Abstract

The authors examined the role of diet in the high-risk population of Central Europe among 1,065 incident kidney cancer cases and 1,509 controls in Russia, Romania, Poland, and the Czech Republic. They observed an increased association with kidney cancer for consumption of milk (odds ratio (OR) = 1.46, 95% confidence interval (CI): 1.15, 1.84) and yogurt (OR = 1.34, 95% CI: 1.07, 1.67), as well as all meat (OR = 1.27, 95% CI: 1.06, 1.51 compared with the lowest tertile). High consumption of all vegetables (OR = 0.64, 95% CI: 0.51, 0.80) and cruciferous vegetables (OR = 0.68, 95% CI: 0.55, 0.84) was inversely associated with kidney cancer. In addition, high consumption of preserved vegetables increased the risk of kidney cancer (OR = 1.66, 95% CI: 1.24, 2.21). Alcohol consumption did not appear to be associated with kidney cancer. This 1999–2003 study provides further evidence that diet may play a role in the development of kidney cancer, with a particularly strong protective association for high vegetable intake. The increased risk associated with dairy products, preserved vegetables, and red meat provides clues to the high rates of kidney cancer in this population.

Kidney cancer incidence has been increasing steadily, particularly in North America (1) and Eastern Europe (2), with cancer of the renal parenchyma accounting for more than 80 percent of cases (3). In 2000, it was estimated that there were approximately 189,000 kidney cancer cases worldwide, with approximately 35,000 new cases in North America and 45,000 new cases in Europe (4). Several countries in Eastern and Central Europe have some of the highest rates of kidney cancer incidence and mortality in the world (5), and reasons for this phenomenon are unclear (6).

Kidney cancer is a multifactorial disease, with both environmental and hereditary components playing a role (7). Diet may be a factor in the pathogenesis of kidney cancer, although results for specific dietary components have been inconsistent (8–20). The World Cancer Research Fund concluded in 1997 that regular consumption of red meat and of dairy products “possibly” increases risk whereas vegetables are “possibly protective,” and that alcohol “possibly” does not affect risk (8). Results from several previously published studies were not adjusted for other dietary exposures, which may limit the inference that can be made regarding associations. To our knowledge, the role of diet and kidney cancer has not been studied in the high-risk countries of Eastern and Central Europe. Of particular interest are cruciferous vegetables, such as cabbage, which are rich in anticarcinogenic isothiocyanates and constitute a significant proportion of the vegetables consumed in this region.

Using part of a large, multicenter case-control study of kidney cancer in four countries in Central and Eastern Europe, we examined the role of total dairy, meat, vegetable, and alcohol consumption, as well as specific dietary components, in relation to risk of kidney cancer.

MATERIALS AND METHODS

Study population

Between August 1999 and January 2003, we conducted a hospital-based case-control study of kidney cancer in Russia (Moscow), Romania (Bucharest), Poland (Lodz), and the Czech Republic (Prague, Olomouc, Ceske Budejovice, and Brno). A total of 1,097 newly diagnosed and histologically confirmed renal cell carcinoma cases (International Classification of Diseases for Oncology, Second Edition, code C64) between the ages of 20 and 79 years were recruited. Trained medical staff reviewed medical records to extract relevant diagnostic information, including date and method of diagnosis, histologic type, tumor location, stage, and grade. Eligible controls (n = 1,555) were patients admitted to the same hospital as cases for conditions unrelated to smoking or genitourinary disorders (except for benign prostatic hyperplasia) who were frequency matched on age to cases. No single disease was present in more than 20 percent of the control group. For controls, the disorders necessitating hospitalization were from the following categories: infectious (1.1 percent), hematologic (3.2 percent), endocrine (2.0 percent), psychiatric (1.4 percent), neurologic (11.2 percent), ophthalmologic or otologic (14.5 percent), cardiovascular (9.6 percent), pulmonary (3.9 percent), gastrointestinal (18.7 percent), dermatologic (2.8 percent), orthopedic or rheumatologic (8.9 percent), genitourinary (benign prostatic hyperplasia) (3.8 percent), obstetric or perinatal (0.1 percent), injury or poisoning (3.0 percent), and other (15.9 percent).

Both cases and controls had to be residents of the study areas for at least 1 year at the time of recruitment. The response rates across study centers among eligible subjects who were requested to participate ranged from 90 percent to 98.6 percent for cases and from 90.3 percent to 96.1 percent for controls. All study subjects and their physicians provided written informed consent. This study was approved by the institutional review boards of all participating centers.

Standardized lifestyle and food frequency questionnaires were piloted in all centers prior to use and were administered in person by trained personnel to elicit information on demographic characteristics, education, exposure to tobacco smoke, alcohol consumption, dietary practices, anthropometry, medical history, family history, and occupational history. Cases were interviewed within 3 months of diagnosis.

Individuals for whom information was missing on diet (n = 6) or alcohol consumption (n = 11) were excluded. Also excluded were those with missing covariates (age, sex, tobacco use, hypertension medication use, body mass index, or education (n = 61)). With the above exclusions, data on 1,065 cases and 1,509 controls remained for our primary analyses.

Assessment of dietary intake and alcohol consumption

The dietary component of the questionnaire comprised 23 food items, which the study investigators selected by consensus during the planning stage of the study and further validated during the pilot stage by asking participants to name common food items not already specified. Frequency of consumption (and score) was assessed for each item (never, less than once per month (1), less than once per week (2), 1–2 times per week (3), 3–5 times per week (4), and daily (5)). A standardized questionnaire was used in each of the study centers that was translated from a common English version and then back-translated into English to ensure the validity of the initial translation. The questionnaire was repeated for two different time periods: 1) the year prior to interview, and 2) prior to political and market changes in 1989 (1991 in Russia). A lifetime weighted average intake for the two time periods was calculated by multiplying the score for each time period by the number of years the participant was alive during the time period, summing the time period scores, and dividing by the total age of the individual. Frequencies of intake of related foods were summed to form food groups (appendix table 1), which were categorized based on tertile cutoff points defined by consumption among controls. Categories of consumption for food-specific items were low (<once per month), medium (≥once per month but <once per week), and high (≥once per week). The high category for cheese consumption was further divided into high (≥once per week but <3 times per week) and very high (≥3 times per week). Sensitivity analyses were conducted and determined that associations of renal cell carcinoma with dietary consumption patterns were similar before and after political and market changes in 1989 (1991 in Russia) in adjusted multivariate logistic regression models (results not shown).

Alcohol consumption was ascertained by usual weekly consumption during different periods of adult life (i.e., ages ≤25, 26–40, 41–50, 51–60, and >60 years) of beer (bottles, 25 cl/bottle), wine (glasses, 10 cl/glass), and liquor/spirits (grams, 2 cl/shot). Lifetime average weekly consumption (grams of ethanol per week) was determined for each type of alcohol and for overall consumption by using standard conversions published by the International Agency for Research on Cancer (21). Those who did not report any weekly consumption of alcohol (274 cases, 362 controls) were chosen as the reference group. Alcohol consumption (grams per week of ethanol) groups of weekly drinkers were categorized into low, medium, and high based on tertile cutpoints defined by consumption among controls for beverage-specific and overall intake: total alcohol consumption (low, <36.5; medium, 36.5–137.5; high, ≥137.5); wine (low, <9.5; medium, 9.5–23.0; high, ≥23.0); beer (low, <15.0; medium, 15.0–49.0; high, ≥49.0); and liquor (low, <30.0; medium, 30.0–157.0; high, ≥157.0).

Assessment of nondietary and nonalcoholic exposures

Smoking status (never smoker, former smoker, current smoker) was defined as status 2 years before the interview. Specifically, participants still smoking in the 24 months prior to the interview were classified as current smokers. Pack-years of smoking were calculated by multiplying the average number of packs of cigarettes smoked per day by years of smoking. Hypertension status was defined by ever use of antihypertensive medication through the questionnaire. Body mass index was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Education was categorized into primary (elementary unfinished and finished), secondary and apprenticeships, and higher education (high school, university, or higher).

Statistical analysis

Statistical analyses were performed by using Stata, version 8 software (Stata Corporation, College Station, Texas). Country of origin was adjusted for as a categorical variable in all multivariate logistic regression models. For each food category, heterogeneity of effects between countries was tested by using the standard Q test. Fixed-effects models were used for pooling. Because of the large degree of heterogeneity between countries for associations between fruit and kidney cancer, pooled estimate of risk was not computed for fruit consumption. For dairy products and alcohol consumption, risk estimates were heterogeneous between Romania and the remaining countries. However, sensitivity analyses demonstrated that results for dairy products and alcohol consumption with or without Romania were comparable, and the results reported below include all countries.

Multivariate logistic regression was used to calculate the odds ratio and 95 percent confidence interval of kidney cancer associated with each exposure of interest. Three multivariate models were used in our analyses examining the risk of kidney cancer: 1) demographic adjusted (covariates: age, country, and gender); 2) risk-factor adjusted (covariates from model 1 and tobacco pack-years of smoking, education, body mass index, hypertension medication use, and categories of weekly alcohol consumption (none, low, medium, and high tertiles); and 3) diet adjusted (covariates from model 2 and adjustment for tertiles of total vegetable, total white meat, and total red meat consumption). Point estimates were comparable across multivariate models, and in this paper we present results for the diet-adjusted models that include all dietary and nondietary confounders. In all analyses examining alcohol as an exposure, the reference category consisted of nondrinkers (274 cases/362 controls with an average adult consumption of 0 g of ethanol/week). Because of collinearity in consumption between the different types of alcoholic beverages, for beverage-specific analyses, we restricted analyses to participants who weekly consumed only the beverage of interest. For example, when examining the association of kidney cancer with wine, we excluded beer and liquor drinkers.

RESULTS

Table 1 describes the clinical and demographic characteristics associated with kidney cancer cases and controls. Individuals from Russia and the Czech Republic constituted the majority of cases and controls (82.1 percent and 75.5 percent, respectively).

TABLE 1.

Characteristics of kidney cancer patients and controls, Eastern and Central Europe, 1999–2003

Characteristic Cases (n = 1,065) Controls (n = 1,509) 
No. No. 
Demographic     
    Age group (years)     
        <45 75 7.0 113 7.5 
        45–54 257 24.1 377 25.0 
        55–64 329 30.9 455 30.2 
        65–74 346 32.5 477 31.6 
        ≥75 58 5.5 87 5.8 
    Gender: male 622 58.4 973 64.5 
Prevalent disease     
    Hypertension medication use 451 42.4 548 36.3 
Smoking     
    Never 502 47.1 621 41.2 
    Former 244 22.9 366 24.3 
    Current 319 30.0 522 34.6 
Body mass index (kg/m2    
    <25 316 29.7 545 36.1 
    25–29 461 43.3 639 42.4 
    ≥30 288 27.0 325 21.5 
Education     
    Primary 116 10.9 126 8.4 
    Secondary/apprenticeships 614 57.7 1,021 67.7 
    Higher education 335 31.5 362 24.0 
Country     
    Romania 94 8.8 173 11.5 
    Poland 97 9.1 196 13.0 
    Russia 313 29.4 465 30.8 
    Czech Republic 561 52.7 675 44.7 
Characteristic Cases (n = 1,065) Controls (n = 1,509) 
No. No. 
Demographic     
    Age group (years)     
        <45 75 7.0 113 7.5 
        45–54 257 24.1 377 25.0 
        55–64 329 30.9 455 30.2 
        65–74 346 32.5 477 31.6 
        ≥75 58 5.5 87 5.8 
    Gender: male 622 58.4 973 64.5 
Prevalent disease     
    Hypertension medication use 451 42.4 548 36.3 
Smoking     
    Never 502 47.1 621 41.2 
    Former 244 22.9 366 24.3 
    Current 319 30.0 522 34.6 
Body mass index (kg/m2    
    <25 316 29.7 545 36.1 
    25–29 461 43.3 639 42.4 
    ≥30 288 27.0 325 21.5 
Education     
    Primary 116 10.9 126 8.4 
    Secondary/apprenticeships 614 57.7 1,021 67.7 
    Higher education 335 31.5 362 24.0 
Country     
    Romania 94 8.8 173 11.5 
    Poland 97 9.1 196 13.0 
    Russia 313 29.4 465 30.8 
    Czech Republic 561 52.7 675 44.7 

When we analyzed all dairy foods combined, we found no association with kidney cancer (p-trend = 0.31) (table 2). Compared with nondrinkers of milk, however, milk drinkers had a significantly elevated risk (odds ratio (OR) = 1.46, 95 percent confidence interval (CI): 1.15, 1.84). Similarly, compared with that for nonconsumers of yogurt, risk was significantly elevated for yogurt consumers (OR = 1.34, 95 percent CI: 1.07, 1.67). However, there was no consistent dose-response pattern of risks across tertiles of intake for milk (p-trend = 0.08) or yogurt (p-trend = 0.46). Compared with those for nonconsumers, risks for individuals with low, medium, and high intakes of milk were 1.86, 1.46, and 1.41 (p < 0.05), respectively (table 2). The corresponding risks for yogurt intake were 1.50, 1.39, and 1.19, respectively. When we examined the odds ratios for daily intake compared with nonconsumption, the odds ratios for milk and yogurt were 1.45 (95 percent CI: 1.09, 1.93) and 0.99 (95 percent CI: 0.66, 1.49), respectively. Because of the prevalence of cheese consumption, we used low levels of consumption as our reference, and higher levels of cheese consumption were not associated with kidney cancer. The findings for milk, yogurt, and cheese were not substantially altered when the dairy products were adjusted for each other.

TABLE 2.

Total dairy consumption and item-specific associations with kidney cancer in Eastern and Central Europe, adjusted for dietary food groups, 1999–2003

Dairy product and category of intake†,‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
Consumption of all dairy products     
    Tertile 1 315 503 1.00  
    Tertile 2 371 503 1.15 0.94, 1.42 
    Tertile 3 379 503 1.13 0.89, 1.42 
Milk     
    No 135 258 1.00  
    Low 105 108 1.86 1.31, 2.65** 
    Medium 149 210 1.46 1.07, 1.98* 
    High 676 933 1.41 1.10, 1.79** 
Yogurt     
    No 257 452 1.00  
    Low 171 205 1.50 1.13, 1.99** 
    Medium 201 250 1.39 1.05, 1.84* 
    High 436 602 1.19 0.92, 1.54 
Cheese     
    No 37 66 1.00  
    Low 206 301 1.19 0.76, 1.88 
    Medium 394 499 1.39 0.90, 2.15 
    Very high 428 643 1.12 0.72, 1.75 
Dairy product and category of intake†,‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
Consumption of all dairy products     
    Tertile 1 315 503 1.00  
    Tertile 2 371 503 1.15 0.94, 1.42 
    Tertile 3 379 503 1.13 0.89, 1.42 
Milk     
    No 135 258 1.00  
    Low 105 108 1.86 1.31, 2.65** 
    Medium 149 210 1.46 1.07, 1.98* 
    High 676 933 1.41 1.10, 1.79** 
Yogurt     
    No 257 452 1.00  
    Low 171 205 1.50 1.13, 1.99** 
    Medium 201 250 1.39 1.05, 1.84* 
    High 436 602 1.19 0.92, 1.54 
Cheese     
    No 37 66 1.00  
    Low 206 301 1.19 0.76, 1.88 
    Medium 394 499 1.39 0.90, 2.15 
    Very high 428 643 1.12 0.72, 1.75 
*

p < 0.05;

**

p < 0.01: statistical significance of point estimates vs. reference category.

Categories of intake: tertiles based on distribution among controls. For frequency: low (<1 time/month); medium (<1 time/week); high (≥1 time/week); very high (≥3 times/week).

Test for trend was not significant (p > 0.05) for all categories of dairy consumption.

§

Adjusted odds ratio (OR) includes covariates for age, country, gender, tobacco pack-years of smoking, education (categorical), body mass index, hypertension medication use, categories of total weekly alcohol consumption (none, low, medium, and high tertiles), and tertiles of total vegetable, total white meat, and total red meat consumption.

CI, confidence interval.

Overall, meat consumption was not related to kidney cancer risk (table 3). However, when data on red and white meats were examined separately, increased risks were observed for high intake of red meat, but not for white meat. Compared with those for individuals in the lowest tertile of red meat consumption, the respective odds ratios were 1.34 (95 percent CI: 1.08, 1.66) and 1.22 (95 percent CI: 1.00, 1.48) for individuals in the second and highest tertiles of consumption. When information for specific types of meat was examined, significant trends of elevated risks with increasing consumption were observed for nonprocessed red meats (beef, pork, lamb) (p-trend = 0.003), with risk doubled for those in the highest category of intake (OR = 2.01, 95 percent CI: 1.02, 3.99). However, daily intake was not associated (OR = 1.22, 95 percent CI: 0.58, 2.58). Daily intake of ham, salami, or sausages was also not associated with kidney cancer (OR = 0.94, 95 percent CI: 0.60, 1.47). No significant dose-response relation was observed for the other types of meat, although elevated risks were related to medium (OR = 2.12, 95 percent CI: 1.35, 3.32) and high (OR = 1.52, 95 percent CI: 0.98, 2.35) intake of poultry.

TABLE 3.

Total meat consumption and item-specific associations with kidney cancer in Eastern and Central Europe, adjusted for vegetable consumption, 1999–2003

Meat product and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
All meats     
    Tertile 1 391 505 1.00  
    Tertile 2 331 465 1.02 0.83, 1.24 
    Tertile 3 343 539 1.01 0.82, 1.24 
All red meats     
    Tertile 1 332 504 1.00  
    Tertile 2 307 376 1.34 1.08, 1.66** 
    Tertile 3 426 629 1.22 1.00, 1.48 
All white meats     
    Tertile 1 364 483 1.00  
    Tertile 2 375 524 1.00 0.82, 1.22 
    Tertile 3 326 502 0.99 0.79, 1.24 
Nonprocessed red meat (beef, pork, lamb)     
    Low 12 31 1.00†  
    Medium 90 173 1.44† 0.70, 2.98 
    High 963 1,305 2.01† 1.02, 3.99* 
Ham, salami, sausages     
    Low 52 72 1.00  
    Medium 109 172 0.85 0.55, 1.33 
    High 904 1,265 1.03 0.71, 1.51 
Poultry     
    Low 32 71 1.00  
    Medium 327 350 2.12 1.35, 3.32** 
    High 706 1,088 1.52 0.98, 2.35 
Fish     
    Low 240 365 1.00  
    Medium 422 534 1.23 0.99, 1.53 
    High 403 610 1.13 0.89, 1.44 
Meat product and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
All meats     
    Tertile 1 391 505 1.00  
    Tertile 2 331 465 1.02 0.83, 1.24 
    Tertile 3 343 539 1.01 0.82, 1.24 
All red meats     
    Tertile 1 332 504 1.00  
    Tertile 2 307 376 1.34 1.08, 1.66** 
    Tertile 3 426 629 1.22 1.00, 1.48 
All white meats     
    Tertile 1 364 483 1.00  
    Tertile 2 375 524 1.00 0.82, 1.22 
    Tertile 3 326 502 0.99 0.79, 1.24 
Nonprocessed red meat (beef, pork, lamb)     
    Low 12 31 1.00†  
    Medium 90 173 1.44† 0.70, 2.98 
    High 963 1,305 2.01† 1.02, 3.99* 
Ham, salami, sausages     
    Low 52 72 1.00  
    Medium 109 172 0.85 0.55, 1.33 
    High 904 1,265 1.03 0.71, 1.51 
Poultry     
    Low 32 71 1.00  
    Medium 327 350 2.12 1.35, 3.32** 
    High 706 1,088 1.52 0.98, 2.35 
Fish     
    Low 240 365 1.00  
    Medium 422 534 1.23 0.99, 1.53 
    High 403 610 1.13 0.89, 1.44 
*

p < 0.05;

**

p < 0.01: statistical significance of point estimates vs. reference category.

p < 0.01: test for trend.

Categories of intake: tertiles based on distribution among controls. For frequency: low (<1 time/month); medium (<1 time/week); high (≥1 time/week).

§

Adjusted odds ratio (OR) includes covariates for age, country, gender, tobacco pack-years of smoking, education (categorical), body mass index, hypertension medication use, categories of total weekly alcohol consumption (none, low, medium, and high tertiles), and tertiles of total vegetable consumption.

CI, confidence interval.

Vegetable consumption in general was inversely associated with kidney cancer, with risks decreased with increasing levels of intake (table 4). The inverse dose-response trends were significant for consumption of total vegetables (p-trend < 0.001), cruciferous vegetables (cabbage, broccoli, and brussels sprouts; p-trend < 0.001), and yellow-orange vegetable (tomatoes, pumpkin, and carrots; p-trend = 0.002). Kidney cancer risks for those in the highest tertile of consumption of these vegetables were 0.64 (95 percent CI: 0.51, 0.80), 0.68 (95 percent CI: 0.55, 0.84), and 0.62 (95 percent CI: 0.48, 0.81), respectively. However, the inverse association observed for cruciferous vegetables was confined mainly to cabbage intake (p-trend = 0.009) but not to broccoli and brussels sprouts. Daily consumption of cabbage was rare (n = 30), but it was associated with a decreased odds ratio of 0.18 (95 percent CI: 0.06, 0.53) for kidney cancer. Of note, vegetables that were preserved or pickled increased risk at both medium (OR = 2.60, 95 percent CI: 1.92, 3.52) and high (OR = 1.66, 95 percent CI: 1.24, 2.21) levels of consumption.

TABLE 4.

Vegetable consumption and item-specific associations with kidney cancer in Eastern and Central Europe, adjusted for white and red meat consumption, 1999–2003

Vegetable and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
All vegetables     
    Tertile 1 362 437 1.00††  
    Tertile 2 426 569 0.92†† 0.75, 1.12 
    Tertile 3 277 503 0.64†† 0.51, 0.80*** 
Cruciferous vegetables     
    Tertile 1 428 503 1.00††  
    Tertile 2 347 503 0.83†† 0.68, 1.01 
    Tertile 3 290 503 0.68†† 0.55, 0.84*** 
Yellow-orange vegetables     
    Tertile 1 310 414 1.00†  
    Tertile 2 563 696 1.09† 0.90, 1.33 
    Tertile 3 192 399 0.62† 0.48, 0.81*** 
Preserved (pickled) vegetables     
    Low 82 189 1.00  
    Medium 406 390 2.60 1.92, 3.52*** 
    High 577 930 1.66 1.24, 2.21** 
Cabbage     
    Low 137 166 1.00†  
    Medium 438 536 0.95† 0.73, 1.24 
    High 490 807 0.76† 0.58, 0.99* 
Broccoli, brussels sprouts     
    Low 908 1,284 1.00  
    Medium 109 152 1.04 0.79, 1.37 
    High 48 73 0.94 0.64, 1.39 
Spinach     
    Low 596 879 1.00  
    Medium 322 441 0.98 0.79, 1.20 
    High 147 189 1.18 0.90, 1.55 
Vegetable and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
All vegetables     
    Tertile 1 362 437 1.00††  
    Tertile 2 426 569 0.92†† 0.75, 1.12 
    Tertile 3 277 503 0.64†† 0.51, 0.80*** 
Cruciferous vegetables     
    Tertile 1 428 503 1.00††  
    Tertile 2 347 503 0.83†† 0.68, 1.01 
    Tertile 3 290 503 0.68†† 0.55, 0.84*** 
Yellow-orange vegetables     
    Tertile 1 310 414 1.00†  
    Tertile 2 563 696 1.09† 0.90, 1.33 
    Tertile 3 192 399 0.62† 0.48, 0.81*** 
Preserved (pickled) vegetables     
    Low 82 189 1.00  
    Medium 406 390 2.60 1.92, 3.52*** 
    High 577 930 1.66 1.24, 2.21** 
Cabbage     
    Low 137 166 1.00†  
    Medium 438 536 0.95† 0.73, 1.24 
    High 490 807 0.76† 0.58, 0.99* 
Broccoli, brussels sprouts     
    Low 908 1,284 1.00  
    Medium 109 152 1.04 0.79, 1.37 
    High 48 73 0.94 0.64, 1.39 
Spinach     
    Low 596 879 1.00  
    Medium 322 441 0.98 0.79, 1.20 
    High 147 189 1.18 0.90, 1.55 
*

p < 0.05; **p < 0.01; ***p < 0.001: statistical significance of point estimates vs. reference category.

p < 0.01;

††

p < 0.001: test for trend.

Categories of intake: tertiles based on distribution among controls. For frequency: low (<1 time/month); medium (<1 time/week); high (≥1 time/week).

§

Adjusted odds ratio (OR) includes covariates for age, country, gender, tobacco pack-years of smoking, education (categorical), body mass index, hypertension medication use, categories of total weekly alcohol consumption (none, low, medium, and high tertiles), and tertiles of total red meat and total white meat consumption.

CI, confidence interval.

Alcohol consumption in general was not related to kidney cancer risk, including intake of total alcohol, wine, beer, or liquor (table 5). Finer categories of total alcohol consumption were examined by using deciles. Compared with the lowest decile, only the upper 10th percentile of total alcohol consumption was inversely associated with kidney cancer (OR = 0.39, 95 percent CI: 0.24, 0.66), and no other decile of consumption was associated with kidney cancer. The inverse association seen between this upper 10th percentile may be due to small numbers (n = 27 cases). A significant inverse association was observed for the highest tertile of liquor intake (OR = 0.51, 95 percent CI: 0.27, 0.97), although this finding was based on relatively small numbers. Results on alcohol intake were comparable when stratified by gender (results not shown).

TABLE 5.

Total weekly alcohol consumption and beverage-specific associations with kidney cancer in Eastern and Central Europe, adjusted for dietary food groups,† 1999–2003

Alcohol consumption and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
Total alcohol consumption (g/week)     
    None 274 362 1.00  
    Low 310 378 1.18 0.93, 1.49 
    Medium 290 378 1.15 0.88, 1.48 
    High 191 391 0.83 0.61, 1.12 
Wine (excluding weekly beer/liquor drinkers)     
    None 274 362 1.00  
    Low 46 50 1.50 0.93, 2.44 
    Medium 20 38 0.73 0.40, 1.30 
    High 15 20 1.05 0.51, 2.18 
Beer (excluding weekly wine/liquor drinkers)     
    None 274 362 1.00  
    Low 38 49 1.20 0.73, 1.97 
    Medium 27 40 1.03 0.58, 1.83 
    High 22 45 0.77 0.42, 1.43 
Liquor (excluding weekly beer/wine drinkers)     
    None 274 362 1.00  
    Low 42 57 1.20 0.71, 2.02 
    Medium 56 88 1.08 0.69, 1.72 
    High 19 82 0.51 0.27, 0.97* 
Alcohol consumption and category of intake‡ No. of cases No. of controls Adjusted OR§ 95% CI¶ 
Total alcohol consumption (g/week)     
    None 274 362 1.00  
    Low 310 378 1.18 0.93, 1.49 
    Medium 290 378 1.15 0.88, 1.48 
    High 191 391 0.83 0.61, 1.12 
Wine (excluding weekly beer/liquor drinkers)     
    None 274 362 1.00  
    Low 46 50 1.50 0.93, 2.44 
    Medium 20 38 0.73 0.40, 1.30 
    High 15 20 1.05 0.51, 2.18 
Beer (excluding weekly wine/liquor drinkers)     
    None 274 362 1.00  
    Low 38 49 1.20 0.73, 1.97 
    Medium 27 40 1.03 0.58, 1.83 
    High 22 45 0.77 0.42, 1.43 
Liquor (excluding weekly beer/wine drinkers)     
    None 274 362 1.00  
    Low 42 57 1.20 0.71, 2.02 
    Medium 56 88 1.08 0.69, 1.72 
    High 19 82 0.51 0.27, 0.97* 
*

p < 0.05: statistical significance of point estimates vs. reference category.

Categories of alcohol consumption: (g/week of ethanol) among weekly drinkers: total alcohol (low, <36.5; medium, 36.5–137.5; high, ≥137.5); wine (low, <9.5; medium, 9.5–23.0; high, ≥23.0); beer (low, <15.0; medium, 15.0–49.0; high, ≥49.0), and liquor (low, <30.0; medium, 30.0–157.0; high, ≥157.0).

Test for trend was not significant (p > 0.05) for all categories of alcohol consumption.

§

Adjusted odds ratio (OR) includes covariates for age, country, gender, tobacco pack-years of smoking, education (categorical), body mass index, hypertension medication use, and tertiles of total vegetable, total white meat, and total red meat consumption.

CI, confidence interval.

DISCUSSION

Our study is the first known to examine the associations of diet and kidney cancer in Eastern and Central Europe, a region with some of the highest rates of kidney cancer in the world (5). For nonconsumers of dairy products compared with consumers of milk and yogurt, but not cheese, associations with kidney cancer were positive. Additionally, red meat and poultry consumption was positively associated with kidney cancer. Vegetable consumption demonstrated a protective association, although consumption of preserved or pickled vegetables appeared to be a risk factor. Unlike several recent studies (15, 16, 22), there did not appear to be an association between kidney cancer and alcohol consumption in our population.

Our study results suggest that milk and yogurt (but not cheese) consumption may have positive associations with kidney cancer. Specifically, compared with those for nonconsumers of dairy products, associations with kidney cancer were positive for individuals with higher intakes of milk and yogurt, but not cheese. The risk effects do not appear to increase across higher levels of consumption (p-trend > 0.05). Additionally, the association may be due to the reference group of nondairy users being a selective group, with residual confounding as a possible explanation. Several studies demonstrated no association (11, 15, 18, 23, 24), although our results are consistent with the majority of the literature, which has shown a risk-enhancing relation with milk (10, 13, 25–30).

It has been hypothesized that specific carcinogens in the feed of cattle may be passed on to humans through milk (31). Specifically, animals fed a diet of bracken fern (Pteridium aquilinum) develop cancer, and cows fed bracken fern produced milk that was able to induce kidney cancer in animal models (31). Although the distribution of bracken fern is widespread (32), the fern is not typically a primary component of feed (but rather a possible contaminant). Unless in times of restricted nourishment, animals do not intentionally graze on bracken fern because both acute and chronic bracken toxicity occurs (32). However, the association between dairy products and kidney cancer should be interpreted with caution, because milk is also an important source of calcium, vitamin A, and vitamin D, which may have anticancer benefits (33). It is important to note that the risk association is seen only when compared with that for individuals who do not consume the specific dairy product, with no association for cheese. Our findings warrant more study into possible mechanisms and other population-based studies to confirm this association.

Consumption of both red meat and poultry was shown to be positively associated with kidney cancer in Eastern and Central Europe, similar to previous studies (11, 18, 26, 28, 34–37). Red meat and poultry consumption may increase risk because of their protein content (11, 28, 30, 34, 38) and fat (10, 28, 30), which have been associated with kidney cancer. Additionally, the method of preparing these foods may affect risk. Some methods of cooking, such as frying or broiling, may generate heterocyclic amines (39) that may increase carcinogenic risk (40). It has been shown that consumption of heterocyclic amines results in accumulation of DNA adducts in kidneys (41). Unfortunately, our questionnaire did not include information on the method of preparing the meats.

An International Agency for Research on Cancer working group previously determined that a protective relation with vegetable consumption for kidney cancer was possible but not conclusive, with several studies demonstrating no association and the potential for confounding from other risk factors (9). Contrary to our findings, several recent prospective studies have shown no association between total vegetable consumption and kidney cancer (15, 19, 20). It is possible that the lowest intake in our reference group in Central and Eastern Europe may be lower than mentioned by the prospective cohort studies that examined populations in the Netherlands and Western Europe (19, 20), although comparisons are not feasible given differences in our questionnaires. However, a recent study in the European Prospective Investigation into Cancer and Nutrition did demonstrate a decreased risk of kidney cancer with intake of root vegetables (relative risk = 0.88, 95 percent CI: 0.78, 0.99 per 8 g/day intake) and emphasized the potential benefits of carrots and carotenes (19). Similarly, our study showed a protective effect for the highest tertile of consumption of yellow-orange vegetables (OR = 0.62, 95 percent CI: 0.48, 0.81), which included vegetables rich in carotenes. The Netherlands Cohort study on diet and cancer demonstrated that individuals in the highest quintile of consumption had a decreased (although nonsignificant) relative risk of 0.84 for kidney cancer. However, in our study, total vegetable consumption was statistically significantly inversely associated with kidney cancer (p-trend < 0.001).

Our study lends weight to the body of evidence, including a recent prospective study (12), for a protective association for vegetables (11, 18, 28, 37, 42, 43). In particular, cruciferous vegetables, such as cabbage, are sources of chemoprotective agents including isothiocyanates, which have been demonstrated to be anticarcinogenic (44). Cruciferous vegetables were inversely associated with kidney cancer in our results, similar to previous studies (11, 12). Cabbage is one of the main vegetables consumed by this population, with 53.5 percent of controls (and 46.0 percent of cases) eating cabbage at least weekly; 12.0 percent of controls (and 6.0 percent of cases) consumed cabbage at least three times per week. In our study, high levels of cabbage consumption were inversely associated with kidney cancer. However, broccoli and brussels sprouts consumption was particularly low in this population, with more than half of the population having never consumed these vegetables, which may explain the lack of association for these items.

Although fresh vegetables were demonstrated to decrease risk, preserved vegetables may increase risk. This association has been seen with cancers of the colon (45) and other sites (46), although, to our knowledge, it has never been studied before in relation to kidney cancer. Preserved vegetables contain N-nitrosodimethylamine and other N-nitroso compounds that demonstrate carcinogenicity in animal models (47, 48). However, the pathogenic role of these agents in kidney cancer needs to be better characterized.

Alcohol may have carcinogenic effects due to mutagenic by-products such as acetaldehyde, and its association with certain cancers of the upper alimentary tract and liver are well established (49). The association with kidney cancer is less clear, however, as is the case with other genitourinary cancers, such as bladder cancer (50). Alcohol consumption was positively associated with kidney cancer in several ecologic studies (51–53) and case-control studies (26), although the majority of studies demonstrated no association (23, 28, 30, 37, 54–60). However, several recent studies (15, 16, 22) identified a protective association of alcohol consumption with kidney cancer, particularly among women and male smokers. We did not find an association between total alcohol consumption and kidney cancer in Eastern and Central Europe, with no sex-specific effects.

Although the results of our study are supported by previous laboratory and population-based research (8, 9, 21, 40, 44), there are limitations. Our controls were hospital based, and hospital-based case-control studies may be more liable to selection bias, particularly if the exposure was related to hospitalization of the control (61). Additionally, the distribution of exposures in our controls may be slightly different than that in the general population. This possibility may be true for the associations seen with milk and yogurt, although other prospective analyses (13, 25) and studies using population-based controls (10, 26, 28, 30) had similar results. However, the majority of our findings have been supported by prospective studies (12, 13, 25), which in general are not affected by such biases. Additionally, the food frequency questionnaire was not comprehensive, but it was developed with local investigators to properly capture dietary consumption in these regions and was unique in assessing dietary patterns before and after the political and economic changes affecting Eastern Europe in the late 1980s and early 1990s. Another limitation is that we were not able to adjust for energy intake, nor were we able to assess quantity consumed, because our metric did not include such information. However, energy intake has been demonstrated to not be associated with kidney cancer (62), and we were able to adjust for body mass index, a known risk factor (62). Furthermore, we were able to examine the effects of specific dietary foods, adjusted for other food groups. In addition, our study is the first known to examine kidney cancer and diet in the context of the high-risk population of Eastern and Central Europe.

In summary, our study suggests that some dairy, red meat, and preserved vegetable consumption is positively associated with kidney cancer, whereas fresh vegetable consumption decreases risk. Especially in high-risk populations, it is particularly important to identify modifiable and preventable risk factors for decreasing the risk of kidney cancer.

Abbreviations

    Abbreviations
  • CI

    confidence interval

  • OR

    odds ratio

APPENDIX TABLE 1.

Individual food items included in calculation of composite food groups

Food group Composition 
Dairy Milk, cheese, yogurt 
Total meat Beef, pork, lamb, meat, liver, salted fish, fresh fish, chicken, ham, salami, sausages, and eggs 
Total red meat Beef, pork, lamb, meat, liver, ham, salami, sausages 
Total white meat Salted fish, fresh fish, chicken 
Processed meats Ham, salami, sausages 
Total fish Salted fish and fresh fish 
Total vegetables Carrots, cabbage, spinach, broccoli, brussels sprouts, onion, pumpkin, tomatoes, other fresh and preserved vegetables 
Cruciferous vegetables Cabbage, broccoli, brussels sprouts 
Yellow-orange vegetables Carrots, tomatoes, pumpkin 
Food group Composition 
Dairy Milk, cheese, yogurt 
Total meat Beef, pork, lamb, meat, liver, salted fish, fresh fish, chicken, ham, salami, sausages, and eggs 
Total red meat Beef, pork, lamb, meat, liver, ham, salami, sausages 
Total white meat Salted fish, fresh fish, chicken 
Processed meats Ham, salami, sausages 
Total fish Salted fish and fresh fish 
Total vegetables Carrots, cabbage, spinach, broccoli, brussels sprouts, onion, pumpkin, tomatoes, other fresh and preserved vegetables 
Cruciferous vegetables Cabbage, broccoli, brussels sprouts 
Yellow-orange vegetables Carrots, tomatoes, pumpkin 

Dr. Charles C. Hsu was supported by a Postdoctoral Fellowship from the International Agency for Research on Cancer.

The authors thank Drs. Olga van der Hel and Mia Hashibe for their assistance in preparing this manuscript.

Conflict of interest: none declared.

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