英国生物库队列中的血浆蛋白质组学和颈动脉内膜中层厚度。

IF 2.8 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Frontiers in Cardiovascular Medicine Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.3389/fcvm.2024.1478600
Ming-Li Chen, Pik Fang Kho, Rodrigo Guarischi-Sousa, Jiayan Zhou, Daniel J Panyard, Zahra Azizi, Trisha Gupte, Kathleen Watson, Fahim Abbasi, Themistocles L Assimes
{"title":"英国生物库队列中的血浆蛋白质组学和颈动脉内膜中层厚度。","authors":"Ming-Li Chen, Pik Fang Kho, Rodrigo Guarischi-Sousa, Jiayan Zhou, Daniel J Panyard, Zahra Azizi, Trisha Gupte, Kathleen Watson, Fahim Abbasi, Themistocles L Assimes","doi":"10.3389/fcvm.2024.1478600","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and aims: </strong>Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.</p><p><strong>Method: </strong>We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (<i>R</i> <sup>2</sup>).</p><p><strong>Result: </strong>The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest <i>R</i> <sup>2</sup> (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower <i>R</i> <sup>2</sup> (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.</p><p><strong>Conclusion: </strong>Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.</p>","PeriodicalId":12414,"journal":{"name":"Frontiers in Cardiovascular Medicine","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480011/pdf/","citationCount":"0","resultStr":"{\"title\":\"Plasma proteomics and carotid intima-media thickness in the UK biobank cohort.\",\"authors\":\"Ming-Li Chen, Pik Fang Kho, Rodrigo Guarischi-Sousa, Jiayan Zhou, Daniel J Panyard, Zahra Azizi, Trisha Gupte, Kathleen Watson, Fahim Abbasi, Themistocles L Assimes\",\"doi\":\"10.3389/fcvm.2024.1478600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and aims: </strong>Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.</p><p><strong>Method: </strong>We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (<i>R</i> <sup>2</sup>).</p><p><strong>Result: </strong>The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest <i>R</i> <sup>2</sup> (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower <i>R</i> <sup>2</sup> (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.</p><p><strong>Conclusion: </strong>Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.</p>\",\"PeriodicalId\":12414,\"journal\":{\"name\":\"Frontiers in Cardiovascular Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480011/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Cardiovascular Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fcvm.2024.1478600\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Cardiovascular Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fcvm.2024.1478600","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

背景和目的:超声波得出的颈动脉内膜中层厚度(cIMT)对心血管风险分层很有价值。我们评估了传统动脉粥样硬化风险因素和血浆蛋白在预测近十年后测量的 cIMT 中的相对重要性:我们对 6,136 名英国生物库参与者进行了研究,他们的基线抽血中使用了接近延伸测定法,对 1,461 种蛋白质进行了分析,随后对他们进行了 cIMT 测量。我们采用线性回归、基于阿凯克信息准则的逐步回归以及最小绝对收缩和选择算子(LASSO)模型来识别潜在的蛋白质组和非蛋白质组预测因子。我们使用解释方差比例(R 2)来评估模型的性能:从基线评估到 cIMT 测量的平均时间为 9.2 年。年龄、血压和人体测量相关变量是 cIMT 的最强预测因素,而躯干区域的去脂质量指数是脂肪测量中最强的预测因素。将年龄、评估中心、遗传风险因素、吸烟、血压、躯干无脂质量指数、载脂蛋白 B 和汤森剥夺指数等变量与 97 种蛋白质结合的 LASSO 模型获得了最高的 R 2(0.308,95% C.I.0.274,0.341)。相比之下,单独使用蛋白质或单独使用非蛋白质组变量建立的模型解释的 R 2 明显较低(分别为 0.261,0.228-0.294 和 0.260,0.226-0.293)。Chromogranin b (CHGB)、胱抑素-M/E (CST6)、瘦素 (LEP) 和前列素 (PRELP) 是所有模型一致选择的蛋白质:结论:血浆蛋白是预测 cIMT 测量值的临床和遗传风险因素的补充。我们的研究结果表明,血压和细胞外基质相关蛋白与 cIMT 病理生理学有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Plasma proteomics and carotid intima-media thickness in the UK biobank cohort.

Background and aims: Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.

Method: We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R 2).

Result: The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R 2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R 2 (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.

Conclusion: Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

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
Frontiers in Cardiovascular Medicine
Frontiers in Cardiovascular Medicine Medicine-Cardiology and Cardiovascular Medicine
CiteScore
3.80
自引率
11.10%
发文量
3529
审稿时长
14 weeks
期刊介绍: Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers? At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.
期刊最新文献
Association between varicose veins and constitution of traditional Chinese medicine plus heart-failure-like symptoms. Clinical decision-making in patients with non-ST-segment-elevation myocardial infarction: more than risk stratification. Evaluating the role of pericoronary adipose tissue on coronary artery disease: insights from CCTA on risk assessment, vascular stenosis, and plaque characteristics. Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram. The correlation between stable angina and inflammatory factors and blood lipids: a case-control study.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1