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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
Keywords Metabolite Identification ; Metabolomics ; Nmr Spectroscopy ; Urine; KORA, Method, Metabolomik
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 12, Issue: 10, Pages: , Article Number: 992 Supplement: ,
Publisher MDPI
Reviewing status Peer reviewed
Institute(s) Research Unit BioGeoChemistry and Analytics (BGC)
Institute of Epidemiology (EPI)
POF-Topic(s) 30202 - Environmental Health
Research field(s) Environmental Sciences
Genetics and Epidemiology
PSP Element(s) G-504800-001
G-504090-001
G-504091-001
G-504000-010
Grants Münchner Zentrum für Gesundheitswissenschaften, Ludwig-Maximilians-Universität München
Helmholtz Zentrum Munchen
Seventh Framework Programme
Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft
DOI 10.3390/metabo12100992
WOS ID WOS:000873253100001
Scopus ID 85140733605
PubMed ID 36295894
Erfassungsdatum 2022-11-04
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