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Inferring food intake from multiple biomarkers using a latent variable model
Date Issued
2021-12
Date Available
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.
Sponsorship
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
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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Inferring food intake from multiple biomarkers using a latent variable model.pdf
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976.17 KB
Format
Owning collection
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2
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