European Journal of Clinical Nutrition (2016), 1–9 © 2016 Macmillan Publishers Limited All rights reserved 0954-3007/16 www.nature.com/ejcn

REVIEW

Body mass index and risk of brain tumors: a systematic review and dose–response meta-analysis D Zhang1,3, J Chen1,3, J Wang1,3, S Gong1,3, H Jin1, P Sheng1, X Qi1, L Lv1, Y Dong1,2 and L Hou1 BACKGROUND/OBJECTIVES: Data regarding the relationships between body mass index (BMI) and brain tumors are inconsistent, especially for the commonly seen gliomas and meningiomas. Therefore, we conducted a dose–response meta-analysis to unravel the issue. SUBJECTS/METHODS: Cochrane Library, PubMed and Embase were searched for pertinent case–control and cohort studies updated to November 2014. Dose–response and quantitative analysis were conducted with random-effect model. RESULTS: Sixteen studies were included, containing 3 887 156 participants and 11 614 cases. In categorical analysis for the relationships between abnormal weight and BMI, the summary risk ratio (RR) of brain tumors was 1.34 (95% confidential interval (CI), 1.15–1.56) for obesity, 1.12 (95% CI, 1.05–1.19) for overweight and 0.77 (95% CI, 0.64–0.93) for underweight; the summary RR of gliomas was 1.13 (95% CI, 1.02–1.26) for overweight and 0.71 (95% CI, 0.58–0.88) for underweight; the summary RR of meningiomas was 1.48 (95% CI, 1.30–1.69) for obesity and 1.18 (95% CI, 1.07–1.31) for overweight. In dose–response analysis, for every 5 kg/m2 increment of BMI, the summary RR was 1.13 (95% CI, 1.07–1.20) for overall brain tumors, 1.07 (95% CI, 0.97–1.19) for gliomas and 1.19 (95% CI, 1.14–1.25) for meningiomas. CONCLUSIONS: Excess weight was associated with increased risk of brain tumors and meningiomas but not with gliomas. Selective screening for brain tumors among obesity, especially for the females, might be more instructive. European Journal of Clinical Nutrition advance online publication, 24 February 2016; doi:10.1038/ejcn.2016.4

INTRODUCTION According to the World Health Organization, in 2008, more than 1.4 billion adults, 20 and older, were overweight; more than 11% of the world’s adults were obese; 42 million children under the age of 5 were overweight or obese in 2013.1 The increasingly apparent prevalence of obesity has severe consequences, among which the direct link between obesity and cancer has been widely accepted. As is reported by the World Cancer Research Fund, main cancers in obese people are predominantly endometrial, esophageal adenocarcinoma, colorectal, postmenopausal breast and prostate.2–4 Arising from different cell types, primary central nervous system tumors are heterogeneous and rare, which account for only 3.0–3.5% of all cancers among adults.5 The two main types of primary central nervous system tumors are meningiomas (34.7%) and gliomas (30%).5 Despite lots of published studies focused on the genesis of brain tumors, the only identified risk factor is ionizing radiation. Recently, several prospective epidemiological studies emerged that obesity might correlate with the increased risk of brain tumors. Possible mechanisms covered increased inflammatory response, prolonged hyperinsulinemia and decreased insulin sensitivity, and increased circulation estrogen levels, especially in women.2,6 However, data regarding the association between excess weight and primary brain tumors have been somewhat inconsistent. Four prospective cohort studies reported positive links between body mass index (BMI) and risk of meningiomas for females,7–10 although similar connection was not found in another 1

cohort study.11 Michaud et al.12 detected positive correlation with obesity for meningiomas but not for gliomas. In another cohort, a nearly four time-increased risk of gliomas was observed among individuals who were obese at age 18 years (NIH-AARP Diet and Health cohort) compared with people of normal weight.13 In view of the discrepancies among literatures, we conducted a dose–response meta-analysis on BMI and brain tumors separately by different types of the disease and assessed the possibility of nonlinear associations. MATERIALS AND METHODS Search strategy We conducted the meta-analysis according to the guidelines from the Meta-analysis of Observational Studies in Epidemiology Group.14 Two investigators (DZ and JC) independently searched Cochrane Library, PubMed and Embase for relevant studies examining the associations between BMI and the risk of brain tumors on 3 November 2014. Gliomas and meningiomas, as well as brain tumors, were investigated, respectively. The language was limited to English. The reference lists of retrieved articles were scrutinized to identify additional studies. The detailed search strategy is provided in Supplementary Text S1. Study selection Articles were included in our study if they (1) were cohort studies or case–control studies, (2) investigated BMI at baseline,

Department of Neurosurgery, Shanghai Institute of Neurosurgery, PLA Institute of Neurosurgery, Changzheng Hospital, Second Military Medical University, Shanghai, China and Department of Neurosurgery, Neuroscience Center, Changzheng Hospital, Second Military Medical University, Shanghai, China. Correspondence: Professor L Hou, Department of Neurosurgery, Neuroscience Center, Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai 200003, China. E-mail: [email protected] 3 These authors contributed equally to this work. Received 22 April 2015; revised 17 December 2015; accepted 17 January 2016 2

BMI and risk of brain tumors D Zhang et al

2 (3) reported brain tumors or gliomas or meningiomas as end point or (4) provided dose–response relative risk for continuous BMI levels or RR or odds ratio or hazard ratio or a number of cases and person-years with 95% confidence intervals (CIs) for ⩾ 3 categories of BMI. Data extraction and quality assessment Three authors (DZ, JW and SG) extracted the data in standardized data-collection forms. The extracted data were as follows: name of the first author, study design, country, follow-up duration, average age and number of the subjects, classification criteria of brain tumors, degree of adjustment for confounding factors and estimates with corresponding 95% CIs. Discrepancies among reviewers were resolved by joint review. Detailed extraction methods are provided at Supplementary Text S1. Methodological quality was assessed by the Newcastle–Ottawa Scale.15 Statistical analysis The effect sizes were RRs and corresponding 95% CIs. Odds ratios and hazard ratios were considered as the estimators of RRs. We used the reference category in each study and classified abnormal BMI into four levels: underweight (BMI o18.5 kg/m2), normal weight (18.5 kg/m2 ⩽ BMIo 25 kg/m2), overweight (25 kg/ m2 ⩽ BMIo 30 kg/m2) and obesity (BMI ⩾ 30 kg/m2).16 We conducted a dose–response meta-analysis to examine the potential nonlinear relationship between BMI and brain tumors, with generalized least squares regression model.17–19 We also calculated summary RRs and corresponding 95% CIs for per five-unit increment in BMI and for quantitative analysis. After obtaining study-specific linear risk parameters, they were expressed into estimates of RR for per five-unit increment in BMI. We generated forest plots for the relationships between brain tumors and underweight, overweight and obesity, as well as for per five-unit increment of BMI by pooling study-specific RR estimates using random-effect model. Heterogeneity among studies was assessed by the I2 statistic.20 Subgroup analyses were conducted according to study design, geography, sex, population size, follow-up duration, BMI measurement (self-reported and directly measured) and degrees of adjustment. In the sensitivity analysis, one study was removed at a time to explore the effect of the study on the result. Categorical analysis exploring the relationships between abnormal weight and BMI and dose–response analysis were explored. Publication bias was assessed by Egger’s test.21 Once the publication bias was detected, trim and fill funnel plots were provided.22 In test for the heterogeneity or publication bias, P-values were one-sided.21,23 In other ways, they were two-sided. Stata release 12 (Stata Corp, College Station, TX, USA) was used for the statistical analyses. Detailed statistical methods are provided in Supplementary Text S1. RESULTS Literature search Study-selection process was available in Figure 1. The initial search produced 69 studies from Cochrane library, 1026 studies from PubMed and 1285 studies from Embase. In the review of abstract, 33 studies were identified after removing duplicates and irrelevant studies. After full-text evaluation, 16 studies were included.7–13,24–32 The review of reference of these studies provided no eligible study. Study characteristics Table 1 reports the main characteristics of the 16 included studies, which come from 22 populations. There were 9 cohort studies,7–10,12,13,24–26 1 nested case–control study11 and 6 European Journal of Clinical Nutrition (2016) 1 – 9

case–control studies.27–32 Seventy-five percent of them (9/16) were published after the year 2009.9,10,12,13,24,25,29,31,32 The 16 studies included 3 887 156 participants and 11 614 brain tumor cases. Eight studies were performed in the United States,8,10,13,27,28,30–32 six in Europe7,9,11,12,24,26 and two in Asia.25,29 The participants were all females in six studies7,8,10,27,28,30 and all males in three studies.25,26,32 In the remaining seven studies, the gender composition is generally balanced.9,11–13,24,29,31 BMI measurements were performed by trained personnel in seven studies,9,11,12,24–26,29 with the left by self-reported. The criteria used to ascertain brain tumors were heterogeneous. In eleven of the sixteen studies, the tumors were diagnosed by linkage to cancer registries.7,9–13,24–26,28,32 In the remaining five studies, the diagnosis was self-reported.8,27,29–31 The categorization, relative risks and quality assessment of included studies were available in Supplementary Tables S1 and S2. The seven studies on gliomas contained 2 589 290 participants with 4289 gliomas cases.7,9,11–13,24,31 Five of these studies were also included in the meta-analysis for meningiomas.7,9,11,12,24 The twelve studies on meningiomas involved 2 470 719 with 4050 meningioma cases.7–12,24,27–30,32 All the relevant articles were included in the analysis for brain tumors. Brain tumors Dose–response meta-analysis. Fifteen studies were identified, involving nine prospective cohort studies,7–10,12,13,24–26 one nested case–control study11 and five case–control studies.27–29,31,32 The dose–response meta-analysis did not detect a nonlinear relationship between BMI and risk of brain tumors (P = 0.20; Figure 2). A statistically significant association was observed for per 5 kg/m2 increment of BMI (RR: 1.13 (1.07–1.20); P o 0.001; Figure 3a). There was moderate heterogeneity among the studies (P-value from the Q-test = 0.007, I2 = 69.7%). Publication bias was detected by Egger’s test. (P = 0.006). The adjusted RR for missing estimates using the trim and fill method was 1.05 (95% CI, 0.99–1.12). Obesity and brain tumors. Associations between obesity and brain tumors were examined in fifteen studies.7–10,12,13,24–32 Nine of them discussed gliomas and meningiomas separately.8,10,13,27–32 Sex-specific RRs were evaluated in eleven studies.7–10,12,25–27,30–32 Two studies reported the brain tumors as end point.25,26 RRs for different subgroups in every study were first aggregated by fixed-effect models, which were then pooled with random-effect model. Meta-analysis of included studies demonstrated a strong bearing between obesity and brain tumors (RR: 1.34; 95% CI, 1.15–1.56; P o 0.001; Figure 3b), with moderate heterogeneity (I2 = 71.6%; P o0.01). Publication bias was not detected by Egger’s test (P = 0.349). Overweight and brain tumors. Thirteen studies examining possible relationship between overweight and brain tumors came up with inconsistent outcomes.7,9,10,12,13,25–29,31,32 Pooled results revealed potential relationship between overweight and brain tumors (RR: 1.12; 95% CI, 1.05–1.19; Po 0.001; Figure 3c), with no heterogeneity (I2 = 3.4%; P = 0.413). Publication bias was not detected by Egger’s test (P = 0.374). Underweight and brain tumors. Six studies evaluated the possible effect of underweight on brain tumors.9,12,13,25,30,31 Summary result implied a reverse relationship between BMI and incidence of brain tumor (RR: 0.77; 95% CI, 0.64–0.93; P = 0.006; Figure 3d), with no heterogeneity (I2 = 1.7%; P = 0.405). Publication bias was not detected by Egger’s test (P = 0.096). In sensitivity analysis, no significance was detected when removing the study by Little et al.31 (RR: 0.86; 95% CI, 0.67–1.10). © 2016 Macmillan Publishers Limited

BMI and risk of brain tumors D Zhang et al

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Figure 1.

The flow diagram for identifying eligible studies.

Glioma Dose–response meta-analysis. Meta-analysis was possible for seven studies, including five prospective cohort studies,7,9,12,13,24 one nested case–control study11 and one case–control study.32 For gliomas, evidence failed to prove a nonlinear relationship with BMI (P = 0.51; Figure 4). Overall, result demonstrated no statistically significant association between BMI and risk of gliomas: for every 5 kg/m2 increment of BMI, the RR was 1.07 (0.97–1.19) (P = 0.16; Figure 5a). Moderate heterogeneity among studies was detected (P-value from the Q-test = 0.001, I2 = 73.4%), with no publication bias (Egger’s test: P = 0.162). In sensitivity analyses, a marginal significance was detected when excluding the study by Helseth et al.11 (RR: 1.11; 95% CI, 1.00–1.22). Obesity and gliomas. Six studies examined the relationships between obesity and gliomas, with five cohort studies7,9,12,13,24 and one case–control study.11 Meta-analysis of related studies failed to prove significant effect of obesity on gliomas, with a pooled RR of 1.22 (95% CI, 0.92–1.61; P = 0.163; Figure 5b). Evidence suggested moderate heterogeneity (I2 = 68.1%; P = 0.008), with no publication bias (Egger’s test: P = 0.217). Overweight and gliomas. Six studies were available for metaanalysis with heterogeneous outcomes.7,9,12,13,24,31 The summary RR (1.13; 95% CI, 1.02–1.26; Figure 5c) favored a significant effect of overweight to bring about gliomas, with no evidence of heterogeneity (I2 = 0; P = 0.652). Egger’s test indicated no © 2016 Macmillan Publishers Limited

publication bias (P = 0.166). In sensitivity analyses, no significance was detected when excluding the study by Benson et al.7 (RR: 1.10; 95% CI, 0.96–1.25) or Little et al.31 (RR: 1.11; 95% CI, 0.99–1.25). Underweight and gliomas. Four studies exploring the link of underweight to gliomas were pooled using random-effect model.9,12,13,31 The summary RR (0.71; 95% CI, 0.58–0.88; P = 0.001; Figure 5d) demonstrated no significant outcomes, with no evidence of heterogeneity (I2 = 0; P = 0.526). Publication bias was not detected by Egger’s test (P = 0.506). In sensitivity analysis, no significance was detected when removing the study by Little et al.31 (RR: 0.78; 95% CI, 0.57–1.06). Meningiomas Dose–response meta-analysis. Six prospective cohort studies,7–10,12,24 one nested case–control study11 and four case–control studies27–29,32 were included. The dose–response meta-analysis indicated no evidence of a nonlinear relationship with BMI for meningiomas (P = 0.29; Figure 6). Statistically significant association between BMI and risk of meningiomas was suggested: for every 5 kg/m2 increase in BMI, the RR was 1.19 (1.14–1.25) (Po 0.001; Figure 7a). No evidence of heterogeneity and publication bias was discovered (P-value from the Q-test = 0.529, I2 = 0%; Egger’s test: P = 0.412). Obesity and meningiomas. The relationship between obesity and meningiomas was assessed in 11 studies, with 6 cohort European Journal of Clinical Nutrition (2016) 1 – 9

Million Women Study cohort

Iowa Women’s Health Study EPIC

Nurses’ Health Study cohort

Prospective cohort, 6.2

Johnson,10 American Prospective cohort, 10.5

Michaud,12 European Prospective cohort, 8.4

Prospective cohort, 10

Prospective cohort, 9.6

Prospective cohort, 8.2

Nested case– control study, 12–14

Prospective cohort, 10

Benson,7 European (English women)

Jhawar,8 American

Edlinger,24 European

European Journal of Clinical Nutrition (2016) 1 – 9

Moore,13 American

Helseth,11 European (Norwegians)

Oh,25 Asian (Korean)

20–79

Unknown

⩾18

54

57

25–89

100

0

0

40

0

54

100

100

⩾ 20

34.3

50

60

50

0

38

0

0

49

% Men

30–69

50–71

41

30–55

51.2

65.0–84.6

50–65

47.4

Age (mean or range) (years)

Self-reported

Self-reported

Self-reported

Measured by trained personnel

Self-reported

Self-reported

Measured by trained personnel

Measured by trained personnel

Measured by trained personnel

Self-reported

Measured by trained personnel

Self-reported

Measured by trained personnel

Self-reported

Self-reported

Measured by trained personnel

BMI measurement

Meningioma (138), glioma (148)

Endpoints (No. of cases)

Glioma (236)

Meningioma (348), low-grade glioma (86), high-grade glioma 367, all brain tumors (1236)

Meningioma (125)

Meningioma (203), glioma (340)

Meningioma (125)

Meningioma (243)

Meningioma (1127)

Gliomas (1111)

Brain cancer (918)

Brain cancer (234)

Rapid Case Ascertainment systems and state cancer registries of the respective sites

Histologically confirmed

Meningioma (456)

Meningioma (219)

National Cancer Institute’s Meningioma (143) Surveillance, Epidemiology, and End Results program for King, Pierce and Snohomish counties of western Washington state

—

Histologically confirmed

Unknown

International Classification of Diseases (ICD), seventh revision

Second revision of the International Classification of Diseases for Oncology

linked to Norwegian Cancer Registry Meningioma (533), glioma (1355), all brain tumor (3049)

International Classification of Diseases, tenth version, C710-719

ICD-7

Self-reported diagnosis of meningioma

International Classification of Diseases-Oncology (ICD-O), second edition

International Classification of Diseases, ninth revision

Tenth revision of the International Meningioma (390), glioma Classification of Diseases (ICD-10) (646), all central nervous and the third edition of Morphology system tumors (n = 1563) and Neoplasms

International Classification of Diseases for Oncology, third edition

Classification criteria

++

+

++

++

+++

++

++

+++

+

+++

++

++

++

+

+++

+++

Degree of covariables adjustment

Abbreviations: BMI, body mass index; EPIC, The European Prospective Investigation into Cancer and Nutrition; HUNT, The Nord–Trøndelag Health Study; KNHIC: Korea National Health Insurance Corporation; Me-Can cohort study: Metabolic Syndrome and Cancer Project.

908

Case–control study

Schildkraut,32 American

—

Case–control study

Lee,30 American

479

Case–control study

Custer,28 American

—

505

—

Case–control study

Duan,29 Asian (Chinese) 429

2219

—

Case–control study

Claus,27 American

—

2207

—

Case–control study

Little,31 American

362 552

Swedish Foundation for Occupational Safety and Health of the Construction Industry

781 283

33 539

270 395

578 462

121 700

380 775

27 791

1 249 670

74 242

No. of participants

Samanic,26 European Prospective (Sweden) cohort, 19

KNHIC study

A screening campaign by the National Mass Radiography Service

NIH-AARP Diet and Health Study

Me-Can cohort study

The HUNT Study

Prospective cohort, 23.5

Wiedmann,9 European (Norwegians)

Study name

Study design, follow-up (years)

Characteristics of studies on BMI and overall brain tumors, gliomas or meningioma

First author,ref. study population

Table 1.

BMI and risk of brain tumors D Zhang et al

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BMI and risk of brain tumors D Zhang et al

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Figure 2. Relative risk (solid line) with 95% CI (long dashed lines) for the association of the BMI level with risk of all brain tumors in a restricted cubic spline random-effect model.

Figure 4. Relative risk (solid line) with 95% CI (long dashed lines) for the association of the BMI level with risk of gliomas in a restricted cubic spline random-effects model.

Figure 3. Forest plots of the BMI level and risk of overall brain tumors. (a) Per 5 kg/m2 increase; (b) obesity and brain tumors; (c) overweight and brain tumors; and (d) underweight and brain tumors.

studies7–10,12,24 and 5 case–control studies.27–30,32 Pooling studyspecific estimates with random-effect model yielded summary RR of 1.48 (95% CI, 1.30–1.69; P o0.001; Figure 7b), with moderate heterogeneity (I2 = 35.8%; P = 0.112). Publication bias was not detected by Egger’s test (P = 0.339). © 2016 Macmillan Publishers Limited

Overweight and meningiomas. Inconsistent outcomes for the relationships overweight and meningiomas derived from nine studies.7,9,10,12,24,27–29,32 Overall, meta-analysis illustrated a potential relationship between overweight and meningiomas (RR: 1.18; 95% CI, 1.07–1.31; P = 0.001; Figure 7c), with no heterogeneity European Journal of Clinical Nutrition (2016) 1 – 9

BMI and risk of brain tumors D Zhang et al

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Figure 5. Forest plots of the BMI level and risk of gliomas. (a) Per 5 kg/m2 increase; (b) obesity and gliomas; (c) overweight and gliomas; and (d) underweight and gliomas.

SUBGROUP ANALYSES The results of the subgroup analyses are available in Supplementary Tables S3 and S4. Brain tumors Dose-response meta-analysis. Statistically significant results were detected in subgroups classified by study type, gender, BMI measurement, sample size and follow-up duration, which were not found in subgroups of European and ‘+’ degree of adjustment. Obesity and brain tumors. Significant associations were detected in subgroups classified by study type and sample size, which were not found in subgroups of male, measured BMI, European, ‘+’ degree of adjustment and ⩾ 10 years of follow-up. Figure 6. Relative risk (solid line) with 95% CI (long dashed lines) for the association of the BMI level with risk of meningiomas in a restricted cubic spline random-effects model.

(I2 = 0; P = 0.454). Publication bias was not detected by Egger’s test (P = 0.929).

Overweight and brain tumors. Significant associations were detected in subgroups classified by study type and sample size, which were not found in subgroups defined by follow-up years and subgroups of male, measured BMI, European and ‘+’ degree of adjustment.

Underweight and meningiomas. Three studies hit our inclusion criterion, with two cohort studies9,12 and one case–control study.30 No statistically significant outcomes were confirmed after pooling the study-specific estimates. The summary RR was 1.03 (95% CI, 0.64–1.64; P = 0.9; Figure 7d), with no evidence of heterogeneity (I2 = 0; P = 0.74). Publication bias was not detected by Egger’s test (P = 0.632).

Gliomas Dose–response meta-analysis. Pooled RRs were not significant in subgroups classified by gender, sample size, degree of adjustment and follow-up duration. However, in subgroups of case–control study and American population, the associations between BMI and risk of gliomas turned out to be statistically significant.

European Journal of Clinical Nutrition (2016) 1 – 9

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Figure 7. Forest plots of the BMI level and risk of meningiomas. (a) Per 5 kg/m2 increase; (b) obesity and meningiomas; (c) overweight and meningiomas; and (d) underweight and meningiomas.

Overweight and gliomas. Positive associations between BMI and the risk of gliomas were detected in subgroups of self-reported BMI, larger sample size and o 10 years of follow-up duration. The summary RRs were not significant in subgroups that were defined by study design, study population and degree of adjustment. In obesity and gliomas analysis, overall subgroup analyses yielded no significant estimate in any subgroup. Meningiomas. In the dose–response meta-analysis and categorical analysis for obesity, the positive relationships were significant in all subgroups. In categorical analysis for overweight, significant RRs were detected in subgroups of case–control studies, measured BMI and smaller sample size, Asian population and ‘++’ degree of adjustment, which were not found in subgroups that were defined by gender and follow-up duration. In categorical analysis for underweight, too few studies were included to conduct subgroup analysis. DISCUSSION In our systematic review, BMI appears to be positively associated with adult brain tumors based on 16 observational studies. When examined for the two main types of brain tumors separately, however, similar result was detected for meningiomas but not for gliomas. Our results revealed that obesity was linked to 34% increment in the risk of brain tumors and 48% increment in the risk of meningiomas. In line with the results, underweight was linked to 23% decrease in the risk of brain tumors. As we can learn from the pooled results, the summary RRs were higher for meningiomas than for all brain tumors whether in dose–response © 2016 Macmillan Publishers Limited

analysis (RR = 1.13 for brain tumors and 1.19 for meningiomas) or in categorical analysis (overweight: RR = 1.12 for brain tumors and 1.18 for meningiomas), possibly demonstrating one of the most strongest link strengths to increased BMI for meningiomas among various types of brain tumors. In subgroup analyses of brain tumors, significant results were detected for adjustment degree of ‘++’ and ‘+++’, but not for ‘+’, which suggested the weakening effect of confounders on the relationships between brain tumors and BMI. In the subgroup analysis of dose–response analyses defined by gender, the RRs were 1.08 (1.02, 1.15) for males and 1.14 (1.04, 1.24) for females, indicating a stronger association with brain tumors for females than for males. In categorical analyses, significant results were detected for females (P = 0.001 for obesity and 0.004 for overweight) but not for males (P = 0.379 for obesity and 0.053 for overweight), which suggested the possible role of estrogen in the tumor genesis. For gliomas, no statistically significant results were raised for all subgroups, except for subgroups of case– control study, American population and self-reported BMI, which might come from heterogeneity among studies. For meningiomas, the results were significant for all subgroups, which indicated a positive relationship between excess body weight and meningiomas. The discrepancies in results for two different brain tumors in our meta-analysis may come from different pathological properties and genesis. On the one hand, the malignancy grades of gliomas and meningiomas are different. Most meningiomas are benign, whereas gliomas are frequently malignant.33,34 On the other hand, there were also discrepancies in etiology, such as a stronger association was detected between exposure to ionizing radiation, European Journal of Clinical Nutrition (2016) 1 – 9

BMI and risk of brain tumors D Zhang et al

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history of head trauma and meningiomas,35–37 but selected medical conditions were only associated with gliomas.38 Therefore, the different effects of excessive BMI on brain cancer genesis were possible too. The biological mechanisms behind the positive associations between BMI and risk of brain cancer remained unclear, but possible ones were suggested. Insulin resistance, the insulin growth factors (IGFs) and IGF-binding proteins, sex hormones, adipokines and vascular growth factors, chronic inflammation and nuclear factor-κB system, obesity-induced hypoxia, oxidative stress, genetic susceptibility, endocrine disruptors and immune dysfunction were discussed in previous publications,39 among which insulin resistance was the shared pathway.40 Elevated level of insulin in serum inhibits the production of IGF-binding proteins, which combines free IGF-1 and suppresses its activity. As a result, there would be more free IGF-1, combining IGF-receptor to promote cell growth.41–45 Investigation of possible relationships between sex hormone and tumors mainly focused on breast cancer and endometrial cancer. As we found in subgroup analyses, excess weight was associated with brain tumors for female but not for males, which indicated the potential role of female sex hormones in cancer genesis. Increased levels of plasma estradiol and estrone facilitate endometrial cell proliferation while inhibiting apoptosis.6,46 Other possible mechanisms involve chronic inflammation, adipokine and vascular growth factors, genetic susceptibility, obesity-related hypoxia and migrating adipose stromal cells.47,48 A recent meta-analysis suggested that obesity but not overweight is associated with an increased risk of meningioma.49 However, only six studies were included in the review, which might leave the authors with biased results and affect the representativeness of conclusion. A dose–response analysis, which would demonstrate relationships between BMI and brain tumors better, was not conducted. In contrast, our study had strengths in including more articles with long follow-up durations and conducting dose–response analyses. We included 16 studies, most of which have large populations involving different races all over the world. The confounders were fully adjusted, rendering the summary results more reliable. Owing to the prospective studies in our analysis, potential bias similar to selection bias and recall bias was largely decreased. Moreover, the negative relationship between underweight and brain tumors in our study strengthened the positive effect of excess weight on brain tumors from the indirect sources. Finally, we conducted analysis for the two main types of brain tumors, respectively. Several limitations, however, are still in order. First, different parameters across studies, particularly from study design and degree of adjustment, contribute a lot to the confounding effect of the review. Second, bias do exist because of defects in the study design of the included studies and meta-analysis itself, such as publication bias, selection bias and recall bias. Publication bias was detected in the dose–response analysis of brain tumors and results from the trim and fill method were not significant, suggesting an overstated positive relationship between BMI and brain tumors. Reporting bias might derive from a different diagnostic criterion of brain tumors and different referent BMI categories. The language of included studies was limited to English, which might lead to the negligence of non-English articles. We tried our best to minimize the bias by rigorous inclusion criteria and subgroup analyses. Finally, we were unable to analyze various types of glioma (for example, glioblastoma, astrocytoma and so on) separately because of the limited data. Therefore, caution is required in the interpretation of these results. In conclusion, our findings indicate that excess weight may increase the risk of adult meningiomas and brain tumors but have no effect on gliomas. On the basis of our results, it is suggested that a healthy body weight might contribute to a decreased risk of European Journal of Clinical Nutrition (2016) 1 – 9

meningioma. The biological mechanisms for the relationships between BMI and brain tumors incidence remain partially unclear. Therefore, more well-designed epidemiological studies and further work in exploring the underlying mechanisms are necessary. More official advice should also be made to avoid obesity and maintain desirable weight. CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS Foundation from the National Natural Science Fund of China (81371382) for LH and Science and Technology of Commission of Shanghai Municipality (14430721300) for YD.

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