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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Analyzing postprandial metabolomics data using multiway models: a simulation study.

Lu Li1, Shi Yan2, Barbara M Bakker3

  • 1Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway. lu@simula.no.

BMC Bioinformatics
|March 4, 2024
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Summary
This summary is machine-generated.

This study introduces a new multiway analysis method using CANDECOMP/PARAFAC (CP) models for time-resolved postprandial metabolomics data. CP models effectively reveal subject groups and metabolic patterns, improving disease diagnosis and precision nutrition.

Keywords:
CANDECOMP/PARAFAC (CP)Meal challenge testPostprandial metabolomics dataPrincipal component analysis (PCA)Tensor factorizations (multiway data analysis)Time-resolved metabolomics dataWhole-body metabolic model

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Computational Biology

Background:

  • Time-resolved postprandial metabolomics data offers insights into metabolic mechanisms, disease biomarkers, and precision nutrition.
  • Traditional analysis methods struggle with complex three-way data (subjects x metabolites x time points).

Purpose of the Study:

  • To develop and evaluate an unsupervised multiway analysis approach for postprandial metabolomics data.
  • To assess the performance of CANDECOMP/PARAFAC (CP) models in identifying subject groups and metabolic processes.

Main Methods:

  • Simulated postprandial metabolomics data generated using a human metabolic model.
  • Comparison of three analysis approaches: principal component analysis (PCA) on fasting data, CP on T0-corrected data, and CP on full-dynamic data.

Main Results:

  • CP models effectively capture stable patterns from simulated meal challenge data.
  • CP models successfully reveal underlying metabolic mechanisms and differences between healthy and diseased groups.
  • Simulations demonstrate the utility of CP for analyzing complex, multi-dimensional metabolomics data.

Conclusions:

  • Analyzing both fasting-state and T0-corrected data is crucial for understanding metabolic differences.
  • CP models applied to T0-corrected or full-dynamic data can achieve optimal group separation.
  • This study advances postprandial metabolomics analysis and informs baseline correction strategies.