Abstract 4349: Identifying causal risk factors of metabolic syndrome for renal cell carcinoma. A Mendelian randomization approach
Authors: Robert Carreras-Torres, Mattias Johansson, Ghislaine Scelo, Philip Haycock, Mark Purdue, Xifeng Wu, Richard Houlston, Stephen Chanock, Paul Brennan
Journal: Cancer Research (2016)

Abstract Epidemiological studies have convincingly demonstrated that several factors of the metabolic syndrome (MetS) are associated with the risk of Renal Cell Carcinoma (RCC)These factors often occur together and it is therefore challenging to disentangle their individual causal relevance in the etiology of RCC. In order to circumvent this limitation, we have applied a Mendelian randomization (MR) approach whereby genetic markers are evaluated in relation to RCC risk as unconfounded markers of the individual MetS factors. We focused on MetS parameters from which genetic instruments could be identified from large-scale genome-wide association studies (GWAS). The following MetS factors were instrumented using multiple gene-variants: general and central obesity (body mass index (BMI) and waist-to-hip ratio), elevated blood pressure (systolic and diastolic blood pressure), dyslipidemia (high and low density cholesterol, total cholesterol, and triglycerides), hyperglycemia (fasting glucose and glucose levels at 2 hours after glucose intolerance tests), and hyperinsulinemia (fasting insulin). Genetic proxies for these parameters were identified from GIANT, ICBP, GLGC and MAGIC Consortia. Summary statistics on RCC risk for instrumental SNPs of each MetS factor, including OR estimates and standard errors, were available from GWAS coordinated by the International Agency for Research on Cancer, the US National Cancer Institute, the Institute for Cancer Research UK, and the MD Anderson Cancer Center US. Together these studies comprised a total of 12,104 RCC cases and 24,999 controls from European origin that were genotyped using different genotyping platforms. Imputation was conducted on each dataset and only SNPs with an imputation quality higher than 0.6 were considered for the analyses. The causal effect of each MetS parameter on RCC risk was subsequently estimated using the MR likelihood-based approach, assuming a linear relationship between the risk factor and the outcome and a bivariate normal distribution for the genetic association estimates. The MR risk analysis using genetic instruments of the individual MetS factors indicated that elevated BMI (P: 1×10-08) and fasting insulin (P: 7×10-04)increased the risk of RCC, whereas elevated post-load glucose levels were associated with a lower risk (P: 2×10-3). The odds ratio per standard deviation increase were estimated at 1.58 (95% CI: 1.35-1.86) for BMI, 1.77 (95%CI: 1.27-2.46) for fasting insulin, and 0.62 (95%CI: 0.46-0.83) for post-load glucose. No associations were seen for genetic instruments of blood pressure or lipids. These results provide a clear support for a causal role of obesity in RCC etiology, and suggest that factors related to hyperglycemia and/or hyperinsulinemia may be involved in the causal pathway. This study may guide future efforts aiming to clarify the biological mechanisms whereby the metabolic syndrome influences RCC pathogenesis. Citation Format: Robert Carreras-Torres, Mattias Johansson, Ghislaine Scelo, Philip Haycock, Mark Purdue, Xifeng Wu, Richard Houlston, Stephen Chanock, Paul Brennan. Identifying causal risk factors of metabolic syndrome for renal cell carcinoma. A Mendelian randomization approach. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4349.