Mean Of Daily Differences Research Articles

Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes

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.

Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes.

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7-14 days in young adults with type 1 diabetes. In addition, person-level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined. In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring over 6-14 days. At baseline, participants completed the Psychomotor Vigilance Test, the Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries. Forty-six participants (mean age, 22.3 ± 3.2 years) wore a wrist actigraph and underwent continuous glucose monitoring concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences; r = .33, P = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences; r = .50, P = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for type 1 diabetes duration (P > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, P = .010, pr2 = 0.40) after controlling for type 1 diabetes duration and accounting for higher daytime sleepiness. Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with type 1 diabetes. Sleep habits in this population may explain higher glucose variability, and optimizing sleep may improve overall diabetes management. Griggs S, Hickman RL Jr, Strohl KP, Redeker NS, Crawford SL, Grey M. Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes. J Clin Sleep Med. 2021;17(9):1865-1874.

Significant abnormal glycemic variability increased the risk for arrhythmias in elderly type 2 diabetic patients

BackgroundLittle is known about whether the influence of glycemic variability on arrhythmia is related to age in type 2 diabetes mellitus (T2DM). Therefore, we aimed to compare the association between glycemic variability and arrhythmia in middle-aged and elderly T2DM patients.MethodsA total of 107 patients were divided into two groups: elderly diabetes mellitus group (EDM, n = 73) and middle-aged diabetes mellitus group (MDM, n = 34). The main clinical data, continuous glucose monitoring (CGM) and dynamic ECG reports were collected. The parameters including standard deviation of blood glucose (SDBG), largest amplitude of glycemic excursions (LAGE), mean amplitude of glycemic excursions (MAGE), absolute means of daily differences (MODD), time in range (TIR), time below range (TBR), time above range (TAR), coefficient of variation (CV) were tested for glycemic variability evaluation.ResultsIn terms of blood glucose fluctuations, MAGE (5.77 ± 2.16 mmol/L vs 4.63 ± 1.89 mmol/L, P = 0.026), SDBG (2.39 ± 1.00 mmol/L vs 2.00 ± 0.82 mmol/L, P = 0.048), LAGE (9.53 ± 3.37 mmol/L vs 7.84 ± 2.64 mmol/L, P = 0.011) was significantly higher in EDM group than those of MDM group. The incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat were significantly higher in EDM group compared with the MDM group (all P < 0.05). Among patients with hypoglycemia events, the incidences of atrial premature beat, couplets of atrial premature beat, atrial tachycardia and ventricular premature beat (all P < 0.05) were significantly higher in the EDM group than those in the MDM group. In EDM group, TIR was negatively correlated with atrial tachycardia in the MAGE1 layer and with atrial tachycardia and ventricular premature beat in the MAGE2 layer, TBR was significantly positively correlated with atrial tachycardia in the MAGE2 layer (all P < 0.05). In MDM group, TAR was positively correlated with ventricular premature beat and atrial tachycardia in the MAGE2 layer (all P < 0.05).ConclusionsThe study demonstrated the elderly patients had greater glycemic variability and were more prone to arrhythmias. Therefore, active control of blood glucose fluctuation in elderly patients will help to reduce the risk of severe arrhythmia.

The Effects of Dietary Nutrition Intake on Glycemic Variability in Type 1 Diabetes Mellitus Adults.

IntroductionType 1 diabetes mellitus (T1DM) is characterized by an absolute deficiency of insulin and dependence on insulin therapy. Therefore, glycemic control and management are important for T1DM patients, particularly glycemic variability, which is associated with the development of diabetic complications. However, insufficient attention has been paid to the glycemic variability in T1DM patients so far. Our objective was to identify the effects of food intake on glycemic variability in T1DM patients.MethodsThis was a single-center study that took place in the outpatient clinics of Peking Union Medical College Hospital. A total of 68 Chinese T1DM patients between June 2018 and June 2019 were enrolled. After the baseline demographic and clinical characteristics were evaluated, each participant underwent 14-day flash glucose monitoring (FGM). They recorded caloric intake of breakfast, lunch, and dinner at least 3 days/week using a “Menthol Health” app. After 2 weeks, we obtained the FGM data and did further data analysis. Baseline characteristics and glycemic variability index generated by FGM were compared among groups. A general linear model was used to compare data among groups after adjusting for potential confounding factors. The quantitative relationship between two continuous variables was explored by constructing a linear regression equation.ResultsThe results showed that the C-peptide level was independently correlated with the mean of daily differences (MODD) after adjusting for the possible confounders (β = − 0.239, p = 0.046). The dietary nutrition intake had no effect on glycemic variability. However, the nutritional composition of carbohydrate, fat, and protein was an independent risk factor for time spent in hypoglycemia (TBR) post adjustment (β = − 0.213, p = 0.054). However, there was no impact of daily total energy intake on glycemic variability index.ConclusionIn our study, dietary nutrition intake had no effect on glycemic variability, but residual β-cell function was identified as an influencing factor for glycemic variability in T1DM adults. However, nutritional macronutrient composition played some roles in the occurrence of hypoglycemia. This might provide new evidence for the clinical glycemic control and management of T1DM in the Chinese population.

Critical illness myopathy precedes hyperglycaemia and high glucose variability

Critical Illness Myopathy (CIM) is a serious ICU complication, and dysglycaemia is widely regarded as a risk factor. Although glucose variability (GV) has been independently linked to ICU mortality, an association with CIM has not been investigated. This study examines the relationship between CIM and GV. Retrospective investigation including ICU patients with SOFA ≥8, mechanical ventilation, and CIM diagnostics. Glucose readings were collected every 6h throughout the first week of treatment, when CIM is thought to develop. GV was measured using standard deviation (SD), coefficient of variability (CV), mean absolute glucose (MAG), mean amplitude of glycaemic excursions (MAGE), and mean of daily difference (MODD). 74 patients were included, and 50 (67.6%) developed CIM. Time on glycaemic target (70-179mg/dL), caloric and insulin intakes, mean, maximum and minimum blood glucose values were similar for all patients until the 5th day, after which CIM patients exhibited higher mean and maximum glucose levels. Significantly higher GV in CIM patients were observed on day 5 (SD, CV, MAG, MAGE), day 6 (MODD), and day 7 (SD, CV, MAG). CIM patients developed transient increases in GV and hyperglycaemia only late in the first week, suggesting that myopathy precedes dysglycaemia.

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

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.

Exenatide once weekly combined with metformin reduced glycemic variability in type 2 diabetes by using flash glucose monitoring system

BACKGROUNDMultiple studies demonstrate that fluctuating blood glucose level produces greater damage compared with sustained hyperglycemia. Flash glucose monitoring system is an effective method in documenting blood glucose variability, contributing to better glucose management and reduced hypoglycemic event occurrence.AIMTo investigate the improvement in glycemic variability (GV), blood glucose level, and metabolic indexes of patients with type 2 diabetes mellitus after combined treatment of exenatide once weekly (EXQW) and metformin.METHODSTwenty-five patients with type 2 diabetes mellitus suffering from poor blood glucose control under metformin treatment were recruited. The recruited patients were prescribed with oral metformin only (maintaining a dosage of metformin at ≥ 1500 mg/day) for 2 wk (screening period), and then given EXQW (2 mg, subcutaneous injection) for 12 wk (experimental period). The flash glucose monitoring system was used to document blood glucose values during the screening period and the last 2 wk of the experimental period. RESULTSFour patients were excluded for various reasons, yielding a total of 21 patients, including 17 males and 4 females, with an average age of 48.8 years, who completed this study. The estimated glycated hemoglobin, mean blood glucose, fasting and postprandial blood glucose levels, and percentage of blood glucose above 7.8 mmol/L decreased compared to those at baseline (P = 0.003, 0.003, 0.008, 0.010, 0.014, 0.017, and 0.005, respectively), while the percentage of blood glucose between 3.9 and 7.8 mmol/L significantly increased (P = 0.005). Parameters of GV including standard deviation of blood glucose, mean amplitude of glycemic excursions, mean of daily difference, area under the curve difference between percentiles 25 and 75, and area under the curve difference between percentiles 10 and 90 were significantly lower compared to that of baseline (P = 0.017, 0.006, 0.000, 0.024, 0.036, respectively). The durations of blood glucose below 3.9 mmol/L during the day and nocturnal periods significantly increased after treatment (P = 0.041 and 0.028, respectively), but there was no significant increase in severe hypoglycemia (< 3.0 mmol/L) compared with that at baseline (P = 0.207). In addition, some metabolic indicators improved after EXQW treatment.CONCLUSIONEXQW combined with metformin can effectively improve blood glucose levels, reduce GV, and improve metabolic indicators. However, there is still a risk of nocturnal hypoglycemia, and careful attention should be paid to patients with EXQW treatment.

Effects of novel flash glucose monitoring system on glycaemic control in adult patients with type 1 diabetes mellitus: protocol of a multicentre randomised controlled trial

IntroductionOptimal glycaemic control is beneficial to prevent and delay microvascular complications in patients with type 1 diabetes mellitus (T1DM). The benefits of flash glucose monitoring (FGM) have been proved among...

The relationship between glycemic variability and blood pressure variability in normoglycemic normotensive individuals.

Glycemic fluctuations around a mean glucose level, referred as glycemic variability and blood pressure variability (BPV) are considered as independent risk factors for cardiovascular diseases, all-cause mortality, and cardiovascular disease-mortality. With this background in mind, we aimed to investigate the association between glycemic variability and BPV and their association in normoglycemic and normotensive individuals. Twenty-seven normotensive normoglycemic individuals were recruited. Twenty-four hour Holter devices were utilized to measure ambulatory blood pressure (BP) while continuous glucose monitoring (CGM) devices were applied to measure glycemic variability simultaneously to the subjects. These devices were kept on for 48 h. For BP recordings, daytime, nighttime, and 24-h BP determinations, their mean and SD were calculated. From CGM measurements, mean blood glucose (MBG), SD of blood glucose, the mean amplitude of glycemic excursions (MAGE), the mean of daily differences (MODD), coefficient of variation (correction of variability for the MBG), and daytime and nighttime blood glucose were determined. The mean age of the subjects was 23.8 ± 2.7 years and 66% were women (18/27). In the correlation analysis between glycemic variability parameters and BPV parameters, SD of 24-h SBP was correlated with the SD of MBG (r = 0.52, P = 0.006), MAGE (r = 0.49, P = 0.009), and MODD (r = 0.46, P = 0.015). SD of daytime SBP was correlated with, MAGE (r = 0.42, P = 0.03) and MODD (r = 0.43, P = 0.02). We report correlation between glycemic variability and BPV variables in normoglycemic and normotensive healthy individuals.

Comparison of Blood Glucose Variability Between Exenatide and Biphasic Insulin Aspart 30 in Chinese Participants with Type 2 Diabetes Inadequately Controlled with Metformin Monotherapy: A Multicenter, Open-Label, Randomized Trial.

IntroductionTo compare blood glucose variability (GV) in Chinese participants with type 2 diabetes mellitus (T2DM) whose blood glucose levels are inadequately controlled with metformin monotherapy after twice-daily exenatide or biphasic insulin aspart 30 (BIAsp30).MethodsIn this 16-week multicenter, randomized clinical trial, 104 participants were randomized 1:1 to receive exenatide (exenatide group) or BIAsp30 (BIAsp30 group) twice daily. All participants continued metformin treatment. The primary outcome was the change in GV as measured by a continuous glucose monitoring system (CGMS) from baseline to 16 weeks.ResultsAt 16 weeks, both the Exenatide and BIAsp30 groups effectively decreased mean glucose (MG), but neither group changed the mean amplitude of glycemic excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), or standard deviation of blood glucose (SDBG). The decrease in 2-h post-breakfast glucose excursions was greater in the Exenatide group compared to the BIAsp30 group, with a least square (LS) mean difference [95% CI] of (1.58 [0.53, 2.63]). Exenatide also significantly reduced 2-h post-lunch glucose excursion compared to BIAsp30 (LS mean difference [95% CI], 1.19 [0.18, 2.20]). The Exenatide group had significantly reduced body weight and body mass index (BMI), while the BIAsp30 group had increased weight and had no change in BMI. Both treatments were well tolerated with no serious hypoglycemic events and with fewer identified hypoglycemic events in the Exenatide group than in the BIAsp30 group (5.77% vs. 17.31%, P < 0.01).ConclusionAlthough there was no difference in change of GV between Exenatide and BIAsp30, exenatide provided more improvement in postprandial glucose excursion and weight control, without increasing the risk of hypoglycemia in Chinese patients with T2DM whose blood glucose was inadequately controlled with metformin. These findings may provide new options for patients who choose further hypoglycemic treatment, especially in patients with obesity who have large postprandial plasma glucose excursions.Trial RegistrationClinicalTrials.gov indentifier: NCT02449603.Electronic supplementary materialThe online version of this article (10.1007/s13300-020-00904-z) contains supplementary material, which is available to authorized users.

Efficacy and safety of Chinese herbal medicine on blood glucose fluctuations in patients with type 2 diabetes mellitus: A protocol of systematic review and meta-analysis.

Background:As one of the major chronic diseases that seriously threaten human health, type 2 diabetes mellitus (T2DM) has become a global public health problem. Blood glucose fluctuation is a risk factor independent of hyperglycemia. At present, the measures to treat blood glucose fluctuations in patients with T2DM are insufficient in effectiveness and safety. Medical practice and clinical studies have proved that Chinese herbal medicine has obvious advantages in reducing blood glucose fluctuations. In this systematic review, we will assess the efficacy and safety of Chinese herbal medicine in the treatment of blood glucose fluctuations in patients with T2DM.Methods:We will search related literature of PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Database, China National Knowledge Infrastructure, China Science and Technology Journal Database, and Wanfang Database, and will manually search grey documents such as literature such as conference articles and references articles. Eligible randomized controlled trials will be screened based on inclusion criteria, and data extraction, risk of bias assessment, publication bias assessment, subgroup analysis, and quality assessment will be performed. Review Manager version 5.3 software and stata version 13 software will be used for data analysis. Each process is independently conducted by 2 researchers, and if there is any objection, it will be submitted to the third researcher for resolution.Results:This study will provide evidence for the efficacy and safety of Chinese herbal medicine in the treatment of blood glucose fluctuations in patients with T2DM. Outcome measures include mean amplitude of glycemic excursions, 24 hours mean blood glucose, standard deviations of blood glucose, mean of daily differences, coefficient of variation, glucose time in range, fasting blood glucose, 2 hours postprandial blood glucose, glycated hemoglobin, HOMA-β, HOMA-IR, quality of life questionnaire, traditional Chinese medicine syndrome score, and adverse event.

Relationship between interstitial glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics; a pilot study

BackgroundTreatment indexes using continuous glucose monitoring (CGM) have become standardized internationally, and the use of ambulatory glucose profile (AGP) is currently recommended. However, the relationship between AGP indexes and standardized CGM metrics has not been investigated. Using flash glucose monitoring (FGM), this retrospective study served to evaluate the association of the inter-quartile range (IQR) of AGP with standardized CGM metrics.MethodsThe study subjects were 30 patients with type 2 diabetes mellitus (T2DM) and 23 non-diabetic patients (control group). We evaluated average IQR (AIQR) and standardized CGM metrics. The primary endpoint was the relationship between AIQR and Time in range (TIR) in a 24-h period.ResultsIn the T2DM group, the AIQR was notably high and correlated negatively with TIR, and positively with Time above range, average interstitial glucose level, standard deviation of interstitial glucose, coefficient of variation of interstitial glucose, and mean of daily difference in blood glucose (MODD). For the T2DM group, the AIQR was notably lower in patients who achieved TIR > 70%, compared to those who did not. The AIQR cutoff value, as determined by ROC analysis, was 28.3 mg/dl for those who achieved TIR > 70%. No association was detected between the presence of hypoglycemia and AIQR.ConclusionsOur study is the first to provide the AIQR cutoff value for achieving the TIR target value. The range of interstitial glucose variability in AGP was associated with indexes of intra- and interday variations and hyperglycemia. Our results provide new perspectives in the yet-to-be established methods for evaluation of AGP in practical clinical settings.

909-P: The Related Factors Accounting for the Discrepancy between Estimated Hemoglobin A1c Derived from CGMS and the Measured A1C in Patients with Type 2 Diabetes

Objective: Continuous glucose monitoring system (CGMS) is emerging as an important tool in management of diabetes. Estimated glycated hemoglobin (eA1c), a CGM-derived metric, can be widely used if it is highly matched with the measured glycated hemoglobin (A1c). This study aimed to evaluate the relationship between eA1c and A1c measured by high performance liquid chromatography. Methods: Data were collected from T2D patients between July 2017 and December 2019. Patients with abnormal hemoglobin levels and acute diabetic complications were excluded. Enrolled patients were divided into 3 groups based on the difference of eA1c and A1c (ΔA1c=eA1c-A1c): matched group (ΔA1c from -0.099% to 0.099%), overestimated group (ΔA1c ≥ +0.1%), and underestimated group (ΔA1c ≤ -0.1%). The indices of glycemic variability (GV) were obtained from CGMS. Results: The 102 enrolled patients were 55.9(12.1) [mean (SD)] years old, with the measured A1c of 6.89(1.25)% and eA1c of 6.20(0.65)%. A positive correlation between eA1c and measured A1c (r= 0.50, p&amp;lt;0.001) was found. Majority of the patients were allocated in the underestimated group (13.7%, 19.6% and 66.7% of the patients in the matched, overestimated and underestimated groups, respectively). ΔA1c was -0.027(0.06)%, 0.59(0.39)% and -1.20(1.03)% (p&amp;lt;0.001) in these 3 groups, respectively. Age, BMI, duration of disease, type of hypoglycemic therapy, duration and intensity of hypoglycemia, blood pressure, lipid profiles, and the indices of intra-day GV among the 3 groups were similar. Multivariate logistic regression analysis revealed that gender (β=7.99, p=0.01), hematocrit (β=4.28, p=0.01) and mean of daily differences (MODD) (β=-7.44, p=0.02) were independently related with ΔA1c. Conclusions: eA1c was generally underestimated compared to measured A1c in T2D patients. Gender, hematocrit and MODD were identified as significant factors accounting for this discrepancy. Disclosure B. Lin: None. W. Xu: None. Z. Liu: None. D. Yang: None. Y. Su: None. Y. Chen: None. J. Yan: None. B. Yao: None. Funding National Key Research and Development Program of China (2016YFC1304801)

0127 Relationship Between Sleep Metrics with Free-Living Glucose Concentrations and Glycemic Variability in Non-Diabetic Adults

Abstract Introduction Insufficient sleep and poor sleep quality have been associated with impaired glucose metabolism at fasting and under experimental conditions. Continuous glucose monitoring (CGM) measures glucose concentrations over an extended, free-living period that can be used to assess glycemic health. Relationships between CGM-assessed glucose concentrations and glycemic variability, an emerging glycemic health marker, with sleep metrics have yet to be elucidated. The purpose of this study was to examine the relationships between sleep metrics with glucose concentrations and glycemic variability in non-diabetic adults. Methods Twenty-four non-diabetic adults (age=46.0±5.8 years; BMI=32.2±5.7 kg/m2) completed actigraphy, sleep diary, and CGM over 7 consecutive days. Time-in-bed (TIB), total sleep time (TST), wake duration after sleep onset, and sleep efficiency [(TST÷TIB)×100%] were determined using actigraphy assisted with sleep diary input. Nightly variability of each sleep metric was calculated as standard deviation (SD) across all nights. Glucose concentrations at waking in the morning, and 1, 2, and 3 hours prior to waking, and diurnal, nocturnal, and 24-hour means were determined. Intra-day glycemic variability, including mean amplitude of glycemic excursions and continuous overlapping of net glycemic action of 1, 2, and 4 hours, and inter-day glycemic variability, mean of daily differences, were calculated. Pearson product correlations between sleep metrics with glucose concentrations and glycemic variability were performed. Results Average TIB and TST were 462.6±61.7 minutes and 403.3±59.7 minutes, respectively. TIB negatively correlated with glucose concentrations at 2 and 3 hours prior to waking (r=-0.42, p=0.04 and r=-0.42, p=0.04, respectively). Nightly variability in sleep efficiency positively correlated with waking, and 1, 2, and 3 hours prior to waking glucose concentrations (0.44≤r≤0.48, p≤0.03 for all). No sleep metrics correlated with glycemic variability measures (p≥0.10 for all). Conclusion Findings suggest a longer amount of sleep opportunity and more consistent sleep efficiency relate to better glucose metabolism in non-diabetic adults. Support American Heart Association 14BGIA20380706 and University of South Carolina Support to Promote Advancement of Research and Creativity Grant #11530-17-43917.

Relationship of ankle-brachial index, vibration perception threshold, and current perception threshold to glycemic variability in type 2 diabetes

To explore the relationship of glycemic variability with lower extremity arterial disease (LEAD) and diabetic peripheral neuropathy (DPN).Seventy-eight patients with type 2 diabetes were enrolled. All patients underwent 72-hour dynamic blood glucose monitoring and obtained mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), standard deviation of blood glucose (SD), largest amplitude of glycemic excursion (LAGE), mean blood glucose (MBG), T≥10.0 (percentage of time for blood glucose levels ≥10.0 mmol/L), T≤3.9 (percentage of time for blood glucose levels ≤3.9 mmol/L), and other glycemic variability parameters. In the meanwhile, in order to explore the correlation of glycemic variability parameters with ankle-brachial index (ABI), vibration perception threshold (VPT), and current perception threshold (CPT), all patients underwent quantitative diabetic foot screening, including ABI for quantitative assessment of lower extremity arterial lesions and VPT and CPT for quantitative assessment of peripheral neuropathy.Patients were divided into abnormal CPT group (n = 21) and normal CPT group (n = 57) according to the CPT values. Compared with the normal CPT group, abnormal CPT group showed significantly higher levels of HbA1c, longer duration of diabetes, and higher levels of T≤3.9 (P < .05). However, there was no significant difference of MAGE, SD, LAGE, MODD, and other glycemic variability parameters between abnormal CPT group and normal CPT group (P > .05). Pearson correlation analysis or Spearman correlation analysis showed that ABI negatively correlated with MBG, T≥10.0, SD, LAGE, and MAGE (P < .05), but no correlation of ABI with T≤3.9 and MODD (P > .05) was shown. VPT showed a positive correlation with T≥10.0 (P < .05), but no correlation with other glycemic variability parameters (P > .05). There was no correlation between the other CPT values and the glycemic variability parameters (P > .05), except that the left and right 250 Hz CPT values were positively correlated with T≤3.9 (P > .05).The higher the blood glucose levels, the severer the degree of LEAD and DPN lesions; the higher the incidence of hypoglycemia, the severer the degree of DPN lesions; the greater the fluctuation of blood glucose, the severer the degree of LEAD lesions. However, the glycemic variability was not significantly correlated with DPN.

Analysis of the effect of liraglutide on glycemic variability in patients with type 2 diabetes.

The efficacy of liraglutide in the treatment of glycemic variability in type 2 diabetic patients remains to be fully elucidated. Some studies evaluated the efficacy and safety of liraglutide in glycemic variability, and this meta-analysis was performed to evaluate the accuracy of the results of existing studies on the efficacy of liraglutide. We conducted a comprehensive search for all relevant studies published in PubMed, EMBASE, Cochrane Library, and China Academic Journal Full-Text Database from the beginning of 2011 to October 31, 2019. The mean ± SD and 95% confidence interval were used for evaluation, and subgroup and sensitivity analysis were carried out. Publication bias was estimated by funnel plots and Egger's tests. A total of 16 studies were included in the meta-analysis involving 492 participants. MAGE (mean amplitude of glycemic excursion), LAGE (largest amplitude of glycemic excursions), SD (standard deviation of blood glucose), and MODD (mean of daily differences) were collected to reflect the variability of blood glucose. The glycemic variability indexes of patients before and after treatment with liraglutide were compared. Patients with treatment had lower glycemic variability compared with patients receiving treatment of liraglutide. Compared with the patients before the treatment, the patients after the treatment had a smaller glycemic variability (MAGE: I2 = 92%, p < 0.01, Z = 11.91, p < 0.01, MD = -2.78, 95%CI: -3.24 - -2.32; LAGE: I2 = 76%, p = 0.08, Z = 9.94, p < 0.01, MD = -2.20, 95%CI: -2.59 - -1.81; MODD: I2 = 74%, p = 0.002, Z = 14.03, p < 0.01, MD = -0.90, 95%CI: -1.02 - -0.77; SD: I2 = 93%, p < 0.01, Z = 3.62, p < 0.01, SMD = -1.77, 95%CI: -2.73 - -0.81). Sensitivity analysis showed that our results were reliable and no evidence of significant publication bias was detected. The results of this study suggest that patients with type 2 diabetes treated with liraglutide are associated with lower glycemic variability.

Use of flash glucose-sensing technology in patients with type 2 diabetes treated with liraglutide combined with CSII: a pilot study.

Glycemic variability (GV) may be linked to the development of diabetic complications by inducing inflammation, oxidative stress, and endothelial dysfunction. Flash glucose monitoring (FGM) provides a novel method of continuously monitoring interstitial glucose levels for up to 14 days. This study randomly assigned poorly controlled type 2 diabetes mellitus patients treated with metformin and multiple daily injections of insulin (n=60) to either continuous subcutaneous insulin infusion (CSII) treatment or CSII in combination with liraglutide (CSII+Lira) treatment for 14 days during hospitalization. GV was assessed using a FGM system; weight and cardiometabolic biomarkers were also evaluated. The coefficient of variation was significantly reduced in the CSII+Lira group (P<0.001), while no significant change was observed in the CSII group. The changes differed significantly between the two groups in mean amplitude of glycemic excursions (P=0.004), standard deviation (P=0.006), and the percentage of time in the target range (4–10 mmol/L, P=0.005 and >10 mmol/L, P=0.028). The changes in mean of daily differences, interquartile range, and percentage of time in hypoglycemia (<3.3 mmol/L) and hyperglycemia (>13.9 mmol/L) identified by FGM showed no difference. Treatment with liraglutide increased serum adiponectin [33.5 (3.5, 47.7) pg/mL, P=0.003] and heme oxygenase-1 levels [0.4 (–0.0, 1.8) ng/mL, P=0.001] and reduced serum leptin levels [–2.8 (3.9) pg/mL, P<0.001]. Adding the glucagon-like peptide-1 analog liraglutide improved GV, weight, and some cardiometabolic risk markers. The FGM system is, therefore, shown to be a novel and useful method for glucose monitoring.

Relationship between glucose variability evaluated by continuous glucose monitoring and clinical factors, including glucagon-stimulated insulin secretion in patients with type 2 diabetes

AimsTo evaluate the clinical factors affecting daily and day-to-day glucose variability by using continuous glucose monitoring. MethodsWe performed a cross-sectional analysis of patients with type 2 diabetes mellitus (T2DM) who underwent a glucagon stimulation test (GST) with 72 h of continuous glucose monitoring. Daily glucose variability was evaluated by mean amplitude of glycemic excursions [MAGE], percentage coefficient of variation for glucose (%CV), and day-to-day glucose variability (mean of daily differences [MODD]) by using continuous glucose monitoring. Correlations of clinical factors, including insulin secretion ability by the GST with MAGE, %CV, and MODD, were analyzed. ResultsIn 83 T2DM with insulin therapy, age and hemoglobin A1c (HbA1c) correlated with MAGE and %CV, fasting plasma glucose with MAGE and MODD, and increment of C-peptide immunoreactivity (ΔCPR) by GST correlated inversely with MAGE, %CV, and MODD. In 126 T2DM without insulin therapy, age, diastolic blood pressure, and triglycerides correlated with MODD, HbA1c with MAGE and MODD, and ΔCPR inversely correlated with %CV. Use of α-glucosidase inhibitors inversely correlated with %CV, whereas that of sulfonylurea was associated with MAGE and %CV. ConclusionsThese results suggest that ΔCPR correlated with stability of glycemic control, whereas poorly controlled diabetes is associated with increase in glucose variability. α-glucosidase inhibitors may be superior to sulfonylureas in reducing the glucose variability in T2DM.

Prepregnancy care in women with type 1 diabetes improves HbA1c and glucose variability without worsening hypoglycaemia time and awareness: Glycaemic variability during prepregnancy care

AimsTo evaluate the impact of a prepregnancy care (PPC) programme, beyond HbA1c, on hypoglycaemia awareness and glycaemic variability (GV). MethodsProspective pilot study. We selected women with Type 1 diabetes who initiated a PPC programme with normal hypoglycaemia awareness (n = 24). Hypoglycaemia awareness, hypoglycaemic events and GV derived from masked-continuous glucose monitoring were evaluated in the first visit and within 2 weeks after pregnancy confirmation. ResultsThe duration was 16.5 ± 13.0 months. HbA1c significantly decreased (−0.8 ± 0.7; p < 0.001). The Clarke score increased (0[0–1] vs. 1[0–2] points, p = 0.164), 2 out of 24 were reclassified as having impaired awareness of hypoglycaemia and 2 presented severe hypoglycaemia. GV decreased: standard deviation (p = 0.008), coefficient of variation (p = 0.021), mean amplitude of glycaemic excursions (p = 0.007), average daily risk range (p < 0.001), J-index (p = 0.010), high blood glucose index (HBGI) (p = 0.004), continuous overall net glycaemic action (CONGA) (p = 0.018), mean of daily differences (p = 0.045) and glycaemic risk assessment diabetes equation (p = 0.012). Final HbA1c was associated with baseline J-index, CONGA and HBGI (β = 0.535, β = 0.466, β = 0.534, respectively; p < 0.05). ConclusionsA PPC programme improved HbA1c as well as GV with no significant impact on hypoglycaemia awareness. Moreover, GV could help to identify women less likely to achieve glycaemic targets. Larger studies are needed to confirm these results.

The impact of shift work on glycemic characteristics assessed by CGM and its association with metabolic indices in non-diabetic subjects.

To explore the glycemic characteristics of non-diabetic shift workers and associations with metabolic indices. In this cross-sectional study, 450 non-diabetic males, including 238 shift workers, aged 23-58years, were recruited after a screening oral glucose tolerance test. Blood samples and anthropometric data were collected. Hundred and fifty of them finished a continuous glucose monitoring for 3-7days. Compared to daytime workers, shift workers presented with larger WHR (p < 0.001), higher HOMA-IR (p < 0.001), higher hs-CRP level (p < 0.001) and worse lipid profiles. In glycemic characteristics, shift workers with normal glucose regulation had a similar mean blood glucose (MBG), daytime MBG, percentage of time of hyperglycemia, hypoglycemia, euglycemia, and fluctuation parameters, including standard deviation of blood glucose (SDBG), mean amplitude of glycemic excursions (MAGE) and mean of daily differences (p > 0.05, respectively), while they had a higher nighttime MBG (p = 0.026) and blood glucose (BG) at 3 a.m. (p = 0.015). For subjects with impaired glucose regulation, both groups had no difference in any clinical characteristics or glycemic parameters (p > 0.05, respectively). Further regression analysis revealed the association between MBG/SDBG/MAGE/nighttime MBG/BG at 3 a.m. and age/WHR/hs-CRP/TC. For non-diabetic shift workers, the glycemic characteristic was the elevated nighttime glycemia, presented as higher nighttime MBG and BG at 3 a.m. And both metrics were closely associated with central obesity. Elevated nighttime glycemia was an early signal of glucose metabolism disorder in shift workers.