Suppr超能文献

社区动脉粥样硬化风险(ARIC)研究中蛋白质组衰老时钟的开发与表征

Development and Characterization of Proteomic Aging Clocks in the Atherosclerosis Risk in Communities (ARIC) Study.

作者信息

Wang Shuo, Rao Zexi, Cao Rui, Blaes Anne H, Coresh Josef, Joshu Corinne E, Lehallier Benoit, Lutsey Pamela L, Pankow James S, Sedaghat Sanaz, Tang Weihong, Thyagarajan Bharat, Walker Keenan A, Ganz Peter, Platz Elizabeth A, Guan Weihua, Prizment Anna

机构信息

Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN.

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN.

出版信息

medRxiv. 2023 Sep 8:2023.09.06.23295174. doi: 10.1101/2023.09.06.23295174.

Abstract

Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in White individuals, and they used proteomic measures from only one-time point. In the Atherosclerosis Risk in Communities (ARIC) study of about 12,000 persons followed for 30 years (around 75% White, 25% Black), we created de novo PACs and compared their performance to published PACs at two different time points. We measured 4,712 plasma proteins by SomaScan in 11,761 midlife participants, aged 46-70 years (1990-92), and 5,183 late-life pariticpants, aged 66-90 years (2011-13). All proteins were log2-transformed to correct for skewness. We created de novo PACs by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and compared their performance to three published PACs. We estimated age acceleration (by regressing each PAC on chronological age) and its change from midlife to late life. We examined their associations with mortality from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in all remaining participants irrespective of health. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per one standard deviation were 1.65 and 1.38 (both p<0.001) for all-cause mortality, 1.37 and 1.20 (both p<0.001) for CVD mortality, 1.21 (p=0.03) and 1.04 (p=0.19) for cancer mortality, and 1.46 and 1.68 (both p<0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to those observed for late-life age acceleration. The association between the change in age acceleration and cancer mortality was insignificant. In this prospective study, the ARIC and published PACs were similarly associated with an increased risk of mortality and advanced testing in relation to various age-related conditions in future studies is suggested.

摘要

生物年龄可以通过蛋白质组衰老时钟(PACs)来估计。先前发表的PACs是在较小规模的研究中构建的,或者主要是在白人个体中构建的,并且它们仅使用了来自一个时间点的蛋白质组测量值。在社区动脉粥样硬化风险(ARIC)研究中,约12000人被随访了30年(约75%为白人,25%为黑人),我们重新创建了PACs,并在两个不同时间点将它们的性能与已发表的PACs进行了比较。我们在11761名46 - 70岁(1990 - 1992年)的中年参与者和5183名66 - 90岁(2011 - 2013年)的老年参与者中,通过SomaScan测量了4712种血浆蛋白。所有蛋白质均进行了log2转换以校正偏态。我们通过在中年和老年三分之二的健康参与者中使用弹性网络回归,根据实际年龄对它们进行训练,从而重新创建了PACs,并将它们的性能与三个已发表的PACs进行了比较。我们估计了年龄加速(通过将每个PAC对实际年龄进行回归)及其从中年到老年的变化。我们在所有其余参与者(无论健康状况如何)中使用Cox比例风险回归,研究了它们与全因死亡率、心血管疾病(CVD)、癌症和下呼吸道疾病(LRD)死亡率的关联。该模型针对实际年龄、吸烟、体重指数(BMI)和其他混杂因素进行了调整。在每个时间点的健康参与者中,ARIC PACs与实际年龄的相关性略强于已发表的PACs。ARIC和已发表的PACs与死亡率的关联相似。对于ARIC PACs的老年和中年年龄加速,每增加一个标准差的风险比(HRs),全因死亡率分别为1.65和1.38(均p<0.001),CVD死亡率分别为1.37和1.20(均p<0.001),癌症死亡率分别为1.21(p = 0.03)和1.04(p = 0.19),LRD死亡率分别为1.46和1.68(均p<0.001)。对于年龄加速的变化,全因、CVD和LRD死亡率的HRs与老年年龄加速时观察到的相似。年龄加速变化与癌症死亡率之间的关联不显著。在这项前瞻性研究中,ARIC和已发表的PACs与死亡率增加的关联相似,建议在未来研究中针对各种与年龄相关的状况进行进一步测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b7/10508816/201f6254952d/nihpp-2023.09.06.23295174v1-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验