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

Disney-Hogg, Linden; Sud, Amit; Law, Philip; Cornish, Alex J.; Kinnersley, Ben; Ostrom, Quinn T.; Labreche, Karim; Eckel‐Passow, Jeanette E.; Armstrong, Georgina; Claus, Elizabeth B.; Il’yasova, Dora; Schildkraut, Joellen M.; Barnholtz‐Sloan, Jill S.; Olson, Sara H.; Bernstein, Jonine L.; Lai, Rose; Swerdlow, Anthony; Simon, Matthias; Hoffmann, Per; Nöthen, Markus M.; Jöckel, Karl Heinz; Chanock, Stephen J.; Rajaraman, Preetha; Johansen, Christoffer; Jenkins, Robert B.; Melin, Beatrice; Wrensch, Margaret; Sanson, Marc; Bondy, Melissa L.; Houlston, Richard S.

doi:10.1038/s41416-018-0009-x

Cited by 36 publications

(31 citation statements)

References 36 publications

(47 reference statements)

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“…This study encompassed 12,488 cases and 18,169 controls. This study found little evidence that indicated that obesity-related factors contribute to glioma ( Disney-Hogg et al, 2018b ).…”

Section: Mr In Glioma Researchmentioning

confidence: 64%

“… Disney-Hogg et al (2018b) carried out an MR analysis to interrogate the observed association between obesity-related factors and risk of glioma. The authors identified variants that were robustly associated with 10 key obesity-related factors: 2-h post-challenge glucose, BMI, fasting glucose, fasting insulin, HDL cholesterol, LDL cholesterol, type-2 diabetes, total cholesterol, triglycerides and waist-hip ratio.…”

Section: Mr In Glioma Researchmentioning

confidence: 99%

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Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors

et al. 2018

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Gliomas are a group of primary brain tumors, the most common and aggressive subtype of which is glioblastoma. Glioblastoma has a median survival of just 15 months after diagnosis. Only previous exposure to ionizing radiation and particular inherited genetic syndromes are accepted risk factors for glioma; the vast majority of cases are thought to occur spontaneously. Previous observational studies have described associations between several risk factors and glioma, but studies are often conflicting and whether these associations reflect true casual relationships is unclear because observational studies may be susceptible to confounding, measurement error and reverse causation. Mendelian randomization (MR) is a form of instrumental variable analysis that can be used to provide supporting evidence for causal relationships between exposures (e.g., risk factors) and outcomes (e.g., disease onset). MR utilizes genetic variants, such as single nucleotide polymorphisms (SNPs), that are robustly associated with an exposure to determine whether there is a causal effect of the exposure on the outcome. MR is less susceptible to confounding, reverse causation and measurement errors as it is based on the random inheritance during conception of genetic variants that can be relatively accurately measured. In previous studies, MR has implicated a genetically predicted increase in telomere length with an increased risk of glioma, and found little evidence that obesity related factors, vitamin D or atopy are causal in glioma risk. In this review, we describe MR and its potential use to discover and validate novel risk factors, mechanistic factors, and therapeutic targets in glioma.

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“…This study encompassed 12,488 cases and 18,169 controls. This study found little evidence that indicated that obesity-related factors contribute to glioma ( Disney-Hogg et al, 2018b ).…”

Section: Mr In Glioma Researchmentioning

confidence: 64%

Section: Mr In Glioma Researchmentioning

confidence: 99%

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors

et al. 2018

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“…This finding contrasts with our earlier work which found no evidence for an association; however, the previous analysis was based on fewer SNPs (26 vs 44). 18 Genetically predicted plasma total TG also showed a suggestive association with risk of GBM (OR SD = 0.87, 95% CI: 0.76-0.99, P = 0.030). Leave-one-out analysis showed that both LDL and TG associations were unstable.…”

Section: Cardiometabolic and Inflammatory Factorsmentioning

confidence: 93%

“…We considered only continuous traits, as analysis of binary traits (such as disease status) with binary outcomes in 2-sample MR frameworks can result in inaccurate causal estimates. 18,19 The analysis was restricted to SNPs associated at genome-wide significance (ie, P ≤ 5 × 10 −8 ) in individuals of European ancestry, to satisfy the MR assumption that genetic variants are associated with the modifiable risk factor. 22 To avoid collinearity between SNPs for each trait, correlated SNPs were excluded using the MR-Base database (linkage disequilibrium threshold, r 2 ≥ 0.01) within each trait, with SNPs with the strongest effect size retained.…”

Section: Genetic Instruments For Putative Risk Factorsmentioning

confidence: 99%

Lack of association between modifiable exposures and glioma risk: A Mendelian randomisation analysis

et al. 2019

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Background The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian Randomisation (MR) framework to examine if lifestyle, cardiometabolic and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation. Methods We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures: lifestyle and dietary factors (height, plasma IGF-1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamin [A1, B12, B6, E and 25-hydroxyvitamin D], fatty acids levels [mono-unsaturated, omega-3 and omega-6] and circulating fetuin-A); cardiometabolic factors (birth weight, HDL cholesterol, LDL cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, HbA1C levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio) were included; inflammatory factors (C-reactive protein (CRP), plasma IL-6 sRa and serum IgE). Results After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism (SNP) no significant association with glioma risk was observed (i.e. PCorrected > 0.05). Conclusions This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk.

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“…In contrast, given that the Central LHIN had the lowest trend, it can be inferred that disease risk for this health unit progressed the slowest over the period of study. As illustrated in Figure 4, six of the twelve LHINs (i.e., IDs 2,6,9,10,11,12) had local trends which were greater than γ. The space-time random effect δ is equivalent to the difference between the temporal trend of an individual LHIN and the mean trend γ of the entire region [31].…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

Persad¹

2019

Medical Sciences

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Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

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

Cited by 36 publications

References 36 publications

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors

Lack of association between modifiable exposures and glioma risk: A Mendelian randomisation analysis

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

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