scholarly journals CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Anne-Esther Breyton ◽  
Stéphanie Lambert-Porcheron ◽  
Martine Laville ◽  
Sophie Vinoy ◽  
Julie-Anne Nazare
Keyword(s):  
Physical Activity ◽  
Glycemic Control ◽  
Glucose Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Mean Amplitude ◽  
Control Parameters ◽  
Postprandial Glycemia ◽  
The Mean ◽  
Mean Blood Glucose

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters’ (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

scholarly journals Liraglutide as additional treatment for type 1 diabetes

2011 ◽  
Vol 165 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Ajay Varanasi ◽  
Natalie Bellini ◽  
Deepti Rawal ◽  
Mehul Vora ◽  
Antoine Makdissi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Additional Treatment ◽  
The Body ◽  
The Mean ◽  
Bolus Insulin

ObjectiveTo determine whether the addition of liraglutide to insulin to treat patients with type 1 diabetes leads to an improvement in glycemic control and diminish glycemic variability.Subjects and methodsIn this study, 14 patients with well-controlled type 1 diabetes on continuous glucose monitoring and intensive insulin therapy were treated with liraglutide for 1 week. Of the 14 patients, eight continued therapy for 24 weeks.ResultsIn all the 14 patients, mean fasting and mean weekly glucose concentrations significantly decreased after 1 week from 130±10 to 110±8 mg/dl (P<0.01) and from 137.5±20 to 115±12 mg/dl (P<0.01) respectively. Glycemic excursions significantly improved at 1 week. The mean s.d. of glucose concentrations decreased from 56±10 to 26±6 mg/dl (P<0.01) and the coefficient of variation decreased from 39.6±10 to 22.6±7 (P<0.01). There was a concomitant fall in the basal insulin from 24.5±6 to 16.5±6 units (P<0.01) and bolus insulin from 22.5±4 to 15.5±4 units (P<0.01).In patients who continued therapy with liraglutide for 24 weeks, mean fasting, mean weekly glucose concentrations, glycemic excursions, and basal and bolus insulin dose also significantly decreased (P<0.01). HbA1c decreased significantly at 24 weeks from 6.5 to 6.1% (P=0.02), as did the body weight by 4.5±1.5 kg (P=0.02).ConclusionLiraglutide treatment provides an additional strategy for improving glycemic control in type 1 diabetes. It also leads to weight loss.

scholarly journals Glycemic Variability Impacted by SGLT2 Inhibitors and GLP 1 Agonists in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 10 (18) ◽  
pp. 4078
Author(s):  
Heeyoung Lee ◽  
Se-eun Park ◽  
Eun-Young Kim
Keyword(s):  
Meta Analysis ◽  
Glycemic Variability ◽  
Mean Amplitude ◽  
Randomized Controlled ◽  
Sodium Glucose Cotransporter ◽  
Patients With Diabetes ◽  
The Mean ◽  
Glucagon Like Peptide ◽  
Glucagon Like Peptide 1 ◽  
Mean Blood Glucose

To investigate the effect of sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) agonists on glycemic variability (GV), the mean amplitude of glucose excursion (MAGE), mean blood glucose (MBG) levels, and percentage of time maintaining euglycemia were evaluated. Randomized controlled trials evaluating the efficacy of SGLT-2 inhibitors and GLP-1 agonists for treating people with diabetes were selected through searches of PubMed, EMBASE, and other databases. Sixteen studies were finally analyzed. There were no differences in the reductions in MAGE after treatment with SGLT-2 inhibitors or GLP-1 agonists (standardized mean difference (SMD) = −0.59, 95% CI = −0.82 to −0.36 vs. SMD = −0.43, 95% CI = −0.51 to −0.35, respectively), and treatment with SGLT-2 inhibitors was associated with an increased reduction in MBG levels (SMD = −0.56, 95% CI = −0.65 to −0.48, p < 0.00001). Monotherapy and add-on therapy with medications were correlated with MAGE and MBG level reductions. In conclusion, SGLT-2 inhibitors and GLP-1 agonists were associated with a reduction in GV and could be alternatives for treating people with diabetes.

scholarly journals Improved Glycemic Control and Variability: Application of Healthy Ingredients in Asian Staples

Nutrients ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 3102
Author(s):  
Stefan Gerardus Camps ◽  
Bhupinder Kaur ◽  
Joseph Lim ◽  
Yi Ting Loo ◽  
Eunice Pang ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Glycemic Load ◽  
Glucose Monitoring ◽  
Area Under The Curve ◽  
Glycemic Variability ◽  
Mean Amplitude ◽  
Carbohydrate Intake ◽  
Functional Ingredients ◽  
Glucose Levels ◽  
Dietary Strategy

A reduction in carbohydrate intake and low-carbohydrate diets are often advocated to prevent and manage diabetes. However, limiting or eliminating carbohydrates may not be a long-term sustainable and maintainable approach for everyone. Alternatively, diet strategies to modulate glycemia can focus on the glycemic index (GI) of foods and glycemic load (GL) of meals. To assess the effect of a reduction in glycemic load of a 24 h diet by incorporating innovative functional ingredients (β-glucan, isomaltulose) and alternative low GI Asian staples (noodles, rice)on glycemic control and variability, twelve Chinese men (Age: 27.0 ± 5.1 years; BMI:21.6 ± 1.8kg/m2) followed two isocaloric, typically Asian, 24h diets with either a reduced glycemic load (LGL) or high glycemic load (HGL) in a randomized, single-blind, controlled, cross-over design. Test meals included breakfast, lunch, snack and dinner and the daily GL was reduced by 37% in the LGL diet. Continuous glucose monitoring provided 24 h glycemic excursion and variability parameters: incremental area under the curve (iAUC), max glucose concentration (Max), max glucose range, glucose standard deviation (SD), and mean amplitude of glycemic excursion (MAGE), time in range (TIR). Over 24h, the LGL diet resulted in a decrease in glucose Max (8.12 vs. 6.90 mmol/L; p = 0.0024), glucose range (3.78 vs. 2.21 mmol/L; p = 0.0005), glucose SD (0.78 vs. 0.43 mmol/L; p = 0.0002), mean amplitude of glycemic excursion (2.109 vs. 1.008; p < 0.0001), and increase in 4.5–6.5mmol/L TIR (82.2 vs. 94.6%; p = 0.009), compared to the HGL diet. The glucose iAUC, MAX, range and SD improved during the 2 h post-prandial window of each LGL meal, and this effect was more pronounced later in the day. The current results validate the dietary strategy of incorporating innovative functional ingredients (β-glucan, isomaltulose) and replacing Asian staples with alternative low GI carbohydrate sources to reduce daily glycemic load to improve glycemic control and variability as a viable alternative to the reduction in carbohydrate intake alone. These observations provide substantial public health support to encourage the consumption of staples of low GI/GL to reduce glucose levels and glycemic variability. Furthermore, there is growing evidence that the role of chrononutrition, as reported in this paper, requires further examination and should be considered as an important addition to the understanding of glucose homeostasis variation throughout the day.

Association between glycemic control, age, and outcomes among intensively treated patients with acute myeloid leukemia (AML).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 10043-10043
Author(s):  
Patrick Kuhlman ◽  
Scott Isom ◽  
Timothy S. Pardee ◽  
Cynthia Burns ◽  
Bernard Tawfik ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Multivariable Analysis ◽  
Glycemic Variability ◽  
Lower Odds ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Models ◽  
Modifiable Factor ◽  
Comorbidity Index ◽  
The Mean ◽  
Mean Blood Glucose

10043 Background: Hyperglycemia and increased glycemic variability are associated with infection and increased mortality. We evaluated the relationship between glycemic control during AML induction and outcomes by age. Methods: We retrospectively evaluated outcomes in 262 consecutive patients (pts) with newly diagnosed AML hospitalized for intensive induction at Wake Forest Baptist Hospital (2002-2009). Data on mean blood glucose (BG) (mg/dL) during hospitalization and standard deviation (SD) of BG (measure of glycemic variability, GV), complete remission ± incomplete count recovery (CR+CRi), and overall survival (OS) were collected. Modified Charlson Comorbidity Index (CCI), diabetes, age, gender, race, cytogenetics, hemoglobin, WBC, LDH, body mass index, and insurance were used in uni- and multi-variate models. We used logistic regression to evaluate CR+CRi, and Cox proportional hazard models for OS, stratified by age ( < 60 vs ≥60 yrs). Results: 124 pts were < 60 (median age 47, median OS 23.1 months), 138 were ≥60 yrs (median age 70, median OS 7.9 months). Older pts had higher baseline comorbidity (CCI > 1 60.1% vs 25.8%) and a higher prevalence of diabetes (20.3% vs 7.3%). The mean ±SD number of BG values obtained per patient during hospitalization was 61±71. The mean ±SD of each individual’s mean BG during hospitalization was 111.6±16.4 in younger versus 121.7±25.9 older pts. The mean SD of BG values [GV] was 26.8±18.6 in younger versus 33±22.8 in older pts. In multivariable analysis higher mean BG was associated with lower odds of CR+CRi in younger (odds ratio (OR) 0.67, 95% CI 0.48-0.93) and older pts (OR 0.78, 95% CI 0.65-0.93) per 10 mg/dL BG increase. Higher mean BG was associated with shorter OS in older adults (HR 1.12, 95% CI 1.04-1.21). Higher GV was associated with lower odds of CR+CRi in younger (OR 0.73, 95% CI 0.56-0.96) and older (OR 0.71, 95% CI 0.57-0.88), as well as shorter OS in older pts (HR 1.17, 95% CI 1.08-1.26) for each 10 mg/dL SD increase in GV. Conclusions: Hyperglycemia and GV during intensive induction are associated with lower CR+CRi rates (all ages) and shorter OS among older adults.Glycemic control during induction may be a modifiable factor to improve AML outcomes.

scholarly journals Continuous glucose monitoring in polycystic ovary syndrome: what interest?

2020 ◽  
Author(s):  
ines kammoun ◽  
Wafa Ben Saada ◽  
Hajer Kandara ◽  
Radhouane Gharbi ◽  
Rania Ben Said ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Polycystic Ovary Syndrome ◽  
Carbohydrate Metabolism ◽  
Continuous Glucose Monitoring ◽  
Polycystic Ovary ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Ovary Syndrome ◽  
The Mean ◽  
Mean Blood Glucose

Abstract Purpose The aim of our study was to detect subclinical abnormalities in carbohydrate metabolism in patients with polycystic ovary syndrome. Methods Cross-sectional study including 20 patients with PCOS diagnosed according to 5- the Rotterdam criteria. All the patients had normal carbohydrate tolerance (fasting blood 6- glucose<5.6 mmol/l, 2-h plasma glucose after a 75-g oral glucose tolerance test<7.8 mmol/l andglycated hemoglobin <5.8%). For each patient, we performed a continuous glucose monitoring over 72h, measuring the interstitial glucose every 5 minutes (288 measurements per day). We collected data about: the mean blood glucose, obtained by determining the mean values of the 288 measurements made by 24h - the mean amplitude of glycemic excursions, which is the difference between the maximum and minimum glycemic values - the time (in hours) in which the blood glucose was <0.7 g/l and / or>1.4 g/l. Results The mean blood glucose (over 72h) was 0.94±0.07 g/l (0.81-1.11).The mean amplitude of glycemic excursions (over 72h) was 0.81 ± 0.23 g/l (0.47-1.31).Fourteen patients (pathologic group) had subclinical glycemic abnormalities: 14 patients had glycemic values<0.7 g/l and 5 patients had also glycemic values>1.4 g/l. The mean amplitude of glycemic excursions was significantly lower (p=0.016) in the normal group (6 patients, 0.64 g/l) compared to the pathologic group(14 patients, 0.88 g/l).The other clinical and biological parameters were comparable between the two groups. Conclusions Our findings confirm the high frequency of subclinical abnormalities of carbohydrate metabolism in patients with polycystic ovary syndrome. A regular follow-up of these patients is necessary.

scholarly journals Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study (Preprint)

2020 ◽  
Author(s):  
Martina Parise ◽  
Linda Tartaglione ◽  
Antonio Cutruzzolà ◽  
Maria Ida Maiorino ◽  
Katherine Esposito ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Disease Management ◽  
Health Care Providers ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Care Providers ◽  
Virtual Visits

BACKGROUND Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. OBJECTIVE During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. METHODS This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. RESULTS Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; <i>P</i>&lt;.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (<i>P</i>=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; <i>P</i>=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; <i>P</i>&lt;.001) than in those with a GMI of &lt;7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; <i>P</i>=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (<i>P</i>=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. CONCLUSIONS These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.

scholarly journals Smoking is Associated With Increased Risk of Not Achieving Glycemic Target, Increased Glycemic Variability, and Increased Risk of Hypoglycemia for People With Type 1 Diabetes

2020 ◽  
pp. 193229682092225
Author(s):  
Morten Hasselstrøm Jensen ◽  
Simon Lebech Cichosz ◽  
Irl B. Hirsch ◽  
Peter Vestergaard ◽  
Ole Hejlesen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Linear Models ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Treatment Intensification ◽  
Increased Risk ◽  
Glycemic Target ◽  
Inadequate Glycemic Control

Background: The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes. Methods: Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions. Results: A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers. Conclusions: In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.

scholarly journals Flash glucose monitoring helps achieve better glycemic control than conventional self-monitoring of blood glucose in non-insulin-treated type 2 diabetes: a randomized controlled trial

2020 ◽  
Vol 8 (1) ◽  
pp. e001115 ◽  
Author(s):  
Eri Wada ◽  
Takeshi Onoue ◽  
Tomoko Kobayashi ◽  
Tomoko Handa ◽  
Ayaka Hayase ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Analysis Of Covariance ◽  
Self Monitoring ◽  
Flash Glucose Monitoring ◽  
Baseline Values ◽  
The Mean

IntroductionThe present study aimed to evaluate the effects of flash glucose monitoring (FGM) and conventional self-monitoring of blood glucose (SMBG) on glycemic control in patients with non-insulin-treated type 2 diabetes.Research design and methodsIn this 24-week, multicenter, open-label, randomized (1:1), parallel-group study, patients with non-insulin-treated type 2 diabetes at five hospitals in Japan were randomly assigned to the FGM (n=49) or SMBG (n=51) groups and were provided each device for 12 weeks. The primary outcome was change in glycated hemoglobin (HbA1c) level, and was compared using analysis of covariance model that included baseline values and group as covariates.ResultsForty-eight participants in the FGM group and 45 in the SMBG group completed the study. The mean HbA1c levels were 7.83% (62.1 mmol/mol) in the FGM group and 7.84% (62.2 mmol/mol) in the SMBG group at baseline, and the values were reduced in both FGM (−0.43% (−4.7 mmol/mol), p<0.001) and SMBG groups (−0.30% (−3.3 mmol/mol), p=0.001) at 12 weeks. On the other hand, HbA1c was significantly decreased from baseline values in the FGM group, but not in the SMBG group at 24 weeks (FGM: −0.46% (−5.0 mmol/mol), p<0.001; SMBG: −0.17% (−1.8 mmol/mol), p=0.124); a significant between-group difference was also observed (difference −0.29% (−3.2 mmol/mol), p=0.022). Diabetes Treatment Satisfaction Questionnaire score was significantly improved, and the mean glucose levels, SD of glucose, mean amplitude of glycemic excursions and time in hyperglycemia were significantly decreased in the FGM group compared with the SMBG group.ConclusionsGlycemic control was better with FGM than with SMBG after cessation of glucose monitoring in patients with non-insulin-treated type 2 diabetes.Trial registration numberUMIN000026452, jRCTs041180082.

scholarly journals Flash Glucose Monitoring: A Review of the Literature with a Special Focus on Type 1 Diabetes

Nutrients ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 992 ◽  
Author(s):  
Giulia Mancini ◽  
Maria Berioli ◽  
Elisa Santi ◽  
Francesco Rogari ◽  
Giada Toni ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Health Care ◽  
Glycemic Control ◽  
Health Care Professionals ◽  
Glucose Monitoring ◽  
Special Focus ◽  
Flash Glucose Monitoring

In people with type 1 diabetes mellitus (T1DM), obtaining good glycemic control is essential to reduce the risk of acute and chronic complications. Frequent glucose monitoring allows the adjustment of insulin therapy to improve metabolic control with near-normal blood glucose concentrations. The recent development of innovative technological devices for the management of T1DM provides new opportunities for patients and health care professionals to improve glycemic control and quality of life. Currently, in addition to traditional self-monitoring of blood glucose (SMBG) through a glucometer, there are new strategies to measure glucose levels, including the detection of interstitial glucose through Continuous Glucose Monitoring (iCGM) or Flash Glucose Monitoring (FGM). In this review, we analyze current evidence on the efficacy and safety of FGM, with a special focus on T1DM. FGM is an effective tool with great potential for the management of T1DM both in the pediatric and adult population that can help patients to improve metabolic control and quality of life. Although FGM might not be included in the development of an artificial pancreas and some models of iCGM are more accurate than FGM and preferable in some specific situations, FGM represents a cheaper and valid alternative for selected patients. In fact, FGM provides significantly more data than the intermittent results obtained by SMBG, which may not capture intervals of extreme variability or nocturnal events. With the help of a log related to insulin doses, meal intake, physical activity and stress factors, people can achieve the full benefits of FGM and work together with health care professionals to act upon the information provided by the sensor. The graphs and trends available with FGM better allow an understanding of how different factors (e.g., physical activity, diet) impact glycemic control, consequently motivating patients to take charge of their health.

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