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International pooled study on diet and bladder cancer: the bladder cancer, epidemiology and nutritional determinants (BLEND) study: design and baseline characteristics

  • Maria E. Goossens1Email author,
  • Fatima Isa2,
  • Maree Brinkman3,
  • David Mak2,
  • Raoul Reulen2,
  • Anke Wesselius4,
  • Simone Benhamou5,
  • Cristina Bosetti6,
  • Bas Bueno-de-Mesquita7, 8, 9, 10,
  • Angela Carta11,
  • Md Farouk Allam12,
  • Klaus Golka13,
  • Eric J. Grant14,
  • Xuejuan Jiang15,
  • Kenneth C. Johnson16,
  • Margaret R. Karagas17,
  • Eliane Kellen18,
  • Carlo La Vecchia19,
  • Chih-Ming Lu20,
  • James Marshall21,
  • Kirsten Moysich21,
  • Hermann Pohlabeln22,
  • Stefano Porru11,
  • Gunnar Steineck23,
  • Marianne C. Stern15,
  • Li Tang21,
  • Jack A. Taylor24,
  • Piet van den Brandt25,
  • Paul J. Villeneuve26,
  • Kenji Wakai27,
  • Elisabete Weiderpass28, 29, 30, 31,
  • Emily White32,
  • Alicja Wolk33,
  • Zuo-Feng Zhang34,
  • Frank Buntinx1, 35 and
  • Maurice P. Zeegers4, 35, 36
Archives of Public HealthThe official journal of the Belgian Public Health Association201674:30

https://doi.org/10.1186/s13690-016-0140-1

Received: 10 March 2016

Accepted: 18 May 2016

Published: 6 July 2016

Abstract

Background

In 2012, more than 400,000 urinary bladder cancer cases occurred worldwide, making it the 7th most common type of cancer. Although many previous studies focused on the relationship between diet and bladder cancer, the evidence related to specific food items or nutrients that could be involved in the development of bladder cancer remains inconclusive. Dietary components can either be, or be activated into, potential carcinogens through metabolism, or act to prevent carcinogen damage.

Methods/design

The BLadder cancer, Epidemiology and Nutritional Determinants (BLEND) study was set up with the purpose of collecting individual patient data from observational studies on diet and bladder cancer. In total, data from 11,261 bladder cancer cases and 675,532 non-cases from 18 case–control and 6 cohort studies from all over the world were included with the aim to investigate the association between individual food items, nutrients and dietary patterns and risk of developing bladder cancer.

Discussion

The substantial number of cases included in this study will enable us to provide evidence with large statistical power, for dietary recommendations on the prevention of bladder cancer.

Keywords

Bladder cancerDietRiskPooled analysis

Background

In 2012, more than 400,000 urinary bladder cancer (UBC) cases occurred worldwide, making it the 7th most common type of cancer [1]. Due to lifetime ongoing cystoscopies and recurrent treatment episodes, UBC is the most expensive malignancy in terms of healthcare expenditure in the USA and in most Western countries [2, 3]. The effect of diet in the prevention of UBC could be more pronounced compared to other types of cancer as dietary components are often excreted through the urine. Dietary components can either be, or be activated into, potential carcinogens through metabolism, or act to prevent carcinogen damage [4].

Although many previous studies focused on the relationship between diet and UBC, the evidence related to specific food items or nutrients that could be involved in the development of UBC remains inconclusive. The World Cancer Research Fund (WCRF) concluded in their most recent WCRF/AICR expert report [5] that there is some evidence for an decreased risk of bladder cancer with greater consumption of vegetables, fruit and tea and strong evidence that drinking water containing arsenic increases the risk of bladder cancer. A potential reason for the absence of evidence between specific foods and nutrients and the risk of UBC is that associations between cancer risk and dietary intake are usually weak and most previous studies may have had insufficient sample size and thus missed adequate statistical power for detailed analyses on individual food items, for subgroup analyses and for food-food interactions. Pooling of individual data of existing epidemiological studies on diet and UBC might therefore be an effective way to increase the current knowledge on the influences of foods, nutrients and dietary patterns on UBC risk. The influence of occupational risk and pollutants in the water, such as arsenic, are not part of this investigation. Occupational risk factors were identified as risk factors for bladder cancer [6]. However, as the frequency of having a high-risk occupation is very low (<3 %) this could not importantly confound the results. For this reason, the BLEND study as well as most previous bladder cancer epidemiological studies have not corrected for occupation in their analyses.

Within the BLadder cancer, Epidemiology and Nutritional Determinants (BLEND) study, we aim to investigate comprehensively the association between individual food items, nutrients, and dietary patterns and risk of developing UBC. The results of this study will likely aid in developing and reviewing current dietary recommendations for the prevention of UBC. In this paper we report on the methodology and baseline characteristics of the BLEND study.

Methods/design

Included epidemiological studies

Possible eligible epidemiological studies reporting on diet and UBC have been identified by a computerized search of Medline (National Library of Medicine, Bethesda, Maryland) (1966-Sept 2009), and Embase (Elsevier B. V., Amderstam, the Netherlands (1974-Sept 2009) using the medical subject headings (MeSH; National Library of Medicine, Bethesda, Maryland) “urinary bladder neoplasms” and “risk” and the free-text word “risk”. The search was restricted to the MeSH term “humans”. All articles from peer-reviewed journals, reporting on the association between diet and risk of UBC were selected. Within these articles, we identified the eligible studies that used a case–control or a cohort design, had data on diet and a minimum number of cases of 40 patients. The principal investigators of these eligible studies were contacted and invited to participate in our collaborative project. There was no restriction about the amount of available diet items, however, data on confounders, especially, smoking, had to be available.

Data harmonization

To harmonize our data, a common codebook was created based on the Eurocode 2 Core classification version 99/2 [7]. The Eurocode 2 Food Coding System was originally developed to serve as a standard instrument for nutritional surveys in Europe and to serve the need for food intake comparisons within the European FLAIR Eurofoods-Enfant Project [8]. The Eurocode 2 classification System unambiguously defines which types of food are covered or not within each food category so that the potential for misclassification is limited. The System provides coding for food items consumed all over the world. Coding has been done centrally by the researchers of the Blend team. One part of the team did the coding, while the other part of the team checked for possible errors. Translation of the questionnaires in English was provided by the principle investigator for studies in other languages. Apart from the variables on diet, we collected non-dietary data such as, study design, age, gender, ethnic group, TNM Classification of Malignant Tumors (TNM), smoking status, smoking frequency and duration, and family history. Each participant was assigned a random and unique identification number. Analyses were restricted to adults, i.e. participants younger than 18 years were excluded. Categorical data have been checked by producing frequency tables to identify inaccurate coding while continuous data have been checked performing descriptive statistics. Possible coding errors and missing data within the provided data of each study were discussed with the principal investigator and updated accordingly. Outliers, defined as values outside the general distribution of the data, were identified after visual inspection of the resultant scatterplots and omitted [9].

Baseline characteristics

In total 67 potentially eligible studies from 156 retrieved articles were identified (Fig. 1). Thirty-eight investigators agreed to participate and 24 [1034] provided data (Table 1). Reasons for non-participation after initially agreement were: no data on diet or the minimum set of confounders available, the workload that was already too high and the wish to publish the results on nutrition first before participating in a pooled study. With some investigators, we lost communication after initial contact. The first datasets and codebooks were collected in March 2009 while the last dataset was included in March 2016. Another two new studies, one case–control and one cohort study are available for inclusion.
Fig. 1

Flow diagram of the Bladder cancer Epidemiology and Nutritional Determinants study (BLEND)

Table 1

Characteristics of the studies included in the pooled analysis of the Bladder cancer Epidemiology and Nutritional Determinants study (BLEND)

Study

Country

Recruitment

period

Study design

Cases

N

Controls

N

    

men

women

total

men

women

total

Case–control studies

 Los-Angeles bladder cancer Case–control study [10]

USA

1987–1999

Population based case–control

1,307

353

1,660

1,237

349

1,586

 Roswell Park Cancer Institute [11]

USA

1982–1998

Hospital-based case–control

164

53

217

501

163

664

 Belgian Case–control study on bladder cancer [12]

Belgium

1999–2004

Population based case–control

172

28

200

228

156

384

 Aichi Prefecture Case–control study [13]

Japan

1996–1999

Hospital-based case–control

245

58

303

244

59

303

 Kaohsiung [14]

Taiwan

1996–1997

Hospital-based case–control

31

9

40

124

36

160

 Hessen Case–control study on bladder cancer [15]

Germany

1989–1992

Hospital-based case–control

239

61

300

239

61

300

 Stockholm Case–control study [16]

Sweden

1985–1987

Population based case–control

204

67

271

281

268

549

 Roswell Park Memorial Institute Case–control study on bladder cancer [17]

USA

1957–1965

Hospital-based case–control

415

138

553

3,253

4,636

7,889

 Reina Sofia University Hospital [18]

Spain

1997

Hospital-based case–control

74

11

85

89

41

130

 New Hampshire bladder cancer study [19]

USA

1994–2001

Population based case–control

286

104

390

185

138

323

 Italian Case–control study on bladder cancer [20]

Italy

1985–1993

Hospital-based case–control

617

110

727

766

298

1,064

 Brescia bladder cancer study [21]

Italy

1997–2000

Hospital-based case–control

200

0

200

214

0

214

 Dortmund Hörde study [22]

Germany

2009–2010

Hospital based case–control

145

48

193

177

56

233

 National Enhanced Cancer Surveillance System (NESCC) [23]

Canada

1994–1997

Population based case–control

600

311

911

2,451

2,423

4,874

 French INSERM study [24]

France

1984–1987

Hospital-based case–control

166

33

199

275

47

322

 South and East China Case–control study on bladder and prostate cancer [25]

China

2005–2008

Hospital-based case–control

390

93

483

364

100

464

 Molecular Epidemiology of Bladder Cancer and Prostate Cancer [26]

USA

1993–1997

Hospital-based case–control

149

45

194

243

58

301

 North Carolina case control study [27]

USA

1987–1991

Hospital-based case–control

188

56

244

174

41

215

Cohort studies

 Swedish Mammography Cohort (SMC) & the Cohort of Swedish Men [28]

Sweden

1987–1990

Population based cohort

538

119

657

2,188

484

2,672

 Netherlands Cohort Study on diet and cancer [29]

The Netherlands

1986–2003

Population based cohort

779

161

940

2,273

2,419

4,692

 Women's Lifestyle and Health Study [30]

Norway, Sweden

1991–2006

Population based cohort

0

49

49

0

48,942

48,942

 RERF atomic bomb survivors Study [31]

Japan

1950–2000

Population based cohort

216

85

301

19,362

28,249

47,611

 VITamins and Lifestyle Study (VITAL) [32]

USA

2000–2008

Population based cohort

338

106

444

36,454

39,983

76,437

 European Prospective Investigation into Cancer and Nutrition (EPIC) [33, 34]

Europe

1993–2006

Population based cohort

1,227

525

1,752

141,872

333,279

475,151

TOTAL

8,657

2,604

11,313

213,227

462,305

675,480

More than 2/3 of the case–control studies [11, 1315, 17, 18, 2022, 2427] had a hospital-based case–control design. Ten studies [12, 16, 1921, 2428] were also part of the International Bladder Cancer Consortium that was formed in 2005 as an open scientific forum for genetic-epidemiologic researchers in the field of UBC. Most of the studies [12, 15, 16, 18, 2022, 24, 2830, 33, 34] were from Europe, eight studies [10, 11, 17, 19, 23, 26, 27, 32] were from the USA and Canada, and four [13, 14, 25, 31] studies were from Asia.

After excluding participants with unknown age (n = 5), unknown case–control status (n = 214) and unknown smoking status (n = 14,028) data of 686,793 participants were available for analyses of which 11,261 cases and 675,532 non-cases. The Brescia bladder cancer study [21] contained only male participants, while the Women’s Lifestyle and Health study consisted of only female participants. Most of the cases were from America to Europe while only 10 % were from Asia.

The cases of the European and Asian case–control studies had the highest male/female ratio (4:1) while their overall male/female ratio was 3:1 (Table 2). In general, controls were younger than cases, 57.0 versus 61.6 years and 51.8 versus 61.1 years, respectively for case–control studies and cohort studies with an exception for the Asian case–control studies (66.1 versus 64.9 years). Most of the participants were Caucasian, whereas only 10 % of the cases were Asian. In contrast with Asia, where one third of the cases were never smoker, only one fifth of the cases never smoked in Europe and USA. Overall, 40 % of the cases were smokers. Controls had significant less current and more never smokers than cases. For cohort studies, nearly half of the controls never smoked. Staging was not reported in 60 and 70 % respectively for the case–control and cohort studies.
Table 2

Characteristics of the study population of the Bladder cancer Epidemiology and Nutritional Determinants study (BLEND)

   

Total

   

Europe

   

America

   

Asia

 
 

Cases

Controls

Cases

Controls

Cases

Controls

Cases

Controls

 

N

(()

N

(%)

N

(%)

N

(%)

N

(%)

N

(%)

N

(%)

N

(%)

Case–control studies

Gender

 Male

5,592

(77.9)

11,045

(55.9)

1,817

(83.5)

2,269

(71.0)

3,109

(74.6)

8,044

(50.7)

666

(80.6)

732

(79.0)

 Female

1,578

(22.1)

8,930

(44.1)

358

(16.5)

927

(29.0)

1,060

(25.4)

7,808

(49.3)

160

(19.4)

195

(21.0)

Age (mean, SD)

61.6

(11.4)

57.0

(14.4)

65.4

(9.7)

63.3

(10.9)

59.0

(11.1)

55.3

(14.5)

64.9

(13.1)

66.1

(12.2)

  < 50

961

(13.4)

5,665

(28.4)

135

(6.2)

308

(9.6)

723

(17.3)

5,271

(33.3)

103

(12.5)

86

(9.3)

 50– 59

1,832

(25.6)

4,501

(22.5)

407

(18.7)

787

(24.6)

1,261

(30.2)

3,549

(22.4)

164

(19.9)

165

(17.8)

 60–64

1,399

(19.5)

2,772

(13.9)

381

(17.5)

555

(17.4)

929

(22.3)

2,1

(13.2)

89

(10.8)

117

(12.6)

 65–69

1,122

(15.6)

2,842

(14.2)

482

(22.2)

567

(17.7)

522

(12.5)

2,138

(13.5)

118

(14.3)

137

(14.8)

  ≥ 70

1,856

(25.9)

4,195

(21.0)

770

(35.4)

979

(30.6)

734

(17.6)

2,794

(17.6)

352

(42.6)

422

(45.5)

Ethnic group

 Caucasian

4,438

(61.9)

15,057

(75.4)

593

(27.3)

831

(26.0)

3,845

(92.2)

14,226

(89.2)

782

(94.7)

767

(82.7)

 Mixed

9

(0.1)

10

(0.1)

9

(0.2)

10

(0.1)

 Asian

788

(11.0)

895

(4.5)

6

(0.1)

128

(0.8)

 Black

52

(0.7)

748

(3.7)

52

(1.2)

748

(4.7)

 Any other ethnic group

64

(0.9)

232

(1.2)

21

(0.5)

72

(0.5)

43

(5.2)

160

(17.3)

 Unknown

1,819

(25.4)

3,033

(15.2)

1,582

(72.7)

2,365

(74.0)

236

(5.7)

668

(4.2)

1

(0.1)

Tobacco smoking status

 Current smoker

2,95

(41.1)

6,98

(34.9)

1,038

(47.7)

1,022

(32.0)

1,564

(37.5)

5,695

(35.9)

348

(42.1)

263

(28.4)

 Former smoker

2,703

(37.7)

5,269

(26.4)

747

(34.3)

1,025

(32.1)

1,731

(41.5)

3,943

(24.9)

225

(27.2)

301

(32.5)

 Never smoker

1,517

(21.2)

7,726

(38.7)

390

(17.9)

1,149

(36.0)

874

(21.0)

6,214

(39.2)

253

(30.6)

363

(39.2)

Staging

 Non–invasive

2,246

(31.3)

511

(23.5)

1,606

(38.5)

129

(15.6)

 Invasive

609

(8.5)

73

(3.4)

366

(8.8)

170

(20.6)

 Unknown

4,315

(60.2)

1,591

(73.1)

2,197

(52.7)

527

(63.8)

Continent

 Europe

2,175

(30.3)

3,196

(16.0)

 America

4,169

(58.1)

15,852

(79.4)

 Asia

826

(11.5)

927

(4.6)

Cohort studies

Gender

 Male

2,866

(69.2)

205,678

(31.4)

2,544

(74.9)

146,333

(27.5)

338

(76.1)

39,983

(52.3)

216

(71.8)

19,362

(40.7)

 Female

1,277

(30.8)

449,827

(68.6)

854

(25.1)

385,124

(72.5)

106

(23.9)

36,454

(47.7)

85

(28.2)

28,249

(59.3)

Age (mean, SD)

61.1

(8.5)

51.8

(10.8)

60.9

(7.9)

50.4

(10.2)

66.4

(6.4)

61.4

(7.4)

55.7

(12.0)

52.0

(13.6)

  < 50

380

(9.2)

270,949

(41.3)

277

(8.2)

249,151

(46.9)

103

(34.2)

21,798

(45.8)

 50–59

1,305

(31.5)

232,316

(35.4)

1,154

(34.0)

184,999

(34.8)

69

(15.5)

35,193

(46.0)

82

(27.2)

12,124

(25.5)

 60–64

1,114

(26.9)

81,842

(12.5)

974

(28.7)

62,868

(11.8)

92

(20.7)

13,923

(18.2)

48

(15.9)

5,051

(10.6)

 65–69

757

(18.3)

39,082

(6.0)

610

(18.0)

22,425

(4.2)

109

(24.5)

12,561

(16.4)

38

(12.6)

4,096

(8.6)

  ≥ 70

587

(14.2)

31,316

(4.8)

383

(11.3)

12,014

(2.3)

174

(39.2)

14,760

(19.3)

30

(10.0)

4,542

(9.5)

Ethnic group

 Caucasian

3,815

(92.1)

602,416

(91.9)

3,398

(100)

531,457

(100)

417

(93.9)

70,959

(92.8)

 Asian

314

(7.6)

50,651

(7.7)

13

(2.9)

3,04

(4.0)

301

(100)

47,611

(100)

 Black

7

(0.2)

969

(0.1)

7

(1.6)

969

1.3)

 Any other ethnic group

1

(0.0)

475

(0.1)

1

(0.2)

475

(0.6)

 Unknown

6

(0.1)

994

(0.2)

6

(1.4)

994

(1.3)

Tobacco smoking status

 Current smoker

1,677

(40.5)

156,467

(23.9)

1,418

(41.7)

130,871

(24.6)

61

(13.7)

6,411

(8.4)

198

(65.8)

19,185

(40.3)

 Former smoker

1,594

(38.5)

185,006

(28.2)

1,296

(38.1)

149,472

(28.1)

280

(63.1)

33,651

(44.0)

18

(6.0)

1,883

(4.0)

 Never smoker

872

(21.0)

314,032

(47.9)

684

(20.1)

251,114

(47.3)

103

(23.2)

36,375

47.6)

85

(28.2)

26,543

(55.7)

Staging

 Non–invasive

1,196

(28.9)

1,196

(35.2)

 Invasive

661

(16.0)

661

(19.5)

 Unknown

2,286

(55.2)

1,541

(45.4)

444

(100)

301

(100)

Continent

 Europe

3,398

(82.0)

531,457

(81.1)

 America

444

(10.7)

76,437

(11.7)

 Asia

301

(7.3)

47,611

(7.3)

Although all of the studies used a food frequency questionnaire (FFQ), the number of food items assessed varied widely (Table 3). Two studies [22, 24] only asked three and two specific items (beer, coffee and decaffeinated coffee), while others assessed dietary intake in more detail (from 9 [27] to 788 food items [12]). The mean number of food items per questionnaire was 107 and 132 after exclusion of those studies that reported only on beverages [14, 22, 24]. Most studies with a FFQ of more than 40 items had detailed information on dietary intake of meat, vegetables, fruit and beverages. The use of a validated FFQ questionnaire was reported in eight studies [12, 19, 23, 2830, 3234], while one study checked the reproducibility of its FFQ [20]. Most of the studies assessed portion size, while four studies [12, 28, 29, 33, 34] reported the quantitative intake of food items in grams. Six studies [10, 19, 28, 30, 3234] also provided data on nutrients.
Table 3

Number of food items and portion size reported by each study within the Bladder cancer Epidemiology and Nutritional Determinants study (BLEND)

 Study

Food items (n)

Portion size

Case–control studies

 Los-Angeles bladder cancer Case–control study [10]

49

Yes

 Roswell Park Cancer Institute [11]

44

Yes

 Belgian Case–control study on bladder cancer [12]

788

Yes

 Aichi Prefecture Case–control study [13]

107

Yes

 Kaohsiung [14]

41

Yes

 Hessen Case–control study on bladder cancer [15]

26

No

 Stockholm Case–control study [16]

188

Yes

 Roswell Park Memorial Institute Case–control study on bladder cancer [17]

64

Yes

 Reina Sofia University [18]

17

No

 New Hampshire bladder cancer study [19]

121

Yes

 Italian Case–control study on bladder cancer [20]

21

No

 Brescia bladder cancer study [21]

40

Yes

 Dortmund Hörde study [22]

3

Yes

 National Enhanced Cancer Surveillance System (NESCC) [23]

69

Yes

 French INSERM study [24]

2

No

 South and East China Case–control study on bladder and prostate cancer [25]

52

No

 Molecular Epidemiology of Bladder Cancer and Prostate Cancer [26]

90

Yes

 North Carolina case control study [27]

9

No

Cohort studies

 Swedish Mammography Cohort (SMC) & the Cohort of Swedish Men [28]

96

No

 Netherlands Cohort Study on diet and cancer, the Netherlands, 1986–2003 [29]

150

Yes

 Women’s Lifestyle and Health Study [30]

98

Yes

 RERF atomic bomb survivors Study [31]

102

No

 Vital study [32]

126

Yes

 European Prospective Investigation into Cancer and Nutrition (EPIC) [33, 34]

260a

Yes

aDietary intake was assessed by a number of different instruments in the participating countries and the number of different food items varied from 88 (Norway) to 2443 (Sweden)

The consumption of beverages was reported in all the eighteen case–control studies. Five case–control studies [12, 13, 16, 19, 26] had detailed information for each of the larger food categories of the Eurocode 2 Food Coding System, while three studies [11, 23, 25] missed only data on sugar and/or fat (Table 4). Fat, grains, nuts and sugar were also missing in another four studies [10, 15, 17, 20]. The six cohort studies [2834] had detailed information in each food categories with the exception of the RERF atomic bomb survivors study [31] which had no data on sugar intake.
Table 4

Numbers of cases and controls available for each food category included in the Bladder cancer Epidemiology and Nutritional Determinants study (BLEND)

 

All Countries

Europe

America

Asia

 

Men

Women

Men

Women

Men

Women

Men

Women

Food category (number of studies)

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Ca

N

Co

N

Case–control studies

Milk and milk products (13) [1017, 19, 20, 23, 25, 26]

4,734

9,251

1,388

7,442

1,231

1,514

266

783

2,838

7,005

962

6,464

665

732

160

195

Eggs and eggs products (11) [1013, 15, 16, 19, 20, 23, 25, 26]

4,299

6,531

1,255

3,974

1,230

1,512

265

781

2,436

4,141

839

3,034

633

605

151

159

Meat and meat products (12) [1013, 1517, 19, 20, 23, 25, 26]

4,699

9235

1,377

7,716

1,231

1,513

265

783

2,833

7,114

961

6,774

635

608

151

159

Fish and fish products (11) [1113, 1517, 19, 20, 23, 25, 26]

3,197

7,391

960

7,144

1,229

1,511

265

781

1,335

5,275

544

6,204

633

605

151

159

Fats and oils (7) [1013, 16, 19, 26]

2,299

2,292

634

984

371

500

94

419

1,689

1,559

484

506

239

233

56

59

Grain and grain products (11) [1013, 16, 17, 19, 21, 23, 25, 26]

4,050

8,481

1,209

7,404

574

721

94

424

2,841

7,153

964

6,821

635

607

151

159

Pulses, seeds and nut products (8) [1113, 16, 19, 23, 25, 26]

2,108

4,255

715

3,270

371

499

94

421

1,106

3,151

470

2,690

631

605

151

159

Vegetables (13) [1013, 1517, 1921, 23, 25, 26]

4,942

10,086

1,403

8,648

1,429

1,727

265

783

2,881

7,754

987

7,706

632

605

151

159

Fruit and fruit products (13) [1013, 1517, 1921, 23, 25, 26]

4,860

9,307

1,376

7,615

1,414

1,713

265

781

2,814

6,989

960

6,675

632

605

151

159

Sugar products (7) [12, 13, 16, 18, 19, 23, 26]

1,613

3,438

582

3,020

446

591

105

463

935

2,615

421

2,499

232

232

56

58

Beverages (18) [1027]

5,509

10193

1,538

7,640

1,814

2,269

357

926

3,030

7,192

1,021

6,519

665

732

160

195

Cohort studies

Milk and milk products (6) [2834]

2,615

184,424

1,159

422,716

2,495

146,183

835

384,864

86

35,753

314

33,742

34

2,488

10

4,110

Eggs and eggs products (6) [2834]

2,585

184,284

1,147

421,392

2,465

146,039

823

383,535

86

35,753

314

33,742

34

2,492

10

4,115

Meat and meat products (6) [2834]

2,614

184,420

1,156

422,122

2,494

146,171

832

384,262

86

35,753

314

33,742

34

2,496

10

4,118

Fish and fish products (6) [2834]

2,613

184,406

1,157

421,976

2,493

146,157

833

384,116

86

35,753

314

33,742

34

2,496

10

4,118

Fats and oils (6) [2834]

2,527

181,544

1,130

421,335

2,420

146,029

810

384,710

86

35,753

314

33,742

21

1,762

6

2,883

Grain and grain products (6) [2834]

2,618

184,446

1,158

422,738

2,498

146,194

834

384,876

86

35,753

314

33,742

34

24,99

10

4,120

Pulses, seeds and nut products (6) [2834]

2,563

184,228

1,143

420,368

2,443

145,984

819

382,512

86

35,753

314

33,742

34

2,491

10

4,114

Vegetables (6) [2834]

2,616

184,432

1,157

422,236

2,496

146,184

833

384,376

86

35,753

314

33,742

34

2495

10

4118

Fruit and fruit products (6) [2834]

2,607

184,416

1,155

421,526

2,487

146,170

831

383,666

86

35,753

314

33,742

34

2493

10

4118

Sugar products (5) [2830, 3234]

2,556

181,860

1,143

417,615

2,470

146,107

829

383,873

86

35,753

314

33,742

0

0

0

0

Beverages (6) [2834]

2,630

187,445

1,172

424,778

2,497

146,190

835

384,868

99

38,760

327

35,793

34

2495

10

4117

Abbreviations: Ca cases, Co controls, N number

Discussion

The high number of cases (11,261) and controls (675,532) from 24 epidemiological studies included in the BLEND study makes the BLEND study the largest dataset on diet and UBC worldwide. A large sample size provides the potential to analyze in more detail food items rarely consumed [35] and allows delineating the generally weak association between UBC cancer and dietary intake for food categories. The advantage of pooling individual data compared to meta-analysis of aggregate data are multiple: it increases the power to detect the effect for food items more rarely consumed, it allows to adjust for the same confounding factors, gender, age, and smoking status, to test for interaction and to perform subgroup analyses [36, 37].

Demographic data in the BLEND study are consistent with the IARC CancerBase [1]. The male/female ratio in our dataset was 3:1. Worldwide the male/female ratio is 3.3:1. Europe is responsible for nearly 40 % of the UBC cases worldwide while the Asian population account for 28 % of the UBC incidence [1]. In our dataset, 49 % of the cases are from Europe while only 10 % of the cases are from Asia. The African and the Eastern Mediterranean region is responsible for only 9 % of the UBC incidence worldwide [1]. These regions are not represented in our dataset. In America and Europe, more than 90 % of the UBC cases are transitional cell carcinoma (TCC), while in Africa, up tot 40 % of the UBC cases can be squamous cell carcinomas (SCC) [38, 39] due to infection with Schistosoma haematobium (Bilharziasis) [40]. The Egyptian multi-center case–control study [41] had not yet been published when we collected our data. So, pooling of the data of the different countries is possible because most industrialized countries are likely to share the same risk factors for UBC. Otherwise, it will be possible to stratify analyses by region given the large number of included participants. We aim to update the BLEND database in the future with new available studies.

Conclusion

The available data in the very large BLEND database will allow us to test associations between individual food items of the different food items categories, even those less commonly consumed, and the risk for UBC. We will also investigate food patterns such as the Mediterranean diet and the influence of nutrients on the risk of UBC. In addition, the large sample size will allow subgroup analyses.

Abbreviations

BLEND, the BLadder cancer, Epidemiology, and Nutritional Determinants study; FFQ, food frequency questionnaire; OR, odds ratio; SCC, squamous cell carcinoma; SD, standard deviation; TCC, transitional cell carcinoma; TNM, TNM Classification of Malignant Tumors; UBC, urinary bladder cancer; WCRF, World Cancer Research Fund.

Declarations

Acknowledgements

We acknowledge all principal investigators for their willingness to participate in this jointed project.

Funding

Hessen Case–control study on bladder cancer was supported by the Bundesanstalt für Arbeitsschutz (No. F 1287). The Aichi Prefecture Case–control study was supported by a Smoking Research Foundation Grant for Biomedical Research. The Kaohsiung study was supported by grant NSC 85-2332-B-037-066 from the National Scientific Council of the Republic of China. The Stockholm Case–control study was supported by grant from the Swedish National Cancer Society and from the Swedish Work Environment Fund. The Roswell Park Memorial Institute Case–control study on bladder cancer was supported by Public Health Service Grants CA11535 and CA16056 from the National Cancer Institute.

The New England bladder cancer study was funded in part by grant numbers 5 P42 ES007373 from the National Institute of Environmental Health Sciences, NIH and CA57494 from the National Cancer Institute, NIH. The Italian Case–control study on bladder cancer was conducted within the framework of the CNR (Italian National Research Council) Applied Project “Clinical Application of Oncological Research” (contracts 94.01321.PF39 and 94.01119.PF39), and with the contributions of the Italian Association for Cancer Research, the Italian League against Tumours, Milan, and Mrs. Angela Marchegiano Borgomainerio.

The Brescia bladder cancer study was partly supported by the International Agency for Research on Cancer. The French INSERM study was supported by a grant from the Direction Générale de la Santé, Ministère des Affaires Sociales, France. The Molecular Epidemiology of Bladder Cancer and Prostate Cancer was supported in part by grants ES06718 (to Z.-F.Z.), U01 CA96116 (to A.B.), and CA09142 from the NIH National Institute of Environmental Health Sciences, the National Cancer Institute, the Department of Health and Human Services, and by the Ann Fitzpatrick Alper Program in Environmental Genomics at the Jonsson Comprehensive Cancer Center, UCLA. The Swedish Mammography Cohort (SMC) & the Cohort of Swedish Men was supported by the Swedish Cancer Foundation, Örebro County Council Research Committee, and Swedish Research Council Committee for Infrastructure. The Netherlands Cohort Study on diet and cancer was supported by the Dutch Cancer Society. The RERF atomic bomb survivors Study was supported by The Radiation Effects Research Foundation (RERF), Hiroshima and Nagasaki, Japan, a public interest foundation funded by the Japanese Ministry of Health, Labour and Welfare (MHLW) and the US Department of Energy (DOE). The research was also funded in part through DOE award DE-HS0000031 to the National Academy of Sciences. This publication was supported by RERF Research Protocol RP-A5-12. The VITamins and Lifestyle Study (VITAL) was supported by a grant (R01CA74846) from the National Cancer Institute. The European Prospective Investigation into Cancer and Nutrition (EPIC) was carried out with financial support of the ‘Europe Against Cancer’ Programme of the European Commision (SANCO); Ligue contre le Cancer (France); Société 3 M (France); Mutuelle Générale de l’Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM); Institute Gustave Roussy; German Cancer Aid; German Cancer Research Centre; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; the Spanish Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra; Cancer Research UK; Medical Research Council, UK; Stroke Association, UK; British Heart Foundation; Department of Health, UK; Food Standards Agency, UK; Wellcome Trust, UK; Greek Ministry of Health; Greek Ministry of Education; Italian Association for Research on Cancer; Italian National Research Council; Dutch Ministry of Public Health, Welfare and Sports; Dutch Prevention Funds; LK Research Funds; Dutch ZON (Zorg Onderzoek Nederland); World Cancer Research Fund; Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skane, Sweden; Norwegian Cancer Society; Norwegian Research Council. Partial support for the publication of this supplement was provided by the Centre de Recherche et d’Information Nutritionnelles (CERIN).

Availability of data and material

The dataset described in this article will be available at Dataverse (https://dataverse.nl/dvn/).

Authors’ contributions

MEG collected and harmonized data, performed the statistical analysis and wrote the manuscript. F.I., D.M., R.R. and A.W. harmonized the data, reviewed and edited the manuscript. M.B. collected the data and reviewed and edited the manuscript. SB, BB, MFA, KG, EG, XJ, KCJ, MRK, EK, ClV, CML, JM, HP, SP, GS, LT, JT, PvdB, PJV, KW, EW, EW, AW and ZFZ provided the data, reviewed and edited the manuscript. FB reviewed and edited the manuscript, and MPZ. supervised the study, reviewed and edited the manuscript. All authors read and approved the final manuscript.

Competing interests

This study was partly funded by the World Cancer Research Fund.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Each participating study has been approved by the local ethic committee.

Open AccessThis 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 General Practice, Katholieke Universiteit Leuven, ACHG-KU Leuven
(2)
Department of Public Health, Epidemiology and Biostatistics, University of Birmingham
(3)
The Cancer Council Victoria
(4)
NUTRIM School for Nutrition and Translational Research in Metabolism, University of Maastricht
(5)
INSERM U946, Variabilite Genetique et Maladies Humaines, Fondation Jean Dausset / CEPH
(6)
Laboratory of General Epidemiology, Istituto di Ricerche Farmacologiche “Mario Negri”
(7)
Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM)
(8)
Gastroenterology and Hepatology, University Medical Centre
(9)
Epidemiology and Biostatistics, The School of Public Health, Imperial College London
(10)
Social and Preventive Medicine, Faculty of Medicine, University of Malaya
(11)
Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Section of Public Health and Human Sciences, University of Brescia
(12)
Department of Preventive Medicine and Public Health, Faculty of Medicine, University of Cordoba
(13)
Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund
(14)
Department of Epidemiology Radiation Effects Research Foundation
(15)
Department of Preventive Medicine, University of Southern California
(16)
Department of Epidemiology and Community Medicine, University of Ottawa
(17)
Department of Epidemiology, Geisel School of Medicine at Dartmouth
(18)
Leuven University Centre for Cancer Prevention (LUCK)
(19)
Department of Clinical Medicine and Community Health, University of Milan
(20)
Department of Urology, Buddhist Dalin Tzu Chi General Hospital
(21)
Department of Cancer Prevention and Control, Roswell Park Cancer Institute
(22)
Leibniz Institute for Prevention Research and Epidemiology – BIPS
(23)
Department of Oncology and Pathology, Division of Clinical Cancer Epidemiology, Karolinska Hospital
(24)
Epidemiology Branch, and Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH
(25)
Department of Epidemiology, Schools for Oncology and Developmental Biology and Public Health and Primary Care, Maastricht University Medical Centre
(26)
Population Studies Division Health Canada
(27)
Department of Preventive medicine, Nagoya University Graduate School of Medicine
(28)
Department of Medical Epidemiology and Biostatistics, Medical Epidemiology, Karolinska Institutet
(29)
Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research
(30)
Genetic Epidemiology Group, Folkhälsan Research Center
(31)
Department of Community Medicine, University of Tromsø, The Arctic University of Norway
(32)
Fred Hutchinson Cancer Research Center
(33)
Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet
(34)
Departments of Epidemiology, UCLA Center for Environmental Genomics, Fielding School of Public Health, University of California, Los Angeles (UCLA)
(35)
CAPHRI School for Public Health and Primary Care, University of Maastricht
(36)
School of Cancer Sciences, University of Birmingham

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Copyright

© The Author(s). 2016