基于大规模蛋白质组学的心肾代谢性疾病风险预测风险评分

IF 5.5 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Adithya K Yadalam, Chang Liu, Qin Hui, Alexander C Razavi, Laurence S Sperling, Arshed A Quyyumi, Yan V Sun
{"title":"基于大规模蛋白质组学的心肾代谢性疾病风险预测风险评分","authors":"Adithya K Yadalam, Chang Liu, Qin Hui, Alexander C Razavi, Laurence S Sperling, Arshed A Quyyumi, Yan V Sun","doi":"10.1161/CIRCGEN.124.005125","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown.</p><p><strong>Methods: </strong>We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by <i>International Classification of Diseases</i>-Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, N=16 671) and validation (30%, N=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices.</p><p><strong>Results: </strong>The study sample had a mean age of 56.1 years; 44% were male, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73-2.03]; <i>P</i><0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72-0.74] versus 0.71 [0.69-0.72]; ΔC-index, 0.03 [0.02-0.04]).</p><p><strong>Conclusions: </strong>A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e005125"},"PeriodicalIF":5.5000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large-Scale Proteomics-Based Risk Score for the Prediction of Incident Cardio-Kidney-Metabolic Disease Risk.\",\"authors\":\"Adithya K Yadalam, Chang Liu, Qin Hui, Alexander C Razavi, Laurence S Sperling, Arshed A Quyyumi, Yan V Sun\",\"doi\":\"10.1161/CIRCGEN.124.005125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown.</p><p><strong>Methods: </strong>We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by <i>International Classification of Diseases</i>-Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, N=16 671) and validation (30%, N=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices.</p><p><strong>Results: </strong>The study sample had a mean age of 56.1 years; 44% were male, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73-2.03]; <i>P</i><0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72-0.74] versus 0.71 [0.69-0.72]; ΔC-index, 0.03 [0.02-0.04]).</p><p><strong>Conclusions: </strong>A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.</p>\",\"PeriodicalId\":10326,\"journal\":{\"name\":\"Circulation: Genomic and Precision Medicine\",\"volume\":\" \",\"pages\":\"e005125\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2025-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Circulation: Genomic and Precision Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1161/CIRCGEN.124.005125\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Circulation: Genomic and Precision Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1161/CIRCGEN.124.005125","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

背景:心肾代谢(CKM)疾病是一个重大的公共卫生挑战。虽然基于蛋白质组学的风险评分(profs)增强了心血管风险预测,但它们在改善传统危险因素之外的复合CKM结果的风险预测方面的应用仍不清楚。方法:我们分析了23815名英国生物银行参与者,他们没有基线CKM疾病,国际疾病分类第十版代码将CKM定义为心血管疾病(冠状动脉疾病、心力衰竭、中风、外周动脉疾病、心房颤动/颤动)、肾脏疾病(慢性肾脏疾病或终末期肾脏疾病)或代谢疾病(2型糖尿病或肥胖)。将样本随机分为训练组(70%,N=16 671)和验证组(30%,N=7144)。利用2913个基于olink的蛋白的最小绝对收缩和基于选择算子的Cox回归模型来开发培训队列中的profs。然后,在调整传统风险因素后,我们在验证队列中使用竞争风险回归评估了profs与CKM疾病发生风险的关联,并评估了其用c指数区分CKM疾病发生风险的能力。结果:研究样本的平均年龄为56.1岁;44%是男性,94%是白人。在中位随访13.5年期间,训练组和验证组分别发生了3235例和1407例CKM疾病事件。基于238个最小绝对收缩和选择操作者选择的蛋白质的加权和的profs与CKM疾病的发生风险显著相关(每1-SD的亚分布风险比为1.87 [95% CI, 1.73-2.03])。结论:在基线时无CKM疾病的人群中,profs与CKM疾病的发生风险独立相关,并且改善了传统危险因素的风险预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Proteomics-Based Risk Score for the Prediction of Incident Cardio-Kidney-Metabolic Disease Risk.

Background: Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown.

Methods: We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by International Classification of Diseases-Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, N=16 671) and validation (30%, N=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices.

Results: The study sample had a mean age of 56.1 years; 44% were male, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73-2.03]; P<0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72-0.74] versus 0.71 [0.69-0.72]; ΔC-index, 0.03 [0.02-0.04]).

Conclusions: A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Circulation: Genomic and Precision Medicine
Circulation: Genomic and Precision Medicine Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
9.20
自引率
5.40%
发文量
144
期刊介绍: Circulation: Genomic and Precision Medicine is a distinguished journal dedicated to advancing the frontiers of cardiovascular genomics and precision medicine. It publishes a diverse array of original research articles that delve into the genetic and molecular underpinnings of cardiovascular diseases. The journal's scope is broad, encompassing studies from human subjects to laboratory models, and from in vitro experiments to computational simulations. Circulation: Genomic and Precision Medicine is committed to publishing studies that have direct relevance to human cardiovascular biology and disease, with the ultimate goal of improving patient care and outcomes. The journal serves as a platform for researchers to share their groundbreaking work, fostering collaboration and innovation in the field of cardiovascular genomics and precision medicine.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信