"Mild dysglycemia" in type 2 diabetes: to be neglected or not?

Comparison of glucose fluctuation between metformin combined with acarbose or sitagliptin in Chinese patients with type 2 diabetes: A multicenter, randomized, active-controlled, open-label, parallel design clinical trial

BACKGROUND

Alpha-glucosidase inhibitors or dipeptidyl peptidase-4 inhibitors are both hypoglycemia agents that specifically impact on postprandial hyperglycemia. We compared the effects of acarbose and sitagliptin add on to metformin on time in range (TIR) and glycemic variability (GV) in Chinese patients with type 2 diabetes mellitus through continuous glucose monitoring (CGM).

METHODS

This study was a randomized, open-label, active-con-trolled, parallel-group trial conducted at 15 centers in China from January 2020 to August 2022. We recruited patients with type 2 diabetes aged 18-65 years with body mass index (BMI) within 19-40 kg/m 2 and hemoglobin A1c (HbA1c) between 6.5% and 9.0%. Eligible patients were randomized to receive either metformin combined with acarbose 100 mg three times daily or metformin combined with sitagliptin 100 mg once daily for 28 days. After the first 14-day treatment period, patients wore CGM and entered another 14-day treatment period. The primary outcome was the level of TIR after treatment between groups. We also performed time series decomposition, dimensionality reduction, and clustering using the CGM data.

RESULTS

A total of 701 participants received either acarbose or sitagliptin treatment in combination with metformin. There was no statistically significant difference in TIR between the two groups. Time below range (TBR) and coefficient of variation (CV) levels in acarbose users were significantly lower than those in sitagliptin users. Median (25th percentile, 75th percentile) of TBR below target level <3.9 mmol/L (TBR 3.9 ): Acarbose: 0.45% (0, 2.13%) vs . Sitagliptin: 0.78% (0, 3.12%), P = 0.042; Median (25th percentile, 75th percentile) of TBR below target level <3.0 mmol/L (TBR 3.0 ): Acarbose: 0 (0, 0.22%) vs . Sitagliptin: 0 (0, 0.63%), P = 0.033; CV: Acarbose: 22.44 ± 5.08% vs . Sitagliptin: 23.96 ± 5.19%, P <0.001. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV (group with small wave, moderate wave and big wave). No significant difference was found in the complexity of glucose time series index (CGI) between acarbose users and sitagliptin users. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV.

CONCLUSIONS

Acarbose had slight advantages over sitagliptin in improving GV and reducing the risk of hypoglycemia. Time series analysis of CGM data may predict GV and the risk of hypoglycemia.

TRIAL REGISTRATION

Chinese Clinical Trial Registry: ChiCTR2000039424.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Flash glucose monitoring in type 2 diabetes managed with basal insulin in the USA: a retrospective real-world chart review study and meta-analysis

INTRODUCTION

Evidence supporting use of continuous glucose monitoring in type 2 diabetes treated with basal insulin is unclear. This real-world study aimed to assess the impact on glycated hemoglobin (HbA1c) of flash glucose monitoring use in adults with type 2 diabetes managed with basal insulin.

RESEARCH DESIGN AND METHODS

Medical records were reviewed for adult individuals with type 2 diabetes using basal insulin for ≥1 year with or without additional antihyperglycemic medication, HbA1c 8.0%-12.0% prior to FreeStyle Libre Flash Glucose Monitoring use for ≥90 days and an HbA1c measurement recorded between 90 and 194 days after device use. Exclusion criteria included utilization of bolus insulin. Meta-analysis data are from the current study (USA) and a similar Canadian cohort.

RESULTS

Medical record analysis (n=100) from 8 USA study sites showed significant HbA1c decrease of 1.4%±1.3%, from 9.4%±1.0% at baseline to 8.0%±1.2% after device use, p<0.0001 (mean±SD).Meta-analysis of medical records from USA and Canada sites (n=191) showed HbA1c significantly decreased by 1.1%±0.14% (mean±SE), from baseline 9.2%±1.0% to 8.1%±1.1%, p≤0.0001, with moderate to high heterogeneity between sites (Q=43.9, I²=74.9, p<0.0001) explained by differences in baseline HbA1c between sites.The HbA1c improvement in both groups was observed by age group, body mass index, duration of insulin use and sex at birth.

CONCLUSIONS

In a real-world retrospective USA study and a meta-analysis of a larger USA and Canada cohort, HbA1c significantly reduced in basal insulin-treated type 2 diabetes, without bolus insulin initiation and following the commencement of flash glucose monitoring technology.

Glycaemic variabilities: Key questions in pursuit of clarity

After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.

The dawn phenomenon in type 2 diabetes: its association with glucose excursions and changes after oral glucose-lowering drugs

Background:

We investigated the association between glucose excursions and the dawn phenomenon, and the effects of oral-glucose lowering drugs on the dawn phenomenon in patients with type 2 diabetes (T2D).

Methods:

We conducted a post hoc analysis using data from a previous randomized trial. Patients with T2D on metformin monotherapy were randomized to receive add-on acarbose or glibenclamide for 16 weeks. Ambulatory continuous glucose monitoring (CGM) was conducted before randomization and at the end of the study. Using the CGM data, we assessed glucose excursions as indicated by mean amplitude of glycemic excursions (MAGE). The magnitude of the dawn phenomenon was calculated as the difference between the nocturnal nadir (0:00 to 6:00 a.m.) and prebreakfast glucose level.

Results:

A total of 50 patients with T2D [mean age 53.5 ± 8.2 years, mean glycated hemoglobin (HbA1c) 8.4 ± 1.2%] were analyzed. There was an independent association between MAGE and the dawn phenomenon [β coefficient 0.199, 95% confidence interval (CI) 0.074–0.325, p = 0.003]. HbA1c improved significantly after treatment with acarbose or glibenclamide. However, only treatment with acarbose significantly improved glucose excursions. The dawn phenomenon decreased significantly only in patients treated with acarbose (from 35.9 ± 15.7–28.3 ± 16.5 mg/dl, p = 0.037), but not in those treated with glibenclamide (from 35.9 ± 20.6–34.6 ± 17.0 mg/dl, p = 0.776).

Conclusion:

Glucose excursions were independently associated with the dawn phenomenon in patients with T2D on metformin monotherapy. Both glucose excursions and the dawn phenomenon improved after treatment with acarbose, but not after treatment with glibenclamide.

Glucose variability and diabetes complications: Risk factor or biomarker? Can we disentangle the "Gordian Knot"?

« Variability in glucose homoeostasis » is a better description than « glycaemic variability » as it encompasses two categories of dysglycaemic disorders: i) the short-term daily glucose fluctuations and ii) long-term weekly, monthly or quarterly changes in either HbA1c, fasting or postprandial plasma glucose. Presently, the relationship between the "variability in glucose homoeostasis" and diabetes complications has never been fully clarified because studies are either observational or limited to retrospective analysis of trials not primarily designed to address this issue. Despite the absence of definitive evidence from randomized controlled trials (RCTs), it is most likely that acute and long-term glucose homoeostasis "cycling", akin to weight and blood pressure "cycling" in obese and hypertensive individuals, are additional risk factors for diabetes complications in the presence of sustained ambient hyperglycaemia. As hypoglycaemic events are strongly associated with short- and long-term glucose variability, two relevant messages can be formulated. Firstly, due consideration should be given to avoid within-day glucose fluctuations in excess of 36% (coefficient of variation) at least for minimizing the inconvenience and dangers associated with hypoglycaemia. Secondly, it seems appropriate to consider that variability in glucose homoeostasis is not only associated with cardiovascular events but is also a causative risk factor via hypoglycaemic episodes as intermediary step. Untangling the" Gordian Knot", to provide confirmation about the impact of variability in glucose homoeostasis and diabetes complications remains a daunting prospect.

Glycemic Variability, Oxidative Stress, and Impact on Complications Related to Type 2 Diabetes Mellitus

Chronic hyperglycemia is an established risk factor for the development of complications in both type 1 and type 2 diabetes, but glycemic variability has emerged as a possible independent risk factor for diabetes complications, possibly through oxidative stress. In this review, methods to access glycemic variability and oxidative stress, as well as their correlations, are discussed. Non-pharmacological and pharmacological strategies are also debated to achieve better glycemic control, not only by HbA1c target but also with reduced glycemic fluctuations, possibly minimizing the risk of diabetes complications.

Glycaemic variability in diabetes: clinical and therapeutic implications

Glycaemic variability is an integral component of glucose homoeostasis. Although it has not yet been definitively confirmed as an independent risk factor for diabetes complications, glycaemic variability can represent the presence of excess glycaemic excursions and, consequently, the risk of hyperglycaemia or hypoglycaemia. Glycaemic variability is currently defined by a large and increasing number of metrics, representing either short-term (within-day and between-day variability) or long-term glycaemic variability, which is usually based on serial measurements of HbA_1c or other measures of glycaemia over a longer period of time. In this Review, we discuss recent evidence examining the association between glycaemic variability and diabetes-related complications, as well as non-pharmacological and pharmacological strategies currently available to address this challenging aspect of diabetes management.

Faster Aspart Versus Insulin Aspart as Part of a Basal-Bolus Regimen in Inadequately Controlled Type 2 Diabetes: The onset 2 Trial

OBJECTIVE

This multicenter, double-blind, treat-to-target, phase 3 trial evaluated the efficacy and safety of fast-acting insulin aspart (faster aspart) versus insulin aspart (IAsp) in adults with type 2 diabetes receiving basal insulin and oral antidiabetic agents. RESEARCH DESIGN AND METHODS: The primary end point was HbA_1c change from baseline after 26 weeks' treatment. After an 8-week run-in to optimize basal insulin, subjects were randomized (1:1) to mealtime faster aspart (n = 345) or IAsp (n = 344), titrated using a simple daily patient-driven algorithm, plus insulin glargine U100 and metformin.

RESULTS

HbA_1c change was -1.38% (faster aspart) and -1.36% (IAsp); mean HbA_1c was 6.6% for both groups. Faster aspart demonstrated noninferiority versus IAsp in reducing HbA_1c (estimated treatment difference [ETD] [95% CI] -0.02% [-0.15; 0.10]). Both treatments improved postprandial plasma glucose (PPG) control; the PPG increment (liquid meal test) was statistically significant in favor of faster aspart after 1 h (ETD [95% CI] -0.59 mmol/L [-1.09; -0.09]; -10.63 mg/dL [-19.56; -1.69]; P = 0.0198), but not after 2-4 h. Change from baseline in fasting plasma glucose, body weight, and overall severe/blood glucose-confirmed hypoglycemia rates (rate ratio [RR] [95% CI] 1.09 [0.88; 1.36]) were similar between treatments. Postmeal hypoglycemia (0-2 h) rates were 2.27 (faster aspart) and 1.49 (IAsp) per patient-year of exposure (RR [95% CI] 1.60 [1.13; 2.27]).

CONCLUSIONS

Faster aspart and IAsp were confirmed noninferior in a basal-bolus regimen regarding change from baseline in HbA_1c. Faster aspart improved 1-h PPG with no differences in 2-4-h PPG versus IAsp. Overall hypoglycemia rates were similar except for an increase in 0-2-h postmeal hypoglycemia with faster aspart.

A global study of the unmet need for glycemic control and predictor factors among patients with type 2 diabetes mellitus who have achieved optimal fasting plasma glucose control on basal insulin

BACKGROUND

This study used data from different sources to identify the extent of the unmet need for postprandial glycemic control in patients with type 2 diabetes mellitus (T2DM) after the initiation of basal insulin therapy in Europe, Asia Pacific, the United States, and Latin America.

METHODS

Different levels of evidence were used as available for each country/region, with data extracted from seven randomized controlled trials (RCTs), three clinical trial registries (CTRs), and three electronic medical record (EMR) databases. Glycemic status was categorized as "well controlled" (glycated hemoglobin [HbA1c ] at target [<7%]), "residual hyperglycemia" (fasting plasma glucose [FPG] but not HbA1c at target [FPG <7.2/7.8 mmol/L, <130/140 mg/dL, depending on country-specific recommendations]), or "uncontrolled" (both FPG and HbA1c above target). Predictor factors were identified from the RCT data set using logistic regression analysis.

RESULTS

RCT data showed that 16.9% to 28.0%, 42.7% to 54.4%, and 16.9% to 38.1% of patients with T2DM had well-controlled glycemia, residual hyperglycemia, and uncontrolled hyperglycemia, respectively. In CTRs, respective ranges were 21.8% to 33.6%, 31.5% to 35.6%, and 30.7% to 46.8%, and in EMR databases were 4.4% to 21.0%, 23.9% to 31.8%, and 53.6% to 63.8%. Significant predictor factors of residual hyperglycemia identified from RCT data included high baseline HbA1c (all countries/regions except Brazil), high baseline FPG (United Kingdom/Japan), longer duration of diabetes (Brazil), and female sex (Europe/Latin America).

CONCLUSIONS

Irrespective of intrinsic differences between data sources, 24% to 54% of patients with T2DM globally had residual hyperglycemia with HbA1c not at target, despite achieving FPG control, indicating a significant unmet need for postprandial glycemic control.

Impact of postprandial glucose control on diabetes-related complications: How is the evidence evolving?

Conflicting findings in the literature and lack of long-term definitive outcome studies have led to difficulty in drawing conclusions about the role of postprandial hyperglycemia in diabetes and its complications. Recent scientific publications support the role of postprandial glucose (PPG) as a key contributor to overall glucose control and a predictor of microvascular and macrovascular events. However, the need remains for definitive evidence to support the precise relationship between PPG excursions and the development and progression of cardiovascular complications of diabetes. Drawing firm conclusions on the relationship between PPG and microvascular and macrovascular complications is challenged by the absence of antidiabetic agents that can specifically exert their action on PPG alone, without a basal glucose-lowering effect. Areas under investigation include interventions that more closely approximate 'normal' physiological postprandial responses, as well as technologies that advance the mode of insulin delivery or optimize methods to sense glycemic levels and variation. In conclusion, the precise role of postprandial hyperglycemia in relation to development of diabetic complications is unclarified and is one of the remaining unanswered questions in diabetes. Nevertheless, current evidence supports PPG control as an important strategy to consider in the comprehensive management plan of individuals with diabetes.

Near normal HbA1c with stable glucose homeostasis: the ultimate target/aim of diabetes therapy

Achieving near normal glucose homeostasis implies that all components of dysglycemia that are present in diabetes states be eliminated. Reducing ambient/overall hyperglycemia is a pre-requisite to eliminate the risk of development and progression of diabetes complications. More controversially however, are the relative and related contributions of postprandial glucose excursions, glucose variability, hypoglycemia and the dawn phenomenon across the spectrum of dysglycemia. For instance, it is likely that the dawn phenomenon contributes to ambient hyperglycemia and that postprandial glucose excursions are at the cross road of ambient hyperglycemia and glucose variability with glucose fluctuations as causative risk factors for hypoglycemia. Proof-of-concept trials such as the ongoing FLAT-SUGAR study are necessary for gaining further insight into the possible harmful effects of some of these features such as excessive glycemic variability and glucose excursions, still considered to be of minor relevance by several diabetologists. Whether their role will be more thoroughly proven through further intervention trials with "hard" endpoints, remains to be seen. In the meantime more consideration should be given to medications aimed at concomitantly reducing ambient/overall hyperglycemia and those additional abnormal glycemic features of dysglycemia.