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Updated: Aug 17, 2025
11:02
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
617
Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification
Thao Vu1, Yuhang Xu2, Yumou Qiu3
1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Fitzsimons Building, 13001 East 17th Place, Aurora, CO 80045, USA.
Biostatistics (Oxford, England)
|December 14, 2022
Summary
This study introduces an automated method for identifying and quantifying metabolites in complex mixtures using nuclear magnetic resonance (NMR) data. The novel approach improves accuracy and efficiency compared to manual analysis.
Area of Science:
- Metabolomics
- Analytical Chemistry
- Biochemistry
Background:
- Accurate metabolite identification and quantification are crucial for understanding biological processes in metabolomics.
- Manual analysis of nuclear magnetic resonance (NMR) data is labor-intensive and requires expert knowledge.
- Variability in experimental procedures can lead to peak shifting errors in NMR spectra.
Purpose of the Study:
- To develop an automated method for identifying and quantifying metabolites in complex mixtures.
- To address and correct peak shifting errors in NMR data.
- To improve the efficiency and accuracy of metabolomics data analysis.
Main Methods:
- A shifting-corrected regularized regression method was developed.
- A detailed algorithm was proposed for implementation.
- A novel weight function was utilized to detect and correct peak shifting.
Main Results:
- The proposed method automatically identifies and quantifies metabolites.
- The method effectively detects and corrects peak shifting errors.
- Simulation studies demonstrated superior performance in metabolite identification and quantification.
- Successful application to real experimental and biological NMR mixtures was shown.
Conclusions:
- The developed method offers an automated and accurate solution for metabolite analysis in complex mixtures.
- This approach enhances the interpretation of biological processes through improved metabolomics data.
- The method provides a robust alternative to manual NMR data analysis.
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