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Heinzmann, S.S. ; Waldenberger, M. ; Peters, A. ; Schmitt-Kopplin, P.

Cluster analysis statistical spectroscopy for the identification of metabolites in 1H NMR metabolomics.

Metabolites 12:992 (2022)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Metabolite identification in non-targeted NMR-based metabolomics remains a challenge. While many peaks of frequently occurring metabolites are assigned, there is a high number of unknowns in high-resolution NMR spectra, hampering biological conclusions for biomarker analysis. Here, we use a cluster analysis approach to guide peak assignment via statistical correlations, which gives important information on possible structural and/or biological correlations from the NMR spectrum. Unknown peaks that cluster in close proximity to known peaks form hypotheses for their metabolite identities, thus, facilitating metabolite annotation. Subsequently, metabolite identification based on a database search, 2D NMR analysis and standard spiking is performed, whereas without a hypothesis, a full structural elucidation approach would be required. The approach allows a higher identification yield in NMR spectra, especially once pathway-related subclusters are identified.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Metabolite Identification ; Metabolomics ; Nmr Spectroscopy ; Urine; KORA, Method, Metabolomik
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 12, Issue: 10, Pages: , Article Number: 992 Supplement: ,
Publisher MDPI
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical BioGeoChemistry (BGC)
Institute of Epidemiology II (EPI2)
Grants Münchner Zentrum für Gesundheitswissenschaften, Ludwig-Maximilians-Universität München
Helmholtz Zentrum Munchen
Seventh Framework Programme
Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft