Effect of Acarbose on Glycemic Variability in Patients with Poorly Controlled Type 2 Diabetes Mellitus Receiving Stable Background Therapy: A Placebo‐Controlled Trial

Derosa, Giuseppe; Franzetti, Ivano; Querci, Fabrizio; D’Angelo, Angela; Maffioli, Pamela

doi:10.1002/phar.1648

Cited by 8 publications

(11 citation statements)

References 19 publications

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“…In comparative clinical studies, significant reductions in GV have been demonstrated with the GLP-1 RAs exenatide, liraglutide and lixisenatide when added to background therapy of oral antidiabetes drugs or high-dose basal-bolus therapy (Table 2). 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58 The DPP-4 inhibitors sitagliptin 53 and vildagliptin 54 significantly reduced GV compared with glimepiride and pioglitazone, respectively, in patients with T2D not adequately controlled on metformin monotherapy, 53, 54 while saxagliptin showed no difference in GV reductions compared with the alpha-glucosidase inhibitor acarbose, 52 which has known PPG-lowering effects (Table 2). 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58…”

Section: Effects Of Diabetes Medications On Gvmentioning

confidence: 99%

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Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes

Umpiérrez

Kovatchev

2018

The American Journal of the Medical Sciences

118

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Glycated hemoglobin A1c (A1C) levels have traditionally been the gold standard for assessing glycemic control and treatment efficacy in patients with type 2 diabetes. However, A1C does not take into account fluctuations in blood glucose levels known as glycemic variability (GV). In recent years, GV has become increasingly clinically relevant, because of a better understanding of the need to reach target A1C while avoiding hypoglycemia. GV relates to both hyperglycemia and hypoglycemia, and has been associated with poorer quality of life. Diabetes treatments targeting multiple pathophysiological mechanisms are most beneficial in controlling A1C and reducing GV. In clinical trials, a number of metrics are used to measure GV, many of which are not well understood in the clinical practice. Until a gold standard metric for GV is established, the variety of measurements available may confound the choice of an optimal treatment for an individual patient.

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Section: Effects Of Diabetes Medications On Gvmentioning

confidence: 99%

“…57 Similarly, insulin detemir did not differ in terms of effect on GV compared with insulin glargine (Table 2). 47, 48, 49, 50, 51, 52, 53,54, 55, 56, 57, 58…”

Section: Effects Of Diabetes Medications On Gvmentioning

confidence: 99%

Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes

Umpiérrez

Kovatchev

2018

The American Journal of the Medical Sciences

118

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“…The glycemic effects of saxagliptin are mediated by glucose‐dependent stimulation of insulin secretion and suppression of glucagon 21 , which has been shown to effectively control the glycemic variability 22‐25 . Although acarbose was also shown to reduce several parameters of glycemic variability 26,27 , the current study showed that its efficacy on blood glucose levels appears to diminish at higher baseline HbA1c levels. This could be due to its non‐insulin‐dependent mechanism of action, which is non‐systemic and limited to delaying the intestinal glucose absorption, and is therefore independent of the baseline hyperglycemia.…”

Section: Discussionmentioning

confidence: 58%

Impact of baseline characteristics on glycemic effects of add‐on saxagliptin or acarbose to metformin therapy: Subgroup analysis of the SMART study in Chinese patients with type 2 diabetes mellitus

Fang

et al. 2020

J of Diabetes Invest

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Aims/Introduction: This secondary analysis of the 24-week SMART study examined the efficacy of add-on saxagliptin or acarbose to metformin across different patient subgroups with type 2 diabetes mellitus, based on baseline characteristics. Materials and Methods: Randomized patients (n = 481) were classified into subgroups based on their baseline age (<65, ≥65 years), body mass index (BMI; <24, 24-<28, ≥28 kg/m 2), glycated hemoglobin (HbA1c; <8%, 8-<9%, 9-<10%, ≥10%) and renal function (creatinine clearance 50-<80, ≥80 mL/min). Treatment effects on primary outcome (HbA1c) and key secondary outcomes of fasting plasma glucose (FPG), 2-h postprandial glucose and homeostatic model assessment of b-cell function were assessed across patient subgroups. Results: For saxagliptin, reductions in HbA1c from baseline to week 24 were consistent across different subgroups regardless of baseline age, body mass index, HbA1c and renal function (range-0.66 to-1.16%). Saxagliptin was associated with consistent reductions in FPG (-0.60 to-1.33 mmol/L) and 2-h postprandial glucose (-0.48 to-1.95 mmol/L) across the majority of subgroups studied. The efficacy of acarbose on FPG attenuated progressively with increasing baseline HbA1c (+0.86 to-1.43 mmol/L); an increase from baseline FPG was observed in patients with HbA1c >9%. The effect of acarbose on postprandial glucose was also variable (+0.23 to-3.38 mmol/L). Conclusions: As add-on to metformin, both saxagliptin and acarbose reduced HbA1c regardless of baseline HbA1c, age, body mass index and renal function; however, only saxagliptin was effective at a stable glycemic control (FPG and PPG). The efficacy of acarbose on FPG and PPG was significantly attenuated in patients with higher baseline HbA1c (≥8%). diabetes worldwide, with a prevalence of 10.9% (114 million) among adults aged 20-79 years; approximately 90% of these patients have type 2 diabetes 1. The high prevalence of type 2 diabetes and the corresponding costs of its associated complications place a considerable strain on China's public health services 1-3 .

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“…The technology has, however, also been judged mature enough to be used as a tool for the assessment of glycemic variation when using different glucose-lowering interventions in people with type 1 or type 2 diabetes. [46][47][48][49][50][51][52] and continues to improve. As the devices provide repeated glucose estimates at very short intervals, a wide variety of derived glycemic status parameters can and have been used for reporting purposes.…”

Section: Metricsmentioning

confidence: 99%

“…51,52 Thus, some studies use historical parameters such as the MAGE, standard deviation (SD) or coefficient of variation about mean plasma glucose level, or the mean of daily difference (MODD). 40 Meanwhile, others use mean glucose level, low/high blood glucose indices, the percentage of time over/under a certain glucose level, the time in target, 32,36 the AUC at certain time points of defined glucose levels, the mean subsequent sensor glucose nadir, the median time to post-prandial peak glucose levels, or the number of excursions above and below some level 19,22,[46][47][48]50 (Table 2). This variability of reporting parameters presently makes comparisons of CGM results between studies difficult, thus limiting generalizability and preventing comparisons among trials, including formal meta-analysis and network analysis.…”

Section: Metricsmentioning

confidence: 99%

Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting

Schnell

Barnard

Bergenstal³

et al. 2017

Diabetes Technology & Therapeutics

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Thanks to significant improvements in the precision, accuracy, and usability of continuous glucose monitoring (CGM), its relevance in both ambulatory diabetes care and clinical research is increasing. In this study, we address the latter perspective and derive provisional reporting recommendations. CGM systems have been available since around the year 2000 and used primarily in people with type 1 diabetes. In contrast to self-measured glucose, CGM can provide continuous real-time measurement of glucose levels, alerts for hypoglycemia and hyperglycemia, and a detailed assessment of glycemic variability. Through a broad spectrum of derived glucose data, CGM should be a useful tool for clinical evaluation of new glucose-lowering medications and strategies. It is the only technology that can measure hyperglycemic and hypoglycemic exposure in ambulatory care, or provide data for comprehensive assessment of glucose variability. Other advantages of current CGM systems include the opportunity for improved self-management of glycemic control, with particular relevance to those at higher risk of or from hypoglycemia. We therefore summarize the current status and limitations of CGM from the perspective of clinical trials and derive suggested recommendations for how these should facilitate optimal CGM use and reporting of data in clinical research.

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Effect of Acarbose on Glycemic Variability in Patients with Poorly Controlled Type 2 Diabetes Mellitus Receiving Stable Background Therapy: A Placebo‐Controlled Trial

Cited by 8 publications

References 19 publications

Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes

Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes

Impact of baseline characteristics on glycemic effects of add‐on saxagliptin or acarbose to metformin therapy: Subgroup analysis of the SMART study in Chinese patients with type 2 diabetes mellitus

Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting

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