The effect of pre-analytical factors on stability of metabolomic epidemiologic research -DCP funding

代谢组学是对小分子的评估，通常只定义为在给定的生物系统中参与细胞代谢的分子。代谢物包括低分子量化合物，如脂质、糖、氨基酸、核苷酸。现代方法，如核磁共振（NMR）和质谱（MS）与液相色谱（LC）或气相色谱（GC）相结合，可以同时识别和量化生物标本中的大量代谢物，捕获其代谢组学特征。这些特征已被用于预测患糖尿病的风险、诊断前列腺癌和识别克罗恩病的生物标志物。人们对将代谢组学分析应用于癌症流行病学研究有着浓厚的兴趣。由于流行病学研究经常涉及现场生物标本采集，样品处理、处理和储存的条件可能会有所不同，这可能会影响结果的可靠性，并增加检测到虚假生物标志物候选物的风险。因此，为了获得对个体代谢组学特征的准确评估，我们需要了解并消除或改变影响测量代谢物水平的样品制备步骤。在这个阶段，我们的兴趣是了解收集方法（例如，冷冻前的时间）或样品制备步骤（例如，代谢物提取）是否会影响测量的偏差或可变性。在进行代谢组学研究时，认识到从样品处理到适当控制分析前参数对代谢物测量的影响的潜在可变性是很重要的。了解这种可变性是将代谢组学技术应用于流行病学研究和汇总来自不同人群研究的测量结果的必要前提。

Metabolomics is the assessment of small molecules, often defined to be only those molecules participating in cellular metabolism, within a given biological system. Metabolites include low-molecular weight compounds, such as lipids, sugars, amino acids, nucleotides. Modern methods, such as Nuclear Magnetic Resonance (NMR) and Mass Spectroscopy (MS) coupled with liquid chromatography (LC) or gas chromatography (GC), can identify and quantify a large number of metabolites simultaneously within a biospecimen, capturing its metabolomic profile. These profiles have been used to predict the risk of diabetes, diagnose prostate cancer, and identify biomarkers of Crohn’s disease. There is a strong interest in applying metabolomic analysis to cancer epidemiologic studies. Since epidemiologic studies often involve field biospecimen collection, the conditions of sample processing, handling, and storage may vary, which may affect the reliability of the results and increase the risk of detecting false biomarker candidates. Therefore, to obtain an accurate assessment of an individual’s metabolomic profile, we need to understand and then eliminate or alter sample preparation steps that affect measured metabolite levels. At this stage, our interest is to understand whether collection methods (e.g. time until freezing) or sample preparation steps (e.g. metabolite extraction) affect the bias or variability of a measurement. It is important to realize the potential variability from the sample handling to properly control for the effect of pre-analytical parameters on metabolite measurements when performing metabolomics research. Understanding this variability is an essential prerequisite to broadening the use of metabolomics technologies to epidemiological studies and pooling measurements from different population-based studies.