scholarly journals Insulin-Related Lipohypertrophy: Ultrasound Characteristics, Risk factors, and Impact of Glucose Fluctuations

2021 ◽  
Author(s):  
Yiyang Lin ◽  
Wei Wang ◽  
Junfeng Hong ◽  
Hua Zeng
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Insulin Injection ◽  
Postprandial Glucose ◽  
General Information ◽  
High Rate ◽  
Detection Methods ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
Glucose Fluctuations

Abstract BackgroundLipohypertrophy (LHT) has been suggested as an outcome of adipogenic effects of insulin injection-related tissue trauma. It is common clinically, but the current understanding of LHT by medical staff and diabetic patients is still insufficient, and it has not attracted attention as a research topic.ObjectiveThe aim of this study was to investigate the ultrasound characterization of LHT, to identify factors associated with the development of LHT by assessing the prevalence of LHT compared to both clinical palpation and ultrasound detection methods, and to further evaluate the possible impact of LHT on patients' blood glucose fluctuations.MethodA cross-sectional study was established, in which 120 patients with type 2 diabetes were selected. General information was registered in the form of a questionnaire, and the patients were evaluated for LHT by ultrasonography and clinical palpation of the abdomen. Patients were instructed to inject equal amounts of insulin in LHT and normal adipose tissue (NAT) on a non-consecutive 2 d in a selected week, and the possible effect of LHT on patients' blood glucose fluctuations was assessed using a continuous glucose monitoring system. .ResultsLHT has special ultrasonic signs. We found a high rate of missed clinical palpation of LHT compared with ultrasonography (P < 0.05). The duration of insulin treatment, whether to rotate the injection site, frequency of needle use, and number of insulin injections per day were the main factors influencing the development of LHT (P < 0.05). Compared to NAT, LHT resulted in elevated largest amplitude of glycemic excursion, mean blood glucose, standard deviation of blood glucose, and postprandial glucose excursion, and large fluctuations in blood glucose (P < 0.05).ConclusionUltrasonography can detect more LHT than can clinical palpation. The development of LHT is related to many factors and can lead to significant blood glucose fluctuations; thus, LHT should be given sufficient attention.

scholarly journals Blood Glucose Monitoring and Its Determinants in Diabetic Patients: A Cross-Sectional Study in Shandong, China

2018 ◽  
Vol 9 (5) ◽  
pp. 2055-2066 ◽  
Author(s):  
Abdul Marouf Raoufi ◽  
Xue Tang ◽  
Zhengyue Jing ◽  
Xinyi Zhang ◽  
Qiongqiong Xu ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Cross Sectional Study ◽  
Blood Glucose Monitoring ◽  
Diabetic Patients ◽  
Sectional Study ◽  
Cross Sectional

scholarly journals Brief communication (Original). Correlation of glycated albumin with self blood glucose monitoring in diabetic patients on hemodialysis taking erythropoietin

2014 ◽  
Vol 8 (3) ◽  
pp. 387-392
Author(s):  
Khalidah M. Bador ◽  
Sharifah K.A. Kamaruddin ◽  
Norita T. Yazid
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Bromocresol Green ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
Albumin Ratio ◽  
Significant Difference

AbstractBackground: Serum glycated albumin (GA) is a marker of glycemic control in diabetic renal patients, but studies were limited by the use of few random glucose values to define glycemic control.Objectives: To determine whether GA correlated with self blood glucose monitoring is better than HbA1c in hemodialyzed diabetic patients taking erythropoietin.Methods: This was a cross-sectional study of diabetic patients on hemodialysis with and without erythropoietin. GA was measured by ELISA and HbA1c by ion-exchange HPLC. GA was reported as the GA/albumin ratio where albumin was measured using bromocresol green. The average capillary blood glucose level over the preceding three months (CBG) was calculated from self-reported daily prebreakfast, prelunch, and prebed glucose meter readings.Results: Thirty-four patients were recruited; 18 were taking erythropoietin (6000 units per week) and 16 had never received erythropoietin. HbA1c correlated poorly with CBG in patients taking erythropoietin (r = -0.014, P = 0.96) compared with patients without erythropoietin (r = 0.579, P = 0.02). The correlation of GA/albumin ratio with CBG in the erythropoietin group (r = 0.612, P = 0.007) was similar to the nonerythropoietin group (r = 0.854, P < 0.001). The slope for HbA1c versus CBG was 2.8-fold greater in patients without erythropoietin compared with those taking erythropoietin. There was no significant difference in the slopes for GA/albumin ratio versus CBG between the two patient groups (P > 0.05).Conclusion: In diabetic patients on hemodialysis and taking low doses of erythropoietin, GA/albumin is a better marker of glycemic control than HbA1c.

scholarly journals Knowledge, Attitude and Practice of Blood Glucose Monitoring in Rural Area among Diabetic Patients

2018 ◽  
Vol 5 (3) ◽  
pp. 225-230
Author(s):  
Saima Shabnum ◽  
Hajra Sarwar
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glucose Monitoring ◽  
Diabetic Control ◽  
Blood Glucose Monitoring ◽  
Diabetic Patients ◽  
Adult Males ◽  
Cross Sectional ◽  
Low Level

Background: Diabetes is main and growing health issue affecting more than 171 million peoples worldwide and the number is expected to rise to 366 million by 2030. Type 2 Diabetes will keep on accounting for 90% of all the cases. According to the WHO, Pakistan positioned seventh in pervasiveness of Diabetes. In 2011, the assessed pervasiveness of diabetes in Pakistan was generally in excess of 350 million and it is depended upon to be in excess of 550 million by year 2030. In Pakistan 9.5% of urban and 9.4% of the provincial population experience the bad effects of type 2 diabetes.  Objective: The reason for this investigation was to survey learning, conduct in regards to blood glucose observing among diabetic in rural group, Lahore. Descriptive cross sectional investigation configuration was led to evaluate learning, disposition and routine with regards to blood glucose monitoring and a sample size of 100 participants was selected for this study through convenient sampling. Data was collected from the adult males and females of Husain Abad community. Result: The result show that there was low level of knowledge, somewhat positive attitudes but very low level of practices regarding the diabetic control and glucose monitoring among the participants. Conclusion: In conclusion, it is stated that this research study the knowledge of participants towards the diabetes was not good except the definition of diabetes. The attitude was comparatively positive and good for following different blood sugar controlling measures. The practices were very poor. No one was following regular exercises, dietary modifications etcetera.Int. J. Soc. Sc. Manage. Vol. 5, Issue-3: 225-230

Indole as a new tentative marker in exhaled breath for non-invasive blood glucose monitoring of diabetic subjects

2021 ◽  
Author(s):  
Herbert Fink ◽  
Tim Maihöfer ◽  
Jeffrey Bender ◽  
Jochen Schulat
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Glucose Measurement ◽  
Diabetic Patients ◽  
Exhaled Breath ◽  
Invasive Approach ◽  
Test Strips ◽  
Non Invasive

Abstract Blood glucose monitoring (BGM) is the most important part of diabetes management. In classical BGM, glucose measurement by test strips involves invasive finger pricking. We present results of a clinical study that focused on a non-invasive approach based on volatile organic compounds (VOCs) in exhaled breath. Main objective was the discovery of markers for prediction of blood glucose levels (BGL) in diabetic patients. Exhaled breath was measured repeatedly in 60 diabetic patients (30 type 1, 30 type 2) in fasting state and after a standardized meal. Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) was used to sample breath every 15 minutes for a total of six hours. BGLs were tested in parallel via BGM test strips. VOC signals were plotted against glucose trends for each subject to identify correlations. Exhaled indole (a bacterial metabolite of tryptophan) showed significant mean correlation to BGL (with negative trend) and significant individual correlation in 36 patients. The type of diabetes did not affect this result. Additional experiments of one healthy male subject by ingestion of lactulose and 13C-labeled glucose (n=3) revealed that exhaled indole does not directly originate from food digestion by intestinal microbiota. As indole has been linked to human glucose metabolism, it might be a tentative marker in breath for non-invasive BGM. Clinical studies with greater diversity are required for confirmation of such results and further investigation of metabolic pathways.

The Impact of Fasting in Ramadan on Metabolic and Anthropometric Indices in Trained Type ΙΙ Diabetic Patients: A Prospective Observational Study

2020 ◽  
Author(s):  
Tayebe Yazdanyar ◽  
Mehrnoush Sohrab ◽  
Atena Ramezani ◽  
Zahra Kashi ◽  
Parastoo Karimi Ali Abadi ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Observational Study ◽  
Blood Sugar ◽  
Prospective Observational Study ◽  
Eating Habits ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Diabetic Patients ◽  
Anthropometric Indices ◽  
Ramadan Fasting

Abstract Background: Fasting has certain effects on metabolic and anthropometric parameters in diabetic patients. It is, therefore, necessary for patients to receive proper education related to their physical activities, eating habits, blood glucose monitoring, and medications. The aim of this study was to investigate the effects of Ramadan fasting on metabolic and anthropometric indices in type ΙΙ diabetic patients.Methods: This prospective observational study was performed during Ramadan 2018. The study population consisted of diabetic patients who desired to fast and received information on physical activity, eating habits, blood glucose monitoring, and taking their medications before Ramadan. Fasting blood sugar (FBS), blood sugar 2-hour postprandial (BS2hpp), glycosylated hemoglobin (HbA1C), and the lipids profile were assessed before and after Ramadan month. FBS and BS2hpp were also evaluated on the fifteenth day of Ramadan. The significance level for data analysis was considered p<0.05.Results: Out of 40 diabetic cases who completed the study, 6 (15%) were male and 34 (75%) were female. The mean age of participants was 55.2 ± 9.3 years. The anthropometric variables, including weight, BMI, waist, and blood pressure, decreased significantly after Ramadan fasting (p<0.05). FBS decreased significantly (125.1 ± 27.4 vs 105.2 ± 21.4, p<0.0001) and serum triglyceride increased significantly (127.5 ± 45.5 vs 166.5±53.5 mg/dl, p<0.001) after fasting compared to pre-Ramadan measurement. Other variables remained unchanged.Conclusion: The results of this study indicate that type II diabetic patients who have controlled blood sugar and received information based on clinical guidelines about their lifestyle and medications can fast safely during the holy month of Ramadan.

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 Glucose Data Classification for Diabetic Patient Monitoring

2019 ◽  
Vol 9 (20) ◽  
pp. 4459 ◽  
Author(s):  
Rghioui ◽  
Lloret ◽  
Parra ◽  
Sendra ◽  
Oumnad
Keyword(s):  
Blood Glucose ◽  
Patient Monitoring ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Prediction Tool ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Patient Monitoring System ◽  
Glucose Fluctuations ◽  
Health Field

Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased.

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