Glucose-lowering Therapies in Patients with Type 2 Diabetes and Cardiovascular Diseases
European journal of preventive cardiology(2019)SCI 1区SCI 2区
IRCCS MultiMed | Karolinska Inst | Univ Hosp Aachen | Ctr Hosp St Joseph St Luc | Jean VERDIER Hosp
Abstract
Type 2 diabetes mellitus is a major risk factor for developing cardiovascular disease, and many patients with diabetes have prevalent cardiovascular complications. Recent cardiovascular outcome clinical trials suggest that certain new glucose-lowering drugs are accompanied by additional cardioprotective properties. Indeed, selected glucagon-like peptide-1 receptor agonists have a proved cardiovascular benefit in terms of a reduced incidence of ischaemic events, while sodium/glucose co-transporter-2 inhibitors have also shown significant protection, with a striking effect on heart failure and renal endpoints. These findings have been integrated in recent guidelines which now recommend prescribing (when initial metformin monotherapy fails) a glucagon-like peptide-1 receptor agonist or a sodium/glucose co-transporter-2 inhibitor with clinical trial-confirmed benefit in patients with diabetes and atherosclerotic cardiovascular disease, and a sodium/glucose co-transporter-2 inhibitor in such patients with heart failure or chronic kidney disease at initial stages. Furthermore, the new 2019 European Society of Cardiology guidelines in collaboration with the European Association for the Study of Diabetes recommend a glucagon-like peptide-1 receptor agonist or a sodium/glucose co-transporter-2 inhibitor in treatment-naive patients with type 2 diabetes mellitus with pre-existing cardiovascular disease or at high cardiovascular risk. Future research will disentangle the mechanisms underpinning these beneficial effects and will also establish to what extent these results are generalisable to the whole diabetes population. In the meantime, available evidence should prompt a wide diffusion of these two classes of drugs among patients with diabetes and cardiovascular disease. Here, we briefly summarise recent findings emerging from cardiovascular outcome clinical trials, discuss their impact on treatment algorithms and propose new possible approaches to improve our knowledge further regarding the cardiovascular effect of glucose-lowering medications.
MoreTranslated text
Key words
Glucose-lowering drugs,type 2 diabetes,cardiovascular disease,cardiovascular outcome trial,DPP-4 inhibitors,GLP-1 receptor agonists,SGLT-2 inhibitors,treatment algorithm
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2017
被引用423 | 浏览
2018
被引用357 | 浏览
2017
被引用11 | 浏览
2019
被引用47 | 浏览
2018
被引用80 | 浏览
2019
被引用1776 | 浏览
2017
被引用173 | 浏览
2018
被引用394 | 浏览
2018
被引用706 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话