Epigenetic scores for the circulating proteome as tools for disease prediction.
Epigenetic scores for the circulating proteome as tools for disease prediction.
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DOI：
10.7554/elife.71802
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发表时间：
2022-01-13
期刊：
影响因子：
7.7
通讯作者：
Marioni RE
中科院分区：
文献类型：
--
作者：
Gadd DA;Hillary RF;McCartney DL;Zaghlool SB;Stevenson AJ;Cheng Y;Fawns-Ritchie C;Nangle C;Campbell A;Flaig R;Harris SE;Walker RM;Shi L;Tucker-Drob EM;Gieger C;Peters A;Waldenberger M;Graumann J;McRae AF;Deary IJ;Porteous DJ;Hayward C;Visscher PM;Cox SR;Evans KL;McIntosh AM;Suhre K;Marioni RE
蛋白质生物标志物已经在许多与年龄相关的疾病中被发现。然而，表征表观遗传影响可以进一步为疾病预测提供信息。在这里，我们利用表观基因组数据来研究循环蛋白质组的DNA甲基化（DNAm）特征与事件疾病之间的联系。使用来自四个队列的数据，我们训练并测试了953种血浆蛋白的表观遗传评分（EpiScores），确定了109个评分，在调整已知蛋白质数量性状位点（pQTL）遗传效应后，解释了1%至58%的蛋白质水平差异。通过将这些episcore投射到一个独立的样本中（苏格兰一代，n = 9537），并将其与14年的随访发病率联系起来，我们发现了130个episcore与疾病的关联。这些关联在很大程度上与免疫细胞比例、常见的生活方式和健康因素以及生物衰老无关。值得注意的是，我们发现我们的糖尿病相关EpiScores突出了之前糖尿病蛋白质组评估的顶级生物标志物关联。因此，这些蛋白水平的episcore可以成为疾病预测和风险分层的宝贵资源。虽然我们的遗传密码在我们的一生中不会改变，但我们的基因可以作为表观遗传学的结果而开启和关闭。表观遗传学可以追踪环境，甚至某些行为如何添加或删除组成基因组的DNA中的小化学标记。这些标记的类型和位置可能会影响基因是活跃还是沉默，也就是说，该基因编码的蛋白质是否产生。一种常见的表观遗传标记被称为DNA甲基化。DNA甲基化与我们细胞中一系列蛋白质的水平以及人们患慢性疾病的风险有关。血液样本可以用来确定一个人基因组上的表观遗传标记，并研究许多蛋白质的丰度。Gadd, Hillary, McCartney， Zaghlool等人研究了德国KORA队列和苏格兰Lothian出生队列1936个体血液样本中DNA甲基化与953种不同蛋白质丰度之间的关系。然后，他们使用机器学习来分析人们血液中发现的表观遗传标记与蛋白质丰度之间的关系，获得每种蛋白质的表观遗传评分或“EpiScores”。他们发现109种蛋白质的DNA甲基化模式可以解释至少1%到58%的蛋白质水平变化。将“EpiScores”与苏格兰世代研究中9000多人的14年医疗记录整合在一起，发现蛋白质的EpiScores与未来常见不良健康结果的诊断之间存在130种联系。这些疾病包括糖尿病、中风、抑郁症、各种癌症和炎症性疾病，如风湿性关节炎和炎症性肠病。与年龄有关的慢性疾病在世界范围内日益严重，并给卫生保健系统带来压力。它们还会严重降低个人多年的生活质量。这项研究表明，基于血液中蛋白质水平的表观遗传评分可以预测一个人患上述几种疾病的风险。在2型糖尿病的案例中，EpiScore的结果重复了先前的研究，将血液中的蛋白质水平与未来的糖尿病诊断联系起来。因此，蛋白质EpiScores可以让研究人员识别出疾病风险最高的人群，从而使早期干预成为可能，并防止这些人随着年龄的增长而患上慢性病。
Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 130 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification. Although our genetic code does not change throughout our lives, our genes can be turned on and off as a result of epigenetics. Epigenetics can track how the environment and even certain behaviors add or remove small chemical markers to the DNA that makes up the genome. The type and location of these markers may affect whether genes are active or silent, this is, whether the protein coded for by that gene is being produced or not. One common epigenetic marker is known as DNA methylation. DNA methylation has been linked to the levels of a range of proteins in our cells and the risk people have of developing chronic diseases. Blood samples can be used to determine the epigenetic markers a person has on their genome and to study the abundance of many proteins. Gadd, Hillary, McCartney, Zaghlool et al. studied the relationships between DNA methylation and the abundance of 953 different proteins in blood samples from individuals in the German KORA cohort and the Scottish Lothian Birth Cohort 1936. They then used machine learning to analyze the relationship between epigenetic markers found in people’s blood and the abundance of proteins, obtaining epigenetic scores or ‘EpiScores’ for each protein. They found 109 proteins for which DNA methylation patterns explained between at least 1% and up to 58% of the variation in protein levels. Integrating the ‘EpiScores’ with 14 years of medical records for more than 9000 individuals from the Generation Scotland study revealed 130 connections between EpiScores for proteins and a future diagnosis of common adverse health outcomes. These included diabetes, stroke, depression, various cancers, and inflammatory conditions such as rheumatoid arthritis and inflammatory bowel disease. Age-related chronic diseases are a growing issue worldwide and place pressure on healthcare systems. They also severely reduce quality of life for individuals over many years. This work shows how epigenetic scores based on protein levels in the blood could predict a person’s risk of several of these diseases. In the case of type 2 diabetes, the EpiScore results replicated previous research linking protein levels in the blood to future diagnosis of diabetes. Protein EpiScores could therefore allow researchers to identify people with the highest risk of disease, making it possible to intervene early and prevent these people from developing chronic conditions as they age.