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Updated: Nov 25, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention

Kathryn J Burton-Pimentel1, Grégory Pimentel1, Maria Hughes2,3,4

  • 1Federal Department of Economic Affairs, Education and Research EAER, Agroscope, Schwarzenburgstrasse 161, Bern, 3003, Switzerland.

Molecular Nutrition & Food Research
|December 16, 2020
PubMed
Summary

Integrating "omics" data enhances understanding of diet

Keywords:
Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics”Similarity Network Fusion toolclassificationdata integrationnutritional intervention

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Area of Science:

  • Nutritional science
  • Bioinformatics
  • Systems biology

Background:

  • Dietary interventions offer insights into human health.
  • Integrating multi-omics data can reveal subtle metabolic responses.
  • Understanding diet-health interactions requires advanced analytical approaches.

Purpose of the Study:

  • To evaluate two data integration tools: Similarity Network Fusion (SNF) and DIABLO (MixOmics).
  • To assess their performance in discriminating diet responses using transcriptomics and metabolomics data.
  • To compare tool efficacy across different human intervention study designs.

Main Methods:

  • Combined transcriptomics and metabolomics datasets from three human intervention studies.
  • Applied SNFtool and DIABLO for data integration and sample classification.
  • Analyzed performance based on study design, dataset size, and sample size.

Main Results:

  • Both SNF and DIABLO improved sample classification in study 1 (postprandial dairy foods).
  • SNF's utility in studies 2 and 3 varied with dietary group comparisons.
  • DIABLO showed good discrimination but not superior to transcriptomics alone in studies 2 and 3.

Conclusions:

  • Integrated multi-omics analysis can clarify nutritional intervention effects.
  • The effectiveness of data integration tools depends on study specifics.
  • Tool performance is influenced by study design, dataset characteristics, and sample size.