Large-scale plasma proteomics for predicting future cardiovascular and all-cause mortality among individuals with cardiovascular-kidney-metabolic syndrome stage 0–3

医学 阶段(地层学) 疾病 内科学 人口 蛋白质组学 梅德林 重症监护医学 生物信息学 流行病学 死亡率 肿瘤科 入射(几何) 风险评估 血压
作者
Bingtao Weng,Jiahe Wei,Han Chen,Yuyan Zhao,Ningjian Wang,Hongliang Feng,Sizhi Ai,Xiao Tan
出处
期刊:Metabolism-clinical and Experimental [Elsevier BV]
卷期号:179: 156600-156600 被引量:3
标识
DOI:10.1016/j.metabol.2026.156600
摘要

BACKGROUND: Identifying high-risk individuals for cardiovascular and all-cause mortality among individuals with cardiovascular-kidney-metabolic (CKM) syndrome stage 0-3 can guide the implementation of targeted interventions. This study aimed to evaluate the predictive value of plasma proteins for future cardiovascular and all-cause mortality. METHODS: This study included 39,007 participants from the UK Biobank (UKB) with CKM stage 0-3 and available proteomic data. Associations between plasma proteins and future risks of cardiovascular and all-cause mortality were assessed using Cox proportional hazards models. Key proteins were identified through an ensemble machine learning approach integrating support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) algorithms. Subsequently, Cox models were applied to evaluate the incremental predictive value of these key proteins and their ability to enhance risk stratification for mortality outcomes. Furthermore, temporal trajectories of protein levels were examined in the years preceding death. RESULTS: During a median follow-up of 15.2 years, 505 participants died from cardiovascular causes and 3368 from any cause. 56 and 269 out of 2911 plasma proteins were significantly associated with cardiovascular and all-cause mortality, respectively (Bonferroni-adjusted P < 0.05). Incorporating seven and eight key proteins into conventional model significantly improved long-term predictive performance (C-statistics: 0.812 versus 0.782 for cardiovascular mortality; 0.772 versus 0.739 for all-cause mortality; both P < 0.001), and also provided incremental predictive value for 5- and 10-year mortality risks. Notably, participants died during follow-up exhibited markedly elevated certain protein levels over a decade before deaths, with progressively increasing trajectories over time. Stratification based on optimal predicted risk thresholds further revealed distinct cumulative mortality risks across groups. CONCLUSIONS: In individuals with CKM stage 0-3, plasma proteins combined with traditional risk factors may predict future cardiovascular and all-cause mortality.
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
差不多姑娘完成签到 ,获得积分10
刚刚
刚刚
rkay完成签到,获得积分10
刚刚
二狗子完成签到,获得积分10
1秒前
1秒前
1秒前
廉穆发布了新的文献求助10
1秒前
Mr.Young完成签到,获得积分10
2秒前
卓垚发布了新的文献求助10
2秒前
脑洞疼应助陈千采纳,获得10
2秒前
asadguy完成签到,获得积分10
2秒前
2秒前
v0id应助why采纳,获得10
2秒前
魔修222完成签到,获得积分10
3秒前
烟花应助dyq采纳,获得10
3秒前
贪玩的秋柔应助初景采纳,获得30
3秒前
大Z发布了新的文献求助10
3秒前
Ke发布了新的文献求助10
3秒前
deer完成签到,获得积分10
3秒前
MMMX完成签到,获得积分10
4秒前
aging00完成签到,获得积分10
4秒前
时尚的半仙完成签到,获得积分10
4秒前
你们都是好人呀完成签到,获得积分10
4秒前
222应助炜大的我采纳,获得10
4秒前
felicia12138完成签到 ,获得积分10
5秒前
橘子29完成签到,获得积分10
5秒前
精明玲发布了新的文献求助10
5秒前
wanjie完成签到,获得积分10
5秒前
5秒前
s1mple完成签到,获得积分10
5秒前
黑粉头头发布了新的文献求助10
5秒前
王思蒙完成签到 ,获得积分10
5秒前
5秒前
内向的太陽完成签到,获得积分10
6秒前
lucky完成签到 ,获得积分10
6秒前
YYYh完成签到,获得积分10
6秒前
fan完成签到 ,获得积分10
6秒前
lalahei完成签到,获得积分0
6秒前
勤奋安波完成签到,获得积分10
6秒前
7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
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Navigating Normative Orders. Interdisciplinary Perspectives 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7760185
求助须知:如何正确求助?哪些是违规求助? 9305375
关于积分的说明 20287848
捐赠科研通 7344333
什么是DOI,文献DOI怎么找? 3312776
关于科研通互助平台的介绍 2463256
邀请新用户注册赠送积分活动 2326815