Dataset Information

Glycemic Variability and Brain Glucose Levels in Type 1 Diabetes.

ABSTRACT: The impact of glycemic variability on brain glucose transport kinetics among individuals with type 1 diabetes mellitus (T1DM) remains unclear. Fourteen individuals with T1DM (age 35 ± 4 years; BMI 26.0 ± 1.4 kg/m²; HbA_1c 7.6 ± 0.3) and nine healthy control participants (age 32 ± 4; BMI 23.1 ± 0.8; HbA_1c 5.0 ± 0.1) wore a continuous glucose monitor (Dexcom) to measure hypoglycemia, hyperglycemia, and glycemic variability for 5 days followed by ¹H MRS scanning in the occipital lobe to measure the change in intracerebral glucose levels during a 2-h glucose clamp (target glucose concentration 220 mg/dL). Hyperglycemic clamps were also performed in a rat model of T1DM to assess regional differences in brain glucose transport and metabolism. Despite a similar change in plasma glucose levels during the hyperglycemic clamp, individuals with T1DM had significantly smaller increments in intracerebral glucose levels (P = 0.0002). Moreover, among individuals with T1DM, the change in brain glucose correlated positively with the lability index (r = 0.67, P = 0.006). Consistent with findings in humans, streptozotocin-treated rats had lower brain glucose levels in the cortex, hippocampus, and striatum compared with control rats. These findings that glycemic variability is associated with brain glucose levels highlight the need for future studies to investigate the impact of glycemic variability on brain glucose kinetics.

SUBMITTER: Hwang JJ

PROVIDER: S-EPMC6302539 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Glycemic Variability and Brain Glucose Levels in Type 1 Diabetes.

Hwang Janice J JJ Jiang Lihong L Sanchez Rangel Elizabeth E Fan Xiaoning X Ding Yuyan Y Lam Wai W Leventhal Jessica J Dai Feng F Rothman Douglas L DL Mason Graeme F GF Sherwin Robert S RS

Diabetes 20181016 1

The impact of glycemic variability on brain glucose transport kinetics among individuals with type 1 diabetes mellitus (T1DM) remains unclear. Fourteen individuals with T1DM (age 35 ± 4 years; BMI 26.0 ± 1.4 kg/m2; HbA1c 7.6 ± 0.3) and nine healthy control participants (age 32 ± 4; BMI 23.1 ± 0.8; HbA1c 5.0 ± 0.1) wore a continuous glucose monitor (Dexcom) to measure hypoglycemia, hyperglycemia, and glycemic variability for 5 days followed by 1H MRS sc ...[more]

PMID: 30327383

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Project description:AimEvidence for contribution of basal and postprandial glucose increment, and glycemic variability to glycated hemoglobin (HbA1c) among adults with type 1 diabetes (T1D) is limited. This study aimed to capture glycemic fluctuation patterns and quantify contributions of these factors to HbA1c levels among adults with T1D.MethodsHbA1c, continuous glucose monitoring (CGM), and diet diaries were collected and pooled from two clinical trials. Available data sets were divided into HbA1c quartiles: group 1 (≤6.7%), group 2 (6.7%-7.3%), group 3 (7.3%-7.8%), and group 4 (≥7.8%). Area under curve above 110 mg/dL (AUC>110mg/dL ) in 24-h profile was defined as overall hyperglycemia and stratified with postprandial hyperglycemia (PHG, AUC>110mg/dL in 3-h period after meals) and basal hyperglycemia (BHG, AUC>110mg/dL in remaining period). Linear regression analysis was used to estimate the proportion of variance in HbA1c explained by BHG, preprandial glucose, PHG, glycemic variability, and non-glycemic factors (age, body mass index, hemoglobin, and duration).ResultsA total of 169 550 glucose data in 2409 meals recorded from 102 patients (male/female, 34/68) were included. Age and duration were 35.2 ± 12.6 and 8.9 (2.9, 13.0) years, with 51.0% using pumps. Overall, BHG was four times higher than PHG (p all <.05) and between-group comparisons showed BHG exhibited a progressive increase (group 1 vs. 2, 3, 4, p = .053, .086, .006) with fasting contribution of 76.1%, 82.6%, 81.5%, and 84.3% from group 1 to 4. The increment was not significant among groups 2, 3, and 4 (p > .05). Factors included in analysis explained a total of 74% of the variance in HbA1c, in which BHG accounted for 32.1% of variance whereas PHG accounted for 24.4%. In group with HbA1c >7.3%, BHG accounted for a higher percentage with 33.8% of the variance in HbA1c.ConclusionsIn our study, basal hyperglycemia better predicts overall glycemic control than postprandial hyperglycemia among adults with T1D. The relative contribution of basal hyperglycemia increased gradually with HbA1c increasing and predominant strategy for insulin titration among T1D is different among different levels of glycemic control.

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: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).

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.

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Glycemic Variability and Brain Glucose Levels in Type 1 Diabetes.

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Glycemic Variability and Brain Glucose Levels in Type 1 Diabetes.

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