Skantze Viktor, Jirstrand Mats, Brunius Carl, Sandberg Ann-Sofie, Landberg Rikard, Wallman Mikael
Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.
Department of Life Sciences, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden.
Front Nutr. 2024 Jan 12;10:1304540. doi: 10.3389/fnut.2023.1304540. eCollection 2023.
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.
We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured ( = 0.40) and simulated data of increasing size (= 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.
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.
在精准营养领域，预测饮食的代谢反应并识别不同反应者群体是制定个性化饮食策略的两个非常理想的步骤。然而，目前缺乏数据分析工具，特别是对于复杂的情况，如具有重复测量的交叉研究。当前的分析方法通常依赖于矩阵或张量分解，这非常适合识别不同反应者，但预测能力不足；或者依赖于动态系统建模，它可用于预测，但通常需要对所研究系统有详细的机理知识。为了弥补这些缺点，我们探索了动态模式分解（DMD），这是一种最近的数据驱动方法，用于从高维数据中推导低秩线性动态系统。结合“参数化DMD”（pDMD）和“带控制的DMD”（DMDc）这两个最新进展，使我们能够（i）整合多种饮食挑战，（ii）仅根据代谢物基线和饮食输入预测所有测量代谢物对新饮食的动态反应，以及（iii）识别个体间的代谢差异，即代谢型。据我们所知，这是首次将DMD应用于分析时间分辨代谢组学数据。
我们在交叉研究环境中展示了pDMDc的潜力。我们能够在测量数据（=0.40）和不断增加规模的模拟数据（=0.65）上预测对未见过的饮食暴露的代谢物反应，以及恢复动态代谢物反应的聚类。我们得出结论，这种方法在个性化营养中具有应用潜力，并且可能有助于将代谢物反应引导至目标水平。
本研究中分析的测量数据可在合理请求下提供。模拟数据以及pDMDc的MATLAB实现可在https://github.com/FraunhoferChalmersCentre/pDMDc上获取。
