Abstract
The authors examined nutritional risk factors for prostate cancer among 9,559 participants in the Prostate Cancer Prevention Trial (United States and Canada, 1994–2003). The presence or absence of cancer was determined by prostate biopsy, which was recommended during the trial because of an elevated prostate-specific antigen level or an abnormal digital rectal examination and was offered to all men at the trial's end. Nutrient intake was assessed using a food frequency questionnaire and a structured supplement-use questionnaire. Cancer was detected in 1,703 men; 127 cancers were high-grade (Gleason score 8–10). There were no associations of any nutrient or supplement with prostate cancer risk overall. Risk of high-grade cancer was associated with high intake of polyunsaturated fats (quartile 4 vs. quartile 1: odds ratio = 2.41, 95% confidence interval (CI): 1.33, 4.38). Dietary calcium was positively associated with low-grade cancer but inversely associated with high-grade cancer (for quartile 4 vs. quartile 1, odds ratios were 1.27 (95% CI: 1.02, 1.57) and 0.43 (95% CI: 0.21, 0.89), respectively). Neither dietary nor supplemental intakes of nutrients often suggested for prostate cancer prevention, including lycopene, long-chain n-3 fatty acids, vitamin D, vitamin E, and selenium, were significantly associated with cancer risk. High intake of n-6 fatty acids, through their effects on inflammation and oxidative stress, may increase prostate cancer risk.
Keywords: diet, dietary supplements, food, micronutrients, prostatic neoplasms
Sound biologic reasoning underlies the hypothesis that dietary patterns, through their effects on steroid hormone and xenobiotic metabolism, oxidative stress, and inflammation, can modify prostate cancer risk. However, the findings from observational and experimental studies examining diet and prostate cancer risk are inconsistent. For example, several cohort studies and secondary analyses from randomized clinical trials found inverse associations of selenium and vitamin E supplementation with prostate cancer risk (1, 2), often restricted to subsets of men such as smokers (3) or men with specific genotypes (4), but a large randomized clinical trial did not find reduced risks after supplementation with vitamin E, selenium, or both (5). Inverse associations found for dietary lycopene in some cohorts (6, 7) have not been consistently corroborated in studies using serum lycopene as a biomarker of intake (8, 9). Both dietary and supplemental calcium have been associated with increased risk in many observational studies (10, 11), but calcium supplementation was found to be protective in a randomized clinical trial (12).
Many factors could explain the discrepancies across these studies. Most important is the widespread adoption of prostate-specific antigen (PSA) screening, which has caused the preponderance of incident prostate cancer cases to be asymptomatic, local-stage, and of uncertain clinical importance (13). It is thus critical to accurately assess the phenotypes of local-stage disease, which currently is best characterized by Gleason grade (14). A related concern is detection bias. The strongest predictor of being diagnosed with prostate cancer is the receipt of PSA screening (15), yet substantial numbers of men with PSA levels below the standard 4.0-ng/mL cutpoint for diagnostic evaluation have prostate cancer that is undiagnosed (16). Thus, if investigators do not carefully control for screening in their analyses, factors associated with the use of screening or serum PSA level could obscure or confound etiologic associations.
Here we present results from a study examining the associations of nutrient intake from food and supplements with the 7-year period prevalence of prostate cancer in a large cohort of men participating in the Prostate Cancer Prevention Trial (PCPT). Several aspects of the PCPT are unique, particularly the biopsy-determined absence or presence of cancer and the centralized and uniform pathologic grading used to define cancer endpoints. Thus, while almost all prostate cancer cases were local-stage, detection bias was minimized and pathologic grading of cases was rigorous and standardized. Analytical results given here are focused on the nutrients and phytochemicals that have been associated with prostate cancer risk in previous studies, including macronutrient density, lycopene, calcium, folate, vitamin D, and n-3 fatty acids.
MATERIALS AND METHODS
Study design and study population
The PCPT (http://www.cancer.gov/pcpt) was a randomized, placebo-controlled trial that tested whether finasteride, a 5α-reductase inhibitor, could reduce the 7-year period prevalence of prostate cancer (16). Briefly, beginning in 1993, 18,880 US and Canadian men aged 55 years or older with normal digital rectal examination (DRE) results, PSA levels of 3 ng/mL or less, and no history of prostate cancer, severe lower urinary tract symptoms, or clinically significant coexisting conditions were randomized to receive finasteride (5 mg/day) or placebo. During the PCPT, men underwent DRE and PSA determination annually, and a prostate biopsy was recommended for participants with an abnormal DRE or a PSA level (adjusted for the effect of finasteride) of 4.0 ng/mL or greater. At the final study visit in year 7 (2000–2003), all men not previously diagnosed with prostate cancer were offered a biopsy, which consisted of a minimum of 6 core samples collected under transrectal ultrasonographic guidance. Biopsies were reviewed for adenocarcinoma by both the pathologist at the local study site and a central pathology laboratory, with full concordance. Clinical stage was assigned locally, and tumors were graded centrally using the Gleason scoring system (14).
Of the 18,880 participants, we excluded 7,615 (40.3%) who did not have an end-of-study biopsy, including 1,225 men who died, 6,381 who were medically unable to have a biopsy or refused, and 9 who underwent prostatectomy for reasons other than cancer; this left 2,401 cases and 8,864 noncases. We then excluded 173 cases diagnosed on or after the trial end date (June 24, 2003), 92 cases diagnosed 180 days or more after their planned end-of-study visit, and 140 cases who were missing Gleason scores. From the 10,860 men remaining for study, we further excluded 102 men who were missing data on body mass index, 770 men who were missing dietary data, and 429 men whose dietary information was judged to be unreliable because of a reported energy intake less than 800 kcal/day or greater than 5,000 kcal/day. Some men did not complete dietary questionnaires because practitioners at their clinical site chose not to participate in the dietary studies or because prostate cancer was diagnosed before the questionnaire was administered. This analysis was based on 1,703 cancer cases diagnosed in 9,559 men.
Data collection
Details regarding demographic and health-related characteristics were collected at baseline using self-administered questionnaires. Level of physical activity was assessed using a 6-item questionnaire (17). Height and weight were measured at the baseline clinic visit.
One year after randomization, the men filled in a 15-page booklet containing 2 questionnaires on diet and the use of nutritional supplements. Diet was assessed using a food frequency questionnaire (FFQ) developed specifically for this population of older men. The FFQ consisted of questions on 99 foods and 9 beverages, plus 18 questions on food preparation and 2 questions on consumption of fruits and vegetables. Algorithms for analysis of data from this questionnaire are available at http://www.fhcrc.org/science/shared_resources/nutrition/ffq/tech_doc.pdf. The nutritional supplement questionnaire has been described in detail previously (18). On the questionnaire, participants reported: the usual number of pills taken per day for multivitamins and antioxidant mixtures; both the number of pills taken per day and the dose for β-carotene, vitamin C, vitamin E, calcium, and zinc; and whether they used stress-type multivitamins, vitamin D, fish oil, or selenium at least 3 times per week. Multivitamin use and supplemental intakes of specific nutrients (the sum of single supplements plus multivitamins) were categorized as low (corresponding to no use or infrequent use of a supplement), moderate (corresponding to the amounts generally obtained from multivitamins), and high (corresponding to amounts that are generally only possible from using high-dose single supplements). Because data for fish oil, selenium, and vitamin D were available only on whether these supplements were used at least 3 times per week, fish oil was coded as 0 or 0.5 g of docosahexaenoic (DHA) plus eicosapentaenoic (EPA) fatty acids per day, selenium was coded as 0 or 200 μg/day, and vitamin D was coded as 0 or 10 μg/day. The vitamin D content of multivitamins is also 10 μg; thus, men who used both multivitamins and single vitamins were placed in the high-dose vitamin D category.
In an inter- and intramethod reliability study carried out among 150 randomly selected men, we compared nutrient intakes calculated from the initial FFQ, intakes from 6 24-hour recalls administered over the following year, and intakes from an additional FFQ completed after all 24-hour recalls had been administered. Based on the 128 men who completed the study, correlations between the first FFQ and the 24-hour recalls (adjusted for energy and deattenuated for measurement error (19)) were: total fat, 0.71; polyunsaturated fat, 0.66; monounsaturated fat, 0.66; saturated fat, 0.75; alcohol, 0.84; carbohydrate, 0.70; protein, 0.50; vitamin C, 0.62; lycopene, 0.58; β-carotene, 0.68; vitamin D, 0.57; EPA + DHA, 0.87; calcium, 0.62; and zinc, 0.51. Correlations between repeat FFQs were above 0.60 for all nutrients, with the exception of 0.54 for EPA + DHA.
Statistical analysis
We used logistic and polytomous logistic regression models to estimate associations of diet and supplement use with risks of total, low-grade (Gleason score 2–7), and high-grade (Gleason score 8–10) prostate cancer. Several alternative categorizations of grade (Gleason score 2–6 vs. 7–10; Gleason score 2–6 vs. 7 vs. 8–10; and Gleason score 2–6 (3 + 4) vs. (4 + 3) 8–10) were examined, but findings were limited to Gleason score 8–10, with no difference between Gleason scores categorized as 2–6 and 2–7. Results given were adjusted for age (continuous), race/ethnicity (white, African-American, other), family history of prostate cancer in first-degree relatives (yes, no), treatment arm (finasteride, placebo), and body mass index (weight (kg)/height (m)2; continuous). Further control for education, diabetes, current smoking, and physical activity did not affect the results, and these factors were not included in the final models. Tests for linear trend across categories were based on an ordinal variable, as described by Breslow and Day (20). Results are given for both study arms combined, because in preliminary analyses there were no discordant findings between arms. All analyses used SAS, version 9.1 (SAS Institute Inc., Cary, North Carolina).
RESULTS
Table 1 shows the demographic and health-related characteristics of the study population. Older age, African-American race/ethnicity, and family history of prostate cancer were associated with increased prostate cancer risk; high body mass index was associated with lower risk of total cancer, but in previously published results, the associations were inverse for low-grade disease and positive for high-grade disease (21). The majority of prostate cancers were low-grade and in an early clinical stage.
Table 1.
Demographic and Health-Related Characteristics of Prostate Cancer Cases and Controls, Prostate Cancer Prevention Trial, 1994–2003
| Cases (n = 1,703) |
Controls (n = 7,856) |
P Valuea | |||||
| No. | % | Mean (SD) | No. | % | Mean (SD) | ||
| Age, years | |||||||
| Mean | 63.6 (5.6) | 62.6 (5.4) | <0.001 | ||||
| <60 | 480 | 28.2 | 2,648 | 33.7 | <0.001 | ||
| 60–64 | 531 | 31.2 | 2,544 | 32.4 | |||
| 65–69 | 418 | 24.5 | 1,715 | 21.8 | |||
| ≥70 | 274 | 16.1 | 949 | 12.1 | |||
| Race/ethnicity | |||||||
| White | 1,587 | 93.2 | 7,372 | 93.8 | <0.001 | ||
| African-American | 75 | 4.4 | 210 | 2.7 | |||
| Asian/Pacific Islander | 5 | 0.3 | 52 | 0.7 | |||
| Hispanic | 32 | 1.9 | 185 | 2.4 | |||
| Other | 4 | 0.2 | 37 | 0.5 | |||
| Family history of prostate cancer | 374 | 22.0 | 1,242 | 15.8 | <0.001 | ||
| Smoking | |||||||
| Never smoker | 612 | 35.9 | 2,701 | 34.4 | 0.473 | ||
| Former smoker | 979 | 57.5 | 4,629 | 58.9 | |||
| Current smoker | 112 | 6.6 | 526 | 6.7 | |||
| Diabetes mellitus | 72 | 4.2 | 550 | 7.0 | <0.001 | ||
| Finasteride study arm | 696 | 40.9 | 3,961 | 50.4 | <0.001 | ||
| Body mass indexb | |||||||
| Mean | 27.4 (4.0) | 27.6 (4.0) | 0.023 | ||||
| <25 | 482 | 28.3 | 2,014 | 25.6 | <0.001 | ||
| 25–29 | 868 | 51.0 | 4,063 | 51.7 | |||
| 30–34 | 272 | 16.0 | 1,392 | 17.7 | |||
| ≥35 | 81 | 4.8 | 387 | 4.9 | |||
| Histologic grade | |||||||
| Low (GS 2–6) | 1,225 | 71.9 | |||||
| Moderate (GS 3 + 4) | 266 | 15.6 | |||||
| Moderate (GS 4 + 3) | 85 | 5.0 | |||||
| High (GS 8–10) | 127 | 7.5 | |||||
| Clinical stage | |||||||
| T1a | 215 | 12.6 | |||||
| T1b | 118 | 6.9 | |||||
| T1c | 901 | 52.9 | |||||
| T2a | 234 | 13.7 | |||||
| T2b | 93 | 5.5 | |||||
| T2c | 73 | 4.3 | |||||
| T3 | 27 | 1.6 | |||||
| T4 | 0 | ||||||
| Unknown | 42 | 2.5 | |||||
Abbreviations: GS, Gleason score; SD, standard deviation.
t tests for mean values and chi-squared tests for categories.
Weight (kg)/height (m)2.
There were no significant associations of any nutrient or nutritional supplement with the risk of total prostate cancer; therefore, results are given by grade only. Table 2 shows adjusted odds ratios for low- and high-grade prostate cancer associated with energy and micronutrient intake.
Table 2.
Associations of Daily Energy and Macronutrient Intake With the Risk of Low- and High-Grade Prostate Cancer, Prostate Cancer Prevention Trial, 1994–2003
| Quartile of Energy or Macronutrient Intakea |
P for Trend | ||||
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | ||
| Energy, kcal | <1,558 | 1,558–2,066 | 2,067–2,678 | >2,678 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.97 (0.83, 1.13) | 1.00 (0.85, 1.17) | 1.07 (0.92, 1.25) | 0.341 |
| No. of cases/total | 375/2,227 | 390/2,405 | 397/2,398 | 414/2,402 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.98 (0.61, 1.58) | 1.00 (0.62, 1.62) | 0.69 (0.40, 1.17) | 0.226 |
| No. of cases/total | 35/1,887 | 35/2,050 | 34/2,035 | 23/2,011 | |
| Total fat | |||||
| Percent energy | <27.4 | 27.4–32.7 | 32.8–37.9 | >37.9 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.98 (0.84, 1.15) | 0.99 (0.85, 1.16) | 0.90 (0.77, 1.06) | 0.242 |
| No. of cases/total | 407/2,390 | 397/2,345 | 397/2,324 | 375/2,373 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.81 (1.08, 3.03) | 1.51 (0.87, 2.59) | 1.36 (0.78, 2.39) | 0.490 |
| No. of cases/total | 23/2,006 | 41/1,989 | 33/1,960 | 30/2,028 | |
| Total energy, kcal | <454 | 454–654 | 655–919 | >919 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.05 (0.89, 1.23) | 0.92 (0.77, 1.09) | 1.07 (0.86, 1.33) | 0.963 |
| No. of cases/total | 386/2,305 | 402/2,319 | 371/2,411 | 417/2,397 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.30 (0.78, 2.16) | 1.38 (0.78, 2.43) | 1.23 (0.58, 2.60) | 0.463 |
| No. of cases/total | 31/1,950 | 36/1,953 | 35/2,075 | 25/2,005 | |
| Saturated fat | |||||
| Percent energy | <8.5 | 8.5–10.4 | 10.5–12.4 | >12.4 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.89 (0.76, 1.04) | 0.95 (0.81, 1.10) | 0.89 (0.76, 1.05) | 0.282 |
| No. of cases/total | 414/2,341 | 380/2,367 | 402/2,372 | 380/2,352 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.18 (0.74, 1.87) | 0.79 (0.47, 1.33) | 0.73 (0.43, 1.26) | 0.125 |
| No. of cases/total | 34/1,961 | 41/2,028 | 28/1,998 | 24/1,996 | |
| Total energy, kcal | <144 | 144–209 | 210–301 | >301 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.99 (0.84, 1.17) | 0.98 (0.80, 1.19) | 1.01 (0.77, 1.34) | 0.959 |
| No. of cases/total | 385/2,280 | 391/2,334 | 392/2,399 | 408/2,419 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.93 (0.56, 1.55) | 0.73 (0.39, 1.37) | 0.37 (0.13, 1.00) | 0.103 |
| No. of cases/total | 35/1,930 | 37/1,980 | 33/2,040 | 22/2,033 | |
| Monounsaturated fat | |||||
| Percent energy | <10.2 | 10.2–12.5 | 12.6–14.7 | >14.7 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.95 (0.81, 1.10) | 0.98 (0.84, 1.14) | 0.90 (0.77, 1.06) | 0.287 |
| No. of cases/total | 407/2,355 | 387/2,355 | 400/2,330 | 382/2,392 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.68 (1.01, 2.78) | 1.30 (0.76, 2.22) | 1.14 (0.65, 1.98) | 0.984 |
| No. of cases/total | 25/1,973 | 42/2,010 | 32/1,962 | 28/2,038 | |
| Total energy, kcal | <170 | 170–249 | 250–352 | >352 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.98 (0.82, 1.16) | 0.89 (0.72, 1.10) | 1.02 (0.73, 1.42) | 0.532 |
| No. of cases/total | 395/2,307 | 388/2,297 | 377/2,432 | 416/2,396 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.80 (1.04, 3.13) | 1.22 (0.58, 2.59) | 1.33 (0.41, 4.37) | 0.656 |
| No. of cases/total | 27/1,939 | 45/1,954 | 29/2,084 | 26/2,006 | |
| Polyunsaturated fat | |||||
| Percent energy | <5.4 | 5.4–6.6 | 6.7–8.0 | >8.0 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.04 (0.89, 1.21) | 1.02 (0.88, 1.19) | 0.95 (0.81, 1.11) | 0.507 |
| No. of cases/total | 396/2,394 | 409/2,372 | 394/2,334 | 377/2,332 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 2.41 (1.33, 4.34) | 2.34 (1.29, 4.25) | 2.41 (1.33, 4.38) | 0.002 |
| No. of cases/total | 16/2,014 | 38/2,001 | 36/1,976 | 37/1,992 | |
| Total energy, kcal | <93 | 93–134 | 135–191 | >191 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.91 (0.77, 1.07) | 0.95 (0.79, 1.15) | 0.88 (0.68, 1.15) | 0.474 |
| No. of cases/total | 394/2,302 | 378/2,369 | 405/2,362 | 399/2,399 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.44 (0.85, 2.45) | 1.77 (0.94, 3.32) | 2.89 (1.24, 6.73) | 0.019 |
| No. of cases/total | 30/1,938 | 34/2,025 | 31/1,988 | 32/2,032 | |
| Carbohydrate | |||||
| Percent energy | <43.1 | 43.1–48.6 | 48.7–54.7 | >54.7 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.95 (0.81, 1.11) | 0.93 (0.80, 1.09) | 1.04 (0.89, 1.22) | 0.684 |
| No. of cases/total | 395/2,342 | 383/2,349 | 379/2,359 | 419/2,382 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.96 (0.58, 1.58) | 1.05 (0.65, 1.72) | 0.82 (0.48, 1.38) | 0.557 |
| No. of cases/total | 31/1,978 | 32/1,998 | 36/2,016 | 28/1,991 | |
| Total energy, kcal | <755 | 755–1,005 | 1,006–1,304 | >1,304 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.00 (0.85, 1.18) | 1.04 (0.87, 1.25) | 1.02 (0.82, 1.27) | 0.766 |
| No. of cases/total | 365/2,211 | 400/2,398 | 411/2,420 | 400/2,403 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.80 (0.49, 1.30) | 0.67 (0.38, 1.19) | 0.64 (0.31, 1.31) | 0.171 |
| No. of cases/total | 41/1,887 | 34/2,032 | 27/2,036 | 25/2,028 | |
| Protein | |||||
| Percent energy | <15.0 | 15.0–16.9 | 17.0–18.9 | >18.9 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.00 (0.86, 1.17) | 0.96 (0.82, 1.12) | 0.93 (0.79, 1.08) | 0.280 |
| No. of cases/total | 407/2,338 | 408/2,376 | 389/2,373 | 372/2,345 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.82 (0.51, 1.34) | 0.78 (0.47, 1.28) | 0.82 (0.51, 1.34) | 0.412 |
| No. of cases/total | 37/1,968 | 30/1,998 | 28/2,012 | 32/2,005 | |
| Total energy, kcal | <258 | 258–349 | 350–460 | >460 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.96 (0.81, 1.13) | 0.95 (0.78, 1.14) | 0.83 (0.64, 1.07) | 0.214 |
| No. of cases/total | 379/2,250 | 401/2,382 | 406/2,382 | 390/2,418 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.97 (0.58, 1.61) | 0.98 (0.54, 1.79) | 0.63 (0.26, 1.51) | 0.474 |
| No. of cases/total | 36/1,907 | 35/2,016 | 34/2,010 | 22/2,050 | |
| Alcohol consumption, drinks/week | <1 | 1–6 | 7–13 | ≥14 | |
| OR (95% CI) for GS 2–7b | 1.00 (referent) | 1.02 (0.89, 1.17) | 1.11 (0.94, 1.30) | 1.10 (0.92, 1.30) | 0.170 |
| No. of cases/total | 677/4,179 | 426/2,577 | 258/1,447 | 215/1,229 | |
| OR (95% CI) for GS 8–10b | 1.00 (referent) | 1.01 (0.64, 1.58) | 1.20 (0.71, 2.03) | 1.73 (1.03, 2.89) | 0.055 |
| No. of cases/total | 54/3,556 | 31/2,182 | 20/1,209 | 22/1,036 | |
| OR (95% CI) for GS 2–7c | 1.00 (referent) | 1.02 (0.90, 1.17) | 1.11 (0.95, 1.30) | 1.11 (0.93, 1.31) | 0.137 |
| No. of cases/total | 677/4,179 | 426/2,577 | 258/1,447 | 215/1,229 | |
| OR (95% CI) for GS 8–10c | 1.00 (referent) | 1.00 (0.64, 1.16) | 1.18 (0.70, 2.00) | 1.63 (0.98, 2.71) | 0.080 |
| No. of cases/total | 54/3,556 | 31/2,182 | 20/1,209 | 22/1,036 | |
Abbreviations: CI, confidence interval; GS, Gleason score; OR, odds ratio.
Results were controlled for age, race/ethnicity, treatment arm, and body mass index.
Results were additionally controlled for total energy intake (substitution of nonalcohol energy for alcohol).
Results were additionally controlled for nonalcohol energy intake (adding energy from alcohol).
For each macronutrient, we present results from 2 statistical models, labeled “Percent energy” and “Total energy.” In the percent energy models, we examined the percentage of energy derived from each macronutrient (for alcohol, models used categorized numbers of drinks per week) and used a linear term for total energy as a covariate; results from this model are interpreted as the effect of substituting energy from each specific macronutrient for other macronutrients. The total energy models examined energy from each macronutrient, and those results can be interpreted as the effect of increasing energy from a specific macronutrient while keeping the energy from other macronutrients constant. In both the percent energy models and the total energy models, there were no associations of energy, carbohydrate, or protein with risk of either high- or low-grade cancer. In the percent energy models, men in the highest category of alcohol intake (≥14 drinks/week) had a 73% increased risk of high-grade cancer in comparison with nondrinkers (P < 0.04); in total energy models, this increase was 63% (P < 0.06). Intake of polyunsaturated fat was positively and significantly associated with risk of high-grade disease: In the percent energy models, there were significant risk increases of approximately 140% in quartiles 2–4 as compared with quartile 1 (all P’s < 0.005), with no dose-response; in the total energy model, there was a significant dose-response, with a nearly 190% increased risk of high-grade disease in quartile 4 as compared with quartile 1 (P < 0.015).
We completed additional analyses to better characterize the findings specific to polyunsaturated fat and high-grade cancer. In a model examining the effect of substituting polyunsaturated fats for saturated fats, substitution of each percentage point of energy from polyunsaturated fat for saturated fat was associated with a 23% (95% confidence interval (CI): 9, 39) increased risk of high-grade disease. In a model examining the effects of adding energy from each type of fat while keeping energy from all other macronutrients and other types of fat constant, only the coefficient for polyunsaturated fat and high-grade disease was statistically significant (P < 0.005), yielding an estimate of a 132% (95% CI: 30, 314) increased risk of high-grade disease associated with each 100-kcal/day increase in energy from polyunsaturated fat.
Table 3 shows the associations of dietary supplement use with cancer risk. There were no significant associations of multivitamin or single supplement use with risk of either low- or high-grade prostate cancer.
Table 3.
Associations of Daily Dietary Supplement Intake With the Risks of Low- and High-Grade Prostate Cancer, Prostate Cancer Prevention Trial, 1994–2003
| Category of Dietary Supplement Intake |
P for Trend | |||
| Lowest | Moderate | Highest | ||
| Multivitamins, pills/week | <1 | 1–6 | >6 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.07 (0.86, 1.33) | 1.09 (0.97, 1.22) | 0.138 |
| No. of cases/total | 858/5,296 | 112/654 | 606/3,482 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.96 (0.46, 2.01) | 1.00 (0.69, 1.45) | 0.989 |
| No. of cases/total | 73/4,511 | 8/550 | 46/2,922 | |
| Vitamin C, mg | <60 | 60–250 | >250 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.09 (0.95, 1.26) | 1.06 (0.94, 1.21) | 0.298 |
| No. of cases/total | 688/4,262 | 381/2,179 | 507/2,991 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.05 (0.66, 1.65) | 1.09 (0.72, 1.64) | 0.679 |
| No. of cases/total | 57/3,631 | 29/1,827 | 41/2,525 | |
| Vitamin E, mg | <8 | 8–30 | >30 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.05 (0.91, 1.21) | 1.08 (0.96, 1.23) | 0.199 |
| No. of cases/total | 718/4,423 | 336/1,996 | 522/3,013 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.82 (0.49, 1.35) | 1.21 (0.82, 1.78) | 0.390 |
| No. of cases/total | 59/3,764 | 21/1,681 | 47/2,538 | |
| Calcium, mg | <150 | 150–199 | >199 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.08 (0.95, 1.23) | 1.11 (0.96, 1.29) | 0.117 |
| No. of cases/total | 917/5,668 | 381/2,197 | 278/1,567 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.02 (0.66, 1.56) | 0.77 (0.46, 1.32) | 0.428 |
| No. of cases/total | 80/4,831 | 30/1,846 | 17/1,306 | |
| Zinc, μg | <15 | 15–22 | >22 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.10 (0.97, 1.24) | 0.95 (0.79, 1.15) | 0.723 |
| No. of cases/total | 894/5,467 | 526/2,974 | 156/991 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.99 (0.67, 1.47) | 1.08 (0.62, 1.90) | 0.847 |
| No. of cases/total | 74/4,647 | 38/2,486 | 15/850 | |
| Fish oil, mg | 0 | 0.5 | ||
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.15 (0.93, 1.42) | 0.184 | |
| No. of cases/total | 1,459/8,805 | 117/627 | ||
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.31 (0.70, 2.45) | 0.399 | |
| No. of cases/total | 116/7,462 | 11/521 | ||
| Selenium, μg | <10 | 10–30 | >30 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.08 (0.96, 1.22) | 1.06 (0.89, 1.25) | 0.184 |
| No. of cases/total | 870/5,351 | 514/2,947 | 192/1,134 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.80 (0.53, 1.21) | 1.00 (0.58, 1.73) | 0.315 |
| No. of cases/total | 78/4,559 | 33/2,466 | 16/958 | |
| Vitamin D, μg | <2.5 | 2.5–10 | >10 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.06 (0.94, 1.19) | 1.10 (0.88, 1.38) | 0.250 |
| No. of cases/total | 874/5,362 | 601/3,494 | 101/576 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.94 (0.64, 1.37) | 1.01 (0.48, 2.10) | 0.832 |
| No. of cases/total | 75/4,563 | 44/2,937 | 8/483 | |
Abbreviations: CI, confidence interval; GS, Gleason score; OR, odds ratio.
Table 4 shows associations for selected micronutrients and food components hypothesized to be associated with prostate cancer risk. Results are given for dietary intake alone and total intake (diet plus supplements) where appropriate. Results for dietary vitamin E and selenium are not reported because, based on very poor correlations between FFQ-based dietary intakes of these nutrients and serum concentrations (22–27), we believe they cannot be assessed using an FFQ. There were significant associations of dietary calcium intake with prostate cancer risk which differed between low- and high-grade disease and showed no evidence of dose-response. For low-grade cancer, men in quartile 4 had a 27% higher risk (P < 0.04) than those in quartile 1. For high-grade cancer, men in quartiles 2, 3, and 4 all had significantly lower risks than those in quartile 1, and in a post-hoc analysis, the odds ratio comparing quartiles 2–4 with quartile 1 was 0.52 (95% CI: 0.33, 0.82). In analyses of total calcium intake, the association with low-grade disease was attenuated and no longer statistically significant, but the association with high-grade disease was unchanged. No antioxidant micronutrient or phytochemical, including vitamin C, nonlycopene carotenoids, lycopene, or EPA + DHA, was associated with prostate cancer risk. There was modest evidence that high dietary zinc intake was associated with reduced risk of high-grade disease; in a post-hoc analysis, there was a borderline statistically significant (P = 0.05) 39% (95% CI: 63, 0) reduced risk of high-grade cancer in quartiles 3–4 as compared with quartiles 1–2. However, there was no association when considering total zinc.
Table 4.
Associations of Daily Dietary and Total Micronutrient Intake With the Risks of Low- and High-Grade Prostate Cancer, Prostate Cancer Prevention Trial, 1994–2003
| Quartile of Dietary or Macronutrient Intakea |
P for Trend | ||||
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | ||
| Vitamin C, mg | |||||
| Diet | <78.7 | 78.7–122.6 | 122.7–179.1 | >179.1 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.09 (0.93, 1.28) | 1.05 (0.89, 1.23) | 1.05 (0.89, 1.25) | 0.701 |
| No. of cases/total | 359/2,277 | 417/2,428 | 402/2,387 | 398/2,340 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.29 (0.79, 2.12) | 0.97 (0.57, 1.67) | 1.24 (0.71, 2.15) | 0.717 |
| No. of cases/total | 30/1,948 | 38/2,049 | 27/2,012 | 32/1,974 | |
| Total | <120.1 | 120.1–217.6 | 217.7–636.4 | >636.4 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.02 (0.88, 1.19) | 0.94 (0.80, 1.10) | 1.03 (0.88, 1.21) | 0.965 |
| No. of cases/total | 399/2,414 | 418/2,442 | 385/2,383 | 374/2,193 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.29 (0.79, 2.09) | 0.87 (0.51, 1.49) | 1.24 (0.75, 2.06) | 0.750 |
| No. of cases/total | 32/2,047 | 38/2,062 | 25/2,023 | 32/1,851 | |
| Zinc, mg | |||||
| Diet | <9.6 | 9.6–13.1 | 13.2–17.7 | >17.7 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.05 (0.89, 1.24) | 0.98 (0.81, 1.19) | 1.13 (0.89, 1.44) | 0.518 |
| No. of cases/total | 368/2,230 | 404/2,386 | 384/2,426 | 420/2,390 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.90 (0.55, 1.48) | 0.54 (0.29, 1.02) | 0.62 (0.28, 1.38) | 0.113 |
| No. of cases/total | 41/1,903 | 37/2,019 | 23/2,065 | 26/1,996 | |
| Total | <13.1 | 13.1–21.7 | 21.8–31.2 | >31.2 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.05 (0.89, 1.24) | 0.99 (0.84, 1.16) | 1.12 (0.94, 1.33) | 0.323 |
| No. of cases/total | 379/2,337 | 410/2,437 | 381/2,374 | 406/2,284 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.87 (0.52, 1.46) | 0.92 (0.56, 1.52) | 0.90 (0.52, 1.54) | 0.772 |
| No. of cases/total | 39/1,997 | 29/2,056 | 31/2,024 | 28/1,906 | |
| Carotenoids (excluding lycopene), μg | |||||
| Diet | <5,342 | 5,342–8,340 | 8,341–12,799 | >12,799 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.12 (0.96, 1.31) | 0.95 (0.81, 1.12) | 1.00 (0.84, 1.18) | 0.504 |
| No. of cases/total | 373/2,319 | 430/2,392 | 379/2,382 | 394/2,339 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.85 (0.52, 1.38) | 0.70 (0.41, 1.17) | 0.82 (0.49, 1.40) | 0.353 |
| No. of cases/total | 38/1,984 | 32/1,994 | 27/2,030 | 30/1,975 | |
| Total | <7,500 | 7,500–12,499 | 12,500–23,450 | >23,450 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.88 (0.75, 1.04) | 0.94 (0.80, 1.09) | 0.98 (0.83, 1.15) | 0.996 |
| No. of cases/total | 383/2,236 | 341/2,182 | 453/2,700 | 399/2,314 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.22 (0.74, 2.04) | 0.92 (0.54, 1.56) | 1.28 (0.77, 2.13) | 0.572 |
| No. of cases/total | 29/1,882 | 33/1,874 | 30/2,277 | 35/1,950 | |
| Dietary lycopene, μg | <3,999 | 3,999–6,646 | 6,647–10,918 | >10,918 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.13 (0.97, 1.32) | 1.00 (0.85, 1.18) | 1.06 (0.89, 1.26) | 0.897 |
| No. of cases/total | 380/2,342 | 419/2,364 | 388/2,411 | 389/2,315 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.22 (0.73, 2.04) | 1.50 (0.90, 2.51) | 1.33 (0.76, 2.34) | 0.221 |
| No. of cases/total | 30/1,992 | 31/1,976 | 37/2,060 | 29/1,955 | |
| Calcium, mg | <598 | 598–841 | 842–1,165 | >1,165 | |
| Diet | |||||
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.19 (1.01, 1.41) | 1.01 (0.84, 1.21) | 1.27 (1.02, 1.57) | 0.165 |
| No. of cases/total | 346/2,210 | 420/2,367 | 368/2,414 | 442/2,441 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.48 (0.29, 0.82) | 0.57 (0.33, 1.00) | 0.43 (0.21, 0.89) | 0.034 |
| No. of cases/total | 48/1,912 | 24/1,971 | 31/2,077 | 24/2,023 | |
| Total | <689 | 689–972 | 973–1,357 | >1,357 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.06 (0.90, 1.25) | 0.95 (0.80, 1.14) | 1.17 (0.97, 1.42) | 0.222 |
| No. of cases/total | 366/2,250 | 403/2,389 | 366/2,384 | 441/2,409 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.51 (0.30, 0.86) | 0.69 (0.41, 1.18) | 0.46 (0.24, 0.89) | 0.053 |
| No. of cases/total | 47/1,931 | 24/2,010 | 34/2,052 | 22/1,990 | |
| Vitamin D, μg | |||||
| Diet | <3.1 | 3.1–4.5 | 4.6–6.7 | >6.7 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.02 (0.87, 1.20) | 1.07 (0.91, 1.27) | 1.10 (0.91, 1.33) | 0.261 |
| No. of cases/total | 360/2,258 | 390/2,388 | 409/2,399 | 417/2,387 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.84 (0.52, 1.36) | 0.70 (0.41, 1.21) | 0.82 (0.45, 1.49) | 0.402 |
| No. of cases/total | 39/1,937 | 33/2,031 | 26/2,016 | 29/1,999 | |
| Total | <4.2 | 4.2–8.1 | 8.2–14.6 | >14.6 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.04 (0.88, 1.22) | 1.02 (0.87, 1.19) | 1.14 (0.97, 1.35) | 0.142 |
| No. of cases/total | 369/2,323 | 397/2,401 | 388/2,369 | 422/2,339 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.83 (0.49, 1.39) | 1.06 (0.66, 1.72) | 0.82 (0.48, 1.41) | 0.757 |
| No. of cases/total | 37/1,991 | 28/2,032 | 36/2,017 | 26/1,943 | |
| Docosahexaenoic acid + eicosapentaenoic acid, mg | |||||
| Diet | <0.06 | 0.06–0.12 | 0.13–0.24 | >0.24 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.09 (0.93, 1.27) | 1.05 (0.90, 1.23) | 1.08 (0.92, 1.28) | 0.456 |
| No. of cases/total | 378/2,372 | 404/2,348 | 399/2,408 | 395/2,304 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.25 (0.75, 2.09) | 1.20 (0.71, 2.03) | 1.52 (0.89, 2.58) | 0.163 |
| No. of cases/total | 28/2,022 | 33/1,977 | 31/2,040 | 35/1,944 | |
| Total | <0.07 | 0.07–0.14 | 0.15–0.28 | >0.28 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.05 (0.90, 1.23) | 1.05 (0.90, 1.24) | 1.11 (0.94, 1.31) | 0.230 |
| No. of cases/total | 379/2,377 | 400/2,381 | 401/2,425 | 396/2,249 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.31 (0.78, 2.20) | 1.30 (0.77, 2.20) | 1.46 (0.86, 2.50) | 0.193 |
| No. of cases/total | 27/2,025 | 34/2,015 | 33/2,057 | 33/1,886 | |
| Folate, μg of α-tocopherol equivalents | |||||
| Diet | <458 | 458–614 | 615–809 | >809 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 1.07 (0.91, 1.27) | 1.00 (0.83, 1.19) | 0.94 (0.75, 1.16) | 0.428 |
| No. of cases/total | 367/2,260 | 409/2,341 | 408/2,423 | 392/2,408 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 0.96 (0.59, 1.59) | 0.80 (0.45, 1.43) | 0.82 (0.41, 1.66) | 0.480 |
| No. of cases/total | 38/1,931 | 34/1,966 | 28/2,043 | 27/2,043 | |
| Total | <582 | 582–1,023 | 1,024–5,291 | >5,291 | |
| OR (95% CI) for GS 2–7 | 1.00 (referent) | 0.97 (0.82, 1.15) | 1.11 (0.94, 1.30) | 1.04 (0.88, 1.23) | 0.306 |
| No. of cases/total | 372/2,298 | 390/2,437 | 413/2,322 | 401/2,375 | |
| OR (95% CI) for GS 8–10 | 1.00 (referent) | 1.09 (0.64, 1.84) | 1.07 (0.66, 1.76) | 0.90 (0.51, 1.56) | 0.714 |
| No. of cases/total | 35/1,961 | 33/2,080 | 33/1,942 | 26/2,000 | |
Abbreviations: CI, confidence interval; GS, Gleason score; OR, odds ratio.
DISCUSSION
In this unique study of primarily local-stage prostate cancer, in which the presence or absence of prostate cancer was determined by prostate biopsy, there were no statistically significant associations of nutrient intake or dietary supplement use with prostate cancer overall. When results were stratified by disease grade (low- vs. high-grade disease (Gleason score 2–7 vs. 8–10)), there were several noteworthy associations. Polyunsaturated fat intake was positively associated with risk of high-grade cancer, and dietary calcium intake was positively associated with risk of low-grade cancer and inversely associated with risk of high-grade cancer. Based on a post-hoc analysis, there was evidence that dietary zinc intake beyond a relatively low threshold was associated with reduced risk of high-grade cancer. There was also some evidence that a high alcohol intake was associated with increased risk of high-grade disease; the associations of alcohol intake with cancer risk in the PCPT are complex and have been described previously (28). Neither use of dietary supplements nor intake of antioxidants, folate, vitamin D, or long-chain n-3 fatty acids was significantly associated with low- or high-grade prostate cancer risk.
Investigators in many large case-control and cohort studies have reported that calcium intake from foods and/or supplements was associated with increased cancer risk (29–34). Our finding of no association with total prostate cancer risk (odds ratios contrasting quartile 4 with quartile 1 were 1.16 (95% CI: 0.95, 1.43) and 1.09 (95% CI: 0.91, 1.32) for dietary intake and total intake, respectively) was consistent with the null findings from several other large cohort studies (35–38). Our finding that calcium intake was inversely associated with high-grade cancer but positively associated with low-grade cancer is inconsistent with several other studies that found associations to be stronger or exclusive for high-grade or advanced-stage disease (29, 31, 32); in particular, we found no evidence that very high dietary calcium intakes (>1,400 mg/day) were associated with increased risk of high-grade disease. Our findings are similar to those reported from the screening arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (33). In both the PCPT and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and in contrast to other studies, almost all prostate cancers were local-stage and screen-detected. It is possible that risk factors for screen-detected cancers are different from those diagnosed clinically. For example, if we assume that low-grade cancers develop into high-grade cancers, perhaps calcium decreases the rate at which low-grade cancers progress. However, lacking a strong biologic rationale, the calcium findings from both the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and the PCPT should be considered provisional until they are replicated in studies that separate screen-detected cancers from clinically detected cancers.
Many investigators have studied associations of dietary fat with prostate cancer risk, and their findings are inconsistent. In a 2004 meta-analysis, Dennis et al. (39) found a significantly increased risk associated with high fat consumption in case-control studies but no association in cohort studies; and in more recently published cohort studies, investigators have found either no associations (40–42) or significant inverse associations for high-grade disease (43). Study results differ somewhat when risk is examined separately by stage and/or grade and when fats are separated into polyunsaturated, monounsaturated, and saturated fats, but overall there is little support for associations of fat with risk. We know of no studies which have found that a high intake of polyunsaturated fat—more specifically, the substitution of polyunsaturated fat for saturated fat—was associated with increased risk of high-grade cancer; however, this finding is biologically plausible. The n-6 fatty acids, which constitute the majority of dietary polyunsaturated fats, are proinflammatory (44), and inflammation may play an important role in prostate cancer pathogenesis (45). A single study of heavy smokers and/or asbestos-exposed men found a substantially increased risk associated with high polyunsaturated fat consumption, which was restricted to the small subset of men with a family history of prostate cancer (41). Nevertheless, our findings are generally inconsistent with those in the literature and require replication in studies of screen-detected cancer.
The most significant weakness in this study was the use of FFQs to measure nutrient intake. Recently, some investigators have questioned the validity of FFQs for dietary assessment (46, 47), and some scientists have challenged their continued use in epidemiologic research (48, 49), although this view is controversial. As demonstrated in studies of dietary fat and breast cancer risk (50, 51), there is a distinct possibility that moderate or weak associations of diet with cancer risk cannot be detected using FFQs but can be detected using multiple-day food records. We believe that strong associations will probably be detected across extreme intake categories, and our concern is that weak but meaningful associations may not be detected. We also chose not to follow several common practices used in nutritional epidemiology. First, we did not adjust model results for multiple dietary factors simultaneously, because most dietary covariates are highly correlated and poorly measured, and their use could therefore lead to unstable models with unpredictable results (52). Second, we did not conduct multiple subgroup analyses—for example, examining results stratified by age or nutrients stratified by type of dietary exposure (e.g., folate from food vs. folate from supplements)— because, lacking a strong biologic rationale, this increases the likelihood of chance findings. It is possible that true, subgroup-specific or nutrient-adjusted associations were missed in our analyses. Our plan is to examine these more complex hypotheses in future analyses based on biomarkers of diet and then attempt to confirm the results using dietary intake data.
There are unique aspects of this study that both increase its quality and limit its generalizability. The most significant are that study participants had PSA levels less than 3 ng/mL at study entry, there was annual screening (PSA plus DRE) during the 7 years of the trial, and determination of the presence or absence of disease was based on endpoint biopsies. Thus, almost all of the cancers that were detected were local-stage, and while the use of endpoint biopsies to identify cancer cases and noncases minimized detection bias, it also identified cancers that would never have been detected by means of either screening or clinical symptoms. A second unique aspect of this study is the use of uniformly graded Gleason scores of 8–10 to define high-grade disease, in contrast to other studies that have used a mix of stage (often surgical and clinical) and grade, as well as long-term clinical outcomes, to define “aggressive” disease. Taken together, the mix of cancer phenotypes in the PCPT may differ markedly from the phenotype mixes in studies that are based on cancers detected by screening alone or by locally defined standards of clinical practice. Thus, risk factors for cancers identified in the PCPT could be quite different from those for clinically detected or advanced-stage disease. Nevertheless, a major strength of this study is the mitigation of the detection biases present in most observational cohort studies in which PSA levels and DREs affect the decision to perform a prostate biopsy. Use of PSA screening is probably associated with dietary patterns (53), such that biases due to screening may have seriously confounded the results of previous studies.
In conclusion, in this unique sample of local-stage, biopsy-detected cancers, we found no evidence that dietary or supplemental intake of nutrients often proposed to prevent prostate cancer, including lycopene, n-3 fatty acids, vitamin D, vitamin E, and selenium, was associated with risk of low- or high-grade cancer. Our finding that polyunsaturated fat was associated with increased risk of high-grade prostate cancer suggests that further research into inflammation and other metabolic processes affected by these fats may be important in understanding prostate cancer etiology. Our finding of a positive association of calcium with low-grade disease and an inverse association with high-grade disease adds to the inconsistency of findings related to calcium, which may be important and may require further inquiry. The consistent and strong findings from ecologic studies that the adoption of a diet high in fat and animal products, characteristic of Western diets (54–56), increases prostate cancer risk are perplexing. It is possible that these ecologic studies are yielding results that do not reflect individual-level cancer risk, that the specific aspects of diet affecting prostate cancer risk have not been adequately measured or identified, or that the association of a Western-style diet with prostate cancer risk cannot be reduced to studies of a single nutrient or set of nutrients.
Acknowledgments
Author affiliations: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (Alan R. Kristal, Kathryn B. Arnold, Marian L. Neuhouser, Phyllis Goodman); Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Washington (Alan R. Kristal); Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland (Elizabeth A. Platz); National Cancer Institute, Bethesda, Maryland (Demetrius Albanes); and Department of Urology, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas (Ian M. Thompson).
This work was supported by the following grants from the National Cancer Institute: R01 CA63164 (Prospective Cohort Study of Diet and Prostate Cancer), P01 CA37429 (Prostate Cancer Prevention Trial), and P01 CA108964 (Biology of the Prostate Cancer Prevention Trial).
Conflict of interest: none declared.
Glossary
Abbreviations
- CI
confidence interval
- DHA
docosahexaenoic acid
- DRE
digital rectal examination
- EPA
eicosapentaenoic acid
- FFQ
food frequency questionnaire
- PCPT
Prostate Cancer Prevention Trial
- PSA
prostate-specific antigen
References
- 1.Clark LC, Dalkin B, Krongrad A, et al. Decreased incidence of prostate cancer with selenium supplementation: results of a double-blind cancer prevention trial. Br J Urol. 1998;81(5):730–734. doi: 10.1046/j.1464-410x.1998.00630.x. [DOI] [PubMed] [Google Scholar]
- 2.Peters U, Littman AJ, Kristal AR, et al. Vitamin E and selenium supplementation and risk of prostate cancer in the VITamins And Lifestyle (VITAL) Study Cohort. Cancer Causes Control. 2008;19(1):75–87. doi: 10.1007/s10552-007-9072-y. [DOI] [PubMed] [Google Scholar]
- 3.Chan JM, Stampfer MJ, Ma J, et al. Supplemental vitamin E intake and prostate cancer risk in a large cohort of men in the United States. Cancer Epidemiol Biomarkers Prev. 1999;8(10):893–899. [PubMed] [Google Scholar]
- 4.Wright ME, Peters U, Gunter MJ, et al. Association of variants in two vitamin E transport genes with circulating vitamin E concentrations and prostate cancer risk. Cancer Res. 2009;69(4):1429–1438. doi: 10.1158/0008-5472.CAN-08-2343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lippman SM, Klein EA, Goodman PJ, et al. Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the Selenium and Vitamin E Cancer Prevention Trial (SELECT) JAMA. 2009;301(1):39–51. doi: 10.1001/jama.2008.864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Giovannucci E, Rimm EB, Liu Y, et al. A prospective study of tomato products, lycopene, and prostate cancer risk. J Natl Cancer Inst. 2002;94(5):391–398. doi: 10.1093/jnci/94.5.391. [DOI] [PubMed] [Google Scholar]
- 7.Giovannucci E. Tomato products, lycopene, and prostate cancer: a review of the epidemiological literature. J Nutr. 2005;135(8):2030S–2031S. doi: 10.1093/jn/135.8.2030S. [DOI] [PubMed] [Google Scholar]
- 8.Kristal AR, Schenk JM. Directions for future epidemiological research in lycopene and prostate cancer risk. J Nutr. 2005;135(8):2037S–2039S. doi: 10.1093/jn/135.8.2037S. [DOI] [PubMed] [Google Scholar]
- 9.Peters U, Leitzmann MF, Chatterjee N, et al. Serum lycopene, other carotenoids, and prostate cancer risk: a nested case-control study in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cancer Epidemiol Biomarkers Prev. 2007;16(5):962–968. doi: 10.1158/1055-9965.EPI-06-0861. [DOI] [PubMed] [Google Scholar]
- 10.Chan JM, Giovannucci EL. Dairy products, calcium, and vitamin D and risk of prostate cancer. Epidemiol Rev. 2001;23(1):87–92. doi: 10.1093/oxfordjournals.epirev.a000800. [DOI] [PubMed] [Google Scholar]
- 11.Park SY, Murphy SP, Wilkens LR, et al. Calcium, vitamin D, and dairy product intake and prostate cancer risk: the Multiethnic Cohort Study. Am J Epidemiol. 2007;166(11):1259–1269. doi: 10.1093/aje/kwm269. [DOI] [PubMed] [Google Scholar]
- 12.Baron JA, Beach M, Wallace K, et al. Risk of prostate cancer in a randomized clinical trial of calcium supplementation. Cancer Epidemiol Biomarkers Prev. 2005;14(3):586–589. doi: 10.1158/1055-9965.EPI-04-0319. [DOI] [PubMed] [Google Scholar]
- 13.Etzioni R, Penson DF, Legler JM, et al. Overdiagnosis due to prostate-specific antigen screening: lessons from U.S. prostate cancer incidence trends. J Natl Cancer Inst. 2002;94(13):981–990. doi: 10.1093/jnci/94.13.981. [DOI] [PubMed] [Google Scholar]
- 14.Gleason DF. Histologic grading of prostate cancer: a perspective. Hum Pathol. 1992;23(3):273–279. doi: 10.1016/0046-8177(92)90108-f. [DOI] [PubMed] [Google Scholar]
- 15.Stanford JL, Wicklund KG, McKnight B, et al. Vasectomy and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 1999;8(6):881–886. [PubMed] [Google Scholar]
- 16.Thompson IM, Goodman PJ, Tangen CM, et al. The influence of finasteride on the development of prostate cancer. N Engl J Med. 2003;349(3):215–224. doi: 10.1056/NEJMoa030660. [DOI] [PubMed] [Google Scholar]
- 17.Satia-Abouta J, Patterson RE, Schiller RN, et al. Energy from fat is associated with obesity in U.S. men: results from the Prostate Cancer Prevention Trial. Prev Med. 2002;34(5):493–501. doi: 10.1006/pmed.2002.1018. [DOI] [PubMed] [Google Scholar]
- 18.Neuhouser ML, Kristal AR, Patterson RE, et al. Dietary supplement use in the Prostate Cancer Prevention Trial: implications for prevention trials. Nutr Cancer. 2001;39(1):12–18. doi: 10.1207/S15327914nc391_2. [DOI] [PubMed] [Google Scholar]
- 19.Willett WC. Nutritional Epidemiology. 2nd ed. New York, NY: Oxford University Press; 1998. Correction of effects of measurement error. 302–320. [Google Scholar]
- 20.Breslow NE, Day NE. Statistical Methods in Cancer Research. Vol 1. The Analysis of Case-Control Studies. Lyon, France: International Agency for Research on Cancer; 1980. [PubMed] [Google Scholar]
- 21.Gong Z, Agalliu I, Lin DW, et al. Obesity is associated with increased risks of prostate cancer metastasis and death after initial cancer diagnosis in middle-aged men. Cancer. 2007;109(6):1192–1202. doi: 10.1002/cncr.22534. [DOI] [PubMed] [Google Scholar]
- 22.Stryker WS, Kaplan LA, Stein EA, et al. The relation of diet, cigarette smoking, and alcohol consumption to plasma beta-carotene and alpha-tocopherol levels. Am J Epidemiol. 1988;127(2):283–296. doi: 10.1093/oxfordjournals.aje.a114804. [DOI] [PubMed] [Google Scholar]
- 23.Levander OA. The need for measures of selenium status. J Am Coll Toxicol. 1986;5(1):37–44. [Google Scholar]
- 24.Hunter DJ, Morris JS, Chute CG, et al. Predictors of selenium concentration in human toenails. Am J Epidemiol. 1990;132(1):114–122. doi: 10.1093/oxfordjournals.aje.a115623. [DOI] [PubMed] [Google Scholar]
- 25.Dixon LB, Subar AF, Wideroff L, et al. Carotenoid and tocopherol estimates from the NCI Diet History Questionnaire are valid compared with multiple recalls and serum biomarkers. J Nutr. 2006;136(12):3054–3061. doi: 10.1093/jn/136.12.3054. [DOI] [PubMed] [Google Scholar]
- 26.Brunner E, Stallone D, Juneja M, et al. Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. Br J Nutr. 2001;86(3):405–414. doi: 10.1079/bjn2001414. [DOI] [PubMed] [Google Scholar]
- 27.Talegawkar SA, Johnson EJ, Carithers T, et al. Total alpha-tocopherol intakes are associated with serum alpha-tocopherol concentrations in African American adults. J Nutr. 2007;137(10):2297–2303. doi: 10.1093/jn/137.10.2297. [DOI] [PubMed] [Google Scholar]
- 28.Gong Z, Kristal AR, Schenk JM, et al. Alcohol consumption, finasteride, and prostate cancer risk: results from the Prostate Cancer Prevention Trial. Cancer. 2009;115(16):3661–3669. doi: 10.1002/cncr.24423. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gao X, LaValley MP, Tucker KL. Prospective studies of dairy product and calcium intakes and prostate cancer risk: a meta-analysis. J Natl Cancer Inst. 2005;97(23):1768–1777. doi: 10.1093/jnci/dji402. [DOI] [PubMed] [Google Scholar]
- 30.Kesse E, Bertrais S, Astorg P, et al. Dairy products, calcium and phosphorus intake, and the risk of prostate cancer: results of the French prospective SU.VI.MAX (Supplémentation en Vitamines et Minéraux Antioxydants) Study. Br J Nutr. 2006;95(3):539–545. doi: 10.1079/bjn20051670. [DOI] [PubMed] [Google Scholar]
- 31.Allen NE, Key TJ, Appleby PN, et al. Animal foods, protein, calcium and prostate cancer risk: the European Prospective Investigation into Cancer and Nutrition. Br J Cancer. 2008;98(9):1574–1581. doi: 10.1038/sj.bjc.6604331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Giovannucci E, Liu Y, Stampfer MJ, et al. A prospective study of calcium intake and incident and fatal prostate cancer. Cancer Epidemiol Biomarkers Prev. 2006;15(2):203–210. doi: 10.1158/1055-9965.EPI-05-0586. [DOI] [PubMed] [Google Scholar]
- 33.Ahn J, Albanes D, Peters U, et al. Dairy products, calcium intake, and risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Cancer Epidemiol Biomarkers Prev. 2007;16(12):2623–2630. doi: 10.1158/1055-9965.EPI-07-0601. [DOI] [PubMed] [Google Scholar]
- 34.Mitrou PN, Albanes D, Weinstein SJ, et al. A prospective study of dietary calcium, dairy products and prostate cancer risk (Finland) Int J Cancer. 2007;120(11):2466–2473. doi: 10.1002/ijc.22553. [DOI] [PubMed] [Google Scholar]
- 35.Schuurman AG, van den Brandt PA, Dorant E, et al. Animal products, calcium and protein and prostate cancer risk in the Netherlands Cohort Study. Br J Cancer. 1999;80(7):1107–1113. doi: 10.1038/sj.bjc.6690472. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Park Y, Mitrou PN, Kipnis V, et al. Calcium, dairy foods, and risk of incident and fatal prostate cancer: the NIH-AARP Diet and Health Study. Am J Epidemiol. 2007;166(11):1270–1279. doi: 10.1093/aje/kwm268. [DOI] [PubMed] [Google Scholar]
- 37.Koh KA, Sesso HD, Paffenbarger RS, Jr., et al. Dairy products, calcium and prostate cancer risk. Br J Cancer. 2006;95(11):1582–1585. doi: 10.1038/sj.bjc.6603475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Severi G, English DR, Hopper JL, et al. Re: Prospective studies of dairy product and calcium intakes and prostate cancer risk: a meta-analysis [letter] J Natl Cancer Inst. 2006;98(11):794–795. doi: 10.1093/jnci/djj215. [DOI] [PubMed] [Google Scholar]
- 39.Dennis LK, Snetselaar LG, Smith BJ, et al. Problems with the assessment of dietary fat in prostate cancer studies. Am J Epidemiol. 2004;160(5):436–444. doi: 10.1093/aje/kwh243. [DOI] [PubMed] [Google Scholar]
- 40.Park SY, Murphy SP, Wilkens LR, et al. Fat and meat intake and prostate cancer risk: the Multiethnic Cohort Study. Int J Cancer. 2007;121(6):1339–1345. doi: 10.1002/ijc.22805. [DOI] [PubMed] [Google Scholar]
- 41.Neuhouser ML, Barnett MJ, Kristal AR, et al. (n-6) PUFA increase and dairy foods decrease prostate cancer risk in heavy smokers. J Nutr. 2007;137(7):1821–1827. doi: 10.1093/jn/137.7.1821. [DOI] [PubMed] [Google Scholar]
- 42.Wallström P, Bjartell A, Gullberg B, et al. A prospective study on dietary fat and incidence of prostate cancer (Malmö, Sweden) Cancer Causes Control. 2007;18(10):1107–1121. doi: 10.1007/s10552-007-9050-4. [DOI] [PubMed] [Google Scholar]
- 43.Crowe FL, Key TJ, Appleby PN, et al. Dietary fat intake and risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr. 2008;87(5):1405–1413. doi: 10.1093/ajcn/87.5.1405. [DOI] [PubMed] [Google Scholar]
- 44.Funk CD. Prostaglandins and leukotrienes: advances in eicosanoid biology. Science. 2001;294(5548):1871–1875. doi: 10.1126/science.294.5548.1871. [DOI] [PubMed] [Google Scholar]
- 45.De Marzo AM, Platz EA, Sutcliffe S, et al. Inflammation in prostate carcinogenesis. Nat Rev Cancer. 2007;7(4):256–269. doi: 10.1038/nrc2090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Schatzkin A, Kipnis V, Carroll RJ, et al. A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) Study. Int J Epidemiol. 2003;32(6):1054–1062. doi: 10.1093/ije/dyg264. [DOI] [PubMed] [Google Scholar]
- 47.Kipnis V, Carroll RJ, Freedman LS, et al. Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies. Am J Epidemiol. 1999;150(6):642–651. doi: 10.1093/oxfordjournals.aje.a010063. [DOI] [PubMed] [Google Scholar]
- 48.Kristal AR, Peters U, Potter JD. Is it time to abandon the food frequency questionnaire? Cancer Epidemiol Biomarkers Prev. 2005;14(12):2826–2828. doi: 10.1158/1055-9965.EPI-12-ED1. [DOI] [PubMed] [Google Scholar]
- 49.Schatzkin A, Kipnis V. Could exposure assessment problems give us wrong answers to nutrition and cancer questions? J Natl Cancer Inst. 2004;96(21):1564–1565. doi: 10.1093/jnci/djh329. [DOI] [PubMed] [Google Scholar]
- 50.Bingham SA, Luben R, Welch A, et al. Are imprecise methods obscuring a relation between fat and breast cancer? Lancet. 2003;362(9379):212–214. doi: 10.1016/S0140-6736(03)13913-X. [DOI] [PubMed] [Google Scholar]
- 51.Freedman LS, Potischman N, Kipnis V, et al. A comparison of two dietary instruments for evaluating the fat-breast cancer relationship. Int J Epidemiol. 2006;35(4):1011–1021. doi: 10.1093/ije/dyl085. [DOI] [PubMed] [Google Scholar]
- 52.Carroll RJ, Ruppert E, Stefanski LA. Measurement Error in Nonlinear Models. London, United Kingdom: Chapman & Hall Ltd; 1995. [Google Scholar]
- 53.Close DR, Kristal AR, Li S, et al. Associations of demographic and health-related characteristics with prostate cancer screening in Washington State. Cancer Epidemiol Biomarkers Prev. 1998;7(7):627–630. [PubMed] [Google Scholar]
- 54.Rose DP, Boyar AP, Wynder EL. International comparisons of mortality rates for cancer of the breast, ovary, prostate, and colon, and per capita food consumption. Cancer. 1986;58(11):2363–2371. doi: 10.1002/1097-0142(19861201)58:11<2363::aid-cncr2820581102>3.0.co;2-#. [DOI] [PubMed] [Google Scholar]
- 55.Hursting SD, Thornquist M, Henderson MM. Types of dietary fat and the incidence of cancer at five sites. Prev Med. 1990;19(3):242–253. doi: 10.1016/0091-7435(90)90025-f. [DOI] [PubMed] [Google Scholar]
- 56.Bakker N, van't Veer P, Zock PL. Adipose fatty acids and cancers of the breast, prostate and colon: an ecological study. EURAMIC Study Group. Int J Cancer. 1997;72(4):587–591. doi: 10.1002/(sici)1097-0215(19970807)72:4<587::aid-ijc6>3.0.co;2-m. [DOI] [PubMed] [Google Scholar]