Inferring food intake from multiple biomarkers using a latent variable model

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Title: Inferring food intake from multiple biomarkers using a latent variable model
Authors: Brennan, LorraineD'Angelo, SilviaGormley, Isobel Claire
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Date: Dec-2021
Online since: 2022-07-25T14:38:10Z
Abstract: 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 quantitatively infer food intake. Furthermore, there is a paucity of approaches for estimating the uncertainty around biomarker-based inferred intake. Here, to estimate 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 multiMarker model integrates factor analytic and mixture of experts models: the observed biomarker values are related to intake which is described as a continuous latent variable which follows a flexible mixture of experts model with Gaussian components. The multiMarker model also facilitates inference on the latent intake when only biomarker data are subsequently observed. A Bayesian hierarchical modelling framework provides flexibility to adapt to different biomarker distributions and facilitates inference of the latent intake along with its associated uncertainty. Simulation studies are conducted to assess the performance of the multiMarker model, prior to its application to the motivating application of quantifying apple intake.
Funding Details: European Commission Horizon 2020
Type of material: Journal Article
Publisher: Institute of Mathematical Statistics
Journal: The Annals of Applied Statistics
Volume: 15
Issue: 4
Start page: 2043
End page: 2060
Copyright (published version): 2021 Institute of Mathematical Statistics
Keywords: Factor analysisLatent variable modelsMetabolomicsMixture of expertsOrdinal regression
DOI: 10.1214/21-AOAS1478
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Conway Institute Research Collection
Mathematics and Statistics Research Collection
Insight Research Collection
Agriculture and Food Science Research Collection

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