SEARCHCASE-CONTROLoncology, epidemiology

case-control·oncology, epidemiology, gastroenterology, biomedical research, observational study·PMC10583301

Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort

Cancer & Metabolism · 9 authors, 9 centres

AI SUMMARY

FIDELITY 100%

POPULATIONAdults from the Northern Sweden Health and Disease Study, including 902 who later developed CRC and 902 matched cancer-free controls

INTERVENTIONUntargeted plasma metabolomics via reverse-phase liquid chromatography–mass spectrometry (LC-MS), yielding 5015 metabolic features

COMPARISONMatched cancer-free controls, matched on sex, age at baseline, cohort, year of blood sampling, freeze–thaw cycles, and fasting status

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AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

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TL;DR

This nested case-control study used untargeted LC-MS metabolomics on prospectively collected plasma samples from 902 CRC cases and 902 matched controls to identify pre-diagnostic metabolic biomarkers of CRC risk. The metabolome could not discriminate between future cases and controls, and the predictive potential for identifying individuals at higher CRC risk was found to be limited.

Full summary

776 CHARS

Plasma samples collected up to 26 years before diagnosis from 902 incident CRC cases and 902 matched controls within the Northern Sweden Health and Disease Study were analyzed by reverse-phase LC-MS, generating 5015 metabolic features. Conditional logistic regression identified seven features associated with CRC risk at an FDR cutoff of 0.25, two of which were annotated: pyroglutamic acid (OR 0.79, 95% CI 0.70–0.89) and hydroxytigecycline (OR 0.77, 95% CI 0.67–0.89), both inversely associated with CRC. Six previously reported metabolite biomarkers, including L-tryptophan and 3-hydroxybutyric acid, also showed associations. These findings suggest that while the circulating metabolome may offer etiological insights, its utility for CRC risk prediction appears limited.

PICO

PPOPULATION

Adults from the Northern Sweden Health and Disease Study, including 902 who later developed CRC and 902 matched cancer-free controls

IINTERVENTION

Untargeted plasma metabolomics via reverse-phase liquid chromatography–mass spectrometry (LC-MS), yielding 5015 metabolic features

OOUTCOME

Incident colorectal cancer risk, assessed via conditional logistic regression and random forest classification

STUDY TYPE

case-control

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10583301

Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort

Cancer & Metabolism

READ THE FULL PAPER

ABSTRACT

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.

CITATION EXPORT

Linda Vidman, Rui Zheng, Stina Bodén et al.. Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort. Cancer & Metabolism. (2023). https://doi.org/10.1186/s40170-023-00319-x

AUTHORS

9 · 9 CENTRES

Linda Vidman, Rui Zheng, Stina Bodén, Anton Ribbenstedt, Marc J. Gunter, Richard Palmqvist, Sophia Harlid, Carl Brunius, Bethany Van Guelpen

1https://ror.org/05kb8h459grid.12650.300000 0001 1034 3451Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
2https://ror.org/048a87296grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Medical Epidemiology, Uppsala University, Uppsala, Sweden
3https://ror.org/05kb8h459grid.12650.300000 0001 1034 3451Department of Clinical Sciences, Pediatrics, Umeå University, Umeå, Sweden
4https://ror.org/040wg7k59grid.5371.00000 0001 0775 6028Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
5https://ror.org/040wg7k59grid.5371.00000 0001 0775 6028Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, Gothenburg, Sweden
6https://ror.org/00v452281grid.17703.320000 0004 0598 0095Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
7https://ror.org/041kmwe10grid.7445.20000 0001 2113 8111Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
8https://ror.org/05kb8h459grid.12650.300000 0001 1034 3451Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
9https://ror.org/05kb8h459grid.12650.300000 0001 1034 3451Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden