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Nutritional and Socioeconomic Factors in Relation to
Prostate Cancer Mortality: a Cross-National Study
James R. Hebert, Thomas G. Hurley, Barbara C. Olendzki, Jane Teas,
Yunsheng Ma, Jeffrey S. Hampl
Background: Large international variations in rates of prostate cancer incidence and mortality suggest that environmental factors have a strong influence on the development of
this disease. The purpose of this study was to identify predictive variables for prostate cancer mortality in data from
59 countries. Methods: Data on prostate cancer mortality,
food consumption, tobacco use, socioeconomic factors, reproductive factors, and health indicators were obtained from
United Nations sources. Linear regression models were fit to
these data. The influence of each variable fit in the regression models was assessed by multiplying the regression coefficient b by the 75th (X75) and 25th (X25) percentile values
of the variable. The difference, bX75 − bX25, is the estimated
effect of the variable across its interquartile range on mortality rates measured as deaths per 100 000 males aged 45–74
years. Reported P values are two-sided. Results: Prostate
cancer mortality was inversely associated with estimated
consumption of cereals (bX75 − bX25 = −7.31 deaths; P =
.001), nuts and oilseeds (bX75 − bX25 = −1.72 deaths; P =
.003), and fish (bX75 − bX25 = −1.47 deaths; P = .001). In the
42 countries for which we had appropriate data, soy products were found to be significantly protective (P = .0001),
with an effect size per kilocalorie at least four times as large
as that of any other dietary factor. Besides variables related
to diet, we observed an association between prostate cancer
mortality rates and a composite of other health-related, sanitation, and economic variables (P = .003). Conclusions: The
specific food-related results from this study are consistent
with previous information and support the current dietary
guidelines and hypothesis that grains, cereals, and nuts are
protective against prostate cancer. The findings also provide
a rationale for future study of soy products in prostate cancer prevention trials. [J Natl Cancer Inst 1998;90:1637–47]
Prostate cancer is the most prevalent cancer among men and
represents a large and growing health problem in the United
States and other Western countries. Incidence and mortality rates
vary widely across populations, with the highest rates in North
America and northern Europe, intermediate rates in southern
Europe and Latin America, and the lowest rates in Asia and
Africa (1,2). Incidence increases markedly with age. Ecologic
studies investigating the health experiences of migrants over
time (3–6) and cross-national comparisons (7–10) suggest that
environmental, rather than genetic, factors are responsible for
large international variations in rates. Against the background of
international variation, increases in prostate cancer mortality and
incidence rates have been reported in the last three decades in the
United States, Asia, South America, and central Europe. Prostate
cancer incidence in the United States is currently increasing at a
rate nearly three times as high as that of female breast cancer
(11). The incidence of prostate cancer is extremely low in populations of western Africa, but in African-Americans, who originate from the same regions of Africa, the incidence is nearly
50% higher than that of U.S. whites (12), indicating a potentially
strong environmental influence on the disease.
Similar to epidemiologic studies of diet and breast cancer,
studies of diet and prostate cancer have produced equivocal
results (13). Ecologic studies and laboratory animal experiments
tend to produce results strongly consistent with dietary hypotheses that propose a role for fat and animal product intake,
whereas data from analytic epidemiologic studies are less consistent (8,9,14–19). Possible explanations include the fact that
data on diet in humans are derived from self-reports that are
subject to measurement error and a variety of biases (20) and
that the range of dietary fat intake (e.g., grams per person per
day or as a percent of dietary energy) within particular study
populations tends to be very narrow (21), hampering efforts to
detect dietary effects. Also, the effect of particular dietary constituents may be modified by other factors, such as the antagonism of the effect of fat by fiber (e.g., through its influence on
enterohepatic circulation) (22).
This study was conducted to identify major predictors of
prostate cancer mortality by use of data from a total of 59 countries on which we had complete data on most covariates of
interest. In addition to a wide range of nutritional predictors
(including alcohol), we examined the significance of estimated
per capita tobacco consumption, a variety of indicators of socioeconomic status (SES), and reproductive factors.
MATERIALS
AND
METHODS
Data Sources
Data on a maximum of 59 countries were available from a number of United
Nations sources (see Appendix Tables 1 and 2). Data were time-lagged so that
the mortality information was from a period approximately 10 years later than
data on other factors potentially affecting risk. Data on prostate cancer mortality
(i.e., prostate cancer deaths per 100 000 male population for ages 45–74 years)
were obtained from a data tape supplied by the World Health Organization
(WHO) (23). Data were averaged for the 5 years from 1985 through 1989 and
then age-truncated to focus on that portion of the distribution (ages 45–74 years)
where cancer mortality rates and quality of reporting tend to be highest (9). The
Affiliations of authors: J. R. Hebert, T. G. Hurley, B. C. Olendzki, J. Teas, Y.
Ma, Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester; J. S. Hampl, Department of Nutrition, Simmons
College, Boston, MA.
Correspondence to: James R. Hebert, Sc.D., Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, 55 Lake Ave.,
North, Worcester, MA 01655.
See ‘‘Note’’ following ‘‘References.’’
© Oxford University Press
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
ARTICLES 1637
rates were then age-standardized to the 1987 world male population average.
Mortality data provided by the WHO excluded countries with more than 15% of
mortality due to senility or ill-defined causes.
National nutrition data were obtained from the Food Balance Sheets—1979
Through 1981, Average, compiled and published by the Food and Agriculture
Organization (FAO) of the United Nations (24). These data often are referred to
as disappearance data. They represent a kind of inventory information that is
based on the sum of imports and production minus exports, with adjustments
made for non-food uses, losses during storage and transport, food fed to livestock, and food used for seeds and in manufacture for food and non-food uses.
Data are prorated on a per capita basis as a surrogate for consumption. Values for
energy intake plus protein and fat intake in grams are available from this source
for overall vegetable and animal product consumption as well as for consumption of the major food groups (i.e., cereals, roots and tubers, sugars, pulses, nuts
and oilseeds, fruits, vegetables, meat, eggs, fish and seafood, milk, oils and fats,
vegetable oils and fats, animal oils and fats, spices, stimulants, and alcoholic
beverages) and a list of about 300 commodities. Some of these items (e.g., soy
products) were not available for every country in the dataset. Data on tobacco
consumption are similar, with the exception that no allowance is made for food
manufacture or livestock feed.
SES data were obtained from a number of sources. Information regarding
basic sanitation (e.g., proportion of the population with safe water and with
excreta disposal facilities) from the late 1970s was obtained from the WHO (25).
The following basic economic and health indicators were obtained from World
Bank compilations (26,27): gross national product (GNP) per capita (1980),
infant mortality rate (1980), crude birth rate (1970), total fertility rate (1970),
annual percent of growth (natural increase) in population (average, 1970 through
1980), and life expectancy (at birth, 1980). Statistics on the number of physicians
per unit population (physicians per 10 000 population) and hospital bed availability (beds per 10 000 population) were obtained from the WHO (28). Data
from 59 countries were sufficiently complete to allow most multivariable analyses of these data.
Statistical Analyses
Preliminary analyses to examine the distributions and to identify outlier values
were performed on all variables to assess the adequacy of the assumptions of
linear regression (i.e., normality, linearity, and independence) (29). These statistics were computed for all 59 countries for which we had complete data in
order to fit the final multivariable model. All P values are two-sided.
Principal-components analysis (PCA) was performed to reduce the number of
predictor variables with minimal loss of information. A complete description of
this technique is beyond the scope of this article; however, excellent descriptions
may be found elsewhere (30,31). The seven variables related to socioeconomic
conditions included in computing the socioeconomic status principal components (SESPCs) were as follows: GNP, life expectancy at birth, infant mortality
rate, proportion of the population without safe water or excreta disposal facilities, and the number of physicians or hospital beds per 10 000 population. To
compute SESPCs, we used the PROC FACTOR procedure with the Varimax
rotation option in SAS (SAS statistical software, version 6.11; SAS Institute,
Cary, NC) (32). This approach allowed us to reduce a larger set of potentially
correlated variables to a smaller set of variables (principal components). The
principal components are optimally weighted combinations of the initial seven
variables that maintain, as much as possible, the explanatory ability of the
original variables. The Varimax rotation option creates principal components
that are not correlated with each other. We selected the three SESPCs that
account for the majority of the variability in the data and excluded the other
factors that did not appreciably increase the explanatory ability.
Similarly, PCA was used to examine the interrelationships between the large
number of nutritional variables in the dataset. Variables were selected to minimize the impact of multicollinearity (i.e., intercorrelation among predictor variables) when fit in the multivariable models, without excluding potentially important predictors of prostate cancer mortality. For example, the intercorrelations
between per capita animal energy (kcal/day—total energy derived from foods
from all animal sources), animal fat energy (kcal/day), animal protein (g/day),
milk energy (kcal/day), meat energy (kcal/day), total fat (g/day), and total energy
(kcal/day) were very high, ranging from 0.72 to 0.99. Fitting such highly correlated variables simultaneously in a model would pose problems in terms of
model specification and interpretation. We used PCA [PROC FACTOR in SAS
(32) with the no-rotation option, which maintains the factor-loading weights
unchanged] to identify nearly collinear sets of variables. We then selected a
1638 ARTICLES
single variable from among each set of highly correlated variables by choosing
the variable with the highest component weighting. In this way, we selected a set
of largely uncorrelated nutrient variables to examine in a multivariable setting
while we retained the inherent representation of the underlying PCA (i.e., the
animal-related variables discussed above were represented by animal energy).
Linear regression models were fit, regressing prostate cancer mortality on
each predictor variable. All regression models were weighted by the malespecific population across countries for the age range 45–74 years. Model specification used stepwise regression techniques as a preliminary approach, as well
as adding and removing candidate variables individually to produce the most
parsimonious model while retaining a high level of explanatory ability. For this
approach, we used a general linear model [the procedure PROC GLM in SAS
(32)]. Diagnostic analytic procedures were used to examine the distribution of
residuals and to identify influential cases and problems associated with multicollinearity (33). Also, we log-transformed both prostate cancer mortality data
and nutrient data and reanalyzed our data. The results were not materially different from those obtained using untransformed data. We chose to present the
untransformed results because the assumptions of the analysis were met (i.e.,
normality) and because use of untransformed data improves interpretability of
results.
To evaluate the actual effect of each variable fit in the final regression models,
we multiplied the regression coefficient b by the 75th percentile value of the
variable (X75) and the 25th percentile value of the variable (X25). The difference
between bX75 and bX25 is the effect of the variable across its interquartile range
(IQR). This manner of presenting the results combines the estimated magnitude
of effect (b) and the range of the variable in the actual dataset in which the
regression models were run. It provides a way to ‘‘standardize’’ the effect in
terms of prostate cancer deaths. Also, we have presented the percentage change
in the mean world rate of prostate cancer, which was calculated as [(bX75 −
bX25)/world mean prostate cancer rate] × 100; this quantity gives a sense of the
relative effect on prostate cancer based on a change across the IQR of the
exposure variable.
Regression model results for the 59-country models excluding tobacco consumption were similar on significant predictors to the 50-country models that
included tobacco in the covariate list. Estimated tobacco consumption (from
disappearance) was consistently unrelated to prostate cancer mortality. By excluding the tobacco consumption variable from the final model, we were able to
include data from 59 countries in the multivariable analyses.
Nuts and oilseeds subsume soybeans in the Food Balance Sheet dataset. We
attempted to separate soybean consumption from this larger food group and were
able to identify a subset of 42 countries on which we could obtain reliable data
on soy disappearance (to estimate consumption). All analyses were repeated in
this smaller dataset by use of the model-building approach described above.
In addition, to assess the effect of total energy on prostate cancer mortality, we
fit a model using energy-adjusted nutrients. Nutrient variables were regressed
upon total calories, and the residuals were calculated as the difference between
the actual value for the nutrient and the predicted value. The residual is that
portion of the nutrient intake that is unexplained by the variation in total energy
intake (34). The model specification process was the same as that described
above.
RESULTS
In Table 1, summary statistics are presented for the dependent
variables (sex-specific, age-truncated, and standardized prostate
cancer mortality rates) and the independent variables (e.g., dietary and SES variables) used in the analyses. There was a slight
rightward (a tendency for a few very high values) skewing for
variables associated with poverty and a slight leftward skewing
for variables associated with affluence. These statistics demonstrate a wide range of variability, as would be expected for such
a diverse set of countries. Exploratory analyses indicated that
data met the assumption of normality, even for the full set of 59
countries.
Table 2 displays the weighting of individual variables in the
SES PCA. SESPC1 is a poverty-related factor, loading heavily
with infant mortality and lack of sanitation or lack of safe drinking water. SESPC2 is related to access to medical services, with
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
Table 1. Summary statistics for prostate cancer mortality, diet, and socioeconomic factors
Percentile values
Variable
No. of countries
Mean
23.27
Minimum
Prostate cancer mortality rate*
59
Diet
Total energy†
Total fat, g/day
Animal fat energy†
Animal energy, including poultry†
Animal protein, g/day
Meat energy†
Milk energy†
Fish energy†
Vegetable energy†
Vegetable oil (extracted oils) energy†
Vegetable fat (all fats), g/day
Sugar energy†
Root and tuber energy†
Cereal energy†
Pulse energy†
Nut and oilseed energy†
Soybean energy†
Cabbage energy†
Fruit energy†
Alcohol energy†
0.53
59
59
59
59
59
59
59
59
59
59
59
59
59
59
59
59
42
59
59
59
3003
88.7
543
791
44
352
233
33
2215
52
28
375
125
1042
48
39
8
4
109
130
Socioeconomic factors
Hospital beds per 10 000 population‡
Physicians per 10 000 population‡
Percentage without sanitation‡
Percentage without safe water‡
Per capita GNP (U.S. dollars)‡,§
Infant mortality rate‡,㛳
Life expectancy‡,¶
59
59
59
59
59
59
59
64
12
16
13
4965
28
70
10
1
0
0
320
7
57
Other variables
Total fertility rate#
Annual percent growth in population**
Crude birth rate††
Tobacco consumption (kg/person per year)
48
46
48
50
4
2
26
17
2
0
14
1
2018
14.1
46
95
9
9
9
2
1431
11
0.1
73
11
604
6
1
0
0
2
0
Maximum
69.50
3699
172.3
1173
1699
104
750
554
195
2966
146
70
644
446
2070
183
312
94
39
326
304
25th
75th
14.61
30.13
2712
52.9
268
406
27
157
116
15
2009
21
20
302
63
703
20
14
0.2
1
69
55
3393
128.6
868
1196
57
511
305
38
2384
70
37
446
187
1265
71
47
4
6
141
209
172
25
85
79
19 870
104
77
35
5
0
0
1430
11
66
101
17
20
20
7920
33
74
8
4
49
54
2
1
17
9
5
3
35
22
*Prostate cancer deaths per 100 000 male population 45–74 years old, age standardized to the 1987 world male population average.
†Total energy based on disappearance data, prorated to kilocalories per person per day.
‡These variables are included in the socioeconomic principal-components analysis. The mean values across countries are derived by averaging the within-country
percentages.
§Per capita gross national product (1980).
㛳Rate per 1000 live births (1980).
¶Life expectancy, in years, at birth (1980).
#Number of children that would be born to a woman if she were to live to the end of her childbearing years (15–49 years) and bear children in accordance with
age-specific fertility rates (1970).
**Average annual growth of population (percent) based on mid-year populations, 1970–1980.
††Number of live births per 1000 population (1970).
large positive loadings on physicians and medical beds per unit
population as well as life expectancy. SESPC3 is an affluencerelated factor, with a large loading on per capita GNP.
The simple linear regression coefficient (b) and the 95% confidence interval along with the Pearson correlation coefficient
(r) are presented in Table 3 for all candidate independent variables in the dataset. For the nutrition variables, positive associations were evident between prostate cancer mortality rates and
variables associated with affluence, especially food-related variables including total fat, animal products including milk and
dairy products, and energy from alcohol and sugar. Negative
associations were observed with energy from cereals, vegetables, cabbage, soybeans, and fish. Among the SESPCs, there
was a negative association with the poverty-related factor and a
positive association with the affluence-related factor.
The results of the regression model analysis are shown in
Table 4. Overall, we observed protective effects from higher
levels of intake of cereals, nuts and oilseeds (which include soy
consumption), and fish. There was also a significant protective
association for decreasing levels of the poverty-related factor
(SESPC1). Animal energy was positively associated with prostate cancer mortality.
Effect sizes, estimated by fitting the parameter estimate
across the interquartile values of the variable (i.e., bX75 − bX25),
are shown in Tables 4 and 5. For cereal energy, the difference in
the estimated effect at the 75th and 25th percentile values was
–7.31 prostate cancer deaths per 100 000 males aged 45–74
years, 31.4% of the world mean average prostate cancer mortality for males in this age group. Across the full range of data
represented by these 59 countries, the difference between the
lowest X0 and the highest X100 consumption of cereal energy
when fitted to the regression coefficient equaled 19.1 deaths. In
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
ARTICLES 1639
Table 4. Results of the general linear model based on prostate cancer
mortality—including energy from soybeans as part of energy from nuts
and oilseeds*
Table 2. Results of principal-components analysis for variables representing
socioeconomic status*
Variable
GNP㛳
Life expectancy
Infant mortality
Percent of households without safe water
Percent of households without sanitation
Physicians per capita
Hospital beds per capita
SESPC1†
SESPC2‡
SESPC3§
−0.207
−0.495
0.746
0.869
0.914
−0.199
−0.301
0.306
0.568
−0.427
−0.213
−0.194
0.893
0.798
0.903
0.463
−0.355
−0.142
−0.145
0.176
0.324
95% confidence interval
Independent
variable
*Principal-components analysis using PROC FACTOR in SAS with Varimax
rotation. The principal components are optimally weighted combinations of the
seven variables that maintain, as much as possible, the explanatory ability of the
original variables. Data used are from the 59 countries on which we had complete data. The numbers in the last three columns indicate optimum factorloading weights for each combination.
†SESPC1 ⳱ socioeconomic status principal component 1, computed as noted
above and in the text. Poverty-related factor.
‡SESPC2 ⳱ socioeconomic status principal component 2, computed as noted
above and in the text. Access to medical care factor.
§SESPC3 ⳱ socioeconomic status principal component 3, computed as noted
above in the text. Affluence-related factor.
㛳GNP ⳱ gross national product (U.S. dollars per person in 1980).
contrast, the increase in mortality associated with an increase in
animal energy across the IQR was 5.53 or 23.8% of the mean of
prostate cancer mortality.
The model focusing on soybean disappearance data for the
subset of 42 countries where that data were available produced
the results shown in Table 5. The same core group of variables
observed to be associated with prostate cancer mortality in the
Cereal energy
Animal energy
Nut and oilseed
energy㛳
Fish energy
SESPC1¶
b†
Lower
Upper
bX75 − bX25‡
P§
−0.013
0.007
−0.052
−0.019
0.001
−0.085
−0.007
0.013
−0.019
−7.31
5.53
−1.72
.001
.02
.003
−0.064
−3.253
−0.101
−5.323
−0.027
−1.183
−1.47
−4.07
.001
.003
*In this model, the dependent variable—prostate cancer mortality rate per
100 000 population, truncated to ages 45–74 years and age standardized to the
1987 world male population—is regressed on all independent variables listed; 59
countries are included; model R2 ⳱ .80.
†Regression coefficient for the independent variable.
‡bX75 − bX25 estimates the effect size of the variable listed by multiplying the
regression coefficient b by the 25th percentile value of the variable and subtracting that value from the product of the regression coefficient b and the 75th
percentile value of the variable. This is the effect of the variable across its
interquartile range (in prostate cancer deaths per 100 000 males aged 45–74
years) in the 59 countries available for this analysis.
§Two-sided P values were obtained from the F test of H0:␤ ⳱ 0.
㛳Includes energy from soy.
¶SESPC1 ⳱ socioeconomic status principal component 1, computed as described in the text. Poverty-related factor.
previous analysis was similarly associated in this model, with the
exception of fish energy. Here, however, energy from alcohol was
significantly associated with increased mortality, while energy
from soy was significantly associated with reduced mortality.
Table 3. Regression coefficients (b) with the 95% confidence interval and Pearson correlation coefficients for candidate independent variables with
age-standardized and truncated prostate cancer mortality rates*
95% confidence interval of b
Variable
Total energy㛳
Total fat, g/day
Animal fat energy㛳
Animal energy, including poultry㛳
Animal protein, g/day
Meat energy㛳
Milk energy㛳
Fish energy㛳
Vegetable energy㛳
Vegetable oil (extracted oils) energy㛳
Sugar energy㛳
Root and tuber energy㛳
Cereal energy㛳
Pulse energy㛳
Nut and oilseed energy㛳
Soybean energy㛳
Cabbage energy㛳
Fruit energy㛳
Alcohol energy㛳
SESPC1 (poverty-related factor)¶
SESPC2 (medical services-related factor)¶
SESPC3 (affluence-related factor)¶
No. of countries
b†
Lower
Upper
r‡
P§
59
59
59
59
59
59
59
59
59
59
59
59
59
59
59
42
59
59
59
59
59
59
0.01
0.19
0.03
0.02
0.43
0.04
0.07
−0.10
−0.01
−0.08
0.06
0.07
−0.02
−0.07
−0.10
−0.24
−0.48
0.01
0.08
−5.06
6.64
3.30
0.004
0.147
0.024
0.016
0.322
0.032
0.054
−0.163
−0.020
−0.182
0.046
0.033
−0.026
−0.148
−0.157
−0.334
−0.858
−0.053
0.053
−8.296
3.702
0.632
0.016
0.233
0.036
0.024
0.538
0.048
0.086
−0.037
0.002
0.022
0.074
0.107
−0.014
0.008
−0.043
−0.146
−0.102
0.069
0.107
−1.824
9.578
5.968
.53
.74
.78
.79
.72
.76
.78
−.39
−.30
−.19
.71
.46
−.72
−.22
−.43
−.62
−.32
.03
.62
−.38
.51
.31
.0001
.0001
.0001
.0001
.0001
.0001
.0001
.002
.02
.14
.0001
.0002
.0001
.10
.0007
.0001
.02
.10
.0001
.003
.0001
.02
*Prostate cancer deaths per 100 000 male population 45–74 years old, age standardized to the 1987 world male population average.
†Regression coefficient from regressing prostate cancer mortality on the independent variables.
‡r ⳱ Pearson correlation coefficient.
§Two-sided P values were obtained from the F test of H0:␤ ⳱ 0, which is identical to the t test of H0:␳ ⳱ 0.
㛳Expressed as kilocalories per person per day.
¶Socioeconomic factors derived from principal-components analysis with Varimax rotation based on the seven socioeconomic status-related variables, as described
in the text and in Table 2.
1640 ARTICLES
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
Table 5. Results of the general linear model based on prostate cancer
mortality—disaggregating energy from soybeans from that derived from nuts
and oilseeds*
95% confidence interval
Independent
variable
Cereal energy
Animal energy
Nut and oilseed
energy㛳
Soy energy㛳
Alcohol energy
SESPC1¶
b†
Lower
Upper
bX75 − bX25‡
P§
−0.009
0.007
−0.035
−0.013
0.001
−0.062
−0.005
0.013
−0.008
−5.62
5.53
−1.16
.0003
.03
.02
−0.147
0.024
−1.961
−0.198
0.003
−3.803
−0.096
0.043
−0.119
−0.56
3.54
−2.46
.0001
.025
.045
*In this model, the dependent variable—prostate cancer mortality rate per
100 000 population, truncated to ages 45–74 years and age standardized to the
1987 world male population—is regressed on all independent variables listed; 42
countries are included; model R2 ⳱ .90.
†Regression coefficient for the independent variable.
‡bX75 − bX25 estimates the effect size of the variable listed by multiplying the
regression coefficient b by the 25th percentile value of the variable and subtracting that value from the product of the regression coefficient b and the 75th
percentile value of the variable. This is the effect of the variable across its
interquartile range (in prostate cancer deaths per 100 000 males aged 45–74
years) in the 42 countries available for this analysis.
§Two-sided P values were obtained from the F test of H0:␤ ⳱ 0.
㛳In this analysis, soy energy is disaggregated from the heading of nut and
oilseed energy.
¶SESPC1 ⳱ socioeconomic status principal component 1, computed as described in the text. Poverty-related factor.
A separate model was developed (data not shown) using energy-adjusted residuals for the nutrient variables and fitting total
energy intake. This approach allows the examination of total
energy intake as a predictor of mortality without being constrained by the problems associated with fitting total energy
intake in a model with other energy-contributing nutrients. Once
again, energy from cereals, fish, nuts and oilseeds, and the poverty-related factor (SESPC1) all demonstrated a protective effect, and the magnitude of these effects was not materially different from that reported in Table 4. However, in this model,
total energy intake was independently and significantly associated with an increase in prostate cancer mortality (b ⳱ 0.005;
bX75 − bX25 ⳱ 3.41) with a change across the IQR of 15%—
calculated as [(bX75 − bX25)/world mean prostate cancer rate] ×
100—of the mean world prostate cancer mortality rate. Animal
energy, even when substituted for total energy intake, exhibited
no association in this model.
DISCUSSION
In this hypothesis-generating study, our results showed that
prostate cancer mortality is positively associated with dietary
factors associated with affluence, including estimated intakes of
energy, total fat, and animal products (specifically, milk, meat,
and poultry). On the other hand, intakes of cereals, soybeans,
other nuts and oilseeds, and fish were negatively associated with
prostate cancer mortality and constituted the strongest effects in
the data. Our results are consistent with the findings of previous
researchers (14–16,35,36), who have shown that prostate cancer
mortality is increased by intakes of animal fat but not of vegetable fat.
Because the etiology of prostate cancer remains unknown, the
mechanism by which fat (or other dietary constituents that cor-
relate with fat) from animal foods may be associated with prostate cancer is still unclear. For prostate cancer and cancers of
other sites that are sensitive to serum hormone levels, animal fat
may influence the risk for cancer by raising adults’ sex hormone
levels. This mechanism was postulated more than 20 years ago
by Hutchison (37). Consistent with this hypothesis is the fact
that, in the United States, African-American men have a rate of
prostate cancer 40% higher than that in whites, whereas their
testosterone levels are about 15% higher (38). Providing further
evidence for the role of diet in control of hormone levels, Dorgan et al. (22) demonstrated, in a controlled feeding trial involving 43 healthy men, that urinary testosterone levels were lower
after a low-fat, high-fiber diet than after a high-fat, low-fiber
diet.
Because cancers of the prostate and the breast are at least
partially hormone mediated, they share several characteristics,
and the mortality rates for prostate and breast cancers are fairly
highly correlated (35). One disparity in the epidemiology of the
two cancer sites is that breast cancer mortality is more strongly
correlated with dietary fat than is prostate cancer mortality (35).
In a previous study using international data (39), we showed that
energy intake from animal foods was a strong predictor of breast
cancer mortality. However, the results of the present study
showed a considerably smaller effect due to energy from animal
sources.
One way in which energy-dense, high-fat foods can exert a
biologic effect is through their effect on total energy intake. In
the energy-adjusted model (fitting nutrients as the residual value
after removing that part of the variability explained by total
energy intake), we found an association for total energy intake.
However, when fitting nutrients as residuals in the model,
the effect of energy from animal foods that was evident in
the unadjusted model disappeared. This result suggests that the
effects of energy from animal foods and total energy intake may
be confounded. When the energy-adjusted animal food residual
was substituted for total energy intake in this model, we found
that energy from animal foods remained unrelated to prostate
cancer, indicating that the original association may be due to the
association of animal energy with total energy intake. These
results must be interpreted with caution because energy adjustment on group level variables may not function the same as
energy adjustment on the individual level. Control for confounding on the group level may not be complete and may introduce
additional bias (40). The evidence from case–control and cohort
studies on the impact of total energy intake is conflicting; it is
not clear if energy intake per se or if the intake of energy-dense
foods, such as animal products, is associated with prostate cancer.
Unlike meat and poultry, fish appears to be protective against
prostate cancer; this effect is perhaps due to ␻-3 fatty acids,
which appear to inhibit the growth of tumors in the prostate as
well as in other sites (41,42). Our results showed a moderate,
inverse association between prostate cancer mortality and energy from fish. In contrast, a study among Seventh-day Adventist men (18) showed several nonsignificant trends of increasing
risk for prostate cancer due to consumption of fish, but only one
statistic was significantly different from unity (relative risk [RR]
⳱ 1.68; 95% confidence interval [CI] ⳱ 1.16–2.43) for men
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
ARTICLES 1641
who ate fish less frequently than once per week compared with
men who never ate fish. These results need to be interpreted
cautiously because the authors do not provide details regarding
their dietary assessment methodology; moreover, their results
for intakes of other foods (e.g., beef, poultry, and milk) do not
agree with the majority of the prostate cancer literature. Also,
fish was not categorized as being from fresh or salt water or by
temperature of the water. All of these factors are known to
influence the ␻-3 fatty acid content of fish. The results of that
study may be unique because their sample comprised a homogeneous group of predominantly white middle-class vegetarian
men.
In our initial model, which did not separate soybeans from
other nuts and oilseeds, energy from fish was associated
with prostate cancer mortality. However, when soybeans
were entered separately from nuts and oilseeds, the effect of fish
was not significant. Our results showed a strong correlation between energy from fish and soy products (r ⳱ .85) because
countries in our dataset that had a high soy intake also had a high
fish intake. Future studies assessing the role of diet in prostate
cancer should ensure that a protective effect from fish is not
actually due to the consumption of other foods, such as soy
products.
Apart from some countries where reported death rates are not
reliable, the lowest death rates from prostate cancer are in the
Far East (i.e., Japan and Hong Kong) (2). Although many hypotheses have been offered for the lower rates of prostate cancer
mortality among Asians, the one that possibly has received the
greatest amount of attention and for which the data are most
consistent concerns the dietary differences between populations
of Western and Eastern nations. In particular, Asians have a
greater per capita consumption of soy products, consumed primarily as tofu, soymilk, tempeh, and miso. Our results showed
that energy from soybeans was inversely associated with prostate cancer mortality. On a per unit energy (kilocalories) basis,
the soy effect was the largest in the dataset, although its effect
across the IQR of the 42 countries was relatively small, owing to
the low per capita disappearance of soy products. Although the
soybeans in our dataset were used for human consumption, we
do not have data indicating what types of soy products were
consumed (e.g., fermented products like tempeh). However, our
results are similar to those of Severson et al. (17), who showed
that an increased consumption of tofu was associated with a
decreased risk for prostate cancer, and to those of another study
(43) showing that soy products are protective against cancer at
other sites.
It has been proposed that certain compounds in soy, isoflavonoids and lignans, have a metabolism similar to that of
biologic steroids but have a profoundly different effect on
hormone-sensitive target cells (44,45). A protective effect
of these compounds may be the result of a competitive
binding to hormone receptors, with the weaker phytoestrogen
activity displacing the harmful stimulatory effect of endogenous
hormones. Isoflavones and lignans also exhibit antioxidative activity and appear to increase serum levels of sex hormonebinding globulin (SHBG) production in the liver, which in turn
results in a lowering of free testosterone, thus reducing hormonal
activity (45). Higher levels of SHBG and decreased concentra1642 ARTICLES
tions of free testosterone have been reported both in Asian men
and in men who are vegetarian (46,47). Vegetarian and Asian
diets typically contain other foods, such as lentils, that have
phytoestrogenic activity and other beneficial properties (48,49).
However, these foods are not itemized in the Food Balance
Sheets (24) because they are of minor economic importance.
Besides isoflavonoids and lignans that affect hormone metabolism, soybeans (and other legumes) contain compounds that
could protect through other mechanisms. These other compounds include protease inhibitors, phytic acid, and plant sterols
(50).
We could not examine all categories of vegetables and fruits
in these data, even though we know that some of them contain
isoflavonoids and other potentially protective factors (51). However, our univariate results did show a moderate, inverse association between prostate cancer mortality and cabbage consumption (Brassica sp.). The Brassica genus comprises other
nutrient-dense vegetables such as kale, cauliflower, Brussels
sprouts, kohlrabi, and broccoli. Although not rich in phytoestrogens, the Brassica vegetables do have a relatively high content
of glucosinolates, which are hydrolyzed to produce indoles and
isothiocyanates (52,53). Sulforaphane, a potent isothiocyanate
that has been extracted from broccoli, blocks chemical carcinogenesis, indicating that these vegetables could have anticarcinogenic activity (54). Perhaps because they are not a plentiful
source of phytoestrogens, the Brassica vegetables have not before been found to consistently protect against cancers of the
prostate, ovary, and endometrium, which are hormonedependent sites; however, their chemopreventive role against
cancers of the lung and the digestive system is fairly well established (52).
Similar to consumption of vegetables, consumption of cereals
typically has been associated with a reduced risk for cancers of
the digestive system (55). Evidence is also growing that consumption of cereals is inversely related to prostate cancer mortality. Our results showed a strong negative association between
cereal energy and prostate cancer (b ⳱ –0.013 and b ⳱ −0.009
for the overall and soy-specific models, respectively). Our findings support those of Rose et al. (35), who showed that cereal
intake was more strongly related to prostate cancer mortality
than was intake of meat or milk. The protection afforded by
cereals is likely related to the consumption of traditional breads,
which frequently contain whole flaxseeds, rye, and buckwheat
flour (47). These foods are rich in lignans that are weaker than
isoflavonoids but may have an independent effect against cancer
at hormone-sensitive sites (47,51). Plant lignans are located next
to the aleurone layer of the grain and are often removed in the
milling processes particular to Western societies. Lignan end
products are excreted when subjects have ingested the whole
grain. Lignans appear to act as protease inhibitors by a mechanism similar to that of the pharmaceutical Finasteride used to
treat prostate cancer (47). The combined action of both isoflavonoids and lignans, from their dietary sources of soy, legumes,
cereals, and vegetables, may be protective against prostate cancer.
Our results showed that consumption of milk was strongly
and positively associated with prostate cancer mortality, a finding consistent with the results of other studies that have found
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
that high intakes of milk and other dairy products are associated
with increased risk of prostate cancer (35,36). La Vecchia et al.
(16) showed that two or more servings of milk per day significantly increased risk (RR ⳱ 5.1; 95% CI ⳱ 1.5–16.6), although
intakes of high-fat dairy foods like cheese and butter did not. A
case–control investigation from the Roswell Park Memorial Institute (56) found a strong association of prostate cancer
incidence with consumption of whole milk but not of skim
milk, supporting the general hypothesis that prostate cancer is
related to intake of animal fats. Although data regarding skim
milk were available from the FAO dataset, we did not
include this information in analyses because quantities were
not specified for most countries. Also, unlike soybeans,
for which the putative biologically active constituents are not
represented in the summary statistics in the dataset, displacement of high-fat dairy products by skim milk products is reflected in summary measures including total fat, animal fat, and
milk energy.
The results of the PCA of the socioeconomic variables can be
interpreted in the context of developed versus undeveloped nations. Each of the three principal components had a moderate
correlation with prostate cancer mortality. Unlike the other principal components, SESPC1, which was associated with poverty
factors, was negatively correlated with prostate cancer mortality.
SESPC1 also was negatively associated with prostate cancer
mortality in the regression models (b ⳱ −3.253 and b ⳱ −1.961
for the overall and soy-specific models, respectively). Hence,
poverty is associated with lower prostate cancer mortality.
This effect might be achieved because of a lower life expectancy
or greater number of deaths due to other causes among populations of undeveloped nations. However, because all of our data
were age adjusted and excluded countries with obvious problems in reporting vital statistics, it is unlikely that this was a
major problem. Alternatively, the principal components
may be acting as a proxy for the plethora of sociodemographic
factors that characterize a population, such as total wealth and
access to health-related resources. Further evidence for correlates of affluence exerting an effect to increase prostate cancer
mortality comes from the univariate analysis of SESPC2 and
SESPC3. These variables, which are related to access to medical
services and affluence, respectively, were directly associated
with prostate cancer mortality (r ⳱ .51 and r ⳱ .31, respectively).
The datasets from the FAO and WHO are a rich source of
information, but they are not without limitations. Criticisms can
be made that countries differ in how accurately they report mortality data, although none of the countries included in these
analyses reported having more than 15% of mortality due to
senility and ill-defined causes. In addition, although the food
disappearance data measure total food that is produced and imported, minus food that is exported, fed to animals, or unavailable for human use, they do not account completely for food
wastage, selective avoidance of foods, or food fed to domestic
animals and pets. Therefore, the data apply to populations; they
cannot be interpreted as having a cause–effect relationship, and
they should not be used to make dietary guidelines for individuals. Nevertheless, each method for determining the dietary intake of individuals or groups has its unique limitations; e.g., we
are not convinced that the food-frequency questionnaires used in
many prostate cancer studies were able to better describe the
food intakes of their samples (20,57).
In this study, we have used prostate cancer mortality rather
than prostate cancer incidence because of the availability and the
quality of the data. Clearly, these two measures are not the same
because mortality is a combination of incidence, stage and age at
diagnosis, and survival. Although incidence rates of prostate
cancer have increased sharply in recent years among some of the
more developed countries (due in part to the implementation of
widespread prostate-specific antigen screening), mortality rates
for this cancer have not changed appreciably (58–60). Apparently, this phenomenon is a result of the identification of earlier
stage, more slowly growing neoplasms and the generally indolent nature of the disease. Moreover, the lack of a decrease in
mortality rates may be due to a lag in the effect of screening
(61).
Analysis of ecologic data can present a problem with the
misinterpretation of results analyzed at the group level as indicative of relationships on the individual level (i.e., the ecologic
fallacy) (62). Aggregating data by country can obscure interesting relationships within a country, and it limits control of potential confounders and effect modifiers. In our analyses, we
have attempted to account for factors related to SES and level of
development by examining and controlling for the effects of the
principal component factors (poverty, access to medical facilities, and affluence) in our multivariable models, while acknowledging that control in this manner cannot completely remove
these biases (40).
In our analyses, we have used age-standardized and truncated
mortality rates to reduce the impact of reporting errors. However, there is potential for bias when regressing age-adjusted
rates on crude measures of exposure (63). The extent of this bias
in our study should be small because the within-country variation in dietary intake is generally much smaller than the between-country variation in dietary intake, suggesting that crude
estimates and age-adjusted estimates of aggregate measures of
diet would be relatively similar. Other kinds of studies would be
needed to relate specific, individual exposures to prostate cancer-related outcomes. In Table 3, we have presented Pearson
correlation coefficients to facilitate comparisons of our results
with those from previous studies. However, caution must be
used in the interpretation of these results because the associations may be overestimated as a result of the effects of aggregation bias (64). Prostate cancer is considered to be a disease of
the affluent because the incidence is greater in developed nations. For example, Armstrong and Doll (9) showed that the
GNP was strongly and positively correlated with prostate cancer.
Among wealthy nations, the effects of occupation, environment,
and lack of physical activity should not be ignored (65,66).
However, even with consideration of these factors, estimates of
dietary intake consistently have been shown to be associated
with prostate cancer incidence and mortality. Western diets typically are rich in total energy, animal fat, and meat but low in
vegetables, soy products, and other nuts and oilseeds. On the
basis of the results of this study and other studies, it appears that
the Western diet may contribute to risk for prostate cancer mortality.
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
ARTICLES 1643
Appendix Table 1. Country-specific values of prostate cancer mortality and selected dietary variables for the 59 countries used in regression analyses
Country
Argentina
Australia
Austria
Bahamas
Barbados
Belgium
Belize
Bulgaria
Canada
Chile
Costa Rica
Cuba
Czechoslovakia
Dominica
Dominican Republic
Ecuador
Egypt
England/Wales
Federal Republic of Germany
France
German Democratic Republic
Greece
Guatemala
Honduras
Hong Kong
Hungary
Iceland
Ireland
Israel
Italy
Japan
Kuwait
Malta
Mauritius
Mexico
The Netherlands
Netherlands Antilles
New Zealand
Northern Ireland
Panama
Paraguay
Peru
Poland
Portugal
Republic of Korea
Romania
Scotland
Singapore
Spain
Sri Lanka
St. Lucia
Suriname
Syrian Arab Republic
Thailand
Trinidad/Tobago
United States
Uruguay
Venezuela
Yugoslavia
Prostate
cancer
mortality*
Total
energy†
Fish
energy†
Nut and
oilseed
energy†
Soy
energy†
Alcohol
energy†
Animal
energy†
Cereal
energy†
25.24
29.59
31.23
63.49
51.61
33.78
25.03
17.12
29.20
23.48
24.17
29.86
30.13
69.50
22.47
15.80
2.75
28.38
32.21
31.43
25.88
15.83
5.77
1.12
5.44
33.72
31.72
28.16
16.10
22.57
5.87
15.21
24.36
7.09
14.61
29.98
28.63
35.09
25.48
24.22
13.84
12.59
22.46
25.60
0.90
17.98
27.40
7.47
24.86
0.70
12.27
32.22
0.67
0.53
46.88
32.19
34.94
24.14
22.16
3380
3055
3575
2200
3020
3639
2714
3619
3340
2759
2653
2796
3393
2018
2130
2114
3175
3249
3351
3529
3689
3668
2138
2135
2771
3484
3087
3699
3060
3688
2852
3344
2843
2766
2890
3617
2712
3573
3249
2338
2839
2195
3479
3204
3056
3346
3249
3165
3294
2251
2390
2590
3005
2330
2837
3641
2886
2646
3550
10
22
15
19
59
28
10
12
29
38
13
34
17
38
15
35
9
22
24
34
27
29
2
3
89
8
184
27
26
25
195
20
41
39
19
17
36
13
22
14
2
49
35
47
67
15
22
63
53
31
53
46
4
37
24
23
11
21
8
14
33
43
7
51
16
48
29
68
3
21
15
24
47
27
10
20
34
39
22
16
86
10
2
81
16
7
5
88
43
115
45
37
13
24
29
7
15
34
30
50
13
1
24
113
18
34
35
46
312
20
30
93
65
56
75
9
8
11
5
0
0
—
—
1
—
0
0
—
4
9
0
—
3
4
4
0
1
0
0
0
3
—
36
0
0
0
1
0
93
—
—
—
—
2
—
—
0
—
32
1
0
0
94
0
0
29
1
1
—
3
—
18
—
1
—
2
0
216
179
238
160
134
255
80
232
129
122
71
37
263
63
48
47
1
209
304
269
280
111
32
31
52
252
54
150
61
254
142
0
66
46
55
177
153
158
209
103
94
111
135
210
161
183
209
34
236
5
115
93
9
21
88
186
98
91
150
1107
1019
1233
768
850
1483
705
756
1297
447
500
650
1157
364
287
403
209
1196
1200
1343
1307
810
211
227
834
1210
1432
1315
705
913
590
870
794
311
406
1512
836
1699
1196
405
573
287
1151
609
269
823
1196
689
843
95
518
325
405
152
516
1316
1019
543
804
987
798
701
640
869
697
958
1582
662
1343
911
1050
1034
607
703
655
2032
685
696
784
907
1165
1240
1153
956
1131
604
960
1082
1282
1239
1265
980
1382
1443
631
808
762
685
909
851
960
1224
1258
2070
1455
685
1443
853
1275
661
1306
1514
1540
1140
661
938
971
1668
*Prostate cancer mortality per 100 000 male population aged 45–74 years and age standardized to the 1987 world male population.
†Values are based on food disappearance data, as described in text, and prorated to an energy equivalent per capita (kilocalories per person per day). — ⳱ missing
data.
1644 ARTICLES
Journal of the National Cancer Institute, Vol. 90, No. 21, November 4, 1998
Appendix Table 2. Country-specific values of prostate cancer mortality and socioeconomic variables for the 59 countries used in regression analyses
Country
Argentina
Australia
Austria
Bahamas
Barbados
Belgium
Belize
Bulgaria
Canada
Chile
Costa Rica
Cuba
Czechoslovakia
Dominica
Dominican Republic
Ecuador
Egypt
England/Wales
Federal Republic of Germany
France
German Democratic Republic
Greece
Guatemala
Honduras
Hong Kong
Hungary
Iceland
Ireland
Israel
Italy
Japan
Kuwait
Malta
Mauritius
Mexico
The Netherlands
Netherlands Antilles
New Zealand
Northern Ireland
Panama
Paraguay
Peru
Poland
Portugal
Republic of Korea
Romania
Scotland
Singapore
Spain
Sri Lanka
St. Lucia
Suriname
Syrian Arab Republic
Thailand
Trinidad/Tobago
United States
Uruguay
Venezuela
Yugoslavia
Prostate
cancer
mortality*
Per
capita
GNP†
Infant
mortality‡
Hospital
beds§
25.24
29.59
31.23
63.49
51.61
33.78
25.03
17.12
29.20
23.48
24.17
29.86
30.13
69.50
22.47
15.80
2.75
28.38
32.21
31.43
25.88
15.83
5.77
1.12
5.44
33.72
31.72
28.16
16.10
22.57
5.87
15.21
24.36
7.09
14.61
29.98
28.63
35.09
25.48
24.22
13.84
12.59
22.46
25.60
0.90
17.98
27.40
7.47
24.86
0.70
12.27
32.22
0.67
0.53
46.88
32.19
34.94
24.14
22.16
2520
11 140
11 140
4650
3040
10 760
1000
2625
10 193
2210
1430
840
5800
460
1330
1350
690
9660
12 460
11 680
5340
4290
1130
660
5340
2270
9000
5150
5090
6840
10 080
19 870
2036
1052
2270
10 930
10 930
7920
5150
2120
1610
1310
4670
2450
1187
2560
9660
5910
5430
320
698
2600
1680
790
6840
13 160
2650
4140
2800
44
10
10
32
28
12
28
20
15
27
18
17
16
21
65
68
104
11
12
10
12
14
66
83
10
20
10
11
16
14
7
32
15
33
53
8
8
12
11
33
45
83
20
26
32
29
11
11
10
32
22
30
58
51
26
11
34
39
34
56
124
113
40
87
89
45
87
87
35
35
41
123
43
29
20
21
86
118
106
107
64
22
14
43
88
172
105
56
104
106
39
106
35
12
101
92
102
112
39
14
18
76
53
16
92
116
38
54
29
51
54
10
12
45
63
43
31
60
Physicians§
Percentage
without
sanitation
Percentage
without
safe water
Life
expectancy㛳
19
15
23
7
7
23
3
23
18
6
7
9
25
2
5
6
9
15
20
16
19
22
2
3
9
23
17
12
5
21
12
13
13
4
8
17
6
14
15
8
5
6
17
14
5
14
17
8
18
2
4
5
4
1
5
17
14
11
13
21
0
0
0
0
0
0
0
0
20
13
0
0
0
85
75
74
0
0
0
0
0
71
65
7
0
74
0
0
0
0
0
4
6
63
0
0
0
0
55
9
63
0
0
0
0
0
20
0
32
0
0
55
54
7
0
41
15
0
43
0
0
0
0
0
0
0
0
16
16
0
0
0
41
55
25
0
0
0
0
0
55
56
0
0
62
0
0
0
0
0
0
1
44
0
0
0
0
18
79
49
0
0
22
0
0
0
0
67
0
0
29
37
2
0
20
19
0
70
74
74
66
71
73
60
72
73
70
74
75
72
58
62
63
57
74
73
75
73
74
60
60
75
71
76
73
74
74
77
71
71
69
65
76
76
73
73
71
65
58
72
71
64
71
74
72
74
69
68
66
66
63
68
75
73
68
71
*Prostate cancer mortality per 100 000 male population aged 45–74 years and age standardized to the 1987 world male population.
†Per capita gross national product in U.S. dollars (1980).
‡Rate per 1000 live births (1980).
§Per 10 000 population.
㛳Life expectancy, in years, at birth (1980).
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NOTE
Manuscript received July 21, 1997; revised August 14, 1998; accepted August
27, 1998.
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