Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study

Caroline J Bull, Joshua A Bell, Neil Murphy, Eleanor Sanderson, George Davey Smith, Nicholas J Timpson, Barbara L Banbury, Demetrius Albanes, Sonja I Berndt, Stéphane Bézieau, D Timothy Bishop, Hermann Brenner, Daniel D Buchanan, Andrea Burnett-Hartman, Graham Casey, Sergi Castellví-Bel, Andrew T Chan, Jenny Chang-Claude, Amanda J Cross, Albert de la Chapelle, Jane C Figueiredo, Steven J Gallinger, Susan M Gapstur, Graham G Giles, Stephen B Gruber, Andrea Gsur, Jochen Hampe, Heather Hampel, Tabitha A Harrison, Michael Hoffmeister, Li Hsu, Wen-Yi Huang, Jeroen R Huyghe, Mark A Jenkins, Corinne E Joshu, Temitope O Keku, Tilman Kühn, Sun-Seog Kweon, Loic Le Marchand, Christopher I Li, Li Li, Annika Lindblom, Vicente Martín, Anne M May, Roger L Milne, Victor Moreno, Polly A Newcomb, Kenneth Offit, Shuji Ogino, Amanda I Phipps, Elizabeth A Platz, John D Potter, Conghui Qu, J Ramón Quirós, Gad Rennert, Elio Riboli, Lori C Sakoda, Clemens Schafmayer, Robert E Schoen, Martha L Slattery, Catherine M Tangen, Kostas K Tsilidis, Cornelia M Ulrich, Fränzel J B van Duijnhoven, Bethany van Guelpen, Kala Visvanathan, Pavel Vodicka, Ludmila Vodickova, Hansong Wang, Emily White, Alicja Wolk, Michael O Woods, Anna H Wu, Peter T Campbell, Wei Zheng, Ulrike Peters, Emma E Vincent, Marc J Gunter, Caroline J Bull, Joshua A Bell, Neil Murphy, Eleanor Sanderson, George Davey Smith, Nicholas J Timpson, Barbara L Banbury, Demetrius Albanes, Sonja I Berndt, Stéphane Bézieau, D Timothy Bishop, Hermann Brenner, Daniel D Buchanan, Andrea Burnett-Hartman, Graham Casey, Sergi Castellví-Bel, Andrew T Chan, Jenny Chang-Claude, Amanda J Cross, Albert de la Chapelle, Jane C Figueiredo, Steven J Gallinger, Susan M Gapstur, Graham G Giles, Stephen B Gruber, Andrea Gsur, Jochen Hampe, Heather Hampel, Tabitha A Harrison, Michael Hoffmeister, Li Hsu, Wen-Yi Huang, Jeroen R Huyghe, Mark A Jenkins, Corinne E Joshu, Temitope O Keku, Tilman Kühn, Sun-Seog Kweon, Loic Le Marchand, Christopher I Li, Li Li, Annika Lindblom, Vicente Martín, Anne M May, Roger L Milne, Victor Moreno, Polly A Newcomb, Kenneth Offit, Shuji Ogino, Amanda I Phipps, Elizabeth A Platz, John D Potter, Conghui Qu, J Ramón Quirós, Gad Rennert, Elio Riboli, Lori C Sakoda, Clemens Schafmayer, Robert E Schoen, Martha L Slattery, Catherine M Tangen, Kostas K Tsilidis, Cornelia M Ulrich, Fränzel J B van Duijnhoven, Bethany van Guelpen, Kala Visvanathan, Pavel Vodicka, Ludmila Vodickova, Hansong Wang, Emily White, Alicja Wolk, Michael O Woods, Anna H Wu, Peter T Campbell, Wei Zheng, Ulrike Peters, Emma E Vincent, Marc J Gunter

Abstract

Background: Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood.

Methods: We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models.

Results: In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles.

Conclusions: Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

Keywords: Body mass index; CCFR; CORECT; Colorectal cancer; Epidemiology; GECCO; Mendelian randomization; Metabolism; NMR; Waist-to-hip ratio.

Conflict of interest statement

None to declare.

Figures

Fig. 1
Fig. 1
Study aims and assumptions. Study aims are to (1) estimate the total effect of adiposity on CRC risk using genetic instruments for BMI and WHR ((i) unadjusted for BMI) and (2) estimate the mediated effect of adiposity on CRC risk by metabolites from targeted NMR metabolomics. Aim 2 is addressed using two approaches: (1) two-step MR wherein effects are examined of adiposity on metabolites (ii) and of adiposity-related metabolites on CRC risk (iii) and (2) multivariable MR wherein effects of adiposity on CRC (i) are examined with adjustment for the effect of representative metabolite classes on CRC (iii). Sex-specific analyses were performed when sex-specific GWAS estimates for exposure and outcome were both available. When ≥ 2 SNP instruments were available, up to 4 MR models were applied: the inverse-variance-weighted (IVW) model which assumes that none of the SNPs are pleiotropic [28], the weighted median (WM) model which allows up to half of the included SNPs to be pleiotropic and is less influenced by outliers [28], the weighted mode model which assumes that the most common effect is consistent with the true causal effect [29], and the MR-Egger model which provides an estimate of association magnitude allowing all SNPs to be pleiotropic [30]. Analyses with metabolites as outcomes were conducted within discovery aims wherein P value thresholds are applied to prioritize traits with the strongest evidence of association to be taken forward into further stages of analysis (with CRC risk). Analyses with CRC as outcomes were conducted within estimation aims wherein P values are interpreted as continuous indicators of evidence strength and focus is on effect size and precision [31, 32]
Fig. 2
Fig. 2
Associations of BMI and WHR with CRC risk based on two-sample MR. Sex-combined estimates are based on GWAS done among women and men together (for both exposure and outcome). Sex-specific estimates are based on GWAS done separately among women and men (for exposure as well as outcome)
Fig. 3
Fig. 3
Associations of BMI- or WHR-related lipid metabolites with CRC risk based on two-sample MR (IVW method). Estimates reflect the OR (95% CI) for CRC per SD higher metabolite that is associated (as an outcome) with BMI or WHR. +/− symbols indicate the direction of association of BMI or WHR with that metabolite
Fig. 4
Fig. 4
Associations of BMI- or WHR-related non-lipid metabolites with CRC risk based on two-sample MR (IVW method). Estimates reflect the OR (95% CI) for CRC per SD higher metabolite that is associated (as an outcome) with BMI or WHR. +/− symbols indicate the direction of association of BMI or WHR with that metabolite
Fig. 5
Fig. 5
Associations of BMI and WHR with CRC risk independent of various metabolite classes based on multivariable MR. Metabolite classes are based on a single representative metabolite from a previous network analysis [43], as follows: VLDL (triglycerides in small VLDL); IDL and LDL (total cholesterol in medium LDL), HDL (triglycerides in very large HDL), Omega-3 and PUFA (other polyunsaturated fatty acids than 18:2), Omega-6 (18:2, linoleic acid), MUFA and other fatty acids (Omega-9 and saturated fatty acids), glycemia (glucose), substrates (citrate), branched-chain amino acids (leucine), and other amino acids (glutamine). Adipose adjustments include the alternative adiposity trait (WHR or BMI) as a positive control

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