Allometric versus traditional body-shape indices and risk of colorectal cancer: a Mendelian randomization analysis

Background Traditional body-shape indices such as Waist Circumference (WC), Hip Circumference (HC), and Waist-to-Hip Ratio (WHR) are associated with colorectal cancer (CRC) risk, but are correlated with Body Mass Index (BMI), and adjustment for BMI introduces a strong correlation with height. Thus, new allometric indices have been developed, namely A Body Shape Index (ABSI), Hip Index (HI), and Waist-to-Hip Index (WHI), which are uncorrelated with weight and height; these have also been associated with CRC risk in observational studies, but information from Mendelian randomization (MR) studies is missing. Methods We used two-sample MR to examine potential causal cancer site- and sex-specific associations of the genetically-predicted allometric body-shape indices with CRC risk, and compared them with BMI-adjusted traditional body-shape indices, and BMI. Data were obtained from UK Biobank and the GIANT consortium, and from GECCO, CORECT and CCFR consortia. Results WHI was positively associated with CRC in men (OR per SD: 1.20, 95% CI: 1.03–1.39) and in women (1.15, 1.06–1.24), and similarly for colon and rectal cancer. ABSI was positively associated with colon and rectal cancer in men (1.27, 1.03–1.57; and 1.40, 1.10–1.77, respectively), and with colon cancer in women (1.20, 1.07–1.35). There was little evidence for association between HI and colon or rectal cancer. The BMI-adjusted WHR and HC showed similar associations to WHI and HI, whereas WC showed similar associations to ABSI only in women. Conclusions This large MR study provides strong evidence for a potential causal positive association of the allometric indices ABSI and WHI with CRC in both sexes, thus establishing the association between abdominal fat and CRC without the limitations of the traditional waist size indices and independently of BMI. Among the BMI-adjusted traditional indices, WHR and HC provided equivalent associations with WHI and HI, while differences were observed between WC and ABSI.


INTRODUCTION 2
Background Explain the scientific background and rationale for the reported study.What is the exposure?Is a potential causal relationship between exposure and outcome plausible?Justify why MR is a helpful method to address the study question 3 Objectives State specific objectives clearly, including pre-specified causal hypotheses (if any).
State that MR is a method that, under specific assumptions, intends to estimate causal effects

Study design and data sources
Present key elements of the study design early in the article.Consider including a table listing sources of data for all phases of the study.For each data source contributing to the analysis, describe the following: Supplementary Tables 2, 3 Figures 2-4

Data and data sharing
Provide the data used to perform all analyses or report where and how the data can be accessed, and reference these sources in the article.Provide the statistical code needed to reproduce the results in the article, or report whether the code is publicly accessible and if so, where 20

Conflicts of Interest
All authors should declare all potential conflicts of interest This checklist is copyrighted by the Equator Network under the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license.
a) Setting: Describe the study design and the underlying population, if possible.Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection, when available.b) Participants: Give the eligibility criteria, and the sources and methods of selection of participants.Report the sample size, and whether any power or sample size calculations were carried out prior to the main analysis c) Describe measurement, quality control and selection of genetic variants d) For each exposure, outcome, and other relevant variables, describe methods of assessment and diagnostic criteria for diseases e) Provide details of ethics committee approval and participant informed consent, if relevant 5 Assumptions Explicitly state the three core IV assumptions for the main analysis (relevance, independence and exclusion restriction) as well assumptions for any additional or sensitivity analysis 6 Statistical methods: main analysis Describe statistical methods and statistics used 2 a) Describe how quantitative variables were handled in the analyses (i.e., scale, units, model) b) Describe how genetic variants were handled in the analyses and, if applicable, how their weights were selected c) Describe the MR estimator (e.g.two-stage least squares, Wald ratio) and related statistics.Detail the included covariates and, in case of two-sample MR, whether the same covariate set was used for adjustment in the two samples d) Explain how missing data were addressed e) If applicable, indicate how multiple testing was addressed Assessment of assumptions Describe any methods or prior knowledge used to assess the assumptions or justify their validity Sensitivity analyses and additional analyses Describe any sensitivity analyses or additional analyses performed (e.g.comparison of effect estimates from different approaches, independent replication, bias analytic techniques, validation of instruments, simulations) Software and preregistration a) Name statistical software and package(s), including version and settings used b) State whether the study protocol and details were pre-registered (as well as when and where) RESULTS Descriptive data a) Report the numbers of individuals at each stage of included studies and reasons for exclusion.Consider use of a flow diagram b) Report summary statistics for phenotypic exposure(s), outcome(s), and other relevant variables (e.g.means, SDs, proportions) c) If the data sources include meta-analyses of previous studies, provide the assessments of heterogeneity across these studies d) For two-sample MR: i. Provide justification of the similarity of the genetic variant-exposure associations between the exposure and outcome samples Methods -statistical analysis (par.5) Methods -statistical analysis (par.3, 4) Methods -statistical analysis (par.5) Main results a) Report the associations between genetic variant and exposure, and between genetic variant and outcome, preferably on an interpretable scale b) Report MR estimates of the relationship between exposure and outcome, and the measures of uncertainty from the MR analysis, on an interpretable scale, such as odds ratio or relative risk per SD difference c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period d) Consider plots to visualize results (e.g.forest plot, scatterplot of associations between genetic variants and outcome versus between genetic variants and exposure) Assessment of assumptions a) Report the assessment of the validity of the assumptions b) Report any additional statistics (e.g., assessments of heterogeneity across genetic variants, such as I 2 , Q statistic or E-value) Sensitivity analyses and additional analyses a) Report any sensitivity analyses to assess the robustness of the main results to violations of the assumptions b) Report results from other sensitivity analyses or additional analyses c) Report any assessment of direction of causal relationship (e.g., bidirectional MR) d) When relevant, report and compare with estimates from non-MR analyses e) Consider additional plots to visualize results (e.g., leave-one-out analyses) DISCUSSION Key results Summarize key results with reference to study objectives Limitations Discuss limitations of the study, taking into account the validity of the IV assumptions, other sources of potential bias, and imprecision.Discuss both direction and magnitude of any potential bias and any efforts to address them Results -Associations of body shape with CRC, Associations of body size with CRC, f igures 2-4, suppl.table 1 Figures2-4 1. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al.Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) Statement.JAMA.2021;underreview.andR01 CA81488 (to SBG).The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 (to SBG) and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I.Additional funding for the OFCCR/ARCTIC was through award GL201-043 from the Ontario Research Fund (to BWZ), award 112746 from the Canadian Institutes of Health Research (to TJH), through a Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society (to SG), and through generous support from the Ontario Ministry of Research and Innovation.The SFCCR Illumina HumanCytoSNP array was supported in part through NCI/NIH awards U01/U24 CA074794 and R01 CA076366 (to PAN).The content of this manuscript does not necessarily reflect the views or policies of the NCI, NIH or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.COLON: The COLON study is sponsored by Wereld Kanker Onderzoek Fonds, including funds from grant 2014/1179 as part of the World Cancer Research Fund International Regular Grant Programme, by Alpe d'Huzes and the Dutch Cancer Society (UM 2012-5653, UW 2013-5927, UW2015-7946), and by TRANSCAN (JTC2012-MetaboCCC, JTC2013-FOCUS).The Nqplus study is sponsored by a ZonMW investment grant (98-10030); by PREVIEW, the project PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW) project which received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant no.312057; by funds from TI Food and Nutrition (cardiovascular health theme), a public-private partnership on precompetitive research in food and nutrition; and by FOODBALL, the Food Biomarker Alliance, a project from JPI Healthy Diet for a Healthy Life.