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[Preprint]. 2023 Nov 9:2023.03.10.23287084.

doi: 10.1101/2023.03.10.23287084.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Caroline J Bull^{1

2

3}, Emma Hazelwood^{1

2}, Joshua A Bell^{1

2}, Vanessa Y Tan^{1

2}, Andrei-Emil Constantinescu^{1

2}, Maria Carolina Borges^{1

2}, Danny N Legge³, Kimberly Burrows^{1

2}, Jeroen R Huyghe⁴, Hermann Brenner^{5

6

7}, Sergi Castellví-Bel⁸, Andrew T Chan^{9

10

11

12

13

14}, Sun-Seog Kweon^{15

16}, Loic Le Marchand¹⁷, Li Li¹⁸, Iona Cheng^{19

20}, Rish K Pai²¹, Jane C Figueiredo²², Neil Murphy²³, Marc J Gunter^{23

24}, Nicholas J Timpson^{1

2}, Emma E Vincent^{1

2

3}

Affiliations

¹ MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
² Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
³ Translational Health Sciences, Bristol Medical School, University of Bristol, UK.
⁴ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
⁵ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁶ Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.
⁷ German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁸ Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain.
⁹ Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁰ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹² Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
¹³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.
¹⁴ Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.
¹⁵ Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea.
¹⁶ Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea.
¹⁷ University of Hawaii Cancer Center, Honolulu, Hawaii, USA.
¹⁸ Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA.
¹⁹ Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
²⁰ University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, San Francisco, California, USA.
²¹ Department of Pathology and Laboratory Medicine, Mayo Clinic, Arizona, Scottsdale, Arizona, USA.
²² Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
²³ Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
²⁴ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom.

PMID: 36945480
PMCID: PMC10029059
DOI: 10.1101/2023.03.10.23287084

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Caroline J Bull et al. medRxiv. 2023.

[Preprint]. 2023 Nov 9:2023.03.10.23287084.

doi: 10.1101/2023.03.10.23287084.

Authors

Affiliations

¹ MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
² Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
³ Translational Health Sciences, Bristol Medical School, University of Bristol, UK.
⁴ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
⁵ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁶ Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.
⁷ German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁸ Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain.
⁹ Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁰ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹² Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
¹³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.
¹⁴ Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.
¹⁵ Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea.
¹⁶ Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea.
¹⁷ University of Hawaii Cancer Center, Honolulu, Hawaii, USA.
¹⁸ Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA.
¹⁹ Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
²⁰ University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, San Francisco, California, USA.
²¹ Department of Pathology and Laboratory Medicine, Mayo Clinic, Arizona, Scottsdale, Arizona, USA.
²² Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
²³ Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
²⁴ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom.

PMID: 36945480
PMCID: PMC10029059
DOI: 10.1101/2023.03.10.23287084

Update in

Identifying metabolic features of colorectal cancer liability using Mendelian randomization.
Bull C, Hazelwood E, Bell JA, Tan V, Constantinescu AE, Borges C, Legge D, Burrows K, Huyghe JR, Brenner H, Castellvi-Bel S, Chan AT, Kweon SS, Le Marchand L, Li L, Cheng I, Pai RK, Figueiredo JC, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Bull C, et al. Elife. 2023 Dec 21;12:RP87894. doi: 10.7554/eLife.87894. Elife. 2023. PMID: 38127078 Free PMC article.

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 characterise the early stages of colorectal cancer (CRC) development, we examined 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=6,221). Linear regression models were applied to examine associations between genetic liability to colorectal cancer and circulating metabolites measured in the same individuals at age 8, 16, 18 and 25 years.

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 58y) 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: ALSPAC; CCFR; CCTS; Colorectal cancer; Epidemiology; GECCO; Genetics; Mendelian randomization; Metabolism; NMR; UK Biobank.

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Figures

**Figure 1:**
Study design. First, linear regression models were used to examine the relationship between genetic susceptibility to adult colorectal cancer and circulating metabolites measured in ALSPAC participants at age 8, 16, 18 and 25 years. 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:**
Associations of genetic liability to adult colorectal cancer (based on a 72 SNP genetic risk score) with clinically validated metabolic traits at different early life stages among ALSPAC offspring (age 8y (N=4,767), 16y (N=2,930), 18y (N=2,613), and 25y (N=2,559)). Estimates shown are beta coefficients representing the SD difference in metabolic trait per doubling of genetic liability to colorectal cancer (purple, 8y; turquoise, 16y; red, 18y; black, 25y). Filled point estimates are those that pass a Benjamini–Hochberg FDR multiple-testing correction (FDR < 0.05).

**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 58y) 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 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 58y) . 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).

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References

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This is a preprint.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Affiliations

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Authors

Affiliations

Update in

Abstract

Figures

References

Publication types

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