Effect of dapagliflozin on kidney and cardiovascular outcomes by baseline KDIGO risk categories: a post hoc analysis of the DAPA-CKD trial

析因分析 医学 达帕格列嗪 基线(sea) 内科学 事后 肾脏疾病 肾功能 糖尿病 内分泌学 2型糖尿病 生物 渔业
作者
Simke W. Waijer,Priya Vart,David Z.I. Cherney,Glenn M. Chertow,Niels Jongs,Anna Maria Langkilde,Johannes F.E. Mann,Ofri Mosenzon,John J.V. McMurray,Peter Rossing,Ricardo Correa‐Rotter,Bergur V. Stefánsson,Robert D. Toto,David C. Wheeler,Hiddo J.L. Heerspink
出处
期刊:Diabetologia [Springer Science+Business Media]
卷期号:65 (7): 1085-1097 被引量:68
标识
DOI:10.1007/s00125-022-05694-6
摘要

AIMS/HYPOTHESIS: In the Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD) trial, dapagliflozin reduced the risks of progressive kidney disease, hospitalised heart failure or cardiovascular death, and death from all causes in patients with chronic kidney disease (CKD) with or without type 2 diabetes. Patients with more severe CKD are at higher risk of kidney failure, cardiovascular events and all-cause mortality. In this post hoc analysis, we assessed the efficacy and safety of dapagliflozin according to baseline Kidney Disease Improving Global Outcomes (KDIGO) risk categories. METHODS: and urinary albumin/creatinine ratio (UACR) of ≥22.6 and <565.0 mg/mmol (200-5000 mg/g) to dapagliflozin 10 mg/day or placebo. The primary endpoint was a composite of ≥50% reduction in eGFR, end-stage kidney disease (ESKD), and death from a kidney or cardiovascular cause. Secondary endpoints included a kidney composite (≥50% reduction in eGFR, ESKD and death from a kidney cause), a cardiovascular composite (heart failure hospitalisation or cardiovascular death), and death from all causes. We used Cox proportional hazards regression analyses to assess relative and absolute effects of dapagliflozin across KDIGO risk categories. RESULTS: Of the 4304 participants in the DAPA-CKD study, 619 (14.4%) were moderately high risk, 1349 (31.3%) were high risk and 2336 (54.3%) were very high risk when categorised by KDIGO risk categories at baseline. Dapagliflozin reduced the hazard of the primary composite (HR 0.61; 95% CI 0.51, 0.72) and secondary endpoints consistently across KDIGO risk categories (all p for interaction >0.09). Absolute risk reductions for the primary outcome were also consistent irrespective of KDIGO risk category (p for interaction 0.26). Analysing patients with and without type 2 diabetes separately, the relative risk reduction with dapagliflozin in terms of the primary outcome was consistent across subgroups of KDIGO risk categories. The relative frequencies of adverse events and serious adverse events were also similar across KDIGO risk categories. CONCLUSION/INTERPRETATIONS: The consistent benefits of dapagliflozin on kidney and cardiovascular outcomes across KDIGO risk categories indicate that dapagliflozin is efficacious and safe across a wide spectrum of kidney disease severity. TRIAL REGISTRATION: ClinicalTrials.gov NCT03036150. FUNDING: The study was funded by AstraZeneca.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
香蕉觅云应助河师大采纳,获得10
刚刚
陈文思完成签到 ,获得积分10
2秒前
黎谱谱完成签到 ,获得积分10
2秒前
Akim应助exile采纳,获得10
2秒前
邱大楚发布了新的文献求助10
2秒前
李好发布了新的文献求助10
4秒前
4秒前
wujiaman345发布了新的文献求助10
5秒前
思源应助樊傲云采纳,获得10
5秒前
Lucas应助忧郁画板采纳,获得10
5秒前
xing_xing应助ww采纳,获得20
6秒前
科研通AI6.4应助杨新苗采纳,获得10
6秒前
7秒前
7秒前
大胆的巧蕊完成签到,获得积分10
7秒前
册册哇发布了新的文献求助10
7秒前
molihuakai应助老简采纳,获得10
9秒前
9秒前
GRACE发布了新的文献求助10
9秒前
10秒前
10秒前
10秒前
Akim应助Kenny采纳,获得10
11秒前
薛定谔的猫完成签到,获得积分10
11秒前
董以宁完成签到,获得积分20
13秒前
13秒前
舒心的小鸭子完成签到,获得积分10
14秒前
14秒前
14秒前
eve发布了新的文献求助10
14秒前
14秒前
河师大发布了新的文献求助10
14秒前
Fish应助Yy采纳,获得10
15秒前
顺利的草丛完成签到,获得积分10
15秒前
积极又向上完成签到,获得积分10
15秒前
nihao完成签到,获得积分10
16秒前
laomo应助fhbsdufh采纳,获得10
16秒前
16秒前
丘比特应助优秀不愁采纳,获得10
17秒前
17秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Great Hymn to Šamaš 500
Positive Obsession: The Life and Times of Octavia E. Butler 500
Interpolation and Regression Models for the Chemical Engineer: Solving Numerical Problems 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7693464
求助须知:如何正确求助?哪些是违规求助? 9254268
关于积分的说明 19988542
捐赠科研通 7266700
什么是DOI,文献DOI怎么找? 3291605
关于科研通互助平台的介绍 2447638
邀请新用户注册赠送积分活动 2297022