概述代谢组学（细胞、器官、组织或生物体液代谢物的高通量表征）在基于人群的研究中的应用如何帮助我们了解乳腺癌病因。我们评估了将代谢组学分析应用于诊断前的研究来自前瞻性流行病学研究的血液样本，用于确定与总体乳腺癌风险相关的循环代谢物以及乳腺癌亚型和绝经状态。我们为代谢组学方法在这种情况下的应用和解释提供了一些重要的考虑因素。总体而言，特定的脂质和氨基酸被认为是与乳腺癌发展相关的最常见的代谢物类别。然而，由于实验室技术、分析方法、样本量和应用统计方法的异质性，跨研究的结果比较具有挑战性。代谢组学正越来越多地应用于基于人群的研究，以识别与代谢相关的新病因假设和/或机制。乳腺癌的发展。尽管其在流行病学应用中取得了成功，但仍需要进行更大样本量的研究，并提供有关绝经状态、乳腺癌亚型的详细信息，以及随时间推移收集的重复生物样本，以改善研究之间的结果比较并增强结果的验证，从而实现潜在的临床转化的调查结果。

To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology.We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context.Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods.Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.