scholarly journals Time in range is a tool for assessing the quality of glycemic control in diabetes

2021 ◽  
Vol 24 (3) ◽  
pp. 282-290
Author(s):  
L. A. Suplotova ◽  
A. S. Sudnitsyna ◽  
N. V. Romanova ◽  
M. V. Shestakova
Keyword(s):  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Macrovascular Complications ◽  
International Consensus ◽  
Glucose Levels ◽  
Target Values ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Diagnostic Capabilities

The presence of continuous glucose monitoring (CGM) systems has expanded diagnostic capabilities. The implementation of this technology into clinical practice allowed to determine the patterns and tendencies of excursions in glucose levels, to obtain reliable data concerning short-term glycemic control. Taking into consideration the large amount of obtained information using CGM systems, more than 30 different indicators characterizing glycemic variability were proposed. However, it is very difficult for a practitioner to interpret the data obtained due to the variety of indicators and the lack of their target values. The first step in the standardization of indices was the creation of the International Guidelines for CGM in 2017, where the Time in Range (TIR) (3,9–10,0 mmol/l, less often 3,9–7,8 mmol/l) was significant. To complement the agreed parameters and simplify the interpretation of obtained data using CGM, in 2019 the recommendations were prepared for the International Consensus on Time in Range, where TIR was validated as an additional component of the assessment of glycemic control along with HbA1c. In the literature review the issues of the association of TIR with the development of micro- and macrovascular complications in type 1 and 2 diabetes are considered. The relationship with other indicators of the glycemic control assessment was also analyzed and the dependence of insulin therapy on TIR was shown. TIR is a simple and convenient indicator, it has a proven link with micro- and macrovascular complications of diabetes and can be recommended as a new tool for assessing the glycemic control. The main disadvantage of TIR usage is the insufficient apply of CGM technology by the majority of patients with diabetes.

scholarly journals Glycemic variability and cardiovascular disease in patients with type 2 diabetes

2021 ◽  
Vol 9 (1) ◽  
pp. e002032
Author(s):  
Marcela Martinez ◽  
Jimena Santamarina ◽  
Adrian Pavesi ◽  
Carla Musso ◽  
Guillermo E Umpierrez
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Cardiovascular Complications ◽  
Glucose Levels ◽  
Increased Risk ◽  
Patients With Diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

scholarly journals The Association Between Continuous Glucose Monitoring-derived Metrics, Including Time in Range, and Cardiovascular Autonomic Neuropathy in Outpatients With Type 2 Diabetes

2020 ◽  
Author(s):  
Min Young Kim ◽  
Gyuri Kim ◽  
Ji Yun Park ◽  
Min Sun Choi ◽  
Ji Eun Jun ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Autonomic Neuropathy ◽  
Autonomic Function ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Cardiovascular Autonomic Neuropathy ◽  
Inverse Association ◽  
International Consensus ◽  
Time In Range

Abstract BackgroundContinuous glucose monitoring (CGM)-derived metrics including time in range (TIR) are attracting attention as new indicators of glycemic control and diabetes complications beyond hemoglobin A1c. This study investigated the association between CGM-derived TIR, hyperglycemia, hypoglycemia metrics, and cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes.MethodsA total of 284 patients with type 2 diabetes who underwent CGM for three days and autonomic function tests within three months based on outpatient data were recruited. The definition of CGM-derived metrics was subject to the most recent international consensus. CAN was defined as an abnormal case in two or more parasympathetic and the severity of CAN was estimated as the sum of the scores of the five cardiovascular autonomic function tests.ResultsMultiple logistic regression analysis revealed that the odds ratio of definite CAN was 0.876 [95% confidence interval (CI): 0.79–0.98] per 10% increase in the TIR of 70 to 180 mg/dL, after adjusting for age, sex, diabetes duration, any medications, and glycemic variability. A 10% increase in TIR was significantly inversely associated with the presence of advanced CAN (OR: 0.89, 95% CI: 0.81–0.98). In addition, there was a strong inverse association between a 10% increase in the TIR and the total CAN score (p for trend = 0.001). Among the metrics of hyperglycemia, a time above range (TAR) of greater than 180 mg/dL was also independently correlated with the presence of definite CAN (OR: 1.013, 95% CI: 1.00–1.02) and advanced CAN (OR: 1.01, 95% CI: 1.00–1.02).ConclusionsA TIR value of 70 to 180 mg/dL and a TAR value of greater than 180 mg/dL were significantly associated with cardiovascular autonomic neuropathy in outpatients with type 2 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.

scholarly journals Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jen-Hung Huang ◽  
Yung-Kuo Lin ◽  
Ting-Wei Lee ◽  
Han-Wen Liu ◽  
Yu-Mei Chien ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Before And After ◽  
Patients With Diabetes

Abstract Background Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM. Methods This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1–3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM. Results The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2–3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1–2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1–3 months before and 1–2 months after CGM. Conclusions CGM can predict HbA1c values within 1 month after CGM in patients with DM.

scholarly journals Correlation Between Short- and Mid-Term Hemoglobin A1c and Glycemic Control Determined by Continuous Glucose Monitoring

2021 ◽  
Author(s):  
Jen-Hung Huang ◽  
Yung-Kuo Lin ◽  
Ting-Wei Lee ◽  
Han-Wen Liu ◽  
Yu-Mei Chien ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Before And After ◽  
Patients With Diabetes

Abstract Background: Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM. Methods: This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1-3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM. Results: The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2-3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1-2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1-3 months before and 1-2 months after CGM.Conclusions: CGM can predict HbA1c values within 1 month after CGM in patients with DM.

scholarly journals Analysis of time in range depending on the method of assessing the glucose level

2021 ◽  
pp. 46-55
Author(s):  
L. A. Suplotova ◽  
A. S. Sudnitsyna ◽  
N. V. Romanova ◽  
K. A. Sidorenko ◽  
L. U. Radionova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Level ◽  
Glucose Monitoring ◽  
Diabetes Type ◽  
Diabetes Type 1 ◽  
Self Monitoring ◽  
Patients With Diabetes ◽  
Time In Range

Introduction. In recent years, there has been an increase in the prevalence and incidence diabetes type 1. The high-quality glycemic control is critical in reducing the risk of developing and progression of vascular complications and adverse outcomes of diabetes. Self-monitoring blood glucose (SMBG) and professional continuous glucose monitoring (PCGM) provide the data set which must be interpreted using multiple indicators of glycemic control. A number of researchers have demonstrated the relationship between the time in range (TIR) and the risk of developing both micro- and macrovascular complications of diabetes. Considering the insufficient amount of data on TIR differences depending on the glucose level assessment method and the significant potential of using this indicator for the stratification of the risk of both micro- and macrovascular complications of diabetes, the study of TIR differences based on the data of PCGM and SMBG is relevant at present.Aims. To estimate the time range according to professional continuous glucose monitoring and self-monitoring of blood glucose levels in the patients with diabetes type 1 among the adult population to improve the control of the disease course.Materials and methods. An interventional open-label multicenter study in the patients with diabetes type 1 was conducted. The patients with diabetes type 1 aged 18 and older, with the disease duration of more than 1 year receiving the therapy with analog insulin was enrolled into the study. The calculation of the indicators of the time spent in the ranges of glycemia was carried out on the basis of the data of PCGM and SMBG.Results and discussion. We examined 218 patients who met the inclusion criteria and did not have exclusion criteria. The presented differences in the indicators of time in ranges indicate the comparability of the SMBG and PCGM methods.Conclusions. When assessing the indicators of time in the ranges of glycemia obtained on the basis of the data of PCGM and SMBG, clear correlations and linear dependence were demonstrated, which indicates the comparability of these parameters regardless of the measurement method.

scholarly journals The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring

2020 ◽  
Author(s):  
Pamela R. Kushner ◽  
Davida F. Kruger
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Treatment Decisions ◽  
Diabetes Treatment ◽  
Good Glycemic Control ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Glycemic Targets

Continuous glucose monitoring (CGM) provides comprehensive assessment of daily glucose measurements for patients with diabetes and can reveal high and low blood glucose values that may occur even when a patient’s A1C is adequately controlled. Among the measures captured by CGM, the percentage of time in the target glycemic range, or “time in range,” (typically 70–180 mg/dL) has emerged as one of the strongest indicators of good glycemic control. This review examines the shift to using CGM to assess glycemic control and guide diabetes treatment decisions, with a focus on time in range as the key metric of glycemic control.

scholarly journals The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring

2020 ◽  
Author(s):  
Pamela R. Kushner ◽  
Davida F. Kruger
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Treatment Decisions ◽  
Diabetes Treatment ◽  
Good Glycemic Control ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Glycemic Targets

Continuous glucose monitoring (CGM) provides comprehensive assessment of daily glucose measurements for patients with diabetes and can reveal high and low blood glucose values that may occur even when a patient’s A1C is adequately controlled. Among the measures captured by CGM, the percentage of time in the target glycemic range, or “time in range,” (typically 70–180 mg/dL) has emerged as one of the strongest indicators of good glycemic control. This review examines the shift to using CGM to assess glycemic control and guide diabetes treatment decisions, with a focus on time in range as the key metric of glycemic control.

scholarly journals Evaluating the Infuence of Mood and Stress on Glycemic Variability in T1DM Patients using Glucose Monitoring Sensors and Pools

2020 ◽  
Author(s):  
Sergio Contador Pachón ◽  
Marta Botella Serrano ◽  
Aranzazu Aramendi Zurimendi ◽  
Remedios Rodríguez Martínez ◽  
Esther Maqueda Villaizán ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
P Value ◽  
Glucose Levels ◽  
Blood Glucose Levels

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.

scholarly journals Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

2021 ◽  
Author(s):  
Yun Shen ◽  
Xiaohong Fan ◽  
Lei Zhang ◽  
Yaxin Wang ◽  
Cheng Li ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Adverse Outcomes ◽  
Glucose Levels ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Sensor Glucose ◽  
Elevated Glucose ◽  
Time In Range ◽  
Design And Methods

<i>Objective: </i>Although elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19. <p><i>Research design and methods:</i> Glucose levels were assessed via intermittently scanned continuous glucose monitoring in 35 patients with an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to intensive care unit, need for mechanic ventilation, or morbidity with critical illness. </p> <p><i>Results:</i> TARs with the threshold from 160 mg/dL - 200 mg/dL were all significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR of 70 mg/dL - 160 mg/dL, but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across its tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively. </p> <p><i>Conclusions:</i> Patients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels over 160 mg/dL, below 70 mg/dL, and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.</p>

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