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The Impact of Physical Activity on Glycemic Variability Assessed by Continuous Glucose Monitoring in Patients With Type 2 Diabetes Mellitus: A Systematic Review.

ABSTRACT: Aim: Patients with Type 2 Diabetes Mellitus (T2DM) have increased risk of developing vascular complications due to chronic hyperglycemia. Glycemic variability (GV) has been suggested to play an even more important role in the risk of developing diabetic complications than sustained hyperglycemia. Physical activity (PA) has shown reducing effects on mean plasma glucose; however, the effect on GV in T2DM needs further description. The objective of this review is to evaluate the effect of PA on GV, assessed by continuous glucose monitoring (CGM) in people with T2DM. Methods: A systematic literature search was conducted on MEDLINE and Embase to find randomized controlled trials (RCTs) covering the aspects T2DM, PA, and CGM. Following eligibility screening, variables of population characteristics, PA interventions, and GV outcomes were extracted and processed through qualitative synthesis. Risk of bias (ROB) was assessed using Cochrane ROB tool v2.0. Results: Of 1,825 identified articles, 40 full texts were screened. In the ten included RCTs matching the eligibility criteria, sample sizes ranged from nine to 63, mean age from 51 (SD 11) to 65 (SD 2) years and mean T2DM duration from four (SD 3) to ten (SD 6) years. Eight RCTs examined GV following single bouts of exercise, while two RCTs examined GV following training interventions. One RCT applied parallel group design, while nine RCTs applied crossover design. Numeric reductions in GV following acute exercise were seen, with four RCTs reaching statistical significance. Numeric reductions in GV were seen following training interventions, with one RCT reaching statistical significance. Numeric reductions of GV after PA appeared independently of intensity and T2DM progression but higher in participants with high baseline HbA1c and GV than with low. 80% of the trials were evaluated as uncertain/high ROB. Conclusion: The systematic literature search revealed limited and biased evidence showing that acute PA numerically reduced GV in patients with T2DM. PA reduced GV independently of PA intensity and T2DM progression. Prolonged RCTs with low ROB are needed to confirm reducing effects of PA on GV and to assess the influence of patient- and intervention characteristics on the effect of PA on GV.

SUBMITTER: Bennetsen SL

PROVIDER: S-EPMC7438766 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Impact of Physical Activity on Glycemic Variability Assessed by Continuous Glucose Monitoring in Patients With Type 2 Diabetes Mellitus: A Systematic Review.

Bennetsen Sebastian L SL Feineis Camilla S CS Legaard Grit E GE Lyngbæk Mark P P MPP Karstoft Kristian K Ried-Larsen Mathias M

Frontiers in endocrinology 20200731

<b>Aim:</b> Patients with Type 2 Diabetes Mellitus (T2DM) have increased risk of developing vascular complications due to chronic hyperglycemia. Glycemic variability (GV) has been suggested to play an even more important role in the risk of developing diabetic complications than sustained hyperglycemia. Physical activity (PA) has shown reducing effects on mean plasma glucose; however, the effect on GV in T2DM needs further description. The objective of this review is to evaluate the effect of PA ...[more]

PMID: 32903679

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Project description:INTRODUCTION:The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM). METHODS:Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed. RESULTS:Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption. CONCLUSIONS:Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Project description:BackgroundDeterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D).MethodsWe recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9-10 mmol/L (TIR3.9-10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose.ResultsHbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9-10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9-10 (r = - 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9-10 (β = - 0.401, t = - 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177).ConclusionsIncreased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.

Project description:BackgroundIt is presently unclear whether glycemic variability (GV) is associated with baroreflex sensitivity (BRS), which is an early indicator of cardiovascular autonomic neuropathy. The present study is the first to examine the relationships between BRS and GV measured using continuous glucose monitoring (CGM).MethodsThis was a multicenter, prospective, open-label clinical trial. A total of 102 patients with type 2 diabetes were consecutively recruited for this study. GV was assessed by measuring the standard deviation (SD), glucose coefficient of variation (CV), and the mean amplitude of glycemic excursions (MAGE) during CGM. The BRS was analyzed from electrocardiogram and blood pressure recordings using the sequence method on the first day of hospitalization.ResultsA total of 94 patients (mean diabetes duration 9.7?±?9.6 years, mean HbA1c 61.0?±?16.8 mmol/mol [7.7?±?1.5%]) were analyzed. In the univariate analysis, CGM-SD (r?=?-?0.375, p?=?0.000), CGM-CV (r?=?-?0.386, p?=?0.000), and MAGE (r?=?-?0.395, p?=?0.000) were inversely related to BRS. In addition to GV, the level of BRS correlated with the coefficient of variation in the R-R intervals (CVR-R) (r?=?0.520, p?=?0.000), heart rate (HR) (r?=?-?0.310, p?=?0.002), cardio-ankle vascular index (CAVI) (r?=?-?0.326, p?=?0.001), age (r?=?-?0.519, p?=?0.000), and estimated glomerular filtration rate (eGFR) (r?=?0.276, p?=?0.007). Multiple regression analysis showed that CGM-CV and MAGE were significantly related to a decrease in BRS. These findings remained after adjusting the BRS for age, sex, hypertension, dyslipidemia, HR, eGFR, CAVI, and CGM-mean glucose. Additionally, BRS was divided according to quartiles of the duration of diabetes (Q1-4). BRS decreased after a 2-year duration of diabetes independently of age and sex.ConclusionsGV was inversely related to BRS independently of blood glucose levels in type 2 diabetic patients. Measurement of BRS may have the potential to predict CV events in consideration of GV. Trial registration UMIN Clinical Trials Registry UMIN000025964, 28/02/2017.

Project description:BackgroundHbA1c variability is independent of mean HbA1c, and associated with mortality due to vascular complications. However, the significance of HbA1c variability is unknown at present. In this study, we used flash glucose monitoring (FGM) and evaluated glycemic intraday variations, and then examined the association with HbA1c variability.MethodsWe conducted a retrospective pilot study of 26 patients treated at the Outpatient department for type 2 diabetes mellitus (T2DM), and evaluated the following items associated with blood glucose levels and their changes/variations using FGM. The primary endpoint was factor(s) associated with standard deviation (SD) HbA1c over a 6-month period. To adjust for the effect of varying numbers of HbA1c measurements, we used the adjusted SD of HbA1c.ResultsThere were significant correlations between mean HbA1c and each of glucose management indicator, maximum, percent time at glucose > 180 mg/day, mean of daily difference of blood glucose, and high blood glucose index. Adjusted SD HbA1c correlated significantly with percent time at glucose < 70 mg/dL and low blood glucose index. We estimated the regression coefficient of adjusted SD HbA1c using multivariate linear regression analysis, and noted that the presence of hypoglycemia affected Adjusted SD HbA1c (β = 0.130, SE = 0.044, P = 0.008). Hypoglycemia was noted in 17 patients, and adjusted SD HbA1c was significantly higher (p = 0.001) in the hypoglycemic group (0.22 ± 0.12%), compared with the non-hypoglycemic group (0.08 ± 0.05%). The cut-off value of adjusted SD HbA1c was 0.109% in the hypoglycemic group.ConclusionsThe results showed that HbA1c variability is associated with hypoglycemia. For patients with high HbA1c variability, we recommend evaluation for the presence of hypoglycemia and reconsideration of their treatment regimen including their glucose-lowering medications. Trial registration The study protocol and opt-out method of informed consent were approved by the ethics committees of the University of Occupational and Environmental Health (Trial registration: H27-186, Registered 25 Dec 2015).

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The Impact of Physical Activity on Glycemic Variability Assessed by Continuous Glucose Monitoring in Patients With Type 2 Diabetes Mellitus: A Systematic Review.

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The Impact of Physical Activity on Glycemic Variability Assessed by Continuous Glucose Monitoring in Patients With Type 2 Diabetes Mellitus: A Systematic Review.

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