Inferring food intake from multiple biomarkers using a latent variable model

TL;DR

This paper introduces a Bayesian latent variable model called multiMarker that combines multiple metabolomic biomarkers to objectively infer food intake and estimate associated uncertainties, addressing a key gap in nutritional assessment methods.

Contribution

The paper presents a novel statistical framework that integrates factor analysis and mixture models within a Bayesian hierarchy to infer food intake from multiple biomarkers.

Findings

The model accurately estimates food intake in simulation studies.

It effectively captures uncertainty in biomarker-based predictions.

Application to apple intake demonstrates practical utility.

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 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.