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What is Metabolism?00:52

What is Metabolism?

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Overview of Metabolism

Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
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Related Experiment Video

Updated: Jun 21, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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MetaboVariation: Exploring Individual Variation in Metabolite Levels.

Shubbham Gupta1, Isobel Claire Gormley2, Lorraine Brennan1

  • 1School of Agriculture and Food Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland.

Metabolites
|February 25, 2023
PubMed
Summary

MetaboVariation identifies early metabolic dysfunction by analyzing individual metabolite fluctuations. This method flags people needing lifestyle interventions, advancing personalized healthcare.

Keywords:
Bayesian generalised linear modelintra-individual variationmetabolite levelspersonalised healthcare

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Area of Science:

  • Metabolomics
  • Personalized Medicine
  • Biomarker Discovery

Background:

  • Current metabolomics research primarily focuses on disease biomarkers.
  • There is a critical need for biomarkers indicating early metabolic dysfunction to guide lifestyle interventions.
  • Analyzing metabolomics data at the individual level requires novel strategies.

Purpose of the Study:

  • To introduce MetaboVariation, a novel method for analyzing metabolite level fluctuations in individuals.
  • To enable the identification of individuals with early metabolic dysfunction using repeated metabolomics measurements.
  • To facilitate personalized healthcare through individual-level metabolomics data analysis.

Main Methods:

  • MetaboVariation utilizes a Bayesian generalized linear model to assess intra-individual metabolite variations.
  • The method models repeated metabolite levels, accounting for individual-specific fluctuations and explanatory variables.
  • Individuals are flagged if their observed metabolite levels fall outside the 95% highest posterior density prediction interval.

Main Results:

  • MetaboVariation was applied to metabolomics data from 164 individuals with repeated measurements.
  • A significant percentage of individuals (28%) were identified with intra-individual variations in three or more metabolites.
  • An R package with an R Shiny web application was developed for MetaboVariation.

Conclusions:

  • MetaboVariation represents a significant advancement in analyzing metabolomics data at the individual level.
  • The method aids in identifying early metabolic dysfunction, crucial for timely lifestyle interventions.
  • This approach paves the way for more personalized healthcare strategies based on individual metabolic profiles.