论文标题
使用潜在变量模型从多个生物标志物中推断出食物摄入量
Inferring food intake from multiple biomarkers using a latent variable model
论文作者
论文摘要
基于代谢组的方法近年来引起了人们的关注，因为它们有望提供客观的食物摄入量的客观工具。特别是，单个食物已经出现了多种生物标志物。但是，缺乏可用于合并多种生物标记物来推断食物摄入量的统计工具。此外，对于估计基于生物标志物的摄入预测的不确定性的方法很少。 在这里，提出了在A-Diet研究计划下进行的一项干预研究中，提出了多种代谢组生物标志物与食物摄入量之间的关系，提出了一种潜在变量模型，多标志物。所提出的模型借鉴了专家模型的因子分析和混合物，将摄入描述为一个连续的潜在变量，其值会增加观察到的生物标志物值。我们采用高斯分布的混合物来灵活地对潜在变量进行建模。贝叶斯分层建模框架提供了适应不同生物标志物分布的灵活性，并促进了潜在摄入量及其相关的不确定性的预测。 进行了仿真研究，以评估所提出的多标志物框架的性能，然后将其应用于量化苹果摄入量的激励应用。
Metabolomic based approaches have gained much attention in recent years due to their promising potential to deliver objective tools for assessment of food intake. In particular, multiple biomarkers have emerged for single foods. However, there is a lack of statistical tools available for combining multiple biomarkers to infer food intake. Furthermore, there is a paucity of approaches for estimating the uncertainty around biomarker based prediction of intake. Here, to facilitate inference on the relationship between multiple metabolomic biomarkers and food intake in an intervention study conducted under the A-DIET research programme, a latent variable model, multiMarker, is proposed. The proposed model draws on factor analytic and mixture of experts models, describing intake as a continuous latent variable whose value gives raise to the observed biomarker values. We employ a mixture of Gaussian distributions to flexibly model the latent variable. A Bayesian hierarchical modelling framework provides flexibility to adapt to different biomarker distributions and facilitates prediction of the latent intake along with its associated uncertainty. Simulation studies are conducted to assess the performance of the proposed multiMarker framework, prior to its application to the motivating application of quantifying apple intake.
