2183-PUB: Standard Deviation Calculated from the 7-Point SMBG Glucose Profiles Is a Good Index of Glycemic Variability Reflecting MAGE Obtained from CGMS

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2183-PUB
Author(s):  
ZHIGU LIU ◽  
BEISI LIN ◽  
WEN XU ◽  
LI GONG ◽  
XUBIN YANG ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Glycemic Variability ◽  
Good Index ◽  
Glucose Profiles

scholarly journals Association of Long-Term Change and Variability in Glycemia with Risk of Incident Heart Failure among Patients with Type 2 Diabetes Mellitus – A Secondary Analysis of the ACCORD Trial

2020 ◽  
Author(s):  
Matthew W. Segar ◽  
Kershaw V. Patel ◽  
Muthiah Vaduganathan ◽  
Melissa C. Caughey ◽  
Javed Butler ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Secondary Analysis ◽  
Glycemic Variability ◽  
Baseline Risk ◽  
Absolute Difference ◽  
Cox Models ◽  
Term Change ◽  
Alternative Measures

<b>Objective</b>: Evaluate the associations between long-term change and variability in glycemia with risk of HF among patients with T2DM. <p><b>Research Design and Methods: </b>Among participants with T2DM enrolled in the ACCORD trial, variability in HbA1c was assessed from stabilization of HbA1c following enrollment (8 months) to 3 years of follow-up as follows: average successive variability (ASV=average absolute difference between successive values), coefficient of variation (CV=standard deviation/mean), and standard deviation. Participants with HF at baseline or within 3 years of enrollment were excluded. Adjusted Cox models were used to evaluate the association of % change (from baseline to 3 years of follow-up) and variability in HbA1c over the first 3 years of enrollment and subsequent risk of HF.</p> <p><b>Results</b>: The study included 8,576 patients. Over a median follow-up of 6.4 years from the end of variability measurements at year 3, 388 patients had an incident HF hospitalization. Substantial changes in HbA1c were significantly associated with higher risk of HF [HR (95% CI) for ≥10% decrease = 1.32 (1.08-1.75), ≥10% increase = 1.55 (1.19-2.04), ref: <10% change in HbA1c]. Higher long-term variability in HbA1c was significantly associated with higher risk of HF [HR (95% CI) per 1 SD of ASV = 1.34 (1.17-1.54)] independent of baseline risk factors and interval changes in cardiometabolic parameters. Consistent patterns of association were observed using alternative measures of glycemic variability.</p> <p><b>Conclusions:</b> Substantial long-term changes and variability in HbA1c were independently associated with risk of HF among patients with T2DM.</p>

scholarly journals Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaoli Ren ◽  
Zhiyun Wang ◽  
Congfang Guo
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Meta Analysis ◽  
Glycemic Variability ◽  
Diabetic Patients ◽  
Increased Risk ◽  
Follow Up Studies ◽  
Patients With Diabetes

Abstract Objectives Long-term glycemic variability has been related to increased risk of vascular complication in patients with diabetes. However, the association between parameters of long-term glycemic variability and risk of stroke remains not fully determined. We performed a meta-analysis to systematically evaluate the above association. Methods Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of stroke in diabetic patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies were used to pool the results. Results Seven follow-up studies with 725,784 diabetic patients were included, and 98% of them were with type 2 diabetes mellitus (T2DM). The mean follow-up duration was 7.7 years. Pooled results showed that compared to those with lowest category of glycemic variability, diabetic patients with the highest patients had significantly increased risk of stroke, as evidenced by glycemic variability analyzed by fasting plasma glucose coefficient of variation (FPG-CV: risk ratio [RR] = 1.24, 95% confidence interval [CI] 1.11 to 1.39, P < 0.001; I2 = 53%), standard deviation of FPG (FPG-SD: RR = 1.16, 95% CI 1.02 to 1.31, P = 0.02; I2 = 74%), HbA1c coefficient of variation (HbA1c-CV: RR = 1.88, 95% CI 1.61 to 2.19 P < 0.001; I2 = 0%), and standard deviation of HbA1c (HbA1c-SD: RR = 1.73, 95% CI 1.49 to 2.00, P < 0.001; I2 = 0%). Conclusions Long-term glycemic variability is associated with higher risk of stroke in T2DM patients.

scholarly journals Maximal Glycemic Difference, the Possible Strongest Glycemic Variability Parameter to Predict Mortality in ICU Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Thanaphruet Issarawattana ◽  
Rungsun Bhurayanontachai
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Blood Glucose ◽  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Intensive Care Unit Mortality ◽  
Pairwise Comparison ◽  
Binary Logistic Regression ◽  
Glycemic Variability ◽  
Glucose Levels

Background. This retrospective study aimed to determine the correlation of blood glucose and glycemic variability with mortality and to identify the strongest glycemic variability parameter for predicting mortality in critically ill patients. Methods. A total of 528 patients admitted to the medical intensive care unit were included in this study. Blood glucose levels during the first 24 hours of admission were recorded and calculated to determine the glycemic variability. Significant glycemic variability parameters, including the standard deviation, coefficient of variation, maximal blood glucose difference, and J-index, were subsequently compared between intensive care unit survivors and nonsurvivors. A binary logistic regression was performed to identify independent factors associated with mortality. To determine the strongest glycemic variability parameter to predict mortality, the area under the receiver operating characteristic of each glycemic variability parameter was determined, and a pairwise comparison was performed. Results. Among the 528 patients, 17.8% (96/528) were nonsurvivors. Both survivor and nonsurvivor groups were clinically comparable. However, nonsurvivors had significantly higher median APACHE-II scores (23 [21, 27] vs. 18 [14, 22]; p < 0.01) and a higher mechanical ventilator support rate (97.4% vs. 74.9%; p < 0.01). The mean blood glucose level and significant glycemic variability parameters were higher in nonsurvivors than in survivors. The maximal blood glucose difference yielded a similar power to the coefficient of variation (p = 0.21) but was significantly stronger than the standard deviation (p = 0.005) and J-index (p = 0.006). Conclusions. Glycemic variability was independently associated with intensive care unit mortality. Higher glycemic variability was identified in the nonsurvivor group regardless of preexisting diabetes mellitus. The maximal blood glucose difference and coefficient of variation of the blood glucose were the two strongest parameters for predicting intensive care unit mortality in this study.

scholarly journals Continuous Blood Glucose Monitoring to Determine the Glycemic Variability in Patients Having SARS CoV-2 Infection with ARDS and Its Bearing on the Severity of the Disease

2020 ◽  
pp. 51-56
Author(s):  
Jitendra D. Lakhani ◽  
Hetal Pandya ◽  
Archit Jain ◽  
Sachin Ghadiya
Keyword(s):  
Blood Glucose ◽  
Standard Deviation ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Blood Glucose Monitoring ◽  
Cross Sectional ◽  
Medical College ◽  
Continuous Blood Glucose Monitoring ◽  
Frequent Episode ◽  
Severity Of The Disease

Aims and Objectives: A study to determine the effect of glycemic variability measured by continuous blood glucose monitoring as assessed by standard deviation of each SARS CoV -2 patient's mean glucose level and to correlate with the severity of the disease. Study Design: Cross-sectional observational study of 13 patients with SARS CoV-2 infection with Acute Respiratory Distress Syndrome (ARDS) with and without diabetes. Place and Duration of Study: Department of Medicine, Dhiraj Hospital, Smt. Bhikhiben Kanjibhai Shah Medical College and Research Institute; between June 2020 to July 2020. Results: 13 patients of SARS CoV-2 with ARDS were enrolled in the study. The median age of the enrolled patients was 55±12 years. Out of the 13 patients, 5 patients belonged to mild and severe category of ARDS each respectively and 3 patients belonged to the moderate category of ARDS. There was a gradual rise in inflammatory markers such as serum LDH, Ferritin, CRP from mild to severe ARDS and D-dimer level was more than double in severe category as compared to the mild ARDS. Normal glycemic variability in adults is 0-3 SD, and we found that there was a significant co-relation of glycemic variability with severity of the disease evidenced by the mean standard deviation of severe ARDS patients as 27.44 SD; whereas 19.26 SD and 9.7 SD for moderate and mild ARDS patients respectively. Hypoglycemia was documented in 10 patients. The maximum stay in the hospital was that of the patients with high glycemic variability that is 22 ± 2 days Conclusion: This preliminary study relates glycemic variability with severity of ARDS in patients of severe SARS CoV-2. Frequent episode of hypoglycemia is not uncommon and should be monitored.

scholarly journals Association of Long-Term Change and Variability in Glycemia with Risk of Incident Heart Failure among Patients with Type 2 Diabetes Mellitus – A Secondary Analysis of the ACCORD Trial

2020 ◽  
Author(s):  
Matthew W. Segar ◽  
Kershaw V. Patel ◽  
Muthiah Vaduganathan ◽  
Melissa C. Caughey ◽  
Javed Butler ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Secondary Analysis ◽  
Glycemic Variability ◽  
Baseline Risk ◽  
Absolute Difference ◽  
Cox Models ◽  
Term Change ◽  
Alternative Measures

<b>Objective</b>: Evaluate the associations between long-term change and variability in glycemia with risk of HF among patients with T2DM. <p><b>Research Design and Methods: </b>Among participants with T2DM enrolled in the ACCORD trial, variability in HbA1c was assessed from stabilization of HbA1c following enrollment (8 months) to 3 years of follow-up as follows: average successive variability (ASV=average absolute difference between successive values), coefficient of variation (CV=standard deviation/mean), and standard deviation. Participants with HF at baseline or within 3 years of enrollment were excluded. Adjusted Cox models were used to evaluate the association of % change (from baseline to 3 years of follow-up) and variability in HbA1c over the first 3 years of enrollment and subsequent risk of HF.</p> <p><b>Results</b>: The study included 8,576 patients. Over a median follow-up of 6.4 years from the end of variability measurements at year 3, 388 patients had an incident HF hospitalization. Substantial changes in HbA1c were significantly associated with higher risk of HF [HR (95% CI) for ≥10% decrease = 1.32 (1.08-1.75), ≥10% increase = 1.55 (1.19-2.04), ref: <10% change in HbA1c]. Higher long-term variability in HbA1c was significantly associated with higher risk of HF [HR (95% CI) per 1 SD of ASV = 1.34 (1.17-1.54)] independent of baseline risk factors and interval changes in cardiometabolic parameters. Consistent patterns of association were observed using alternative measures of glycemic variability.</p> <p><b>Conclusions:</b> Substantial long-term changes and variability in HbA1c were independently associated with risk of HF among patients with T2DM.</p>

scholarly journals Association of Long-Term Change and Variability in Glycemia with Risk of Incident Heart Failure among Patients with Type 2 Diabetes Mellitus – A Secondary Analysis of the ACCORD Trial

2020 ◽  
Author(s):  
Matthew W. Segar ◽  
Kershaw V. Patel ◽  
Muthiah Vaduganathan ◽  
Melissa C. Caughey ◽  
Javed Butler ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Secondary Analysis ◽  
Glycemic Variability ◽  
Baseline Risk ◽  
Absolute Difference ◽  
Cox Models ◽  
Term Change ◽  
Alternative Measures

<b>Objective</b>: Evaluate the associations between long-term change and variability in glycemia with risk of HF among patients with T2DM. <p><b>Research Design and Methods: </b>Among participants with T2DM enrolled in the ACCORD trial, variability in HbA1c was assessed from stabilization of HbA1c following enrollment (8 months) to 3 years of follow-up as follows: average successive variability (ASV=average absolute difference between successive values), coefficient of variation (CV=standard deviation/mean), and standard deviation. Participants with HF at baseline or within 3 years of enrollment were excluded. Adjusted Cox models were used to evaluate the association of % change (from baseline to 3 years of follow-up) and variability in HbA1c over the first 3 years of enrollment and subsequent risk of HF.</p> <p><b>Results</b>: The study included 8,576 patients. Over a median follow-up of 6.4 years from the end of variability measurements at year 3, 388 patients had an incident HF hospitalization. Substantial changes in HbA1c were significantly associated with higher risk of HF [HR (95% CI) for ≥10% decrease = 1.32 (1.08-1.75), ≥10% increase = 1.55 (1.19-2.04), ref: <10% change in HbA1c]. Higher long-term variability in HbA1c was significantly associated with higher risk of HF [HR (95% CI) per 1 SD of ASV = 1.34 (1.17-1.54)] independent of baseline risk factors and interval changes in cardiometabolic parameters. Consistent patterns of association were observed using alternative measures of glycemic variability.</p> <p><b>Conclusions:</b> Substantial long-term changes and variability in HbA1c were independently associated with risk of HF among patients with T2DM.</p>

Nucleic Acid Molecules Prepared by Monolayer Techniques

1972 ◽  
Vol 30 ◽  
pp. 178-179
Author(s):  
Dimitrij Lang
Keyword(s):  
Electron Microscopy ◽  
Standard Deviation ◽  
Nucleic Acid ◽  
Cytochrome C ◽  
Nucleic Acids ◽  
Contour Length ◽  
Sample Standard Deviation ◽  
Molecular Weights ◽  
Length Measurements ◽  
Protein Monolayer

The success of the protein monolayer technique for electron microscopy of individual DNA molecules is based on the prevention of aggregation and orientation of the molecules during drying on specimen grids. DNA adsorbs first to a surface-denatured, insoluble cytochrome c monolayer which is then transferred to grids, without major distortion, by touching. Fig. 1 shows three basic procedures which, modified or not, permit the study of various important properties of nucleic acids, either in concert with other methods or exclusively:1) Molecular weights relative to DNA standards as well as number distributions of molecular weights can be obtained from contour length measurements with a sample standard deviation between 1 and 4%.

Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

2020 ◽  
Vol 29 (3) ◽  
pp. 429-435
Author(s):  
Patricia C. Mancini ◽  
Richard S. Tyler ◽  
Hyung Jin Jun ◽  
Tang-Chuan Wang ◽  
Helena Ji ◽  
...  
Keyword(s):  
Standard Deviation ◽  
High Frequency ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Outcome Variable ◽  
Reliable Estimate ◽  
Moderate Correlation ◽  
Trained Subjects ◽  
The Relationship ◽  
Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

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