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Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort
BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics. METHODS: This study included pros...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583301/ https://www.ncbi.nlm.nih.gov/pubmed/37849011 http://dx.doi.org/10.1186/s40170-023-00319-x |
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author | Vidman, Linda Zheng, Rui Bodén, Stina Ribbenstedt, Anton Gunter, Marc J. Palmqvist, Richard Harlid, Sophia Brunius, Carl Van Guelpen, Bethany |
author_facet | Vidman, Linda Zheng, Rui Bodén, Stina Ribbenstedt, Anton Gunter, Marc J. Palmqvist, Richard Harlid, Sophia Brunius, Carl Van Guelpen, Bethany |
author_sort | Vidman, Linda |
collection | PubMed |
description | BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics. METHODS: This study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography–mass spectrometry (LC–MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population. RESULTS: In the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70–0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67–0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations). CONCLUSIONS: These findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40170-023-00319-x. |
format | Online Article Text |
id | pubmed-10583301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105833012023-10-19 Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort Vidman, Linda Zheng, Rui Bodén, Stina Ribbenstedt, Anton Gunter, Marc J. Palmqvist, Richard Harlid, Sophia Brunius, Carl Van Guelpen, Bethany Cancer Metab Research BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics. METHODS: This study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography–mass spectrometry (LC–MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population. RESULTS: In the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70–0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67–0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations). CONCLUSIONS: These findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40170-023-00319-x. BioMed Central 2023-10-17 /pmc/articles/PMC10583301/ /pubmed/37849011 http://dx.doi.org/10.1186/s40170-023-00319-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Vidman, Linda Zheng, Rui Bodén, Stina Ribbenstedt, Anton Gunter, Marc J. Palmqvist, Richard Harlid, Sophia Brunius, Carl Van Guelpen, Bethany Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title | Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title_full | Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title_fullStr | Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title_full_unstemmed | Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title_short | Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
title_sort | untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583301/ https://www.ncbi.nlm.nih.gov/pubmed/37849011 http://dx.doi.org/10.1186/s40170-023-00319-x |
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