{"title":"循环炎症相关蛋白质组改善心血管风险预测。来自两个大型欧洲队列研究的结果。","authors":"Ruijie Xie,Sha Sha,Hermann Brenner,Ben Schöttker","doi":"10.1007/s10654-025-01285-y","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nInflammation plays a crucial role in cardiovascular disease (CVD), but the value of inflammation-related proteins in predicting major adverse cardiovascular events (MACE) is unclear. This study evaluated whether incorporating inflammation-related proteins into the SCORE2 model improves 10-year MACE risk prediction.\r\n\r\nMETHODS\r\nThis study included 47,382 participants from the UK Biobank and 4,135 participants from the German ESTHER study without prior CVD or diabetes. We tested C-reactive protein (CRP) and 73 inflammation-related proteins measured with Olink® panels. Biomarker selection was performed using least absolute shrinkage and selection operator (LASSO) regression with bootstrapping separately for males and females. Selected proteins were added to the SCORE2 model variables. Model performance was evaluated using Harrell's C-index, net reclassification index (NRI), and integrated discrimination index (IDI).\r\n\r\nRESULTS\r\nSeven inflammation-related proteins but not CRP were selected, including two for both sexes, three specifically for males, and two specifically for females. Incorporating these proteins significantly improved the C-index (95% confidence interval (95%CI)) of the refitted SCORE2 model from 0.716 (0.698, 0.734) to 0.750 (0.732, 0.768) in internal validation in the UK Biobank and from 0.677 (0.644, 0.710) to 0.713 (0.681, 0.745) in external validation in the ESTHER study. The NRI with 95%CI was 12.4% (5.2%, 16.3%) in internal validation and 4.2% (0.5%, 23.6%) in external validation. The IDI also improved significantly.\r\n\r\nCONCLUSION\r\nIncorporating inflammation-related proteins into the SCORE2 model significantly improves the prediction of 10-year MACE risk among individuals without prior CVD or diabetes. Measuring these proteins may enhance risk stratification in clinical practice.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"114 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Circulating inflammation-related proteome improves cardiovascular risk prediction. Results from two large European cohort studies.\",\"authors\":\"Ruijie Xie,Sha Sha,Hermann Brenner,Ben Schöttker\",\"doi\":\"10.1007/s10654-025-01285-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\r\\nInflammation plays a crucial role in cardiovascular disease (CVD), but the value of inflammation-related proteins in predicting major adverse cardiovascular events (MACE) is unclear. This study evaluated whether incorporating inflammation-related proteins into the SCORE2 model improves 10-year MACE risk prediction.\\r\\n\\r\\nMETHODS\\r\\nThis study included 47,382 participants from the UK Biobank and 4,135 participants from the German ESTHER study without prior CVD or diabetes. We tested C-reactive protein (CRP) and 73 inflammation-related proteins measured with Olink® panels. Biomarker selection was performed using least absolute shrinkage and selection operator (LASSO) regression with bootstrapping separately for males and females. Selected proteins were added to the SCORE2 model variables. Model performance was evaluated using Harrell's C-index, net reclassification index (NRI), and integrated discrimination index (IDI).\\r\\n\\r\\nRESULTS\\r\\nSeven inflammation-related proteins but not CRP were selected, including two for both sexes, three specifically for males, and two specifically for females. Incorporating these proteins significantly improved the C-index (95% confidence interval (95%CI)) of the refitted SCORE2 model from 0.716 (0.698, 0.734) to 0.750 (0.732, 0.768) in internal validation in the UK Biobank and from 0.677 (0.644, 0.710) to 0.713 (0.681, 0.745) in external validation in the ESTHER study. The NRI with 95%CI was 12.4% (5.2%, 16.3%) in internal validation and 4.2% (0.5%, 23.6%) in external validation. The IDI also improved significantly.\\r\\n\\r\\nCONCLUSION\\r\\nIncorporating inflammation-related proteins into the SCORE2 model significantly improves the prediction of 10-year MACE risk among individuals without prior CVD or diabetes. Measuring these proteins may enhance risk stratification in clinical practice.\",\"PeriodicalId\":11907,\"journal\":{\"name\":\"European Journal of Epidemiology\",\"volume\":\"114 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10654-025-01285-y\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10654-025-01285-y","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Circulating inflammation-related proteome improves cardiovascular risk prediction. Results from two large European cohort studies.
BACKGROUND
Inflammation plays a crucial role in cardiovascular disease (CVD), but the value of inflammation-related proteins in predicting major adverse cardiovascular events (MACE) is unclear. This study evaluated whether incorporating inflammation-related proteins into the SCORE2 model improves 10-year MACE risk prediction.
METHODS
This study included 47,382 participants from the UK Biobank and 4,135 participants from the German ESTHER study without prior CVD or diabetes. We tested C-reactive protein (CRP) and 73 inflammation-related proteins measured with Olink® panels. Biomarker selection was performed using least absolute shrinkage and selection operator (LASSO) regression with bootstrapping separately for males and females. Selected proteins were added to the SCORE2 model variables. Model performance was evaluated using Harrell's C-index, net reclassification index (NRI), and integrated discrimination index (IDI).
RESULTS
Seven inflammation-related proteins but not CRP were selected, including two for both sexes, three specifically for males, and two specifically for females. Incorporating these proteins significantly improved the C-index (95% confidence interval (95%CI)) of the refitted SCORE2 model from 0.716 (0.698, 0.734) to 0.750 (0.732, 0.768) in internal validation in the UK Biobank and from 0.677 (0.644, 0.710) to 0.713 (0.681, 0.745) in external validation in the ESTHER study. The NRI with 95%CI was 12.4% (5.2%, 16.3%) in internal validation and 4.2% (0.5%, 23.6%) in external validation. The IDI also improved significantly.
CONCLUSION
Incorporating inflammation-related proteins into the SCORE2 model significantly improves the prediction of 10-year MACE risk among individuals without prior CVD or diabetes. Measuring these proteins may enhance risk stratification in clinical practice.
期刊介绍:
The European Journal of Epidemiology, established in 1985, is a peer-reviewed publication that provides a platform for discussions on epidemiology in its broadest sense. It covers various aspects of epidemiologic research and statistical methods. The journal facilitates communication between researchers, educators, and practitioners in epidemiology, including those in clinical and community medicine. Contributions from diverse fields such as public health, preventive medicine, clinical medicine, health economics, and computational biology and data science, in relation to health and disease, are encouraged. While accepting submissions from all over the world, the journal particularly emphasizes European topics relevant to epidemiology. The published articles consist of empirical research findings, developments in methodology, and opinion pieces.