Accepted Manuscript Association between fruit and vegetables intake and risk of glioma: A meta-analysis Ying Li, B.S. PII:
S0899-9007(14)00189-0
DOI:
10.1016/j.nut.2014.03.027
Reference:
NUT 9275
To appear in:
Nutrition
Received Date: 2 March 2014 Revised Date:
29 March 2014
Accepted Date: 31 March 2014
Please cite this article as: Li Y, Association between fruit and vegetables intake and risk of glioma: A meta-analysis, Nutrition (2014), doi: 10.1016/j.nut.2014.03.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Association between fruit and vegetables intake and risk of glioma: A meta-analysis ★ Ying Li B.S.
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Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Running head: Association of fruit and vegetables and risk of glioma
Corresponding author: Ying Li, Neurosurgery, the First Affiliated Hospital of
Chongqing Medical University, Chongqing 400016, China
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Email: [email protected] Tel: +8613508322799 Fax: +8613508322799
Word count for the entire manuscript (including references, figures and tables): 4178 Number of figures: 4
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Number of tables: 2
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Role of author in the work: YL conceived of the study, carried out the literature searching, data extraction, analyzed the data and draft the manuscript.
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ACCEPTED MANUSCRIPT Abstract Objective: Epidemiological studies evaluating the association on the intake of vegetables and fruit with glioma risk have produced inconsistent results. Therefore,
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we performed a comprehensive meta-analysis to test the hypothesis that higher vegetables and fruit intake may be a protective effect on glioma risk.
Methods: Pertinent studies were identified by a search in PubMed, Web of Knowledge
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and Wan Fang Med Online up to January 2014. Random-effect model was used to
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combine study-specific results. Publication bias was estimated using Begg’ funnel plot and Egger’s regression asymmetry test.
Results: 15 studies involving 5562 cases about vegetables intake and 17 studies involving 3994 cases about fruit intake with risk of glioma were included in this
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meta-analysis. The combined relative risk (RR) of glioma associated with vegetables intake was 0.775 (95%CI = 0.688-0.872) overall, and the association for subgroup analysis by study design, sources of control, ethnicity and number of cases was
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consistent with overall data. For fruit intake and glioma risk, significant protective
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associations was found in Asia population (RR=0.573, 95%CI=0.346-0.947), but not in Caucasian population. No publication bias was found. Conclusions: Our analysis indicated that intake of vegetables might have a protective effect on glioma. The intake of fruit may have a protective effect on glioma in Asia population, and the results need to be confirmed.
Keywords: vegetable, fruit, glioma, meta-analysis
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Introduction Glioma is the most common primary brain tumor that occurs most frequently in
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brain among adults, accounting for approximately 70 % of adult brain malignancies [1, 2]. It has an incidence of 5-10 cases per 100,000 [1]. Evidence has suggested that a genetic predisposition, ionizing radiation is an established risk factor for brain tumor
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patients [3-5]. In addition, other potential risk factors include exposure to
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environmental chemical carcinogens, such as chemical agents and, among dietary factors, exposure to N-nitroso compounds [1, 3]. Due to the highly invasive character of glioma, complete resection is hard to achieve [6]. Thus, prevention of glioma progression has become an important strategy for fighting against the disease [7].
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The intake of fruit and vegetables has long been associated with a decreased risk of various diseases [8, 9]. The suggested mechanisms for the major role of vegetables and fruit include: modulation of DNA methylation; protection from and repair of
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DNA damage; promotion of apoptosis and induction of detoxifying phase-II enzymes
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[10]. Up to date, a number of epidemiologic studies have been published to explore the relationship between fruit and vegetables intake and glioma risk. However, the results are not consistent [11-14]. Thus, to better characterize this issue, we conducted a comprehensive meta-analysis to evaluate the evidence from observational studies on vegetables and fruit intake with the risk of glioma by summarizing it quantitatively with a meta-analysis approach. Methods
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ACCEPTED MANUSCRIPT Search strategy A comprehensive search was conducted for available articles published in English or Chinese using the databases of PubMed, Web of Knowledge and Wan Fang Med
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Online up to January 2014 and by hand-searching the reference lists of the computer retrieved articles. The following search terms were used: ‘glioma’ AND (neoplasm OR carcinoma OR cancer) combined with “nutrition OR diet OR lifestyle OR fruit
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OR vegetable.” Two investigators searched articles and reviewed of all retrieved
consensus with a third reviewer. Inclusion criteria
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studies independently. Disagreements between the two investigators were resolved by
All relevant studies reporting the association of vegetables and fruit and glioma risk
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were considered for inclusion. The inclusion criteria were as follows: (1) use a case-control, nested case-control or cohort design; (2) the exposure of interest were vegetables or fruit; (3) the outcome of interest was glioma; (4) report associations in
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the form of RR (or OR) with the 95% confidence intervals (CI) for vegetables or fruit
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or providing us sufficient information to calculate them. Accordingly, the following exclusion criteria were also used: (1) reviews and (2) repeated or overlapped publications. In the present meta-analysis, we included the studies evaluating fruit or vegetable groups classified as “all” or “total.” Exposures presented as cooked vegetables, raw vegetables, other vegetables, citrus fruit or other fruits were not considered as equivalent to “all” or “total” and thus were not included. Studies that reported “fresh vegetables” or “fresh fruit” were included because fresh vegetables or
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ACCEPTED MANUSCRIPT fruit accounts for a very high proportion of the total consumption. Data extraction Two researchers independently extracted the following information: name of the
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first author, publication year, study design, ethnicity, the number of cases and controls or participants, type of controls, the methods used for collection of data on exposure, exposure classification, confounders adjusted for and the RR estimates with
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corresponding 95% CI for the highest versus lowest level. From each study, we
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extracted the risk estimates adjusted for the greatest number of potential confounders. If there was disagreement between the two investigators about eligibility of the data, it was resolved by consensus with a third reviewer. Statistical analysis
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The pooled measure was calculated as the inverse variance-weighted mean of the natural logarithm of multivariate adjusted RR with 95% CI for the highest vs. lowest levels to assess the association of vegetables and fruit intake with the risk of glioma.
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Random-effects model was used to combine study-specific RR (95%CI), which
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considers both within-study and between-study variation [15]. The Q test and I2 of Higgins and Thompson [16] were used to assess heterogeneity among included studies. I2 describes the proportion of total variation attributable to between-study heterogeneity as opposed to random error or chance, and I2 values of 0, 25, 50 and 75% represent no, low, moderate and high heterogeneity, respectively [17]. Meta-regression with restricted maximum likelihood estimation was performed to describe the potentially important covariates [18]. If no significant covariates were
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ACCEPTED MANUSCRIPT found to be heterogeneous, the subgroup analysis was conducted. Publication bias was estimated using Begg’ funnel plot [19] and Egger’s regression asymmetry test [20]. A study of influence analysis [21] was conducted to describe how robust the
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pooled estimator is to removal of individual studies. An individual study is suspected of excessive influence, if the point estimate of its omitted analysis lies outside the 95% CI of the combined analysis. All analyses were conducted using STATA software,
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version 11.0 (StataCorp LP, College Station, Texas). Two-tailed P≤0.05 was accepted
Results Search results and study characteristics
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as statistically significant.
The search strategy identified 312 articles from Pubmed, 11 articles from Wan Fang
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Med Online and 432 articles from the Web of Knowledge, and 29 articles were reviewed in full after reviewing the title/abstract. By studying reference lists, we identified 2 additional articles. Sixteen of these 31 articles were subsequently
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excluded from the meta-analysis for various reasons. Hence, 12 articles [12-14, 22-30]
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with 15 studies (1 prospective study and 14 case-control studies) involving 5562 cases about vegetables intake and glioma risk and 15 articles [11-13, 22-33] with 17 studies (2 prospective studies and 15 case-control studies) involving 3994 cases about fruit intake and glioma risk were used in this meta-analysis. The detailed steps of our literature search are shown in Figure 1. The characteristics of these studies are presented in Table 1. Total vegetables
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ACCEPTED MANUSCRIPT High versus low analyses. For vegetable intake and glioma, data from 15 studies (1 prospective study and 14 case-control studies) including 5562 glioma cases were used. Inverse association of
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vegetable intake with risk of glioma was reported in 7 studies, and no significant association of vegetable intake with risk of glioma was reported in 8 studies. Pooled results suggested that highest vegetable intake versus lowest level was significantly
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associated with the risk of glioma [summary RR=0.775, 95%CI=0.688-0.872,
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I2=41.3%, Pheterogeneity=0.048] (Figure 2). Sources of heterogeneity and subgroup analyses
As seen in Figure 2, moderate of heterogeneity (I2=41.3%, Pheterogeneity=0.048) was found in the pooled results. However, univariate meta-regression analysis, with the
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covariates of publication year, ethnicity, number of cases, and sources of controls showed no covariate having a significant impact on between-study heterogeneity, respectively.
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Fourteen case-control studies were included in this meta-analysis, and the pooled
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RR was 0.757 (95%CI=0.675-0.850) for the highest category of vegetable intake versus the lowest category and glioma risk. In subgroup analyses of ethnicity, when we restricted the analysis to Asia and Caucasian, the pooled RR of glioma were 0.785 (95%CI=0.675-0.914) and 0.782(95%CI=0.646-0.946), respectively. When we conducted the subgroup analysis by sources of control and number of cases, the significant protective associations were also found between vegetable intake and glioma in all strata. The main results are summarized in Table 2.
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ACCEPTED MANUSCRIPT Influence analysis and publication bias Influence analysis showed that no individual study had excessive influence on the association of vegetable intake and glioma. Begg’s funnel plot (Figure 3) and Egger’s
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test showed no evidence of significant publication bias between vegetable intake and glioma (P= 0.595). Total fruit
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High versus low analyses.
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Data from 17 studies (2 prospective studies and 15 case-control studies) for fruit intake and glioma risk were used including 3994 glioma cases. Seven studies reported that fruit intake can reduce the glioma risk, while 10 studies didn’t show the significant association between fruit intake and glioma risk. The meta-analysis
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showed no significant association between fruit intake and glioma risk (summary RR=0.828, 95%CI=0.659-1.039, I2=83.0%, Pheterogeneity=0.000) (Figure 4). Sources of heterogeneity and Subgroup analyses
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As seen in Figure 4, evidence of heterogeneity (I2=83.0%, Pheterogeneity=0.000) was
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found in the pooled results. However, univariate meta-regression analysis, with the covariates of publication year, ethnicity, number of cases, and sources of controls showed no covariate having a significant impact on between-study heterogeneity, respectively.
For the subgroup analyses by source of controls, the associations were significant in the hospital-based case-control studies (RR=0.586, 95%CI=0.398-0.863), but not in population-based case-control studies for the highest category of fruit intake versus
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ACCEPTED MANUSCRIPT the lowest category and glioma risk. In subgroup analyses of ethnicity, when we restricted the analysis to Asia and Caucasian, significant protective associations was only found in Asia population (RR=0.573, 95%CI=0.346-0.947), but not in Caucasian
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population. The associations of fruit intake with risk of glioma were not found in all strata for the subgroups of case-control studies and number of cases. The main results are summarized in Table 2.
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Influence analysis and publication bias
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No individual study had excessive influence on the association of fruit intake and glioma risk in the influence analysis. Begg’s funnel plot and Egger’s test (P= 0.795) showed no evidence of significant publication bias between fruit intake and glioma risk.
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Discussion
Finding from this meta-analysis suggested that the intake of vegetables might have a protective effect on glioma (pooled RR=0.775, 95%CI=0.688-0.872) for overall data
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and all subgroup analyses. The intake of fruit may have a protective effect on glioma
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only in Asia population (RR=0.573, 95%CI=0.346-0.947) and hospital-based case-control studies (RR=0.586, 95%CI=0.398-0.863), but not in other subgroups. Previous meta-analysis has suggested that a favorable effect was found between the intake of vegetables and risk of esophageal squamous cell carcinoma and renal cell carcinoma [8, 34]. Inverse associations on vegetables intake with colorectal neoplasm risk were also found [35]. No association was found between fruits intake with risk of non-Hodgkin's lymphoma and prostate cancer [36, 37], although significant protective
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ACCEPTED MANUSCRIPT association was found between fruits intake with a lower risk of type 2 diabetes and cardiovascular disease [9, 38]. The mechanisms of the anti-glioma properties of fruit and vegetables have not been
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thoroughly investigated. Antioxidants and dietary fibers might play a key role in the prevention of glioma development. The protective effects of vegetables and fruit are thought to be mediated by multiple components, including beta-carotene, fiber,
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vitamins, retinoids, phytoestrogens and folate [39].These components are involved in
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the inhibition of cell growth, the normal synthesis and methylation of DNA, and protection against oxidative stress and DNA damage.
Between-study heterogeneity is common in meta-analysis [40], and exploring the potential sources of between-study heterogeneity is the essential component of
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meta-analysis. For fruit intake and glioma, evidence of heterogeneity (I2=83.0%, Pheterogeneity=0.000) was found in the pooled results. The between-study heterogeneity might arise from publication year, study region, number of cases and sources of
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controls. Thus, we used meta-regression to explore the causes of heterogeneity for
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covariates. However, no covariate having a significant impact on between-study heterogeneity for the above mentioned covariates. Considering the pooled meta-analysis was fraught with the problem of heterogeneity, subgroup analyses by the type of study design (case-control studies), sources of controls (Population-based and Hospital-based), ethnicity (Asia and Caucasian) and Number of cases (G polymorphism of epidermal growth factor gene and susceptibility to glioma. Tumour Biol 2013.
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5. Xu G, Wang M, Xie W, Bai X. Three polymorphisms of DNA repair gene XRCC1 and the risk of glioma: a case-control study in northwest China. Tumour Biol 2013. 6. Bellail AC, Hunter SB, Brat DJ, Tan C, Van Meir EG. Microregional extracellular
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2004;36:1046-69.
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matrix heterogeneity in brain modulates glioma cell invasion. Int J Biochem Cell Biol
7. Xu Z, Ma W, Gao L, Xing B. Association between ERCC1 C8092A and ERCC2 K751Q polymorphisms and risk of adult glioma: a meta-analysis. Tumour Biol 2013. 8. Liu J, Wang J, Leng Y, Lv C. Intake of fruit and vegetables and risk of esophageal squamous cell carcinoma: a meta-analysis of observational studies. Int J Cancer 2013;133:473-85. 9. Woodside JV, Young IS, McKinley MC. Fruit and vegetable intake and risk of
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ACCEPTED MANUSCRIPT cardiovascular disease. Proc Nutr Soc 2013;72:399-406. 10. McCullough ML, Giovannucci EL. Diet and cancer prevention. Oncogene 2004;23:6349-64.
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11. Cabaniols C, Giorgi R, Chinot O, Ferahta N, Spinelli V, Alla P, et al. Links between private habits, psychological stress and brain cancer: a case-control pilot study in France. J Neurooncol 2011;103:307-16.
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12. Holick CN, Giovannucci EL, Rosner B, Stampfer MJ, Michaud DS. Prospective
Clin Nutr 2007;85:877-86.
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study of intake of fruit, vegetables, and carotenoids and the risk of adult glioma. Am J
13. Terry MB, Howe G, Pogoda JM, Zhang FF, Ahlbom A, Choi W, et al. An international case-control study of adult diet and brain tumor risk: a histology-specific
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analysis by food group. Ann Epidemiol 2009;19:161-71.
14. Tedeschi-Blok N, Lee M, Sison JD, Miike R, Wrensch M. Inverse association of antioxidant and phytoestrogen nutrient intake with adult glioma in the San Francisco
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Bay Area: a case-control study. BMC Cancer 2006;6:148.
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15. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-88.
16. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539-58.
17. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60. 18. Higgins JP, Thompson SG. Controlling the risk of spurious findings from
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ACCEPTED MANUSCRIPT meta-regression. Stat Med 2004;23:1663-82. 19. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088-101.
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20. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34.
21. Tobias A. Assessing the in fluence of a single study in the meta-analysis estimate.
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Stata Tech Bull 1999;47:
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22. Blowers L, Preston-Martin S, Mack WJ. Dietary and other lifestyle factors of women with brain gliomas in Los Angeles County (California, USA). Cancer Causes Control 1997;8:5-12.
23. Boeing H, Schlehofer B, Blettner M, Wahrendorf J. Dietary carcinogens and the
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risk for glioma and meningioma in Germany. Int J Cancer 1993;53:561-5. 24. Burch JD, Craib KJ, Choi BC, Miller AB, Risch HA, Howe GR. An exploratory case-control study of brain tumors in adults. J Natl Cancer Inst 1987;78:601-9.
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25. Chen H, Ward MH, Tucker KL, Graubard BI, McComb RD, Potischman NA, et al.
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Diet and risk of adult glioma in eastern Nebraska, United States. Cancer Causes Control 2002;13:647-55. 26. Giles GG, McNeil JJ, Donnan G, Webley C, Staples MP, Ireland PD, et al. Dietary factors and the risk of glioma in adults: results of a case-control study in Melbourne, Australia. Int J Cancer 1994;59:357-62. 27. Hu J, La Vecchia C, Negri E, Chatenoud L, Bosetti C, Jia X, et al. Diet and brain cancer in adults: a case-control study in northeast China. Int J Cancer 1999;81:20-3.
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ACCEPTED MANUSCRIPT 28. Kaplan S, Novikov I, Modan B. Nutritional factors in the etiology of brain tumors: potential role of nitrosamines, fat, and cholesterol. Am J Epidemiol 1997;146:832-41. 29. Xu Y, Shi L, Shi L, Zhao JB, Hu JF. Diet Factors Associated with Glioma in a
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Case-control Study. Chin J Prev Contr Chron Non-commun Dis 1999;7:222-223. 30. Hu J, Johnson KC, Mao Y, Guo L, Zhao X, Jia X, et al. Risk factors for glioma in adults: a case-control study in northeast China. Cancer Detect Prev 1998;22:100-8.
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31. Howe GR, Burch JD, Chiarelli AM, Risch HA, Choi BC. An exploratory
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case-control study of brain tumors in children. Cancer Res 1989;49:4349-52. 32. Mills PK, Preston-Martin S, Annegers JF, Beeson WL, Phillips RL, Fraser GE. Risk factors for tumors of the brain and cranial meninges in Seventh-Day Adventists. Neuroepidemiology 1989;8:266-75.
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33. Preston-Martin S, Mack W. Gliomas and meningiomas in men in Los Angeles County: investigation of exposures to N-nitroso compounds. IARC Sci Publ 1991;197-203.
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34. Liu B, Mao Q, Wang X, Zhou F, Luo J, Wang C, et al. Cruciferous vegetables
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consumption and risk of renal cell carcinoma: a meta-analysis. Nutr Cancer 2013;65:668-76.
35. Tse G, Eslick GD. Cruciferous vegetables and risk of colorectal neoplasms: a systematic review and meta-analysis. Nutr Cancer 2014;66:128-39. 36. Chen GC, Lv DB, Pang Z, Liu QF. Fruits and vegetables consumption and risk of non-Hodgkin's lymphoma: a meta-analysis of observational studies. Int J Cancer 2013;133:190-200.
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ACCEPTED MANUSCRIPT 37. Meng H, Hu W, Chen Z, Shen Y. Fruit and vegetable intake and prostate cancer risk: A meta-analysis. Asia Pac J Clin Oncol 2013. 38. Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, van Dam RM, et al. Fruit
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consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ 2013;347:f5001.
Cancer Epidemiol Biomarkers Prev 2006;15:1765-77.
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39. Chang ET, Adami HO. The enigmatic epidemiology of nasopharyngeal carcinoma.
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40. Munafo MR, Flint J. Meta-analysis of genetic association studies. Trends Genet
Figure Legends:
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2004;20:439-44.
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Figure 1 The detailed steps of our literature search. Figure 2 The forest plot between highest versus lowest categories of vegetables intake and glioma risk. Figure 3 Begg’s funnel plot for publication bias of vegetables intake and glioma risk. Figure 4 The forest plot between highest versus lowest categories of fruit intake and glioma risk.
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Adjustment or matched for Adjusted for age and total calorie intake.
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Table 1 Characteristics of studies on vegetables and fruit and glioma risk. First author, Country Study Cases, RR (95%CI) for highest year design age versus lowest category Holick et al. America Cohort 296, 1.17 (0.78-1.75) for vegetable 2007 30-75 1.41 (0.95-2.10) for fruit Blowers et al. America Case-control 94, 1.3 (0.5-3.2) for vegetable 1995 (PCC) 25-74 1.3 (0.5-3)for fruit Boeing et al. Germany Case-control 115, 0.9 (0.5-1.7) for vegetable 1993 (PCC) 25-75 1.1 (0.6-1.9) for fruit Canada
Case-control (HCC)
215, 25-80
1.33(0.56-3.16) for vegetable 0.455(0.336-0.618) for fruit
Cabaniols et al. 2011 Chen et al. 2002
France
Case-control (HCC) Case-control (PCC)
122, ≥18 236, ≥21
0.85 (0.49-1.47) for fruit
Giles et al. 1994
Australia
Case-control (PCC)
Howe et al. 1989 Hu et al. 1999
Canada
Case-control (PCC) Case-control (HCC)
China
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EP 416, 20-70
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America
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Burch et al. 1987
74, ≤19 73, 20-74
0.5 (0.3-1.0)for vegetable 1.0 (0.6-1.7)for fruit
Men 1.01(0.62-1.63)for vegetable 1.51(0.95-2.39)for fruit Women 0.53(0. 29-0.95)for vegetable 0.69(0.36-1.31)for fruit 0.858(0.614-1.20)for fruit 0.29 (0.1-0.7) for vegetable 0.15 (0.1-0.4) for fruit
Matched the patient on age (within five years) and race (Black or White). Adjusted for age, gender, tobacco-smoking and alcohol consumption. Matched to each case on the basis of sex, area of residence, marital status, year of birth (within 5 yr), date of diagnosis (within 1yr) for live cases, and date of death (within 1 yr) for dead cases. Adjusted for age and sex. Adjusting for age, age squared, gender, total energy intake, respondent type, education level, family history, and farming experience. Adjusted for alcohol and tobacco.
Adjusted for age at diagnosis. Matched to each case by sex, age within 5-year intervals and area of residence
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Preston-Martin et al. 1991 Hu et al. 1998
Case-control (PCC)
Case-control (HCC)
86, ≥18
America
Case-control (PCC) Case-control (HCC)
202, Na 218, 39.6
(same or adjacent city and country). Matched to case by age (±5years), sex, and ethnic origin. Na.
0.8 (0.6-1.0)for leafy green vegetables 0.7 (0.5-0.9) for yellow-orange vegetables 1.4 (1.1-1.8) for fruit
Adjusted for age, sex, center and the following food groups: leafy green vegetables, yellow–orange vegetables, cured meat, noncured meat, fresh fish, dairy eggs, grains, and citrus fruit.
0.84(0.73-0.96) for vegetable 0.81(0.73-0.89) for vegetable 0.85(0.77-0.95) for fruit 0.70(0.54-0.91) for fruit 0.8(0.4-1.6)for fruit
Adjusted for age, sex, economic conditions.
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0.51(0.29-0.89)for vegetable 0.28(0.16-0.51)for fruit
Na.
Adjusted for income, education, number of years of drinking liquor, and occupational exposure; vegetables adjusted for fruit and fruit also adjusted for total vegetables. Tedeschi-Blok et al. America Case-control 802, 0.57(0.42-0.78)for vegetable Adjusted for age, gender, ethnicity, 2006 (PCC) ≥20 SES, total calories, and supplement use Abbreviations: Na: not available; PCC: population-based case–control study; HCC: hospital-based case–control study; BMI: Body Mass Index
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China
0.91(0.55-1.52) for vegetable 1.76(1.03-3.01) for fruit 0.85(0.28-2.60)for fruit
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Germany, France, Canada, Sweden, Australia, USA China
139, 18-75 21, ≥25 1185, 20-80
America
Case-control (HCC) Cohort
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Xu et al. 1999
Israel
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Kaplan et al. 1997 Mills et al. 1989 Terry et al. 2009
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Table 2 Summary risk estimates of the association between vegetables and fruit and glioma risk. Vegetables Sub-groups Studies, n RR(95%CI) I2 (%) Pheterogeneity Studies, n All 15 0.775(0.688-0.872) 41.3 0.048 17 Case-control 14 0.757(0.675-0.850) 35.9 0.088 15 Sources of control Population-based 8 0.717(0.603-0.851) 24.4 0.235 8 Hospital-based 6 0.796(0.684-0.927) 42.0 0.125 7 Ethnicity Asia 5 0.785(0.675-0.914) 45.7 0.118 5 Caucasian 10 0.782(0.646-0.946) 43.5 0.068 12 Number of cases
S0899-9007(14)00189-0
DOI:
10.1016/j.nut.2014.03.027
Reference:
NUT 9275
To appear in:
Nutrition
Received Date: 2 March 2014 Revised Date:
29 March 2014
Accepted Date: 31 March 2014
Please cite this article as: Li Y, Association between fruit and vegetables intake and risk of glioma: A meta-analysis, Nutrition (2014), doi: 10.1016/j.nut.2014.03.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Association between fruit and vegetables intake and risk of glioma: A meta-analysis ★ Ying Li B.S.
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Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Running head: Association of fruit and vegetables and risk of glioma
Corresponding author: Ying Li, Neurosurgery, the First Affiliated Hospital of
Chongqing Medical University, Chongqing 400016, China
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Email: [email protected] Tel: +8613508322799 Fax: +8613508322799
Word count for the entire manuscript (including references, figures and tables): 4178 Number of figures: 4
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Number of tables: 2
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Role of author in the work: YL conceived of the study, carried out the literature searching, data extraction, analyzed the data and draft the manuscript.
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ACCEPTED MANUSCRIPT Abstract Objective: Epidemiological studies evaluating the association on the intake of vegetables and fruit with glioma risk have produced inconsistent results. Therefore,
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we performed a comprehensive meta-analysis to test the hypothesis that higher vegetables and fruit intake may be a protective effect on glioma risk.
Methods: Pertinent studies were identified by a search in PubMed, Web of Knowledge
SC
and Wan Fang Med Online up to January 2014. Random-effect model was used to
M AN U
combine study-specific results. Publication bias was estimated using Begg’ funnel plot and Egger’s regression asymmetry test.
Results: 15 studies involving 5562 cases about vegetables intake and 17 studies involving 3994 cases about fruit intake with risk of glioma were included in this
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meta-analysis. The combined relative risk (RR) of glioma associated with vegetables intake was 0.775 (95%CI = 0.688-0.872) overall, and the association for subgroup analysis by study design, sources of control, ethnicity and number of cases was
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consistent with overall data. For fruit intake and glioma risk, significant protective
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associations was found in Asia population (RR=0.573, 95%CI=0.346-0.947), but not in Caucasian population. No publication bias was found. Conclusions: Our analysis indicated that intake of vegetables might have a protective effect on glioma. The intake of fruit may have a protective effect on glioma in Asia population, and the results need to be confirmed.
Keywords: vegetable, fruit, glioma, meta-analysis
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Introduction Glioma is the most common primary brain tumor that occurs most frequently in
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brain among adults, accounting for approximately 70 % of adult brain malignancies [1, 2]. It has an incidence of 5-10 cases per 100,000 [1]. Evidence has suggested that a genetic predisposition, ionizing radiation is an established risk factor for brain tumor
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patients [3-5]. In addition, other potential risk factors include exposure to
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environmental chemical carcinogens, such as chemical agents and, among dietary factors, exposure to N-nitroso compounds [1, 3]. Due to the highly invasive character of glioma, complete resection is hard to achieve [6]. Thus, prevention of glioma progression has become an important strategy for fighting against the disease [7].
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The intake of fruit and vegetables has long been associated with a decreased risk of various diseases [8, 9]. The suggested mechanisms for the major role of vegetables and fruit include: modulation of DNA methylation; protection from and repair of
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DNA damage; promotion of apoptosis and induction of detoxifying phase-II enzymes
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[10]. Up to date, a number of epidemiologic studies have been published to explore the relationship between fruit and vegetables intake and glioma risk. However, the results are not consistent [11-14]. Thus, to better characterize this issue, we conducted a comprehensive meta-analysis to evaluate the evidence from observational studies on vegetables and fruit intake with the risk of glioma by summarizing it quantitatively with a meta-analysis approach. Methods
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ACCEPTED MANUSCRIPT Search strategy A comprehensive search was conducted for available articles published in English or Chinese using the databases of PubMed, Web of Knowledge and Wan Fang Med
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Online up to January 2014 and by hand-searching the reference lists of the computer retrieved articles. The following search terms were used: ‘glioma’ AND (neoplasm OR carcinoma OR cancer) combined with “nutrition OR diet OR lifestyle OR fruit
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OR vegetable.” Two investigators searched articles and reviewed of all retrieved
consensus with a third reviewer. Inclusion criteria
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studies independently. Disagreements between the two investigators were resolved by
All relevant studies reporting the association of vegetables and fruit and glioma risk
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were considered for inclusion. The inclusion criteria were as follows: (1) use a case-control, nested case-control or cohort design; (2) the exposure of interest were vegetables or fruit; (3) the outcome of interest was glioma; (4) report associations in
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the form of RR (or OR) with the 95% confidence intervals (CI) for vegetables or fruit
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or providing us sufficient information to calculate them. Accordingly, the following exclusion criteria were also used: (1) reviews and (2) repeated or overlapped publications. In the present meta-analysis, we included the studies evaluating fruit or vegetable groups classified as “all” or “total.” Exposures presented as cooked vegetables, raw vegetables, other vegetables, citrus fruit or other fruits were not considered as equivalent to “all” or “total” and thus were not included. Studies that reported “fresh vegetables” or “fresh fruit” were included because fresh vegetables or
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ACCEPTED MANUSCRIPT fruit accounts for a very high proportion of the total consumption. Data extraction Two researchers independently extracted the following information: name of the
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first author, publication year, study design, ethnicity, the number of cases and controls or participants, type of controls, the methods used for collection of data on exposure, exposure classification, confounders adjusted for and the RR estimates with
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corresponding 95% CI for the highest versus lowest level. From each study, we
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extracted the risk estimates adjusted for the greatest number of potential confounders. If there was disagreement between the two investigators about eligibility of the data, it was resolved by consensus with a third reviewer. Statistical analysis
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The pooled measure was calculated as the inverse variance-weighted mean of the natural logarithm of multivariate adjusted RR with 95% CI for the highest vs. lowest levels to assess the association of vegetables and fruit intake with the risk of glioma.
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Random-effects model was used to combine study-specific RR (95%CI), which
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considers both within-study and between-study variation [15]. The Q test and I2 of Higgins and Thompson [16] were used to assess heterogeneity among included studies. I2 describes the proportion of total variation attributable to between-study heterogeneity as opposed to random error or chance, and I2 values of 0, 25, 50 and 75% represent no, low, moderate and high heterogeneity, respectively [17]. Meta-regression with restricted maximum likelihood estimation was performed to describe the potentially important covariates [18]. If no significant covariates were
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ACCEPTED MANUSCRIPT found to be heterogeneous, the subgroup analysis was conducted. Publication bias was estimated using Begg’ funnel plot [19] and Egger’s regression asymmetry test [20]. A study of influence analysis [21] was conducted to describe how robust the
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pooled estimator is to removal of individual studies. An individual study is suspected of excessive influence, if the point estimate of its omitted analysis lies outside the 95% CI of the combined analysis. All analyses were conducted using STATA software,
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version 11.0 (StataCorp LP, College Station, Texas). Two-tailed P≤0.05 was accepted
Results Search results and study characteristics
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as statistically significant.
The search strategy identified 312 articles from Pubmed, 11 articles from Wan Fang
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Med Online and 432 articles from the Web of Knowledge, and 29 articles were reviewed in full after reviewing the title/abstract. By studying reference lists, we identified 2 additional articles. Sixteen of these 31 articles were subsequently
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excluded from the meta-analysis for various reasons. Hence, 12 articles [12-14, 22-30]
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with 15 studies (1 prospective study and 14 case-control studies) involving 5562 cases about vegetables intake and glioma risk and 15 articles [11-13, 22-33] with 17 studies (2 prospective studies and 15 case-control studies) involving 3994 cases about fruit intake and glioma risk were used in this meta-analysis. The detailed steps of our literature search are shown in Figure 1. The characteristics of these studies are presented in Table 1. Total vegetables
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ACCEPTED MANUSCRIPT High versus low analyses. For vegetable intake and glioma, data from 15 studies (1 prospective study and 14 case-control studies) including 5562 glioma cases were used. Inverse association of
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vegetable intake with risk of glioma was reported in 7 studies, and no significant association of vegetable intake with risk of glioma was reported in 8 studies. Pooled results suggested that highest vegetable intake versus lowest level was significantly
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associated with the risk of glioma [summary RR=0.775, 95%CI=0.688-0.872,
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I2=41.3%, Pheterogeneity=0.048] (Figure 2). Sources of heterogeneity and subgroup analyses
As seen in Figure 2, moderate of heterogeneity (I2=41.3%, Pheterogeneity=0.048) was found in the pooled results. However, univariate meta-regression analysis, with the
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covariates of publication year, ethnicity, number of cases, and sources of controls showed no covariate having a significant impact on between-study heterogeneity, respectively.
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Fourteen case-control studies were included in this meta-analysis, and the pooled
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RR was 0.757 (95%CI=0.675-0.850) for the highest category of vegetable intake versus the lowest category and glioma risk. In subgroup analyses of ethnicity, when we restricted the analysis to Asia and Caucasian, the pooled RR of glioma were 0.785 (95%CI=0.675-0.914) and 0.782(95%CI=0.646-0.946), respectively. When we conducted the subgroup analysis by sources of control and number of cases, the significant protective associations were also found between vegetable intake and glioma in all strata. The main results are summarized in Table 2.
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ACCEPTED MANUSCRIPT Influence analysis and publication bias Influence analysis showed that no individual study had excessive influence on the association of vegetable intake and glioma. Begg’s funnel plot (Figure 3) and Egger’s
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test showed no evidence of significant publication bias between vegetable intake and glioma (P= 0.595). Total fruit
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High versus low analyses.
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Data from 17 studies (2 prospective studies and 15 case-control studies) for fruit intake and glioma risk were used including 3994 glioma cases. Seven studies reported that fruit intake can reduce the glioma risk, while 10 studies didn’t show the significant association between fruit intake and glioma risk. The meta-analysis
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showed no significant association between fruit intake and glioma risk (summary RR=0.828, 95%CI=0.659-1.039, I2=83.0%, Pheterogeneity=0.000) (Figure 4). Sources of heterogeneity and Subgroup analyses
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As seen in Figure 4, evidence of heterogeneity (I2=83.0%, Pheterogeneity=0.000) was
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found in the pooled results. However, univariate meta-regression analysis, with the covariates of publication year, ethnicity, number of cases, and sources of controls showed no covariate having a significant impact on between-study heterogeneity, respectively.
For the subgroup analyses by source of controls, the associations were significant in the hospital-based case-control studies (RR=0.586, 95%CI=0.398-0.863), but not in population-based case-control studies for the highest category of fruit intake versus
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ACCEPTED MANUSCRIPT the lowest category and glioma risk. In subgroup analyses of ethnicity, when we restricted the analysis to Asia and Caucasian, significant protective associations was only found in Asia population (RR=0.573, 95%CI=0.346-0.947), but not in Caucasian
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population. The associations of fruit intake with risk of glioma were not found in all strata for the subgroups of case-control studies and number of cases. The main results are summarized in Table 2.
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Influence analysis and publication bias
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No individual study had excessive influence on the association of fruit intake and glioma risk in the influence analysis. Begg’s funnel plot and Egger’s test (P= 0.795) showed no evidence of significant publication bias between fruit intake and glioma risk.
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Discussion
Finding from this meta-analysis suggested that the intake of vegetables might have a protective effect on glioma (pooled RR=0.775, 95%CI=0.688-0.872) for overall data
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and all subgroup analyses. The intake of fruit may have a protective effect on glioma
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only in Asia population (RR=0.573, 95%CI=0.346-0.947) and hospital-based case-control studies (RR=0.586, 95%CI=0.398-0.863), but not in other subgroups. Previous meta-analysis has suggested that a favorable effect was found between the intake of vegetables and risk of esophageal squamous cell carcinoma and renal cell carcinoma [8, 34]. Inverse associations on vegetables intake with colorectal neoplasm risk were also found [35]. No association was found between fruits intake with risk of non-Hodgkin's lymphoma and prostate cancer [36, 37], although significant protective
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ACCEPTED MANUSCRIPT association was found between fruits intake with a lower risk of type 2 diabetes and cardiovascular disease [9, 38]. The mechanisms of the anti-glioma properties of fruit and vegetables have not been
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thoroughly investigated. Antioxidants and dietary fibers might play a key role in the prevention of glioma development. The protective effects of vegetables and fruit are thought to be mediated by multiple components, including beta-carotene, fiber,
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vitamins, retinoids, phytoestrogens and folate [39].These components are involved in
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the inhibition of cell growth, the normal synthesis and methylation of DNA, and protection against oxidative stress and DNA damage.
Between-study heterogeneity is common in meta-analysis [40], and exploring the potential sources of between-study heterogeneity is the essential component of
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meta-analysis. For fruit intake and glioma, evidence of heterogeneity (I2=83.0%, Pheterogeneity=0.000) was found in the pooled results. The between-study heterogeneity might arise from publication year, study region, number of cases and sources of
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controls. Thus, we used meta-regression to explore the causes of heterogeneity for
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covariates. However, no covariate having a significant impact on between-study heterogeneity for the above mentioned covariates. Considering the pooled meta-analysis was fraught with the problem of heterogeneity, subgroup analyses by the type of study design (case-control studies), sources of controls (Population-based and Hospital-based), ethnicity (Asia and Caucasian) and Number of cases (G polymorphism of epidermal growth factor gene and susceptibility to glioma. Tumour Biol 2013.
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5. Xu G, Wang M, Xie W, Bai X. Three polymorphisms of DNA repair gene XRCC1 and the risk of glioma: a case-control study in northwest China. Tumour Biol 2013. 6. Bellail AC, Hunter SB, Brat DJ, Tan C, Van Meir EG. Microregional extracellular
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2004;36:1046-69.
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matrix heterogeneity in brain modulates glioma cell invasion. Int J Biochem Cell Biol
7. Xu Z, Ma W, Gao L, Xing B. Association between ERCC1 C8092A and ERCC2 K751Q polymorphisms and risk of adult glioma: a meta-analysis. Tumour Biol 2013. 8. Liu J, Wang J, Leng Y, Lv C. Intake of fruit and vegetables and risk of esophageal squamous cell carcinoma: a meta-analysis of observational studies. Int J Cancer 2013;133:473-85. 9. Woodside JV, Young IS, McKinley MC. Fruit and vegetable intake and risk of
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ACCEPTED MANUSCRIPT cardiovascular disease. Proc Nutr Soc 2013;72:399-406. 10. McCullough ML, Giovannucci EL. Diet and cancer prevention. Oncogene 2004;23:6349-64.
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11. Cabaniols C, Giorgi R, Chinot O, Ferahta N, Spinelli V, Alla P, et al. Links between private habits, psychological stress and brain cancer: a case-control pilot study in France. J Neurooncol 2011;103:307-16.
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12. Holick CN, Giovannucci EL, Rosner B, Stampfer MJ, Michaud DS. Prospective
Clin Nutr 2007;85:877-86.
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study of intake of fruit, vegetables, and carotenoids and the risk of adult glioma. Am J
13. Terry MB, Howe G, Pogoda JM, Zhang FF, Ahlbom A, Choi W, et al. An international case-control study of adult diet and brain tumor risk: a histology-specific
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analysis by food group. Ann Epidemiol 2009;19:161-71.
14. Tedeschi-Blok N, Lee M, Sison JD, Miike R, Wrensch M. Inverse association of antioxidant and phytoestrogen nutrient intake with adult glioma in the San Francisco
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Bay Area: a case-control study. BMC Cancer 2006;6:148.
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15. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-88.
16. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539-58.
17. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60. 18. Higgins JP, Thompson SG. Controlling the risk of spurious findings from
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ACCEPTED MANUSCRIPT meta-regression. Stat Med 2004;23:1663-82. 19. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088-101.
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20. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34.
21. Tobias A. Assessing the in fluence of a single study in the meta-analysis estimate.
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Stata Tech Bull 1999;47:
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22. Blowers L, Preston-Martin S, Mack WJ. Dietary and other lifestyle factors of women with brain gliomas in Los Angeles County (California, USA). Cancer Causes Control 1997;8:5-12.
23. Boeing H, Schlehofer B, Blettner M, Wahrendorf J. Dietary carcinogens and the
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risk for glioma and meningioma in Germany. Int J Cancer 1993;53:561-5. 24. Burch JD, Craib KJ, Choi BC, Miller AB, Risch HA, Howe GR. An exploratory case-control study of brain tumors in adults. J Natl Cancer Inst 1987;78:601-9.
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25. Chen H, Ward MH, Tucker KL, Graubard BI, McComb RD, Potischman NA, et al.
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Diet and risk of adult glioma in eastern Nebraska, United States. Cancer Causes Control 2002;13:647-55. 26. Giles GG, McNeil JJ, Donnan G, Webley C, Staples MP, Ireland PD, et al. Dietary factors and the risk of glioma in adults: results of a case-control study in Melbourne, Australia. Int J Cancer 1994;59:357-62. 27. Hu J, La Vecchia C, Negri E, Chatenoud L, Bosetti C, Jia X, et al. Diet and brain cancer in adults: a case-control study in northeast China. Int J Cancer 1999;81:20-3.
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ACCEPTED MANUSCRIPT 28. Kaplan S, Novikov I, Modan B. Nutritional factors in the etiology of brain tumors: potential role of nitrosamines, fat, and cholesterol. Am J Epidemiol 1997;146:832-41. 29. Xu Y, Shi L, Shi L, Zhao JB, Hu JF. Diet Factors Associated with Glioma in a
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Case-control Study. Chin J Prev Contr Chron Non-commun Dis 1999;7:222-223. 30. Hu J, Johnson KC, Mao Y, Guo L, Zhao X, Jia X, et al. Risk factors for glioma in adults: a case-control study in northeast China. Cancer Detect Prev 1998;22:100-8.
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31. Howe GR, Burch JD, Chiarelli AM, Risch HA, Choi BC. An exploratory
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case-control study of brain tumors in children. Cancer Res 1989;49:4349-52. 32. Mills PK, Preston-Martin S, Annegers JF, Beeson WL, Phillips RL, Fraser GE. Risk factors for tumors of the brain and cranial meninges in Seventh-Day Adventists. Neuroepidemiology 1989;8:266-75.
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33. Preston-Martin S, Mack W. Gliomas and meningiomas in men in Los Angeles County: investigation of exposures to N-nitroso compounds. IARC Sci Publ 1991;197-203.
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34. Liu B, Mao Q, Wang X, Zhou F, Luo J, Wang C, et al. Cruciferous vegetables
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consumption and risk of renal cell carcinoma: a meta-analysis. Nutr Cancer 2013;65:668-76.
35. Tse G, Eslick GD. Cruciferous vegetables and risk of colorectal neoplasms: a systematic review and meta-analysis. Nutr Cancer 2014;66:128-39. 36. Chen GC, Lv DB, Pang Z, Liu QF. Fruits and vegetables consumption and risk of non-Hodgkin's lymphoma: a meta-analysis of observational studies. Int J Cancer 2013;133:190-200.
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ACCEPTED MANUSCRIPT 37. Meng H, Hu W, Chen Z, Shen Y. Fruit and vegetable intake and prostate cancer risk: A meta-analysis. Asia Pac J Clin Oncol 2013. 38. Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, van Dam RM, et al. Fruit
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consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ 2013;347:f5001.
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40. Munafo MR, Flint J. Meta-analysis of genetic association studies. Trends Genet
Figure Legends:
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2004;20:439-44.
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Figure 1 The detailed steps of our literature search. Figure 2 The forest plot between highest versus lowest categories of vegetables intake and glioma risk. Figure 3 Begg’s funnel plot for publication bias of vegetables intake and glioma risk. Figure 4 The forest plot between highest versus lowest categories of fruit intake and glioma risk.
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Adjustment or matched for Adjusted for age and total calorie intake.
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Table 1 Characteristics of studies on vegetables and fruit and glioma risk. First author, Country Study Cases, RR (95%CI) for highest year design age versus lowest category Holick et al. America Cohort 296, 1.17 (0.78-1.75) for vegetable 2007 30-75 1.41 (0.95-2.10) for fruit Blowers et al. America Case-control 94, 1.3 (0.5-3.2) for vegetable 1995 (PCC) 25-74 1.3 (0.5-3)for fruit Boeing et al. Germany Case-control 115, 0.9 (0.5-1.7) for vegetable 1993 (PCC) 25-75 1.1 (0.6-1.9) for fruit Canada
Case-control (HCC)
215, 25-80
1.33(0.56-3.16) for vegetable 0.455(0.336-0.618) for fruit
Cabaniols et al. 2011 Chen et al. 2002
France
Case-control (HCC) Case-control (PCC)
122, ≥18 236, ≥21
0.85 (0.49-1.47) for fruit
Giles et al. 1994
Australia
Case-control (PCC)
Howe et al. 1989 Hu et al. 1999
Canada
Case-control (PCC) Case-control (HCC)
China
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America
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Burch et al. 1987
74, ≤19 73, 20-74
0.5 (0.3-1.0)for vegetable 1.0 (0.6-1.7)for fruit
Men 1.01(0.62-1.63)for vegetable 1.51(0.95-2.39)for fruit Women 0.53(0. 29-0.95)for vegetable 0.69(0.36-1.31)for fruit 0.858(0.614-1.20)for fruit 0.29 (0.1-0.7) for vegetable 0.15 (0.1-0.4) for fruit
Matched the patient on age (within five years) and race (Black or White). Adjusted for age, gender, tobacco-smoking and alcohol consumption. Matched to each case on the basis of sex, area of residence, marital status, year of birth (within 5 yr), date of diagnosis (within 1yr) for live cases, and date of death (within 1 yr) for dead cases. Adjusted for age and sex. Adjusting for age, age squared, gender, total energy intake, respondent type, education level, family history, and farming experience. Adjusted for alcohol and tobacco.
Adjusted for age at diagnosis. Matched to each case by sex, age within 5-year intervals and area of residence
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Preston-Martin et al. 1991 Hu et al. 1998
Case-control (PCC)
Case-control (HCC)
86, ≥18
America
Case-control (PCC) Case-control (HCC)
202, Na 218, 39.6
(same or adjacent city and country). Matched to case by age (±5years), sex, and ethnic origin. Na.
0.8 (0.6-1.0)for leafy green vegetables 0.7 (0.5-0.9) for yellow-orange vegetables 1.4 (1.1-1.8) for fruit
Adjusted for age, sex, center and the following food groups: leafy green vegetables, yellow–orange vegetables, cured meat, noncured meat, fresh fish, dairy eggs, grains, and citrus fruit.
0.84(0.73-0.96) for vegetable 0.81(0.73-0.89) for vegetable 0.85(0.77-0.95) for fruit 0.70(0.54-0.91) for fruit 0.8(0.4-1.6)for fruit
Adjusted for age, sex, economic conditions.
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0.51(0.29-0.89)for vegetable 0.28(0.16-0.51)for fruit
Na.
Adjusted for income, education, number of years of drinking liquor, and occupational exposure; vegetables adjusted for fruit and fruit also adjusted for total vegetables. Tedeschi-Blok et al. America Case-control 802, 0.57(0.42-0.78)for vegetable Adjusted for age, gender, ethnicity, 2006 (PCC) ≥20 SES, total calories, and supplement use Abbreviations: Na: not available; PCC: population-based case–control study; HCC: hospital-based case–control study; BMI: Body Mass Index
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China
0.91(0.55-1.52) for vegetable 1.76(1.03-3.01) for fruit 0.85(0.28-2.60)for fruit
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Germany, France, Canada, Sweden, Australia, USA China
139, 18-75 21, ≥25 1185, 20-80
America
Case-control (HCC) Cohort
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Xu et al. 1999
Israel
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Kaplan et al. 1997 Mills et al. 1989 Terry et al. 2009
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Table 2 Summary risk estimates of the association between vegetables and fruit and glioma risk. Vegetables Sub-groups Studies, n RR(95%CI) I2 (%) Pheterogeneity Studies, n All 15 0.775(0.688-0.872) 41.3 0.048 17 Case-control 14 0.757(0.675-0.850) 35.9 0.088 15 Sources of control Population-based 8 0.717(0.603-0.851) 24.4 0.235 8 Hospital-based 6 0.796(0.684-0.927) 42.0 0.125 7 Ethnicity Asia 5 0.785(0.675-0.914) 45.7 0.118 5 Caucasian 10 0.782(0.646-0.946) 43.5 0.068 12 Number of cases
