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}
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.
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.