Diet and risk of glioma: combined analysis of 3 large prospective studies in the UK and USA

Abstract

Background Available evidence on diet and glioma risk comes mainly from studies with retrospective collection of dietary data. To minimize possible differential dietary recall between those with and without glioma, we present findings from 3 large prospective studies. Methods Participants included 692 176 from the UK Million Women Study, 470 780 from the US National Institutes of Health–AARP study, and 99 148 from the US Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression yielded study-specific adjusted relative risks for glioma in relation to 15 food groups, 14 nutrients, and 3 dietary patterns, which were combined, weighted by inverse variances of the relative risks. Separate analyses by <5 and ≥5 years follow-up assessed potential biases related to changes of diet before glioma diagnosis. Results The 1 262 104 participants (mean age, 60.6 y [SD 5.5] at baseline) were followed for 15.4 million person-years (mean 12.2 y/participant), during which 2313 incident gliomas occurred, at mean age 68.2 (SD 6.4). Overall, there was weak evidence for increased glioma risks associated with increasing intakes of total fruit, citrus fruit, and fiber and healthy dietary patterns, but these associations were generally null after excluding the first 5 years of follow-up. There was little evidence for heterogeneity of results by study or by sex. Conclusions The largest prospective evidence to date suggests little, if any, association between major food groups, nutrients, or common healthy dietary patterns and glioma incidence. With the statistical power of this study and the comprehensive nature of the investigation here, it seems unlikely we have overlooked major effects of diet on risk of glioma that would be of public health concern.

Cancer Stem Cell Enrichment and Metabolic Substrate Adaptability are Driven by Hydrogen Sulfide Suppression in Glioblastoma

“…For example, the recent completion of a massive meta-analysis leveraging clinical data from more than 10 million subjects led to the conclusion that overweight (BMI: 25 -30 kg /m 2 ) and obese (BMI > 30 kg /m 2 ) status represents a risk factor for glioma development specifically for women (41). However, other reports fail to substantiate this finding (42,43). Little to no attention has been paid to the possibility that a long-term pattern of HFD consumption may profoundly alter GBM evolution as it develops within the brain.…”

“…Recently, a diet-focused meta-analysis of 1.2 million subjects was presented involving self-reported dietary patterns. Once again, investigators were limited to interrogating this dataset for risk factor identification, concluding that no specific diets served as risk factors for GBM initiation (43). Including factors such as progressionfree survival and long-term dietary pattern will help to clarify the degree to which obesogenic diets modulate the course of human GBM.…”

The Influence of Nutritional and Lifestyle Factors on Glioma Incidence

“…No significant correlation was observed between alcohol consumption and neuroepithelial tumors risk, but there was a linear decreasing dose-response relationship trend for meningioma (odds ratio (OR): 0.33; p = 0.001) and overall CNS tumors (including all studied groups, p = 0.003) and alcohol intake [ 12 ]. The investigation carried out by Kuan et al [ 13 ] on three large prospective studies, taking into consideration the data divided into three time periods of observation (all follow-up, first 5 years of follow-up, and 5+ years of follow-up), showed no influence of alcohol consumption (counted per 10 g of alcohol) on the risk of glioma incidence [ 13 ].…”

Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study

“…4 Epidemiological studies of other potential risk factors have been inconsistent, null or not independently validated. [5][6][7][8][9][10][11][12] These observational studies are, however, prone to reverse causation, unmeasured confounding and recall bias, which can preclude causal inferences. 13 Additionally, the high frequency of exposure ascertainment by proxy is another source of bias.…”