Continuous Glucose Monitoring System Data Research Articles

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18–87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39–0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27–0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31–0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21–0.30]) than the older subjects (ICC 0.39 [CI 0.32–0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

1034-P: Access to Continuous Glucose Monitoring Systems in a Primary Care Clinic for Underserved Patients with Type 2 Diabetes—A Pilot Study

B-Disparities in diabetes care are well documented. Continuous Glucose Monitoring Systems (CGMs) have improved patient experience without the need for finger sticks and have been shown to effectively lower Hemoglobin A1c (HbA1c), decrease clinical inertia and decrease the number of hypoglycemic events. Medically underserved and historically marginalized persons are disproportionately affected by diabetes, yet limited data exist on use of CGMs in this population. M-We report a pilot initiative that brought CGM access (FreeStyle Libre 2) to a Federally Qualified Community Health Center (FQHC). Patients with type 2 diabetes and an HBA1c >8.0% were referred to be enrolled in CGM. Each patient had an initial nurse visit for device placement of FreeStyle Libre (FSL) and several follow up visits to review glycemic trends and make medication changes with a provider. Baseline HBA1c was collected and repeated at the end of the study. Provider and patient surveys were distributed at each visit. R-Fifty participants were enrolled in the CGM study and had FSL placed. The CGM active time was greater than 50% in 76% of participants and those who continued with CGM had an increase in active time. Initial mean A1C for the entire cohort was 9.64% and average glucose after 2 weeks was 195 mg/dl. Hypoglycemia, especially overnight, reduced overtime. The use of CGM data was associated with clinician-initiated medication changes. Provider surveys revealed an increase in likelihood of medication changes based on glycemic trends and patients reported better understanding of their disease process. C-This pilot study demonstrates the feasibility of utilizing diabetes technology (FreeStyle Libre 2) in underserved patients at a primary care clinic staffed with internal medicine residents, physicians, and nurse practitioners. CGM is beneficial in under resourced primary care outpatient settings to determine glycemic trends and make appropriate medication recommendations. Disclosure R.G.Sgroi: None. S.Gumireddy: None. W.Tan: None. R.Kaplan: None. Y.Milambwe: None. L.H.Rosoph: None. D.A.Salama: None. C.R.Feher: None. N.J.Rennert: None.

‘You have to use everything and come to some equilibrium’: a qualitative study on hypoglycemia self-management in users of continuous glucose monitor with diverse hypoglycemia experiences

IntroductionAlthough continuous glucose monitoring systems (CGMs) can help reduce hypoglycemia, about one-quarter of people with type 1 diabetes (T1D) who use CGMs still either spend at least 1% of the...

Beneficial effects of premeal almond load on glucose profile on oral glucose tolerance and continuous glucose monitoring: randomized crossover trials in Asian Indians with prediabetes

BackgroundRapid conversion from prediabetes to diabetes and frequent postprandial hyperglycemia (PPHG) is seen in Asian Indians. These should be the target of dietary strategies.ObjectivesWe hypothesized that dietary intervention of preloading major meals with almonds in participants with prediabetes will decrease overall glycemia and PPHG.DesignThe study included two phases: (1) an oral glucose tolerance test (OGTT)-based crossover randomized control study, the effect of a single premeal almond load (20 g) given before OGTT was evaluated (n = 60, 30 each period). (2) The continuous glucose monitoring system (CGMS)-based study for 3 days including premeal almond load before three major meals was a free-living, open-labeled, crossover randomized control trial, where control and premeal almond load diets were compared for glycaemic control (n = 60, 30 in each period). The study was registered at clinicaltrials.gov (registration no. NCT04769726).ResultsIn the OGTT-based study phase, the overall AUC for blood glucose, serum insulin, C-peptide, and plasma glucagon post-75 g oral glucose load was significantly lower for treatment vs. control diet (p < 0.001). Specifically, with the former diet, PPHG was significantly lower (18.05% in AUC on OGTT, 24.8% at 1-h, 28.9% at 2-h post OGTT, and 10.07% during CGMS). The CGMS data showed that premeal almond load significantly improved 24-glucose variability; SD of mean glucose concentration and mean of daily differences. Daily glycaemic control improved significantly as per the following: mean 24-h blood glucose concentration (M), time spent above 7.8 mmol/L of blood glucose, together with the corresponding AUC values. Premeal almond load significantly decreased following: overall hyperglycemia (glucose AUC), PPHG, peak 24-h glycaemia, and minimum glucose level during night.ConclusionIncorporation of 20 g of almonds, 30 min before each major meal led to a significant decrease in PPHG (as revealed in OGTT-based study phase) and also improved insulin, C-peptide, glucagon levels, and improved glucose variability and glycemic parameters on CGMS in participants with prediabetes.Clinical trial registryThe study was registered at clinicaltrials.gov (registration no. NCT04769726).

Blood Glucose Fluctuation in Older Adults with Diabetes Mellitus and End-Stage Renal Disease on Maintenance Hemodialysis: An Observational Study.

IntroductionPatients with diabetes mellitus and end-stage renal disease are at a high risk of developing coronary, cerebrovascular, and peripheral vascular diseases. This study aimed to characterize hypoglycemia and blood glucose fluctuations associated with maintenance hemodialysis in older adult patients with diabetes mellitus and end-stage renal disease using a continuous glucose monitoring system.MethodsSeven patients were enrolled in this study, and 13 pairs of continuous glucose monitoring system data were collected. Each pair included data of 1 dialysis-on day and 1 dialysis-off day. Information on basic patient characteristics, including age, diabetes mellitus duration, hemodialysis duration, and proportions of hemoglobin A1c and glycated albumin, were collected. Differences in blood glucose fluctuation were compared between dialysis-on days and dialysis-off days.ResultsThe mean blood glucose on dialysis-on days (6.96 ± 2.57 mmol/L) was significantly lower than that on dialysis-off days (7.68 ± 2.31 mmol/L; P < 0.05). In contrast, the following parameters had significantly higher values (all P < 0.05) on dialysis-on days compared to dialysis-off days: large amplitude of glycemic excursion level (5.82 ± 2.86 mmol/L versus 4.21 ± 1.71 mmol/L), large amplitude of glycemic excursion level from 8 a.m. to 2 p.m. (3.6 ± 1.74 mmol/L versus 2.8 ± 1.33 mmol/L), mean amplitude of glycemic excursion level (4.78 ± 1.68 mmol/L versus 3.89 ± 1.67 mmol/L), mean amplitude of glycemic excursion level from 8 a.m. to 2 p.m. (4.01 ± 1.03 mmol/L versus 3.12. ± 0.97 mmol/L), standard deviation of blood glucose (1.55 ± 0.89 mmol/L versus 1.03 ± 0.4 mmol/L), and time below a target glucose range of less than 3.9 mmol/L (8.27% versus 4.25%).ConclusionFluctuations in blood glucose levels were larger on dialysis-on days, particularly from the start of hemodialysis to 2 h post-hemodialysis, than on dialysis-off days. Hypoglycemia, as indicated by the time below a target glucose range of less than 3.9 mmol/L, occurred more frequently on dialysis-on days than on dialysis-off days.Graphical

Beliefs Around Hypoglycemia and Their Impacts on Hypoglycemia Outcomes in Individuals with Type 1 Diabetes and High Risks for Hypoglycemia Despite Using Advanced Diabetes Technologies.

OBJECTIVEThis study aimed to 1) identify the frequency of severe and level 2 hypoglycemia presenting in individuals with type 1 diabetes using continuous glucose monitoring systems (CGMs), including those with concomitant closed-loop insulin pumps, in a clinical practice setting and 2) evaluate the impact of beliefs around hypoglycemia in the development of severe and level 2 hypoglycemia in this population.RESEARCH DESIGN AND METHODSA cross-sectional survey study in adults with type 1 diabetes using CGMs >6 months was conducted at a large tertiary academic center. Participant demographics, 6-month severe hypoglycemia history, hypoglycemia beliefs (with the Attitude to Awareness of Hypoglycemia questionnaire), and 4-week CGM glucose data were collected. Statistical analysis was performed to assess the presentation of severe and level 2 hypoglycemia and identify associated risk factors.RESULTSA total of 289 participants were recruited (including 257 participants with CGM data within the last 3 months). Of these, 25.6% experienced at least one severe hypoglycemic episode in the last 6 months, and 13.6% presented with ≥1% of time in level 2 hypoglycemia on CGMs. Reporting beliefs about prioritizing hyperglycemia avoidance was associated with severe hypoglycemia development (P < 0.001), while having beliefs of minimal concerns for hypoglycemia was associated with spending ≥1% of time in level 2 hypoglycemia (P = 0.038).CONCLUSIONSDespite the use of advanced diabetes technologies, severe and level 2 hypoglycemia continues to occur in individuals with type 1 diabetes and high hypoglycemia risks. Human factors, including beliefs around hypoglycemia, may continue to impact the effectiveness of glucose self-management.

Continuous Glucose Monitoring System Versus Self-Monitoring Blood Glucose in Type 1 Diabetes Mellitus Children: A Randomised Controlled Trial (RoSEC)

Objectives: The primary and secondary objectives were to compare the glycaemic control and frequency of hypoglycaemia between continuous glucose monitoring system (CGMS) and self-monitoring blood glucose (SMBG). Methods: A single centre, randomised, parallel-group controlled trial was conducted involving twenty-two type one Diabetes Mellitus (T1DM) patients with the mean age of 13.8 years assigned to either intervention or control group. All respondents wore the CGMS device at the beginning of the study. Intervention group (n=11) had their insulin adjusted based on the CGMS data, while the control group (n=11) was based on SMBG. Monthly average blood sugar level (BSL) and monthly mean hypoglycemic events per week (HE/wk) were measured at baseline, first month, second month, and third month. HbA1c levels were measured at baseline and in the third month. Results: The baseline characteristics were similar. The data were analysed using repeated measure analysis of variance (ANOVA). The mean difference of HbA1c within the group was not statistically significant with p=0.322. There were significant differences in the monthly mean HE/wk within and between groups, p=0.004, and p=0.037. Conclusion: In conclusion, CGMS is equivalent to SMBG in optimising glycaemic control but is more effective in detecting hypoglycaemia in children.

608-P: Relationship between Time-in-Range and Estimated HbA1c Derived from Continuous Glucose Monitoring System in Adults with Type 1 Diabetes Mellitus

Aims: Time-in-range (TIR) and estimated glycated hemoglobin (eHbA1c), two key metrics derived from continuous glucose monitoring system (CGMS), are valuable for glycemic control. This study is to investigate the relationship between TIR and eHbA1c, and the impact of glycemic variability on their relationship. Methods: The CGMS data were collected from baseline and follow-up visits in a clinical trial (NCT03522870) and yearly visits in the Guangdong Type 1 Diabetes Translational Medicine Study. Adult patients with type 1 diabetes (≥18 yrs) who had available CGM data for &amp;gt; 3 days were included. Correlation between TIR and eHbA1c was explored according to different stratification of glycemic variability assessed by glucose coefficient of variation (CV). Predicted TIR in the fixed eHbA1c value was calculated via the linear regression equations performed in the respective interquartile group of CV. Results: From March 2014 to July 2020, a total of 260 CGMS data were collected. The mean wearing time of available CGM data was 11.8±3.8 days, and the mean value of TIR, eHbA1c, and CV was 62.9±14.1%, 7.2±0.8%, and 39.4±8.1%, respectively. There was a strong negative correlation between TIR and eHbA1c (R=-0.83, P&amp;lt;0.001). For patients with stable glycemic variability (CV&amp;lt;36%), the correlation between the two metrics was stronger (R=-0.95) while in the unstable group the coefficient was decreased (R=-0.80). Based on the regression equations, the predicted %TIR value was decreased across the ascending quartiles with 73.0% in the lowest quartile of CV (&amp;lt;25th quartile, &amp;lt;33.8%), 67.4% in 25th - 50th quartile (33.8-39.2%), 61.4% in 50th - 75th quartile (39.2-44.9%) and 58.5% in the highest quartile (&amp;gt;75th quartile, CV &amp;gt;44.9%) when eHbA1c was set as 7%. Conclusions: There is a strong correlation between TIR and eHbA1c in adult patients with type 1 diabetes. Patients with lower glycemic variability usually have higher %TIR in the same eHbA1c level. Disclosure Y. Zhou: None. J. Weng: None. H. Liu: None. D. Yang: None. H. Ai: None. J. Lv: None. X. Mai: None. W. Xu: None. B. Yao: None. J. Yan: None. Funding National Key Research and Development Program of China (2017YFC1309600)

Coronary Flow Reserve and Glycemic Variability in Patients with Coronary Artery Disease.

Objective Glycemic variability is being increasingly recognized as an early indicator of glucose metabolic disorder and may contribute to the development of diabetic vascular complications, such as coronary microvascular dysfunction. The present study sought to investigate the relationship between coronary microvascular function assessed by intracoronary thermodilution method and glycemic variability on a continuous glucose monitoring system (CGMS). Methods We prospectively enrolled 40 patients with or without known diabetes mellitus who had epicardial coronary artery disease referred for coronary angiography and were not treated with diabetic medications. Of these, two had a significant stenosis in the left main coronary artery and were therefore excluded from the analyses. In the end, 38 patients were equipped with a CGMS and underwent intracoronary physiological assessments in the unobstructed left anterior descending artery. The mean amplitude of glycemic excursion (MAGE) and standard deviation were calculated from the obtained CGMS data as indicators of glucose variability. Results Coronary flow reserve (CFR) was negatively correlated with MAGE (r=-0.328, p=0.044) and standard deviation (r=-0.339, p=0.037) on CGMS, while the index of microcirculatory resistance showed no such correlation. Multivariable linear regression analyses showed that MAGE on CGMS was significantly associated with CFR after adjusting for age, sex, fractional flow reserve and hemoglobin A1c. Conclusion Higher MAGE on CGMS was associated with reduced CFR in stable patients with coronary artery disease, suggesting a potential effect of glycemic variability on coronary microvascular flow regulation. A further study with a larger sample size needs to be conducted to confirm our findings.

Improving blood glucose level predictability using machine learning.

This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-specific supervised-machine-learning (SML) analysis of glucose level based on a continuous-glucose-monitoring system (CGM) that needs no human intervention, and minimises false-positive alerts. The CGM data over 7 to 50 non-consecutive days from 11 type-1 diabetic patients aged 18 to 39 with a mean HbA1C of 7.5% ± 1.2% were analysed using four SML models. The algorithm was constructed to choose the best-fit model for each patient. Several statistical parameters were calculated to aggregate the magnitudes of the prediction errors. The personalised solutions provided by the algorithm were effective in predicting glucose levels 30 minutes after the last measurement. The average root-mean-square-error was 20.48 mg/dL and the average absolute-mean-error was 15.36 mg/dL when the best-fit model was selected for each patient. Using the best-fit-model, the true-positive-hypoglycemia-prediction-rate was 64%, whereas the false-positive- rate was 4.0%, and the false-negative-rate was 0.015%. Similar results were found even when only CGM samples below 70 were considered. The true-positive-hyperglycemia-prediction-rate was 61%. State-of-the-art SML tools are effective in predicting the glucose level values of patients with type-1diabetes and notifying these patients of future hypoglycemic and hyperglycemic events, thus improving glycemic control. The algorithm can be used to improve the calculation of the basal insulin rate and bolus insulin, and suitable for a closed loop "artificial pancreas" system. The algorithm provides a personalised medical solution that can successfully identify the best-fit method for each patient.

Improved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive Alerts.

Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated. Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes. Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 (P < 0.001, 95% CI, 1.88-1.93) and 3.3 (P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled. The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.

943-P: Improvement in Glycemic Variability with Dulaglutide Therapy Evaluated by Continuous Glucose Monitoring System: A Real-World Experience of Indian Patients with Type 2 DM

Dulaglutide-based therapy may potentiate improved glycemic variability (GV) in patients with type 2 diabetes (T2D) owing to their several pancreatic and extra-pancreatic effects. This study assessed the effects of dulaglutide on GV using continuous glucose monitoring system (CGMS) in patients with T2D. Patients (N = 27) from a CGMS specialty center in India received dulaglutide as add-on to oral antidiabetics (OADs) with or without insulin and were subsequently monitored for continuous glucose data using CGMS (FreeStyle Libre Pro) for ≤15 days considering 70-140 mg/dL as target. CGMS data revealed a significant reduction (P&amp;lt;.05) in overall within-patient mean CGM glucose on Day 5 relative to Day 1 which further reduced at Day 15 (Figure), warranting titration of doses of OADs and/or insulin. Interestingly, the coefficient of variation for overall within-patient mean glucose on Day 15 (21.99%) was less compared to Day 1 (31.56%). Through Day 1 to Day 15, overall within-patient mean % time of glucose in target significantly (P&amp;lt;.05) increased (25.19% to 66.42%) along with reduced mean % time above target (68.96% to 17.42%). A non-significant increase in overall mean % time below target (5.86% to 16.16%) was observed with no events of severe hypoglycemia. Dulaglutide improved GV and is well tolerated in patients with T2D. Disclosure S.A. Patange: None.

Role of continuous glucose monitoring in the management of glycogen storage disorders.

Management of liver glycogen storage diseases (GSDs) primarily involves maintaining normoglycemia through dietary modifications and regular glucose monitoring. Self-monitoring of blood glucose is typically done 3-6 times per day, and may not sufficiently capture periods of asymptomatic hypoglycemia, particularly during sleep. Continuous glucose monitoring systems (CGMS) provide 24-h continuous glucose data and have been used effectively in diabetes mellitus to monitor metabolic control and optimize treatment. This is a relatively new approach in GSDs with only a handful of studies exploring this modality. In this study we used Dexcom CGMS to study the glycemic profile of 14 pediatric and six adult patients with GSD I, III, and IX. A total of 176days of CGMS data were available. The CGMS was found to be a reliable tool in monitoring glucose levels and trends at all times of the day with good concordance with finger-stick glucose values. This study revealed that in addition to overnight hypoglycemia, CGMS can uncover previously undetected, subclinical, low glucose levels during daytime hours. Additionally, the CGMS detected daytime and overnight hyperglycemia, an often overlooked concern in liver GSDs. The CGMS with concurrent dietary adjustments made by a metabolic dietitian improved metabolic parameters and stabilized blood glucose levels. The CGMS was found to be a safe, effective, and reliable method for optimizing treatment in patients with GSD I, III, and IX.

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters—age, sex, BMI, and waist circumference—with an accuracy of 87.1%.

Continuous glucose monitoring to evaluate glycaemic abnormalities in cystic fibrosis

ObjectiveThis study aimed to determine the glycaemic profile of patients with cystic fibrosis using a continuous glucose monitoring system (CGMS), and to evaluate the associations of glycaemic abnormalities with sex,...

Short-Acting Insulin Reduction Strategies Before Continuous Ergometer Exercises In Patients With Type 1 Diabetes Mellitus

Patients with type 1 diabetes mellitus (T1DM) often refuse performing exercise because of its associations with negative glycemic disturbances. However, it was shown that a higher compliance to exercise adaptation strategies reduced the fear of hypoglycemia. PURPOSE: To investigate exercise-intensity dependent short-acting insulin reduction, catecholamine and glucagon response in order to avoid hypoglycemia applying theory based standardized exercise workloads. METHODS: 7 male subjects with T1DM (age: 24.7 ± 5.3 years, height: 1.76 ± 0.40 m, weight: 74 ± 5.1 kg, HbA1c: 57 ± 6.3 mmol.mol-1) underwent a 4-week adaptation with basal insulin Degludec (Tresiba; Novo Nordisk, Denmark). Subjects performed a maximal incremental cycle exercise test to determine the first (LTP1) and the second (LTP2) lactate turn points. Pre- and post-exercise short-acting insulin reductions of 25%, 50% and 75% were administered accordingly for defined 30 min continuous cycle ergometer exercise workloads set at 5% Pmax below and above LTP1 (A, B) and LTP2 (C, D). Interstitial glucose was measured by continuous glucose monitoring system (CGMS) 24 h before and after each test. Pre- and post-exercise diet was standardized. At rest, during and at the end of each exercise workload hormones were determined. Blood glucose (BG) and lactate were determined every 5 min and the time (tcrit) to reach 50% of basal BG was calculated. Pulmonary gas-exchange variables were collected continuously. BG decrease and hormone response were analysed by one-sample t-test. RESULTS: With short-acting insulin reduction, there was a significant linear decrease in BG from baseline, which was dependent on exercise intensity: 2.01 ± 1.04 mmol.l-1 (A), 3.00 ± 1.54 mmol.l-1 (B), 3.42 ± 2.34 mmol.l-1 (C) and 3.70 ± 3.36 mmol.l-1 (D) (p<0.05). Catecholamines significantly increased from baseline (p<0.05) dependent on exercise intensity but not glucagon. Due to the same insulin and carbohydrate adaptation immediately after exercise subjects showed no post-exercise hypoglycemia in CGMS data. CONCLUSION: In all 35 tests no hypoglycemia was seen applying standardized insulin reduction, in combination with the mentioned exercise intensities within the given time frame. However, the tcrit to induce hypoglycemia is dependent on exercise intensity and starting BG levels.

Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data.

Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of the time sequence, and, thus, a decreased complexity in this system. No study has investigated the MSE analysis of glucose dynamics in diabetes. This study was designed to compare the complexity of glucose dynamics between the diabetic patients (n = 17) and the control subjects (n = 13), who were matched for sex, age, and body mass index via MSE analysis using the CGMS data. Compared with the control subjects, the diabetic patients revealed a significant increase (P < 0.001) in the mean (diabetic patients 166.0 ± 10.4 vs. control subjects 93.3 ± 1.5 mg/dl), the standard deviation (51.7 ± 4.3 vs. 11.1 ± 0.5 mg/dl), and the mean amplitude of glycemic excursions (127.0 ± 9.2 vs. 27.7 ± 1.3 mg/dl) of the glucose levels; and a significant decrease (P < 0.001) in the MSE complexity index (5.09 ± 0.23 vs. 7.38 ± 0.28). In conclusion, the complexity of glucose dynamics is decreased in diabetes. This finding implies the reactivity of glucoregulation is impaired in the diabetic patients. Such impairment presenting as an increased regularity of glycemic fluctuating pattern could be detected by MSE analysis. Thus, the MSE complexity index could potentially be used as a biomarker in the monitoring of diabetes.

Technology and Pregnancy

Technology and Pregnancy

The Effect on Day-Long Glycemia of Consuming Lower and Higher Glycemic Index Diets in People with Type 2 Diabetes: A Randomized Crossover Study

Objective: Treatment of type 2 diabetes includes pharmacologic and lifestyle modification such as dietary change. The use of the Glycemic Index (GI) to guide food choice has been advocated, although the effectiveness of this dietary strategy in people with type 2 diabetes has had mixed success. Our objective was to investigate daylong glycemic responses to diets differing in GI using continuous glucose monitoring in people with type 2 diabetes. Methods: A randomized crossover trial in 22 adults aged 18 to 75 years diagnosed with type 2 diabetes and without major co-morbidities. Lower and higher GI diets were consumed over a five-day period with food supplied to participants. Diet and physical activity were standardized and medication was maintained for the study period. Main outcomes using a Continuous Glucose Monitoring System (CGMS) were mean 24-hour glucose, three-hour incremental postprandial glycemia (iAUC), total day-long glycemia (AUC), and 48-hour glycemic variability assessed as Mean Amplitude of Glycemic Excursion (MAGE). Results: Complete CGMS data for 18 participants were obtained. The between-treatment difference in GI was 13 GI units (P<0.01). Between low- and high-GI diets, no difference in three-hour iAUC (mmol/L•min) following breakfast (367 vs. 390, P=0.69), lunch (252 vs. 317, P=0.16) or dinner (216 vs. 263, P=0.32) was observed. No difference in mean 24-hour glucose (6.62 vs. 6.31 mmol/L, P=0.31), total day-long glycemia (8,906 vs. 8,786 mmol/ L•min, P=0.82) or MAGE (3.7 vs. 3.9 mmol/L, P=0.61) were observed between diets. Conclusions: Differences in dietary GI were not predictive of improvement in within-day markers of glycemic control in people with type 2 diabetes. These findings are reflective of ongoing difficulties in translating laboratorygenerated GI values of individual foods to glycemic improvement in the whole of diet setting.

Effects of nateglinide and acarbose on glycemic excursions in standardized carbohydrate and mixed-meal tests in drug-naïve type 2 diabetic patients

The aim of this study was to compare the effects of nateglinide and acarbose on glycemic excursions and postprandial glucose profiles with different types of meals (standardized carbohydrate and mixed meals) in drug-naïve type 2 diabetic patients. A randomized, parallel-group prospective design clinical trial was conducted and a total of 39 drug-naïve patients (16 males and 23 females, aged 56.7±10.2 years) were enrolled. The patients were randomly divided into group A [nateglinide 120 mg three times daily (t.i.d.), n=19] and group B (acarbose 50 mg t.i.d., n=20). The standardized carbohydrate and mixed-meal tests were performed at baseline and at the end of study. Continuous glucose monitoring system (CGMS) data were recorded. Various parameters that measure glucose variability were derived from the CGMS data. In the standardized carbohydrate meal tests, the postprandial glucose excursions (PPGEs) were significantly decreased in the two groups after 12 weeks of treatment (P<0.05), whereas the decrease was more prominent in the acarbose compared to the nateglinide group (P=0.138). In the mixed-meal tests, the mean sensor glucose values [24-h mean blood glucose (MBG)] were significantly decreased in the two groups after 12 weeks of treatment (P<0.05) and the parameters of glucose excursions, including standardized deviation (SD), largest amplitude of glycemic excursion (LAGE), mean of daily differences (MODD) and mean amplitude of glycemic excursion (MAGE), were reduced in the two groups. However, the decreases in SD and LAGE in the nateglinide group were statistically significant, whereas in the acarbose group only the decreases in LAGE were statistically significant. The efficiency of nateglinide or acarbose in lowering postprandial 120-min hyperglycemia were similar in the standardized carbohydrate meal test. However, acarbose was more efficient in lowering postprandial 30- and 60-min hyperglycemia (P<0.05) compared to nateglinide. The fasting and postprandial total cholesterol (TC), triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) had a tendency to decrease from the baseline after 12 weeks of treatment with nateglinide, whereas fasting and postprandial high-density lipoprotein cholesterol (HDL-C) had a tendency to increase. Acarbose did not affect the fasting or postprandial lipid profiles after 12 weeks of treatment (P>0.05). In conclusion, nateglinide and acarbose effectively improved postprandial glycemic control, although acarbose was shown to be more efficient in controlling early (30 and 60 min) postprandial glucose excursions in the carbohydrate meal test, whereas nateglinide was shown to be superior to acarbose in controlling postprandial glucose excursions in the mixed-meal test.