Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Department of Nutrition, School of Public Health, University of North Carolina-Chapel Hill, Kannapolis, NC, 28081, USA.
J Expo Sci Environ Epidemiol. 2020 Jan;30(1):16-27. doi: 10.1038/s41370-019-0162-1. Epub 2019 Sep 23.
With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).
随着促进代谢组学全面分析的技术进步，代谢组学在人群健康研究中作为暴露和效应生物标志物的研究也在迅速发展。然而，许多分析方法在流行病学框架内处理高维代谢组学数据的能力有限，包括代谢物的高度共线性和混杂变量的考虑。在这个儿童健康暴露分析资源（CHEAR）网络研究中，我们展示了在代谢组学领域中既已确立又新颖的各种分析方法，包括单变量单代谢物模型、最小绝对收缩和选择算子（LASSO）、随机森林、加权分位数和（WQS）回归、探索性因子分析（EFA）和潜在类别分析（LCA）。在这里，在一个孟加拉国的出生队列（n=199）中，我们说明了每个分析方法可以解决的研究问题，评估脐带血代谢物（NMR 测量）与出生人体测量学测量值（出生体重和头围）之间的关联。
