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

The meta-analysis showed the relationship of glioma with underweight, overweight,
and obesity compared normal weight. Only three studies to date have analyzed the relationship
between the risk of glioma and underweight, and pooled HR of three studies show underweight
could decrease the risk of developing glioma 8], 11], 14]. Excess BMI (BMI???25 kg.m
^?2
) was significantly associated with a danger of developing glioma. Many studies reported
the overweight and obesity were independent risk factors for poor outcome in patients
with glioma 8], 11]–14]. At present, several potential theories have been built to explain how obesity can
influence the development of glioma. The most well-known mechanism is the insulin-like
growth factor (IGF) hypothesis of obesity-related cancer, which has been implicated
in glioma proliferation and progression in vitro 20]–25]. A 22-case-control study showed a positive correlation between serum IGF-1 levels
and glioma risk 26]. IGF-1 inhibitor was found effectively to suppress growth of glioblastoma cell and
induced tumor regression in vitro 27]. There is a peak level of IGF during fetal brain development, and it decreases with
age. But it reappears in nervous tissue of glioma cells 28]. Insulin resistance and hyperinsulinemia are very common among excess body mass especially
obesity 29], which increase the level of free IGF. The free IGF can bind insulin-like growth
factor binding protein 1 (IGFBP-1) and insulin-like growth factor binding protein
2 (IGFBP-2). Correspondence with a decrease of the binding protein, more and more
higher circulating concentrations of free or bioactive insulin-like growth factor
1 (IGF-1), was detected 21], 30].

Basing on our findings, we thought weight loss is beneficial which may reduce insulin
resistance in obese patients. In addition, nutrients and phytochemicals in fruit and
vegetables might decrease glioma risk 31], while socioeconomic level, daily alcohol intake, smoking status, number of full-term
pregnancies, age at first birth, and oral contraceptive use were not significantly
associated with the incidence of glioma 10]. Moore et al. found no link between weight gain between ages 18 and 50 years and
glioma risk 12].

As we know, this is the first meta-analysis illustrating the correlation of different
BMI grades on the risk of glioma. There are some advantages of this meta-analysis.
Firstly, meta-analysis can assess the consistency of result and find the origin of
heterogeneity. Secondly, meta-analysis can evaluate and summarize results from different
studies which can increase the statistical efficiency and accuracy. Thirdly, we could
do detailed subgroup analysis to identify risk factors relative to glioma.

Several potential limitations of this meta-analysis should be noted. First, the number
of included studies was small which might let us underestimate the true association.
Second, as all included studies were observational, we cannot exclude all confounders
like age, region, and race. Third, because of our strict inclusive criteria, many
articles might exclude subject. Forth, the data were not stratified according to the
WHO grade of tumors. Finally, unpublished negative results were needed to be considered.