Lower Interstitial Glucose Concentrations but Higher Glucose Variability during Low-Energy Diet Compared to Regular Diet—An Observational Study in Females with Obesity

This is an observational study of interstitial glucose (IG) concentrations, IG variability and dietary intake under free-living conditions in 46 females with obesity but without diabetes. We used continuous glucose monitoring, open-ended food recording and step monitoring during regular dietary intake followed by a low-energy diet (LED). Thirty-nine participants completed both study periods. The mean BMI at baseline was 43.6 ± 6.2 kg/m2. Three weeks of LED resulted in a mean weight loss of 5.2% with a significant reduction in diurnal IG concentration but with greater glycemic variability observed during LED. The mean 24 h IG concentration decreased from 5.8 ± 0.5 mmol/L during the regular diet period to 5.4 ± 0.5 mmol/L (p < 0.001) during LED, while the mean amplitude of glycemic excursion increased from 1.5 ± 0.7 to 1.7 ± 0.7 mmol/L (p = 0.031). The positive incremental area under the curve at breakfast was significantly larger for LED compared to regular diet. The daily fiber intake and the glycemic index of breakfast meals were significantly associated with the glycemic variability during regular dietary intake. In conclusion, the 24 h mean IG concentration was lower but with more pronounced glycemic variability during LED compared to a regular diet.

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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study (Preprint)

10.2196/preprints.26107 ◽

2020 ◽

Author(s):

Robert P Hirten ◽

Matteo Danieletto ◽

Lewis Tomalin ◽

Katie Hyewon Choi ◽

Micol Zweig ◽

...

Keyword(s):

Observational Study ◽

Predictive Ability ◽

Mean Amplitude ◽

Wearable Device ◽

Physiological Data ◽

Circadian Pattern ◽

Mixed Effect ◽

Normal Sinus ◽

Nervous System Function ◽

The Mean

BACKGROUND Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

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P6441New continuous glucose monitoring reveals hypoglycemia risk in both diabetic and nondiabetic patients with acute myocardial infarction

European Heart Journal ◽

10.1093/eurheartj/ehz746.1035 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

M Kuroda ◽

M Kawata ◽

A Matsuura ◽

K Adachi ◽

Y Hirayama ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Continuous Glucose Monitoring ◽

Glucose Monitoring ◽

Mean Amplitude ◽

Glucose Fluctuation ◽

Glucose Levels ◽

Interstitial Glucose ◽

The Mean ◽

Artery Disease

Abstract Background There has been growing evidence that the glucose fluctuation is an important contributing factor to the development of coronary artery disease. However, whether large glucose fluctuation, especially hypoglycemia, may be associated with acute myocardial infarction (AMI) remains largely unknown. Aim As new continuous glucose monitoring (CGM) has recently become available to evaluate glucose fluctuation from immediately after an emergency visit, this study sought to investigate glucose fluctuation and the occurrence of hypoglycemia in patients with AMI. Methods In this prospective study, 93 consecutive patients with AMI from April 2017 to November 2018 were enrolled. Subcutaneous interstitial glucose levels were monitored from emergency room to discharge using the CGM System. Based on the CGM data, 24-h mean glucose levels, the time in hyperglycemia and hypoglycemia and the occurrence of hypoglycemia, defined as less than 70 mg/dL, were measured, and the mean amplitude of glycemic excursions (MAGE) were calculated. Results The majority of patients [n=57, 61% (non-DM)] did not have diabetes and 36 patients had diabetes (DM). The occurrence of hypoglycemia within 24 hours after admission was observed in 49 patients [DM: n=11 (30.6%), non-DM: n=38 (66.7%)]. MAGE within 24 hours after admission were 100±47 in DM patients and 67±20 in non-DM patients. The mean time in hypoglycemia within 24 hours after admission was 148 minutes [DM: 100±260 minutes, non-DM: 178±287 minutes]. The occurrence of hypoglycemia during a hospital stay (mean 11.5 days) was detected in 76 patients [DM: n=28 (77.8%), non-DM: n=48 (84.2%)]. Representative case of hypoglycemia Conclusion Not only in DM patients but also in non-DM patients with AMI, large glucose fluctuation and high incidence of hypoglycemia were observed using new CGM system. Further investigations should address the rationale for the early detection and control of glucose fluctuation for AMI patients.

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Attrition Bias in an Observational Study of Very Low‐Energy Diet: A Cohort Study

Obesity ◽

10.1002/oby.23031 ◽

2020 ◽

Vol 29 (1) ◽

pp. 213-219

Author(s):

Deanna J. M. Isaman ◽

William H. Herman ◽

Amy E. Rothberg

Keyword(s):

Cohort Study ◽

Observational Study ◽

Low Energy ◽

Attrition Bias ◽

Very Low Energy Diet ◽

Low Energy Diet

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Glycemic Variability Impacted by SGLT2 Inhibitors and GLP 1 Agonists in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine ◽

10.3390/jcm10184078 ◽

2021 ◽

Vol 10 (18) ◽

pp. 4078

Author(s):

Heeyoung Lee ◽

Se-eun Park ◽

Eun-Young Kim

Keyword(s):

Meta Analysis ◽

Glycemic Variability ◽

Mean Amplitude ◽

Randomized Controlled ◽

Sodium Glucose Cotransporter ◽

Patients With Diabetes ◽

The Mean ◽

Glucagon Like Peptide ◽

Glucagon Like Peptide 1 ◽

Mean Blood Glucose

To investigate the effect of sodium-glucose cotransporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) agonists on glycemic variability (GV), the mean amplitude of glucose excursion (MAGE), mean blood glucose (MBG) levels, and percentage of time maintaining euglycemia were evaluated. Randomized controlled trials evaluating the efficacy of SGLT-2 inhibitors and GLP-1 agonists for treating people with diabetes were selected through searches of PubMed, EMBASE, and other databases. Sixteen studies were finally analyzed. There were no differences in the reductions in MAGE after treatment with SGLT-2 inhibitors or GLP-1 agonists (standardized mean difference (SMD) = −0.59, 95% CI = −0.82 to −0.36 vs. SMD = −0.43, 95% CI = −0.51 to −0.35, respectively), and treatment with SGLT-2 inhibitors was associated with an increased reduction in MBG levels (SMD = −0.56, 95% CI = −0.65 to −0.48, p < 0.00001). Monotherapy and add-on therapy with medications were correlated with MAGE and MBG level reductions. In conclusion, SGLT-2 inhibitors and GLP-1 agonists were associated with a reduction in GV and could be alternatives for treating people with diabetes.

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Improved Glycemic Control and Variability: Application of Healthy Ingredients in Asian Staples

Nutrients ◽

10.3390/nu13093102 ◽

2021 ◽

Vol 13 (9) ◽

pp. 3102

Author(s):

Stefan Gerardus Camps ◽

Bhupinder Kaur ◽

Joseph Lim ◽

Yi Ting Loo ◽

Eunice Pang ◽

...

Keyword(s):

Glycemic Control ◽

Glycemic Load ◽

Glucose Monitoring ◽

Area Under The Curve ◽

Glycemic Variability ◽

Mean Amplitude ◽

Carbohydrate Intake ◽

Functional Ingredients ◽

Glucose Levels ◽

Dietary Strategy

A reduction in carbohydrate intake and low-carbohydrate diets are often advocated to prevent and manage diabetes. However, limiting or eliminating carbohydrates may not be a long-term sustainable and maintainable approach for everyone. Alternatively, diet strategies to modulate glycemia can focus on the glycemic index (GI) of foods and glycemic load (GL) of meals. To assess the effect of a reduction in glycemic load of a 24 h diet by incorporating innovative functional ingredients (β-glucan, isomaltulose) and alternative low GI Asian staples (noodles, rice)on glycemic control and variability, twelve Chinese men (Age: 27.0 ± 5.1 years; BMI:21.6 ± 1.8kg/m2) followed two isocaloric, typically Asian, 24h diets with either a reduced glycemic load (LGL) or high glycemic load (HGL) in a randomized, single-blind, controlled, cross-over design. Test meals included breakfast, lunch, snack and dinner and the daily GL was reduced by 37% in the LGL diet. Continuous glucose monitoring provided 24 h glycemic excursion and variability parameters: incremental area under the curve (iAUC), max glucose concentration (Max), max glucose range, glucose standard deviation (SD), and mean amplitude of glycemic excursion (MAGE), time in range (TIR). Over 24h, the LGL diet resulted in a decrease in glucose Max (8.12 vs. 6.90 mmol/L; p = 0.0024), glucose range (3.78 vs. 2.21 mmol/L; p = 0.0005), glucose SD (0.78 vs. 0.43 mmol/L; p = 0.0002), mean amplitude of glycemic excursion (2.109 vs. 1.008; p < 0.0001), and increase in 4.5–6.5mmol/L TIR (82.2 vs. 94.6%; p = 0.009), compared to the HGL diet. The glucose iAUC, MAX, range and SD improved during the 2 h post-prandial window of each LGL meal, and this effect was more pronounced later in the day. The current results validate the dietary strategy of incorporating innovative functional ingredients (β-glucan, isomaltulose) and replacing Asian staples with alternative low GI carbohydrate sources to reduce daily glycemic load to improve glycemic control and variability as a viable alternative to the reduction in carbohydrate intake alone. These observations provide substantial public health support to encourage the consumption of staples of low GI/GL to reduce glucose levels and glycemic variability. Furthermore, there is growing evidence that the role of chrononutrition, as reported in this paper, requires further examination and should be considered as an important addition to the understanding of glucose homeostasis variation throughout the day.

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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Journal of Medical Internet Research ◽

10.2196/26107 ◽

2021 ◽

Vol 23 (2) ◽

pp. e26107 ◽

Cited By ~ 1

Author(s):

Robert P Hirten ◽

Matteo Danieletto ◽

Lewis Tomalin ◽

Katie Hyewon Choi ◽

Micol Zweig ◽

...

Keyword(s):

Observational Study ◽

Predictive Ability ◽

Mean Amplitude ◽

Wearable Device ◽

Physiological Data ◽

Circadian Pattern ◽

Mixed Effect ◽

Normal Sinus ◽

Nervous System Function ◽

The Mean

Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

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Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Clinical Medicine Insights Endocrinology and Diabetes ◽

10.1177/11795514211013789 ◽

2021 ◽

Vol 14 ◽

pp. 117955142110137

Author(s):

Bader Alzahrani ◽

Saad Alzahrani ◽

Mussa H Almalki ◽

Souha S Elabd ◽

Shawana Abdulhamid Khan ◽

...

Keyword(s):

Type 1 Diabetes ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Mean Amplitude ◽

Glucose Variability ◽

International Standards ◽

Medical City ◽

Interstitial Glucose

Background: Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM. Methods: We prospectively assessed interstitial glucose using FreeStyle® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks. Results: Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001). Conclusion: Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

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CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

Frontiers in Endocrinology ◽

10.3389/fendo.2021.666008 ◽

2021 ◽

Vol 12 ◽

Author(s):

Anne-Esther Breyton ◽

Stéphanie Lambert-Porcheron ◽

Martine Laville ◽

Sophie Vinoy ◽

Julie-Anne Nazare

Keyword(s):

Physical Activity ◽

Glycemic Control ◽

Glucose Control ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Mean Amplitude ◽

Control Parameters ◽

Postprandial Glycemia ◽

The Mean ◽

Mean Blood Glucose

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters’ (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

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What Is the Profile of Overweight Individuals Who Are Unsuccessful Responders to a Low-Energy Diet? A PREVIEW Sub-study

Frontiers in Nutrition ◽

10.3389/fnut.2021.707682 ◽

2021 ◽

Vol 8 ◽

Author(s):

Angelo Tremblay ◽

Mikael Fogelholm ◽

Elli Jalo ◽

Margriet S. Westerterp-Plantenga ◽

Tanja C. Adam ◽

...

Keyword(s):

Weight Loss ◽

Body Weight ◽

Body Weight Loss ◽

Low Energy ◽

Energy Deficit ◽

Diet Restriction ◽

Daily Energy ◽

The Mean ◽

Improve Sleep Quality ◽

Low Energy Diet

This study was performed to evaluate the profile of overweight individuals with pre-diabetes enrolled in PREVIEW who were unable to achieve a body weight loss of ≥8% of the baseline value in response to a 2-month low-energy diet (LED). Their baseline profile reflected potential stress-related vulnerability that predicted a reduced response of body weight to a LED programme. The mean daily energy deficit maintained by unsuccessful weight responders of both sexes was less than the estimated level in successful female (656 vs. 1,299 kcal, p < 0.01) and male (815 vs. 1,659 kcal, p < 0.01) responders. Despite this smaller energy deficit, unsuccessful responders displayed less favorable changes in susceptibility to hunger and appetite sensations. They also did not benefit from the intervention regarding the ability to improve sleep quality. In summary, these results show that some individuals display a behavioral vulnerability which may reduce the ability to lose weight in response to a diet-based weight loss program. They also suggest that this vulnerability may be accentuated by a prolonged diet restriction.

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Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable Algorithm Based on the Integer Nonlinear Method

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/6286893 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Xuefei Yu ◽

Liangzhuo Lin ◽

Jie Shen ◽

Zhi Chen ◽

Jun Jian ◽

...

Keyword(s):

Glycemic Variability ◽

Mean Amplitude ◽

Nonlinear Method ◽

Manual Method ◽

Mathematical Algorithm ◽

Software Products ◽

Huge Data ◽

The Mean ◽

Open Code ◽

Manual Calculation

The mean amplitude of glycemic excursions (MAGE) is an essential index for glycemic variability assessment, which is treated as a key reference for blood glucose controlling at clinic. However, the traditional “ruler and pencil” manual method for the calculation of MAGE is time-consuming and prone to error due to the huge data size, making the development of robust computer-aided program an urgent requirement. Although several software products are available instead of manual calculation, poor agreement among them is reported. Therefore, more studies are required in this field. In this paper, we developed a mathematical algorithm based on integer nonlinear programming. Following the proposed mathematical method, an open-code computer program named MAGECAA v1.0 was developed and validated. The results of the statistical analysis indicated that the developed program was robust compared to the manual method. The agreement among the developed program and currently available popular software is satisfied, indicating that the worry about the disagreement among different software products is not necessary. The open-code programmable algorithm is an extra resource for those peers who are interested in the related study on methodology in the future.

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