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Related Concept Videos

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics11:02

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Nuclear magnetic resonance (NMR) spectroscopy is used to identify dysregulation in the metabolites in patients with various diseases. This technique allows the quantification of the deranged metabolites, unraveling the pathophysiological insights. Here, we describe the step-by-step procedure of the NMR-based approach for the metabolic characterization of the patients.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Updated: Jan 20, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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Advanced Statistical Methods for NMR-Based Metabolomics.

Dabao Zhang1, Min Zhang2

  • 1Department of Statistics, Purdue University, West Lafayette, IN, USA.

Methods in Molecular Biology (Clifton, N.J.)
|August 30, 2019
PubMed
Summary
This summary is machine-generated.

Advanced statistical methods can improve biomarker discovery in metabolomics by analyzing multiple metabolites and confounding factors simultaneously. This enhances statistical power and reproducibility for identifying reliable metabolite biomarkers.

Keywords:
Omics dataPenalized orthogonal components regressionSeemingly unrelated regressionSupervised dimension reduction

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Area of Science:

  • Biostatistics
  • Metabolomics
  • Biomarker Discovery

Background:

  • Metabolomics is increasingly used for biomarker identification.
  • Data analysis faces challenges due to small sample sizes, numerous metabolites, and confounding variables.
  • Low statistical power and reproducibility hinder accurate biomarker discovery.

Purpose of the Study:

  • To advocate for advanced statistical methods to enhance statistical power and reproducibility in metabolomics biomarker identification.
  • To describe methods that simultaneously analyze multiple metabolites and confounding variables.
  • To introduce approaches for handling large-scale omics data.

Main Methods:

  • Seemingly unrelated regression (SUR) for simultaneous analysis of multiple metabolites.
  • Controlling for demographic and clinical confounders (e.g., gender, age, BMI, smoking status).
  • Penalized orthogonal components regression (POCR) for screening large omics datasets.

Main Results:

  • SUR enables simultaneous evaluation of metabolite-biomarker relationships while accounting for confounders.
  • POCR provides a screening approach for high-dimensional omics data with millions of predictors.
  • These methods improve the statistical rigor of metabolomics data analysis.

Conclusions:

  • Advanced statistical methods like SUR and POCR are crucial for robust metabolomics biomarker discovery.
  • Simultaneous analysis of metabolites and confounders enhances statistical power and reproducibility.
  • These approaches address key challenges in analyzing complex omics data for biomarker identification.