scholarly journals The Correlation of Hemoglobin A1c to Blood Glucose

2009 ◽  
Vol 3 (3) ◽  
pp. 429-438 ◽  
Author(s):  
Ken Sikaris
Keyword(s):  
Blood Glucose ◽  
Hemoglobin A1c ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Glucose Measurement ◽  
Glucose Levels ◽  
Average Blood Glucose ◽  
Current Concerns ◽  
Average Glucose ◽  
Hba1c Levels

The understanding that hemoglobin A1c (HbA1c) represents the average blood glucose level of patients over the previous 120 days underlies the current management of diabetes. Even in making such a statement, we speak of “average blood glucose” as though “blood glucose” were itself a simple idea. When we consider all the blood glucose forms—arterial versus venous versus capillary, whole blood versus serum versus fluoride-preserved plasma, fasting versus nonfasting—we can start to see that this is not a simple issue. Nevertheless, it seems as though HbA1c correlates to any single glucose measurement. Having more than one measurement and taking those measurements in the preceding month improves the correlation further. In particular, by having glucose measurements that reflect both the relatively lower overnight glucose levels and measurements that reflect the postprandial peaks improves not only our ability to manage diabetes patients, but also our understanding of how HbA1c levels are determined. Modern continuous glucose monitoring (CGM) devices may take thousands of glucose results over a week. Several studies have shown that CGM glucose averages account for the vast proportion of the variation of HbA1c. The ability to relate HbA1c to average glucose may become a popular method for reporting HbA1c, eliminating current concerns regarding differences in HbA1c standardization. Hemoglobin A1c expressed as an average glucose may be more understandable to patients and improve not only their understanding, but also their ability to improve their diabetes management.

scholarly journals Discrepancies Between Blood Glucose and Interstitial Glucose—Technological Artifacts or Physiology: Implications for Selection of the Appropriate Therapeutic Target

2017 ◽  
Vol 11 (4) ◽  
pp. 766-772 ◽  
Author(s):  
Thorsten Siegmund ◽  
Lutz Heinemann ◽  
Ralf Kolassa ◽  
Andreas Thomas
Keyword(s):  
Blood Glucose ◽  
Therapeutic Target ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Blood Glucose Measurement ◽  
Glucose Measurement ◽  
Glucose Levels ◽  
Therapeutic Decisions ◽  
The Impact ◽  
Selection Of

Background: For decades, the major source of information used to make therapeutic decisions by patients with diabetes has been glucose measurements using capillary blood samples. Knowledge gained from clinical studies, for example, on the impact of metabolic control on diabetes-related complications, is based on such measurements. Different to traditional blood glucose measurement systems, systems for continuous glucose monitoring (CGM) measure glucose in interstitial fluid (ISF). The assumption is that glucose levels in blood and ISF are practically the same and that the information provided can be used interchangeably. Thus, therapeutic decisions, that is, the selection of insulin doses, are based on CGM system results interpreted as though they were blood glucose values. Methods: We performed a more detailed analysis and interpretation of glucose profiles obtained with CGM in situations with high glucose dynamics to evaluate this potentially misleading assumption. Results: Considering physical activity, hypoglycemic episodes, and meal-related differences between glucose levels in blood and ISF uncover clinically relevant differences that can make it risky from a therapeutic point of view to use blood glucose for therapeutic decisions. Conclusions: Further systematic and structured evaluation as to whether the use of ISF glucose is more safe and efficient when it comes to acute therapeutic decisions is necessary. These data might also have a higher prognostic relevance when it comes to long-term metabolic consequences of diabetes. In the long run, it may be reasonable to abandon blood glucose measurements as the basis for diabetes management and switch to using ISF glucose as the appropriate therapeutic target.

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.

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

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 Longitudinal Changes in the Relationship Between Hemoglobin A1c and Glucose Tolerance Across Pregnancy and Postpartum

2020 ◽  
Vol 105 (5) ◽  
pp. e1999-e2007 ◽  
Author(s):  
P Kaitlyn Edelson ◽  
Kaitlyn E James ◽  
Aaron Leong ◽  
Juliana Arenas ◽  
Melody Cayford ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Gestational Age ◽  
Hemoglobin A1c ◽  
Longitudinal Changes ◽  
Glucose Levels ◽  
Pregnancy And Postpartum ◽  
Clinical Records ◽  
The Relationship ◽  
Average Glucose ◽  
Hba1c Levels

Abstract Objective To characterize the relationship between hemoglobin A1c (HbA1c) levels and glucose tolerance across pregnancy and postpartum. Design and Participants In a longitudinal study of pregnant women with gestational diabetes risk factors (N = 102), we performed oral glucose tolerance testing (OGTT) and HbA1c measurements at 10–15 weeks of gestation, 24–30 weeks of gestation (N = 73), and 6–24 weeks postpartum (N = 42). Complete blood counts were obtained from clinical records. We calculated HbA1c-estimated average glucose levels and compared them with mean OGTT glucose levels (average of fasting, 1- and 2-hour glucose levels). Linear mixed effects models were used to test for longitudinal changes in measurements. Results Mean OGTT glucose increased between 10–15 and 24–30 weeks of gestation (β = 8.1 mg/dL, P = .001), while HbA1c decreased during the same time period (β = –0.13%, P < .001). At 10–15 weeks of gestation and postpartum the discrepancy between mean OGTT glucose and HbA1c-estimated average glucose was minimal (mean [standard deviation]: 1.2 [20.5] mg/dL and 0.16 [18.1] mg/dL). At 24–30 weeks of gestation, the discrepancy widened (13.2 [17.9] mg/dL, β = 12.7 mg/dL, P < .001, compared to 10–15 weeks of gestation, with mean OGTT glucose being higher than HbA1c-estimated average glucose). Lower hemoglobin at 24–30 weeks of gestation was associated with a greater discrepancy (β = 6.4 mg/dL per 1 g/dL lower hemoglobin, P = .03 in an age- and gestational age-adjusted linear regression model). Conclusions HbA1c accurately reflects glycemia in the 1st trimester, but underestimates glucose intolerance in the late 2nd trimester. Lower hemoglobin level is associated with greater underestimation. Accounting for gestational age and maternal hemoglobin may improve the clinical interpretation of HbA1c levels during pregnancy.

Blood Glucose Monitoring In Pediatric Patients—Looking Toward Better Diabetes Management and Perspectives for the Future

2010 ◽  
Vol 06 (01) ◽  
pp. 54
Author(s):  
William L Clarke ◽  
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Blood Glucose Monitoring ◽  
Intensive Glucose Control ◽  
Nocturnal Hypoglycemia ◽  
Glucose Levels ◽  
Analytical Accuracy

Self-blood glucose monitoring (SBGM) is an important component of day-to-day diabetes management for children and their families. Despite some recent concerns in terms of its analytical accuracy, it has been used successfully to implement intensive glucose control in the Diabetes Control and Complications Trial, reduce glycated hemoglobin (HbA1c) levels, prevent acute complications, and make it possible for children to attend school and participate in sports activities safely. While still in its infancy, continuous glucose monitoring (CGM) has been shown to be useful in reducing the occurrence of nocturnal hypoglycemia, lowering HbA1clevels, and reducing glycemic variability. Its analytical accuracy has prevented its approval as an alternative to SBGM for insulin decision-making. However, it has made possible the development and testing of closed-loop ‘artificial pancreas’ systems for controlling glucose levels in adults and adolescents.

scholarly journals Longitudinal Average Glucose Levels and Variance and Risk of Stroke: A Chinese Cohort Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Xuenan Peng ◽  
Jinzhuo Ge ◽  
Congju Wang ◽  
Hongpeng Sun ◽  
Qinghua Ma ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glucose Level ◽  
Cox Regression ◽  
Diabetes Diagnosis ◽  
Glucose Levels ◽  
Diabetes Status ◽  
Chinese Cohort ◽  
Average Blood Glucose ◽  
Average Glucose

Background. Diabetes is a known independent risk factor for stroke. However, whether higher glucose levels (126–139.9 mg/dl) can increase the risk of stroke in people without diabetes is still unknown. Moreover, as a fluctuating parameter, long-term glucose levels may also be related to the risk of stroke outcome. It is important to explore the correlation between long-term average blood glucose, as well as its variability, and stroke. Methods. We used 40,975 clinical measurements of glucose levels and 367 measurements of glycated hemoglobin A1c levels from 12,321 participants without stroke to examine the relationship between glucose levels and the risk of stroke. Participants were from the Weitang Geriatric Diseases study, including 5,707 men and 6,614 women whose mean age at baseline was 60.8 years; 1,011 participants had diabetes, and 11,310 did not. We estimated the long-term average blood glucose level based on the multilevel Bayesian model and fit in Cox regression models, stratified according to diabetes status. Results. Over a median follow-up period of 5 years, stroke developed in 279 of the 12,321 participants (244 without diabetes and 35 with). For people with an average glucose level of 126–139.9 mg per deciliter, compared with 90–99.9 mg per deciliter, the adjusted hazard ratio (HR) for total stroke was 1.78 (95% confidence interval (CI), 1.16–2.75), and the HR for levels higher than 140 mg per deciliter was 1.89 (95% CI, 1.09–3.29). Among those without diabetes whose glucose level was higher than 140 mg per deciliter, compared with 90–99.9 mg per deciliter, the adjusted HRs for total stroke and fatal stroke were 3.66 (95% CI, 1.47–9.08) and 5 (95% CI, 1.77–14.15), respectively. For a glucose standard deviation level higher than 13.83 mg per deciliter, compared with that lower than 5.91 mg per deciliter, the adjusted HR for total stroke was 2.31 (95% CI, 1.19–4.48). Conclusions. Our results suggest that higher average glucose levels (126–139.9 mg/dl) and variance may be risk factors for stroke, even among people without diabetes diagnosis.

scholarly journals Outcome of Patients with ST-T Changes in Non STSegment Elevation Myocardial Infarction

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mohammad Arifur Rahman
Keyword(s):  
Myocardial Infarction ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Measurement Methods ◽  
Glucose Levels ◽  
Sensor Device ◽  
Sensor Glucose ◽  
Average Glucose

This article discusses the fundamental characteristics of measured glucose levels and predicted glycated hemoglobin A1c (HbA1c) values among three sets of collected data, measured finger-piercing and continuous glucose monitoring (CGM) sensor device collected glucose levels at 15-minute (15-min) and 5-minute (5-min) intervals. The average glucose (in milligram per deciliter-mg/dL) is listed below: Finger glucose: 109 mg/dL (100%) Sensor at 15-min: 120 mg/dL (109%) Sensor at 5-min: 117 mg/dL (107%) Using candlestick chart, the comparison of average glucoses during this period between two sensor glucose (mg/dL) data (15-min/5-min) are as follows: Open glucose: 108/111 Close glucose: 115/115 Maximum (max) glucose: 170 /175 Minimum (min) glucose: 85/83 Average glucose: 120/117 Additional analysis of time above range (TAR)≥140 mg/dL for hyperglycemia, time within the range (TIR) from 70-140 mg/dL for normal, time below range (TBR)≤70 mg/dL for hypoglycemia based on two sensor candlesticks revealing the following information in a specific format of TAR%/ TIR%/TBR%. 15-min:18.3%, 80.5%, 1.2% 5-min: 17.0%, 81.9%, 1.1% By evaluating the results of the TIR analysis, the 5-min glucose levels appear to be marginally healthier (1.4%) than the 15-min ones. During the coronavirus pandemic (COVID 19) quarantine period, the author lived a rather unique lifestyle which is extremely calm with regular routines, such as eating home-cooked meals and exercising on a regular basis. As a result, his HbA1c has decreased from 6.6% to 6.3% with an average A1c of 6.4% without taking any diabetes medications. However, these three different measurement methods still provide three different sets of glucoses levels which are within a 10% margin of differences, while the HbA1c values are particularly close to each other between the finger-piercing and CGM 15-min.

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 Hypoglycemia after initiating insulin therapy based on the results of structured self-control and monitoring of blood glucose

2018 ◽  
pp. 112-116
Author(s):  
A. V. Petrov ◽  
L. G. Strongin
Keyword(s):  
Blood Glucose ◽  
Roc Analysis ◽  
Optimal Schedule ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Control ◽  
Self Monitoring ◽  
Glucose Levels ◽  
Diagnostic Threshold ◽  
Average Glucose

Hypoglycemia detection in T2DM patients is an important issue which is usually accomplished with self-monitoring of blood glucose (SMBG). Optimal schedule of testing and diagnostic threshold are important for effective SMBG. Aims of study: To evaluate hypoglycemia frequency by SMBG using structured AccuChek 360 View protocol and glucose monitoring and increase SMBG efficacy in the detection of hypoglycemia. Study design: 16 T2DM patients after initiation of insulin treatment were included. Each patient had 3 days of glucose monitoring together with SMBG 7 times a day after hospital discharge and 3 months later. Results: Hypoglycemia was detected in 38% of monitoring periods; this patients had higher glucose variability and lower average glucose. SMBG detected hypoglycemia in 16% of periods. 6 out of 7 unrecognized hypoglycemias were during night. This 6 cases were characterized by minimal daytime glucose levels by SMBG below 5 mmol/l. ROC-analysis demonstrated minimal glucose level during daytime of 4.8 mmol/l to have 92% sensitivity and 74% specificity for detection of any hypoglycemia by glucose monitoring. Conclusion: Structured Accu-Chek 360 View SMBG can reliably detect daytime hypoglycemia but regular nighttime testing is recommended in all T2DM patients using insulin for detection of nighttime hypoglycemia. Minimal glucose levels below 4.8-5 mmol/l during daytime corresponds with high hypoglycemia risk. 

Sign in / Sign up

Export Citation Format

Share Document