scholarly journals Markers of glycemic control in the mouse: comparisons of 6-h- and overnight-fasted blood glucoses to Hb A1c

2008 ◽  
Vol 295 (4) ◽  
pp. E981-E986 ◽  
Author(s):  
Byoung Geun Han ◽  
Chuan-Ming Hao ◽  
Elena E. Tchekneva ◽  
Ying-Ying Wang ◽  
Chieh Allen Lee ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glycosylated Hemoglobin ◽  
Fasting Blood Glucose ◽  
Glucose Monitoring ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Overnight Fasting ◽  
Highly Correlated ◽  
Method R

The present studies examined the relationship between fasting blood glucose and Hb A1cin C57BL/6J, DBA/2J, and KK/HlJ mice with and without diabetes mellitus. Daily averaged blood glucose levels based on continuous glucose monitoring and effects of 6-h vs. overnight fasting on blood glucose were determined. Daily averaged blood glucose levels were highly correlated with Hb A1c, as determined with a hand-held automated device using an immunodetection method. R2values were 0.90, 0.95, and 0.99 in KK/HIJ, C57BL/6J, and DBA/2J, respectively. Six-hour fasting blood glucose correlated more closely with the level of daily averaged blood glucose and with Hb A1cthan did blood glucose following an overnight fast. To validate the immunoassay-determined Hb A1c, we also measured total glycosylated hemoglobin using boronate HPLC. Hb A1cvalues correlated well with total glycosylated hemoglobin in all three strains but were relatively lower than total glycosylated hemoglobin in diabetic DBA/2J mice. These results show that 6-h fasting glucose provides a superior index of glycemic control and correlates more closely with Hb A1cthan overnight-fasted blood glucose in these strains of mice.

Perioperative blood glucose concentrations in kittens following overnight fasting and gonadectomy

2017 ◽  
Vol 20 (4) ◽  
pp. 344-348
Author(s):  
Danielle N Semick ◽  
Stephanie L Shaver ◽  
Heather N Cornell ◽  
Nancy C Bradley ◽  
Rachael E Kreisler
Keyword(s):  
Blood Glucose ◽  
Body Condition Score ◽  
Fasting Blood Glucose ◽  
Clinical Signs ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Condition Score ◽  
Prolonged Fast ◽  
Overnight Fasting ◽  
Blood Glucose Concentrations

Objectives The objective of this study was to determine if hypoglycemia is an effect of overnight fasting and gonadectomy in kittens, as well as to determine predictors of baseline and postoperative blood glucose. Methods This was a prospective observational study. Seventy-five kittens between the age of 8 and 16 weeks undergoing routine castration or ovariohysterectomy at an animal shelter were included. Two blood glucose measurements were analyzed per kitten after an overnight fast: a baseline reading prior to preoperative examination, and a reading immediately postoperatively. Predictors of the baseline and postoperative blood glucose levels were determined using multi-level mixed-effects linear regression. Results Kittens, when fasted overnight, were not hypoglycemic (<60 mg/dl). No kittens exhibited clinical signs consistent with hypoglycemia. No kittens had a blood glucose <70 mg/dl postoperatively. Postoperative hyperglycemia (>150 mg/dl) was observed in 44% of kittens. The only predictor of fasted blood glucose levels was body condition score. The only predictor of postoperative blood glucose levels was the fasting blood glucose value. Conclusions and relevance Overnight fasting prior to elective sterilization in 8- to 16-week-old kittens did not result in hypoglycemia. Concern regarding hypoglycemia after a prolonged fast in kittens may be unwarranted for short procedures in healthy animals.

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.

Evaluation of Nursing Work Effort and Perceptions About Blood Glucose Testing in Tight Glycemic Control

2006 ◽  
Vol 15 (4) ◽  
pp. 370-377 ◽  
Author(s):  
Daleen Aragon
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Tight Glycemic Control ◽  
Blood Glucose Monitoring ◽  
Glucose Levels ◽  
Glucose Testing ◽  
Intravenous Insulin ◽  
Blood Glucose Levels ◽  
Blood Glucose Testing

• Background Tight glycemic control is important in critically ill patients and involves insulin infusions and monitoring of blood glucose levels. Hourly measurements of blood glucose levels and adjustments of intravenous insulin doses require additional work by nurses. • Objectives To evaluate the nursing work incurred with and nursing perceptions about tight glycemic control and blood glucose monitoring. • Methods A variety of intensive care units were studied. Surveys were used to gain information about nurses’ perceptions. Time-in-motion observations were used to determine the time taken to measure blood glucose levels and adjust insulin doses. • Results Nurses thought that tight glycemic control was important and that the work associated with it was substantial. Nurses thought that easier and automated forms of blood glucose monitoring are needed. They preferred using an arterial catheter to obtain blood samples to avoid excessive finger sticks. The total number of blood glucose measurements was 77 954. The mean time taken for hourly blood glucose monitoring and adjustment of insulin doses was 4.72 minutes. The estimated costs of time spent on glycemic control during a 1-year period were $182 488 for nurses’ salaries and $58 500 for supplies. • Conclusions Although most nurses endorse tight glycemic control, the work associated with it is burdensome and costly. Because up to 2 hours might be required for tight glycemic control for a single patient in a 24-hour period, the costs in time and money are high. Easier clinical methods for monitoring blood glucose levels are needed.

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 Clinical Update on Optimal Prandial Insulin Dosing Using a Refined Run-to-Run Control Algorithm

2009 ◽  
Vol 3 (3) ◽  
pp. 487-491 ◽  
Author(s):  
Howard Zisser ◽  
Cesar C. Palerm ◽  
Wendy C. Bevier ◽  
Francis J. Doyle ◽  
Lois Jovanovic
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Concentration ◽  
Fasting Blood Glucose ◽  
Glucose Monitoring ◽  
Postprandial Glucose ◽  
Carbohydrate Content ◽  
Postprandial Blood Glucose ◽  
Glucose Levels ◽  
Prandial Insulin ◽  
Blood Glucose Levels

Background: This article provides a clinical update using a novel run-to-run algorithm to optimize prandial insulin dosing based on sparse glucose measurements from the previous day's meals. The objective was to use a refined run-to-run algorithm to calculate prandial insulin-to-carbohydrate ratios (I:CHO) for meals of variable carbohydrate content in subjects with type 1 diabetes (T1DM). Method: The open-labeled, nonrandomized study took place over a 6-week period in a nonprofit research center. Nine subjects with T1DM using continuous subcutaneous insulin infusion participated. Basal insulin rates were optimized using continuous glucose monitoring, with a target fasting blood glucose of 90 mg/dl. Subjects monitored blood glucose concentration at the beginning of the meal and at 60 and 120 minutes after the start of the meal. They were instructed to start meals with blood glucose levels between 70 and 130 mg/dl. Subjects were contacted daily to collect data for the previous 24-hour period and to give them the physician-approved, algorithm-derived I:CHO ratios for the next 24 hours. Subjects calculated the amount of the insulin bolus for each meal based on the corresponding I:CHO and their estimate of the meal's carbohydrate content. One- and 2-hour postprandial glucose concentrations served as the main outcome measures. Results: The mean 1-hour postprandial blood glucose level was 104 ± 19 mg/dl. The 2-hour postprandial levels (96.5 ± 18 mg/dl) approached the preprandial levels (90.1 ± 13 mg/dl). Conclusions: Run-to-run algorithms are able to improve postprandial blood glucose levels in subjects with T1DM.

scholarly journals THE EFFECTIVENESS OF KARELA COMPARED WITH VIJAYASAR IN THE MANAGEMENT OF MADHUMEHA

AYUSHDHARA ◽  
2016 ◽  
Author(s):  
Kalouni Om Prakash ◽  
Singh Binod Kumar ◽  
Roka D.B
Keyword(s):  
Diabetes Mellitus ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Fasting Blood Glucose ◽  
Treated Group ◽  
Indian Council ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Treatment Changes ◽  
The Mean

Vijayasar (Pterocarpus marsupium) has been mentioned in Charak Samhita, as a remedy for Madhumeha (Diabetes Mellitus). A study conducted by ICMR (Indian Council of Medical Research) revealed that the hypoglycemic effects of Vijayasar are comparable to that of tolbutamide. Karela (Mormordica charantia) is another herb used in Madhumeha and it is a routinely used vegetable in Nepal. In this study we measured the effectiveness of Karela in patients of Madhumeha and compared with that of Vijayasar. A total of sixty four patients diagnosed with Madhumeha (Fasting Blood Glucose ≥126mg/dl or Post Prandial Blood Glucose≥200mg/dl) were given either Karela or Vijayasar powder two times a day for one month, along with dietary and lifestyle advices and their blood glucose levels were measured before initiating treatment and after one month of treatment. Changes in the subjective complaints of Madhumeha like Prabhutamutrata, Avilmutrata, etc. and appearance of adverse events were also evaluated. Randomization of treatment was done and dosage was titrated on the basis of glycemic control and duration of Madhumeha. The mean reductions in fasting blood glucose and post prandial blood glucose in Karela treated group are 60.83 mg/dl and 79.74 mg/dl respectively and that in Vijayasar treated. Karela is a safe and effective medicine in the management of Madhumeha and it is as effective as Vijayasar.

scholarly journals In search of the Somogyi effect

2016 ◽  
Vol 19 (1) ◽  
pp. 80-88
Author(s):  
Evgenia Mikhaylovna Patrakeeva ◽  
Ksenia Andreevna Solovyova ◽  
Natalia Sergeevna Novoselova ◽  
Alsu Gafurovna Zalevskaya
Keyword(s):  
Blood Glucose ◽  
Fasting Blood Glucose ◽  
Experimental Studies ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Nocturnal Hypoglycaemia ◽  
Monitoring Methods ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Somogyi Phenomenon

The Somogyi phenomenon or rebound hyperglycaemia is known as the counterregulatory response to asymptomatic nocturnal hypoglycaemia; the Somogyi phenomenon occurs with high fasting blood glucose levels and hyperglycaemia the following morning. Most published trials, however, do not agree with this theory. Although data from some experimental studies may suggest a pathophysiological link. Perhaps, the differences in research results are caused by the evolution of blood glucose monitoring methods. Nevertheless, it cannot be excluded that the results of Michael Somogyi’s studies were misunderstood.

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.

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