joshi2012

Carcinogenesis vol.33 no.11 pp.2108–2118, 2012
doi:10.1093/carcin/bgs242
Advance Access publication July 20, 2012
Red meat and poultry, cooking practices, genetic susceptibility and risk of prostate
cancer: results from a multiethnic case–control study
Amit D.Joshi1,4, Román Corral1, Chelsea Catsburg1,
Juan Pablo Lewinger1, Jocelyn Koo2, Esther M.John2,3,
Sue A.Ingles1 and Mariana C.Stern1,*
1
*To whom correspondence should be addressed. Mariana C. Stern, Ph.D.,
University of Southern California Keck School of Medicine, Norris
Comprehensive Cancer Center, 1441 Eastlake Avenue, room 5421A,
Los Angeles, CA 90089, USA. Tel: +1 323 865 0811
Email: [email protected]
Red meat, processed and unprocessed, has been considered a
potential prostate cancer (PCA) risk factor; epidemiological
evidence, however, is inconclusive. An association between meat
intake and PCA may be due to potent chemical carcinogens that
are generated when meats are cooked at high temperatures. We
investigated the association between red meat and poultry intake
and localized and advanced PCA taking into account cooking
practices and polymorphisms in enzymes that metabolize carcinogens that accumulate in cooked meats. We analyzed data
for 1096 controls, 717 localized and 1140 advanced cases from
the California Collaborative Prostate Cancer Study, a multiethnic, population-based case–control study. We examined nutrient density-adjusted intake of red meat and poultry and tested
for effect modification by 12 SNPs and 2 copy number variants
in 10 carcinogen metabolism genes: GSTP1, PTGS2, CYP1A2,
CYP2E1, EPHX1, CYP1B1, UGT1A6, NAT2, GSTM1 and GSTT1.
We observed a positive association between risk of advanced
PCA and high intake of red meat cooked at high temperatures
(trend P = 0.026), cooked by pan-frying (trend P = 0.035), and
cooked until well-done (trend P = 0.013). An inverse association
was observed for baked poultry and advanced PCA risk (trend
P = 0.023). A gene-by-diet interaction was observed between an
SNP in the PTGS2 gene and the estimated levels of meat mutagens
(interaction P = 0.008). Our results support a role for carcinogens
that accumulate in meats cooked at high temperatures as potential PCA risk factors, and may support a role for heterocyclic
amines (HCAs) in PCA etiology.
Introduction
Other than age, the only well-established risk factors for prostate cancer (PCA) are race/ethnicity, family history of prostate cancer and
low-penetrance genetic variants that have emerged from genome-wide
association studies. However, migrant studies and comparisons of
ethnically similar populations across different countries suggest that
environmental and/or lifestyle factors play an important role in the etiology of PCA (1). Among these factors, dietary factors such as intake
of meat have been considered as a potential PCA risk factor. Given
that few modifiable risk factors are known for PCA, understanding
Abbreviations: BMI, body mass index; CI, confidence interval; FFQ, food
frequency questionnaire; H, Hispanic; HCA, heterocyclic amine; LAC, Los
Angeles County; NHW, non-Hispanic white; OR, odds ratio; PAH, polycyclic aromatic hydrocarbon; PCA, prostate cancer; SEER, Surveillance,
Epidemiology, and End Result; SES, socioeconomic status; SFBA, San
Francisco Bay Area.
© The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
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Department of Preventive Medicine, University of Southern California Keck
School of Medicine, Norris Comprehensive Cancer Center, Los Angeles,
CA 90033, USA, 2Cancer Prevention Institute of California, Fremont,
CA 94538, USA and 3Division of Epidemiology, Department of Health
Research and Policy, Stanford University School of Medicine, and Stanford
Cancer Institute, Stanford, CA 94305, USA4Present address: Department of
Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
the role of dietary factors in PCA etiology is of high public health
relevance.
A review by the World Cancer Research Fund concluded that,
although evidence was limited, consumption of processed meat may
increase the risk of PCA (2). Several cohort studies have reported positive associations (3–6); however, a recent review and meta-analysis
of prospective studies (3), and a few additional recent cohort studies (7,8) provide limited evidence for an association between total
red meat intake and PCA risk. Similarly, several case–control studies
around the world reported positive associations with high intake of
red meat (4,9–12), whereas others did not (4,13–15). Some of these
studies also examined processed meats as a separate food category
and, similar to total red meat, found inconsistent results, with several
cohort (3,16) and case–control (13,15,17,18) studies reporting positive associations, whereas others did not (3,11,13,19,20). In addition,
an ecological study of data from 1930 to 2000 reported that meat
intake was the strongest dietary correlate of the temporal trend of
increasing PCA mortality (21). Overall, the role of high intake of red
meat, processed and unprocessed, remains inconclusive.
Several explanations have been put forward for the association
between red meat and PCA. A prominent one is the relationship
between meat and dietary fat, which has long been proposed as a PCA
risk factor. However, studies of dietary fat and PCA are even less
consistent than those of red meat (4), and some studies have found
positive associations with red meat in the absence of associations with
dietary fat (22). Other potential explanations include the presence of
zinc in meats, which is essential for testosterone synthesis (23), the
presence of heme iron in red meats, which may catalyze oxidative
reactions (24), and the inverse relationship between consumption of
meat and vegetables, which are known to contain many potentially
anti-carcinogenic factors. Finally, an association between PCA and
meat intake may be due to potent chemical carcinogens generated
during cooking and/or processing of red meat and poultry, such as heterocyclic amines (HCAs), polycyclic aromatic hydrocarbons (PAHs)
and N-nitroso compounds. The prostate gland is able to metabolize
these chemicals into activated carcinogens (25); therefore, meat carcinogens are plausible PCA risk factors.
The relative proportion of each of these compounds in cooked
meats depends on the amount and type of meat consumed, cooking
and processing method, and cooking temperature and time. PAHs are
deposited on the surface of smoked or grilled meat due to pyrolysis
of fat droppings (26). HCAs are formed from the interaction of
creatine/creatinine, amino acids and reducing sugars, which occurs
at high temperatures (27). HCA formation increases with increasing
cooking temperature and duration. Cooking methods that produce
high levels of mutagens are broiling, grilling and pan-frying (28), with
pan-frying yielding higher mutagenic activity when compared with
grilling at a similar temperature (29). Since PAHs and HCAs have
been shown to induce damage to prostatic epithelium cells (30), and
were associated with formation of DNA adducts in prostatic tissue
(31,32), it is biologically plausible that consumption of red and white
meats cooked in conditions that favor PAH and HCA formation may
increase the risk of PCA.
Among all epidemiological studies of meat and PCA conducted
to date, few have considered level of doneness and cooking methods. Some studies found support for an association with high
intake of meat cooked with high temperature cooking methods
(5,13) or well-done meat (33–35), whereas others did not (7,8).
Among studies that estimated levels of carcinogens, one cohort
study reported a positive association with the HCA 2-amino-1-meth
yl1-6-phenylimidazo[4,5-b]pyridine (PhIP) (33) and another reported
an association with the PAH benzo-a-pyrene (BaP) (5). Two other
studies reported no associations (8,34).
Red meat, poultry, cooking practices, metabolism and prostate cancer risk
Materials and methods
The California Collaborative Prostate Cancer Study is a multiethnic,
population-based case–control study conducted in Los Angeles County and
in the San Francisco Bay area (SFBA) (37–39). Incident cases of PCA were
identified through two regional cancer registries (Los Angeles County Registry
and the Greater Bay Area Cancer Registry) that participate in the Surveillance,
Epidemiology, and End Result (SEER) program and the California Cancer
Registry. In both study sites, PCA was classified as advanced if the tumor
extended beyond the prostatic capsule or into the adjacent tissue or involved
regional lymph nodes or metastasized to distant locations (SEER 1995 clinical
and pathologic extent of disease codes 41–85). At the Los Angeles site, controls were identified through a standard neighborhood walk algorithm, as we
previously described (39). At the San Francisco site, controls were identified
through random-digit dialing and, for those aged 65–79 years, through random selections from the rosters of beneficiaries of the Health Care Financing
Administration. At both sites, controls were matched to cases on race/ethnicity
and the expected 5-year age distribution of cases.
Study population
San Francisco Bay Area. Characteristics of this study, including eligibility
criteria and ascertainment rates, have been previously reported (36,38). Briefly,
incident cases aged 40–79 years diagnosed with a first primary localized prostate cancer from 1997 to 1998 were randomly sampled (15% of NHW cases
and 60% of African-American cases). Cases with a first primary advanced
prostate cancer included all NHW and African-American men diagnosed
from 1997 to 2000. In-person interviews were completed by 208 (73 African
American and 135 NHW) localized cases, 568 (118 African American and 450
NHW) advanced cases and 545 (90 African American and 455 NHW) controls.
Blood or mouthwash samples were collected for controls and advanced cases
only. The genotype analyses were based on 378 advanced cases (77 African
American and 301 NHW) and 252 controls (35 African American and 217
NHW) with DNA from blood and reliable dietary information from food frequency questionnaire (FFQ).
Los Angeles County. Characteristics of this study, including eligibility criteria and ascertainment rates, have been previously reported (39). Briefly,
African-American, Hispanics and NHW males diagnosed with incident PCA
from 1999 to 2003 were identified using the rapid case ascertainment protocol
of the Los Angeles County Cancer Surveillance Program and the Los Angeles
County cancer registry. A total of 594 controls (163 African American,
122 Hispanics and 309 NHW) and 1184 cases (351 African American, 333
Hispanics and 500 NHW) completed the in-person interview and provided a
blood sample. The analyses in this study were based on 559 advanced cases
(126 African American, 157 Hispanic and 276 NHW), 499 localized cases
(194 African American, 112 Hispanic and 193 NHW) and 511 controls (143
African American, 81 Hispanic and 287 NHW) with DNA from blood and
reliable dietary information from FFQ.
Written informed consent was obtained from all study participants, and the
study was approved by the institutional review boards at the Cancer Prevention
Institute of California and the University of Southern California.
Data collection
Trained professional interviewers administered a structured questionnaire at
the participants’ home that asked about demographic background, medical history and various lifestyle factors, and obtained measurements of height and
weight. Usual dietary intake was assessed for the reference year, defined as
the calendar year prior to diagnosis for cases and the calendar year prior to
selection into the study for controls, using a modified version of the Block
FFQ (40). Questions on cooking methods and degree of doneness and browning were adapted from a commonly used cooking module developed by
Sinha et al. (41). An aggregate level socioeconomic status (SES) variable was
derived from 2000 census data as previously described (36,38). Body mass
index (BMI) was calculated as reported weight (in kilograms) in the reference
year divided by measured height (in meters) squared. For subjects with missing information on self-reported weight (1 case and 1 control), the BMI calculation was based on measured weight. Similarly, for subjects who declined
the measurement of height (4.9% of cases and 4.8% of controls), BMI was
based on self-reported height. Even though weight was measured at the time of
diagnosis, we chose to use self-reported weight given the concern of potential
bias introduced by weight loss among cancer patients at the time of interview.
Whereas using self-reported weight for the reference year may be associated
with misclassification, we speculated that this approach would lead to less misclassification than the one introduced by case weight loss at the time of interview. BMI was categorized as normal weight (BMI <25), overweight (BMI
25–29.9) and obese (BMI ≥30). Underweight men (BMI <18.5, n = 15) were
grouped with normal-weight men.
Exposure variables
The FFQ assessed the usual portion size and frequency of consumption of red
meat (all types of beef and pork, hamburgers and steak), poultry (chicken and
turkey) and processed meat (sausages made from red or white meat, bacon
and cold cuts). Information was obtained on usual method of preparation (e.g.,
pan-frying, oven-broiling, grilling and other methods) and level of doneness
(by choosing from a series of color photographs showing increasing degrees of
doneness and browning) was obtained for hamburgers, steak, poultry, bacon,
sausages and hot dogs.
We utilized the multivariate nutrient density approach (42) to adjust for
energy intake of red meat, poultry, processed meat and meat mutagen variables. These nutrient density variables were categorized into quintiles based on
the distribution among controls. Variables for specific red meats (hamburger,
steak), processed meats (bacon, sausage), homemade gravy, meats cooked by
specific cooking methods (pan-frying, oven-broiling, baking and grilling) or
well-done meats (browned or charred on the surface) were categorized into
four levels of consumption: never/rarely, low (quintiles 2 and 3), medium
(quintile 4) and high (quintile 5). The categorization between the second and
third quintile was highly influenced by the variation in the denominator of the
nutrient density variable (inverse calorie intake). To minimize spurious associations due to this effect, second and third quintile were collapsed to form the
low intake category. Given that oven-broiling, grilling and pan-frying expose
foods to high cooking temperatures, we combined these methods into a single
category termed “high temperature cooking methods”, and nutrient density
variables for specific meats cooked with high temperature methods were created and categorized into four levels as described above.
Polymorphism data
We collected data on 12 SNPs in 8 genes, which were selected given previous
reports on their impact on enzyme function and/or associations with PCA, as
we previously described (43): GSTP1 Ile105Val (rs1695), PTGS2 −765 G/C
(rs20417), CYP1A2 −154 A/C (rs762551), EPHX1 Tyr113His (rs1051740),
CYP1B1 Leu432Val (rs1056836), UGT1A6 Thr181Ala and Arg184Ser
(rs1105879, rs2070959), NAT2 Ile114Thr, Arg197Gln, Gly286Glu and
Arg64Gln (rs1799930, rs1799931, rs1801279, rs180120) and CYP2E1 −1054
C>T (rs2031920). In addition we genotyped two copy number variants in
GSTM1 and GSTT1. We used the information on the four NAT2 polymorphisms to construct haplotypes using haplo.stats package in R. These polymorphisms define different NAT2 alleles, which have been characterized for their
impact on protein function, consistent with the existing classification (44), we
classified carriers of two copies of the slow haplotype as ‘”slow” and carriers of all other haplotypes as “fast” phenotype. All genotypes were obtained
using Taqman assays, available “on demand” from ABI (Applied Biosystems,
Foster City, CA), following manufacturer's instructions. Call rates were >97%.
No differences were found between observed genotypic frequencies and those
expected assuming Hardy–Weinberg equilibrium.
Statistical analysis
After excluding 43 controls, 44 localized cases and 59 advanced cases with
dietary data considered unreliable (i.e., daily caloric intake <600 kcal or >6000
kcal), the analyses of questionnaire data were based on 1096 controls, 717
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The amount of DNA-damaging carcinogens in the prostate is determined by the amount and type of meat consumed, the cooking method
used and the level of activity of key metabolism enzymes that activate
and detoxify carcinogens. Therefore, it is plausible that genetic variation
in key enzymes that activate or detoxify HCAs and PAHs may modify
the association between diets high in red or white meat and PCA risk.
Recently, using data from the San Francisco Bay area component
of the California Collaborative Prostate Cancer Study, we reported
positive associations between consumption of hamburgers, processed
meat, grilled red meat and well-done or very well-done red meat and
advanced, but not localized, PCA risk (36). Moreover, we reported an
association between PhIP intake and advanced PCA, although a dose–
response relationship was lacking, since increased risk was associated
with intermediate, but not high, intake. We now extend these analyses
to the entire California Collaborative Prostate Cancer Study, which
includes non-Hispanic white (NHW), African-American (AA) and
Hispanic cases and controls from the San Francisco Bay area and
from Los Angeles County (LAC). We investigated the associations of
different red meats, processed meats and poultry with risk of localized
and advanced PCA, taking into account cooking methods, level of
doneness, estimated levels of carcinogens and the potential modifying role of selected polymorphisms in enzymes that metabolize meat
mutagens.
Amit D.Joshi et al.
(g/day), total fruit consumption (g/day), total vegetable consumption (g/day)
and alcohol consumption (g/day) during the reference year and lifetime cigarette smoking (pack-years).
To examine the potential modifying role of the selected polymorphisms
on the associations between red meat/poultry and PCA risk, we performed
SNP × red meat or poultry interaction tests coding each SNP as log-additive
and each meat variable as a three-level variable (using the median level of
exposure among controls at each of the levels). We conducted both 2-df interaction tests by treating the three-level meat exposure variable as categorical
1-df tests by treating meat exposures as ordinal. For these gene × environment
analyses we evaluated whether genotypes for eight SNPs and two null variants
in nine carcinogen metabolism genes (CYP1A2, CYP1B1, CYP2E1, EPHX1,
GSTP1, PTGS2, UGT1A6, GSTT1 and GSTM1), and the predicted phenotype
for NAT2, modified the associations with the following dietary variables: high
temperature-cooked red meat, well-done red meat, high temperature-cooked
poultry, well-done poultry, processed meat, well-done processed meat and estimated level of carcinogens. These variables were selected because they capture
meat dietary patterns that have been associated with the accumulation of chemical carcinogens that require metabolism by proteins coded by the selected
genes. The main effects of these SNPs have been previously reported (43). Due
to strong LD between the UGT1A6 Thr181Ala and UGT1A6 Arg184Ser SNPs,
only the former was utilized for the purpose of interaction analyses.
For all analyses, tests for trend were performed by assigning median values
to each quartile and modeling these categories as a continuous variable. All
hypothesis tests were two-sided and all analyses were done using the statistical software Stata S/E 11.0 for Windows (STATA Corporation, College
Station, TX).
Results
Table I shows the sociodemographic characteristics and lifestyle/
dietary patterns of cases and controls. Cases were more likely to be
Table I. Sociodemographic, lifestyle and dietary characteristics of cases and controls by study site
Controls, N (%)
Race/ethnicity
African American
Hispanic
Non-Hispanic white
Excluded from analysisa
Family history of PCA
No
Yes
Excluded from analysisa
Socioeconomic status
1 (Low)
2
3
4
5 (High)
Excluded from analysisa
Body mass index (kg/m2)
<25
25–29
≥30
Excluded from analysisa
Age (years)
Cigarette smoking (pack-years)
Dietary consumption (g/day)
Calorie intake (kcal/day)
Total fat consumption
Fruit consumption
Vegetable consumption
Alcohol consumption
Total dairy consumption
Localized PCA cases, N (%)
Advanced PCA cases, N (%)
P-value* (cases
versus controls)
LAC
SFBA
<0.001
0.001
0.001
<0.001
<0.001
0.003
LAC
SFBA
LAC
SFBA
LAC
SFBA
156 (26)
101 (17)
301 (51)
36 (6)
84 (15)
0 (0)
454 (83)
7 (1)
199 (36)
119 (22)
198 (36)
37 (7)
66 (32)
0 (0)
135 (65)
7 (3)
129 (20)
174 (28)
286 (45)
42 (7)
106 (19)
0 (0)
445 (78)
17 (3)
484 (81)
74 (12)
36 (6)
473 (87)
65 (12)
7 (1)
407 (74)
109 (20)
37 (7)
160 (77)
41 (20)
7 (3)
475 (75)
114 (18)
42 (7)
445 (78)
106 (19)
17 (3)
91 (15)
104 (18)
112 (19)
117 (20)
134 (23)
36 (6)
11 (2)
32 (6)
89 (16)
157 (29)
249 (46)
7 (1)
138 (25)
113 (20)
86 (16)
98 (18)
81 (15)
37 (7)
8 (4)
16 (8)
34 (16)
35 (17)
108 (52)
7 (3)
126 (20)
108 (17)
136 (22)
102 (16)
117 (19)
42 (7)
17 (3)
32 (6)
71 (13)
125 (22)
306 (54)
17 (3)
151 (26)
248 (42)
156 (26)
36 (6)
Mean (SD)
63 (10)
17 (24)
135 (25)
249 (46)
154 (28)
7 (1)
Mean (SD)
64 (8)
20 (26)
137 (25)
253 (46)
124 (23)
37 (7)
Mean (SD)
68 (9)
24 (31)
60 (29)
102 (49)
39 (19)
7 (3)
Mean (SD)
66 (8)
18 (24)
133 (21)
284 (45)
171 (27)
42 (7)
Mean (SD)
64 (9)
18 (27)
153 (27)
271 (48)
127 (22)
17 (3)
Mean (SD)
64 (8)
20 (26)
0.326
0.025
<0.001
0.003
0.694
0.833
2708 (1150)
110 (67)
155 (217)
153 (197)
10 (19)
298 (320)
2543 (996)
93 (47)
71 (125)
119 (154)
16 (33)
316 (316)
2947 (1169)
122 (67)
136 (182)
149 (192)
11 (22)
303 (343)
2582 (1007)
91 (46)
62 (129)
136 (179)
20 (30)
298 (271)
3022 (1179)
123 (70)
134 (192)
139 (169)
9 (20)
349 (347)
2672 (1062)
101 (52)
70 (119)
128 (169)
18 (52)
361 (384)
<0.001
<0.001
0.131
0.374
0.644
0.083
0.072
<0.053
0.670
0.246
0.213
0.134
a
Excluded from analysis if total daily energy intake <600 or >6000 kcal.
*P-value for difference between cases and controls was calculated using χ2 test for categorical variables and t-test for continuous variables.
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localized cases and 1140 advanced cases. Analyses of genotype data were
based on subjects with DNA from blood, including 800 controls, 535 localized cases and 988 advanced cases. These individuals did not differ from those
without DNA with regards to age, calorie intake, family history, SES and BMI
in either study site (data not shown).
To best correct for differences in race/ethnicity, SES and the case–control
ratio across the two study sites, we created a variable that classified subjects
according to study site (SFBA or LAC), SES (five-level variable from low to
high) and race/ethnicity (African American, Hispanic, NHW), and used it to
group individuals into 18 categories in conditional logistic regression models.
Five quintiles of SES were derived using 2000 census data at the level of the
block group, based on levels of education, income and occupation as described
previously (36,45). At the LAC site, the three ethnic groups were combined
with four SES categories (quintiles 1, 2, 3, 4–5) to form 12 groups. SES was
collapsed into three categories (quintiles 1–2, 3, 4–5) at the SFBA site and
combined with the two ethnic groups (AA and NHW) to form six groups. SES
quintiles were collapsed to maintain sufficient number of individuals in each
group, thus allowing for stable variance estimates in regression models after
adjusting for multiple covariates. The fourth and the fifth quintiles of SES were
collapsed together because among Hispanic and African-American individuals
there were low numbers in the high SES category. Similarly for the SFBA site,
which was predominantly composed of non-Hispanic white participants, the
first and second quintile groups were collapsed together.
Differences in sociodemographic, lifestyle and dietary characteristics
between cases and controls were computed within each study site, using
Pearson’s χ2 test for categorical variables and t-test for continuous variables.
For the exposure variables, odds ratios (ORs) and 95% confidence intervals
(CIs) were estimated from conditional logistic regression, with separate analyses performed for localized and advanced cases. In multivariate model 1,
analyses were adjusted for age (years, continuous), BMI (<25, 25.0–29.9,
≥30), total calorie intake (kcal/day, continuous) and family history of PCA in
first-degree relatives (yes or no). In multivariate model 2, we further adjusted
for potentially confounding dietary and lifestyle factors such as total fat intake
Red meat, poultry, cooking practices, metabolism and prostate cancer risk
Meat consumption and prostate cancer risk
Intake of red meat or processed meat was not associated with localized or advanced PCA risk (Table II). Poultry intake was inversely
associated with advanced PCA risk only (Ptrend = 0.009), with an OR
of 0.7 (95% CI = 0.6–1.0) for highest versus lowest quartile of intake.
The associations with specific meat types did not vary significantly
across the three racial/ethnic groups (Supplementary Table I, available at Carcinogenesis Online).
Meat types, cooking practices and prostate cancer risk
As shown in Table III, increased risks of advanced PCA were associated with high consumption (highest versus lowest quartile) of
pan-fried red meat (OR = 1.3; 95% CI = 1.0–1.8, Ptrend = 0.035)
and red meat cooked by high temperature cooking methods such as
grilling, oven-broiling or pan-frying (OR = 1.4, 95% CI = 1.0–1.9,
Ptrend = 0.026). When individual meat types were considered, high
intake (highest versus lowest quartile) of hamburgers cooked with
high temperature methods was positively associated with both localized and advanced PCA risk, with a stronger association for advanced
PCA (OR = 1.7, 95% CI = 1.3–2.2, Ptrend < 0.001). Steak cooked at
high temperature was not associated with localized or advanced PCA.
High intake (highest versus lowest quartile) of well-done red meat
was also positively associated with advanced PCA (OR = 1.4, 95%
CI = 1.1–1.8, Ptrend = 0.013). However, no individual meat type contributed predominantly to this association.
Baked poultry was inversely associated with both localized
(Ptrend = 0.073) and advanced (Ptrend = 0.028) PCA, whereas pan-fried
poultry consumption was marginally associated with increased risk
of advanced PCA (Ptrend = 0.069) (Table IV). No associations were
observed with poultry cooked by grilling or oven broiling or with
well-done poultry.
We found no evidence of association between sausage, bacon or
total processed meat intake and PCA risk when considering different cooking methods and doneness levels (Supplementary Table II,
available at Carcinogenesis Online). Similarly, for both localized and
advanced PCA, we found no evidence of association with energyadjusted estimated levels of HCA mutagens, BaP and total meat mutagens (revertant colonies) (Table V).
The associations with various meat types when considering cooking practices did not vary significantly across the three racial/ethnic
groups (data not shown). An exception was the association between
hamburgers cooked at high temperature and advanced PCA, which
was observed in NHW (Ptrend < 0.001) and Hispanic (Ptrend = 0.041)
men, but not African-American men (Supplementary Table III, available at Carcinogenesis Online). Although no significant heterogeneity was detected by race/ethnicity in the associations between
estimated meat mutagens and localized or advanced PCA, elevated
ORs were observed for high intake of meat mutagens and localized
PCA risk among Hispanic men (Supplementary Table IV, available
at Carcinogenesis Online). These findings, however, were based on
a very small sample size. No such associations were observed for
advanced PCA.
Polymorphisms in carcinogen metabolism enzymes and
interactions with red meat, poultry and processed meats
and prostate cancer risk
Using a P-value cutoff of 0.005 (Bonferroni multiple testing corrected
P-value = 0.05 for testing nine polymorphisms and one predicted phenotype), for heterogeneity of trends, we observed that the −765G>C
promoter SNP in PTGS2, modified the association between estimated
meat mutagens and advanced PCA risk (Table VI). Estimated mutagenic activity (actual and predicted revertant colonies) was associated
with advanced PCA in carriers of the G/G genotype at the PTGS2 promoter locus. However, this association was not observed among carriers of at least one C allele (PHeterogeneity of trend = 0.004). There was no
other evidence of effect modification of the association between any
of the meat cooking/processing variables and advanced or localized
PCA by the selected carcinogen metabolism SNPs when considering
a P-value cutoff of 0.005.
Discussion
In this multiethnic population-based case–control study we observed
that high intake of red meat per se was not associated with PCA risk.
Only when cooking practices were considered, did we find an association between high intake of red meats cooked at high temperature,
especially pan-fried red meats, and advanced PCA. When considering specific red meats, intake of hamburgers, but not steak, was associated with risk. Intake of well-done red meat was also associated
with advanced PCA risk. Whereas intake of baked poultry showed
an unexpected inverse association with advanced PCA, intake of
pan-fried poultry showed a positive association.
Pan-frying has been consistently implicated in the formation of
meat mutagens (28). The oil used in pan-frying acts as an efficient
heat transfer medium between the pan and the surface of the meat,
and therefore high surface temperatures are reached. Pan-frying does
not expose meats to open flames and fats from the meats do not have
an opportunity to drip on the flames undergoing incomplete combustion. Thus, pan-frying is typically not associated with accumulation of
PAHs (46). Similarly, there is no evidence for formation of N-nitroso
compounds during pan-frying. Therefore, the most relevant carcinogens generated by pan-frying seem to be HCAs, which may drive the
observed associations between pan-fried red meat and poultry and
PCA risk.
We observed an association between consumption of high
temperature-cooked red meats, especially hamburgers, and the risk
2111
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African American and less likely to be non-Hispanic white, as compared with controls in both the study sites; at the LAC site, cases also
had an overrepresentation of Hispanic men. In both the sites the frequency of individuals with at least one first-degree relative with prostate cancer was higher among cases. Cases were older than controls at
the LAC site, but not in the SFBA site. Differences between distributions of SES were observed at both the sites between cases and controls. Cigarette smoking, total calorie intake and total fat consumption
were higher among cases as compared with controls in the LAC site,
but no such differences were seen at the SFBA site. BMI was lower
among cases in the SFBA study site, but not in the LAC site. Other
potential risk factors of prostate cancer were not observed to be different between cases and controls in both the study sites (Table I).
Energy-adjusted red meat, poultry and processed meat consumption patterns by race/ethnicity and prostate cancer defined by stage
at diagnosis are summarized in Supplementary Table I, available at
Carcinogenesis Online. NHW male controls had a lower intake of red
meat (mean = 13.9 g/1000 kcal/day) and poultry (mean = 21.0 g/1000
kcal/day) than African-American (mean = 16.3 and 27.6 g/1000 kcal/
day, respectively) or Hispanic (mean = 16.8 and 26.5 g/1000 kcal/day,
respectively) controls. Hispanic controls had a lower intake of hamburger, but a higher intake of steak. African-American controls had a
higher intake of bacon and sausage as well as total processed meats
as compared with NHW and Hispanic controls. Differences were
also observed in red meat cooking preferences; African-American
controls had a higher intake of oven-broiled meats than NHW or
Hispanic men, whereas Hispanic controls had a higher consumption
of grilled and pan-fried meats, which contributed to an overall higher
consumption of high temperature-cooked red meats and poultry as
compared with the remaining two racial/ethnic groups. There were
72% African-American, 52% Hispanic and 62% NHW controls who
did not consume any high temperature-cooked poultry; baking was
the most preferred way of cooking poultry. No racial/ethnic differences in consumption of well-done poultry were observed. AfricanAmerican controls had the highest consumption of pan-fried and
well-done processed meats. The mean consumption in g/1000 kcal/
day of pan-fried processed meat was 3.6 among African-American
controls, 1.6 among Hispanic controls and 1.7 among NHW controls,
whereas that of well-done processed meat was 4.1, 1.7 and 1.8 among
African-American, Hispanic and NHW controls, respectively.
Amit D.Joshi et al.
Table II. Meat types and prostate cancer risk, by cancer stage
Meat group consumption
(g/1000 kcal/day)
Localized cases
n
N
ORa
218
218
219
219
218
124
142
140
141
168
1.0Ref
1.2
1.1
1.1
1.2
267
413
207
206
167
285
130
133
1.0Ref
1.2
1.2
1.1
315
390
194
194
185
257
124
149
1.0Ref
1.1
1.0
1.2
219
219
218
219
218
139
150
156
134
136
1.0Ref
1.1
1.0
0.9
0.9
218
219
219
218
219
126
120
135
187
147
1.0Ref
0.9
0.9
1.1
0.8
495
299
150
149
314
195
90
116
1.0Ref
1.0
0.8
0.8
513
290
145
145
334
173
86
122
1.0Ref
1.1
0.9
1.0
741
177
87
88
475
116
55
69
1.0Ref
0.9
0.8
0.7
Advanced cases
95% CI
0.9–1.6
0.8–1.6
0.8–1.5
0.9–1.6
0.541
0.9–1.5
0.9–1.7
0.8–1.5
0.640
0.8–1.4
0.8–1.4
0.9–1.6
0.371
0.8–1.6
0.7–1.4
0.7–1.3
0.6–1.2
0.126
0.6–1.2
0.7–1.3
0.8–1.6
0.6–1.2
0.494
0.8–1.3
0.6–1.1
0.6–1.1
0.126
0.8–1.4
0.6–1.2
0.8–1.4
0.915
0.7–1.2
0.5–1.2
0.5–1.1
0.109
ORb
1.0Ref
1.2
1.1
1.0
1.1
1.0Ref
1.1
1.2
1.1
1.0Ref
1.1
1.0
1.1
1.0Ref
1.1
1.0
0.9
0.9
1.0Ref
0.9
0.9
1.1
0.8
1.0Ref
1.0
0.7
0.8
1.0Ref
1.0
0.9
1.0
1.0Ref
0.9
0.7
0.7
95% CI
0.8–1.6
0.8–1.5
0.7–1.4
0.8–1.6
0.822
0.9–1.5
0.9–1.7
0.8–1.5
0.881
0.8–1.4
0.7–1.4
0.8–1.6
0.502
0.8–1.5
0.7–1.4
0.7–1.3
0.6–1.2
0.198
0.6–1.2
0.6–1.2
0.8–1.5
0.6–1.2
0.515
0.7–1.2
0.5–1.1
0.6–1.1
0.134
0.8–1.4
0.6–1.2
0.7–1.4
0.799
0.6–1.2
0.5–1.1
0.5–1.0
0.060
n
ORa
209
200
250
257
223
1.0Ref
0.9
1.2
1.2
1.1
232
468
223
216
1.0Ref
1.2
1.3
1.3
297
413
207
222
1.0Ref
1.1
1.1
1.1
255
255
234
219
176
1.0Ref
1.1
0.9
0.9
0.7
206
203
213
276
236
1.0Ref
0.9
1.0
1.3
1.1
487
341
152
159
1.0Ref
1.1
1.0
1.1
510
327
136
166
1.0Ref
1.1
0.9
1.2
762
177
97
103
1.0Ref
1.0
1.2
1.1
95% CI
0.7–1.2
0.9–1.6
0.9–1.6
0.8–1.4
0.315
1.0–1.6
1.0–1.8
1.0–1.7
0.143
0.9–1.3
0.9–1.4
0.8–1.4
0.541
0.8–1.4
0.7–1.2
0.7–1.2
0.5–1.0
0.006
0.7–1.2
0.7–1.3
1.0–1.7
0.9–1.5
0.115
0.9–1.4
0.8–1.3
0.8–1.4
0.853
0.9–1.3
0.7–1.2
0.9–1.6
0.351
0.8–1.2
0.9–1.7
0.8–1.5
0.394
ORb
1.0Ref
0.9
1.2
1.1
1.0
1.0Ref
1.2
1.3
1.2
1.0Ref
1.0
1.1
1.0
1.0Ref
1.1
0.9
0.9
0.7
1.0Ref
0.9
0.9
1.2
1.1
1.0Ref
1.1
0.9
1.0
1.0Ref
1.0
0.9
1.1
1.0Ref
0.9
1.2
1.1
95% CI
0.7–1.2
0.9–1.5
0.8–1.5
0.8–1.4
0.667
1.0–1.5
1.0–1.7
0.9–1.6
0.322
0.8–1.3
0.8–1.4
0.8–1.4
0.780
0.8–1.4
0.7–1.2
0.7–1.2
0.6–1.0
0.009
0.7–1.2
0.7–1.2
0.9–1.7
0.8–1.5
0.270
0.9–1.3
0.7–1.2
0.7–1.3
0.719
0.8–1.3
0.7–1.2
0.9–1.5
0.549
0.7–1.2
0.9–1.6
0.8–1.5
0.617
a
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day) and family history of PCA (yes/ no).
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day), family history of PCA (yes/no), total fat intake (g/day), alcohol consumption
(g/day), cigarette smoking (pack-years),total fruit consumption (g/day), total vegetable consumption (g/day).
b
of advanced prostate cancer. The association for steak cooked at high
temperatures or to well-done levels was weaker in magnitude and not
statistically significant. We speculate that these observations may be
due to the formation of different amounts and types of HCAs in these
specific meat types and also because hamburgers attain high internal
and external temperatures faster than steak (47).
Contrary to our expectation that HCAs drive the observed associations, the estimated levels of HCAs were not associated with PCA
2112
risk. Interestingly, one other study has also reported an association
with well-done red meat intake and PCA risk, but no evidence of association with estimated meat mutagens (34). There are several potential
explanations for this discrepancy. First, of more than 15 HCAs identified to date that are known to accumulate in cooked meat (48), only
PhIP, MeIQx and DiMeIQx are captured by the CHARRED database
that all epidemiological studies to date, including the current study,
have used to estimate meat mutagens. Compounds formed at lower
Downloaded from http://carcin.oxfordjournals.org/ at University of Illinois at Urbana-Champaign on March 13, 2015
Red meat
Q1: ≥0 to <4.6
Q2: ≥4.6 to <8.9
Q3: ≥8.9 to <14.4
Q4: ≥14.4 to <23.3
Q5: ≥23.3
Ptrend
Hamburger
Never/rarely: 0.0
Low: >0.0 to <4.8
Medium: ≥4.8 to <9.6
High: ≥9.6
Ptrend
Steak
Never/rarely: 0.0
Low: >0.0 to <5.4
Medium: ≥5.4 to <9.6
High: ≥9.6
Ptrend
Poultry
Q1: ≥0 to <7.9
Q2: ≥7.9 to <14.3
Q3: ≥14.3 to <22.9
Q4: ≥22.9 to <35.6
Q5: ≥35.7
Ptrend
Processed meat
Q1: ≥0 to <0.5
Q2: ≥0.5 to <2.6
Q3: ≥2.6 to <4.8
Q4: ≥4.8 to <10.3
Q5: ≥10.3
Ptrend
Bacon
Never/rarely: 0.0
Low: >0.0 to <0.9
Medium: ≥0.9 to <2.0
High: ≥2.0
Ptrend
Sausage
Never/rarely: 0.0
Low: >0.0 to <2.2
Medium: ≥2.2 to <3.5
High: ≥3.5
Ptrend
Home made gravy
Never/rarely: 0.0
Low: >0.0 to <1.2
Medium: ≥1.2 to <2.3
High: ≥2.3
Ptrend
Controls
Red meat, poultry, cooking practices, metabolism and prostate cancer risk
Table III. Red meat intake by cooking practices and prostate cancer risk, by cancer stage
Meat group consumption
(g/1000 kcal/day)
Localized cases
N
n
ORa
576
259
129
129
398
150
84
83
1.0Ref
0.9
1.1
0.9
745
174
87
87
478
119
54
64
1.0Ref
0.9
0.9
1.2
591
251
126
125
359
152
86
118
1.0Ref
0.9
1.1
1.1
985
54
27
27
661
26
10
18
1.0Ref
0.6
0.6
1.0
172
460
231
230
99
284
151
181
1.0Ref
1.0
1.1
1.2
442
326
163
162
241
186
121
167
1.0Ref
0.9
1.2
1.3
577
258
129
129
332
184
94
105
1.0Ref
1.2
1.4
1.4
586
254
127
126
348
186
93
88
1.0Ref
1.0
1.2
1.0
318
388
193
194
187
256
121
151
1.0Ref
1.1
1.0
1.2
800
147
73
73
438
128
64
85
1.0Ref
1.2
1.2
1.4
Advanced cases
95% CI
0.7–1.2
0.8–1.5
0.7–1.3
0.897
0.7–1.2
0.6–1.4
0.9–1.8
0.382
0.7–1.2
0.8–1.5
0.8–1.5
0.439
0.4–1.0
0.3–1.3
0.5–2.0
0.185
0.7–1.4
0.7–1.5
0.9–1.7
0.160
0.7–1.2
0.8–1.6
0.9–1.7
0.072
0.9–1.5
1.0–1.9
1.0–1.9
0.029
0.8–1.3
0.9–1.7
0.7–1.4
0.712
0.8–1.4
0.7–1.4
0.9–1.6
0.328
0.9–1.6
0.8–1.7
1.0–2.0
0.068
ORa
1.0Ref
0.9
1.1
0.9
1.0Ref
0.9
0.9
1.2
1.0Ref
0.9
1.0
1.1
1.0Ref
0.6
0.6
1.0
1.0Ref
1.0
1.0
1.2
1.0Ref
0.9
1.2
1.2
1.0Ref
1.2
1.4
1.4
1.0Ref
1.0
1.2
1.0
1.0Ref
1.1
1.0
1.2
1.0Ref
1.2
1.2
1.4
95% CI
0.7–1.2
0.8–1.5
0.6–1.3
0.728
0.7–1.2
0.6–1.4
0.8–1.8
0.408
0.7–1.1
0.7–1.4
0.8–1.5
0.553
0.4–1.1
0.3–1.3
0.5–1.9
0.179
0.7–1.3
0.7–1.4
0.8–1.7
0.249
0.7–1.2
0.8–1.6
0.9–1.7
0.083
0.9–1.6
1.0–1.9
1.0–1.9
0.040
0.8–1.3
0.9–1.7
0.7–1.4
0.791
0.8–1.4
0.7–1.3
0.8–1.6
0.446
0.9–1.6
0.8–1.7
1.0–2.1
0.067
n
ORa
579
270
140
150
1.0Ref
1.0
1.1
1.1
804
166
76
93
1.0Ref
0.9
0.8
1.0
538
297
137
167
1.0Ref
1.2
1.2
1.4
1049
43
21
26
1.0Ref
0.7
0.8
0.9
133
457
274
275
1.0Ref
1.2
1.4
1.5
392
355
161
231
1.0Ref
1.2
1.1
1.5
501
310
145
183
1.0Ref
1.3
1.4
1.7
601
278
129
131
1.0Ref
1.0
1.0
1.1
299
409
205
226
1.0Ref
1.1
1.1
1.1
780
169
79
111
1.0Ref
1.1
1.1
1.3
95% CI
0.8–1.2
0.8–1.4
0.9–1.5
0.394
0.7–1.2
0.6–1.2
0.8–1.4
0.848
1.0–1.5
0.9–1.6
1.0–1.8
0.024
0.5–1.1
0.4–1.4
0.5–1.6
0.227
0.9–1.6
1.1–1.9
1.1–2.0
0.009
1.0–1.4
0.9–1.5
1.1–1.9
0.007
1.0–1.6
1.0–1.8
1.3–2.2
<0.001
0.8–1.2
0.8–1.4
0.8–1.4
0.573
0.9–1.3
0.9–1.4
0.9–1.4
0.452
0.9–1.4
0.8–1.5
1.0–1.8
0.095
ORb
1.0Ref
1.0
1.0
1.1
1.0Ref
0.9
0.9
1.0
1.0Ref
1.2
1.2
1.3
1.0Ref
0.7
0.8
0.9
1.0Ref
1.1
1.4
1.4
1.0Ref
1.2
1.1
1.4
1.0Ref
1.3
1.4
1.7
1.0Ref
1.0
1.0
1.0
1.0Ref
1.0
1.1
1.1
1.0Ref
1.1
1.1
1.3
95% CI
0.8–1.2
0.8–1.4
0.8–1.5
0.475
0.7–1.1
0.6–1.2
0.7–1.4
0.616
1.0–1.5
0.9–1.6
1.0–1.8
0.035
0.5–1.1
0.4–1.4
0.5–1.6
0.190
0.9–1.5
1.0–1.9
1.0–1.9
0.026
0.9–1.4
0.8–1.4
1.1–1.8
0.013
1.0–1.6
1.0–1.8
1.3–2.2
<0.001
0.8–1.2
0.8–1.3
0.8–1.4
0.747
0.8–1.3
0.8–1.4
0.8–1.4
0.668
0.9–1.4
0.8–1.6
0.9–1.8
0.136
a
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day) and family history of PCA (yes/no).
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day), family history of PCA (yes/no), total fat intake (g/day), alcohol consumption
(g/day), cigarette smoking (pack-years), total fruit consumption (g/day), total vegetable consumption (g/day).
c
High temperature-cooked meat = meat cooked by oven-broiling, grilling and pan-frying.
b
2113
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Grilled red meat
Never/rarely: 0.0
Low: >0.0 to <6.3
Medium: ≥6.3 to <12.4
High: ≥12.4
Ptrend
Oven broiled red meat
Never/rarely: 0.0
Low: >0.0 to <5.5
Medium: ≥5.5 to <9.5
High: ≥9.5
Ptrend
Pan-fried red meat
Never/rarely: 0.0
Low: >0.0 to <5.0
Medium: ≥5.0 to <9.8
High: ≥9.8
Ptrend
Baked red meat
Never/rarely: 0.0
Low: >0.0 to <2.9
Medium: ≥2.9 to <4.1
High: ≥4.1
Ptrend
High temperature-cookedc red meat
Never/rarely: 0.0
Low: >0.0 to <9.4
Medium: ≥9.4 to <16.9
High: ≥16.9
Ptrend
Well-done red meat
Never/rarely: 0.0
Low: >0.0 to <6.1
Medium: ≥6.1 to <11.0
High: ≥11.0
Ptrend
High temperature-cookedc hamburger
Never/rarely: 0.0
Low: >0.0 to <4.4
Medium: ≥4.4 to <7.9
High: ≥7.9
Ptrend
Well-done hamburger
Never/rarely: 0.0
Low: >0.0 to <4.9
Medium: ≥4.9 to <9.9
High: ≥9.9
Ptrend
High temperature-cookedc steak
Never/rarely: 0.0
Low: >0.0 to <5.4
Medium: ≥5.4 to <9.6
High: ≥9.6
Ptrend
Well-done steak
Never/rarely: 0.0
Low: >0.0 to <5.1
Medium: ≥5.1 to <9.2
High: ≥9.2
Ptrend
Controls
Amit D.Joshi et al.
Table IV. Cooked poultry and prostate cancer risk, by cancer stage
Meat group consumption
(g/1000 kcal/day)
Localized cases
n
n
ORa
919
87
44
43
608
64
28
15
1.0Ref
1.4
0.9
0.7
952
71
35
35
612
50
22
31
1.0Ref
0.9
1.1
1.3
1010
42
21
20
634
45
20
16
1.0Ref
1.9
1.3
1.2
506
294
147
146
372
181
87
75
1.0Ref
0.7
0.8
0.7
695
199
100
99
424
156
74
61
1.0Ref
1.4
1.2
1.1
636
339
114
114
451
139
71
54
1.0Ref
1.1
1.0
0.9
Advanced cases
95% CI
1.0–2.1
0.5–1.5
0.4–1.4
0.485
0.6–1.3
0.6–2.0
0.8–2.2
0.375
1.2–3.1
0.6–2.5
0.6–2.5
0.281
0.6–0.9
0.6–1.1
0.5–1.0
0.039
1.1–1.8
0.8–1.7
0.8–1.6
0.493
0.9–1.5
0.7–1.5
0.6–1.2
0.462
ORb
1.0Ref
1.4
0.9
0.7
1.0Ref
0.9
1.2
1.3
1.0Ref
1.9
1.3
1.2
1.0Ref
0.7
0.8
0.7
1.0Ref
1.4
1.2
1.1
1.0Ref
1.1
1.1
0.9
95% CI
1.0–2.1
0.6–1.6
0.4–1.4
0.495
0.6–1.3
0.7–2.1
0.7–2.2
0.393
1.2–3.1
0.6–2.5
0.6–2.5
0.275
0.6–0.9
0.6–1.1
0.5–1.0
0.073
1.1–1.8
0.8–1.7
0.7–1.6
0.493
0.8–1.5
0.7–1.5
0.6–1.3
0.515
n
ORa
957
113
42
27
1.0Ref
1.3
0.9
0.7
990
73
37
39
1.0Ref
1.0
1.0
1.3
1004
73
28
34
1.0Ref
1.5
1.2
1.6
596
296
135
112
1.0Ref
0.8
0.7
0.7
673
260
104
102
1.0Ref
1.3
1.0
1.2
624
279
144
92
1.0Ref
1.2
1.2
0.9
95% CI
1.0–1.8
0.6–1.4
0.4–1.2
0.242
0.7–1.4
0.6–1.6
0.8–2.0
0.438
1.0–2.3
0.7–2.2
0.9–2.8
0.059
0.7–1.0
0.6–0.9
0.5–1.0
0.008
1.1–1.7
0.7–1.4
0.9–1.6
0.424
1.0–1.5
0.9–1.6
0.6–1.2
0.531
ORb
1.0Ref
1.3
0.9
0.7
1.0Ref
1.0
1.0
1.2
1.0Ref
1.5
1.2
1.5
1.0Ref
0.8
0.7
0.8
1.0Ref
1.3
1.0
1.1
1.0Ref
1.2
1.2
0.8
95% CI
0.9–1.7
0.6–1.4
0.4–1.2
0.251
0.7–1.4
0.6–1.6
0.8–2.0
0.521
1.0–2.3
0.7–2.2
0.9–2.7
0.069
0.7–1.0
0.6–1.0
0.6–1.0
0.023
1.1–1.6
0.7–1.4
0.8–1.5
0.508
1.0–1.5
0.9–1.6
0.6–1.1
0.424
a
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day) and family history of PCA (yes/no).
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake(kcal/day), family history of PCA (yes/no), total fat intake (g/day), alcohol consumption
(g/day), cigarette smoking (pack-years), total fruit consumption (g/day), total vegetable consumption (g/day).
c
High temperature-cooked poultry = poultry cooked by oven-broiling, grilling and pan-frying.
b
concentrations, and hence not measured, or not-yet identified HCAs,
may play a more relevant role in cancer causation. For example,
in a case–control study of PCA in New Zealand (49), intake of
well-cooked red meat (beef and steak) was associated with increased
PCA risk, whereas no associations were found with estimated PhIP,
MeIQx and DiMeIQx. A fourth HCA (IFP), however, was weakly
associated with PCA risk. Second, although the assessment of meat
mutagens using the cooking module and CHARRED, is vastly better than previously used methods, it is subject to measurement error.
Interpolation from laboratory measurements of different meat samples cooked under different conditions, and adaptation of the cooking module to different FFQs, add additional approximations, thereby
contributing to exposure misclassification and biasing risk estimates
towards the null, assuming non-differential misclassification between
cases and controls.
Alternative explanations for our findings include the absorption
of culinary fat, free radical formation from fats and exposure to
cookware-related substances such as perfluorooctanoate (50). Since
red meats naturally have a high fat content, they are less likely to
imbibe culinary fats. However, the fatty acid content in red meat, particularly corn fed meats, may be higher in n-6 polyunsaturated fatty
acids, which are substrate to oxidation and free radicals formation
(51). It is also possible that pan-fried red meat tags other intangible
2114
aspects of lifestyle, not considered in our analyses, which may confer
increased PCA risk.
There is insufficient evidence to draw firm conclusions about the
role of poultry in PCA risk. Studies have reported that while red
meats predominantly accumulate higher levels of MeIQx-like HCAs,
high temperature-cooked poultry accumulates much higher levels of
PhIP-like HCAs, as compared with red meat (52,53). Our finding of a
positive association between pan-fried poultry and PCA risk supports
this fact. However, we also report an inverse association of baked
poultry intake with advanced and localized PCA risk. This inverse
association may be explained by a form of vitamin K2, menaquinone,
present in dark poultry meat that has been associated with reduced
risk of advanced PCA in a European cohort (54). Further studies need
to test this hypothesis.
We did not observe any association between processed meat intake
and PCA risk. According to the WCRF report, associations between
processed meats and PCA risk are stronger in prospective studies than
in case–control studies (55). Case–control studies typically assess dietary intake for the period 1–2 years before diagnosis, whereas cohort
studies obtain dietary information at baseline. It is possible that the
stronger association found in prospective studies may be due to the
capturing of exposures during a more relevant time period in cohort
studies.
Downloaded from http://carcin.oxfordjournals.org/ at University of Illinois at Urbana-Champaign on March 13, 2015
Grilled poultry
Never/rarely: 0.0
Low: >0.0 to <11.2
Medium: ≥11.2 to <22.5
High: ≥22.5
Ptrend
Oven broiled poultry
Never/rarely: 0.0
Low: >0.0 to <8.8
Medium: ≥8.8 to <18.4
High: ≥18.4
Ptrend
Pan-fried poultry
Never/rarely: 0.0
Low: >0.0 to <7.7
Medium: ≥7.7 to <15.8
High: ≥15.8
Ptrend
Baked poultry
Never/rarely: 0.0
Low: >0.0 to <8.1
Medium: ≥8.1 to <19.0
High: ≥19.0
Ptrend
High temperature-cookedc poultry
Never/rarely: 0.0
Low: >0.0 to <9.7
Medium: ≥9.7 to <18.8
High: ≥18.8
Ptrend
Well-done poultry
Never/rarely: 0.0
Low: >0.0 to <7.7
Medium: ≥7.7 to <18.0
High: ≥18.0
Ptrend
Controls
Red meat, poultry, cooking practices, metabolism and prostate cancer risk
Table V. Meat mutagens and prostate cancer risk, by cancer stage
Meat mutagens (ng/1000 kcal/day)
Localized cases
n
n
ORa
120
120
135
166
174
1.0Ref
1.0
1.1
1.3
1.2
134
94
133
148
206
1.0Ref
0.6
0.9
0.9
1.0
148
99
126
157
185
1.0Ref
0.8
0.9
1.0
1.0
138
160
168
112
137
1.0Ref
0.9
1.0
0.8
0.9
113
97
149
176
180
1.0Ref
0.8
1.2
1.4
1.1
129
107
133
138
208
1.0Ref
0.8
0.9
0.8
1.0
PhIP
Q1: ≥0 to <6.3
219
Q2: ≥6.3 to <21.5
217
Q3: ≥21.5 to <47.0
219
Q4: ≥47.0 to <105.4
219
Q5: ≥105.4
219
Ptrend
MelQx
Q1: ≥0 to <4.0
219
Q2: ≥4.0 to <9.7
217
Q3: ≥9.7 to <17.3
219
Q4: ≥17.3 to <31.9
219
Q5: ≥31.9
219
Ptrend
DiMelQx
Q1: ≥0 to <0.1
247
Q2: ≥0.1 to <0.4
190
Q3: ≥0.4 to <1.0
219
Q4: ≥1.0 to <2.2
218
Q5: ≥2.2
219
Ptrend
BAP
Q1: ≥0 to <0.6
219
Q2: ≥0.6 to <2.4
219
Q3: ≥2.4 to <10.0
219
Q4: ≥10.0 to <27.1
218
Q5: ≥27.1
218
Ptrend
Mutagenic activity (number of revertant colonies)
Q1: ≥0 to <629
218
Q2: ≥629 to <1433
219
Q3: ≥1433 to <2626
219
Q4: ≥2626 to <4950
218
Q5: ≥4950
219
Ptrend
Mutagenic activity (number of predicted revertant colonies)
Q1: ≥0 to <735
219
Q2: ≥735 to <1620
218
Q3: ≥1620 to <2665
218
Q4: ≥2665 to <4409
219
Q5: ≥4409
219
Ptrend
Advanced cases
95% CI
0.7–1.4
0.8–1.6
1.0–1.8
0.9–1.7
0.144
0.4–0.9
0.6–1.2
0.6–1.2
0.7–1.4
0.279
0.6–1.2
0.6–1.2
0.7–1.4
0.7–1.3
0.586
0.7–1.3
0.8–1.4
0.6–1.2
0.7–1.3
0.757
0.5–1.1
0.8–1.7
1.0–1.9
0.8–1.5
0.286
0.5–1.1
0.6–1.2
0.6–1.2
0.7–1.4
0.478
ORb
1.0Ref
0.9
1.1
1.3
1.2
1.0Ref
0.6
0.9
0.9
1.0
1.0Ref
0.8
0.9
1.0
1.0
1.0Ref
0.9
1.0
0.8
0.9
1.0Ref
0.7
1.2
1.4
1.1
1.0Ref
0.8
0.9
0.8
1.0
95% CI
0.7–1.3
0.8–1.6
0.9–1.8
0.9–1.7
0.156
0.4–0.9
0.6–1.2
0.6–1.2
0.7–1.4
0.308
0.6–1.1
0.6–1.2
0.8–1.4
0.7–1.3
0.657
0.6–1.2
0.7–1.4
0.6–1.2
0.6–1.3
0.660
0.5–1.1
0.8–1.7
1.0–2.0
0.8–1.5
0.341
0.5–1.1
0.6–1.2
0.6–1.2
0.7–1.4
0.480
n
ORa
193
208
257
231
250
1.0Ref
1.1
1.3
1.2
1.3
217
189
231
252
250
1.0Ref
0.9
1.0
1.1
1.1
247
162
246
234
250
1.0Ref
0.8
1.1
1.0
1.1
234
249
231
209
216
1.0Ref
1.0
1.0
0.9
0.9
197
201
257
231
253
1.0Ref
1.0
1.3
1.2
1.2
222
206
234
226
251
1.0Ref
0.9
1.0
1.0
1.1
95% CI
0.8–1.4
1.0–1.7
0.9–1.5
1.0–1.7
0.158
0.6–1.1
0.8–1.3
0.9–1.5
0.8–1.4
0.233
0.6–1.1
0.9–1.5
0.8–1.4
0.8–1.4
0.411
0.8–1.3
0.7–1.3
0.7–1.2
0.7–1.2
0.544
0.8–1.3
1.0–1.7
0.9–1.6
0.9–1.6
0.208
0.7–1.2
0.8–1.3
0.8–1.3
0.8–1.4
0.417
ORb
1.0Ref
1.0
1.3
1.1
1.2
1.0Ref
0.8
1.0
1.1
1.0
1.0Ref
0.8
1.1
1.0
1.0
1.0Ref
1.0
0.9
0.9
0.9
1.0Ref
0.9
1.3
1.1
1.1
1.0Ref
0.9
1.0
0.9
1.0
95% CI
0.8–1.4
1.0–1.7
0.8–1.5
0.9–1.6
0.245
0.6–1.1
0.7–1.3
0.8–1.4
0.8–1.4
0.417
0.6–1.1
0.8–1.4
0.8–1.3
0.8–1.3
0.680
0.7–1.3
0.7–1.2
0.7–1.1
0.7–1.1
0.396
0.7–1.3
1.0–1.7
0.8–1.5
0.9–1.5
0.329
0.7–1.1
0.7–1.3
0.7–1.2
0.8–1.3
0.664
a
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day) and family history of PCA (yes/no).
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake(kcal/day), family history of PCA (yes/ no), total fat intake (g/day), alcohol consumption
(g/day), cigarette smoking (pack-years), total fruit consumption (g/day), total vegetable consumption (g/ day).
b
Although overall, the findings in this study were in agreement with
those reported previously within the San Francisco Bay area component (36), certain differences were also observed. In both reports there
were null associations between all types of meat and cooking variables and localized prostate cancer risk. For advanced prostate cancer
risk we could validate the associations of well-done/very well-done
red meats observed in the previous study. However, the previously
reported positive main effect associations of hamburgers, steak and
processed meats with advanced prostate cancer could not be replicated in this combined study. These discrepancies could potentially
be due to differences in ethnic, socioeconomic and life-style patterns
in the LAC site as compared with the SFBA site, and the fact that the
previous study was more homogeneous than this study. However, the
pooled analysis of the two sites allowed us to improve power, while
statistically adjusting for these heterogeneous effects. Therefore, we
were able to test for associations for cooking methods within specific meat types that may have previously been missed, such as associations of pan-fried hamburgers and advanced prostate cancer risk.
Both studies reported no associations between estimated meat mutagens and risk of localized or advanced prostate cancer.
When we considered genes that play key roles in the metabolism of
meat mutagens, the strongest genetic modifier identified was a PTGS2
promoter polymorphism (−765 G>C, rs20417) (56). The PTGS2 gene
codes for the COX-2 protein, which is overexpressed in prostate cancer cells and is induced by HCAs and PAHs, which it can activate
into carcinogens (57–60). PAH activation in the prostate can also be
carried out by CYP1A1 and CYP1B1 (61); therefore, if COX-2 levels
are reduced, other enzymes might be able to compensate for this. In
contrast, COX-2 activity levels may be more critical for HCA activation in the prostate given the absence of other enzymes capable of carrying out this step. CYP1A2 is a critical enzyme for HCA activation
in the liver; however, it is not expressed in the prostate (62). Given
our observation of a role of pan-frying in PCA risk, it is plausible
that the estimated mutagenic activity modified by a polymorphism in
PTGS2 is capturing potential HCAs that may be activated by COX-2
in the prostate. Since COX-2 is only expressed during periods of
2115
Downloaded from http://carcin.oxfordjournals.org/ at University of Illinois at Urbana-Champaign on March 13, 2015
Controls
Amit D.Joshi et al.
Table VI. Interaction of meat derived mutagens/ mutagenic activity and PTGS2 SNP (rs20417) in the risk of advanced prostate cancer
GG (Co/Ca)
G/C (Co/Ca)
C/C (Co/Ca)
G/G genotype
G/C genotype
C/C genotype
PGTS2 −765G>C
G/G
G/C
C/C
ORa,b
ORa,b
95% CI
ORa,b
95% CI
1.0
0.9–1.2
1.1
0.7–1.5
1.4
1.5
1.2
1.0–1.9
1.1–2.1
0.8–1.6
0.246
1.9
1.8
0.9
1.0–3.6
1.0–3.2
0.5–1.5
0.016
1.2
1.4
0.9
0.8–1.6
1.0–2.0
0.7–1.3
0.086
1.3
2.0
0.6
0.7–2.6
1.1–3.4
0.4–1.1
0.011
1.1
1.4
1.0
0.8–1.6
1.0–1.9
0.7–1.4
0.263
1.3
1.9
0.7
0.7–2.4
1.1–3.3
0.4–1.3
0.037
Marginal effect of rs20417
481/595
Mutagenic activity (number of revertant colonies)
≥0 to <1186
164/163
≥1186 to <3260
170/206
≥3260
147/226
Ptrend
PHeterogeneity of trend
0.002
PLR test for interaction
0.008
Mutagenic activity (number of predicted revertant colonies)
≥0 to <1305
157/179
≥1305 to <3210
177/198
≥3210
147/218
Ptrend
PHeterogeneity of trend
0.004
PLR test for interaction
0.003
MeIQx
≥0 to <7.8
153/166
≥7.8 to <21.1
179/198
≥21.1
149/231
Ptrend
PHeterogeneity of trend
0.010
PLR test for interaction
0.007
95% CI
Ref
237/304
38/36
1.0
70/98
74/102
93/104
9/10
10/13
19/13
1.0Ref
1.3
1.6
77/96
66/112
94/96
5/8
13/17
20/11
1.0Ref
1.0
1.3
73/93
66/101
98/110
8/8
12/17
18/11
1.0Ref
1.0
1.4
0.9–1.7
1.2–2.2
0.004
0.7–1.4
1.0–1.8
0.059
0.7–1.4
1.0–1.9
0.028
a
Adjusted for age (years), BMI (<25, 25–29, ≥30), total calorie intake (kcal/day) and family history of PCA (yes/ no).
Odds ratios are from models with genotype coded as log-additive (i.e., number of C alleles, treated as continuous).
b
inflammatory stress, it is possible that meat derived HCAs may be
relevant in prostate carcinogenesis during periods of inflammation
in the prostate. In support of this, animal experiments have shown
that the HCA PhIP can induce inflammation in regions of the prostate
that coincide with areas of tumor development, which suggests that in
addition to bulky adducts and cell proliferation, HCAs may also cause
inflammation and thus promote cancer development (63). This dual
role for HCAs suggest that red meat cooked at high temperature might
contribute to inflammation in the prostate and in this way contribute to
progression of existing tumors that otherwise might remain localized.
If this were true, it would explain why our study, along with many
others, found an association between red meat intake and PCA risk
exclusively, or stronger, for advanced than localized disease.
We observed that mutagenic activity was associated with PCA risk
only among individuals carrying the PTGS2 −765 G>C (rs20417)
G/G genotype. The effects of this polymorphism, which is located
in a binding site upstream of the translation initiation site, on COX-2
protein function are still unclear. In vitro studies done with different
cell types have reported lower expression of the protein coded by the
C allele than the one coded by the G allele or vice versa, depending
on which cell type was used (56,64). These inconsistencies make it
difficult to interpret our findings. If the C allele conferred reduced
PTGS2 gene expression, as reported by some (56,65), this may lead to
reduced activation of HCAs or PAHs in the prostate during inflammatory stress. This would be consistent with our finding of an association
between higher mutagenic activity and PCA risk among carriers of the
G allele. However, recently we reported that high intake of white fish,
in particular white fish cooked at high temperature, was associated
with PCA risk (66), and that this association was stronger among carriers of the PTGS2 (−765 G>C, rs20417) C allele (43). Given the dual
role of COX-2, in both carcinogen metabolism and fatty acid metabolism, it is a possibility that the impact of the -765 G>C (rs20417)
polymorphism may be different for each of these two functions. If
that were the case, our finding of effect modification of PTGS2 on
the association between mutagenic activity and PCA risk would be
pointing to a role of chemical carcinogens that accumulate in red meat
and poultry, and require COX-2 activation, whereas the association
2116
between high intake of white fish could be pointing to a role of fatty
acids, that also require COX-2 metabolism. We note that the estimated
levels of mutagenic activity obtained with the CHARRED database
do not include fish intake, and that in our data set correlations between
red meat, poultry and fish variables were low. To our knowledge, no
previous study has assessed interactions between PTGS2 variants and
estimates of meat mutagens from the CHARRED database in relation
to PCA risk. Therefore, further studies that include a more comprehensive coverage of this gene may help clarify these opposite findings.
Strengths of our study include its population-based design with
cases ascertained using two SEER cancer registries, a large sample size, the inclusion of a diverse study sample, enrichment with
advanced cases and the ability to distinguish between different meat
types, cooking methods and level of doneness ascertained using
colored photographs. The questionnaire used in this study allowed us
to estimate intake of specific nutrients and meat mutagens. However,
we acknowledge potential weaknesses, such as possible misclassification of meat intake and cooking practices due to measurement
error via questionnaire. Although differential misclassification due to
recall bias among cases is unlikely given that at the time of interview there was no widespread knowledge that consumption of meat
or certain cooking methods may be associated with PCA risk, we
cannot exclude the possibility of non-differential misclassification.
Moreover, in our study we did not consider the effect of using marinades in the cooking of meats, which have been shown to decrease
the formation of HCAs (67). Therefore, lack of information on marinating practices may have contributed to exposure misclassification,
potentially biasing the results towards the null. Lastly, even though
our study included a larger number of men from racial/ethnic minority populations as compared with other studies, the sample size for
specific exposure categories was limited. It is therefore possible that
we may have missed associations present in certain subgroups only.
In summary, our findings support the hypothesis that mutagenic
compounds formed during the process of cooking meats may contribute to prostate carcinogenesis. Our findings may have implications for
public health recommendations. Exposure to HCAs from pan-fried
meats may be reduced by cooking meats for longer time using lower
Downloaded from http://carcin.oxfordjournals.org/ at University of Illinois at Urbana-Champaign on March 13, 2015
Exposure (ng/1000 kcal/ day)
Red meat, poultry, cooking practices, metabolism and prostate cancer risk
surface temperatures, frequent meat flipping and pretreatment of meat
in the microwave before pan-frying, which alters concentrations of
precursors (e.g., creatine, sugars) (68). If these findings can be replicated in independent studies, and pan-frying of meats can indeed be
established as a relevant risk factor for PCA through an HCA mediated mechanism, then guidelines can be given to the public on how
to modify meat cooking practices to reduce the formation of meat
mutagens.
Supplementary material
Supplementary materials can be found at http://carcin.oxfordjournals.
org/.
We are grateful to the men who participated in this study, without whom this
research would not be possible. We also thank Mr. Andre Kim for help with
genotyping and Mr. Chris Yoon for assistance with data cleaning.
The project described was supported in part by the Prostate Cancer
Foundation (to M.C.S). M.C.S. received support from grant RSF-09-020-01CNE from the American Cancer Society and from award number 5P30
ES07048 from the National Institute of Environmental Health Sciences
and award number P30CA014089 from the National Cancer Institute.
The Northern and Southern California studies were funded by grants
99-00527V-10182 (to E.M.J.) and 99-00524V-10258 (to S.A.I.) from the
Cancer Research Fund, under Interagency Agreement #97-12013 (University
of California contract #98-00924V) with the Department of Health Services
Cancer Research Program, and by grant R01CA84979 (to S.A.I.) from the
National Cancer Institute, National Institutes of Health. The collection of
cancer incidence data used in this study was supported by the California
Department of Health Services as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the
National Cancer Institute’s Surveillance, Epidemiology, and End Results
Program under contract HHSN261201000140C awarded to the Cancer
Prevention Institute of California, contract HHSN261201000035C awarded
to the University of Southern California, and contract HHSN261201000034C
awarded to the Public Health Institute; and the Centers for Disease Control and
Prevention’s National Program of Cancer Registries, under agreement #1U58
DP000807-01 awarded to the Public Health Institute. The ideas and opinions
expressed herein are those of the authors, and endorsement by the State of
California, the California Department of Health Services, the National Cancer
Institute, or the Centers for Disease Control and Prevention or their contractors
and subcontractors is not intended nor should be inferred.
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