2 exposure results in statistically significant differences in glucose measurements obtained with GO-based devices, but not a GD-based device. Standard HBO2 treatment results in statistically significant effects on glucose concentrations. These differences are of unlikely clinical significance.
Acarbose (ACA) can effectively reduce the postprandial blood glucose and has similar antidiabetic effects as metformin (MET). To our knowledge, few studies have compared the effect of ACA or MET on glucose fluctuations. In the present study, we explored the effect of ACA or MET combined with premixed insulin (INS) on glycemic control and glycemic variability (GV).
This was an open-label randomized trial that was conducted in type 2 diabetic patients taking premixed insulin. The patients were assigned to 12 weeks of MET (n=62) or ACA (n=62) treatment combined with INS. The main outcomes were changes in GV with the use of continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) compared with baseline.
Compared with baseline, several GV indices [standard deviation (SD), mean amplitude of glycemic excursions (MAGE)] and blood glucose control indices [mean glucose (MG), time in range (TIR) and HbA1c] were both significantly improved in INS+ACA and INS+MET after 12-week therapy. However, coefficient of variation (CV) was significantly reduced in INS+ACA but not in INS+MET. Moreover, compared with INS+MET, INS+ACA led to a more pronounced percentage change from baseline in CV [26.3% (1.7% - 44.6%) vs. 11.9% (-7.0% - 29.9%), P = 0.022], MAGE [40.5% (20.1% - 60.5%) vs. 25.2% (-2.1% - 43.4%), P = 0.007] and SD [38.6% (25.2% - 57.9%) vs. 30.1% (10.8% - 46.5%), P = 0.041].
Both MET and ACE combined with INS effectively reduced blood glucose. Compared with MET, ACA combined with INS reduced GV.
Currently there is no data among Asian Indians on the correlation between Time In Range(TIR) or Time In Target(TIT) and A1c. In this study, we have made an attempt to assess the correlation if any, from CGM obtained from our diabetes centres in Kerala, South India.
We divided the patient cohort of 200 into 4 categories; those with A1c values <7, 7 to 8, 8 to 9 and 9 to 10%. Subsequently, HbA1c values were paired against their corresponding TIT values from CGM data(Libre Pro) and reviewed to determine if any correlation existed between these metrics. Informed consent were obtained from study participants.
HbA1c was paired with TIT% data and evaluated by Pearson’s Coefficient ratio. Among the subjects, those with HbA1c between 7-8% experienced stronger correlation ( R=0.4399, R2=0.1935) with a significant p value followed by patient cohort group 8-9%, with a moderate correlation while the other two cohorts gave a weak correlation value and had a p value of no clinical significance. In elderly subset of subjects (> 60 years), in accordance with International consensus for TIR, the target value > 50% was seen as HbA1c drops below 8%.
This study in Asian Indians shows a close alignment with TIR in accordance with the International Consensus on TIR for A1c between 7 and 9%. However in the other 2 cohorts the correlation is weaker implying the role of TIR as complementary and not as a replacement for A1c. In elderly, TIR becomes significant as A1c drops below 8%.
The anticipated size of the injectable Sencell Glucose Sensor (Lifecare, Norway) is planned to be 2 x 0.8 x 0.6 mm² to fit into a larger injection needle. It uses an active fluid in an osmotic pressure chamber with a glucose binding molecule (GBM) and a glucose-like ligand. The reversible reaction affinity in the chamber does not destroy any molecule when generating the signal, resulting in a long-term survival of the sensor in the body.
To miniaturize the core sensor without losing pressure sensing sensitivity, a 3D-printed nanosensor technology was employed. MEMS technology was used to build a sensor chamber with less than 1 mm³ volume, and the pressure membrane was equipped with a nano-strain sensor (4 x 1 µm²). The chamber was embedded into a circuit board chip and connected to an electronic read-out interface for sensor calibration using a standardized gas-pressure protocol.
The collected signals showed a very sensitive and linear pressure to signal relation (r²=0.996), a high reproducibility (CV = 0.2 %), no hysteresis/drift over time, and a high stability even when performing continuous repetitive calibration procedures. The observed sensor specifications (pressure range: <-300 to >300 mbar, pressure resolution 480 µbar, membrane Size 1x1mm², 300 nm thickness) would allow to track glucose changes with a resolution of 1-2 mg/dL
In conclusion, the first pilot attempt to miniaturize the core Sencell sensor technology by means of a nanostrain pressure sensor resulted in a very small osmotic pressure chamber (1mm²) suitable for the anticipated purpose
Evaluating the effectiveness of an implantable continuous glucose monitoring (CGM) sensor in a center in Spain.
Data from patients with type 1 diabetes mellitus who started using an implantable glucose sensor were retrospectively analyzed. Age, sex, diabetes duration, diabetes chronic complications, treatment, sensor time of use and previous use of CGM were evaluated. HbA1c was compared before the start of the implantable sensor and at the end of the follow-up.
19 patients were included with a mean age of 38±12 years, 84.2% men, diabetes duration 14 ± 11 years. 78.9% were treated with multiple doses of insulin and the remaining 21.1% (n = 4) had an insulin pump. 7 patients had previously used another CGM system (3 Dexcom®, 2 Guardian®, 2 FreeStyle Libre®). Of these, 3 patients suffered from diabetic retinopathy and one also from diabetic nephropathy.
The median follow-up was 6 months [4-18], with a maximum follow-up of 15 months.The average time of sensor use was 91.4%. HbA1c decreased from 7.34±0.19 to 7.22±016% (p=0.3). 35 insertions and 18 extractions were performed. No episodes of bleeding, infection or failed extractions were counted. Two patients stopped using the implantable CGM system (one due to a change of address and imposible follow-up and another for issues with the smartphone).
The use of the implantable CGM sensor is safe and provides benefit in glycemic control with a low dropout rate.
Continuous glucose monitoring (CGM) provides important information to improve glycemic targets in people with diabetes. We performed a meta-analysis of randomized controlled trials (RCTs) comparing CGM with usual care on glycemic control in both type 1 and type 2 diabetes.
We conducted an electronic search until June 2019 to identify RCTs assessing changes in HbA1c, time in target range (TIR), time below range (TBR), time above range (TAR) and glucose variability expressed as coefficient of variation (CV). We used a random-effects model to calculate the weighted mean difference (WMD) with the 95% CI.
We identified 15 RCTs, lasting 12–36 weeks, with 2,461 patients. Compared with the usual care (overall data), CGM was associated with reduction in HbA1c (WMD = −0.17%, 95% CI −0.29 to −0.06, I2 = 96.2%), increase in TIR (WMD = 70.74 min, 95% CI 46.73 to 94.76, I2 = 66.3%), and lower TAR, TBR and CV, with heterogeneity among studies. In pre-planned subgroup analysis, intermittent glucose monitoring was similar to control strategy for HbA1c , with less time spent in both level 1 hypoglycemia (<70 mg/dL, WMD = -56.26, 95% CI -88.91 to -23.60, I2 = 93.7%) and level 2 hypoglycemia (<54 mg/dL, WMD = -26.23, 95% CI -49.07 to -3.40, I2 = 86.8%), and lower CV (WMD = -3.86%, 95% CI -5.15 to -2.57, I2 = 78.1%).
CGM improves glycemic control by expanding TIR and decreasing TBR, TAR and glucose variability in both type 1 and 2 diabetes.
In 2018, the Russified version of Free Style Libre (FSL) was registered in the Russian Federation.
To evaluate the metabolic profile and satisfaction with the use of the Russified version of FSL in children.
During 3 months, 10 children were held FSL. The age of the children was 4.4-14.4 years (7 boys, 3 girls). Initial HbA1с values were 5.8-9.0%. At the beginning and the end of the study, time in range, HbA1c, insulin dose, survey of children and parents with an assessment of satisfaction with the monitoring were analyzed. Satisfaction was rated on a five-point scale, where 5 - fully satisfied.
1. HbA1с — in 50% of cases increased by 0.2-0.5%, 50% decreased by 0.3-0.7% 2. Time in range - 60% of children increased their time in range, 20% did not change and 20% decreased. In 80% of observations, the frequency and duration of hypoglycemia decreased. In 60% of children, hyperglycemia time decreased, in 40% this time slightly increased. 3. Insulin requirement - there was a general trend towards a significant reduction in the requirement and total daily dose of insulin. 4. Survey of participants - Patients and parents were fully satisfied with the monitoring system in 60% of cases, and 40% were satisfied.
FSL allows improve the glycemic profile of children, increase time in range, reduce the frequency and duration of hypo and hyperglycemia. Participants were highly satisfied with the use of FSL and the main wish was to include a warning system.
Background: Glycemic variability (GV) considers intraday glycemic excursions as well as glucose fluctuations that occur at the same time on different days.
Aim: To evaluate several GV indices in patients with type 1 diabetes mellitus (T1D) and the association among GV indices and microvascular complications and lipid abnormalities.
Methods: patients from a diabetes clinic in a tertiary care center were divided into 2 groups: HbA1c <7% (10) and between 7–8% (10). A continuous glucose monitoring device (iPro2) was placed for 4 days.
Results: 20 patients were included, 12 (60%) women, median age and diagnosis were 34 (22-46 years) and 16 years, respectively. The median of GV indices were: standard deviation (SD) of 55 mg/dL (40-66 mg/dL), coefficient of variation (CV) of 38%, time in range (TIR) of 50% (40-63%) and time below range was 8%. Patients with HbA1c <7% had a lower average glucose (129 mg/dL vs.146 mg/dL, p=0.04), longer TIR (61% vs. 42%, p=0.006). A HbA1c >7% was significantly associated with higher average glucose and SD, shorter TIR before (p=0.016) and after lunch (p=0.005). Patients with CV >36% had less TIR (42% vs. 58%, p=0.030), longer time below range (18% vs. 3%, p=0.001) and more low excursions.
Conclusions: CV >36% is associated with a shorter time in range, longer hypoglycemia time and more glycemic excursions. HbA1c is related to the average of glucose and TIR but it is not an accurate predictor of hypoglycemia. GV parameters should be measured to assess the possibility of hypoglycemia and inter-day variation.
Latest generation of FreeStyle Libre (FSL) has been recently approved by FDA as equivalent to glucose meters for making therapeutic decisions. However, real-life evidence for this equivalence is needed.
Adolescents with type 1 diabetes (N=58) taking part in a summer camp were invited into the prospective observational study. For 14 days, they used FSL participating in camp activities. During four consecutive days, an 8-point glucose profile was collected. Capillary blood glucose (BG) were measured using Contour Plus One glucose meter and followed within 1 minute by FSL scan. Glucose trends arrows were also recorded. Accuracy of the system was assessed by calculation of bias and mean absolute relative difference (MARD), while clinical utility was checked against surveillance error grid (SEG).
Mean age equaled 13.8 years, (95%CI:13.2–14.3), diabetes duration 7.9 years (6.5-9.4), HbA1c 7.5% (7.2-7.8%). Altogether we were able to collect 1796 valid pairs of measurements of FSL and BG (median 32/patient). FSL overestimated BG by a mean of 6.5mg/dl (5.5-7.6) and overall MARD was 11.3% (10.8-11.8). SEG classified 97.5% of scans as clinically accurate (class A:85.3%, B:12.2%) and 2.5% as class C. FGM presented significantly worse MARD during rapid glucose decrease [17.9% (16.0-19.7) vs 10.2% (9.5-10.8), p<0.0001), during mountain hikes (16.6% (12.3-20.8) vs 11.1% (10.6-11.6), p=0.0131] and in children who presented adverse skin reactions to the sensor (13.5% (11.3-15.6) vs 11.1% (10.6-11.6), p=0.0381).
The new generation of FSL demonstrated good agreement with point-of-care BG measurements in real-life conditions. However, in some scenarios additional verification of measurements might be advised.
The glucose-lowering effect of exercise has been well characterized in controlled settings. However, the extent to which real-world physical activities modulate glucose levels is less understood. This study evaluated sensor glucose (SG) change during logged activity events in individuals wearing continuous glucose monitor (CGM) and fitness tracker devices.
Data were uploaded between 6/2017-10/2017 by 5 individuals aged 27-63 years (mean 44.9±12.9 years) who used the MiniMedTM 530G system with the Sugar.IQTM diabetes assistant. Corresponding activity logs, steps, and metabolic equivalents (METs) were acquired with Fitbit® Charge 2. Change in SG during activities ranging 15 to 154 minutes in duration was calculated as the difference between the end and start SG level of within each logged activity event. Only events with a negative SG change, and no active insulin from bolus, were retained for analyses. The association of SG change with sum of METs, sum of steps, duration, and heart rate (HR), were assessed with Pearson’s correlation.
There were 16 events out of a total 174 that met both criteria of no active insulin from bolus (21%) and negative SG change (63%), which included walking, running, cleaning, and gardening. Strong correlations were observed for SG change against METs (r= -0.986, p<0.001), duration (r= -0.899, p<0.001), steps (r= -0.829, p<0.001), and HR (p= -0.566, p=0.028).
Glucose reduction during exercise can be observed and characterized in real-world scenarios with CGM and fitness tracking. Future models of exercise impact on SG may optimize algorithm prediction accuracy.
The use of continuous glucose monitoring (CGM) systems provide information to define the correlation between average glucose (AG) and plasma glycosylated hemoglobin (A1c). Factors that can affect this association are unknown. The aim of the study was to determine whether metabolic control could influence the relationship between A1c and AG.
Caucasian individuals with type 1 diabetes using CGM (11Dexcom G4®, 21Dexcom G5®,4 DexcomG6®,6 Enlite2® and 1 Enlite3®) were enrolled in the study. The AG values measured by CGM from the previous 90,60,30 and 14 days were correlated with A1c. To analyze the possible effect of glycemic control upon A1c-AG relationship, we divided the subjects into two similar size subgroups according to glycemic control. Pearson test and linear regression analysis were performed.
43 patients were studied: 67% women with mean A1c 7.2±0.7%. Data at table 1 show different correlations according to glycemic control, with a stronger association in the subgroup of A1c>7.2%. We found different slopes in linear regression according to the degree of metabolic control (Figure 1). The subgroup with A1c≤ 7.2% showed the lowest correlation, and the subgroup with A1c>7.2% displayed a slope closest to 1, especially for 30 and 60 days.
Metabolic control can influence the correlation between A1c and AG. This correlation is weak, especially in subjects with good metabolic control. Other factors beyond glycemia levels may influence plasma A1c, especially in the presence of good metabolic control.
Frequent glucose testing is required for optimal management of type 1 diabetes mellitus (T1DM). Limited data are available regarding real-world experience of the novel technology for monitoring by interstitial glucose monitoring (IGM) systems. We aimed to assess the effect of IGM systems in a real-life clinical setting on glycemic control parameters, among adult T1DM patients.
Cross-sectional analysis about effectiveness and safety of IGM systems on intensive-treated (continuous subcutaneous insulin infusion or multiple dose insulin injections) T1DM adult patients in routine clinical practice. All insulin-pump treated patients were recruited. Multiple dose insulin injections (MDI) treated patients were randomized selected (1:1) from our T1DM database. The protocol was approved by the reference Castilla-La Mancha Public Health Institute Ethic Committee.
Ninety patients (45 on insulin-pumps and 45 on MDI). Mean age 36.8±11.0 yrs. and mean T1DM duration 18.6±10.7 yrs. Mean insulin-pump treatment was 5.6±3.9 yrs. Forty-seven percent (42) of the patients were treated with interstitial glucose monitoring (IGM) system (FreeStyle Libre, 13%; Guardian Sensor 3, 24%). Glycated haemoblobin and percentage of glucose values <3.9 mmol/L (70 mg/dL) were lower among IGM users compared with those using exclusively self-monitoring of blood glucose (7.0±0.8% vs. 7.7±1.2%, P=0.001; 5.9±6.1% vs. 11.2±9.2%; P=0.003). Furthermore, IGM treated patients administered higher number of daily insulin boluses (4.9±1.7 vs. 3.3±1.1, P<0.001).
Real-life observational data in an adult intensive-treated T1DM population demonstrated a significant reduction in HbA1c and hypoglycaemia with IGM systems.
Continuous glucose monitoring (CGM) may help to improve diabetes therapy. An international consensus paper calls for standards in the CGM data analysis. The aim of this study is to evaluate the CGM-based clinical targets in italian adult patients with Type1 (DM1) and Type2 (DM2) diabetes using an IS-CGM (intermittently scanned CGM - FreeStyle LibreTM system).
We evaluate data of 227 patients (130 DM2; 97 DM1) using IS-CGM for at least 4 weeks. All patients were treated with insulin (basal-bolus regimen). Data were obtained from the cloud-based management system LibreView®.
The DM1 patients showed: mean glucose level 183 ± 45 mg/dl; 8.7 scans per day; mean time in range (70-180 mg/dl, TIR) 49.8% ± 19, time below range (TBR) 4.8% and an estimated HbA1c of 8.1%±1.4. 17% of patients had TIR ≥ 70%.
The DM2 patients showed mean glucose level 162 ± 31.7 mg/dl, 5 scans per day, TIR 63.2% ± 18, TBR 3.1% and an estimated HbA1c 7.2%±0.8. 33% of patients had TIR ≥ 70%.
The TIR increased with the number of scans per day in the DM1 patients: scans/day < 4: TIR 35.7% ± 17 ; scans/day 4-6 : TIR 49.4 %± 17; scans/day >6: TIR 54.0 %± 10 (p<0.05). There were no differences in DM2 patients with the increased numbers of scans per day.
Our study show that it is difficult to achieve the clinical targets identified by the international consensus without specific patient's education in using and interpretation the IS-CGM data.
Miniaturization is key to improve integration of minimally invasive CGM sensors with local tissue. The better is this integration, the lesser is the foreign body response, the faster and more accurate is the sensor, and the better suited it is for glucose monitoring. IMS has used semiconductor technology to develop world’s first fully-integrated CGM sensor that is also the smallest glucose sensor developed to date. The integrated CGM includes an electrochemical glucose oxidase based sensor, a small potentiostat, signal processing circuit, and wireless powering and communication capability. The sensor is powered by and communicates with a wearable wireless transmitter using RFID. The transmitter communicates with a smartphone, and smart insulin pumps/pens via BLE. The CGM is manufactured using highly scalable semiconductor technology which provides high yield and low-cost for extremely small sensor. The thin polymer coated sensor has flexibility and mechanical integrity that improves integration with local tissue. The sensor is insertable at home, eliminating the need for doctor’s office procedure.
IMS has developed and tested the system with > 13 months of stable readings in lab, and > 1 month of in-vivo lifetime in a swine study. No adverse safety effects are observed in detailed animal (rats, swine) experiments. The system offers less blood-sensor lag time (< 3 minutes) and better accuracy in hypoglycemia (< 10% overall, < 6.5% in hypoglycemia).
Work is in progress to get regulatory approval for human feasibility testing.
IMS has developed world's smallest CGM sensor and has validated it in lab and an in animals.
Technology is fast-growing in the field of wearable healthcare devices to provide ease and comfort to humankind. Sensors play a vital role in designing any wearable devices. Wearable devices for diabetes is an emerging field. Research shows various trends from using the first generation glucose sensors to continuous glucose monitoring. Different sensors measure glucose from the blood, dermi-layer of the skin, sweat and/or saliva. Several studies show a relation between blood glucose and salivary glucose or blood glucose and Interstitial fluid glucose which discloses a path to replace the traditional finger prick test to non-invasive glucose measurement devices. Along with this, technology is having a paradigm shift towards continuous glucose monitoring. Continuous glucose monitoring is challenging as it holds expectations to provide real time accurate data with lesser calibration and respective sensors that are only sensitive to glucose. A wearable device with biocompatible sensor and reliable performance is the expectation of the healthcare device. Our analysis shares different amperometric sensors from a range of invasive to non-invasive glucose monitoring with the help of electrochemical/bioimpedance methodology. The objective is to study the state-of-art sensors which will be helpful in designing a wearable and continuous glucose monitoring device specially for people suffering from Diabetes mellitus type 2.
*This study is sponsored by witooth dental technologies sociedad limitada
included in Background and Aims
included in Background and Aims
included in Background and Aims
A new device has been developed for non-invasive assessment of glucose, endothelial vascular function, and other body parameters, which includes an optical circuit configured to detect several photoplethysmography (PPG) signals. A PPG signal includes a first spectral response obtained from light reflected around a first wavelength, and a second PPG signal includes a second spectral response obtained from light reflected around a second wavelength from the tissue of the user. This technology was investigated in a pilot study, which served the primary purpose to improve the underlying algorithm used to extract the glucose concentration from the obtained readings.
Twelve patients participated in the trial (5 female, 7 male, 5 Type 1, 7 Type 2 diabetes, age: 57±18 yrs). The received a standardized meal and glucose was measured at 11 time-points over a period of 3 h. YSI 2300Statplus served as reference method for the non-invasive readings.
The device worked well in all patients with one exception (male patient with very thick skin). All results were included into the analysis. The observed measurement range was 59 mg/dL to 371 mg/dL. Mean absolute relative difference over the entire data set (n = 104) was 8.0 %. In the consensus error grid, 98 % of the results were in zone A and 1.9 % were found in zone B.
The prototypes tested with a new and promising non-invasive glucose assessment technology showed to reliably and accurately measure glucose levels in this first pilot study.
Since the approval of continuous glucose monitoring (CGM) devices for insulin dosing in type 1 diabetes (T1D) treatment, one open issue is how to exploit CGM-derived information, e.g. blood glucose (BG) rate-of-change (ROC), to improve the calculation of meal-time insulin bolus provided by the standard formula (SF). The aim of this work is performing an in-silico comprehensive comparison of literature methods proposed so far for such a purpose.
The UVa/Padova T1D Simulator was used to generate data of 100 virtual patients undergoing multiple single meal scenarios with different preprandial BG (70, 120, 180 mg/dL) and ROC (-2, -1, 1, 2 mg/dL/min). We considered six literature methodologies that adjust SF according to future CGM predictions (Scheiner, Pettus/Edelman), by a percentage modulation (Buckingham), adding/subtracting a fixed insulin quantity (Klonoff), also based on insulin sensitivity (Aleppo/Laffel, Ziegler). The quality of glycemic control has been quantified as the difference between the blood glucose risk indexes (ΔBGRI) of final glucose profiles (SF vs literature methods).
Results are reported in Figure 1.
Literature methods accounting for ROC are effective, but no method prevails over the others. Particularly, the improvement is limited to the combination low BG/positive ROC, and high BG/negative ROC. As such, further investigations are required to optimize insulin therapy using CGM information.
Pregnancy in T1D is associated with increased maternal and fetal morbidity and mortality. Tight glycemic control is correlated with improved outcomes. CGM use during pregnancy is frequently encouraged to help women achieve glucose targets and reduce complications.
CGM use and delivery outcomes are reported from retrospective data from 50 T1D pregnancies from 2/2012-7/2019 in which Dexcom G4, G5, or G6 was used.
Dexcom CGM was used in every trimester in 84% of patients. No patients discontinued use during pregnancy. The median wear time was 93% (range: 52-100%). Women wore sensors on the front and back abdomen, arms, buttocks, and thighs in all stages of pregnancy, labor and delivery, and postpartum period. Pre-eclampsia was diagnosed in 12%; C-section was performed in 60% of births. One fetus had intrauterine growth restriction, and one infant had neonatal respiratory distress. 6% were early pre-term (<34 weeks), and 15% were late pre-term (34-36 weeks). Median birth weight was 3530g (13% >4000g). The Dexcom CGM was well tolerated. One patient had recurrent severe hypoglycemia requiring emergency care prior to starting a sensor, but she had no severe events after starting her Dexcom G5 CGM. No patients experienced severe hypoglycemia or reported severe skin reactions with sensor use.
CGM is a useful and well-tolerated tool to help manage diabetes during pregnancy. Our outcomes, which are better than many of those reported for standard of care, further support the benefits of CGM in T1D pregnancies. Further prospective data are needed to further validate the benefits of CGM use.
Over the last four decades, advancements in both insulin delivery and glucose monitoring technologies have promised to facilitate and improve the management of T1D. The present study investigated whether T1D patients’ use of insulin pumps and glucose sensors was associated with self-reported evaluations of burden of care and glycemic control.
A total of 2,416 T1D patients from research panels in Canada and Europe completed an online survey. Half of all respondents used a pump and glucose sensor, 10% used a pump and no glucose sensor, 26% were on MDI and a glucose sensor, and 15% were on MDI alone.
Overall, the greater one’s use of diabetes technology, the more time spent on management. Those using a pump and glucose sensor were the most likely to spend over 30 minutes a day on their diabetes care (74%), followed by those on just a pump (65%), those on just a glucose sensor (58%), and those on MDI alone (45%). Notably, sensor users were significantly more likely than non-users to report an A1c at or below 7 (46%, 34% respectively). Among sensor users, those on a pump were significantly more likely to report 60-80% time-in-range (33%) than those on MDI (26%).
Our data suggests that adoption of diabetes technologies is associated with greater time spent on management as well as improved glycemic outcomes. Future research should elucidate whether this increase in time is required to achieve greater control, or whether future technologies can minimize burden of care while continuing to offer better results.
This study evaluated the TensorTip Combo glucose meter (CoG, CNOGA Medical, Cesarea, Israel), which measures blood glucose invasively (INV) and tissue glucose non-invasively (NI). The optical non-invasive device module is individually calibrated over a period of 3-4 days.
Standardized meal experiments were performed at baseline and endpoint (after three months of home use). During the meal experiments, glucose was assessed at 11 time-points resulting in 2729 comparator readings vs. the YSI Stat2300 reference method. A subgroup of patients used two devices analysis of precision. The statistical analysis included parameters of glycemic control (HbA1c, hypoglycemia etc.) and system accuracy (mean absolute relative difference; MARD and consensus error grid analysis; CEG).
The study was performed with 88 patients (male/female: 43/45, type1/type2: 24/64, HbA1c: 7.4±1.0%; Ethnicities: 40 Caucasian, 19 African-American, 12 Hispanic, 14 Asian). The observed glucose range was 53.5-399 mg/dL. A positive NI-MARD and a negative NI-MARD of 5.1% and 11.5% were observed, respectively (total NI-MARD: 16.6%). 99.3 % of the NI-data-points were seen in zones A+B of the CEG. No differences were seen in the meal test results between baseline and endpoint. The frequency of use of the NI module increased significantly (baseline NI/INV measurement ratio: 1.04 vs. 2.62 at endpoint, p<0.001). HbA1c remained stable (endpoint: 7.5±1.1%, n.s.), while frequency of hypoglycemia (invasive readings <70 mg/dL) was substantially reduced by 54% (p<0.01).
Using the CoG for 3 months at home resulted in an increased measurement frequency, and in a major improvement of glycemic control in patients with both types of diabetes.
Nutritional substances, over-the-counter drugs, and popular nutritional supplements can influence blood flow. This study was performed to evaluate possible interferences for the non-invasive module of the TensorTip Combo glucose meter (CoG, CNOGA Medical, Cesarea, Israel).
Ten healthy subjects were included in this trial (6 male, 4 female, age: 41±14 yrs). They arrived after an overnight fast and ingested the test substance (ten visits/patient). Six glucose assessment were performed at time-points related to the anticipated pharmacokinetic profiles. YSI Stat2300 plus and COBAS served as reference methods. Mean absolute relative bias for each concentration was calculated and plotted against the plasma substance concentration. Interference was assumed when the slope of the regression line was >10% or <-10%.
No interference was seen with any of the tested substances (acetaminophen, acetyl salicylic acid, ascorbic acid, caffeine, diclofenac, ethyl alcohol, ibuprofen, mannose, xylose, 3Ω-fatty acids). Uptake of ethyl alcohol (0.2 g/kg) caused interference with the YSI results (-11 % vs. COBAS) and the invasive device module results (-11 % vs COBAS, non-invasive module: 0.6 % vs. COBAS). Overall MARD vs. YSI (n = 600) in the observed glucose range (71 to 158 mg/dL) was 8.8 % and 7.4 % for the non-invasive and the invasive modules, respectively.
The non-invasive CoG module was not influenced by any of the tested substances. Operating the invasive CoG module after alcohol uptake may result in too low results (e.g. plasma alcohol levels of 2.0 o/oo result in a 20 % underestimation by the invasive CoG module).
Flash Glucose Monitoring (FGM) is associated with significant improvements in HbA1c but the impact on quality of life (QoL) stays unknown. This study aimed to examine the effect of FGM on glycemic control and QoL in a cohort of adults with Type 1 Diabetes.
We undertook a prospective, real-world, case-control study with a six-month follow up involving patients treated with multiple insulin injections. Individuals who started with a FGM system for the first time (cases) were compared with those who were using it previously (controls). The end points were metabolic outcomes and the scores of QoL questionnaires (EsDQUOL and Hypoglycemia Fear Survey-HFS-).
41 patients were included in the case group ( 61% women, mean age: 42,8 ±12,1 years, diagnosed for 1-53 years, mean HbA1c: 7,6% ± 1,2) and 23 patients in the control group (52% women, mean age: 42,8 ± 16,1 years, diagnosed for 1-40 years, mean HbA1c: 7,1% ± 0,7). EsDQUOL score improved in the case group (96,53 vs 83,58 after 6 months; p=0,005) but not in the control group. There were no significant differences in HbA1c or in the rest of the analyzed outcomes (Body Mass Index, dose of insulin/kg, rapid insulin percentage, severe hypoglycemias and HFS score).
FGM could improve QoL of patients with Type 1 Diabetes. Additional studies with a larger number of patients and a longer follow up are probably needed to observe changes in metabolic outcomes.
One of the most promising technologies for non-invasive glucose monitoring (NIGM) in diabetes is Raman spectroscopy. We assessed the performance of a novel prototype in comparison with standard capillary blood glucose monitoring (BGM) and intermittent scanning continuous glucose monitoring (iscCGM).
In total, 15 subjects with type 1 diabetes underwent a carbohydrate-rich meal challenge during which BGM, iscCGM and NIGM measurements were performed for 450 min.
Mean glucose values for the 3 monitoring systems are shown in the figure exhibiting a good match. The closeness of agreement generally improved with lower rates of change. During the initial 60 min after the standardized meal, the rates of change were: BGM, 2.0 ± 1.7 mg/dl/min, iscCGM, 1.5 ± 1.2 mg/dl/min, and NIGM, 0.8 ± 1.5 mg/dl/min (mean ± standard deviation).
In this proof-of-concept study, average glucose readings using NIGM showed good agreement with mean BGM and iscCGM data. The agreement improved with lower rates of change, indicating an effect of varying signal delay between the methods.
Outcomes of using flash glucose monitoring (FGM) have been reported in adults and in clinical trials, less information is available in children and adolescents during real-life settings. This study is evaluating the use and outcomes of FGM in children and teenagers with type 1 diabetes.
FGM had been initiated in children and adolescents (6 to 17 years) from April to June 2019 in real-life settings. Educational program included 2 visits in groups of 4-8 patients at the time of initiation and 1 individualised visit during of first month of sensor wearing.
A total of 31 children and teenagers (54.8% male; age 11.9±3.0 years, type 1 diabetes duration 4.7±3.1 years) had been included from April to June of 2019. Baseline HbA1c was 8.4±1.1. During the first 2 weeks of sensor wearing 75.5±27.9 % of data were collected. TIR was 38.8±15.7%, with 56.5±17.7% of time in hyperglycaemia (>180mg/dL) and 4.9±4.5% of time in hypoglycaemia (<70mg/dL). Patients performed 7.5±1.8 SMBG/day before FGM initiation and scanned 7.5±5.5 times/day during the first 2 weeks of sensor wearing. Diabetes related quality of life had been measured (PedsQL tests for patients and parents) baseline.
The same data will be collected at 6 months and compared with baseline. Moreover, % of therapy discontinuation, the reasons and technical incidences will be collected.
The evaluation of the effectiveness in terms of metabolic control and quality of life of FGM in real-life settings is important in order to corroborate the clinical trials data.
Gestational diabetes (GD) is defined as “any degree of glucose intolerance, onset or first recognition during pregnancy”. Treatment of GD is based on diet, and if necessary insulin therapy. Blood glucose monitoring is carried out by fasting and post-prandial (PP) capillary self-monitoring. Continuous glucose monitoring systems have been designed to minimize inconveniences related to capillary self-monitoring. The FreeStyle Libre® (FSL) is a “continuous” measurement device of interstitial glucose concentration. It has been validated in the management of type 1 and type 2 diabetes.
The purpose of this study is to evaluate the accuracy of FSL and to assess the readings of FSL in the management of GD.
The study involved 14 patients. It lasted from 39 to 70 days. Patients performed a capillary glucose test combined with FSL measurements four times a day (fasting and 1h PP).
The accuracy of the FSL was assessed by Clarke's Error Grid (CEG) analysis and Bland and Altman's (BA) analysis (based on ISO 15197 consensus).
CEG analysis shows 99.6% of paired data in the A and B zones, considered clinically acceptable according to Clarke’s method.Some fasting FSL readings are lower than the threshold recommended by ISO criteria. BA method reveals also insufficient concordance (67%) of the measurements. Comparison between the average of both measurements shows that FSL readings are lower than capillary averages. Most of PP peaks (68%) are observed after one hour.
Based on our results, FSL should not currently be recommended in the monitoring and therapeutic decision-making of patients with DG.
Continuous subcutaneous glucose monitoring (CGM) is commonly used in diabetes care. Still there are concerns with regard to sensor accuracy especially in periods of hypo- and hyperglycaemia. The aim of this study was to assess accuracy of commonly used CGM systems in a diabetes camp in children with type 1 diabetes (T1D) ages 9-14 years.
Data was gathered during a 2-week summer camp under physicians’ supervision. Out of 38 children, 37 wore a CGM system while participating at the camp. Baseline characteristics: age: 11.0 (9.9;12.1) years; 57% girls, HbA1c 7.2% (6.9; 7.7); diabetes duration: 3.6 (2.7; 6.3) years (median (interquartile range)). CGM was performed throughout the camp. Capillary glucose measurements were performed prior to main meals, at bedtime and when required by medical staff after thoroughly cleaning the finger tips. Calibrations of Medtronic Enlite was performed twice daily when glycemia was stable. All concomitantly available data pairs were used for the analysis.
Sensor distribution was 51% Abbott Libre, 35% Medtronic Enlite and 14% Dexcom G6. Sensor accuracy data are displayed in Table 1:
Abbott | Dexcom | Medtronic | |
All | 13.3 (6.7; 21.6) n=1165 | 10.3 (5.8; 16.7) n=242 | 8.5 (3.6; 15.6) n=671 |
<70mg/dl | 17.7 (9.0; 27.7) n=210 | 18.7 (10.1; 23.5) n=21 | 15.0 (9.9; 30.0) n=62 |
70-180mg/dl | 13.2 (6.7; 21.3) n=692 | 9.8 (5.9; 15.4) n=178 | 7.6 (3.2; 14.5) n=419 |
>180mg/dl | 11.2 (5.5; 17.7) n=263 | 11.1 (4.7; 17.0) n=43 | 8.6 (3.9; 14.9) n=190 |
Sensor performance of the adequately calibrated Medtronic system outperformed the factory-calibrated sensors. All sensors performed worst in hypoglycemia.
Flash glucose monitoring (FGM) allows non-invasive glucose level assessment. Studies have shown that patients using FGM test their glucose levels more often than those who use traditional blood glucose testing. Our objective was to describe the change in the glycemic control expressed as HbA1c and time in euglycemic range (70-180 mg/dL), above range and below range in patients with type 1 diabetes (DM1) after FGM implementation and the effect of the number of scans in this control.
Observational longitudinal clinical study between June 2018 and September 2019 in patients with DM1 and subsidized FGM implementation
115 patients included. Mean age: 16.30 ± 1.15 years. Mean DM1 evolution time: 6.41 ± 4.59 years. 44.3% women. 47% had used FGM before it was subsidized. 4 patients decided not to use FGM anymore.
In patients with more than 70% FGM data, one year after implementation estimated HbA1c levels were reduced from 7.47% to 7.09% but this was not statistically significant.
After 1 year, time in range (TIR) was higher and time in hypoglycemia (Hypo) was reduced the more daily scans (DS) patients performed. (TIR = 1.50 ·DS + 39.88; R2 = 21,1%. Hypo = -1.54·DS + 50.77; R2=16,7%; p<0.01). However, time in hyperglycemia didn’t show a significative reduction.
In our series, FGM implementation reduced HbA1c levels and the time patients spent in hyperglycemia, although it was not statistically significant.
The more scans performed the more time patients spent in range and the less time in hypoglycemia (p<0.01). This improvement wasn’t seen in time in hyperglycemia.
Blood glucose monitoring systems (BGMSs) have the potential to provide inaccurate results in the presence of interfering substances found in the blood resulting in several serious and some fatal events being documented. Furthermore, some conditions such as anemia, cancers, renal disease, dietary deficiencies, rheumatoid arthritis and during pregnancy may affect concentrations of endogenous interfering substances. The effects of interfering substances is of clinical relevance during BGMS selection.
A new BGMS platform utilizing the CONTOUR®CARE test strip encompassing the FAD-GDH enzyme was assessed for common endogenous and exogenous substances for interference effect in accordance with ISO 15197:2013 and FDA guidelines.
Interfering substances were tested in pooled venous blood at glucose concentrations of 60 mg/dL, 120 mg/dL and 260 mg/dL with guidance from the Interference Testing in ISO 15197:2013 and FDA guidelines. Each interferent was assessed at each of the three glucose concentrations with three test strip lots in ten meters resulting in 90 measurements per interferent.
Results were analyzed according to ISO 15197:2013 Section 6.4.4 guidelines (interference effects shall be described in the instructions for use if the average difference between test and control samples exceeds 10 mg/dL [0.55 mmol/L] or 10% at glucose concentrations < 100 mg/dL [5.55 mmol/L] and ≥ 100 mg/dL [5.55 mmol/L], respectively). All of the interfering substances passed with the exception of xylose.
All of the assessed interfering substances passed the acceptance criteria with the exception of xylose which is no longer commonly used in clinical practice.
Accuracy and safety of the implantable Eversense CGM System has been demonstrated in 3 pivotal trials. Analysis of the first 205 US commercial users who completed a 90-day sensor wear cycle has been recently published and results demonstrated accuracy and safety consistent with pivotal trial outcomes.
Ninety-day de‐identified sensor glucose (SG) data from the Eversense Data Management System (DMS) were analyzed for these first 205 patients to determine the percent of patients meeting the recommended targets of 1) 70% of time in range (TIR) between 70-180mg/dL, 2) <4% of time below 70mg/dL, and 3) <1% of time below 54mg/dL.
Analyses showed the following: 1) 42% of the users achieved a TIR > 70% with a mean TIR of 62.3%. 2) 62% of users achieved <4% of time <70 mg/dL with a mean time < 70 mg/dL of 4.1%. 3) 64% achieved <1% of time <54 mg/dL with a mean time <54 mg/dL of 1.2%. Percent of the 205 users achieving all three targets of TIR>70%, time <70mg/dL of 4%, and time <54mg/dL of <1% was 23.4%.
In the real-world setting, the Eversense CGM System was shown to assist patients in achieving recommended glucose goals regarding hypoglycemia, with ~64% of patients avoiding what has been defined as an excess of very low SG values. In addition, over 40% achieved targeted TIR. These data support the use of the first long-term implanted CGM system as a viable tool to manage diabetes.
Recently, we showed that a linear two-compartment model (Schiavon et al., DTT 2015) is able to describe plasma-to-interstitial fluid (ISF) glucose kinetics both in steady as well as non-steady state conditions (Schiavon et al., ATTD 2019) using multi-tracer plasma and microdialysis data. The model allows estimation of plasma-to-ISF equilibration time (τ). However, on average, slower kinetics and greater variability was shown in non-steady than steady state conditions. Here we aim to test in silico the role that experiment design variables may have on τ estimation.
The 100 virtual adult population of the UVA/Padova T1D simulator (Visentin at al., JDST 2018) was used to simulate plasma-to-ISF glucose kinetics in fasting (steady state) and postprandial (non-steady state) conditions. In addition, a primed-constant i.v. infusion of glucose tracer was simulated. Measurements of glucose concentrations and tracer enrichments were simulated in both plasma and ISF. Different experimental settings were simulated while τ estimation was performed by fitting the model to ISF glucose tracer data using plasma measurements as forcing functions.
The model is able to describe the data in the various experimental settings. An effect of sampling schedule and data pooling in both steady state and non-steady state conditions have been observed. The role of measuring glucose concentration in ISF has also been assessed.
Experiment design is critical to accurately assess plasma-to-ISF glucose kinetics and should be taken into account in evaluating the plasma-to-ISF equilibration time.
Practical skills and comprehensive knowledge are indispensable to benefit from real time continuous glucose monitoring (rtCGM) systems. The German Federal Joint Committee (G-BA) demands a qualified training for rtCGM users. To assess users’ rtCGM-knowledge, a questionnaire based on the independent training program SPECTRUM was developed and evaluated.
Diabetes professionals defined central knowledge content about rtCGM (content validity). They suggested 50 relevant multiple-choice (MC) items with 5 answer options. After a pilot study, the final knowledge questionnaire comprises 40 MC items. This version was answered by adults and adolescents with type 1 diabetes and diabetes professionals.
The total sample (n=233; 70% female) consisted of 111 people with diabetes (mean (SD) age: 42 (14) years, diabetes duration: 20 (14) years, 64% CSII, 38% CGM, HbA1c 8.0 (1.7)%) and 122 diabetes professionals (46 (11) years).
Internal consistency (Cronbach‘s alpha) was 0.92 for the total sample, for people with diabetes 0.94 and for professionals 0.84. Item difficulty ranged from 0.12 to 0.88 for people with diabetes and from 0.27 to 0.97 for professionals.
On average, 24.1 ± 9.9 items were answered correctly by people with diabetes and 29.2 ± 5.2 by professionals (p<0.001). People with diabetes without rtCGM-experiences reached lower values than rtCGM-experienced ones (21.0 ±10.4 vs. 29.2 ± 6.2; p=0.001). Their HbA1c correlated negatively with the sum-score of the questionnaire (r=- 0.36; p<0.001).
The knowledge questionnaire “rtCGM-Profi-Check” showed a good internal consistency and a high content validity. The questionnaire is an objective, reliable and valid measure to assess persons’ knowledge about rtCGM.
Not all blood glucose monitoring systems (BGMSs) that meet ISO 15917:2013 or FDA 2016 accuracy criteria yield accurate results in the low blood glucose range (LBGR), defined as BG below 70 mg/dl. Accuracy in the LBGR is important for hypoglycemia detection/management. This post hoc analysis utilizes probability methodology to estimate BGMS performance in the LBGR.
All data were computed from capillary blood samples from diabetes patients. CONTOUR®PLUS BGMS (CP) data were derived from a previous study comparing the accuracy of five BGMSs; CONTOUR®PLUS ONE BGMS (CPO) data were from a separate study. To estimate likelihood of accurate BGMS results (±15% of reference values) in range of 20–450 mg/dl, probability curves were computed using linear regression models. with BGMS results expressed as a function of laboratory data. The 95% ranges of BGMS results expected at specific reference values (40, 54, 60 and 70 mg/dl) were also computed.
In the LBGR probability curves demonstrated that CP and CPO BGMSs maintained high accuracy. The 95% ranges of BGMS results at specific reference values indicated that some BGMSs would have a low likelihood of having 95% of results within ±15 % of reference values, particularly in the LBGR.
In this analysis, the CP and CPO BGMSs were highly accurate in the LBGR, which is important for safe and effective diabetes management, especially in insulin-treated patients, patients with history of severe hypoglycemia or hypoglycemia unawareness, diabetes during pregnancy and patients using CGM and FSL, when BGM is recommended.
Croatia has one National Health Insurance HZZO, all decisions made by HZZO directly affect all people with diabetes in Croatia. Less than two years ago, there was no stand-alone CGM system available through health insurance. Pharmaceutical companies, often refer to Croatia as a small and poor market and therefore insignificant.
Great number of patients from Croatia were self-funding CGM sensors for better control of diabetes. The aim of the initiative “Life with less pain” was to bring advanced CGM technology to Croatia and to reimbursement list.
Aims:
1. create the pressure to the rtCGM/fCGM manufactures to come to Croatia
2. pressure Health Insurance to expand reimbursement list
Patients with diabetes played a crucial role in the initiative “Life with less pain”. But the importance of other stakeholders was greatly endorsed and appreciated. Without great support from doctors, Politicians, Media and the general public – nothing would be done.
Diabetics were connected through closed Facebook group and associations. They exchanged knowledge, ideas, and motivation for a common goal. “Everybody knows somebody, who knows somebody, who’s important”.
Abbott fGMS is reimbursed under certain conditions, so all T1D patients can get a recommendation if they meet the terms. As a result, the expansion of CGMS in Croatia is significant despite some obstacles. Almost all children are using sensors in Croatia. Also, one company registered the first stand-alone rtCGMS system and the process of reimbursing through health insurance is underway.
Patients with diabetes should be active because they carry important role in their rights
Data analyzing blood glucose monitoring system (BGMS) accuracy in the low blood glucose range (LBGR: ≤70 mg/dL) are lacking, warranting further research. This post hoc analysis utilizes previously presented probability methodology to estimate the likelihood of accurate BGMS performance in the LBGR.
Data were computed from capillary blood samples obtained by study staff from patients in two separate trials. Trial 1 (Christiansen M, et al. J Diabetes Sci Technol. 2017;11:567-573) was conducted in the US and included the CONTOUR®NEXT ONE (CNO) BGMS only. Trial 2 (Jendrike N, et al. Curr Med Res Opin. 2019;35:301-311), conducted in Europe, compared five systems including CNO BGMS (and is included here to corroborate results from Trial 1). To estimate likelihood of accurate BGMS performance (results ±15% of reference values) in entire range of BG concentrations (20–460 mg/dL), probability curves were computed based on a linear regression model with BGMS results expressed as a function of laboratory data.
For CNO BGMS, the probability of accurate system performance at specific BG concentrations in the LBGR (40, 54, 60 and 70 mg/dL) was >95% in both trials.
In this analysis, CNO BGMS was highly accurate in the LBGR, which is important for safe and effective diabetes management, especially in insulin-treated patients, diabetes patients with history of severe hypoglycemia or hypoglycemia unawareness, diabetes during pregnancy, and/or patients using CGM when BGM monitoring is recommended. This analysis also further demonstrates the utility of probability methodology for assessing BGMS accuracy. Hypoglycemia
Measurement frequency of continuous glucose monitor (CGM) may affect accuracy of glycemic variability (GV) metric values. We compared GV calculated from sensor glucose levels (SG) measured every 5 and 15 min.
We cross-sectionally investigated CGM (iPro2) data in 110 patients with type 2 diabetes whose 24-h SG was measured continuously during hospitalization for type 2 diabetes treatment. Mean glucose level, standard deviation (SD), coefficient of variation (CV), percentage of time in target range (70–180 mg/dL) [TIR 70–180], mean absolute glucose (MAG), glycemic variability percentage (GVP), low blood glucose index and high blood glucose index were calculated as GV. GV calculated from SG measured every 5 min in iPro2 and GV calculated from SG extracted every 15 min in iPro2 were compared.
GV calculated from SG measured every 5 min and GV calculated from SG extracted every 15 min were almost equal numerically in all the GVs evaluated in this study. However, GV calculated from SG extracted every 15 min was statistically significantly higher than that calculated from SG measured every 5 min in SD and CV. Also, GV calculated from SG extracted every 15 min was statistically significantly lower than that calculated from SG measured every 5 min in MAG and GVP (Table).
GV calculated from SG measured every 5 min and GV calculated from SG extracted every 15 min were almost equal numerically in all GVs evaluated in this study.
The management of Type 1 Diabetes Mellitus (T1DM) is improved by diabetes technology including continuous monitoring (CGM) and insulin pumps. Data management tools are very useful to optimize therapy, with improvement of patients and caregivers’ quality of life.
Evaluate motivation, satisfaction and effective use of diabetes technology by people with diabetes and their caregivers.
Semistructured questionnaire was filled out by 48 patient using technologies (F/M 35/13, 2 to 60 age years old). Caregivers filled out the questionnaire for children ≤10 years.
96% of patients uses CGM (54.3% RTCGM, 32% isCGM). Insulin pump is used by 87.2%, 83% used both CGM and pump. 74% uses CGM every day, only 4% less than 10 days/month. 28% were motivated to use technology by family, friends and other patients, 30% by the diabetologist. Other motivations (42%) are comfort, pregnancy planning and need to improve glycemic control. Anxiety was a barrier to CGM use for 24% of patients, as fear of pain and esthetic issues. Download data was carried out by 63% of patients and it was found useful by most of them. Carbohydrate counting is used almost every day by 61% patients, as well as bolus calculator (68%).
Appropriated sensor use is frequent in our patients. Above 60% uses Carbohydrate counting and bolus calculator. Download data is used and appreciated by most of patients, unlike previous report, but it is important to share with diabetologist. Anxiety, fear of pain and esthetic issues are barriers to break down.
Background: The long-term implantable Eversense CGM System was first commericalized in 2016 in Europe and South Africa for adults with diabetes. Sensor performance over sequential 90- or 180-day cycles has not been published.
Methods: Sensor glucose (SG) and SMBG measurements obtained from the Eversense data managment system from 6/2016 to 8/2019 were used to evaluate accuracy in Eversense patients with at least four sensor cycles from European and South African clinics. Twenty-four hour transmitter wear time, mean SG with variability metrics, glucose management indicator (GMI), and percent and time in various glycemic ranges were calculated over each cycle.
Results: Among the 945 users included in the analysis (Table), the mean absolute relative difference (MARD) using 152,206 to 206,024 calibration-matched pairs against SMBG ranged from 11.5 to 11.9% over the four sensor cycles. Mean values of the CGM metrics ranged from 156.5 mg/dL to 158.2 mg/dL for SG, 54.7 mg/dL to 55.8 mg/dL for SD, 0.35 to 0.36 for CV, and 7.04% to 7.08% for GMI. The range of %SG across the 4 cycles in the various glycemic ranges was as follows: 1.1-1.3% (<54 mg/dL), 4.6-5.0% (<70 mg/dL), 63.2-64.5% ( ≥70 to 180 mg/dL), 22.4-23.2% (>180-250 mg/dL) and 8.1-8.8% (>250 mg/dL). Median transmitter wear time ranged from 83.2-85.8%.
Conclusion: This real-world longitudinal evaluation of the implantable Eversense CGM System demonstrated that the accuracy and glucometrics are stable over multiple, consecutive sensor cycles with no degradation of performance.
Since 1/2019 health insurance in Czechia partly covers consumables for using Flash Glucose Monitoring (FGM) for every patient with T1D.Aim of the study is to compare parameters of metabolic control of glycaemia in T1D patients after 3 and 6-9 months of using FGM based on retrospective CGM analyses and to check patients’ knowledge on using the sensor and their satisfaction with the sensor.
27 patients with T1D (mean age 47.3, mean HbA1C 59.7 mmol/mol, 13 males, 15 CSII/12 MDI) using FGM since 1/2019 filled in 8-9/2019 a questionnaire on: technical aspects of using sensor, data interpretation, general educational principles, Gold score, DDS2 and satisfaction with use (GMSS-total score, four subscales). Clinical data (TIR, TBR, GMI, HbA1C) were obtained from medical documentation and from two retrospective CGM analyses over period of 3-6 months produced before regular medical check-up.
Use of FGM proved a positive trend in prolonging the time-in-range period (TIR) for 4.1% (p=0,052). In the second CGM analysis target values of TIR>70% were reached by 3 patients (11.1%), while in the first one it was only 1 patient (3.7%). Average knowledge score was 39.7 (range of 0-54), average overall satisfaction score 61.7 (range of 15-75).
Improvement of metabolic control of glycaemia in our patient group on FGM didn´t reach statistical significance. The probable reason is short monitoring period and uneven interval between assessed CGM analyses. A certain positive trend was however observed. Effective use of FGM requires education and engagement of both patients and clinicians.
In this work we present initial validation results for novel optical biosensor technology based on gallium antimonide laser technology, which offers access to a largely unexplored 1.9-2.5 micron wavelength spectral region, containing molecule-specific ro-vibrational absorption bands and favorable skin transmission properties, allowing remote sensing of such molecules as glucose, lactate, urea, ethanol, etc. as has been demonstrated in previous proof-of-concept studies.
Here, we take a further step towards a real-life sensing scenario, and present experimental results of initial in vivo glucose detection study with pigs. Presented study contains data from more than 40 pigs. Each animal was sedated for the duration of the day and the blood glucose level was altered by means of intravenous glucose infusion. Optical data was gathered continuously by means of transdermal illumination and diffuse reflectance collection from the belly of the pig. In parallel, a blood sample was drawn and analyzed with a clinical blood analyzer, serving as a gold standard.
To analyze the data, we have built a statistical linear regression-based algorithm involving machine learning for data grouping. Figure 1 illustrates excellent sensor performance of in vivo transdermal blood glucose level prediction in a wide dynamic concentration range for a single pig. Here, green dots represent data points that were used to build a PLS calibration model, whereas the red data points represent the validation data set.
We discuss the effects of the change of sensing position, motion, test subject, statistical batch size and provide outlook towards requirements for bringing the technology to market.
Continuous and flash glucose monitoring have revealed weaknesses of HbA1c as standard measure of glycemic control and introduced new indices for assessing glycemia. Glycemic variability (GV) is a measure of glycemic instability that can be mathematically defined as %CV. GV relates to both hyper/hypoglycemia and is a candidate for predicting diabetes specific outcomes. Our aim was to determine the correlation of CGM-derived glycemic parameters to the gold standard; HbA1c.
22 participants (HbA1c=7.1±1.3%,) with type 1 (n=11; %CV=42.3±6.9) and type 2 (n=11, %CV=35.4±6.7) diabetes, treated by basal bolus MDI were equipped with blinded CGM. CGM data were analyzed to determine glycemic control parameters. Correlation was assessed by Pearson correlation. Difference in glycemic variability between groups with HbA1c above or below 7%, was compared by paired sample t-test. Results were reported as mean±SD.
Glycemic variability (%CV) and time in hyper/hypoglycemia significantly correlated to HbA1c (Table 1). Patients with higher HbA1c experienced significantly higher %CV (Table 2).
HbA1c | |||
CGM-derived glycemic parameters | Pearson correlation coefficient | Strength of correlation | P-value |
time in hyperglycemia | 0.452 | moderate | 0.035 |
time in range | -0.191 | weak | 0.394 |
time in hypoglycemia | -0.432 | moderate | 0.044 |
time in grade II hypoglycemia | -0.540 | strong | 0.010 |
% CV | 0.428 | moderate | 0.047 |
Table 1.
HbA1c | %CV | P-value |
below 7.0% | 38.9±9.5 | 0.007 |
above 7.0% | 55.7±15.5 |
Table 2.
Assessment of glycemia should go beyond HbA1c, however HbA1c is widely used as a predictor of diabetes complications. We found a positive association of GV with HbA1c, which adds to the pool of indirect evidence on GV impact on diabetic complications.
Despite decades of work, current continuous glucose monitoring systems (CGMs) are still suffering signal drift, whether enzymatic or non-enzymatic, electrochemical or optical. Consequently, many CGMs still require frequent calibration from finger sticks, which in turn contributes the high cost and complexity of CMG products and the reluctance of rapid widespread adoption. In this work, we show a new strategy with the capability of self-calibration and self-correction to solve the instability by developing a dual-mode glucose sensing system.
Together with a newly developed diboronic acid molecule (DBA2+) we developed to sense glucose, a fluorescent molecule alizarin red S (ARS), was used for both optically and electrochemically probe glucose concentration. ARS is able to reversibly bind with boronic acids and has different electrochemical and optical signatures in different bound states. These signals changes of ARS upon binding with DBA2+ can be recovered by glucose, offering fluorescence and electrochemical signals. These dual-mode signals in one system can be used for self-calibration with algorithms.
The fluorescence of ARS exhibits more than 10 times enhancement in the presence of DBA2+ and decreases with the increase of glucose concentration (A and C), and ARS redox peak is also recovered by glucose (B and D). More importantly, the two signals show different response curves, which offers the basis for self-calibration.
Dual-mode (fluorescent and electrochemical) glucose sensing in one system was designed and demonstrated. The algorithms for self-calibration and self-correction is underway and will afford a solution for long-term stable continuous glucose monitoring.
There is limited literature regarding flash glucose monitoring (FGM) associated cutaneous adverse events (AE). This study aimed to evaluate participant reported cutaneous AEs and sensor use associated with the Freestyle Libre FGM compared to usual care with SMBG as part of a six-month multisite randomised controlled trial (RCT) among adolescents with T1D.
Patients aged 13-20 years with type 1 diabetes ( [pre-enrolment mean HbA1c ≥75mmol/mol (≥9%)] were randomised to intervention (FGM and usual care), or control (self-monitoring blood glucose and usual care). Participants self-reported AEs every 14 days for six months. Reports were analysed to determine frequency, type, and severity and cause, as well as premature sensor loss.
64 participants were recruited, 33 randomised to FGM and 31 controls. In total, 80 cutaneous AEs were reported, 40 in each group, however the proportion of participants experiencing AEs was greater in the FGM group compared to control (58% and 23% respectively, p = 0.004). FGM participants most frequently reported erythema (50%) while controls most commonly reported skin hardening (60%). For both groups the majority of AEs were rated as mild. One participant ceased using FGM due to reoccurring cutaneous AEs. 27/33 (82%) FGM participants experienced at least one premature sensor loss, largely unrelated to an AE.
Frequency of cutaneous AEs were similar for FGM compared to SMBG, with most AEs rated as mild. For FGM, the majority of users continued use despite AEs. Awareness of cutaneous complications and efforts to mitigate them may reduce cutaneous AEs associated with FGM use.
MiaoMiao is a Bluetooth™ transmitter, which when paired with a smart phone/device, converts the Abbott FreeStyle Libre flash glucose monitoring system into a do-it-yourself (DIY) continuous glucose monitor (CGM). Families are adopting MiaoMiao, but little is known about parent and child experiences with this add-on technology. We aimed to explore experiences of families using MiaoMiao CGM including challenges faced and their advice to others who may choose to use the technology.
Between May and July 2019, we conducted twelve semi-structured interviews (in person or via videoconference) with parents of children (aged <17 years) with type 1 diabetes using MiaoMiao CGM. Interviews were audio recorded; professionally transcribed and key themes were identified through thematic analysis.
Overall, parents used MiaoMiao CGM to proactively manage their child’s blood glucose. In all participants, this led to a perceived decrease in frequency of hypoglycaemia. Participants reported that the visibility and easy access to blood glucose readings, glucose trends, and customised alarms on parent’s phones decreased their disease burden and improved their sleep quality. Common barriers to using MiaoMiao CGM included difficulty of the setting up process, connectivity issues, and lack of support from medical teams.
This study highlights the potential feasibility of using a DIY CGM system like MiaoMiao CGM, which could be an empowering and cost-effective tool for enabling remote monitoring of blood glucose in real time.
This study sought to evaluate the impact of early continuous glucose monitoring (CGM) initiation on psychosocial functioning among children and adolescents soon after type 1 diabetes diagnosis.
The study sample included 55 youth ages 2.9-17.9 years (M=11.0, SD=3.6) and their parents. Within 40 days of diagnosis, participants were randomized to either a CGM (Dexcom G5 training and initiation) or control condition. Psychosocial survey data were collected from youth and parents at baseline and 3-, 6-, and 12-month follow-ups. Regression models tested interactions between condition assignment and days since diagnosis, adjusting for gender, age, income, and baseline A1c.
A beneficial main effect (d=1.1, p=0.019) of early CGM initiation was found on youths’ hypoglycemia confidence at 1 year, with further beneficial effects on youth (d=-0.3, p=0.030) and parent (d=-1.0, p=0.048) emotional burden related to glucose monitoring, parent trust in glucose monitoring (d=1.0, p=0.046), and parent satisfaction with glucose monitoring (d=1.3, p=0.012) only among youth started on CGM after >22 days from diagnosis. Within the intervention group, early increases in youth hypoglycemia confidence (Figure 1) and parent glucose monitoring satisfaction (Figure 2) were followed by continued gains from 6- through 12-month follow-ups only for youths diagnosed with 22+ days since diagnosis, which tapered off for those with less (<22) days.
Early initiation of CGM use has stronger and more widespread beneficial effects when initiation occurs slightly later (more than 3 weeks) following diagnosis. Providers may consider a short delay in CGM initiation to achieve maximal long-term gains.
The association between β-cell function and glycemic variability remains to be clarified in insulin-treated patients with type 2 diabetes. Therefore, the study sought to examine the association of various indices of β-cell function with glycemic variability in Chinese insulin-treated patients with type 2 diabetes.
Glycemic variability was assessed by the coefficient of variation (CV) of glucose levels with the use of continuous glucose monitoring (CGM). Basal β-cell function was evaluated by fasting C-peptide (FCP) and the homeostasis model assessment 2 for β-cell function (HOMA2-%β). Postload β-cell function was measured by 2-hour C-peptide (2hCP) and the acute C-peptide response (ACPR) to arginine.
When a cutoff value of CV≥36% was used to define unstable glucose, the multivariable-adjusted odds ratios for labile glycemic control were 0.34 (95% CI 0.18–0.64) for each 1 ng/mL increase in ACPR, 0.47 (95% CI 0.27-0.81) for each 1 ng/mL increase in FCP, 0.77 (95% CI 0.61-0.97) for each 1 ng/mL increase in 2hCP, and 1.00 (95% CI 0.98-1.01) for each 1% increase in HOMA2-%β. When we further adjusted for 2hCP and HOMA2-%β in the ACPR and FCP analyses, and adjusted for ACPR or FCP in the 2hCP analyses, only ACPR but not FCP and 2hPC remained to be a significant and inverse predictor for labile glycemic control.
ACPR evaluated by the arginine stimulation test may be superior to other commonly used β-cell function parameters to reflect glycemic fluctuation in insulin-treated patients with type 2 diabetes.