December 2021 Inferring food intake from multiple biomarkers using a latent variable model
Silvia D’Angelo, Lorraine Brennan, Isobel Claire Gormley
Author Affiliations +
Ann. Appl. Stat. 15(4): 2043-2060 (December 2021). DOI: 10.1214/21-AOAS1478


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 Statement

Supported by a research grant from the European Research Council (ERC) (647783).
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number SFI/12/RC/2289_P2. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.


We would like to acknowledge the help of the following people with respect to the Apple Biomarker data: Aoife E. McNamara, Cassandra Collins, Pedapati S. C. Sri Harsha and Diana Gonzalez-Pena.


Download Citation

Silvia D’Angelo. Lorraine Brennan. Isobel Claire Gormley. "Inferring food intake from multiple biomarkers using a latent variable model." Ann. Appl. Stat. 15 (4) 2043 - 2060, December 2021.


Received: 1 December 2020; Revised: 1 March 2021; Published: December 2021
First available in Project Euclid: 21 December 2021

MathSciNet: MR4355088
zbMATH: 1498.62205
Digital Object Identifier: 10.1214/21-AOAS1478

Keywords: factor analysis , latent variable models , Metabolomics , mixture of experts , ordinal regression

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 4 • December 2021
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