Glycemic variability correlates strongly with postprandial beta-cell dysfunction in a segment of type 2 diabetic patients using oral hypoglycemic agents

Exploring the potential role of C-peptide in type 2 diabetes management

Type 2 diabetes (T2D) is a complex condition characterised by the interaction between insulin resistance and beta cell dysfunction. C-peptide, a key biomarker of endogenous insulin secretion, has a role in diagnosing type 1 diabetes (T1D). However, its utility in T2D has not been extensively studied. This review provides an overview of the progression of C-peptide levels over time in T2D and discuss its interpretation in clinical settings. We reviewed current evidence on the relationship between C-peptide levels and response to antidiabetic drugs, as well as the utility of C-peptide testing in T2D treatment strategies. We also reviewed available evidence for C-peptide in predicting future outcomes in T2D. In this review, we hoped to clarify the value of C-peptide testing in understanding and managing T2D and to highlight areas where further research is needed.

A Narrative Review: Relationship Between Glycemic Variability and Emerging Complications of Diabetes Mellitus

A growing body of evidence emphasizes the role of glycemic variability (GV) in the development of conventional diabetes-related complications. Furthermore, advancements in diabetes management and increased life expectancy have led to the emergence of new complications, such as cancer, liver disease, fractures, infections, and cognitive dysfunction. GV is considered to exacerbate oxidative stress and inflammation, acting as a major mechanism underlying these complications. However, few reviews have synthesized the association between GV and these emerging complications or examined their underlying mechanisms. Hence, this narrative review provides a comprehensive discussion of the burden, risks, and mechanisms of GV in these complications, offering further evidence supporting GV as a potential therapeutic target for diabetes management.

A comparative study of the relationship between time in range assessed by self-monitoring of blood glucose and continuous glucose monitoring with microalbuminuria outcome, HOMA-IR and HOMA-β test

AIMS

To compare the time in range (TIR) obtained from self-monitoring of blood glucose (SMBG) with that obtained from continuous glucose monitoring (CGM), and explore the relationship of TIR with microalbuminuria outcome, HOMA-IR and HOMA-β test.

METHODS

We recruited 400 patients with type 2 diabetes to carry out blood glucose monitoring by both SMBG and CGM for 3 consecutive days. TIR, TAR, TBR and other blood glucose variation indices were calculated respectively through the glucose data achieved from SMBG and CGM. The HOMA-IR and HOMA-β test was evaluated by an oral glucose tolerance test. Urinary microalbumin-to-creatinine ratio completed in the laboratory.

RESULTS

The median (25 %, 75 % quartile) of TIRCGM and TIRSMBG were 74.94(44.90, 88.04) and 70.83(46.88, 87.50) respectively, and there was no significant difference, p = 0.489; For every 1 % increase in TIRCGM, the risk of microalbuminuria decreased by 1.6 % (95%CI:0.973, 0.995, p = 0.006) and for every 1 % increase in TIRSMBG, the risk of microalbuminuria decreased by 1.3 % (95%CI:0.975, 0.999, p = 0.033). Stepwise multiple linear regression analysis showed an independent positive correlation between TIR (including TIRCGM and TIRSBMG) and LnDI30 and LnDI120 levels (p = 0.000).

CONCLUSIONS

The TIR calculated by SMBG was highly consistent with that reported by CGM and was significantly associated with the risk of microalbuminuria and the HOMA-β. Higher TIR quartiles were associated with lower incidence of microalbuminuria as well as higher lever of HOMA-β. For patients with limited CGM application, SMBG-derived TIR may be an alternative to CGM-derived TIR, to assess blood glucose control.

Initiation of Insulin Degludec in Chinese Hospitalized Patients with Type 2 Diabetes - A Single Center's Experience

Introduction

The long-acting insulin analogue insulin degludec (IDeg) is increasingly recommended for type two diabetes (T2DM), yet clinical experience in China remains limited. This retrospective study aimed to delineate the initiation strategy for IDeg in Chinese hospitalized patients with T2DM.

Methods

We retrospectively analyzed 217 Chinese hospitalized patients with T2DM who initiated IDeg from December 2018 to June 2020, calculating the initial dose and examining correlations between clinical characteristics and glucose profiles.

Results

The initial IDeg doses ranged from 0.15 to 0.18 IU/kg·d, showing no association with clinical characteristics. During titration, mean blood glucose levels (MEAN) correlated positively with diabetes duration, age, and Glycosylated Hemoglobin (HbA1c), and negatively with body mass index (BMI), triglycerides (TG), and low-density lipoprotein (LDL). The coefficient of variation (CV) in glucose levels correlated positively with HbA1c and negatively with BMI and TG. The mean amplitude of glycemic excursions (MAGE) mirrored these trends, with additional negative correlations to estimated glomerular filtration rate (eGFR) and serum albumin (ALB). Notably, glycemic variability parameters did not correlate with the presence of diabetic ketoacidosis (DKA) at admission. Hypoglycemia was observed in 21 patients, with differences in MEAN and CV during titration being the only significant findings.

Conclusion

The initial IDeg dosing was inadequate and not tailored to clinical features, and there were weak correlations between diabetes duration, age, BMI, eGFR, LDL, and ALB levels and glucose profile post-initiation.

Patients with type 2 diabetes who achieve reduced postprandial glucose levels during insulin intensive therapy may have a better recovery of β-cell function

OBJECTIVES

To explore parameters that may determine the improvement in C-peptide levels in patients with type 2 diabetes (T2D) receiving continuous subcutaneous insulin infusion (CSII) therapy.

METHODS

The trial included a lead-in period for collecting baseline parameters and correcting hyperglycemia, a 4-day CGM period, and a 2-3 weeks treatment period. After screening, patients were hospitalized and randomized to the metformin add-on NovoRapid group or the Prandilin group. Once the glycemic target was reached, all patients underwent a 4-day CGM, with treatments maintained for 2-3 weeks. OGTTs were performed at baseline and endpoint. The primary endpoint was identifying factors contributing to better β-cell function recovery after CSII therapy.

RESULTS

A total of 99 recruited patients were admitted as inpatients and achieved glycemic control within 3.8 ± 1.1 days. Of these, 83 (84 %) patients showed improvement in C-peptide levels, while 16 (16 %) did not show any change in C-peptide levels at the endpoint. Pearson analysis showed a negative correlation between the incremental AUC of glucose concentration (from 0700 to 1000) and the increase in incremental AUC of C-peptide levels (r = -0.199, P < 0.05).

CONCLUSIONS

Drug-naïve T2D patients with lower postprandial glucose concentration during CSII therapy exhibit better β-cell function recovery.

Hepatic insulin resistance affects the efficacy of metformin in patients with newly diagnosed type 2 diabetes: A Sub-Analysis of the MARCH trial

AIM

Our study aimed to analyze how hepatic insulin resistance (IR) influences the efficacy of 48 weeks of metformin treatment in newly diagnosed type 2 diabetes patients.

METHODS

We chose 291 participants who were allocated to a 48-week metformin treatment in the "Metformin and Acarbose in Chinese as initial Hypoglycemic treatment" (MARCH) trial and calculated their hepatic insulin resistance indexes (HIRI). We equally grouped the subjects into tertiles: low, medium, and high HIRI groups based on baseline HIRI; Low, medium, and high ΔHIRI groups based on the decreasing extent of HIRI after a 48-week metformin treatment.

RESULTS

Multiple linear regression showed that baseline HIRI was positively associated with the rising degree of Matsuda index and the falling range of fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), and HIRI. Furthermore, baseline fasting insulin, homeostatic model assessment of β cell function (HOMA-β), HOMA-IR, and HIRI were positively associated with the decreasing extent of HIRI, while baseline Matsuda index had a negative association with the falling extent of HIRI.

CONCLUSIONS

Patients with higher levels of hepatic IR obtained better curative effects from metformin in terms of glycemic control, insulin saving, insulin sensitivity enhancement, and IR improvement. Higher fasting blood glucose, fasting insulin, HOMA-β, IR, and lower Matsuda index were indicators of better hepatic IR improvement.

Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes

AIMS

To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic β-cell function of subjects with type 2 diabetes mellitus (T2DM).

METHODS

A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic β-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables.

RESULTS

Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of β-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects.

CONCLUSION

Our study suggests that impaired pancreatic β-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review

Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

Association between residual islet beta-cell function and achieving the target of time in range in inpatients with type 2 diabetes undergoing antidiabetic treatment: An observation study

AIM

To assess whether the beta-cell function of inpatients undergoing antidiabetic treatment influences achieving time in range (TIR) and time above range (TAR) targets.

MATERIALS AND METHODS

This cross-sectional study included 180 inpatients with type 2 diabetes. TIR and TAR were assessed by a continuous glucose monitoring system, with target achievement defined as TIR more than 70% and TAR less than 25%. Beta-cell function was assessed by the insulin secretion-sensitivity index-2 (ISSI2).

RESULTS

Following antidiabetic treatment, logistic regression analysis showed that lower ISSI2 was associated with a decreased number of inpatients achieving TIR (OR = 3.10, 95% CI: 1.19-8.06) and TAR (OR = 3.40, 95% CI: 1.35-8.55) targets after adjusting for potential confounders. Similar associations still existed in those participants treated with insulin secretagogues (TIR: OR = 2.91, 95% CI: 0.90-9.36, P = .07; TAR, OR = 3.14, 95% CI: 1.01-9.80) or adequate insulin therapy (TIR: OR = 2.84, 95% CI: 0.91-8.81, P = .07; TAR, OR = 3.24, 95% CI: 1.08-9.67). Furthermore, receiver operating characteristic curves showed that the diagnostic value of the ISSI2 for achieving TIR and TAR targets was 0.73 (95% CI: 0.66-0.80) and 0.71 (95% CI: 0.63-0.79), respectively.

CONCLUSIONS

Beta-cell function was associated with achieving TIR and TAR targets. Stimulating insulin secretion or exogenous insulin treatment could not overcome the disadvantage of lower beta-cell function on glycaemic control.

Responses of INS-1 cells to glucose stimulation patterns

Diabetes has become a major public health problem in the world for many years, and it is driving us to probe into its complex mechanism of insulin secretion in pancreatic β cells. The nanoscale resolution characterization of pancreatic β cells in response to glucose led to insights into diverse mechanical and functional processes at the single cell level. Recent advances allowed the direct observations of cytoskeleton dynamics which were quantitatively determined. Here, we firstly performed the glucose stimulation with multiple physiologically relevant glucose patterns. Atomic force microscopy (AFM) produced high spatial resolution mechanical images together with the insulin secretions linking the physical interactions to the biochemical process of INS-1 cells. Altered material properties of the INS-1 cells revealed the regulation of multiple glucose stimulation patterns. Rapidly responded to high glucose (HG), INS-1 cells presented the unique meshing networks of elasticities. The decreases of Young's modulus (YM) and insulin secretion suggested that mechanical changes affected the insulin release. Furthermore, the frequency and gradient of glucose patterns induced nanomechanical and secreting changes of the INS-1 cells and gained the knowledge on the potential controllability of glucose. The relationships between the cellular mechanics and insulin secretion of INS-1 cells could contribute to establish a mechanical cell model for the study of β cells in diabetes. The results also indicated the cell mechanics as promising mechanical biomarkers for β cells, and promoted the understanding of specific mechanical mechanism of glucose regulation, which lighted on the further application of functional glucose regulation in therapy.

Precision nutrition in diabetes: when population-based dietary advice gets personal

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course.

Insulin secretion and action affect glucose variability in the early stages of glucose intolerance

AIMS

Since it is unknown whether glucose variability (GV) is increased and whether this is related to worsening of insulin secretion and action in prediabetes, we have assessed insulin secretion and sensitivity, and daily GV in early stages of dysglycemia.

MATERIALS AND METHODS

Twenty subjects with normal glucose tolerance (NGT; age 45.0 ± 9.5 years; BMI 31.1 ± 6.4 kg/m2), 25 with NGT and 1hrOGTT>8.6 mmol/L (1hrOGTT; 45.7 ± 8.5 years; 32.4 ± 7.0 kg/m2), and 59 with isolated impaired glucose tolerance (iIGT; 47.7 ± 11.2 years; 31.3 ± 6.1 kg/m2) underwent OGTT and MMTT. CGM was performed with blinded FreeStyle Libre Pro for 24 h under standard conditions. Parameters of beta-cell function, insulin sensitivity and GV were calculated.

RESULTS

Overall insulin secretion and action as well as GV progressively worsened across glucose tolerance categories. On a matrix analysis, GV parameters were inversely related to ISSI-2; r = -0.37 to -0.52; p < 0.0001; and IGI; r = -0.28 to -0.48; p < 0.0001 for CV, SD, J-index, LI, HBGI and MAGE. Insulin secretion (IGI) and b-cell function (ISSI-2) emerged as independent contributors to GV in early stage of dysglycemia accounting for about 16%-38% of its variability.

CONCLUSIONS

Our results show that daily GV worsens already with mild impairment of glucose tolerance. The increase in GV is inversely related to insulin secretion and action.

Time in Range Assessed by Capillary Blood Glucose in Relation to Insulin Sensitivity and β-Cell Function in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in China

AIMS

This study investigated the association of capillary blood glucose (CBG)-assessed time in range (TIR) (3.9-10.0 mmol/L) with insulin sensitivity and islet β-cell function (BCF).

MATERIALS AND METHODS

We recruited 455 patients with type 2 diabetes mellitus. Seven-point glucose-profile data (pre- and 120-min post-main meals, bedtime) were collected over three consecutive days. Plasma glucose and serum insulin concentrations were measured at 0, 60, and 120 min after a 100-g standard steamed bread meal test. The homeostasis model assessment of insulin resistance (HOMA-IR) and Matsuda index were computed to evaluate insulin resistance (IR). HOMA of β-cell function (HOMA-β) and the area under the curve between insulin and blood glucose (IAUC_0-120 /GAUC_0-120 ) were used to estimate BCF.

RESULTS

TIR was positively correlated with 60- and 120-min insulin values, IAUC_0-120 , the Matsuda index, HOMA-β, and IAUC_0-120 /GAUC_0-120 (r_s : 0.154, 0.129, 0.137, 0.194, 0.341, and 0.334, respectively; P <0.05) but inversely correlated with HOMA-IR (r_s : -0.239, P <0.001). After adjusting for confounders, multinomial multiple logistic regression analysis revealed that the odds ratios (ORs) of achieving the target TIR (>70%) increased by 12% (95% confidence interval [CI]: 3-21%), 7% (95% CI: 1-14%), 10% (95% CI: 5-16%), and 45% (95% CI: 25-68%) for each 10-mIU/L increase in 60- and 120-min insulin value, 10-unit increase in HOMA-β, and unit increase in IAUC_0-120 /GAUC_0-120 , respectively (P <0.05). Nevertheless, the OR decreased by 10% (95% CI: 1-18%) for each unit increase in HOMA-IR (P <0.05).

CONCLUSIONS

IR and BCF are related to CBG-assessed TIR.

Enterocyte HKDC1 Modulates Intestinal Glucose Absorption in Male Mice Fed a High-fat Diet

Hexokinase domain containing protein-1, or HKDC1, is a widely expressed hexokinase that is genetically associated with elevated 2-hour gestational blood glucose levels during an oral glucose tolerance test, suggesting a role for HKDC1 in postprandial glucose regulation during pregnancy. Our earlier studies utilizing mice containing global HKDC1 knockdown, as well as hepatic HKDC1 overexpression and knockout, indicated that HKDC1 is important for whole-body glucose homeostasis in aging and pregnancy, through modulation of glucose tolerance, peripheral tissue glucose utilization, and hepatic energy storage. However, our knowledge of the precise role(s) of HKDC1 in regulating postprandial glucose homeostasis under normal and diabetic conditions is lacking. Since the intestine is the main entry portal for dietary glucose, here we have developed an intestine-specific HKDC1 knockout mouse model, HKDC1Int-/-, to determine the in vivo role of intestinal HKDC1 in regulating glucose homeostasis. While no overt glycemic phenotype was observed, aged HKDC1Int-/- mice fed a high-fat diet exhibited an increased glucose excursion following an oral glucose load compared with mice expressing intestinal HKDC1. This finding resulted from glucose entry via the intestinal epithelium and is not due to differences in insulin levels, enterocyte glucose utilization, or reduction in peripheral skeletal muscle glucose uptake. Assessment of intestinal glucose transporters in high-fat diet-fed HKDC1Int-/- mice suggested increased apical GLUT2 expression in the fasting state. Taken together, our results indicate that intestinal HKDC1 contributes to the modulation of postprandial dietary glucose transport across the intestinal epithelium under conditions of enhanced metabolic stress, such as high-fat diet.

Basal Insulin Reduces Glucose Variability and Hypoglycaemia Compared to Premixed Insulin in Type 2 Diabetes Patients: A Study Based on Continuous Glucose Monitoring Systems

AIMS

To examine the glycaemic variability and safety of basal and premixed insulin by using continuous glucose monitoring (CGM) systems.

METHODS

393 patients with type 2 diabetes mellitus (T2DM) treated with basal or premixed insulin for more than 3 months were enrolled. Patients were classified into a basal insulin group or premixed insulin group according to their insulin regimens. CGMs were used for 72 h with their previous hypoglycaemic regimen unchanged. The following glycaemic parameters were calculated for each 24 h using CGM data.

RESULTS

Despite similar HbA1c and fasting C-peptide concentrations, glycaemic variability (GV), including the mean amplitude of glycaemic excursion (MAGE), standard deviation (SD) and coefficient of variation (CV), and the time below range (TBR) were significantly lower in the basal insulin group than these in the premixed insulin group. Night-time hypoglycaemia was lower in the basal insulin group than that in the premixed insulin group (p<0.01). Among participants with haemoglobin A1c (HbA1c) < 7%, the GV and TBR were higher in the premixed insulin group than that in the basal insulin group.

CONCLUSION

Compared with basal insulin, the patients who use premixed insulin had higher GV, smaller TIR and an increased incidence of hypoglycaemia. For patients who use premixed insulin and with HbA1c < 7%, more attention needs to be given to hypoglycaemic events and asymptomatic hypoglycaemia.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, identifier NCT03566472.

Variability, Mean, and Baseline Values of Metabolic Parameters in Predicting Risk of Type 2 Diabetes

CONTEXT

The effect of baseline (B) and alteration of metabolic parameters (MPs), including plasma glucose (PG) testing, insulin resistance surrogates, and lipid profile and their mutual interactions on the development of type 2 diabetes mellitus (T2DM), has not been investigated systematically.

OBJECTIVE

To access the association of the past variability (V), past mean (M), and B values of various MPs and their mutual interaction with the risk of T2DM.

METHODS

A community-based, longitudinal analysis was conducted using the Korean Genome and Epidemiology Study comprising 3829 nondiabetic participants with completed MPs measurements during 3 biannually visits who were followed over the next 10 years. Outcomes included the incidence of T2DM during follow-up.

RESULTS

Among predictors, PG concentrations measured during the oral glucose tolerance test were the most prominent T2DM determinants, in which the M of the average value of fasting PG (FPG), 1-hour, and 2-hour PGs had the strongest discriminative power (hazard ratios and 95% CI for an increment of SD: 3.00 (2.5-3.26), AUC: 0.82). The M values of MPs were superior to their B and V values in predicting T2DM, especially among postload PGs. Various mutual interactions between indices and among MPs were found. The most consistent interactants were the M values of high-density lipoprotein cholesterol and the M and V values of FPG. The findings were similar in normal glucose tolerance participants and were confirmed by sensitivity analyses.

CONCLUSION

Postload PG, past alteration of measurements, and mutual interactions among indices of MPs are important risk factors for T2DM development.

Increased glycated albumin levels in patients with acromegaly related to glucose fluctuation caused by growth hormone excess but not albumin metabolism

Acromegaly is often complicated by impaired glucose tolerance. The accuracy of glycated hemoglobin (HbA_1c) and glycated albumin (GA) levels in representing glycemic profiles in patients with endocrine disorders, such as acromegaly, is unclear. This retrospective study reviewed data from patients whose GA levels had been recorded. 14 patients with acromegaly without diabetes mellitus (DM) (the acromegaly group), 15 patients with severe adult GH deficiency without DM (the growth hormone deficiency (GHD) group), and 55 nondiabetic patients (the control group) were included in this study. GA levels were significantly increased in the acromegaly group compared with the control and GHD groups, but no significant differences were observed between the control and GHD groups. The three groups were matched using propensity score matching (13 patients with acromegaly, 13 with GHD, and 13 control patients). Nonetheless, the results after matching were the same as those before matching. GA levels in the acromegaly group were significantly associated with plasma glucose (PG) levels at 0, 30, and 120 min after a 75-g oral glucose tolerance test (OGTT). Further, GH levels at 120 min after a 75-g OGTT in the acromegaly group were significantly correlated with GA levels and the difference in PG levels at baseline and 30 min. Our findings suggest that increases in PG levels attributable to excess GH after glucose loading are related to increases in GA levels in patients with acromegaly without DM. Hence, both HbA_1c and GA should be checked to accurately assess impaired glucose tolerance in patients with acromegaly.

Association of low fasting C-peptide levels with cardiovascular risk, visit-to-visit glucose variation and severe hypoglycemia in the Veterans Affairs Diabetes Trial (VADT)

Aims

Low C-peptide levels, indicating beta-cell dysfunction, are associated with increased within-day glucose variation and hypoglycemia. In advanced type 2 diabetes, severe hypoglycemia and increased glucose variation predict cardiovascular (CVD) risk. The present study examined the association between C-peptide levels and CVD risk and whether it can be explained by visit-to-visit glucose variation and severe hypoglycemia.

Materials and methods

Fasting C-peptide levels at baseline, composite CVD outcome, severe hypoglycemia, and visit-to-visit fasting glucose coefficient of variation (CV) and average real variability (ARV) were assessed in 1565 Veterans Affairs Diabetes Trial participants.

Results

There was a U-shaped relationship between C-peptide and CVD risk with increased risk with declining levels in the low range (< 0.50 nmol/l, HR 1.30 [95%CI 1.05–1.60], p = 0.02) and with rising levels in the high range (> 1.23 nmol/l, 1.27 [1.00–1.63], p = 0.05). C-peptide levels were inversely associated with the risk of severe hypoglycemia (OR 0.68 [0.60–0.77]) and visit-to-visit glucose variation (CV, standardized beta-estimate − 0.12 [SE 0.01]; ARV, − 0.10 [0.01]) (p < 0.0001 all). The association of low C-peptide levels with CVD risk was independent of cardiometabolic risk factors (1.48 [1.17–1.87, p = 0.001) and remained associated with CVD when tested in the same model with severe hypoglycemia and glucose CV.

Conclusions

Low C-peptide levels were associated with increased CVD risk in advanced type 2 diabetes. The association was independent of increases in glucose variation or severe hypoglycemia. C-peptide levels may predict future glucose control patterns and CVD risk, and identify phenotypes influencing clinical decision making in advanced type 2 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01418-z.

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

BACKGROUND

Deterioration 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).

METHODS

We 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 (TIR_3.9-10). 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.

RESULTS

HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR_3.9-10 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 TIR_3.9-10 (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 TIR_3.9-10 (β  =  - 0.401, t  =  - 3.930, p  <  0.001) but not for MODD (β  =  0.188, t  =  1.374, p  =  0.177).

CONCLUSIONS

Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.

Acute glucose fluctuation induces inflammation and neurons apoptosis in hippocampal tissues of diabetic rats

It is well-recognized that glycemic disorders are leading causes of diabetic complications and acute fluctuation of blood glucose and reported more likely being related to oxidative stress, vasculopathy, and other diabetic complications than continuous hyperglycemia in patients with diabetic and animal models. To explore the hypothesis that acute glucose fluctuation (GF) aggravates inflammatory lesions and neuron apoptosis in the hippocampus of diabetic rats. Twenty female GK rats were randomly allocated into a glucose fluctuating group (GK-GF) and a continuous hyperglycemia group (GK-CHG) and 10 age-matched female Wistar rats served as controls. GF was induced in the GK-GF group by injection with glucose and insulin at different periods of time per day for 6 weeks. Body weight was determined weekly. At the end of the study, blood hemoglobin A1c (HbA1c) and serum lipids were measured. Serum and hippocampus interleukin 1β (IL-1β), IL-6, IL-8, and tumor necrosis factor-α (TNF-α) were measured by enzyme-linked immunosorbent assay and real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Hippocampus Bcl-2, Bax, Pten, fas, and myc were quantified by qRT-PCR and Western blot analysis and Mirror Water Maze (MWM) test was performed. We successfully established an animal model with daily GF and a control model with CHG using GK diabetic rats. The GF and CHG rats showed lower weight gain during the 6-week experimental period with no significant difference in the levels of serum lipids such as total triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol compared with the control rats at the end of the study. Meanwhile, the GF and CHG rats showed higher blood HbA1c levels than that of control rats. MWM trainings tests detected that glucose disorders in GF and CHG rats tend to present longer latencies, more cross times and longer path length compared with those of the control rats, indicating impaired the hippocampus-regulated behavioral function such as spatial orientating and memory. Importantly, it was found that GF promoted the expression of TNF-α and IL-1β in the hippocampus of the GF rats while continuous hyperglycemia in CHG rats had little effect on that. Furthermore, both GF and CHG diabetic rats had abnormal expression of apoptosis-associated genes in the hippocampus compared with control Wistar rats and neurons apoptosis in GF rats appears to be more severe than CHG rats. Overall, this study confirmed that GF is a more critical factor that would promote the neuron apoptosis and induce inflammation in the hippocampus than continuous hyperglycemia in diabetic animals, which shed new light on the importance of monitoring and administration of blood glucose in the prevention and therapy for diabetes.

The Relationship Between β-cell Function Indices and Sudomotor Function in Chinese Patients with Type 2 Diabetes

PURPOSE

SUDOSCAN, a new non-invasive, quick, sensitive and quantitative technique, has been developed to detect diabetic peripheral neuropathy, and the latter is believed to be correlated with impaired β-cell function. The purpose of the present study was to investigate the associations between β-cell function indices and sudomotor function in Chinese type 2 diabetes.

METHODS

A total of 266 Chinese patients with type 2 diabetes were enrolled. Sudomotor function was assessed using electrochemical skin conductance of hands and feet. Pancreatic β-cell function was determined by homeostasis model assessment of β-cell function index, early-phase β-cell function indices and total β-cell function indices. Pearson correlation analysis and multiple linear stepwise regression analysis were carried out to explore the associations between β-cell function indices and sudomotor function.

RESULTS

Patients with lower early-phase β-cell function had lower electrochemical skin conductance levels of hands and feet and higher asymmetry ratio of hands and feet. Both Pearson correlation analysis and multiple linear stepwise regression analysis showed significantly positive relationships between early-phase β-cell function and electrochemical skin conductance levels of hands and feet, after controlling for potential confounders (P<0.05).

CONCLUSIONS

Impaired early-phase β-cell function was positively associated with sudomotor dysfunction in Chinese patients with type 2 diabetes. We speculated that impaired early-phase β-cell function may be associated with the incidence of sudomotor dysfunction in patients with T2DM.

Flash Glucose Monitoring to Assess Glycemic Control and Variability in Hemodialysis Patients: The GIOTTO Study

Background: Flash glucose monitoring (FGM) is a technology with considerable differences compared to continuous glucose monitoring (CGM), but it has been scarcely studied in hemodialysis patients. Thus, we aimed assessing the performance of FGM in such patients by comparison to self-monitoring of blood glucose (SMBG). We will also focus on estimation of glycemic control and variability, and their relationships with parameters of glucose homeostasis.

Methods: Thirty-one patients (20 with type 2 diabetes, T2DM, 11 diabetes-free, NODM) collected readings by FGM and SMBG for about 12 days on average. Readings by FGM and SMBG were compared by linear regression, Clarke error grid, and Bland-Altman analyses. Several indices of glycemic control and variability were computed. Ten patients also underwent oral glucose tolerance test (OGTT) for assessment of insulin sensitivity/resistance and insulin secretion/beta-cell function.

Results: Flash glucose monitoring and SMBG readings showed very good agreement in both T2DM and NODM (on average, 97 and 99% of readings during hemodialysis in A+B Clarke regions, respectively). Some glycemic control and variability indices were similar by FGM and SMBG (p = 0.06–0.9), whereas others were different (p = 0.0001–0.03). The majority of control and variability indices were higher in T2DM than in NODM, according to both FGM and SMBG (p = 0.0005–0.03). OGTT-based insulin secretion was inversely related to some variability indices according to FGM (R < −0.72, p < 0.02).

Conclusions: Based on our dataset, FGM appeared acceptable for glucose monitoring in hemodialysis patients, though partial disagreement with SMBG in glycemic control/variability assessment needs further investigations.

Glycemic variability and the risk of nonalcoholic fatty liver disease : A nationwide population-based cohort study

AIM

Although few recent studies have reported the association between the glycemic variability and the development of type 2 diabetes mellitus and cardiovascular disease in individuals without diabetes mellitus, the impact of the long-term variability in fasting plasma glucose (FPG) levels on the incident nonalcoholic fatty liver disease (NAFLD) has not been evaluated.

METHODS

The study included 57,636 Korean men and women without NAFLD and diabetes mellitus from the Korean National Health Insurance System cohort. FPG variability was calculated using the coefficient of variation (FPG-CV), standard deviation (FPG-SD), variability independent of the mean (FPG-VIM), and average successive variability (FPG-ASV).

RESULTS

The cumulative incidence of NAFLD demonstrated progressively increasing trends according to the higher quartiles of FPG variability in Kaplan-Meier curves. A multivariable Cox proportional hazard analysis revealed that the hazard ratio for incident NAFLD was 1.15 (95% confidence interval, 1.06-1.24) in the highest quartile of FPG-CV compared with the lowest quartile of FPG-CV after adjusting for various confounding factors, including mean FPG levels. When using FPG-SD, FPG-VIM, and FPG-ASV, the results were similar. The 10-unit increase in FPG variability was associated with a 14% increased risk of NAFLD in the fully adjusted model. Moreover, this effect remained consistent in the subgroup and sensitivity analyses.

CONCLUSION

Increased long-term FPG variability is associated with the development of NAFLD, independent of confounding risk variables including mean FPG levels.

Primary Percutaneous Coronary Intervention in Patients With Type 2 Diabetes With Late/Very Late Stent Thrombosis and de novo Lesions: A Single-Center Observational Cohort Study of Clinical Outcomes and Influencing Factors

Background: This study compared differences in the risk factors and clinical outcomes of primary percutaneous coronary intervention (PCI) in type 2 diabetes mellitus (DM) and non-DM patients with de novo lesions (DNLs) and late or very late stent thrombosis (LST/VLST).

Methods: We used angiography to screen 4,151 patients with acute coronary syndrome for DNL and LST/VLST lesions. Overall, 3,941 patients were included in the analysis and were allocated to the DM (n = 1,286) or non-DM (n = 2,665) group at admission. The primary endpoint was a composite of major adverse cardiovascular events (MACEs), defined as death, myocardial infarction, revascularization, and ischemic stroke, within a median follow-up period of 698 days.

Results: In the group with a total white blood cell count >10 × 10⁹/L (P = 0.004), a neutral granular cell count >7 × 10⁹/L (P = 0.030), and neutrophil–lymphocyte ratio >1.5 (P = 0.041), revascularization was better for DNL than for LST/VLST lesions. Among DM patients with DNLs, each unit increase in age was associated with a 53.6% increase in the risk of MACEs [hazard ratio (HR): 1.536, 95% confidence interval (CI), 1.300–1.815, P < 0.0001]. Older age (≥65 years) was associated with a significantly greater risk of MACEs (P < 0.0001). Furthermore, each standard deviation (SD) increase in the level of peak white blood cell counts was associated with a 50.1% increase in the risk of MACEs (HR, 1.501; 95% CI, 1.208–1.864; P = 0.0002). When stratifying the DM population with DNLs according to the D-dimer baseline and peak levels <0.5 vs. ≥0.5 mg/L, the high D-dimer group at baseline had a 2.066-fold higher risk of MACEs (P < 0.0001), and the high peak level D-dimer group had a 1.877-fold higher risk of MACEs (P = 0.001) compared to the low-level groups. Among DM patients with LST/VLST, each unit increase in age was associated with a 75.9% increase in the risk of MACEs (HR: 1.759, 95% CI, 1.052–2.940, P = 0.032). Furthermore, for each SD increase in the peak D-dimer level, the risk of MACEs increased by 59.7% (HR, 1.597; 95% CI, 1.110–2.295; P = 0.041).

Conclusion: Following successful primary PCI, the measurement of baseline and peak D-dimer values may help identify individuals at high cardiovascular risk. This suggests a potential benefit of lowering D-dimer levels among T2DM patients with DNL. Furthermore, age and the peak D-dimer values may facilitate the risk stratification of T2DM patients with LST/VLST.

Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring

The contribution of endogenous insulin secretion to glycemic variability (GV) may differ between patients with impaired insulin secretion and those with preserved secretion. Our objective was to determine the linearity of the relationship between fasting C-peptide (CPR) as a marker of endogenous insulin secretion and GV in type 2 diabetes (T2DM), regardless of the type of antidiabetic treatment. We conducted a prospective observational study using continuous glucose monitoring obtained from 284 Japanese outpatients with T2DM with various HbA1c values and antidiabetic treatment. We constructed a prediction curve of base-line CPR versus coefficient of variation (CV) and identified the clinical factors associated with CV using multiple regression analysis. Fasting CPR showed a significant negative log-linear relationship with CV (P < 0.0001), and the latter being strikingly high in the low-CPR group. The multiple regression analysis showed that low CPR was an independent predictor of high CV (P < 0.0001). The significant correlations were sustained in both patients with/without insulin treatment. The contribution of endogenous insulin secretion to GV depends on the extent of insulin secretion impairment. Fasting CPR may represent a useful indicator of GV instability in T2DM.

Intervention by clinical pharmacists can improve blood glucose fluctuation in patients with diabetes and acute myocardial infarction: A propensity score-matched analysis

Acute phase hyperglycemia and exaggerated glucose fluctuation may be associated with poor outcomes in diabetic patients after acute myocardial infarction (AMI). This study aimed to determine whether intervention by clinical pharmacists can mitigate blood glucose and glucose fluctuations in these fragile patients. This retrospective study enrolled patients with diabetes and AMI, from 1 January 2019 to 30 June 2020 in our institution. Blood glucose and glucose fluctuations were calculated before and after the pharmacist's intervention and between patients who underwent intervention and those who did not. Propensity score matching (PSM) was used to reduce the impact of patient characteristics on the results. A total of 170 patients were included in our primary analysis, including 29 patients who received the pharmacist intervention and 141 patients who did not. After the pharmacist's intervention, blood glucose (fasting blood glucose-FBG, from 11.9 to 9.8; postprandial blood glucose-PBG, from 15.3 to 13.2; mean blood glucose-BG, 14.5 to 12.3 mmol/L; p < .001), and glucose fluctuations (standard deviation of blood glucose-SDBG, from 3.8 to 3.0, mmol/L, p = .005) were significantly improved. Before PSM, no clear effects were found in intervention versus nonintervention patients, in terms of blood glucose and glucose fluctuation indicators, except for FBG (9.3 vs. 8.0. mmol/L, p = .005). Further analysis indicated a high incidence of FBG <7.8 mmol/L in nonintervention versus intervention patients (51.5% vs. 27.6%, p = .003). After PSM, a significant reduction in blood glucose fluctuation (SDBG, 3.0 vs. 4.1, p = .031; PBGE, 2.1 vs. 4.1, p = .017; LAGE, 4.7 vs. 7.2, mmol/L, p = .004), and PBG (11.1 vs. 13.0, mmol/L, p = .048) was observed in the intervention group than in the nonintervention group. The clinical pharmacist intervention contributed to improved outcomes, specifically, in reducing blood glucose fluctuations and potential hypoglycemia risk.

New Insights into the Role of Visit-to-Visit Glycemic Variability and Blood Pressure Variability in Cardiovascular Disease Risk

PURPOSE OF REVIEW

There is evidence from epidemiologic studies that variability in cardiovascular risk factors influences risk of cardiovascular disease. We review new studies and novel findings in the relationship between visit-to-visit glycemic variability and blood pressure variability and risk of adverse outcomes.

RECENT FINDINGS

Visit-to-visit glycemic variability is consistently linked to macrovascular disease. This relationship has been observed in both clinical trials and retrospective studies of electronic health records. Long-term blood pressure variability also predicts cardiovascular outcomes, and the association appears stronger in those with lower levels of systolic and diastolic function. As epidemiologic evidence increases in support of a role for metabolic risk factor variability in cardiovascular risk, there is a corresponding rise in interest in applying this information toward improving risk factor prediction and treatment. Future investigation of underlying mechanisms for these associations as well as implications for therapy is also warranted. The potential additive contribution of variability of multiple parameters also merits additional scrutiny. As our technology for capturing risk factor variability continues to improve, this will only enhance our understanding of its links with vascular disease and how to best utilize this information to reduce cardiovascular outcomes.

Association between time in range, a novel measurement of glycemic control and islet secretory function in chinese patients with type 2 diabetes mellitus-An observational study

AIMS

To explore the association between dynamic islet secretory function and TIR (time in range), a new valuable metric of glycemic control in type 2 diabetes (T2D).

METHODS

In this observational study 256 patients with type 2 diabetes were included and continuous glucose monitoring system (CGMS) were applied to monitor blood glucose and also the calculation of TIR [the time spent in an individual's target glucose range (usually 3.9-10 mmol/L)]. The participants were divided into 3 groups according to the tertiles of TIR, 85 cases with TIR ≥ 65.05% (T1 group), 86 cases with 41.84 < TIR ≤ 65.05% (T2 group) and 85 cases with TIR < 41.84% (T3 group). Serum glucagon (GLA0h, GLA0.5h, GLA1h, GLA2h, GLA3h), C-peptide (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h) concentration at different time points were measured after a 100 g standard steamed buns meal test to assess the pancreatic alpha cell and beta cell function. Spearman correlation analysis and multivariate linear stepwise regression analysis were adopted for statistical analysis.

RESULTS

The average age and diabetes duration of all the participants were separately 56.09 ± 13.8 years and 8.0 (4.0,15.0) years. Compared with patients in T1 group, participants in group T2 and T3 tend to have a lower concentration of C-peptide at all time points, as well as GLA0h, GLA2h and GLA3h (p < 0.05). TIR was positively correlated with C-peptide at different time points, area under the curve of C-peptide in half an hour (AUCCp0.5h), GLA0h, GLA3h, area under the curve of glucagon in half an hour (AUCGLA0.5h)(rs = 0.263, 0.414, 0.510, 0.587, 0.528, 0.360, 0.259, 0.144 and 0.208, respectively, p < 0.05) and was negatively correlated with the increment of serum glucagon from baseline at 0.5 h, 1 h and 2 h after the standard energy loaded(△GLA0.5h, △GLA1h, △GLA2h)(rs = -0.152,-0.172 and -0.203, respectively, p < 0.05). Cp2h, Cp0h and GLA0h were independent factors for TIR (β = 6.558,-6.930, 0.247, respectively, p < 0.01).

CONCLUSION

Both islet alpha cell and beta cell secretory function have important influence on TIR, a novel vital index of glycemic fluctuation.

Flash glucose monitoring data analysed by detrended fluctuation function on beta-cell function and diabetes classification

AIM

We aimed to use data-driven glucose pattern analysis to unveil the correlation between the metrics reflecting glucose fluctuation and beta-cell function, and to identify the possible role of this metric in diabetes classification.

MATERIALS AND METHODS

In total, 78 participants with type 1 diabetes and 59 with type 2 diabetes were enrolled in this study. All participants wore a flash glucose monitoring system, and glucose data were collected. A detrended fluctuation function (DFF) was utilized to extract glucose fluctuation information from flash glucose monitoring data and a DFF-based glucose fluctuation metric was proposed.

RESULTS

For the entire study population, a significant negative correlation between the DFF-based glucose fluctuation metric and fasting C-peptide was observed (r = -0.667; P <.001), which was larger than the correlation coefficient between the fasting C-peptide and mean amplitude of plasma glucose excursions (r = -0.639; P < .001), standard deviation (r = -0.649; P <.001), mean blood glucose (r = -0.519; P < .001) and time in range (r = 0.593; P < .001). As glucose data analysed by DFF revealed a clear bimodal distribution among the total participants, we randomly assigned the 137 participants into discovery cohorts (n = 100) and validation cohorts (n = 37) for 10 times to evaluate the consistency and effectiveness of the proposed metric for diabetes classification. The confidence interval for area under the curve according to the receiver operating characteristic analysis in the 10 discovery cohorts achieved (0.846, 0.868) and that for the 10 validation cohorts was (0.799, 0.862). In addition, the confidence intervals for sensitivity and specificity in the discovery cohorts were (75.5%, 83.0%), (81.3%, 88.5%) and (71.8%, 88.3%), (76.5%, 90.3%) in the validation cohorts, indicating the potential capacity of DFF in distinguishing type 1 and type 2 diabetes.

CONCLUSIONS

Our study first proposed the possible role of data-driven analysis acquired glucose metric in predicting beta-cell function and diabetes classification, and a large-scale, multicentre study will be needed in the future.

Association between glucose fluctuation during 2-hour oral glucose tolerance test, inflammation and oxidative stress markers, and β-cell function in a Chinese population with normal glucose tolerance

Backgrounds

Glucose fluctuation (GF) may have detrimental effects in individuals with diabetes; however, clinical data on the association between short-term GF, inflammation/oxidative stress markers, and islet β-cell function based on a population with normal glucose tolerance (NGT) are insufficient. Therefore, we aimed to explore these associations in a Chinese population of 209 individuals with NGT in a cross-sectional analysis.

Methods

Individuals were categorized based on GF tertiles, calculated as the maximum-minimum glucose levels among four time points (0, 30, 60, 120 min) during 2-hour oral glucose tolerance test (OGTT). Plasma inflammation markers tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), and oxidative stress markers superoxide dismutase (SOD), and 8-oxo-2'-deoxyguanosine (8-oxo-dG) were measured. Islet β-cell function was estimated according to the disposition index (DI) at the early (30 min) and total (120 min) phase of the OGTT, adjusted for insulin sensitivity.

Results

Individuals in the middle and highest tertile of GF had reduced β-cell function, and increased plasma SOD and TNF-α levels compared with those in the lowest tertile of GF (P<0.05). Multiple linear regression analysis indicated that GF was positively associated with TNF-α, 8-oxo-dG and SOD levels, but negatively associated with β-cell function, whereas IL-6, TNF-α, 8-oxo-dG and SOD levels were negatively associated with β-cell function (P<0.05).

Conclusions

GF may increase inflammation and oxidative stress markers in individuals with NGT, which could contribute to reduced β-cell function. Thus, maintaining glucose stability after a meal may have beneficial effects on delaying β-cell dysfunction, suggesting that diet and exercise strategies to decrease diet related GF are warranted.

Visit-to-visit HbA1c variability is associated with in-stent restenosis in patients with type 2 diabetes after percutaneous coronary intervention

Background

Patients with type 2 diabetes are under substantially higher risk of in-stent restenosis (ISR) after coronary stent implantation. We sought to investigate whether visit-to-visit HbA_1c variability is a potential predictor of ISR in diabetic patients after stent implantation.

Methods

We consecutively enrolled type 2 diabetic patients who underwent successful elective percutaneous coronary intervention and performed follow-up coronary angiography after around 12 months. The incidence of ISR and its relationship with visit-to-visit HbA_1c variability, expressed as coefficient of variation (CV), standard deviation (SD) and variability independent of the mean (VIM), were studied. Multivariable Cox proportional hazards models were constructed to analyze the predictive value of HbA_1c variability for ISR.

Results

From September 2014 to July 2018 in Ruijin Hospital, a total of 420 diabetic patients (688 lesions) after stent implantation were included in the final analysis. During a mean follow-up of 12.8 ± 1.3 months, the incidence of ISR was 8.6%, which was significantly increased in patients with higher CV of HbA_1c (P = 0.001). The mean diameter stenosis (DS), net luminal loss and net luminal gain were 22.9 ± 16.8%, 0.42 ± 0.88 mm and 1.66 ± 0.83 mm, respectively. Greater DS was observed in subjects with higher tertiles of CV of HbA_1c (P < 0.001), and this trend was more prominent in patients with optimal glycemic control (HbA_1c ≤ 7%) in the baseline. In multivariate analysis, HbA_1c variability was independently associated with incidence of ISR after adjustment for traditional risk factors and mean HbA_1c (HR: 3.00 [95% CI 1.14–7.92] for highest vs. lowest tertile). Inclusion of CV of HbA_1c led to a better risk stratification accuracy. Assessing HbA_1c variability by SD or VIM yielded similar findings.

Conclusions

This study suggests that visit-to-visit HbA_1c variability is an independent predictor of incidence of ISR in patients with type 2 diabetes after stent implantation.

Trial registration NCT02089360: NCT

Small changes in glucose variability induced by low and high glycemic index diets are not associated with changes in β-cell function in adults with pre-diabetes

Oscillating glucose levels can increase oxidative stress and may contribute to β-cell dysfunction. We tested the hypothesis that increased glycemic variability contributes to β-cell dysfunction by experimentally altering glucose variability with controlled diets varying in glycemic index (GI). Fifty-two adults with prediabetes received a 2-week moderate GI (GI = 55-58) control diet followed by randomization to a four-week low GI (LGI: GI < 35) or high GI (HGI HI > 70) diet. Those on the HGI diet were randomized to placebo or the antioxidant N-acetylcysteine (NAC). Participants underwent blinded CGMS, fasting oxidative stress markers and an intravenous glucose tolerance test to estimate β-cell function (disposition index: DI). On the control diet, DI was inversely correlated with SD glucose (r = -0.314, p = 0.03), but neither DI nor glucose variability were associated with oxidative stress markers. The LGI diet decreased SD glucose (Control 0.96 ± 0.08 vs. LGI 0.79 ± 0.06, p = 0.02) while the HGI diet increased it (Control 0.88 ± 0.06 vs. HGI 1.06 ± 0.07, p = 0.03). Neither DI nor oxidative stress markers changed after the LGI or HGI diets. NAC had no effect on DI, glucose variability or oxidative stress markers. We conclude small changes in glucose variability induced by dietary GI in adults with pre-diabetes are unlikely to contribute to β-cell dysfunction.

The effect of Sancai powder on glycemic variability of type 2 diabetes in the elderly: A randomized controlled trial

BACKGROUND

Type 2 diabetes is a kind of metabolic disease. Its clinical characteristic is hyperglycemia. Recently, more and more elderly people suffer from type 2 diabetes, and the glycemic variability of the elderly is greater. In addition, blood sugar variation is more likely to cause diabetes complications than simple hyperglycemia. Sancai podwer (SC) is based on the theory of traditional Chinese medicine and gradually formed in the summary of clinical experience. It has the effect of lowering blood sugar and alleviating clinical symptoms of diabetes. But the existing evidence of its efficacy on glycemic variability is insufficient. So, in our study, the randomized controlled trials will be used as a research method to explore the effects of SC on glycemic variability of type 2 diabetes.

METHOD

We will use randomized controlled experiments based on the recommended diagnostic criteria, inclusion and exclusion criteria. A total of 60 elderly patients with type 2 diabetes will be randomly divided into treatment group and control group, 30 cases in each group. The control group will receive conventional western medicine and the intervention group will receive SC combined with western medicine. The standard deviation and coefficient of variation of blood glucose level will be used as evaluation indexes.

DISCUSSION

This study can provide evidence for the clinical efficacy and safety of SC in elderly patients with type 2 diabetes mellitus.

TRIAL REGISTRATION

This study is registered on the Chinese Clinical Trial Registry: ChiCTR2000032611.

Glycemic variability indices determined by self-monitoring of blood glucose are associated with β-cell function in Chinese patients with type 2 diabetes

INTRODUCTION

Glycemic control plays an important role in diabetes management, and self-monitoring of blood glucose (SMBG) is critical to achieving good glycemic control. However, there are few studies about the relationship between SMBG-estimated glycemic indices and β-cell function. Here we investigated the association between glucose variation indices estimated by SMBG and β-cell function among Chinese patients with type 2 diabetes mellitus (T2DM).

METHODS

In this cross‑sectional study, 397 patients with T2DM were recruited from February 2015 to October 2016. β-cell function was monitored using the Homeostasis Model Assessment 2 (HOMA2)-%β index. The parameters evaluated by SMBG were the mean blood glucose (MBG), standard deviation of MBG (SDBG), largest amplitude of glycemic excursions (LAGE), and postprandial glucose excursion (PPGE).

RESULTS

HOMA2-%β was negatively correlated with SDBG, LAGE, PPGE, and MBG (r = -0.350, -0.346, -0.178, and -0.631, respectively; all p < 0.01). After adjusting for confounding characteristics (diabetic duration, triglyceride, total cholesterol, fasting C-peptide, HOMA2-insulin resistance index, hypoglycemia, and diabetic treatments) and glycated hemoglobin A1c on a continuous scale, odds ratios of SDBG, LAGE, PPGE, and MBG between the patients in the lowest and highest HOMA2-%β quartiles were 2.02 (1.14-3.57), 1.24 (1.04-1.49), 1.13 (0.86-1.51), and 2.26 (1.70-3.00). HOMA2-%β was independently associated with SDBG, LAGE, and MBG.

CONCLUSIONS

Increased SDBG and LAGE assessed by SMBG are associated with β-cell dysfunction in Chinese patients with T2DM.

Impact of acute-phase insulin secretion on glycemic variability in insulin-treated patients with type 2 diabetes

AIMS

The association between β-cell function and glycemic variability remains to be clarified in insulin-treated patients with type 2 diabetes. Therefore, the study sought to examine the association of various indices of β-cell function with glycemic variability in Chinese insulin-treated patients with type 2 diabetes.

METHODS

Glycemic variability was assessed by the coefficient of variation (CV) of glucose levels with the use of continuous glucose monitoring (CGM). Basal β-cell function was evaluated by fasting C-peptide (FCP) and the homeostasis model assessment 2 for β-cell function (HOMA2-%β). Postload β-cell function was measured by 2-hour C-peptide (2hCP) and the acute C-peptide response (ACPR) to arginine.

RESULTS

When a cutoff value of CV ≥ 36% was used to define unstable glucose, the multivariable-adjusted odds ratios for labile glycemic control were 0.34 (95% CI 0.18-0.64) for each 1 ng/mL increase in ACPR, 0.47 (95% CI 0.27-0.81) for each 1 ng/mL increase in FCP, 0.77 (95% CI 0.61-0.97) for each 1 ng/mL increase in 2hCP, and 1.00 (95% CI 0.98-1.01) for each 1% increase in HOMA2-%β. When we further adjusted for 2hCP and HOMA2-%β in the ACPR and FCP analyses, and adjusted for ACPR or FCP in the 2hCP analyses, only ACPR but not FCP or 2hPC remained to be a significant and inverse predictor for labile glycemic control.

CONCLUSIONS

ACPR evaluated by the arginine stimulation test may be superior to other commonly used β-cell function parameters to reflect glycemic fluctuation in insulin-treated patients with type 2 diabetes.

Investigation of the Effect of Canagliflozin on the Disposition Index, a Marker of Pancreatic Beta Cell Function, in Patients with Type 2 Diabetes

AIM

Our aim was to investigate the effects of add-on canagliflozin with glimepiride dose adjustment or glimepiride dose adjustment on pancreatic beta cell function in patients with type 2 diabetes mellitus and inadequate glycemic control despite stable triple therapy (metformin, teneligliptin, and glimepiride) plus diet/exercise therapy.

METHODS

Forty patients on stable triple therapy were randomized to glimepiride dose adjustment without (glimepiride group) or with add-on canagliflozin 100 mg (canagliflozin group) for 24 weeks. The glimepiride dose was adjusted every 4 weeks based on continuous glucose monitoring over the previous 2 weeks according to a prespecified algorithm. After the 24-week treatment period, the patients returned to the pre-intervention regimen for 1 week (wash-out period). Patients underwent 75 g OGTTs at the start of the run-in period and at the end of the wash-out period. The primary endpoint was the change in disposition index (DI).

RESULTS

Thirty-nine patients completed the study (canagliflozin, n = 19; glimepiride, n = 20). The change in DI was +5.1% and -11.0% in the canagliflozin and glimepiride groups, respectively, with a between-group difference ratio of 18.0% (P = 0.330). HbA1c, fasting plasma glucose, body weight, and daily-life continuous glucose monitoring-derived parameters improved in the canagliflozin group. Hypoglycemia occurred in 60% (44 episodes) and 70% (79 episodes) of patients in the canagliflozin and glimepiride groups, respectively. The change in DI was significantly correlated with the changes in glycemic control and variability in overall cohort.

CONCLUSION

Adding canagliflozin to the triple therapy improved beta cell function by 18%, but it did not reach statistical significance. This study also demonstrated a correlation between the change in DI and glycemic control. As canagliflozin improved both glucose level and variability with relatively lower risk of hypoglycemia compared with glimepiride dose adjustment, adding canagliflozin to the triple therapy may be clinically beneficial.

TRIAL REGISTRATION

UMIN000030208/jRCTs051180036.

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

AIMS

To evaluate the clinical factors affecting daily and day-to-day glucose variability by using continuous glucose monitoring.

METHODS

We 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.

RESULTS

In 83 T2DM with insulin therapy, age and hemoglobin A_1c (HbA_1c) 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, HbA_1c 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.

CONCLUSIONS

These 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.

Effect of DPP-IV Inhibitors on Glycemic Variability in Patients with T2DM: A Systematic Review and Meta-Analysis

Glycemic variability (GV) has been an emerging target for preventing complications related to type 2 diabetes. For reducing GV, DPP-IV inhibitors have shown effectiveness compared to other oral anti-hyperglycemic drugs (OADs), but systematic evaluation has yet to be existed. A systematic review and meta-analysis of randomized controlled trials (RCTs) were performed to evaluate the effect of DPP-IV inhibitors compared with other OADs, on GV as measured by mean amplitude of glycemic excursions (MAGE). Searches were conducted using Pubmed, EMBASE, and the Cochrane Library, from which eligible studies were retrieved; seven RCTs were included in the analysis. DPP-IV inhibitors were found to significantly reduce MAGE compared to other OADs (mean difference = -14.61; 95% CI = -19.00 to -10.21; p < 0.0001) without significant heterogeneity among sulfonylureas (mean difference = -14.93; 95% CI = -21.60 to -8.26; p < 0.0001). Initial combination therapy with DPP-IV inhibitors more effectively reduced MAGE than stepwise add-on therapies (p = 0.006), although no differences in MAGE were found based on HbA1c values. These findings indicate that DPP-IV inhibitors are promising alternatives for reducing GV in type 2 diabetes patients. However, further studies utilizing larger numbers of patients and longer-term follow-ups are needed.

Chinese clinical guidelines for continuous glucose monitoring (2018 edition)

Blood glucose monitoring is an important part of diabetes management. Continuous glucose monitoring (CGM) technology has become an effective complement to conventional blood glucose monitoring methods and has been widely applied in clinical practice. The indications for its use, the accuracy of the generated data, the interpretation of the CGM results, and the application of the results must be standardized. In December 2009, the Chinese Diabetes Society (CDS) drafted and published the first Chinese Clinical Guideline for Continuous Glucose Monitoring (2009 edition), providing a basis for the standardization of CGM in clinical application. Based on the updates of international guidelines and the increasing evidence of domestic studies, it is necessary to revise the latest CGM guidelines in China so that the recent clinical evidence can be effectively translated into clinical benefit for diabetic patients. To this end, the CDS revised the Chinese Clinical Guideline for Continuous Glucose Monitoring (2012 Edition) based on the most recent evidence from international and domestic studies.

Resolution on the results of the first working meeting of the scientific advisory board «Actual problems of glycemic variability as a new criterion of glycemic control and safety of diabetes therapy»

The Scientific Advisory Board, chaired by Professor G. R. Galstyan (cochair - A.V. Zilov), met in Moscow on 19 June 2018 to discuss the possibilities of improving the results of diabetes mellitus (DM) treatment by considering glycaemic variability (GV) as an additional criterion for effective glycaemic control (especially in patients receiving insulin therapy) and as one of the goals of treatment in patients with unstable glycaemia. The purpose of the working meeting was to develop a strategy for the introduction of GV as a predictor and as an additional criterion for assessing the effectiveness and safety of hypoglycaemic therapy to improve the pharmacotherapy of diabetes and reduce cardiovascular and total mortality. The aims of the working meeting were to conduct a comprehensive data analysis of the relationship between GV and hypoglycaemia; to gather and analyse published data and the experience of decrease in GV and improved outcomes of diabetes against the background of different types of insulin therapy; to compare existing methods of glycaemia monitoring and GV assessment and examine their validity and availability in real practice in the context of limited budget and to analyse the informativeness and clinical and prognostic significance of various parameters of GV assessment and to determine their reasonable minimum for a comprehensive assessment of GV as a criterion for evaluating the effectiveness of DM treatment and the predictors of negative diabetes outcomes. The following reports were presented during the discussion: Glycemic variability: clinical and prognostic value. Types of glycemic variability (Candidate of Medical Sciences, assistant Professor Zilov A.V.); Methods of assessment of variability of glycemia in clinical trials and routine practice (PhD, Professor Markova T. N.); Current international and national recommendations on glycemic monitoring (PhD, Professor Galstyan G. R.) and Peculiarities of glycemic variability and its evaluation among children and adolescents (Candidate of Medical Sciences Vitebskaya A.V.).

Association of high circulating testosterone with increased glycaemic variability in type 2 diabetes: A cross-sectional study in China

BACKGROUND

Testosterone affects insulin resistance, but the effect of testosterone treatment on type 2 diabetes (T2D) remains controversial. We aimed to investigate the association between circulating total testosterone (TT) and glycaemic variability using continuous glucose monitoring (CGM) in patients with T2D.

METHODS

A total of 248 men with T2D were enrolled in the study. Clinical characteristics and plasma for glycated haemoglobin (HbA1c) and C-peptide assessment were collected. TT was measured using a chemiluminescent immunometric assay. All patients were subjected to a 3-day CGM before making adjustments for hypoglycaemic therapy.

RESULTS

TT positively correlated with the standard deviation of mean blood glucose (SDBG) (P < 0.05), especially in older patients. Linear regression analysis showed that SDBG was associated with HbA1c (β = 0.354, P < 0.001) and TT (β = 0.164, P = 0.008) after adjusting for age, duration of diabetes, body mass index, fasting/postprandial C-peptide, and use of different hypoglycaemic drugs. The cut-off value of TT for predicting glycaemic variability was 14.76 mmol/L according to receiver operating characteristic (ROC) analysis. SDBG, the coefficient of variation, the incremental area under the curve of glucose (AUC) > 10 mmol/L, and AUC night were increased in the group with TT > 14.76 nmol/L (P < 0.01 for all variables). Body mass index and fasting/postprandial C-peptide were lower in the group with TT > 14.76 nmol/L than in the group with TT ≤ 14.76 nmol/L (P < 0.05).

CONCLUSIONS

Circulating TT levels should be assessed in patients with T2D in addition to HbA1c for predicting glycaemic variability. More frequent blood glucose monitoring or CGM is suggested for patients with T2D and high testosterone levels.

CLINICAL TRIALS REGISTRATION

NCT03519529, ClinicalTrials.gov.

Impact of Visit-to-Visit Fasting Plasma Glucose Variability on the Development of Type 2 Diabetes: A Nationwide Population-Based Cohort Study

OBJECTIVE

Although increasing evidence suggests the association between short-term variability of fasting plasma glucose (FPG) and diabetic complications or mortality, the impact of visit-to-visit variability of FPG on the development of type 2 diabetes (T2D) has not been evaluated.

RESEARCH DESIGN AND METHODS

Our analysis included 131,744 Korean men and women without diabetes using the Korean National Health Insurance System cohort with periodic health examination program. FPG variability was calculated using the coefficient of variation (FPG-CV), SD (FPG-SD), and variability independent of the mean (FPG-VIM).

RESULTS

During the median follow-up time of 8.3 years, Kaplan-Meier curves demonstrated lower disease-free probability in the higher FPG variability group compared with the lower FPG variability group. Multivariable Cox proportional hazards analysis exhibited that the hazard ratio for incident T2D was 1.67 (95% CI 1.58-1.77, P < 0.001) in the highest quartile of FPG-CV compared with the lowest quartile of FPG-CV after adjusting for confounding variables, including mean FPG. The association between FPG variability and the risk of T2D was consistent when modeling using FPG-SD and FPG-VIM in both normal and impaired fasting glucose groups. A 1 SD increase in the FPG-CV was associated with a 24% increased risk of T2D in the fully adjusted model.

CONCLUSIONS

Increased variability of FPG is associated with the development of T2D independently of diverse risk factors.

Characteristics of people with high visit-to-visit glycaemic variability in Type 2 diabetes

AIMS

Increased visit-to-visit glycaemic variability is independently associated with adverse outcomes in Type 2 diabetes. Our aim was to identify the patient characteristics associated with raised visit-to-visit glycaemic variability in people with Type 2 diabetes.

METHODS

A case-control study was conducted to establish associations between HbA_1c variability and clinical covariates in 10 130 people with Type 2 diabetes. Variability was calculated by two metrics [sd and coefficient of variation (CV)] from a minimum of four HbA_1c readings obtained over a 4-year period. High and low variability groups were defined as the top and bottom tertile of the sd or CV, and used in logistic regression analyses including a number of clinical and biochemical covariates. The analyses were stratified into low mean (< 53 mmol/mol; 7%) and high mean (≥ 53 mmol/mol; 7%) HbA_1c groups.

RESULTS

Findings were consistent across both HbA_1c groups and variability metrics. Treatment, independent of other factors, was the most strongly associated covariate for the risk of high HbA_1c variability. A six-fold increased risk was observed in the low HbA_1c group, between the most and least intense treatment regimens (P < 0.001). Similar findings were present in the high HbA_1c group with a three-fold increase in risk (P < 0.001). In addition, male gender, younger age, reduced HDL-cholesterol and increased BMI were all found to be independently associated with raised visit-to-visit glycaemic variability.

CONCLUSIONS

Intensive treatment resulting in low mean HbA_1c was associated with marked increase in HbA_1c variability. Irrespective of diabetes control, the greatest visit-to-visit variability was observed in young, insulin resistant men.

Association of lower body mass index with increased glycemic variability in patients with newly diagnosed type 2 diabetes: a cross-sectional study in China

Previous studies have indicated that the pathogenesis of diabetes differs between obese and lean patients. We investigated whether newly diagnosed Chinese diabetic patients with different body mass indices (BMIs) have different glycemic variability, and we assessed the relationship between BMI and glycemic variability. This was a cross-sectional study that included 169 newly diagnosed and drug-naïve type 2 diabetic patients (mean age, 51.33 ± 9.83 years; 110 men). The clinical factors and results of the 75-g oral glucose tolerance test were all recorded. Glycemic variability was assessed using continuous glucose monitoring. Compared with overweight or obese patients (BMI ≥ 24 kg/m2), underweight or normal-weight patients (BMI < 24 kg/m2) had higher levels of blood glucose fluctuation parameters, particularly in terms of mean amplitude of glycemic excursion (MAGE 6.64 ± 2.38 vs. 5.67 ± 2.05; P = 0.007) and postprandial glucose excursions (PPGEs) (PPGE at breakfast, 7.72 ± 2.79 vs. 6.79 ± 2.40, P = 0.028; PPGE at lunch, 5.53 ± 2.70 vs. 5.07 ± 2.40, P = 0.285; PPGE at dinner, 5.96 ± 2.24 vs. 4.87 ± 2.50, P = 0.008). BMI was negatively correlated with glycemic variability (r = -0.243, P = 0.002). On multiple linear regression analyses, BMI (β = -0.231, P = 0.013) and Insulin Secretion Sensitivity Index-2 (β = -0.204, P = 0.048) were two independent predictors of glycemic variability. In conclusion, lower BMI was associated with increased glycemic variability, characterized by elevated PPGEs, in newly diagnosed Chinese type 2 diabetic patients.

Influencing factors of glycemic variability in hospitalized type 2 diabetes patients with insulin therapy: A Strobe-compliant article

Previous studies have shown that glucose fluctuation is closely related to oxidative stress and diabetic complications. However, only few studies have evaluated the influencing factors of glycemic variability (GV) in type 2 diabetes (T2D) patients so far.This was a cross-sectional study design. A total of 366 cases of hospitalized patients with T2D using insulin therapy, whom received continuous glucose monitoring from January 2014 to December 2016, were enrolled for this study. The evaluation variables of GV included standard deviation of blood glucose, coefficient of variation (CV%), mean amplitude of glycemic excursion, and absolute means of daily differences.In 366 T2D patients with insulin therapy, 148 were used multiple daily injections (MDI) insulin regimen; 144 were on premixed insulin injection; and 74 were treated with continuous subcutaneous insulin injection. Compared with MDI insulin regimen, patients on premixed insulin injection have less insulin dose per day, lower mean blood glucose, and better glycated hemoglobin (HbA1c) (all P <.05). Generalized linear model showed that family history of diabetes, duration of diabetes, higher HbA1c, and higher level of aspartate aminotransferase and high-density lipoprotein cholesterol were positively associated with GV parameters. Otherwise, serum levels of C-peptide, premixed insulin injection, history of cardiovascular disease, and serum concentration of uric acid were inversely associated with GV parameters.Dysfunction of pancreatic β-cell and better insulin sensitivity were independent contributors to the fluctuation of blood glucose. Moreover, premixed insulin therapy may obtain better glucose control and lower within-day and day-to-day glucose variability for Chinese T2D patients with insulin therapy.

Therapies to Preserve β-Cell Function in Type 1 Diabetes

In spite of modern techniques, the burden for patients with type 1 diabetes mellitus will not disappear, and type 1 diabetes will remain a life-threatening disease causing severe complications and increased mortality. We have to learn of ways to stop the destructive process, preserve residual insulin secretion or even improve the disease via β-cell regeneration. This will give a milder disease, a more stable metabolism, simpler treatment and perhaps even cure. Therapies based on single drugs have not shown sufficient efficacy; however, there are several treatments with encouraging efficacy and no apparent, or rather mild, adverse events. As the disease process is heterogeneous, treatments have to be chosen to fit relevant subgroups of patients, and step by step efficacy can possibly be improved by the use of combination therapies. Thus immunosuppressive therapies like anti-CD3 and anti-CD20 monoclonal antibodies might be combined with fusion proteins such as etanercept [tumor necrosis factor (TNF)-α inhibitor] and/or abatacept (CTLA4-Ig) early after onset to stop the destructive process, supported by β-cell protective agents. The effect may be prolonged by using autoantigen therapy [glutamate decarboxylase (GAD) proinsulin], and by adding agents facilitating β-cell regeneration [e.g. glucagon-like peptide-1 (GLP-1)] there should be a good chance to make the disease milder, perhaps leading to cure in some patients.

The effect of Astragalus as an adjuvant treatment in type 2 diabetes mellitus: A (preliminary) meta-analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Astragalus is a traditional Chinese medicine that is widely used for tonifying Qi (Qi mainly means life energy) to treat diabetes mellitus and its complications.

AIM OF THE STUDY

We performed a meta-analysis to evaluate the effect of Astragalus in adjuvant treatment of type 2 diabetes mellitus (T2DM), and to provide novel information to improve clinical decision-making.

MATERIALS AND METHODS

We conducted an exhaustive database search (PubMed, EMbase, Cochrane Library, China Knowledge Resource Integrated Database (CNKI), Wanfang data and SinoMed) of studies associated with "Astragalus" and "type 2 diabetes mellitus" until December 2015. Following quality assessment of study eligibility, the extracted data were statistically analyzed using STATA, ver. 12.0 (Stata Corp.).

RESULTS

A total of 13 studies with 1054 participants were included in this meta-analysis. Two subgroups were identified, based on Astragalus dosing regimens: control group vs. Astragalus injection (AI); control group vs. Astragalus aqueous decoction (AAD). The pooled results showed that, in comparison with control group, Astragalus administration significantly reduced fasting plasma glucose (FPG) in both the AI group (WMD=-0.28, 95% CI=-0.46 to -0.10, P=0.002, I(2)=18.5%) and the AAD group (WMD=-0.83, 95% CI=-1.07 to -0.58, P=0.000, I(2)=0.0%); postprandial plasma glucose (PPG) was also significantly reduced in the AI group (WMD=-0.47, 95% CI=-0.77 to -0.17, P=0.002, I(2)=46.8%) and the AAD group (WMD=-1.19, 95% CI=-1.63 to -0.75, P=0.000, I(2)=49.3%). Fasting insulin (Fins) was significantly reduced only in the AAD treatment group (SMD=-0.33, 95% CI=-0.55 to -0.10, P=0.005, I(2)=1.0%) as was the homeostasis model assessment insulin resistance index (HOMA-IRI) levels (SMD=-1.66, 95% CI=-3.24 to -0.09, P=0.038, I(2)=94.0%). Although AAD treatment significantly reduced levels of glycated hemoglobin A1c (HbA1c) (WMD=-1.77, 95% CI=-3.06 to -0.47, P=0.007, I(2)=90.8%), AI treatment failed to show significant efficacy (WMD=-0.28, 95% CI=-0.63 to 0.06, P=0.102, I(2)=83.8%). Sensitivity analysis failed to detect outliers in all studies while Egger's linear regression test revealed a lack of publication bias in this meta-analysis (P=0.771, 95%CI =-3.51 to 4.56).

CONCLUSIONS

Astragalus may be beneficial as an adjuvant therapy in the treatment of type 2 diabetes. However, due to the limited quality of existing studies, further high-quality studies are warranted before definitive conclusions may be reached.

Glycemic variability is an important risk factor for cardiovascular autonomic neuropathy in newly diagnosed type 2 diabetic patients

BACKGROUND

The relationship between glycemic variability, another component of glycemic disorders as well as chronic sustained hyperglycemia, and cardiovascular autonomic neuropathy (CAN) has not been clarified. Our aim is to investigate the association between glycemic variability and CAN in newly diagnosed type 2 diabetic patients.

METHODS

Ewing tests were performed in 90 newly diagnosed type 2 diabetic patients and 37 participants with normal glucose tolerance as control from May 1, 2009, through September 30, 2010. According to the scores from Ewing tests, diabetic patients were divided into two groups: without CAN (CAN-) and with CAN (CAN+). All participants underwent a 48-h to 72-h continuous glucose monitoring (CGM). Coefficient of variability of glycemia (%CV), mean amplitude of glycemic excursions (MAGE) and means of daily differences (MODD) were calculated with the CGM data.

RESULTS

The prevalence of CAN in patients with newly diagnosed type 2 diabetes was 22.2%. An increasing trend of glycemic variability was found from control group, CAN- group to CAN+ group. MAGE in CAN+ group was significantly higher than that in CAN- group (5.27±1.99mmol/L vs. 4.04±1.39mmol/L, P=0.001). In the Logistic regression analysis, a significant relationship was shown between MAGE and CAN [odds ratio (OR): 1.73, 95% confidence interval (CI): 1.01-2.73, P=0.018)]. The area under the receiver-operating characteristic curve for MAGE was superior to those for other dysglycemic indices in detecting CAN.

CONCLUSIONS

Glycemic variability is associated with CAN in patients with newly diagnosed type 2 diabetes. Among the glycemic variability indices, MAGE is a significant indicator for detecting CAN.

Factors Associated with Glycemic Variability in Patients with Type 2 Diabetes: Focus on Oral Hypoglycemic Agents and Cardiovascular Risk Factors

BACKGROUND

The role of glycemic variability (GV) in development of cardiovascular diseases remains controversial, and factors that determine glucose fluctuation in patients with diabetes are unknown. We investigated relationships between GV indices, kinds of oral hypoglycemic agents (OHAs), and cardiovascular risk factors in patients with type 2 diabetes mellitus (T2DM).

METHODS

We analyzed 209 patients with T2DM. The GV index (standard deviation [SD] and mean absolute glucose change [MAG]) were calculated from 7-point self-monitoring of blood glucose profiles. The patients were classified into four groups according to whether they take OHAs known as GV-lowering (A) and GV-increasing (B): 1 (A only), 2 (neither), 3 (both A and B), and 4 (B only). The 10-year risk for atherosclerotic cardiovascular disease (ASCVD) was calculated using the Pooled Cohort Equations.

RESULTS

GV indices were significantly higher in patients taking sulfonylureas (SUs), but lower in those taking dipeptidyl peptidase-4 inhibitors. In hierarchical regression analysis, the use of SUs remained independent correlates of the SD (γ=0.209, P=0.009) and MAG (γ=0.214, P=0.011). In four OHA groups, GV indices increased progressively from group 1 to group 4. However, these did not differ according to quartiles of 10-year ASCVD risk.

CONCLUSION

GV indices correlated significantly with the use of OHAs, particularly SU, and differed significantly according to combination of OHAs. However, cardiovascular risk factors and 10-year ASCVD risk were not related to GV indices. These findings suggest that GV is largely determined by properties of OHAs and not to cardiovascular complications in patients with T2DM.

Efficacy of mitiglinide and sitagliptin, alone or in combination, on postprandial excursion and glycemic variability assessed by continuous glucose monitoring: a post hoc analysis with single-day treatment

OBJECTIVE

To compare the efficacy of mitiglinide and sitagliptin, alone or in combination, on postprandial excursion and glycemic variability assessed by continuous glucose monitoring (CGM) in a single-day treatment setting.

METHODS

This was a post hoc analysis of a randomized crossover study comparing the efficacy of sitagliptin, mitiglinide and the combination of these two drugs. Twenty-four hour CGM was performed before and after a single-day treatment with each drug alone or in combination.

RESULTS

Mean glucose levels were decreased in all groups. The average of three postprandial glucose excursions AUC (average of all three 4-h postprandial periods throughout the day) (AUCpp-average) decreased in the mitiglinide and combination treatment groups, but not in the sitagliptin group. The lowering effect on AUCpp-average was greater in patients given mitiglinide (-47 mg/dl, p < 0.001) or combination treatment (-66 mg/dl, p < 0.001) compared with sitagliptin alone (-18 mg/dl). The reduction in mean amplitude of glycemic excursion was greater with mitiglinide (-29.3 mg/dl, p < 0.001) and combination treatment (-28.3 mg/dl, p < 0.01) than with sitagliptin alone (-8.9 mg/dl).

CONCLUSIONS

Mitiglinide or combination treatment resulted in lower glycemic variability and postprandial glucose excursion than sitagliptin alone; however, the results of this single-day pharmacodynamics study cannot be generalized to a clinical setting.