26-LB: Exenatide and Renal Outcomes in Patients with Type 2 Diabetes and Diabetic Kidney Disease: A Multicenter, Randomized, Parallel Study

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 26-LB
Author(s):  
XIANGYU WANG ◽  
QIAN ZHANG ◽  
MEIPING GUAN ◽  
SHUYUE SHENG ◽  
WEI MO ◽  
...  
2020 ◽  
Author(s):  
Xiangyu Wang ◽  
Qian Zhang ◽  
Meiping Guan ◽  
Shuyue Sheng ◽  
Wei Mo ◽  
...  

Abstract Background: This trial aimed to assess the effects of exenatide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), on renal outcomes in patients with type 2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD).Methods: We performed a randomized, parallel study conducted in 4 general hospitals. T2DM patients with an estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73m2 and macroalbuminuria, defined as 24-hour urinary albumin excretion rate (UAER) >0.3 g/24-h, were randomized 1:1 to receive exenatide twice daily plus insulin glargine or insulin lispro plus glargine for 24 weeks. The primary outcome was percentage change in UAER after 24 weeks of intervention comparing to baseline measurement. Rates of hypoglycemia, adverse events and change in eGFR during the follow up were set as safety outcomes.Results: Between March 2016 and April 2019, 92 patients were randomized and took at least one dose of study drug. The mean age of the participants was 56 years. At baseline, the median UAER was 1512.0 mg/24-h, and mean eGFR was 70.4 mL/min/1.73 m2. After 24 weeks, exenatide reduced 29.7% of the UAER (p = 0.0255). Meanwhile, the body weight declined by 1.3 kg with exenatide (difference between groups was 2.7 kg, p = 0.0001). Comparing to the control group, lower frequency of hypoglycemia as well as more gastrointestinal adverse events were in intervention group. Conclusions: Exenatide plus insulin glargine for 24 weeks resulted in significant reduction of albuminuria in T2DM patients with DKD.Trial registration: Clinicaltrials.gov, NCT02690883. Registered 20 February, 2016, https://clinicaltrials.gov/ct2/show/NCT02690883


2021 ◽  
Vol 12 ◽  
pp. 204201882110206
Author(s):  
Áine M. de Bhailís ◽  
Shazli Azmi ◽  
Philip A. Kalra

Type 2 diabetes is a leading cause of chronic kidney disease worldwide and continues to increase in prevalence. This in turn has significant implications for healthcare provision and the economy. In recent years there have been multiple advances in the glucose-lowering agents available for the treatment of diabetes, which not only modify the disease itself but also have important benefits in terms of the associated cardiovascular outcomes. The cardiovascular outcome trials of agents such as glucagon-like peptide-1 receptor agonists (GLP-RAs) and sodium glucose cotransporter 2 inhibitors (SGLT-2) have demonstrated significant benefits in reducing major adverse cardiovascular events, admissions for heart failure and in some cases mortality. Secondary analysis of these trials has also indicated significant renoprotective benefit. Canagliflozin and Renal Outcomes in Type 2 Diabetes Mellitus and Nephropathy (CREDENCE) a renal-specific trial, has shown major benefits with canagliflozin for renal outcomes in diabetic kidney disease, and similar trials with other SGLT-2 inhibitors are either underway or awaiting analysis. In this article we review current goals of treatment of diabetes and the implications of advancing renal impairment on choice of treatments. Areas discussed include the diagnosis of diabetic kidney disease and current treatment strategies for diabetic kidney disease ranging from lifestyle modifications to glycaemic control. This review focuses on the role of GLP-RAs and SGLT-2 inhibitors in treating those with diabetes and chronic kidney disease with some illustrative cases. It is clear that these agents should now be considered first choice in combination with metformin in those with diabetes and increased cardiovascular risk and/or reduced renal function, and in preference to other classes such as dipeptidyl peptidase-4 (DPP-4) inhibitors or sulphonylureas.


2020 ◽  
Vol 51 (10) ◽  
pp. 806-814 ◽  
Author(s):  
Xiangyu Wang ◽  
Huijie Zhang ◽  
Qian Zhang ◽  
Meiping Guan ◽  
Shuyue Sheng ◽  
...  

<b><i>Background:</i></b> Cardiovascular outcomes in clinical trials with type 2 diabetes mellitus (T2DM) patients have shown that glucagon-like peptide-1 receptor agonist can have a beneficial effect on the kidney. This trial aimed to assess the effects of exenatide on renal outcomes in patients with T2DM and diabetic kidney disease (DKD). <b><i>Methods:</i></b> We performed a randomized parallel study encompassing 4 general hospitals. T2DM patients with an estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m<sup>2</sup> and macroalbuminuria, defined as 24-h urinary albumin excretion rate (UAER) &#x3e;0.3 g/24 h were randomized 1:1 to receive exenatide twice daily plus insulin glargine (intervention group) or insulin lispro plus glargine (control group) for 24 weeks. The primary outcome was the UAER percentage change from the baseline after 24 weeks of intervention. The rates of hypoglycemia, adverse events (AEs), and change in eGFR during the follow-up were measured as safety outcomes. <b><i>Results:</i></b> Between March 2016 and April 2019, 92 patients were randomized and took at least 1 dose of the study drug. The mean age of the participants was 56 years. At baseline, the median UAER was 1,512.0 mg/24 h and mean eGFR was 70.4 mL/min/1.73 m<sup>2</sup>. After 24 weeks of treatment, the UAER percentage change was significantly lower in the intervention group than in the control group (<i>p</i> = 0.0255). Moreover, the body weight declined by 1.3 kg in the intervention group (the difference between the 2 groups was 2.7 kg, <i>p</i> = 0.0001). Compared to the control group, a lower frequency of hypoglycemia and more gastrointestinal AEs were observed in the intervention group. <b><i>Conclusion:</i></b> Exenatide plus insulin glargine treatment for 24 weeks resulted in a reduction of albuminuria in T2DM patients with DKD.


Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 443-P
Author(s):  
YOSHINORI KAKUTANI ◽  
MASANORI EMOTO ◽  
YUKO YAMAZAKI ◽  
KOKA MOTOYAMA ◽  
TOMOAKI MORIOKA ◽  
...  

2019 ◽  
Vol 95 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Guozhi Jiang ◽  
Andrea On Yan Luk ◽  
Claudia Ha Ting Tam ◽  
Fangying Xie ◽  
Bendix Carstensen ◽  
...  

2021 ◽  
Vol 18 (3) ◽  
pp. 17-25
Author(s):  
Stoiţă Marcel ◽  
Popa Amorin Remus

Abstract The presence of albuminuria in patients with type 2 diabetes mellitus is a marker of endothelial dysfunction and also one of the criteria for diagnosing diabetic kidney disease. The present study aimed to identify associations between cardiovascular risk factors and renal albumin excretion in a group of 218 patients with type 2 diabetes mellitus. HbA1c values, systolic blood pressure, diastolic blood pressure were statistically significantly higher in patients with microalbuinuria or macroalbuminuria compared to patients with normoalbuminuria (p <0.01). We identified a statistically significant positive association between uric acid values and albuminuria, respectively 25- (OH)2 vitamin D3 deficiency and microalbuminuria (p <0.01).


2008 ◽  
Vol 11 (4) ◽  
pp. 988-991
Author(s):  
Robert C Atkins ◽  
Paul Zimmet

In 2003, the International Society of Nephrology and the International Diabetes Federation launched a booklet called “Diabetes in the Kidney: Time to act” [1] to highlight the global pandemic of type 2 diabetes and diabetic kidney disease. ration (PZ)


2021 ◽  
Author(s):  
Ning Zhang ◽  
Rui Fan ◽  
Jing Ke ◽  
Qinghua Cui ◽  
Dong ZHAO

Abstract BackgroundMicroalbuminuria is the main characteristic of Diabetic kidney disease (DKD), but it fluctuates greatly under the influence of blood glucose. Our aim was to establish some common clinical variables which could be easily collected to predict the risk of DKD in patients with type 2 diabetes. Methods and resultsWe build an artificial intelligence (AI) model to quantitively predict the risk of DKD based on the biomedical parameters from 1239 patients. An information entropy-based feature selection method was applied to screen out the risk factors of DKD. The dataset was divided with 4/5 into the training set and 1/5 into the test set. By using the selected risk factors, 5-fold cross-validation is applied to train the prediction model and it finally got AUC of 0.72 and 0.71 in the training set and test set respectively. In addition, we provide a method of calculating risk factors’ contribution for individuals to provide personalized guidance for treatment. We set up web-based application available on http://www.cuilab.cn/dkd for self-check and early warning. ConclusionsWe establish a feasible prediction model for DKD and suggest the degree of risk contribution of each indicator for each individual, which has certain clinical significance for early intervention and prevention.


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