Department of Population Health, New York University School of Medicine , New York, New York, USA.
Department of Environmental Medicine, New York University School of Medicine , New York, New York, USA.
Environ Health Perspect. 2018 Jan 12;126(1):017005. doi: 10.1289/EHP1992.
Chronic exposure to inorganic arsenic from drinking water has been associated with a host of cancer and noncancer diseases. The application of metabolomics in epidemiologic studies may allow researchers to identify biomarkers associated with arsenic exposure and its health effects.
Our goal was to evaluate the long-term reproducibility of urinary metabolites and associations between reproducible metabolites and arsenic exposure.
We studied samples and data from 112 nonsmoking participants (58 men and 54 women) who were free of any major chronic diseases and who were enrolled in the Health Effects of Arsenic Longitudinal Study (HEALS), a large prospective cohort study in Bangladesh. Using a global gas chromatography-mass spectrometry platform, we measured metabolites in their urine samples, which were collected at baseline and again 2 y apart, and estimated intraclass correlation coefficients (ICCs). Linear regression was used to assess the association between arsenic exposure at baseline and metabolite levels in baseline urine samples.
We identified 2,519 molecular features that were present in all 224 urine samples from the 112 participants, of which 301 had an ICC of ≥0.60. Of the 301 molecular features, water arsenic was significantly related to 31 molecular features and urinary arsenic was significantly related to 74 molecular features after adjusting for multiple comparisons. Six metabolites with a confirmed identity were identified from the 82 molecular features that were significantly associated with either water arsenic or urinary arsenic after adjustment for multiple comparisons.
Our study identified urinary metabolites with long-term reproducibility that were associated with arsenic exposure. The data established the feasibility of using metabolomics in future larger studies. https://doi.org/10.1289/EHP1992.
长期饮用含无机砷的水会导致一系列癌症和非癌症疾病。代谢组学在流行病学研究中的应用，可能使研究人员能够识别与砷暴露及其健康影响相关的生物标志物。
我们的目标是评估尿液代谢物的长期重现性，以及重现性代谢物与砷暴露之间的关联。
我们研究了来自 112 名不吸烟的参与者（58 名男性和 54 名女性）的样本和数据，这些参与者没有任何重大慢性疾病，并且参加了孟加拉国的一项大型前瞻性队列研究——砷暴露纵向研究（HEALS）。我们使用全球气相色谱-质谱平台测量了他们尿液样本中的代谢物，这些样本是在基线和 2 年后采集的，并估计了组内相关系数（ICC）。线性回归用于评估基线时的砷暴露与基线尿液样本中代谢物水平之间的关系。
我们鉴定出 224 个尿液样本中存在的 2519 个分子特征，其中 112 名参与者中有 301 个的 ICC 值≥0.60。在调整了多重比较后，301 个分子特征中，水中砷与 31 个分子特征显著相关，尿砷与 74 个分子特征显著相关。在调整了多重比较后，有 82 个分子特征与水中砷或尿砷显著相关，从中鉴定出 6 种具有确定身份的代谢物。
我们的研究发现了具有长期重现性的尿液代谢物，这些代谢物与砷暴露有关。这些数据为未来更大规模的研究中使用代谢组学奠定了可行性。https://doi.org/10.1289/EHP1992.
