Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition
Artikel i vetenskaplig tidskrift, 2023

Motivation: In the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures. Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data. Combining the two recent developments “parametric DMD” (pDMD) and “DMD with control” (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data. Results: We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size ((Formula presented.) = 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels. Availability and implementation: The measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.

differential responders

precision nutrition

metabotypes

dynamic mode decomposition

personalized nutrition

Författare

Viktor Skantze

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Chalmers, Life sciences, Livsmedelsvetenskap

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Carl Brunius

Chalmers, Life sciences, Livsmedelsvetenskap

Ann-Sofie Sandberg

Chalmers, Life sciences, Livsmedelsvetenskap

Rikard Landberg

Chalmers, Life sciences, Livsmedelsvetenskap

Mikael Wallman

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Frontiers in Nutrition

2296861X (eISSN)

Vol. 10 1304540

Optimal kost utifrån metabotyp för hälsa och vällevnad

Formas (2016-00314), 2016-01-01 -- 2021-12-31.

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Näringslära

DOI

10.3389/fnut.2023.1304540

Mer information

Senast uppdaterat

2024-02-09