Influence of obesity-related risk factors in the aetiology of glioma

Abstract

Background: Obesity and related factors have been implicated as possible aetiological factors for the development of glioma in epidemiological observation studies. We used genetic markers in a Mendelian randomisation framework to examine whether obesity-related traits influence glioma risk. This methodology reduces bias from confounding and is not affected by reverse causation.

Methods: Genetic instruments were identified for 10 key obesity-related risk factors, and their association with glioma risk was evaluated using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. The estimated odds ratio of glioma associated with each of the genetically defined obesity-related traits was used to infer evidence for a causal relationship.

Results: No convincing association with glioma risk was seen for genetic instruments for body mass index, waist-to-hip ratio, lipids, type-2 diabetes, hyperglycaemia or insulin resistance. Similarly, we found no evidence to support a relationship between obesity-related traits with subtypes of glioma-glioblastoma (GBM) or non-GBM tumours.

Conclusions: This study provides no evidence to implicate obesity-related factors as causes of glioma.

Fig. 1

Study power against OR for each obesity-related trait and all glioma (P = 0.05, two-sided). A line indicating a power of 80% is shown. BMI body mass index, HDL high-density lipoprotein, LDL low-density lipoprotein, OR odds ratio

Fig. 2

SNP-specific effects for risk of all glioma. For each figure, the effect size of the respective measure for: a 2-h post-challenge glucose, b BMI, c fasting glucose, d fasting insulin, e HDL cholesterol, f LDL cholesterol, g type-2 diabetes, h total cholesterol, i triglycerides and j WHR is plotted against the effect for all glioma. Error bars represent one SD. The GSMR estimate is plotted as a dashed line for reference. BMI body mass index, GSMR generalised summary data-based Mendelian randomisation, HDL high-density lipoprotein, LDL low-density lipoprotein, SD standard deviation, WHR waist–hip ratio

Fig. 3

SNP-specific effects for risk of GBM glioma. For each figure, the effect size of the respective measure for a 2-h post-challenge glucose, b BMI, c fasting glucose, d fasting insulin, e HDL cholesterol, f LDL cholesterol, g type-2 diabetes, h total cholesterol, i triglycerides and j WHR is plotted against the effect for GBM glioma. Error bars represent one SD. The GSMR estimate is plotted as a dashed line for reference. BMI body mass index, GBM glioblastoma mulitforme, GSMR generalised summary data-based Mendelian randomisation, HDL high-density lipoprotein, LDL low-density lipoprotein, SD standard deviation, WHR waist–hip ratio

Fig. 4

SNP-specific effects for risk of non-GBM glioma. For each figure, the effect size of the respective measure for a 2-h post-challenge glucose, b BMI, c fasting glucose, d fasting insulin, e HDL cholesterol, f LDL cholesterol, g type-2 diabetes, h total cholesterol, i triglycerides and j WHR, is plotted against the effect for non-GBM glioma. Error bars represent one SD. The GSMR estimate is plotted as a dashed line for reference. BMI body mass index, GBM glioblastoma mulitforme, GSMR generalised summary data-based Mendelian randomisation, HDL high-density lipoprotein, LDL low-density lipoprotein, SD standard deviation, WHR waist–hip ratio