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. 2023 Dec 21:12:RP87894.
doi: 10.7554/eLife.87894.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Caroline Bull #  1   2   3 , Emma Hazelwood #  1   2 , Joshua A Bell  1   2 , Vanessa Tan  1   2 , Andrei-Emil Constantinescu  1   2 , Carolina Borges  1   2 , Danny Legge  3 , Kimberley Burrows  1   2 , Jeroen R Huyghe  4 , Hermann Brenner  5   6   7 , Sergi Castellvi-Bel  8 , Andrew T Chan  9   10   11   12   13   14 , Sun-Seog Kweon  15   16 , Loic Le Marchand  17 , Li Li  18 , Iona Cheng  19   20 , Rish K Pai  21 , Jane C Figueiredo  22 , Neil Murphy  23 , Marc J Gunter  23   24 , Nicholas J Timpson  1   2 , Emma E Vincent  1   2   3
Affiliations

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Caroline Bull et al. Elife. .

Abstract

Background: Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival.

Methods: To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y.

Results: The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk.

Conclusions: These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development.

Funding: This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.

Keywords: Mendelian randomization; cancer biology; colorectal cancer; epidemiology; global health; human; metabolomics; obesity.

Plain language summary

Colorectal cancer, or bowel cancer, is the fourth most common cause of death from cancer worldwide. Understanding how the cancer develops and recognizing early signs is essential, as people who receive treatment early on have higher survival rates. One way to boost early detection and disease survival rates is through identifying early colorectal cancer biomarkers. For example, metabolites produced when cells process nutrients have been shown to play a role in the development of colon cancer. Certain metabolites could therefore serve as biomarkers, which can be detected in routine blood tests. But first, scientists need to identify the exact metabolic processes involved in cancer development. Bull, Hazelwood et al. show that fat metabolites during early adulthood may help predict colorectal cancer risk. In the experiments, the team assessed the link between an individual’s genetic risk for developing colorectal cancer and metabolites in their blood. By looking at data from over 6,000 individuals living in the UK, followed from early life into adulthood, they found higher fatty acid and low-density lipoprotein levels in young adults at risk of colorectal cancer. However, the results could not be replicated in a separate cohort study of middle-aged adults. Bull, Hazelwood et al. noted that many individuals in this older age group use fat-targeting drugs called statins, which may have obscured this connection. The study of Bull, Hazelwood et al. shows that colorectal cancer risk indicators may be present from adolescence to around 40 years, before most individuals are diagnosed. The results suggest this may be a window for early detection and preventive interventions. It also highlights that differences in fat metabolism, possibly linked to genetic differences, may underlie colorectal cancer risk. More studies are needed to better understand how and whether interventions targeting fat levels may help prevent colorectal cancer development.

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Conflict of interest statement

CB, EH, JB, VT, AC, CB, DL, KB, JH, HB, SC, AC, SK, LL, LL, IC, RP, JF, NM, MG, NT, EV No competing interests declared

Figures

Figure 1.
Figure 1.. Study design.
First, linear regression models were used to examine the relationship between genetic susceptibility to adult colorectal cancer (CRC) and circulating metabolites measured in the Avon Longitudinal Study of Parents and Children (ALSPAC) participants at age 8 y, 16 y, 18 y, and 25 y. Next, we performed a reverse Mendelian randomization analysis to identify metabolites influenced by CRC susceptibility in an independent population of adults. Finally, we performed a conventional (forward) Mendelian randomization analysis of circulating metabolites on CRC to identify metabolites causally associated with CRC risk. Consistent evidence across all three methodological approaches was interpreted to indicate a causal role for a given metabolite in CRC aetiology.
Figure 2.
Figure 2.. Associations of genetic liability to adult colorectal cancer (based on a 72 single-nucleotide polymorphism [SNP] genetic risk score) with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y [N = 4767], 16 y [N = 2930], 18 y [N = 2613], and 25 y [N = 2559]).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colorectal cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Associations of genetic liability to adult colon cancer with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colon cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 2.
Figure 2—figure supplement 2.. Associations of genetic liability to proximal colon cancer with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to proximal colon cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 3.
Figure 2—figure supplement 3.. Associations of genetic liability to distal colon cancer with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to distal colon cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 4.
Figure 2—figure supplement 4.. Associations of genetic liability to rectal cancer with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to rectal cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 5.
Figure 2—figure supplement 5.. Associations of genetic liability to adult colorectal cancer (excluding rs174533) with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colorectal cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 2—figure supplement 6.
Figure 2—figure supplement 6.. Associations of genetic liability to adult colon cancer (excluding rs174535) with clinically validated metabolic traits at different early life stages among the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (age 8 y, 16 y, 18 y, and 25 y).
Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colorectal cancer (purple, 8 y; turquoise, 16 y; red, 18 y; black, 25 y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 3.
Figure 3.. Associations of genetic liability to colorectal cancer with clinically validated metabolic traits in an independent sample of adults (UK Biobank, N = 118,466, median age 58 y) based on reverse two-sample Mendelian randomization analyses.
Estimates shown are beta coefficients representing the SD-unit difference in metabolic trait per doubling of liability to colorectal cancer. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Associations of genetic liability to colorectal cancer with clinically validated metabolic traits in an independent sample of adults based on reverse two-sample Mendelian randomization analyses.
Estimates shown are beta coefficients representing the SD-unit difference in metabolic trait per doubling of liability to colorectal cancer by site (colorectal, colon, distal colon, proximal colon, and rectal cancer). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. Associations of genetic liability to colorectal cancer (excluding genetic variants in the FADS gene region) with clinically validated metabolic traits in an independent sample of adults based on reverse two-sample Mendelian randomization analyses.
Estimates shown are beta coefficients representing the SD-unit difference in metabolic trait per doubling of liability to colorectal cancer. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 3—figure supplement 3.
Figure 3—figure supplement 3.. Associations of genetic liability to colorectal and colon cancer with clinically validated metabolic traits in an independent sample of adults based on reverse two-sample Mendelian randomization analyses with FADS variants excluded from colorectal cancer instruments.
Estimates shown are beta coefficients representing the SD-unit difference in metabolic trait per doubling of liability to colorectal cancer by site (colorectal, colon). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 4.
Figure 4.. Associations of clinically validated metabolites with colorectal cancer based on conventional (forward) two-sample Mendelian randomization analyses in individuals from UK Biobank (N = 118,466, median age 58 y).
Estimates shown are beta coefficients representing the logOR for colorectal cancer per SD metabolite. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Associations of clinically validated metabolites with colorectal cancer by site (colorectal, colon, distal colon, proximal colon, and rectal cancer) based on conventional (forward) two-sample Mendelian randomization analyses.
Estimates shown are ORs for colorectal cancer per SD metabolite. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Associations of clinically validated metabolites with colorectal cancer based on conventional (forward) two sample Mendelian randomization analyses with FADS variants excluded from metabolite instruments.
Estimates shown are ORs for colorectal cancer per SD metabolite. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
Figure 4—figure supplement 3.
Figure 4—figure supplement 3.. Associations of clinically validated metabolites with colorectal cancer by site (colorectal, colon) based on conventional (forward) two sample Mendelian randomization analyses with FADS variants excluded from metabolite instruments.
Estimates shown are ORs for colorectal cancer per SD metabolite. Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).
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