scholarly journals Fluctuation of blood glucose levels in an infant with an ileostomy on continuous glucose monitoring: A case report

2018 ◽  
Vol 27 (1) ◽  
pp. 39-43
Author(s):  
Seiichi Tomotaki ◽  
Tetsuo Naramura ◽  
Junko Hanakawa ◽  
Katsuaki Toyoshima ◽  
Koji Muroya ◽  
...  
Keyword(s):  
Case Report ◽  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Levels ◽  
Blood Glucose Levels

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.

Advantages and Disadvantages of Realtime Continuous Glucose Monitoring in People with Type 2 Diabetes

2012 ◽  
Vol 08 (01) ◽  
pp. 22 ◽  
Author(s):  
M Susan Walker ◽  
Stephanie J Fonda ◽  
Sara Salkind ◽  
Robert A Vigersky ◽  
◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Levels ◽  
Advantages And Disadvantages ◽  
Blood Glucose Levels

Previous research has shown that realtime continuous glucose monitoring (RT-CGM) is a useful clinical and lifestyle aid for people with type 1 diabetes. However, its usefulness and efficacy for people with type 2 diabetes is less known and potentially controversial, given the continuing controversy over the efficacy of self-monitoring of blood glucose (SMBG) in this cohort. This article reviews theextantliterature on RT-CGM for people with type 2 diabetes, and enumerates several of the advantages and disadvantages of this technology from the perspective of providers and patients. Even patients with type 2 diabetes who are not using insulin and/or are relatively well controlled on oral medications have been shown to spend a significant amount of time each day in hyperglycemia. Additional tools beyond SMBG are necessary to enable providers and patients to clearly grasp and manage the frequency and amplitude of glucose excursions in people with type 2 diabetes who are not on insulin. While SMBG is useful for measuring blood glucose levels, patients do not regularly check and SMBG does not enable many to adequately manage blood glucose levels or capture marked and sustained hyperglycemic excursions. RT-CGM systems, valuable diabetes management tools for people with type 1 diabetes or insulin-treated type 2 diabetes, have recently been used in type 2 diabetes patients. Theextantstudies, although few, have demonstrated that the use of RT-CGM has empowered people with type 2 diabetes to improve their glycemic control by making and sustaining healthy lifestyle choices.

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 Continuous glucose monitoring in obese pregnant women with no hyperglycemia on glucose tolerance test

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253047
Author(s):  
Rosa Maria Rahmi ◽  
Priscila de Oliveira ◽  
Luciano Selistre ◽  
Paulo Cury Rezende ◽  
Gabriela Neuvald Pezzella ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glucose Tolerance ◽  
Pregnant Women ◽  
Continuous Glucose Monitoring ◽  
Glucose Tolerance Test ◽  
Glucose Monitoring ◽  
Tolerance Test ◽  
Oral Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels

Objective The objective of the present study was to compare 24-hour glycemic levels between obese pregnant women with normal glucose tolerance and non-obese pregnant women. Methods In the present observational, longitudinal study, continuous glucose monitoring was performed in obese pregnant women with normal oral glucose tolerance test with 75 g of glucose between the 24th and the 28th gestational weeks. The control group (CG) consisted of pregnant women with normal weight who were selected by matching the maternal age and parity with the same characteristics of the obese group (OG). Glucose measurements were obtained during 72 hours. Results Both the groups were balanced in terms of baseline characteristics (age: 33.5 [28.7–36.0] vs. 32.0 [26.0–34.5] years, p = 0.5 and length of pregnancy: 25.0 [24.0–25.0] vs. 25.5 [24.0–28.0] weeks, p = 0.6 in the CG and in the OG, respectively). Pre-breakfast glycemic levels were 77.77 ± 10.55 mg/dL in the CG and 82.02 ± 11.06 mg/dL in the OG (p<0.01). Glycemic levels at 2 hours after breakfast were 87.31 ± 13.10 mg/dL in the CG and 93.48 ± 18.74 mg/dL in the OG (p<0.001). Daytime blood glucose levels were 87.6 ± 15.4 vs. 93.1 ± 18.3 mg/dL (p<0.001) and nighttime blood glucose levels were 79.3 ± 15.8 vs. 84.7 ± 16.3 mg/dL (p<0.001) in the CG and in the OG, respectively. The 24-hour, daytime, and nighttime values of the area under the curve were higher in the OG when compared with the CG (85.1 ± 0.16 vs. 87.9 ± 0.12, 65.6 ± 0.14 vs. 67.5 ± 0.10, 19.5 ± 0.07 vs. 20.4 ± 0.05, respectively; p<0.001). Conclusion The results of the present study showed that obesity in pregnancy was associated with higher glycemic levels even in the presence of normal findings on glucose tolerance test.

scholarly journals A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6820
Author(s):  
Bushra Alsunaidi ◽  
Murad Althobaiti ◽  
Mahbubunnabi Tamal ◽  
Waleed Albaker ◽  
Ibraheem Al-Naib
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Daily Basis ◽  
Glucose Sensing ◽  
Blood Glucose Monitoring ◽  
Optical Systems ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Modern technologies in diabetes management - continuous glucose monitoring and insulin pump therapy

2021 ◽  
Vol 87 (1) ◽  
pp. 50-55
Author(s):  
E.Yu. Pyankova ◽  
◽  
L.A. Anshakova ◽  
I.A. Pyankov ◽  
S.V. Yegorova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Insulin Pump ◽  
Glucose Monitoring ◽  
Insulin Pump Therapy ◽  
Pump Therapy ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
The Individual ◽  
Glucose Lowering Therapy

The problems of complications of diabetes mellitus cannot be solved without constant monitoring of blood glucose levels. The evolution of additional technologies for the determination of glucose in the blood of the last decades makes it possible to more accurately predict the risks of complications, both in the individual and in the patient population as a whole. The article provides an overview of the methods used in modern diabetology, facilitating control over the variability of blood glucose levels and helping in a more accurate selection of glucose-lowering therapy. All presented methods are currently working in real clinical practice in the Khabarovsk Krai

Online Prediction of Blood Glucose Levels using Genetic Algorithm

2014 ◽  
pp. 299-310
Author(s):  
Khaled Eskaf ◽  
Tim Ritchings ◽  
Osama Bedawy
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Simulation Software ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Learning Techniques ◽  
Preliminary Study ◽  
Online Prediction

Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years: from 115 million in 2000 to 284 million in 2030. This chapter is concerned with helping diabetic patients to manage themselves by developing a computer system that predicts their Blood Glucose Level (BGL) after 30 minutes on the basis of their current levels, so that they can administer insulin. This will enable the diabetic patient to continue living a normal daily life, as much as is possible. The prediction of BGLs based on the current levels BGLs become feasible through the advent of Continuous Glucose Monitoring (CGM) systems, which are able to sample patients' BGLs, typically 5 minutes, and computer systems that can process and analyse these samples. The approach taken in this chapter uses machine-learning techniques, specifically Genetic Algorithms (GA), to learn BGL patterns over an hour and the resulting value 30 minutes later, without questioning the patients about their food intake and activities. The GAs were invested using the raw BGLs as input and metadata derived from a Diabetic Dynamic Model of BGLs supplemented by the changes in patients' BGLs over the previous hour. The results obtained in a preliminary study including 4 virtual patients taken from the AIDA diabetes simulation software and 3 volunteers using the DexCom SEVEN system, show that the metadata approach gives more accurate predictions. Online learning, whereby new BGL patterns were incorporated into the prediction system as they were encountered, improved the results further.

scholarly journals A Novel Five-Microlitre Capillary System for Home Glucose Monitoring

1981 ◽  
Vol 18 (5) ◽  
pp. 304-307 ◽  
Author(s):  
C P Williams ◽  
G K Davies ◽  
D F Child
Keyword(s):  
Blood Glucose ◽  
Capillary Tube ◽  
Glucose Monitoring ◽  
Diabetic Patients ◽  
Collection System ◽  
Patient Acceptability ◽  
Glucose Levels ◽  
Capillary System ◽  
Blood Glucose Levels ◽  
High Degree

Improvement in the control of diabetic patients is aided by a knowledge of blood glucose levels during a ‘normal’ (non-hospitalised) day. We have devised a 5 μl capillary tube collection system as a ‘kit’ for home use by diabetics. Blood collected into 5 μl capillary tubes is washed into a protein precipitant by the patient. The completed kit is posted to the laboratory for analysis. The technique has achieved a high degree of patient acceptability. Subsequent analysis involves the addition of a single reagent. Reagents, patient samples, and standards are stable, and the precision of the technique compares favourably with our routine glucose procedure.

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