Open Access

No association between fiber intake and prostate cancer risk: a meta-analysis of epidemiological studies

World Journal of Surgical Oncology201513:264

https://doi.org/10.1186/s12957-015-0681-8

Received: 9 April 2015

Accepted: 17 August 2015

Published: 28 August 2015

Abstract

Background

The findings of epidemiologic studies on the association between fiber intake and prostate cancer risk remain conflicting. We aimed to examine this association by conducting a meta-analysis of epidemiological studies.

Methods

Relevant studies were identified by PubMed (1966 to March 2015) and Embase (1974 to March 2015) database search through March 2015. We included epidemiological studies that reported relative risks (RRs) or odds ratios (ORs) with 95 % confidence intervals (CIs) for the association between dietary fiber intake and prostate cancer risk. Random effects models were used to calculate the summary risk estimates.

Results

For the highest compared with the lowest dietary fiber intake, a significantly decreased risk with prostate cancer was observed in case-control studies (OR = 0.82; 95 % CI, 0.68–0.96), but not in cohort studies (RR = 0.94; 95 % CI, 0.77–1.11). The combined risk estimate of all studies was 0.89 (95 % CI, 0.77, 1.01). A significant heterogeneity was observed across studies (p = 0.005). There was no evidence of significant publication bias based on Begg’s funnel plot (p = 0.753) or Egger’s test (p = 0.946).

Conclusions

This meta-analysis suggests the absence of evidence for association between dietary fiber intake and prostate cancer risk.

Keywords

Prostatic neoplasms Dietary fiber Meta-analysis Epidemiology

Background

Prostate cancer is the second most common cancer among men in the world, with 1.1 million new cases diagnosed in 2012 worldwide, accounting for about 7.9 % of all cases of cancer [1]. The high prevalence and incidence of prostate cancer have resulted in a large public health burden. Age and family history are well-established and strong risk factors for prostate cancer [2]. Environmental factors such as diet are believed to play an important role in the prevention of prostate cancer because of the wide international variation in incidence [3].

Although dietary factors have long been suspected to be implicated in the development of prostate cancer, no major modifiable risk factor has been established. During the last few years, increased intake of dietary fibers has been associated with decreased risk of several cancers, such as colorectal, breast, ovarian, and upper aerodigestive tract cancers [47]. However, results from epidemiological studies regarding prostate cancer are sparse and inconsistent. The 2007 World Cancer Research Fund (WCRF) Second Expert Report concluded that the data were too inconsistent to draw a conclusion on the association between dietary fiber intake and prostate cancer risk [8]. Since that report was released, five prospective studies have been published on this association [913]. To quantitatively assess the accumulated evidence for a role of dietary fiber consumption on prostate cancer risk, we carried out a systematic review and meta-analysis of published epidemiological studies.

Methods

Selection of studies

Two authors performed a computerized blinded search of MEDLINE (1966 to March 2015) and Embase (1974 to March 2015) databases for relevant epidemiologic studies of dietary fiber consumption in relation to the risk of prostate cancer published in English. Additional publications identified by hand-searching of references of retrieved articles were also included. For computer searches, we used the following words in any field: “fiber” or “fibre” combined with “prostate carcinoma” or “prostatic cancer” or “prostate cancer” or “prostatic carcinoma”. Studies were included in the meta-analyses if they presented estimates of the odds ratio (OR) or relative risk (RR) and the corresponding confidence interval (CI) from a case-control or cohort study on the association between fiber intake and incidence of prostate cancer. When multiple reports were published on the same study population, we included the study with the largest number of cases.

Figure 1 gives the flowchart for selection of articles. The primary literature search identified 505 records. After screening the titles and abstracts, 486 articles were excluded because they were either duplicates, review articles, or irrelevant to the current study. Nineteen full-text papers were retrieved. In addition, we included ten studies after reviewing reference lists of retrieved articles or preceding reviews. Twelve studies [1425] were excluded mostly because of insufficient information to compute its RR or OR and 95 % CI. Finally, we identified 5 prospective studies [913] and 12 case-control studies [2637] with data that were eligible for inclusion in the meta-analysis.
Fig. 1

Flowchart of study selection

Data extraction and classification

The following pieces of information were extracted from published studies: the name of the first author, the year of publication, the country in which the study was conducted, study design, year of follow-up (cohort studies), year of data collection (case-control studies), sample size, evaluation of exposures, the RR or OR and its 95 % CIs, exposure assessment and range of exposure, and adjusted covariates. Data extraction was conducted independently by two authors, with disagreements resolved by consensus. Considering that prostate cancer is a relatively rare disease, the RR was assumed approximately the same as OR, and the OR was used as the study outcome. If a study provided several ORs, we extracted the ORs reflecting the greatest degree of control for potential confounders. Oishi et al. [26] presented two ORs for benign prostatic hyperplasia (BPH) and hospital controls, respectively. We chose the risk estimate comparing prostate cancer with hospital controls instead of BPH because it may increase the chance of diagnosing an incidental prostate cancer [38].

Quality assessment

The study quality was assessed using the nine-star Newcastle-Ottawa Scale (The Newcastle-Ottawa Scale for assessing the quality of nonrandomized studies in meta-analyses. Ottawa, Canada: Dept of Epidemiology and Community Medicine, University of Ottawa. http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm). NOS is an eight-item instrument that allows for the assessment of the patient selection, study comparability, and exposure (for case-control study) or outcome (for cohort study). The range of possible scores is 0–9. The study with score more than 6 was considered of high quality.

Statistical analysis

We used random effects models to calculate summary ORs and 95 % CIs for the highest vs. the lowest levels of dietary fiber because it used a combination of within-study variance and between-study variance for computing weights. We evaluated the heterogeneity among studies with the Cochrane Q test [39] and I 2 score [40]. We also estimated the 95 % prediction interval, which further accounts for between-study heterogeneity and evaluates the uncertainty for the effect that would be expected in a new study addressing that same association [41]. To explore the sources of heterogeneity across studies, subgroup analyses were conducted according to study design, study quality, geographic region, and method of dietary assessment. Because adjustments for confounding factors were not consistent between the studies, we also conducted the subgroup analysis according to whether the risk estimates had been adjusted for family history of prostate cancer, body mass index (BMI), and total energy intake. In addition, we further performed a sensitivity analysis to explore sources of heterogeneity. Each study was omitted at a time to assess robustness of the results. In addition to those methods, the Galbraith plot was also used to detect the possible sources of heterogeneity, and a re-analysis was conducted with exclusion of the studies possibly causing the heterogeneity. Meta-regression was also applied to measure the subgroup interaction. The p value for interaction between two groups is the comparison of subgroup vs. the other. We used p < 0.10 as the indicator of significant interaction. Publication bias was assessed by Begg’s [42] and Egger’s [43] test. All analyses were performed by using STATA version 11.0 (StataCorp). A p value < 0.05 was considered significant.

Results

The characteristics of these studies and the variables evaluated are listed in Table 1. Six studies were conducted in North America [10, 31, 33, 34, 36, 37], seven in Europe [9, 11, 12, 28, 30, 32, 35], two in Japan [13, 26], one in South Africa [27], and one in Uruguay [29]. Overall, this meta-analysis included more than 8000 cases of prostate cancer. Information on fiber intake was obtained by interview or self-administered questionnaire using food frequency questionnaires (FFQ) except one using 24-h dietary record [12]. All of the included studies adjusted for age, and 14 of them included adjustment for energy intake [6, 913, 2831, 3336], 8 adjusted for family history [6, 10, 12, 2931, 36, 37], and 8 adjusted for BMI [10, 12, 13, 29, 30, 33, 36, 37].
Table 1

Study characteristics of published cohort and case-control studies on dietary fiber intake and prostate cancer

Authors and publication year

Study design

Country

Study period

Cases/subjects

Exposure range

RR (95 % CI)

Variables of adjustment

Study qualitya

Other variables evaluated

Assessment

Oishi et al. 1988 [26]

HCC

Japan

1981–1984

100/200

Ever vs. none

0.78 (0.45–1.37)

Age

5

None

Interview FFQ (31 items)

Walker et al. 1992 [27]

PCC

South Africa

1998–1990

166/332

≥15 vs. <15 g/day

0.6 (0.4–1.0)

Age

6

None

Interview FFQ (unknown items)

Andersson et al. [35]

PCC

Sweden

1989–1994

526/1062

The highest quartile (≥25.9 g/day) vs. the lowest (<15.9 g/day)

0.82 (0.58–1.15)

Age, energy

6

Advanced prostate cancer

Interview and self-administered questionnaire FFQ (68 items)

Vlajinac et al. 1997 [28]

HCC

Serbia

1990–1994

101/303

The highest tertile vs. the lowest

4.02 (1.38–11.73)

Age, residence, energy, protein, fat total, saturated fatty acids, carbohydrate, total sugar, retinol, retinol equivalent, a-tocopherol, folic acid, vitamin B12, sodium, potassium, calcium, phosphorus, magnesium, and iron

6

None

Interview FFQ (150 items)

Deneo-Pellegrini et al. 1999 [29]

HCC

Uruguay

1993–1997

175/408

The highest quartile (≥27.2 g/day) vs. the lowest (<18.2 g/day)

1.5 (0.8–2.6)

Age, residence, urban/rural status, education, family history of prostate cancer, BMI, and total energy intake

6

None

Interview FFQ (64 items)

Ramon et al. 2000 [30]

HCC

Spain

1994–1998

270/704

The highest quartile (≥39.5 g/day) vs. the lowest (<13.1 g/day)

1.0 (0.7–1.5)

Age, residence, family history of prostate cancer, BMI, and energy intake

8

None

Interview FFQ (141 items)

Lu et al. 2001 [31]

PCC

USA

1993–1997

65/197

The highest quartile (≥13.7 g/day) vs. the lowest (<7.9 g/day)

1.81 (0.55–5.96)

Age, race, education, alcohol drinking, pack-years of smoking, family history of prostate cancer, and total dietary caloric intake

8

None

Interview FFQ (98 items)

Pelucchi et al. 2004 [32]

HCC

Italy

1991–2002

1294/1745

The highest quintile (≥21.1 g/day) vs. the lowest (<12.3 g/day)

0.93 (0.71–1.22)

Age, study center, education, family history of prostate cancer, smoking habit, alcohol consumption and total energy intake

7

Insoluble fiber, cellulose, vegetable fiber, fruit fiber, grain fiber.

Interview FFQ (78 items)

McCann et al. 2005 [33]

PCC

USA

1986–1991

433/971

The highest quartile (>38 g/day) vs. the lowest ≤15 g/day

1.21 (0.73–2.01)

Age, education, BMI, cigarette smoking status, and total energy

7

None

Interview FFQ (172 items)

Walker et al. 2005 [34]

HCC

Canada

1997–1999

80/414

The highest tertile vs. the lowest

1.10 (0.58–2.07)

Age, alcohol, energy, fat, carbohydrate, calcium, protein, and cholesterol intake

6

None

Interview FFQ (66 items)

Lewis et al. 2009 [36]

HCC

USA

1998–2004

478/860

The highest tertile (≥20.7 g/day) vs. the lowest (<13.7 g/day)

0.56 (0.35–0.89)

Age, education, BMI, smoking history, family history of prostate cancer in first-degree relatives, and total caloric intake

6

None

Self-administered questionnaire FFQ (100 items)

Suzuki et al. 2009 [9]

Cohort

Europe

1993–2007

2747/142,590

The highest quintile (≥30.4 g/day) vs. the lowest (<17.8 g/day)

1.02 (0.87–1.19)

Age, energy intake, height, weight, smoking, education, and marital status

8

Vegetables fiber, fruit fiber, cereal fiber

Local, advanced, low-grade, and high-grade prostate cancer

 

Nimptsch et al. 2011 [10]

Cohort

USA

1986–2002

5112/49,934

The highest quintile (≥26 g/day) vs. the lowest (≤15.4 g/day)

1.01 (0.92–1.12)

Age, BMI, height, history of diabetes, family history of prostate cancer, race, smoking, vigorous physical activity, energy intake, alcohol intake, calcium intake, alphalinolenic acid, and tomato sauce

7

Local, advanced, low-grade and high-grade prostate cancer

Self-administered questionnaire FFQ (131 items)

Drake et al. 2012 [11]

Cohort

Sweden

1991–2009

817/8128

The highest quintile (≥23.7 g/day) vs. (17.6 g/day) the lowest

1.15 (0.89–1.49)

Age, year of study entry, season of data collection, energy intake, height, waist, physical activity, smoking, educational level, birth in Sweden, alcohol, calcium, selenium

9

Low-risk, high-risk, and symptomatic prostate cancer

Interview FFQ (168 items)

Deschasaux et al. 2014 [12]

Cohort

France

1994–2007

139/3313

The highest quartile vs. the lowest

0.47 (0.27–0.81)

Age, energy intake without alcohol, intervention group, number of 24-h dietary records, smoking status, educational level, physical activity, height, BMI, alcohol intake, family history of prostate cancer, prostate-specific antigen, calcium intake, processed meat intake, tomato product intake, vitamin E intake, and blood selenium

7

Soluble fiber, insoluble fiber, cereal fiber, vegetable fiber, fruit fiber, legume fiber

24-h dietary record

Vidal et al. 2015 [37]

HCC

USA

2007–2012

156/430

The highest tertile vs. the lowest

0.79 (0.31–1.97)

Age, race, family history, caloric intake, carbohydrate intake, BMI, diabetes, physical activity, alcohol, and smoking status

6

Low-grade and high-grade prostate cancer

Interview FFQ (61 items)

Sawada et al. 2015 [13]

Cohort

Japan

1995–2009

825/43,435

The highest quartile vs. the lowest

1.00 (0.77, 1.29)

Age, public health center area, smoking status, drinking frequency, marital status, BMI, and intakes of green tea, genistein, SFAs, and carbohydrate

7

Soluble fiber, insoluble fiber, local and advanced prostate cancer

Self-administered questionnaire FFQ (138 items)

PCC population-based case-control studies, HCC hospital-based case-control studies, FFQ food-frequency questionnaire, BMI body mass index

aEvaluated by nine-star Newcastle-Ottawa Scale

As shown in Fig. 2, a statistically significant protective effect of dietary fiber intake on prostate was observed in case-control studies (OR = 0.82; 95 % CI, 0.68–0.96), while no such effect was observed in cohort studies (RR = 0.94; 95 % CI, 0.77–1.11). There was no evidence of heterogeneity among case-control (p = 0.277, I 2 = 17 %), but significant heterogeneity among cohort studies (p = 0.004, I 2 = 74.3 %). When all these studies were analyzed together, no association was observed between fiber intake and risk of prostate cancer (summary OR = 0.89; 95 % CI, 0.77–1.01), with significant heterogeneity among studies (p = 0.005, I 2 = 53.6 %). The wide 95 % prediction interval also included the null value and reflected the significant heterogeneity (0.59, 1.52). In a sensitivity analysis excluding one study at a time, the summary OR for prostate cancer ranged from 0.87 (0.75 to 0.99) when the study by Drake et al. [11] was excluded to 0.93 (0.83 to 1.03) when the study by Deschasaux et al. [12] was excluded. Through the Galbraith plot, four studies were identified as the major sources of heterogeneity (Fig. 3). After excluding these four studies, there was no study heterogeneity (p = 0.915, I 2 = 0), and the overall association turned out to be null (OR 1.00, 95 % CI 0.93–1.07). There was no evidence of significant publication bias either with the Egger’s test (p = 0.946) or Begg’s funnel plot (p = 0.753) (Fig. 4).
Fig. 2

Pooled results for 12 case-control and 5 cohort studies of dietary fiber intake and prostate cancer risk

Fig. 3

Galbraith plot analysis indicated that four studies were the potential source of heterogeneity

Fig. 4

Publication bias which was estimated by Begg’s test (a) and Egger’s test (b)

Next, we performed subgroup analyses by study quality, geographical region, and the method of exposure assessment (Table 2). When we stratified by study quality, more significant association was observed in studies of low-quality (OR 0.73, 95 % CI 0.56–0.90) compared with studies of high-quality (OR 0.96, 95 % CI 0.83–1.08). Considering the geographic area, the pooled OR was 0.90 (95 % CI, 0.65–1.16) in European studies, 0.90 (95 % CI 0.64–1.06) in North American studies, and 0.95 (95 % CI, 0.72–1.17) in Japanese studies. When separately analyzed by exposure assessment, the ORs were 0.93 (95 % CI 0.76–1.09) for studies that used an interview and 0.94 (0.76–1.10) for with a self-administered questionnaire, respectively.
Table 2

Subgroup analyses of odds ratios for the association between fiber intake and risk of prostate cancer

Outcome of interest

No. of studies

OR (95 % CI)

p heterogenity

I 2 (%)

p for interaction

Total dietary fiber

17

0.89 (0.77, 1.01)

0.005

53.6

 

Study design

 Cohort

5

0.94 (0.77, 1.11)

0.004

74.3

0.202

 Case-control

12

0.82 (0.68, 0.96)

0.277

17.0

 

Study quality

 Low

8

0.73 (0.56, 0.90)

0.335

12.2

0.033

 High

9

0.96 (0.83, 1.08)

0.04

51.7

 

Geographical region

 Europe

7

0.90 (0.71, 1.09)

0.01

63.5

0.937

 North America

6

0.90 (0.64, 1.16)

0.059

53.1

 

 Japan

2

0.95 (0.72, 1.17)

0.41

0

 

Assessment

 Interview

11

0.94 (0.79, 1.10)

0.313

13.8

0.931

 Questionnaire

4

0.93 (0.76, 1.09)

0.02

69.7

 

Family history

     

 Yes

8

0.84 (0.62, 1.05)

0.002

69.4

0.44

 No

9

0.94 (0.81, 1.08)

0.187

29.0

 

BMI

 Yes

8

0.87 (0.66, 1.08)

0.001

70.3

0.695

 No

9

0.92 (0.79, 1.06)

0.21

26.4

 

Energy intake

 Yes

14

0.91 (0.78, 1.04)

0.007

55.1

0.507

 No

3

0.81 (0.54, 1.07)

0.14

49.2

 

Multiple confoundersa

 Yes

6

0.82 (0.54, 1.09)

0.306

14.4

0.387

 No

11

0.95 (0.84, 1.05)

<0.001

77.7

 

Tumor stage

 Local

3

0.98 (0.89, 1.08)

0.24

30.5

0.562

 Advanced

4

0.93 (0.79, 1.07)

0.24

29.3

 

Source of intake

 Cereal fiber

3

1.05 (0.94, 1.16)

0.52

0

0.02

 Fruit fiber

3

0.92 (0.81, 1.03)

0.55

0

 

 Vegetable fiber

3

0.87 (0.53, 1.21)

0.001

84.8

 

 Legume fiber

1

0.55 (0.32, 0.95)

NA

NA

 

Solubility

 Soluble fiber

2

0.87 (0.52, 1.22)

0.13

57.2

0.777

 Insoluble fiber

3

0.80 (0.46, 1.13)

0.005

81.0

 

aMultiple confounders refer to effect estimates adjusted for at least family history, BMI, and energy intake

We also investigated the impact of some confounding factors on the estimates of ORs (Table 2). Family history is the established risk factor for prostate cancer; BMI and energy are potential confounders of the relationship between fiber intake and the risk of prostate cancer. We found that the non-significant relationships between prostate cancer and fiber intake were consistent in all subgroups, whether controlled for family history, BMI, and energy intake or not. Moreover, six studies in our analysis adjusted for these three confounders simultaneously. Therefore, we examined whether more thoroughly adjusting for potential confounders affected the pooled OR. The effect estimates for studies that adjusted for these three confounders or not were ORs of 0.82 (95 % CI 0.54–1.09) and 0.95 (0.84–1.05), respectively.

In addition, after stratification according to food source and solubility, none of the subtypes could lower the incidence of prostate cancer significantly, except for legume fiber, though it is based on only one cohort study [12]. We also pooled the ORs by clinical characteristics of prostate cancer. The summary ORs did not indicate that high fiber intake had a significant protective association with low- or high-stage disease (Table 2).

Discussion

This is the first meta-analysis for clarification of the association between fiber intake and risk of prostate cancer. Twelve case-control studies and 5 prospective studies involving more than 8000 cases were included in our study. The results suggested no significant association between dietary fiber intake and prostate cancer incidence.

Although the pooled analysis from the case-control studies suggested a significant reduction in risk, the results from the cohort studies were non-significant, suggesting that our conclusion depend mainly on the cohort studies. It is generally thought that cohort studies provide stronger evidence regarding an association than case-control studies because they are less prone to differential recall of dietary habits or selection bias. Therefore, the evidence from case-control studies should be viewed with caution, particularly considering that the combined risk estimates from all studies suggested no association. In the subgroup analysis separated by study quality, we observed that fiber intake was associated with decreased risk of prostate cancer in low-quality studies, but no significant association in high-quality studies. This may account partly for the discrepancy between cohort and case-control studies, since all 5 cohort studies were high-quality studies published after 2009, while 8 of 12 case-control studies were low-quality ones. Moreover, the non-significant relationships were similar independent of study design, geographical region, method of dietary assessment, and adjustment for several essential confounders or not, further strengthening the stability of our findings.

We observed a significant heterogeneity among studies, which was partly explained by the fact that levels in the lowest and highest categories and the range of intake were various and quite heterogeneous across studies. In addition, accurate assessment of fiber intake is a challenge. A previous meta-analysis suggested that the different definition of dietary fiber between included studies may contribute to heterogeneity in the results [44]. However, only one study used the Englyst method for the definition of fiber [32]. Also, the extent to which confounding factors were controlled differed among studies, which may bring heterogeneity and resulted in inaccurate pooled estimates. For the two established risk factors, all studies included in this meta-analysis provided risk estimates adjusted for age, while 8 of 17 studies controlled for a family history of prostate cancer in their analyses [6, 10, 12, 2931, 36, 37]. However, it is unlikely that a family history of prostate cancer is a strong confounder because it is not strongly related to fiber intake. In the subgroup analysis, results that did and did not adjust for family history did not differ in the meta-regression. The summary OR represents the combination of different types of fiber, such as soluble and insoluble fiber, and fiber from different food sources, which may have different effects on prostate cancer, though the pooled estimates of subtypes suggested no association, except for the legume fiber. Intakes of different types of fiber vary across countries, thus providing another explanation for the heterogeneity across studies. We also performed Galbraith plot analysis and identified four studies reporting extreme ORs as the potential sources of heterogeneity [12, 27, 28, 36]. No heterogeneity existed after excluding these four studies.

It has been suggested that dietary fiber may reduce prostate cancer risk possibly by increasing circulating levels of sex hormone-binding globulin [45] and improving insulin sensitivity [46]. Fiber may reduce insulin resistance through a decrease in carbohydrate absorption rate [47]. Insulin resistance and hyperinsulinemia, by decreasing insulin-like growth factor (IGF) binding proteins and increasing IGF concentrations, may stimulate prostate carcinogenesis [46]. Foods rich in dietary fiber also contain dietary lignans, which are postulated to be associated with a decreased risk of prostate cancer [48]. New evidence showed that inositol hexaphosphate (IP6), a major component of high-fiber diet, could control the progression of prostate cancer in mice due to its anti-angiogenic effects [49]. The inconsistency between experimental and epidemiology studies may be partly explained by the low bioavailability of these active ingredients in human plasma, and the drug accumulation could not achieve high levels in prostate. It was noted that most of the included studies were conducted in western countries, and the western diet is typically described as being high fat and low fiber compared with Asian diet [50], probably leading to relatively low blood levels of active ingredients in the subjects, thus the non-significant findings in the meta-analysis.

Our study has several important limitations. First, fiber intake was generally not the main focus of the included studies. Although analysis of total fiber on prostate cancer incidence was based on many studies, results for fiber subtypes and secondary outcomes of local and advanced stage disease were limited. As such, the pooled estimates were more susceptible to the influence from individual studies and should be interpreted with caution. Second, we were unable to conduct dose-responses because some studies did not provide the value of fiber intake in each category, and the number of cases and noncases by stratum were often missing in studies. Third, although most studies included in our analysis had performed adjustment for a wide range of confounders, we could not rule out the possibility that other unidentified or unmeasured factors could affect the association. Fourth, we did not sought to include unpublished data or papers in other languages, yet little evidence of publication bias was observed.

Conclusions

In conclusion, this meta-analysis of epidemiological studies provides evidence that diets with high intake of plant-based foods rich in fiber may have no impact in the prevention of prostate cancer. Additional studies, especially large prospective cohort studies, are warranted to confirm these findings and address the effects of different fiber subtypes and secondary outcomes.

Abbreviations

BMI: 

body mass index

CI: 

confidence interval

FFQ: 

Food frequency questionnaire

IGF: 

insulin-like growth factor

OR: 

odds ratio

RR: 

relative risk

Declarations

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Urology, Jiaxing Affilated Hospital of Zhejiang Chinese Medical University
(2)
Department of Pharmacy, Jiaxing Affilated Hospital of Zhejiang Chinese Medical University

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108.View ArticlePubMedGoogle Scholar
  2. Chan JM, Jou RM, Carroll PR. The relative impact and future burden of prostate cancer in the United States. J Urol. 2004;172:S13–16. discussion S17.View ArticlePubMedGoogle Scholar
  3. Shimizu H, Ross RK, Bernstein L, Yatani R, Henderson BE, Mack TM. Cancers of the prostate and breast among Japanese and white immigrants in Los Angeles County. Br J Cancer. 1991;63:963–6.View ArticlePubMed CentralPubMedGoogle Scholar
  4. Bingham SA, Day NE, Luben R, Ferrari P, Slimani N, Norat T, et al. Dietary fibre in food and protection against colorectal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC): an observational study. Lancet. 2003;361:1496–501.View ArticlePubMedGoogle Scholar
  5. Baghurst PA, Rohan TE. High-fiber diets and reduced risk of breast cancer. Int J Cancer. 1994;56:173–6.View ArticlePubMedGoogle Scholar
  6. Pelucchi C, La Vecchia C, Chatenoud L, Negri E, Conti E, Montella M, et al. Dietary fibres and ovarian cancer risk. Eur J Cancer. 2001;37:2235–9.View ArticlePubMedGoogle Scholar
  7. Soler M, Bosetti C, Franceschi S, Negri E, Zambon P, Talamini R, et al. Fiber intake and the risk of oral, pharyngeal and esophageal cancer. Int J Cancer. 2001;91:283–7.View ArticlePubMedGoogle Scholar
  8. Wiseman M. The second World Cancer Research Fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008;67:253–6.View ArticlePubMedGoogle Scholar
  9. Suzuki R, Allen NE, Key TJ, Appleby PN, Tjonneland A, Johnsen NF, et al. A prospective analysis of the association between dietary fiber intake and prostate cancer risk in EPIC. Int J Cancer. 2009;124:245–9.View ArticlePubMedGoogle Scholar
  10. Nimptsch K, Kenfield S, Jensen MK, Stampfer MJ, Franz M, Sampson L, et al. Dietary glycemic index, glycemic load, insulin index, fiber and whole-grain intake in relation to risk of prostate cancer. Cancer Causes Control. 2011;22:51–61.View ArticlePubMed CentralPubMedGoogle Scholar
  11. Drake I, Sonestedt E, Gullberg B, Ahlgren G, Bjartell A, Wallstrom P, et al. Dietary intakes of carbohydrates in relation to prostate cancer risk: a prospective study in the Malmo Diet and Cancer cohort. Am J Clin Nutr. 2012;96:1409–18.View ArticlePubMedGoogle Scholar
  12. Deschasaux M, Pouchieu C, His M, Hercberg S, Latino-Martel P, Touvier M. Dietary total and insoluble fiber intakes are inversely associated with prostate cancer risk. J Nutr. 2014;144:504–10.View ArticlePubMedGoogle Scholar
  13. Sawada N, Iwasaki M, Yamaji T, Shimazu T, Sasazuki S, Inoue M, et al. Fiber intake and risk of subsequent prostate cancer in Japanese men. Am J Clin Nutr. 2015;101:118–25.View ArticlePubMedGoogle Scholar
  14. Fincham SM, Hill GB, Hanson J, Wijayasinghe C. Epidemiology of prostatic cancer: a case-control study. Prostate. 1990;17:189–206.View ArticlePubMedGoogle Scholar
  15. Augustin LS, Galeone C, Dal Maso L, Pelucchi C, Ramazzotti V, Jenkins DJ, et al. Glycemic index, glycemic load and risk of prostate cancer. Int J Cancer. 2004;112:446–50.View ArticlePubMedGoogle Scholar
  16. Bradbury KE, Appleby PN, Key TJ. Fruit, vegetable, and fiber intake in relation to cancer risk: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC). Am J Clin Nutr. 2014;100 Suppl 1:394S–8S.View ArticlePubMedGoogle Scholar
  17. Westerlund A, Steineck G, Balter K, Stattin P, Gronberg H, Hedelin M. Dietary supplement use patterns in men with prostate cancer: the Cancer Prostate Sweden study. Ann Oncol. 2011;22:967–72.View ArticlePubMedGoogle Scholar
  18. Schuurman AG, Goldbohm RA, Dorant E, van den Brandt PA. Vegetable and fruit consumption and prostate cancer risk: a cohort study in The Netherlands. Cancer Epidemiol Biomarkers Prev. 1998;7:673–80.PubMedGoogle Scholar
  19. Key TJ, Silcocks PB, Davey GK, Appleby PN, Bishop DT. A case-control study of diet and prostate cancer. Br J Cancer. 1997;76:678–87.View ArticlePubMed CentralPubMedGoogle Scholar
  20. Lee MM, Wang RT, Hsing AW, Gu FL, Wang T, Spitz M. Case-control study of diet and prostate cancer in China. Cancer Causes Control. 1998;9:545–52.View ArticlePubMedGoogle Scholar
  21. Hebert JR, Hurley TG, Olendzki BC, Teas J, Ma Y, Hampl JS. Nutritional and socioeconomic factors in relation to prostate cancer mortality: a cross-national study. J Natl Cancer Inst. 1998;90:1637–47.View ArticlePubMedGoogle Scholar
  22. Rosato V, Edefonti V, Bravi F, Bosetti C, Bertuccio P, Talamini R, et al. Nutrient-based dietary patterns and prostate cancer risk: a case-control study from Italy. Cancer Causes Control. 2014;25:525–32.View ArticlePubMedGoogle Scholar
  23. Egeberg R, Olsen A, Christensen J, Johnsen NF, Loft S, Overvad K, et al. Intake of whole-grain products and risk of prostate cancer among men in the Danish Diet, Cancer and Health cohort study. Cancer Causes Control. 2011;22:1133–9.View ArticlePubMedGoogle Scholar
  24. Chhim AS, Fassier P, Latino-Martel P, Druesne-Pecollo N, Zelek L, Duverger L, et al. Prospective association between alcohol intake and hormone-dependent cancer risk: modulation by dietary fiber intake. Am J Clin Nutr. 2015;102:182–9.View ArticlePubMedGoogle Scholar
  25. Rohan TE, Howe GR, Burch JD, Jain M. Dietary factors and risk of prostate cancer: a case-control study in Ontario, Canada. Cancer Causes Control. 1995;6:145–54.View ArticlePubMedGoogle Scholar
  26. Oishi K, Okada K, Yoshida O, Yamabe H, Ohno Y, Hayes RB, et al. A case-control study of prostatic cancer with reference to dietary habits. Prostate. 1988;12:179–90.View ArticlePubMedGoogle Scholar
  27. Walker AR, Walker BF, Tsotetsi NG, Sebitso C, Siwedi D, Walker AJ. Case-control study of prostate cancer in black patients in Soweto, South Africa. Br J Cancer. 1992;65:438–41.View ArticlePubMed CentralPubMedGoogle Scholar
  28. Vlajinac HD, Marinkovic JM, Ilic MD, Kocev NI. Diet and prostate cancer: a case-control study. Eur J Cancer. 1997;33:101–7.View ArticlePubMedGoogle Scholar
  29. Deneo-Pellegrini H, De Stefani E, Ronco A, Mendilaharsu M. Foods, nutrients and prostate cancer: a case-control study in Uruguay. Br J Cancer. 1999;80:591–7.View ArticlePubMed CentralPubMedGoogle Scholar
  30. Ramon JM, Bou R, Romea S, Alkiza ME, Jacas M, Ribes J, et al. Dietary fat intake and prostate cancer risk: a case-control study in Spain. Cancer Causes Control. 2000;11:679–85.View ArticlePubMedGoogle Scholar
  31. Lu QY, Hung JC, Heber D, Go VL, Reuter VE, Cordon-Cardo C, et al. Inverse associations between plasma lycopene and other carotenoids and prostate cancer. Cancer Epidemiol Biomarkers Prev. 2001;10:749–56.PubMedGoogle Scholar
  32. Pelucchi C, Talamini R, Galeone C, Negri E, Franceschi S, Dal Maso L, et al. Fibre intake and prostate cancer risk. Int J Cancer. 2004;109:278–80.View ArticlePubMedGoogle Scholar
  33. McCann SE, Ambrosone CB, Moysich KB, Brasure J, Marshall JR, Freudenheim JL, et al. Intakes of selected nutrients, foods, and phytochemicals and prostate cancer risk in western New York. Nutr Cancer. 2005;53:33–41.View ArticlePubMedGoogle Scholar
  34. Walker M, Aronson KJ, King W, Wilson JW, Fan W, Heaton JP, et al. Dietary patterns and risk of prostate cancer in Ontario, Canada. Int J Cancer. 2005;116:592–8.View ArticlePubMedGoogle Scholar
  35. Andersson SO, Wolk A, Bergstrom R, Giovannucci E, Lindgren C, Baron J, et al. Energy, nutrient intake and prostate cancer risk: a population-based case-control study in Sweden. Int J Cancer. 1996;68:716–22.View ArticlePubMedGoogle Scholar
  36. Lewis JE, Soler-Vila H, Clark PE, Kresty LA, Allen GO, Hu JJ. Intake of plant foods and associated nutrients in prostate cancer risk. Nutr Cancer. 2009;61:216–24.View ArticlePubMedGoogle Scholar
  37. Vidal AC, Williams CD, Allott EH, Howard LE, Grant DJ, McPhail M, et al. Carbohydrate intake, glycemic index and prostate cancer risk. Prostate. 2015;75:430–9.View ArticlePubMedGoogle Scholar
  38. Chang RT, Kirby R, Challacombe BJ. Is there a link between BPH and prostate cancer? Practitioner. 2012;256:13–6. 12.PubMedGoogle Scholar
  39. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88.View ArticlePubMedGoogle Scholar
  40. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60.View ArticlePubMed CentralPubMedGoogle Scholar
  41. Higgins JP, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 2009;172:137–59.View ArticlePubMed CentralPubMedGoogle Scholar
  42. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101.View ArticlePubMedGoogle Scholar
  43. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.View ArticlePubMed CentralPubMedGoogle Scholar
  44. Aune D, Chan DS, Lau R, Vieira R, Greenwood DC, Kampman E, et al. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. BMJ. 2011;343:d6617.View ArticlePubMed CentralPubMedGoogle Scholar
  45. Roddam AW, Allen NE, Appleby P, Key TJ. Endogenous sex hormones and prostate cancer: a collaborative analysis of 18 prospective studies. J Natl Cancer Inst. 2008;100:170–83.View ArticlePubMedGoogle Scholar
  46. Hsing AW, Gao YT, Chua Jr S, Deng J, Stanczyk FZ. Insulin resistance and prostate cancer risk. J Natl Cancer Inst. 2003;95:67–71.View ArticlePubMedGoogle Scholar
  47. Higgins JA. Whole grains, legumes, and the subsequent meal effect: implications for blood glucose control and the role of fermentation. J Nutr Metab. 2012;2012:829238.View ArticlePubMed CentralPubMedGoogle Scholar
  48. Adlercreutz H. Phyto-oestrogens and cancer. Lancet Oncol. 2002;3:364–73.View ArticlePubMedGoogle Scholar
  49. Gu M, Roy S, Raina K, Agarwal C, Agarwal R. Inositol hexaphosphate suppresses growth and induces apoptosis in prostate carcinoma cells in culture and nude mouse xenograft: PI3K-Akt pathway as potential target. Cancer Res. 2009;69:9465–72.View ArticlePubMed CentralPubMedGoogle Scholar
  50. Zhou BF, Stamler J, Dennis B, Moag-Stahlberg A, Okuda N, Robertson C, et al. Nutrient intakes of middle-aged men and women in China, Japan, United Kingdom, and United States in the late 1990s: the INTERMAP study. J Hum Hypertens. 2003;17:623–30.View ArticlePubMedGoogle Scholar

Copyright

© Sheng et al. 2015

Advertisement