Continuous Glucose Monitoring System Readings Research Articles

Performance Evaluation of the Glunovo® Continuous Blood Glucose Monitoring System in Chinese Participants with Diabetes: A Multicenter, Self-Controlled Trial.

IntroductionThe present study was aimed to evaluate the performance and safety of the Glunovo® real-time continuous glucose monitoring system (CGMS) in monitoring interstitial fluid glucose in adult participants with diabetes (at least 18 years old) using venous blood glucose as control.MethodsThis was a multicenter, self-controlled clinical trial, conducted in participants with diabetes from China, between March 2019 to October 2019. The CGMS was used by all the participants for a 14-day wear-in period. The real-time glucose values measured by Glunovo® CGMS were compared with venous blood glucose values measured by the Entwicklung, Konstruktion und Fertigung (EKF) blood glucose detector. The primary outcomes were the consistency rate of CGMS readings and venous blood glucose values (20/20% standard).ResultsA total of 78 participants (41 men, 37 women) and 156 CGMS sensors were included in the study. Among the included participants, 25 and 53 participants had type 1 and type 2 diabetes, respectively, with median age of 52.50 years (range 32–62 years). The overall agreement rate (20/20%) was 89.71% (95% CI 89.18–90.24%). It was observed that 99.08% (95% CI 98.91–99.24%) and 99.82% (95% CI 99.74–99.89%) of the measuring points fell within the A + B zones of the Clarke error grid analysis and Parkes/consensus error grid analysis, respectively. The mean absolute relative difference was 10.30% ± 4.86%. The probability of a glucose measurement falling within a range, when stratified by venous glucose measurements, ranged from 7.14% for 19.44–22.22 mmol/L to 79.21% for 4.44–6.67 mmol/L. There were 73 (41.24%) and 27 (57.45%) successful CGMS alarms for hypoglycemic and hyperglycemic events, respectively.ConclusionFrom the results, Glunovo® CGMS had excellent accuracy and limited clinical risk compared with venous blood glucose in the range of 2.2–22.2 mmol/L over 14 days.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-021-01171-2.

Reliability of continuous glucose monitoring system in the inpatient setting

Aims/HypothesisHyperglycemia and hypoglycemia are associated with increased morbidity and mortality in the inpatient setting. Standard point of care capillary glucose testing (POCT) is commonly used in hospitalized patients to monitor their glucose levels. The goal of this study was to examine the relationships between the glucose readings obtained by a continuous glucose monitoring system (CGMS) (Freestyle Libre) and the capillary blood glucose results obtained by the inpatient glucose POCT meter (Accuchek Inform II) as well as between CGMS readings and the serum glucose values obtained by the hospital laboratory. Study participants had either primary or secondary diagnosis of diabetes mellitus and were admitted to non-critical units. We hypothesized that there exists an acceptable agreement between the capillary blood glucose results obtained by the inpatient glucose POCT meter (Accuchek Inform II) and the readings obtained by the CGMS (Freestyle Libre); and that there exists an acceptable agreement between the serum glucose levels and the glucose values obtained by the CGMS. MethodsThis was an Institutional Review Board approved prospective cohort study for the non– critical inpatient setting. Fifty-two hospitalized patients with diabetes were recruited. After informed consent was obtained, patients were instructed on the application and use of the CGMS. The data were assessed using a standard regression analysis and modified Bland Altman analysis. All analyses were conducted using SAS, release 3.8 Enterprise Edition (SAS Institute Inc., Cary, NC). ResultsFifty-two subjects recruited into the study represented a sample of convenience. There were a total of 467 AccuChek-Libre pairs, The regression analysis showed a negative bias between.Libre and AccuChek, R2 = 0.83, with Libre glucose readings on average being lower than those of AccuChek. Using Bland-Altman analysis, 42% of the 467 Libre-AccuChek pairs had a difference in glucose reading more than 15%. Mean absolute relative difference (MARD) between Libre and AccuChek was 15.6%; mean relative difference (MRD) between Libre and AccuChek was −11.4%.The regression analysis showed a negative bias between Libre and serum glucose, R2 = 0.89. Using Bland Altman analysis, 36% of the 44 Libre-serum pairs had a difference in glucose reading more than 15%. Mean absolute relative difference (MARD) between Libre and serum glucose was 13.2%; mean relative difference (MRD) between Libre and serum glucose was −12.5%.A review of the data pairs showed that 71/467 Accuchek-Libre pairs had one result that was either below 70 mg/dl or above 200 mg/dl (combined American Diabetes Association-ADA-, American College of Physicians-ACP- and American College of Endocrinology-AACE- goals). Thus 85%, of these pairs would have yielded results that engendered the same intervention (e.g. treatment for hypoglycemia or hyperglycemia). Likewise 5/45 Serum-Libre pairs had one result that was either below 70 mg/dl or above 200 mg/dl; thus 89% of these pairs would have yielded results requiring the same intervention. Conclusion/InterpretationThese findings confirm the existent literature and indicate acceptable agreement between the standard POCT and the CGMS as well as between serum glucose and the CGMS values. Because of the advantages of the CGMS over capillary blood glucose testing (reduced patient discomfort and reduced staff exposure to patients in isolation) CGMS use may be preferable to the current bedside capillary blood glucose testing in hospitalized patients with diabetes mellitus. As with other laboratory measures, clinical judgement needs to be exercised when the laboratory values are used to guide patient care.

Relationship of Glycemic Derangement Using Continuous Glucose Monitoring System with Sleep Pattern Among Children with Type 1 Diabetes Mellitus

Background: Glycemic derangement has been linked to sleep disruption. However, the impact of glycemic derangement on sleep pattern among children with type 1 diabetes (C-T1D) remains unraveled. Aim: To assess the effect of nocturnal hyperglycemia and clinically significant-hypoglycemia on sleep pattern among C-T1D. Methodology: Thirty C-T1D were compared to 30 age and sex matched healthy siblings. Patients having other organ disease that might cause sleep disorders or on medications causing sleep disturbance were excluded. History included diabetes-duration, type and dose of insulin therapy, chronic diabetic- complications, and manifestations of sleep disorders. Epworth Sleepiness Scale -Child Adolescent(ESS-CHAD) was used. Continuous-glucose monitoring system(CGMS) and overnight polysomnography(PSG) were done and analyzed. Results:C-T1D had significantly lower sleep efficiency and significantly higher arousal-index(AI), periodic-limb movement- index(PLMI) and apnea-hypopnea index(AHI) compared to controls. Moreover, they had significantly longer sleep-onset latency, light sleep percentage and shorter rapid eye- movement(REM) percentage than controls. According to nocturnal CGMS readings, 15 C-T1D had nocturnal hyperglycemia(50%), 6 experienced clinically significant(CS) hypoglycemia(20%), 2 had level-1 hypoglycemia(6.7%) and 7 were within the normoglycemic range(23.3%). C-T1D experiencing nocturnal CS hypoglycemia had significantly higher stage 3 sleep(P=0.004) than controls. On the other hand, C- T1D experiencing nocturnal hyperglycemia had significantly higher sleep onset latency(P=0.013), light sleep %(P<0.001) and AI(P<0.001) than controls. Nocturnal CS hypoglycemia was positively correlated to deep sleep duration, while hyperglycemia was correlated to number of awakenings, sleep-onset latency, and light sleep duration. Conclusion: CS hypoglycemia is associated with sleep deepening, while hyperglycemia is associated with increased light sleep, sleep onset latency, AI among C-T1D.

Relationship of glycaemic derangement using continuous glucose monitoring system with sleep pattern among children with type 1 diabetes.

Glycaemic derangement has been linked to sleep disruption. However, the impact of glycaemic derangement on sleep pattern among children with type 1 diabetes (C-T1D) remains unraveled. To assess the effect of nocturnal hyperglycaemia and clinically significant (CS) hypoglycaemia on sleep pattern among C-T1D. Thirty C-T1D were compared to 30 age and sex matched healthy siblings. Patients having other organ disease that might cause sleep disorders or on medications causing sleep disturbance were excluded. History included diabetes-duration, type and dose of insulin therapy, chronic diabetic-complications, and manifestations of sleep disorders. Epworth Sleepiness Scale-Child Adolescent was used. Continuous glucose monitoring system (CGMS) and overnight polysomnography were done and analysed. C-T1D had significantly lower sleep efficiency and significantly higher arousal index (AI), periodic limb movement index and apnoea-hypopnoea index compared to controls. Moreover, they had significantly longer sleep-onset latency, light sleep percentage, and shorter rapid eye movement percentage than controls. According to nocturnal CGMS readings, 15 C-T1D had nocturnal hyperglycaemia (50%), six experienced CS hypoglycaemia (20%), two had level-1 hypoglycaemia (6.7%), and seven were within the normoglycaemic range (23.3%). C-T1D experiencing nocturnal CS hypoglycaemia had significantly higher stage 3 sleep (P = 0.004) than controls. On the other hand, C- T1D experiencing nocturnal hyperglycaemia had significantly higher sleep onset latency (P = 0.013), light sleep percentage (P < 0.001), and AI (P < 0.001) than controls. Nocturnal CS hypoglycaemia was positively correlated to deep sleep duration, while hyperglycaemia was correlated to number of awakenings, sleep-onset latency, and light sleep duration. In children with T1D CS hypoglycaemia is associated with sleep deepening, while hyperglycaemia is associated with increased light sleep, sleep onset latency.

Prediction of blood glucose concentration for type 1 diabetes based on echo state networks embedded with incremental learning

Valid prediction of blood glucose concentration can help people to manage diabetes mellitus, alert hypoglycemia/hyperglycemia, exploit artificial pancreas, and plan a treatment program. Along the development of continuous glucose monitoring system (CGMS), the massive historical data require a new modeling framework based on a data-driven perspective. Studies indicate that the glucose time series (i.e., CGMS readings) involve chaotic properties; therefore, echo state networks (ESN) and its improved variants are proposed to establish subject-specific prediction models owing to their superiority in processing chaotic systems. This study mainly has two innovations: (1) a novel combination of incremental learning and ESN is developed to obtain a suitable network structure through partial optimization of parameters; (2) a feedback ESN is proposed to excavate the relationship of different predictions. These methods are assessed on ten patients with diabetes mellitus. Experimental results substantiate that the proposed methods achieve superior prediction performance in terms of four evaluation metrics compared with three conventional methods.

Glucose excursion in senior patients with type 2 diabetes mellitus and coronary artery disease

Objectives: To explore glucose excursion and incidence of hypoglycemia in senior patients with type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) with different hemoglobin A1c (HbA1c) values. Methods: Eighty-nine outpatients [58 male, 31 female, aged 69.0 (66.0, 73.0) years] diagnosed with T2DM and CAD in Beijing Anzhen Hospital between June 2013 and March 2016 were divided into group A (HbA1c<6.5%, n=25) and group B (HbA1c≥6.5%, n=64). Every patient wore continuous glucose monitoring system (CGMS) for 72 h. Glucose excursion parameters and incidence of hypoglycemia from CGMS readings were calculated. Results: Compared with group B, patients in group A experienced shorter diabetes duration [8.0 (3.5, 15.0) vs 15.0 (8.0, 20.0) years, Z=-2.222, P=0.026], lower standard deviation (SD) of blood glucose from CGMS, mean amplitude of glycemic excursions(MAGE) and mean of the daily differences(MODD) (P=0.001, 0.003, 0.001). However, incidence of hypoglycemia was significantly increased [56.0% (14/25) vs 32.8% (21/64), χ(2)=4.051, P=0.044] in group A. Compared to those who had not experienced hypoglycemia, patients with hypoglycemia had higher SD [1.8(1.4, 2.4) mmol/L vs 1.4(1.1, 1.8) mmol/L, Z=-3.198, P=0.001] and MAGE [6.0(3.2, 7.4) mmol/L vs 3.9(2.7, 4.8 )mmol/L, Z=-2.768, P=0.006] which were appropriate tools for assessing intraday glycemic variability. No statistical difference were found in MODD which was the index for estimating interday glycemic variability. Conclusions: Intensive glycemic control in senior patients with T2DM and CAD had higher incidence of hypoglycemia. Those with hypoglycemia experienced greater intraday glucose excursion.

Continuous subcutaneous glucose monitoring is accurate in term and near-term infants at risk of hypoglycaemia.

Postnatal hypoglycaemia increases the risk of adverse neurological outcomes in newborn infants, and adequate glucose control requires repetitive and painful blood sampling. This study evaluated a continuous glucose monitoring system (CGMS) that aims to improve glucose control and decrease the frequency of blood samples taken from neonates. CGMS sensors, which measure glucose values every five minutes and require calibration twice a day, were placed on 20 infants at risk of hypoglycaemia. The infants also underwent blood glucose sampling, and the blood glucose values were compared with CGMS values six times during the first 30 minutes after sampling. We used 97/264 (37%) of the blood glucose values taken for the CGMS calibration. The highest accuracy, a mean of 0.22 (95% confidence interval 0.13-0.30 mmol/L), was found 15-19 minutes after sampling, due to the calibration process. No significant subcutaneous glucose time lag was detectable. The CGMS system was an accurate and feasible method for glucose control, provided earlier detection of hypoglycaemia in newborn infants and reduced their exposure to procedural pain. The delay in calibration in infants was a new finding and needs to be taken into account when comparing CGMS readings to blood glucose values.

Accuracy of a real-time continuous glucose monitoring system in children with septic shock: A pilot study.

Aims:The aim of this prospective, observational study was to determine the accuracy of a real-time continuous glucose monitoring system (CGMS) in children with septic shock.Subjects and Methods:Children aged 30 days to 18 years admitted to the Pediatric Intensive Care Unit with septic shock were included. A real-time CGMS sensor was used to obtain interstitial glucose readings. CGMS readings were compared statistically with simultaneous laboratory blood glucose (BG).Results:Nineteen children were included, and 235 pairs of BG-CGMS readings were obtained. BG and CGMS had a correlation coefficient of 0.61 (P < 0.001) and a median relative absolute difference of 17.29%. On Clarke's error grid analysis, 222 (94.5%) readings were in the clinically acceptable zones (A and B). When BG was < 70, 70–180, and > 180 mg/dL, 44%, 100%, and 76.9% readings were in zones A and B, respectively (P < 0.001). The accuracy of CGMS was not affected by the presence of edema, acidosis, vasopressors, steroids, or renal replacement therapy. On receiver operating characteristics curve analysis, a CGMS reading <97 mg/dL predicted hypoglycemia (sensitivity 85.2%, specificity 75%, area under the curve [AUC] =0.85). A reading > 141 mg/dL predicted hyperglycemia (sensitivity 84.6%, specificity 89.6%, AUC = 0.87).Conclusion:CGMS provides a fairly, accurate estimate of BG in children with septic shock. It is unaffected by a variety of clinical variables. The accuracy over extremes of blood sugar may be a concern. We recommend larger studies to evaluate its use for the early detection of hypoglycemia and hyperglycemia.

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Background and aims: Hourly bedside blood glucose (BG) monitoring using reagent strips is currently the standard of care in the management of diabetic ketoacidosis (DKA) in children. Aims: To study the feasibility and accuracy of a real-time Continuous Glucose Monitoring System (CGMS) compared to BG, for better monitoring and preventing rapid changes in BG in children with DKA. Methods: IRB approval and consent were obtained. An abdominal wall CGMS sensor (Guardian REAL-time, Medtronic, Northridge CA) was inserted in children admitted to the PICU with DKA, to obtain 5-minute readings of interstitial glucose till DKA resolved. Readings were statistically compared with hourly bedside BG (Optium Xceed, Abbott, IL) measurements on arterial samples. Using ROC curves, we assessed how CGMS can predict a rapid fall in BG (>90 mg/dL/hr). Results: 13 patients were studied (age 3–114 months). CGMS readings could be obtained 207 + 171 minutes after sensor insertion. Sensors were used for 54.5+28.7 hours and tolerated well. 630 pairs of BG and CGMS readings were obtained. On Clarke’s Error Grid analysis, 96.5% readings were in the clinically acceptable zones A and B (63.2% in zone A, 33.3% in zone B). On ROC curve analysis, a fall in CGMS of 18 mg/dL over 15 minutes was 62% sensitive and 90% specific in predicting a fall in BG >90 mg/dL in that hour (AUC=0.81). Conclusions: CGMS is a feasible alternative to hourly BG testing in DKA and may predict falls in BG early. Further studies on larger groups are required before it can be recommended for routine use.

Analysis of Chronic Kidney Disease – Asociated Glycemic Variability in Patients with Type 2 Diabetes Using Continuous Glucose Monitoring System

Abstract Background and Aims. In diabetic patients, chronic kidney disease (CKD) requires special attention due to the multitude of factors that determine glycemic variability. We aimed to assess glycemic variability in patients with CKD and type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring system (CGMS) and identify the predictive value of inter-day and intra-day glycemic variability indices for metabolic imbalance. Material and method. We included 20 diabetic patients (10 CKD patients/10 patients without CKD) and 10 healthy volunteers. Anthropometric parameters, glycated hemoglobin (HbA1c), and glycemic variability indices on CGMS readings were registered. Results. CKD diabetic patients presented significantly higher inter-day and intra-day glycemic variability compared to the diabetic patients without CKD. HbA1c was not significantly different between diabetic subjects with/without CKD. ROC curves indicated that just some CGMS parameters had higher predictive value for metabolic imbalance (HbA1c≥6.5%) but only the percentage of time with glucose values&gt;180 mg/dl (p=0.024) was an independent predictor for HbA1c≥6.5%. Conclusions. Subjects with CKD and T2DM had poor glycemic control and significantly higher glycemic variability comparative to those without CKD, and especially to healthy volunteers. Assessment of glycemic variability indices is more accurate than HbA1c for the quantification of glycemic control in CKD diabetic patients

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Blood glucose (BG) prediction plays a very important role in daily BG management of patients with diabetes mellitus. Several algorithms, such as autoregressive (AR) models and artificial neural networks, have been proposed for BG prediction. However, every algorithm has its own subject range (i.e., one algorithm might work well for one diabetes patient but poorly for another patient). Even for one individual patient, this algorithm might perform well during the preprandial period but poorly during the postprandial period. A novel framework was proposed to combine several BG prediction algorithms. The main idea of the novel framework is that an adaptive weight is given to each algorithm where one algorithm's weight is inversely proportional to the sum of the squared prediction errors. In general, this framework can be applied to combine any BG prediction algorithms. As an example, the proposed framework was used to combine an AR model, extreme learning machine, and support vector regression. The new algorithm was compared with these three prediction algorithms on the continuous glucose monitoring system (CGMS) readings of 10 type 1 diabetes mellitus patients; the CGMS readings of each patient included 860 CGMS data points. For each patient, the algorithms were evaluated in terms of root-mean-square error, relative error, Clarke error-grid analysis, and J index. Of the 40 evaluations, the new adaptive-weighted algorithm achieved the best prediction performance in 37 (92.5%). Thus, we conclude that the adaptive-weighted-average framework proposed in this study can give satisfactory predictions and should be used in BG prediction. The new algorithm has great robustness with respect to variations in data characteristics, patients, and prediction horizons. At the same time, it is universal.

Accuracy of continuous glucose monitoring system during exercise in Type 2 Diabetes

a b s t r a c t The concordance of continuous glucose monitoring system (CGMS) and finger-stick blood glucose (FSBG) was assessed in patients with type 2 diabetes during daily activities and two different exercise sessions. Agreement between FSBG and CGMS becomes weaker during exercise, but more than 90% of the CGMS readings are within acceptable range.

Accuracy of continuous glucose monitoring system during exercise in type 2 diabetes

The concordance of continuous glucose monitoring system (CGMS) and finger-stick blood glucose (FSBG) was assessed in patients with type 2 diabetes during daily activities and two different exercise sessions. Agreement between FSBG and CGMS becomes weaker during exercise, but more than 90% of the CGMS readings are within acceptable range.

Effect of Short-Term Use of a Continuous Glucose Monitoring System with a Real-Time Glucose Display and a Low Glucose Alarm on Incidence and Duration of Hypoglycemia in a Home Setting in Type 1 Diabetes Mellitus

The objective of this study was to examine whether setting the low glucose alarm of a Guardian® REAL-Time continuous glucose monitoring system (CGMS) to 80 mg/dl for 3 days and providing instructions to users reduce the risk of hypoglycemia under free-living conditions in individuals with type 1 diabetes mellitus (T1DM). Fourteen participants with T1DM aged 26.1±6.0 years (mean±standard deviation) were fitted with a CGMS and assigned for 3 days to either an alarm [low and high blood glucose (BG) alarms set at 80 and 200 mg/dl, respectively] or no alarm condition, with each treatment administered to all participants following a counterbalanced design. All participants were given detailed instructions on how to respond appropriately to low glucose alarms. The CGMS with alarm reduced the incidence of hypoglycemia (CGMS readings≤65 mg/dl) by 44% as well as the time spent below this hypoglycemic threshold by 64% without increasing average BG levels. However, the CGMS with alarm had no effect on the incidence of symptomatic hypoglycemia. Short-term use of the CGMS with alarm, together with appropriate instructions for users, reduces the incidence and duration of hypoglycemia, but only to a limited extent, in part because it overestimates BG in the low glucose range.

Continuous Glucose Monitoring System in a Rural Intensive Care Unit: A Pilot Study Evaluating Accuracy and Acceptance

Glucose management in an intensive care unit (ICU) is labor-intensive. A continuous glucose monitoring system (CGMS) has the potential to improve efficiency and safety in this setting. The goal of this study was to determine if the Medtronic Guardian REAL-Time CGMS was accurate and tolerated by patients in a rural hospital ICU unit. Differences between individual finger stick blood glucose (FSBG) and CGMS values were compared to American Diabetes Association (ADA) and International Organization for Standardization (ISO) standards. Continuous glucose monitoring system accuracy was evaluated over four ranges: <75, 75-140, 140-200, and >200 mg/dl. Other accuracy measures [mean absolute deviation (MAD), mean absolute relative difference (MARD), and coefficient of linear regression of CGMS on FSBG] were calculated. Nursing staff and patients were surveyed regarding use of the CGMS in the ICU. Twenty-nine participants had 320 FSBG and corresponding CGMS readings. Sixty-two percent of participants were admitted with diabetic ketoacidosis (DKA). Two hundred and thirteen (66.6%) were accurate within the ISO standard, whereas only 70 out of 320 (21.9%) were within the 5% ADA standard. The CGMS was most accurate in euglycemia. Technical difficulties, such as adequate time for "wetting" and calibration of electrodes, arose with the sensors. The MAD was 28.3 mg/dl, the MRAD was 17.4%, and the linear regression coefficient of CGMS on FSBG was 0.834 (p < 0.001). The CGMS is well tolerated by ICU patients but, at present, is not sufficiently accurate to be used for therapeutic decisions in the acute setting, particularly in patients with diabetic ketoacidosis. There is a need to find resolution to the technical issues regarding electrode "wetting" and calibration if CGMS use in the ICU setting is to provide an effective means of diabetes care and management.

Evaluation of a Novel Real‐Time Continuous Glucose‐Monitoring System for Use in Cats

The Guardian REAL-Time is a continuous glucose-monitoring system (CGMS) recently developed to provide instantaneous interstitial glucose concentrations; the system does not require a monitor being fixed to the animal. The CGMS provides accurate and reproducible real-time readings of glucose concentration in cats. Thirty-two diabetic cats, 2 cats with suspected insulinoma, and 5 healthy cats. Prospective, observational study. CGMS accuracy was compared with a reference glucose meter at normal, high, and low blood glucose concentrations using error grid analysis. Reading variability of 2 simultaneously used CGMS was determined in diabetic cats by calculating correlation and percentage of concordance of paired data at different glycemic ranges. The time interval between increasing glycemia and a rise in interstitial fluid glucose measured by the CGMS was assessed in healthy cats receiving glucose IV; the time point of maximal increase in interstitial glucose concentrations was calculated. The CGMS was 100, 96.1, and 91.0% accurate at normal, high, and low blood glucose concentrations. Measurements deviated from reference by -12.7 +/- 70.5 mg/dL at normal, -12.1 +/- 141.5 mg/dL at high, and -1.9 +/- 40.9 mg/dL at low glucose concentrations. Overall, paired CGMS readings correlated significantly (r = 0.95, P < .0001) and concordance was 95.7%. The median delay after IV administration of glucose to an increase in interstitial glucose was 11.4 minutes (range: 8.8-19.7 minutes). Although some readings substantially deviated from reference values, the CGMS yields reproducible results, is clinically accurate in cats with hyperglycemia and euglycemia, and is slightly less accurate if blood glucose concentrations are low. Rapidly increasing interstitial glucose after a glycemic rise suggests that the CGMS is suitable for real-time measurement under clinical conditions.

CPAP Therapy of Obstructive Sleep Apnea in Type 2 Diabetics Improves Glycemic Control During Sleep

Type 2 diabetes and obstructive sleep apnea (OSA) are frequently comorbid conditions. OSA is associated with increased insulin resistance, but studies of continuous positive airway pressure (CPAP) have shown inconsistent effects on glycemic control. However, endpoints such as hemoglobin A1c and insulin sensitivity might not reflect short-term changes in glycemic control during sleep. We used a continuous glucose-monitoring system to measure interstitial glucose every 5 minutes during polysomnography in 20 patients with type 2 diabetes and newly diagnosed OSA. The measurements were repeated after an average of 41 days of CPAP (range 26-96 days). All patients were on a stable diet and medications. Each 30-second epoch of the polysomnogram was matched with a continuous glucose-monitoring system reading, and the sleeping glucose level was calculated as the average for all epochs scored as sleeping. The mean sleeping glucose decreased from untreated (122.0 +/- 61.7 mg/dL) to treated (102.9 +/- 39.4 mg/dL; p = 0.03 by Wilcoxon paired rank test). The sleeping glucose was more stable after treatment, with the median SD decreasing from 20.0 to 13.0 mg/dL (p = 0.005) and the mean difference between maximum and minimum values decreasing from 88 to 57 mg/dL (p= 0.003). The change in the mean hemoglobin A1c from 7.1% to 7.2% was not significant. Our study is limited by the lack of a control group, but the results suggest that sleeping glucose levels decrease and are more stable after patients with type 2 diabetes and OSA are treated with CPAP.

Reproducibility and reliability of hypoglycaemic episodes recorded with Continuous Glucose Monitoring System (CGMS) in daily life

Continuous glucose monitoring may reveal episodes of unrecognized hypoglycaemia. We evaluated reproducibility and reliability of hypoglycaemic episodes recorded in daily life by the Medtronic MiniMed Continuous Glucose Monitoring System (CGMS). Twenty-nine adult patients with Type 1 diabetes underwent 6 days of continuous subcutaneous glucose monitoring, applying one CGMS on each side of the abdomen. Blood glucose was measured by HemoCue B-Glucose Analyzers six times daily and two different 4-point calibration sets were generated (set A and B). Using these calibration sets, CGMS raw data were recalibrated generating four different CGMS data sets [left-A (left side of abdomen, calibration set A), left-B, right-A and right-B]. Agreement between CGMS data sets was evaluated during hypoglycaemic events, comparing CGMS readings = 2.2 mmol/l with nadir values from corresponding CGMS data sets. CGMS readings were also compared with independent self-monitored blood glucose (SMBG) values. With hypoglycaemia (CGMS readings = 2.2 mmol/l) in calibration set left-A, values below 3.5 mmol/l were present in 99% (95% CI: 95-100%) of samples in left-B, 91% (95% CI: 84-96%) of samples in right-A, and 90% (95% CI: 83-95%) of samples in right B. In 84% of these episodes (95% CI: 59-96%) independent SMBG values were below 3.5 mmol/l. Difference in duration was observed with a median difference of 20 min; (left-A vs. right-B). Hypoglycaemic episodes recorded by CGMS are reproducible and agreement with independent SMBG values is acceptable for retrospective recording of hypoglycaemic events with CGMS.

Undiagnosed asymptomatic hypoglycemia: diet, insulin, and glyburide for gestational diabetic pregnancy.

The role of maternal hypoglycemia during pregnancy has not yet been established. We sought to estimate the prevalence of undiagnosed, asymptomatic hypoglycemic events that occur in diabetic patients. All patients were evaluated using a continuous glucose monitoring system for 72 consecutive hours. The continuous glucose monitoring system measures in subcutaneous tissue interstitial glucose levels within a range of 40-400 mg/dL every 5 minutes for a total of 288 measurements per day. All patients were instructed regarding diabetic diet and assigned to pharmacological treatment as needed. Patients documented the time of food intake, insulin or glyburide administration, and all clinical hypoglycemic events. An asymptomatic hypoglycemic episode was defined as more than 30 consecutive minutes of glucose value below 50 mg/dL detected only by continuous glucose monitoring system reading without patient awareness. An evaluation of 82 patients with gestational diabetes was performed; 30 were insulin-treated, 27 were managed by diet only, and 25 were patients treated with glyburide. For purposes of comparison, data were obtained from 35 nondiabetic gravid women. Asymptomatic hypoglycemic events were identified in 19 of 30 (63%) insulin-treated patients and in 7 of 25 (28%) glyburide-treated patients. No hypoglycemic events were identified in patients with gestational diabetes mellitus treated by diet alone or in nondiabetic subjects. The mean recorded hypoglycemic episodes per day was significantly higher in insulin-treated patients (4.2 +/- 2.1) than in glyburide-treated patients (2.1 +/- 1.1), P =.03. In insulin-treated patients, the majority of the hypoglycemic events were nocturnal (84%), whereas in glyburide-treated patients, episodes were identified equally by day and night. Our data suggest that asymptomatic hypoglycemic events are common during pharmacological treatment in gestational diabetic pregnancies. We speculate that this finding may be explained by treatment modality rather than by the disease itself.

Spurious reporting of nocturnal hypoglycemia by CGMS in patients with tightly controlled type 1 diabetes.

The Medtronic MiniMed Continuous Glucose Monitoring System (CGMS) is designed to continuously monitor interstitial fluid glucose levels within a range of 40-400 mg/dl. It is considered an important tool for overnight glucose monitoring. The goal of this study was to determine the accuracy of this system in individuals with tightly controlled diabetes. Seven adolescents and young adults with HbA(1c) levels 6.6 +/- 0.6% (range 5.7-7.1) were admitted to the Clinical Research Center. Simultaneous glucose measurements obtained by glucose analyzer, Accu-Check Advantage meter, and CGMS were compared. The analyzer levels were considered the standard. The CGMS results were lower than analyzer readings in 74% of simultaneous pairs of tests performed during the 24-h period; the average correlation was 0.76. There was a trend for the poorest correlation to occur in patients with the narrowest range in daily glucose levels. When the lowest CGMS reading of the night was compared with the simultaneous analyzer reading, the CGMS level was lower in all cases by an average of 38 +/- 15%. In six of seven patients, the discrepancy was believed to be clinically significant; in at least four patients, overnight glucose levels reported by CGMS were falsely low, in a range that might have resulted in inappropriate reduction of overnight insulin dose. CGMS reports of asymptomatic nighttime hypoglycemia may be spurious and should be interpreted with caution in patients with tightly controlled diabetes.