Science
January 23, 1998
SECTION: No. 5350, Vol. 279; Pg. 563; ISSN: 0036-8075
BYLINE: Chan, June M.
AUTHOR-ABSTRACT:
Insulin-like growth factor-I (IGF-I) is a mitogen for prostate epithelial
cells. To investigate associations between plasma IGF levels and prostate cancer
risk, a nested case-control study within the Physicians' Health Study was
conducted on prospectively collected plasma from 152 cases and 152 controls. A
strong positive association was observed between IGF-I levels and prostate
cancer risk. Men in the highest quartile of IGF-I levels had a relative risk of
4.3 (95 percent confidence interval 1.8 to 10.6) compared with men in the lowest
quartile. This association was independent of baseline prostate-specific antigen
levels. Identification of plasma IGF-I as a predictor of prostate cancer risk
may have implications for risk reduction and treatment.
BODY:
The cell proliferation rate is positively correlated with the risk of
transformation of certain epithelial cells (1). Insulin-like growth factors have
mitogenic and antiapoptotic effects on normal and transformed prostate
epithelial cells (2-4). Most circulating IGF-I originates in the liver, but IGF
bioactivity in tissues is related not only to circulating IGF and IGF binding
protein (IGFBP) levels, but also to local production of IGFs, IGFBPs, and IGFBP
proteases (5). Person-to-person variability in levels of plasma IGF-I and
IGFBP-3 [the major circulating IGFBP (5)] is considerable (6, 7), and plasma
IGF-I levels appear to reflect heterogeneity in tissue IGF-I bioactivity (8-11).
To examine the potential relation between plasma IGF-I, IGF-II, and IGFBP-3
levels and prostate cancer risk, we conducted a prospective case-control study
of men participating in the Physicians' Health Study (12). At the start of the
study (1982), the men (aged 40 to 82) provided medical information via mailed-in
questionnaires, and 14,916 (68%) also provided plasma (12). Through 1992
follow-up was over 99% complete. Reports of prostate cancer were verified by
medical records (13).
Cases and controls were selected from the 14,916 physicians who provided
plasma. By March 1992, we confirmed 520 cases, of whom 152 had adequate volume
for IGF assays in 1997. Levels of plasma steroid hormones (14),
prostate-specific antigen (PSA) (15), and carotenoids, and CAG polymorphisms of
the androgen receptor gene (16) had previously been measured in the same samples
(17). On average, 7 years (minimum = 6 months, maximum = 9.5 years) elapsed
between plasma collection and diagnosis.
We selected controls at random from men who provided blood and had not
reported a diagnosis of prostate cancer up to the diagnosis date of the case. We
excluded men with inadequate sample volume and who had total or partial
prostatectomies by the time of the case diagnosis. We matched one control to
each case on the basis of smoking (never, past, or current), duration of
follow-up, and age within 1 year.
IGF-I, IGF-II, and IGFBP-3 were assayed by enzyme-linked immunoabsorbent
assay (ELISA) with reagents from Diagnostic Systems Laboratory (Webster, Texas)
(18, 19). A single IGF-I measurement is generally representative of levels over
time (20, 21). We used paired t tests to compare the means of IGF-I, IGF-II, and
IGFBP-3 levels between cases and controls. We examined age-standardized (using
five groups: 40 to 50, 51 to 55, 56 to 60, 61 to 65,and 66 to 80) mean values of
various predictors for prostate cancer within quartiles of IGF-I among the
controls. Conditional logistic regression was used to analyze the associations
between IGF and prostate cancer, after adjustment for other possible risk
factors--PSA, height, weight, body mass index, CAG polymorphisms of the androgen
receptor gene, and plasma levels of lycopene, estrogen, testosterone (T),
dihydrotestosterone (DHT), sex hormone binding globulin (SHBG), prolactin, and 3
[Alpha]-androstanediol glucuronide (AAG) (14-16, 22-24). In view of the
growth-inhibitory properties of IGFBP-3 and its potential to reduce the
bioactivity of IGF (25), we hypothesized that high levels of IGFBP-3 would be
inversely related to risk. Because levels of IGF-I and IGFBP-3 were highly
correlated, it was necessary to simultaneously adjust for these factors in
regression models to observe their independent effects.
We estimated relative risks (RRs) from the odds ratios and computed 95%
confidence intervals (CIs) (24). In stratified analyses, we used unconditional
logistic regression models and adjusted for age (eight 5-year categories) and
smoking (never, past, and current) in the models to make full use of the data
without restriction to the matched pairs (24). We also separately examined high
grade/stage cases, low grade/stage cases, and cases occurring after the first 5
years of follow-up.
Because PSA acts as an IGFBP protease in prostatic tissue (26), we
investigated possible interactions involving PSA. We classified men by quartile
of IGF-I and low ([is less than or equal to] 4 ng/ml) versus high ([is greater
than] 4 ng/ml) PSA level, creating eight categories of IGF-I and PSA. Similar
methods were used to examine potential interactions between IGF-I and plasma
androgens (with the controls' medians as cutpoints for low and high androgen
levels).
All exposures of interest and covariates, with the exceptions of CAG repeats
(dichotomized), age, and smoking, were analyzed in quartile groups (based on
controls' distributions) with the lowest quartile as the reference category. We
tested linear trends for statistical significance by assigning the medians of
each quartile as scores (24).
The mean level of IGF-I among the cases (269.4 ng/ml) was significantly
higher than among controls (248.9 ng/ml) (P = 0.03). Means of IGF-II and IGFBP-3
were similar among cases and controls (P = 0.85 and 0.95, respectively). Table
1 presents age-standardized means of IGF-II, IGFBP-3, lycopene, estrogen, T,
DHT, SHBG, weight, height, body mass index, and medians of PSA among 152 control
men, within quartiles of IGF-I. PSA and estrogen had I levels, and lycopene
levels were slightly lower among men in the highest quartile of IGF-I. There was
no significant correlation between IGF-I and any of these factors except IGF-II
(r = 0.5) and IGFBP-3 (r - 0.6).
Table 1. Age-standardized characteristics among 152 control men within
quartiles of IGF-I. Abbreviations: PSA, prostate-specific antigen; DHT,
dihydrotestosterone; SHBG, sex hormone binding globulin; BMI, body mass index.
Quartile IGF-I (range, ng/ml)
Characteristic(*) 1 2
([dagger]) (99.4 to 184.8) (184.9 to 236.95)
n = 38 n = 38
Age (years) 63.9 58.9
IGF-II (ng/ml) 418 536
IGFBP-3 (ng/ml) 2234 2841
PSA (ng/ml) 2.19 2.27
Lycopene (ng/ml) 445 430
Estrogen (ng/ml) 35.9 37.2
Testosterone 5.27 4.74
(ng/ml)
DHT (ng/ml) 0.41 0.41
SHBG (nmol/liter) 27.9 20.9
Weight (kg) 77.1 78.6
Height (m) 1.77 1.76
BMI (kg/[m.sup.2]) 24.7 25.4
Quartile IGF-I (range, ng/ml)
Characteristic(*) 3 4
([dagger]) (236.96 to 293.75) (293.76 to 499.6)
n = 38 n = 38
Age (years) 59.0 59.3
IGF-II (ng/ml) 509 583
IGFBP-3 (ng/ml) 2829 3473
PSA (ng/ml) 2.81 2.49
Lycopene (ng/ml) 438 388
Estrogen (ng/ml) 38.6 39.4
Testosterone 5.28 5.60
(ng/ml)
DHT (ng/ml) 0.44 0.43
SHBG (nmol/liter) 24.8 21.7
Weight (kg) 78.7 77.4
Height (m) 1.77 1.76
BMI (kg/[m.sup.2]) 25.0 24.9
(*) All measurements are means, except for PSA, which is presented as median.
([dagger]) Standardized by five categories of age (40 to 50, 51 to 55, 56 to
60, 61 to 65, and 66 to 80).
IGF-I was significantly associated with prostate cancer risk in a univariate
analysis; men in the highest quartile had an RR of 2.4 (95% CI 1.2 to 4.7) as
compared with men in the lowest quartile (Table 2). With further adjustment
for IGFBP-3, these men had more than four times the risk of prostate cancer
compared with men in the reference group (RR = 4.3, 95% CI 1.8 to 10.6). IGF-II
and IGFBP-3 were not associated with prostate cancer risk when examined
individually, but IGFBP-3 was inversely associated with risk after controlling
for IGF-I (RR for fourth versus first quartile 0.4, 95% CI 0.2 to 1.0). This
suggests that the inverse association with IGFBP-3 in the univariate analysis
was masked because of its high correlation with IGF-I; the multivariate model
shows the independent effects of each of these factors. There was a significant
linear trend between IGF-I and prostate cancer risk, especially after adjustment
for IGFBP-3; a 100 ng/ml increase in IGF-I corresponded to an approximate
doubling of risk (RR = 2.1 per 100 ng/ml increase 95% CI 1.3 to 3.2).
Table 2. Relative risk (RR) of prostate cancer according to quartiles of
IGF-I, IGF II, and IGFBP-3.
RR associated with quartiles
Characteristic 1 2
Univariate analysis
IGF-I([dagger]) 1.00 1.32
(0.62 to 2.80)([double dagger])
IGF-II 1.00 1.00
(0.54 to 1.84)
IGFBP-3 1.00 0.92
(0.48 to 1.79)
Multivariate analysis (simultaneous adjustment
for IGF-I or IGFBP-3)
IGF-I([dagger]) 1.00 1.94
(0.83 to 4.56)
IGFBP-3 1.00 0.50
(0.23 to 1.10)
RR associated with quartiles
Trend(*)
Characteristic 3 4 P-Value
IGF-I([dagger]) 1.81 2.41 0.006
(0.92 to 3.56) (1.23 to 4.74)
IGF-II 0.67 0.97 0.74
(0.33 to 1.37) (0.48 to 1.95)
IGFBP-3 0.69 1.07 0.96
(0.33 to 1.44) (0.54 to 2.11)
Multivariate analysis (simultaneous adjustment
for IGF-I or IGFBP-3)
IGF-I([dagger]) 2.83 4.32 0.001
(1.27 to 6.28) (1.76 to 10.6)
IGFBP-3 0.33 0.41 0.09
(0.14 to 0.82) (0.17 to 1.03)
(*) Test for linear trend calculated by assigning the medians of the
quartiles as scores.
([dagger]) 151 or cases, one case missing measure of IGF-I.
([double dagger]) Ninety-five percent confidence intervals.
IGF-I remained a significant independent predictor of prostate cancer risk
even after inclusion of quartiles of weight, height, body mass index, lycopene,
androgen receptor CAG repeats, and plasma hormone levels (estrogen, T, DHT,
SHBG, prolactin, and AAG) in the multivariate models. Adding quartiles of PSA to
the model attenuated the association for IGF-I slightly, although the results
remained significant (RR = 3.3, 95% CI 1.1 to 10.1 for the fourth versus first
quartile, after adjustment for IGFBP-3). This underestimates the effect of IGF-I
because controlling for PSA levels partly controls for the presence of
undiagnosed prostate cancer.
To investigate whether the associations between IGF-I and prostate cancer
could be due to increased IGF-I levels among preclinical undiagnosed cases in
1982, we repeated the basic analyses including only those men who were diagnosed
5 years or more after the start of follow-up. With the remaining 125 cases and
152 controls, we observed similar results to those from previous analyses with
all cases and controls. The effect of IGF-I adjusted for PSA was also unchanged.
Comparison of the association between IGF-I and prostate cancer risk among
men with high grade/stage versus low grade/stage cancer at diagnosis revealed no
significant difference [RR for the fourth versus first quartile of IGF-I = 3.4
(95% CI 1.1 to 10.1) for high grade/stage and 5.5 (95% CI 1.9 to 15.5) for low
grade/stage cancers].
When we stratified subjects by the median case baseline age of 60, the
increased risk associated with IGF-I was stronger among the older men. Men over
age 60 and in the highest quartile of IGF-I had an RR of 7.9 (95% CI 2.1 to
30.7) with adjustment for IGFBP-3, compared with men of similar age and in the
lowest quartile; the comparable RR among men age 60 or less was 1.6 (95% CI 0.4
to 6.1). There was a significant linear trend, however, among both older (P =
0.006) and younger (P = 0.047) men, and a formal test for interaction between
age and IGF-I was not significant (P = 0.43). We also examined IGF-I within
strata of smoking and within strata of six plasma hormones but observed no
evidence of interaction.
As previously reported (15), men with elevated baseline PSA levels were more
likely to be subsequently diagnosed with prostate cancer than those with PSA
levels less than or equal to 4 ng/ml (Table 3). However, plasma IGF-I level was
strongly related to risk of developing prostate cancer even among men with a
baseline PSA level less than or equal to 4 ng/ml (the multivariate RR of
clinical diagnosis during followup increased from 1.0 to 4.6 across quartiles of
IGF-I, adjusted for IGFBP-3, age, and smoking). Given that men with PSA levels
greater than 4 ng/ml have a high likelihood of harboring occult prostate cancer
(15) these data suggest a possible effect of IGF-I on the natural history of
clinically occult prostate cancer (the multivariate RR increased from 3.9 to
17.5 across quartiles of IGF-I). Men in the highest quartile of IGF-I had 4.5
times greater risk than men in the lowest quartile regardless of their PSA
levels, hence, a combined assessment of IGF-I and PSA levels may better predict
prostate cancer than a PSA measure alone. These results were unchanged when we
excluded cases occurring during the first 5 years of follow-up.
Table 3. IGF-I and multivariate relative risk (RR) of prostate cancer by
prediagnostic PSA levels.
RR(*) associated with IGF-I quartile
PSA level
1 2
[is less than or 1.00([dagger]) 1.66
equal to] 4 ng/ml -- (0.70 to 3.92)([double dagger])
[is greater than] 3.92 11.0
4 ng/ml (1.01 to 15.3) (1.84 to 65.4)
RR(*) associated with IGF-I quartile
PSA level
3 4
[is less than or 2.07 4.57
equal to] 4 ng/ml (0.84 to 5.09) (1.79 to 11.6)
[is greater than] 16.0 17.5
4 ng/ml (4.08 to 62.6) (3.83 to 80.1)
(*) Multivariate RR adjusted for age (40 to 44, 45 to 49, 50 to 54, 55 to 59,
60 to 64, 65 to 69, 70 to 74, and 75 to 80) smoking (never, past, and current),
and IGFBP-3 (quartiles).
([dagger]) Reference group: men in first quartile of IGF-I and with PSA [is
less than or equal to] 4 ng/ml.
([double dagger]) Ninety-five percent confidence intervals.
Our data support the hypothesis that higher plasma IGF-I levels are
associated with higher rates of malignancy in the prostate gland. It is possible
that other measures of IGF-I physiology (for example, adolescent or early
adulthood mean IGF-I assessed over time, tissue IGF bioactivity, or rate of cell
turnover in the prostate gland) would better capture the true etiologically
relevant variable. To the extent that our single measurement is a proxy for such
a variable and that the measurement errors are nonsystematic and proportionately
equal among cases and controls, we have reduced the observable variation between
our cases and controls, and our results may underestimate the true association
between IGF-I and prostate cancer risk (24). Measurement error in assessing
prostate cancer outcome is minimal given the physician study base and the
histologic confirmation of all cases, and any underascertainment of existing
cases would not bias the relative risks in our study (24).
A small case-control study (n = 52 cases), in which blood samples were drawn
from men already diagnosed with prostate cancer and healthy controls, observed a
multivariate relative risk of prostate cancer of 1.9 per 60 ng/ml increase in
IGF-I (95% CI 1.0 to 3.7) (27). However, the retrospective design used in that
study could not rule out an effect of the cancer on IGF-I levels.
The association between circulating IGF-I level and risk of prostate cancer
is stronger than that of any previously reported risk factor, including
steroid hormone levels (14) or anthropomorphic variables (22, 23, 28-30).
Previous reports showing a weak relation between prostate cancer risk and height
(22, 28, 29) are of particular interest in the context of our results, as IGF-I
levels have been reported to be loosely correlated with height (6), and height
may act as a weak surrogate for IGF-I. Plasma IGF-I level in turn, may be
related to risk because it represents a determinant of or a surrogate for
prostate tissue IGF bioactivity, which affects cellular proliferation rate. In
this study population, height was moderately associated with prostate cancer
risk, independent of weight, age, smoking, IGF-I, and IGFBP-3 (RR = 1.1 per
centimeter increase in height, P = 0.05). However, we did not observe an
association between IGF-I or IGFBP-3 and height in this study, possibly as a
result of small sample size or older age of the subjects. A small study (n = 21
cases) that found high birth weight to be associated with a higher incidence of
prostate cancer may also be consistent with the observed effect of IGF (30), as
birth weight may be positively correlated with IGF-I level (31).
Reduction of androgen action has been the principal strategy under
investigation for prostate cancer prevention. If our results are confirmed,
pharmacological approaches to decreasing IGF-I bioactivity may warrant
investigation as risk-reduction strategies specifically targeted at men at high
risk due to increased IGF-I levels. Partial suppression of the growth hormone
(GH)-IGF-I axis by somatostatin analogs (32) or GH-releasing hormone
antagonists (33) are two possibilities. Finally, our results raise concern that
administration of GH or IGF-I over long periods, as proposed for elderly men to
delay the effects of aging (34), may increase risk of prostate cancer.
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cancer. Previous study has shown that cases who did and did not provide blood
samples were not appreciably different in their baseline lifestyle
characteristics (15).
(18.) The IGF-I values obtained by the ELISA were highly correlated (Pearson
r = 0.97) with values obtained by radioimmunoassay after acid chromatography.
All assays were carried out in a blinded manner, and quality control samples
were included within assay nuns. Average intra-assay coefficients of variation
for IGF-I, IGF-II, and IGFBP-3 were 4.9%, 3.0%, and 9.0%, respectively.
(19.) To evaluate the effect of our blood collection methods on IGF-Revels,
we compared IGF-I and IGFBP-3 levels in blood samples that were processed and
serum frozen immediately after venipuncture (the usual collection and processing
methods) to samples that were stored as heparinized whole blood for 24 and 36
hours before processing (mimicking our collection conditions). The mean IGF-I
and IGFBP-3 values were almost identical and the interclass correlations between
results of the two collection methods were 0.98 for IGF-I and 0.96 for IGFBP-3,
indicating that our collection methods did not adversely affect sample
integrity.
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over time, we collected two blood samples each from 16 people,8 weeks apart
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(35.) Supported by National Research Service Award 5 T32 CA 09001-20,
National Cancer Institute grants CA-58684 and CA-42182, and the National Cancer
Institute of Canada.
1 August 1997; accepted 4 December 1997
J. M. Chan and M. J. Stampfer, Department of Epidemiology, Harvard School
of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
E. Giovannucci and J. Ma, Channing Laboratory, Department of Medicine,
Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
P. H. Gann, Department of Preventive Medicine, Northwestern University
Medical School, Chicago, IL 60611, USA.
P. Wilkinson and M. Pollak, Cancer Prevention Research Unit, Departments of
Medicine and Oncology, Lady Davis Research Institute of the Jewish General
Hospital and McGill University, Montreal, Canada H3T1E2.
C. H. Hennekens, Division of Preventive Medicine, Department of Medicine and
Department of Ambulatory Care and Prevention, Brigham and Women's Hospital and
Harvard Medical School, Boston, MA 02115, USA.
June M. Chan, To whom correspondence should be addressed. E-mail:
jmlchan@hsph.harvard.edu
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