Different body mass index grade on the risk of developing glioma: a meta-analysis

Impact of abdominal obesity on the risk of glioma development in patients with diabetes: A nationwide population-based cohort study in Korea

BACKGROUND

Abdominal obesity has been suggested as a risk factor for glioma; however, it is unclear whether this association applies to people with diabetes. This study examined the association between abdominal obesity and the risk of developing gliomas in diabetic patients.

METHODS

We conducted a retrospective cohort study using the National Health Insurance System of South Korea from 2009 to 2012. The primary outcome was the incidence of newly diagnosed gliomas according to waist circumference (WC), and subgroup analyses were performed according to demographic characteristics and diabetes status including disease duration, number of oral hypoglycemic agents, and insulin use.

RESULTS

Of a total of 1,893,057 participants, 1,846 (0.10%) cases of gliomas occurred. After adjusting for confounding factors, WC ≥90 cm (men)/85 cm (women) was associated with significantly higher risks of gliomas (adjusted HR [95% CI]; 1.279 [1.053, 1.554], 1.317 [1.048, 1.655], and 1.369 [1.037, 1.807] in the WC <95 cm (men)/90 cm (women) group, WC <100 cm (men)/95 cm (women) group, and WC ≥100 cm (men)/95 cm (women) group, respectively). Subgroup analysis showed that patients with larger WC had a consistently higher incidence of glioma than their lean counterparts, except for insulin users (insulin user vs. nonuser, P for interaction = .03).

CONCLUSIONS

Abdominal obesity was associated with the development of gliomas in diabetic patients in a nationwide population-based database. Further study is needed in diabetic patients to stratify the risk for glioma development according to WC and to establish the underlying mechanism of carcinogenesis.

The association of BMI and sarcopenia with survival in patients with glioblastoma multiforme

Background: The association between obesity and sarcopenia (via temporal muscle thickness) with overall survival (OS) has been evaluated in several glioblastoma multiforme studies, however, the data are inconclusive. Methods: The authors conducted meta-analyses via the generic inverse-variance method with a random-effects model. Results: In the pooled analysis of five studies, including 973 patients, patients with lower temporal muscle thickness had significantly decreased OS (HR: 1.62, 95% CI: 1.16-2.28, p = 0.005). The pooled analysis of five studies, including 2131 patients, demonstrated decreased OS in patients with lower BMI compared with patients with obesity (HR: 1.45, 95% CI: 1.12-1.88, p = 0.005). Conclusion: Readily available body composition parameters could be used for prognosis prediction and to aid in treatment decisions in patients with glioblastoma multiforme.

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

BACKGROUND

The aetiology of glioma is poorly understood. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to search for glioma risk factors.

METHODS

We performed an MR-PheWAS analysing 316 phenotypes, proxied by 8387 genetic variants, and summary genetic data from a GWAS of 12,488 glioma cases and 18,169 controls. Causal effects were estimated under a random-effects inverse-variance-weighted (IVW-RE) model, with robust adjusted profile score (MR-RAPS), weighted median and mode-based estimates computed to assess the robustness of findings. Odds ratios per one standard deviation increase in each phenotype were calculated for all glioma, glioblastoma (GBM) and non-GBM tumours.

RESULTS

No significant associations (P < 1.58 × 10-4) were observed between phenotypes and glioma under the IVW-RE model. Suggestive associations (1.58 × 10-4 < P < 0.05) were observed between leukocyte telomere length (LTL) with all glioma (ORSD = 3.91, P = 9.24 × 10-3) and GBM (ORSD = 4.86, P = 3.23 × 10-2), but the association was primarily driven by the TERT variant rs2736100. Serum low-density lipoprotein cholesterol and plasma HbA1C showed suggestive associations with glioma (ORSD = 1.11, P = 1.39 × 10-2 and ORSD = 1.28, P = 1.73 × 10-2, respectively), both associations being reliant on single genetic variants.

CONCLUSIONS

Our study provides further insight into the aetiological basis of glioma for which published data have been mixed.

Association of metabolic syndrome with glioblastoma: a retrospective cohort study and review

Background

Metabolic syndrome is identified as a risk factor for the development of several systemic cancers, but its frequency among patients with glioblastoma and its association with clinical outcomes have yet to be determined. The aim of this study was to investigate metabolic syndrome as a risk factor for and affecting survival in glioblastoma patients.

Methods

A retrospective cohort study, consisting of patients with diagnoses at a single institution between 2007 and 2013, was conducted. Clinical records were reviewed, and clinical and laboratory data pertaining to 5 metabolic criteria were extrapolated. Overall survival was determined by time from initial surgical diagnosis to date of death or last follow-up.

Results

The frequency of metabolic syndrome among patients diagnosed with glioblastoma was slightly greater than the frequency of metabolic syndrome among the general population. Within a subset of patients (n = 91) receiving the full schedule of concurrent radiation and temozolomide and adjuvant temozolomide, median overall survival was significantly shorter for patients with metabolic syndrome compared with those without. In addition, the presence of all 5 elements of the metabolic syndrome resulted in significantly decreased median survival in these patients.

Conclusions

We identified the metabolic syndrome at a slightly higher frequency in patients with diagnosed glioblastoma compared with the general population. In addition, metabolic syndrome with each of its individual components is associated with an overall worse prognosis in patients receiving the standard schedule of radiation and temozolomide after adjustment for age.

The Influence of Nutritional and Lifestyle Factors on Glioma Incidence

Cancers are the first main cause of premature death in developed countries. Since brain tumors, especially gliomas, are the most lethal type of cancers, risk factors for their prevalence are still being discussed. Nearly 30–50% of all cancers could be prevented by proper nutritional habits and other lifestyle factors, but their influence on the tumors of the central nervous system has not been explained completely and still requires further studies. That is why we attempted to review the available research in this field, with a special focus on the factors with the proven protective activity observed in other cancers. Adequate vegetables and antioxidants (such as vitamins C and A) provided with a diet could have a protective effect, while other factors have shown no correlation with the incidence of glioma. However, further studies are necessary to determine whether fish, coffee, and tea consumption may prevent glioma. Maintaining proper body weight and undertaking a sufficient level of daily physical activity also seem to be important. Excessive body mass index (BMI) and higher attained height have increased the risk of glioma. In order to link more accurately the chosen factors to the prevalence of gliomas, it seems necessary to conduct large cohort, prospective, controlled studies in different world regions.

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

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.