Molecular epidemiology
Molecular epidemiology
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DOI：
10.1093/eurpub/ckad160.455
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发表时间：
2023-10-24
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环境暴露会增加多种疾病的风险，包括痴呆症。现代工业化的扩展和化石燃料的燃烧，加上农业中大量使用化肥，导致空气、水和食物中有毒元素的含量增加。这项工作的一个关键部分是确定与砷等有毒元素暴露相关的生物特征及其对慢性疾病的影响。本报告将展示一个工作的例子，说明如何砷暴露将在尿样评估。将在贝尔法斯特皇后大学Andy Meharg教授实验室通过离子色谱法与电感耦合等离子体质谱检测（ICP-MS）接口分析尿液中的iAs、MMA、DMA和AsB。将通过Rnbeads 2.0处理DNAm数据。然后使用主成分分析（PCA）来确定DNA m数据中的技术变异性（批次效应）。去除批次效应后，Clean DNAm数据将用于线性回归模型，以确定与砷暴露相关的CpG位点。
Environmental exposures increase risk for multiple diseases, including dementia. The modern expansion of industrialization and the combustion of fossil fuels, coupled with extensive application of chemical fertilizers in farming, have led to an increase in the levels of toxic elements in the air, water and food. A key part of this work is to identify biological signatures associated with toxic element exposure, such as arsenic, and its effects on chronic disease conditions. This presentation will showcase a worked example illustrating how arsenic exposure will be assessed in urine samples. iAs, MMA, DMA and AsB will be analysed in urine by ion chromatography interfaced with inductively coupled plasma-mass spectrometry detection (ICP-MS) in Prof. Andy Meharg's lab, Queens's University, Belfast. DNAm data will be processed through Rnbeads 2.0. A principal-component analysis (PCA) was then used to identify technical variability (batch effect) in DNAm data. After removing batch effect, Clean DNAm data will be used in linear regression models to determine CpG loci associated with arsenic exposure.