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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Ann Intern Med. Author manuscript; available in PMC Oct 7, 2009.
Published in final edited form as:
PMCID: PMC2584869
NIHMSID: NIHMS73457

Insulin-like Growth Factors, Their Binding Proteins, and Prostate Cancer Risk: Analysis of Individual Patient Data from 12 Prospective Studies

Abstract

Background

Some, but not all, published results have shown an association between circulating blood levels of some insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) and the subsequent risk for prostate cancer.

Purpose

To assess the association between levels of IGFs and IGFBPs and the subsequent risk for prostate cancer.

Data Sources

Studies identified in PubMed, Web of Science, and CancerLit.

Study Selection

The principal investigators of all studies that published data on circulating concentrations of sex steroids, IGFs, or IGFBPs and prostate cancer risk using prospectively collected blood samples were invited to collaborate.

Data Extraction

Investigators provided individual participant data on circulating concentrations of IGF-I, IGF-II, IGFBP-II, and IGFBP-III and participant characteristics to a central data set in Oxford, United Kingdom.

Data Synthesis

The study included data on 3700 men with prostate cancer and 5200 control participants. On average, case patients were 61.5 years of age at blood collection and received a diagnosis of prostate cancer 5 years after blood collection. The greater the serum IGF-I concentration, the greater the subsequent risk for prostate cancer (odds ratio [OR] in the highest vs. lowest quintile, 1.38 [95% CI, 1.19 to 1.60]; P < 0.001 for trend). Neither IGF-II nor IGFBP-II concentrations were associated with prostate cancer risk, but statistical power was limited. Insulin-like growth factor I and IGFBP-III were correlated (r = 0.58), and although IGFBP-III concentration seemed to be associated with prostate cancer risk, this was secondary to its association with IGF-I levels. Insulin-like growth factor I concentrations seemed to be more positively associated with low-grade than high-grade disease; otherwise, the association between IGFs and IGFBPs and prostate cancer risk had no statistically significant heterogeneity related to stage or grade of disease, time between blood collection and diagnosis, age and year of diagnosis, prostate-specific antigen level at recruitment, body mass index, smoking, or alcohol intake.

Limitations

Insulin-like growth factor concentrations were measured in only 1 sample for each participant, and the laboratory methods to measure IGFs differed in each study. Not all patients had disease stage or grade information, and the diagnosis of prostate cancer may differ among the studies.

Conclusion

High circulating IGF-I concentrations are associated with a moderately increased risk for prostate cancer.

Prostate cancer is one of the most common types of cancer in men, yet few risk factors for the disease, other than age, race, and a family history, have been established (1, 2). Insulin-like growth factors (IGFs) and their associated binding proteins (IGFBPs) have been the subject of many epidemiologic investigations of prostate cancer because they are known to help regulate cell proliferation, differentiation, and apoptosis (3). Although results from some, but not all, studies suggest an association between IGFs and IGFBPs and prostate cancer risk, there has been much uncertainty about its consistency and magnitude. A previous meta-analysis that included only 3 prospective studies suggested that high levels could be associated with more than a 2-fold increase in risk (4), although recent studies have suggested the risk is lower. Furthermore, given that these peptides are correlated with each other, uncertainty remains about any observed relationships. The individual studies are rarely large enough to allow proper mutual adjustment for these correlated factors, and they are insufficiently powered to investigate the consistency of their findings in key subgroups (for example, stage and grade of disease). Such analyses are important because studies have suggested that IGF-I might be more associated with advanced than with localized disease (5, 6).

The Endogenous Hormones and Prostate Cancer Collaborative Group was established to conduct collaborative reanalyses of individual data from prospective studies on the relationships between circulating levels of sex hormones and IGFs and subsequent prostate cancer risk. Results for the sex hormones have been reported elsewhere and show no statistically significant relation between androgen or estrogen levels in men and the subsequent risk for prostate cancer (7). We report results for concentrations of IGFs and IGFBPs.

Context

Insulin-like growth factors (IGFs) and IGF binding proteins may be associated with some cancers.

Contribution

This reanalysis of individual patient data from 12 studies of the association between IGFs and IGF binding proteins and prostate cancer suggests that higher levels of serum IGF-I are associated with higher risk for prostate cancer.

Caution

The 12 studies varied in the types of patients they studied and in how they measured IGFs.

Implication

High IGF-I levels seem to be a risk factor for prostate cancer.

Methods

Participants

The Endogenous Hormones and Prostate Cancer Collaborative Group is described in detail elsewhere (7). In brief, the group invited principal investigators of all studies, found by searching PubMed, Web of Science, and CancerLit, that provided data on circulating concentrations of sex steroids, IGFs or IGFBPs, and prostate cancer risk by using prospectively collected blood samples to join the collaboration. Thirteen studies collected data on circulating IGF concentrations and the subsequent risk for prostate cancer (5, 6, 820), of which 1 contributed only data on sex hormones (20). Eleven of the studies used a matched case–control design nested within a prospective cohort study (5, 6, 812, 16, 19) or a randomized trial (1315, 17). One study used a case–cohort design (18) and was converted into a matched case–control design by randomly matching up to 3 control participants to each case patient by age at recruitment, time between blood collection and diagnosis, time of blood draw, and race. (Table 1 provides a full description of the studies and matching criteria used.) Most of the prospective studies were population-based, with the exception of 1 based on health plan members (9), 1 that recruited male health professionals (16), and 1 that was a combination of an intervention study and a monitoring study for cardiovascular disease (6, 10). Two of the randomized trials did not have prostate cancer as a primary end point (5, 8, 15); the other 2 were based within a screening trial (13) or were about treatment of prostate-specific antigen (PSA)–detected prostate cancer (14).

Table 1
Study Characteristics

Individual participant data were available for age; height; weight; smoking status; alcohol consumption; marital status; socioeconomic status (assessed by educational achievement); race; concentrations of IGFs, IGFBPs, and endogenous sex steroids; and PSA level. Information sought about prostate cancer included date of diagnosis, stage and grade of disease, and method of case patient ascertainment.

Some studies (5, 6, 8, 10, 16) published more than 1 article or performed assays at different times on the association between IGFs and prostate cancer risk, sometimes with different matched case–control sets, laboratory measurements, and durations of follow-up. For each study, we created a single data set in which each participant appeared only once. In our analysis, we treated any participant who appeared in a study as both a control participant and a case patient as a case patient only. We removed matched set identifiers, and we generated a series of strata (equivalent to matched sets) in which participants in each study were grouped according to age at recruitment (2-year age bands) and date of recruitment (by year), because these matching criteria were common to most studies (Table 1). The number of strata used in the collaborative analysis was slightly less than that of matched sets used in the original analyses. To ensure that this process did not introduce any bias, we checked that the results for each study, using the original matched sets, were the same as those using the strata described above.

Tumors were classified as advanced if the tumor was described as extending beyond the prostate capsule (T3/T4), and/or there was lymph node involvement (N1/N2/N3), and/or there were distant metastases (M1); tumors were classified as localized if they were T0/T1/T2 and N0/NX and M0. We classified tumors as high-grade if they had a Gleason score of 7 or more or were moderately poorly or poorly differentiated; otherwise, they were classified as low-grade.

Statistical Analysis

We calculated partial correlation coefficients between log-transformed IGF and IGFBP concentrations among control participants, adjusted for age at blood collection (<50, 50 to 59, 60 to 69, or ≥70 years) and study. For each IGF and IGFBP, we categorized men into quintiles of IGF and IGFBP serum concentrations, with cut-points defined by the study-specific quintiles of the distribution within control participants. For studies with more than 1 publication or in which the serum assays were done at different times, resulting in different absolute levels of IGFs (5, 6, 8, 10, 16), we calculated cut-points separately for each substudy. We used a conditional logistic regression stratified by study, age at recruitment (2-year age bands), and date of recruitment (single year) as our main method of analysis. To provide a summary measure of risk, we calculated a linear trend by scoring the quintiles of the serum IGF or IGFBP concentrations as 0, 0.25, 0.5, 0.75, and 1. Under the assumption of linearity, a unit change in this trend variable is equivalent to the odds ratio (OR) comparing the highest with the lowest quintile.

All results are unadjusted for participant characteristics, except for those controlled by the stratification variables. We examined the possible influence of 5 participant characteristics by adjusting the relevant conditional logistic regression models for body mass index (BMI) (<22.5, 22.5 to 24.9, 25.0 to 27.4, 27.5 to 29.9, or >30 kg/m2), marital status (married or cohabiting, or not married or cohabiting), educational status (did not attend college or university, or attended college or university), smoking (never, previous, or current), and alcohol consumption (<10 or ≥10 g/d). We excluded participants from the analysis if they had a missing value for the characteristic under examination.

We assessed heterogeneity in linear trends among studies by using a chi-square statistic to test whether the study-specific ORs were statistically different from the overall OR (21). Heterogeneity among studies was also quantified by calculating the H and I2 statistics (22).

To test whether the linear trend OR estimates for each IGF and IGFBP varied according to case patient characteristics, we estimated a series of subsets for each characteristic: stage at diagnosis (localized or advanced), grade at diagnosis (low or high), year of diagnosis (before 1990, 1990 to 1994, or 1995 onward; these year cutoffs were chosen to attempt to reflect differences in the use of the PSA test for cancer detection), age at diagnosis (<60, 60 to 69, or ≥70 years), and time between blood collection and diagnosis (<3, 3 to 6, or ≥7 years). We excluded case patients from the analyses of stage and grade at diagnosis if the relevant information was not available. For each of these case patient characteristics, we calculated a heterogeneity chi-square statistic to assess whether the estimated ORs statistically differed from each other (21). To assess whether the OR estimate of the linear trend for each IGF or IGFBP varied according to PSA level at recruitment (<2 μg/L or ≥2 μg/L), we entered an interaction term into the conditional logistic regression model for each IGF or IGFBP, and we tested the statistical significance of the interaction term with a likelihood ratio test.

Statistical significance was set at the 5% level. All statistical tests were 2-sided. All statistical analyses were done with Stata, version 9.0 (StataCorp, College Station, Texas).

Results

Table 1 shows the characteristics of the studies. The 12 prospective studies included approximately 3700 case patients with prostate cancer and 5200 control participants. Insulin-like growth factor I and IGFBP-III measurements were available for all and 3600 case patients, respectively. However, IGF-II and IGFBP-II measurements were available for only 379 and 419 case patients, respectively (Table 2). Mean age at blood collection was 61.5 years (range, 55 to 73 years). Data on race were available for most studies; however, we did not explore associations by race because more than 95% of participants were white. The median concentration of IGF-I was higher in case patients than in control participants in 9 of 12 studies; the picture is less clear for IGFBP-III, and 5 of 11 studies showed lower concentrations in case patients than in control participants (Table 2). Insulin-like growth factor II and IGFBP-II concentrations were similar between case patients and control participants. On average, case patients received a diagnosis 5 years after their blood was drawn, were age 67 years at diagnosis, and received the diagnosis after 1995 (Table 3). When data were available, most case patients had localized disease (range across studies, 70% to 80%) and most were low-grade lesions (range across studies, 60% to 80%).

Table 2
Participant Characteristics*
Table 3
Characteristics of Case Patients with Prostate Cancer*

Insulin-like growth factor I and IGF-II concentrations were correlated with each other (r = 0.39), but both were more strongly correlated with IGFBP-III (r = 0.58 and 0.51, respectively); IGFBP-II was weakly correlated with other components of the IGF system. The IGF or IGFBP concentrations and PSA levels, however, had no statistically significant correlations. We found no correlations between IGFs or IGFBPs and endogenous sex hormone concentrations; however, sex hormone–binding globulin was correlated with IGF-I, IGF-II, IGFBP-II, and IGFBP-III (r = −0.12, −0.19, 0.39, and −0.28, respectively).

Figure 1 shows that the higher the concentration of IGF-I, the greater the risk for prostate cancer: The OR in the highest versus lowest quintile was 1.38 (95% CI, 1.19 to 1.60), with a highly statistically significant trend (P < 0.001 for trend). This result is based on 3299 case patients and 4436 control participants from 12 studies with no statistically significant heterogeneity in the findings among studies (Figure 2). Restricting the analysis to population-based cohort studies did not materially change the results (data not shown). Neither IGF-II nor IGFBP-II was associated with prostate cancer risk (Figure 1), and no statistically significant heterogeneity was seen among studies (Appendix Figures 1 and and2,2, available at www.annals.org), although not all studies measured these factors and statistical power was limited. Insulin-like growth factor binding protein III concentration was associated with prostate cancer risk with the OR in the highest versus lowest quintile of 1.23 (CI, 1.06 to 1.43). The test for linear trend was statistically significant (P = 0.009 for trend), but this was mainly because of the difference between the lowest and all other quintiles, because there seemed to be little difference between the second lowest and the other quintiles (Figure 1). Statistically significant heterogeneity was observed across studies for IGFBP-III (P = 0.044 for heterogeneity) (Figure 3), with 47% of the variation due to heterogeneity.

Figure 1
Association of prostate cancer risk with increasing quintiles of insulin-like growth factors (IGFs) and their main binding protein concentrations
Figure 2
Association of prostate cancer risk with insulin-like growth factor I concentration, by study
Figure 3
Association of prostate cancer risk with insulin-like growth factor binding protein III concentration, by study
Appendix Figure 1
Association of prostate cancer risk with insulin-like growth factor II concentration, by study
Appendix Figure 2
Association of prostate cancer risk with insulin-like growth factor binding protein II concentration, by study

Adjustment of the results for IGF-I by IGFBP-III and vice versa resulted in the findings for IGF-I remaining highly statistically significant. The OR for linear trend in IGF-I concentration was 1.42 (CI, 1.24 to 1.63; P < 0.001 for trend) before and 1.42 (CI, 1.21 to 1.68; P < 0.001 for trend) after adjustment for IGFBP-III. In contrast, the OR for linear trend in IGFBP-III concentration was 1.19 (CI, 1.04 to 1.37; P = 0.010 for trend) before and 0.98 (CI, 0.83 to 1.15; P = 0.79 for trend) after adjustment for IGF-I concentration.

To further explore the joint relationship among IGF-I, IGFBP-III, and prostate cancer risk, within each study we calculated the residuals from a linear regression of IGF-I on IGFBP-III—this new variable being an estimate of IGF-I adjusted for IGFBP-III—and categorized it into quintiles. We calculated the residuals from linear regression of IGFBP-III on IGF-I in a similar way. By using this alternative method, the association between IGF-I adjusted for IGFBP-III was statistically significantly related to prostate cancer risk, with an OR of 1.25 (CI, 1.08 to 1.46; P = 0.002 for trend) for the highest versus lowest quintile. However, IGFBP-III adjusted for IGF-I by this method was not related to risk, with an OR of 1.09 (CI, 0.93 to 1.26; P = 0.36 for trend) for the highest versus lowest quintile. It would thus seem that the results for IGFBP-III are indirect because of its association with IGF-I.

Adjustment of the results for IGF-I by levels of testosterone, free testosterone, estradiol, free estradiol, and sex hormone–binding globulin (in the subset of 8 studies that measured them) made no material difference to the estimated ORs for IGF-I, nor did it change the statistical significance of the relationship between IGF-I and prostate cancer risk (data not shown). The unadjusted estimates of association between IGF and IGFBP and prostate cancer risk were similar to those adjusted for patient characteristics (BMI, marital status, educational status, smoking, and usual alcohol consumption [data not shown]).

The association between IGF-I and prostate cancer risk had no statistically significant heterogeneity by patient characteristics (PSA level at blood collection, BMI, smoking habits, alcohol consumption, or family history of prostate cancer), age at diagnosis, year of diagnosis, time between blood collection and diagnosis, or tumor stage. The only statistically significant difference was for grade of disease (P = 0.027 for heterogeneity) (Figure 4). The OR for the linear trend in IGF-I was 1.57 (CI, 1.32 to 1.87) for low-grade disease and 1.12 (CI, 0.87 to 1.43) for high-grade disease; however, given the number of statistical tests, this could be due to chance. Analyses jointly classifying tumors by both stage and grade did not provide evidence of additional heterogeneity in risk for any of the subgroups compared with the differences seen in analyses of stage and grade reported above (results not shown). The association of any other IGF components with prostate cancer risk had no statistically significant heterogeneity according to any of the subgroups considered (Appendix Figures 3, ,4,4, and and5,5, available at www.annals.org). Subgroup results remained unchanged after adjustment for potential confounding variables, including BMI (data not shown).

Figure 4
Association of prostate cancer risk with insulin-like growth factor I concentration, by tumor and participant characteristics
Appendix Figure 3
Association of prostate cancer risk with insulin-like growth factor II concentration, by tumor and participant characteristics
Appendix Figure 4
Association of prostate cancer risk with insulin-like growth factor binding protein II concentration, by tumor and participant characteristics
Appendix Figure 5
Association of prostate cancer risk with insulin-like growth factor binding protein III concentration, by tumor and participant characteristics

Discussion

This collaborative analysis of individual data from 12 studies found that increasing levels of circulating IGF-I were statistically significantly associated with a moderately increased risk for subsequent prostate cancer. Insulin-like growth factor binding protein III concentrations were also associated with an increased risk, but IGFBP-III is correlated with IGF-I, and the association was no longer evident after adjustment for IGF-I. Neither IGF-II nor IGFBP-II was associated with risk for prostate cancer, although these analyses were based on much less information than that for IGF-I and IG-FBP-III. Further adjustment for potential confounding variables made little difference to any of the risk estimates. The association of serum IGF-I levels was somewhat stronger for low-grade than high-grade cancer, but this could be due to chance.

This collaborative analysis includes information from 12 of the 13 prospective studies that published information on IGFs, IGFBPs, and prostate cancer. The only study that we did not include had 100 case patients with prostate cancer (20) and reported no association between IGF-I or IGFBP-III levels and prostate cancer risk. We also include further unpublished data from the Health Professionals Follow-up Study. After the database was closed for analysis, 3 further studies with 141, 727, and 96 case patients of prostate cancer have been published (2325). One reported a small association between IGF-I and prostate cancer risk (23), 1 reported no association (25), and 1 reported an association of a similar magnitude to our collaboration (24). Including these additional studies in the collaboration would not have materially changed our results, and our findings therefore provide a reliable summary of the totality of the evidence on the association between IGF and IGFBP levels and prostate cancer risk.

The increase in prostate cancer risk associated with serum IGF-I concentration is thought to be related to the mitogenic and antiapoptotic effects of IGF-I (3, 2628). The overall bioactivity of IGF-I is the result of a series of complex interactions among IGF-I, its binding proteins, and their cellular receptors. More than 90% of circulating IGF-I is bound to IGFBP-III and an acid-labile subunit, which cannot transfer from the circulation to the target tissues. A decrease in circulating levels of IGFBP-III has been suggested to result in a relative increase in bioactive IGF-I. Thus, a decreased IGFBP-III concentration might perhaps be expected to be associated with an increased risk for prostate cancer. However, recent in vitro experiments have shown that IGFBP-III can modulate the effects of IGF-I and, under some conditions, enhance the proliferative effects of IGFs (28, 29).

Our study showed a modest correlation between IGF-I and IGFBP-III levels, reflecting the fact that growth hormone largely controls synthesis of both peptides and IGF-I is bound and stabilized by IGFBP-III. After mutual adjustment, the increased risk between IGF-I and prostate cancer remained, whereas the association with IGFBP-III was attenuated. This suggests that the association of IGFBP-III with prostate cancer risk is secondary to the association with IGF-I. In addition, the association of IGFBP-III and prostate cancer risk had statistically significant heterogeneity among studies, which may reflect differences in the assays used by different studies (Appendix Table 1, available at www.annals.org, shows detailed descriptions of laboratory methods). It has been suggested that different assays may have different specificities for the intact and the nonintact, proteolytically cleaved forms of IGFBP-III; furthermore, specificities may have changed over time owing to recalibration, making comparisons among methods (and hence studies) difficult to interpret (30).

Appendix Table
Assay Method, Manufacturer, and Reported Intra-assay Coefficients of Variation (CVs) for Each Study

No obvious biological mechanism can explain the apparent stronger association of IGF-I with low-grade than high-grade disease, and this may be a chance finding. The distinction between low- versus high-grade cancer is unlikely to represent 2 distinct types of disease, and prostate cancer grading has varied considerably over time, making interpretation of this finding difficult (31). Studies with uniform procedures for grading cancer are needed to investigate this finding further.

We found no evidence that high circulating levels of IGF-II or IGFBP-II are related to an increased risk for prostate cancer, although with few case patients, statistical power was limited (we had approximately 80% power to detect an OR of 1.7).

Detection of localized prostate cancer has increased substantially since the introduction of the PSA test in the late 1980s (32). The mix of a growing proportion of early, localized cancers with a decreasing number of advanced cancers can lead to difficulty in the interpretation of studies, particularly because some early-stage PSA-detected cancers never progress to clinical disease (33). The lead time associated with PSA testing (number of years earlier the tumor is detected by testing) has been estimated to be as high as 12 years in men age 55 years (34). We did not have detailed information on each participant's PSA screening history or on which of the tumors were PSA detected. However, the lack of any detectable heterogeneity in risk estimates, according to tumor characteristics, suggests that the introduction of PSA testing and differences in its use in various populations are unlikely to have unduly influenced the associations.

Our study has several limitations. The analysis relies on measurement of IGF in only 1 sample at 1 time point. These single measures provide an imperfect estimate of a man's usual hormonal status and are influenced both by within-person errors and analytic errors. However, because both types of error are likely to lead to attenuation of the relationship between IGF concentration and risk, this would imply that the true association between IGF-I and prostate cancer risk may be greater. Although a single IGF measurement has been shown to reliably reflect average exposure over a few years (16), whether it also adequately reflects lifetime exposure is unknown. Insulin-like growth factors play a major role in growth during childhood (35), and circulating IGF concentration during this period could also be an important exposure window for subsequent prostate cancer development.

A further limitation is that many of the studies did not record information on the clinical diagnosis of cancer, such as basis of diagnosis, biopsy protocol, or staging criteria, or on how these may have changed over time. However, with no evidence of heterogeneity among studies and stability in the estimates with year of diagnosis, such differences are unlikely to have had a major influence on the results. Furthermore, some of the studies were based within randomized trials, and the participants may therefore have benefited from closer investigation and clinical follow-up. However, after excluding these studies, we obtained essentially the same results. Finally, the IGF levels vary among studies, which may be mostly due to differences in assay methods (Appendix Table 1, available at www.annals.org). Our method of analysis allows for this by defining study quintiles of hormone concentration and pooling study specific estimates of ORs. This method assumes that the quintiles are similar among studies, and if this assumption is not true, estimates of the OR may be biased. However, because heterogeneity was not evident among studies and the distributions of IGF-I concentration were not expected to differ greatly among the men in the different studies, this assumption seems reasonable.

In summary, this collaborative analysis of worldwide data on IGFs and their main binding proteins and prostate cancer risk demonstrates that the higher the circulating level of IGF-I, the greater the subsequent risk for prostate cancer. Given the need to identify modifiable risk factors for prostate cancer (36), the current results suggest IGF-I as a possible candidate because it is both associated with the disease and is potentially modifiable through its association with many dietary and lifestyle factors (3740).

Acknowledgments

Grant Support: The central pooling and analysis of these data were supported by Cancer Research UK, London, United Kingdom.

Drs. Roddam, Allen, and Key and Mr. Appleby: Cancer Epidemiology Unit, University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford OX3 7LF, United Kingdom.

Drs. Ferrucci and Metter: Clinical Research Branch, National Institute on Aging, Harbor Hospital, 3001 Hanover Street, Baltimore, MD 21225.

Dr. Carter: Johns Hopkins Hospital, Urology–Marburg 145, 600 North Wolfe Street, Baltimore, MD 21287.

Dr. Chen: Program in Epidemiology, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024.

Dr. Weiss: Box 357236, Department of Epidemiology, 1959 NE Pacific Street, Health Sciences F-262D, Seattle, WA 98195.

Dr. Fitzpatrick: Box 354922, Collaborative Health Studies Coordinating Center, 6200 NE 74th Street, Suite 310, Building 29, Seattle, WA 98155.

Dr. Hsing: Division of Cancer Epidemiology and Genetics, Executive Plaza South, Room 5024, National Cancer Institute, 6120 Executive Boulevard, MSC 7242, Bethesda, MD 20892-7335.

Dr. Lacey: Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Boulevard, MSC 7234, Bethesda, MD 20852-7234.

Dr. Helzlsouer: Prevention and Research Center, Mercy Medical Center, 227 Saint Paul Place, Baltimore, MD 21202.

Dr. Rinaldi: International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France.

Dr. Riboli: Division of Epidemiology, Public Health and Primary Care, Faculty of Medicine, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom.

Dr. Kaaks: Division of Cancer Epidemiology, German National Cancer Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.

Dr. Janssen: Erasmus MC, Department of Internal Medicine, 's Gravendijkwal 230, 3015 CE Rotterdam, the Netherlands.

Dr. Wildhagen: Erasmus MC, Departments of Urology and Obstetrics & Gynaecology, 's Gravendijkwal 230, 3015 CE Rotterdam, the Netherlands.

Dr. Schröder: Department of Urology, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands.

Dr. Platz: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room E6132, Baltimore, MD 21205.

Dr. Pollak: Cancer Prevention Research Unit, Jewish General Hospital–Lady Davis Institute, Pollak Research Lab–E 423, 3755 Côte Ste Catherine Road, Montreal, Quebec H3T 1E2, Canada.

Dr. Giovannucci: Department of Nutrition and Epidemiology, Harvard School of Public Health, 655 Huntington Avenue, Building II Room 319, Boston, MA 02115.

Drs. Schaefer and Quesenberry: Kaiser Division of Research, 2000 Broadway, Oakland CA, 94612.

Dr. Vogelman: Orentreich Foundation for the Advancement of Science, Cold Spring, NY 10516.

Drs. Severi, English, and Giles: Cancer Epidemiology Centre, The Cancer Council Victoria, 1 Rathdowne Street, Carlton, Victoria 3053, Australia.

Drs. Stattin and Johansson: Department of Surgical and Perioperative Sciences Urology and Andrology, Umeå University, 901 85 Umeå, Sweden.

Dr. Hallmans: Department of Clinical Medicine and Public Health, Umeå University, 901 85 Umeå, Sweden.

Dr. Chan: Epidemiology & Biostatistics, and Urology, University of California, San Francisco, Box 1695, San Francisco, CA 94143-1695.

Dr. Gann: Northwestern University, Feinberg School of Medicine, Department of Preventive Medicine, 680 North Lake Shore Drive, Suite 1102, Chicago, IL 60611.

Dr. Oliver: Department of Health Sciences, University of York, The Hull York Medical School, Seebohm Rowntree Building, Heslington, York YO10 5DD, United Kingdom.

Dr. Holly: Department of Clinical Science at North Bristol, University of Bristol, Paul O'Gorman Lifeline Centre, Southmead Hospital, Bristol BS10 5NB, United Kingdom.

Dr. Donovan: Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Clifton, Bristol BS8 2PR, United Kingdom.

Drs. Meyer and Bairati: Centre de Recherche en Cancérologie, Université Laval, 11 côte du Palais, Québec G1R 2J6, Canada.

Dr. Galan: UMR U557 Inserm, U1125 Inra, Cnam, Paris 13, SMBH—Université Paris 13, 74 rue Marcel Cachin, 93017 Bobigny Cedex, France.

Footnotes

Potential Financial Conflicts of Interest: None disclosed.

Reproducible Research Statement: Study protocol and statistical code: Available from Dr. Roddam (e-mail, ku.ca.xo.uec@maddor.werdna). Data set: Not available.

Author Contributions: Conception and design: A.W. Roddam, N.E. Allen, T.J. Key, C. Chen.

Analysis and interpretation of the data: A.W. Roddam, P. Appleby, T.J. Key, C. Chen, M. Pollak.

Drafting of the article: A.W. Roddam, N.E. Allen, P. Appleby.

Critical revision of the article for important intellectual content: N.E. Allen, T.J. Key, C. Chen, N.S. Weiss, S. Rinaldi, R. Kaaks, J.A.M.J.L. Janssen, M.F. Wildhagen, E.A. Platz, E. Giovannucci, C. Schaefer, C.P. Quesenberry, J.H. Vogelman, G. Severi, D.R. English, G.G. Giles, P. Stattin, M. Johansson, J.M. Chan, P. Gann, S.E. Oliver, J.M. Holly, F. Meyer, I. Bairati.

Final approval of the article: A.W. Roddam, N.E. Allen, P. Appleby, T.J. Key, H.B. Carter, C. Chen, N.S. Weiss, A. Fitzpatrick, S. Rinaldi, R. Kaaks, J.A.M.J.L. Janssen, M.F. Wildhagen, E.A. Platz, M. Pollak, E. Giovannucci, C. Schaefer, C.P. Quesenberry, J.H. Vogelman, G. Severi, D.R. English, G.G. Giles, P. Stattin, M. Johansson, J.M. Chan, P. Gann, S.E. Oliver, J.M. Holly, J. Donovan, F. Meyer, I. Bairati. Provision of study materials or patients: N.E. Allen, T.J. Key, C. Chen, N.S. Weiss, A. Fitzpatrick, S. Rinaldi, R. Kaaks, J.A.M.J.L. Janssen, M.F. Wildhagen, E.A. Platz, E. Giovannucci, C. Schaefer, C.P. Quesenberry, J.H. Vogelman, G. Severi, D.R. English, G.G. Giles, P. Stattin, M. Johansson, J.M. Chan, P. Gann, S.E. Oliver, J.M. Holly, J. Donovan, F. Meyer, I. Bairati.

Statistical expertise: A.W. Roddam, N.E. Allen, P. Appleby, D.R. English.

Obtaining of funding: A.W. Roddam, N.E. Allen, T.J. Key, C. Chen, N.S. Weiss, S. Rinaldi, R. Kaaks, J.A.M.J.L. Janssen, E.A. Platz, E. Giovannucci, C. Schaefer, C.P. Quesenberry, J.H. Vogelman, G. Severi, D.R. English, G.G. Giles, P. Stattin, M. Johansson, J.M. Chan, P. Gann, S.E. Oliver, J.M. Holly, J. Donovan, F. Meyer, I. Bairati. Administrative, technical, or logistic support: A. Fitzpatrick, G. Severi. Collection and assembly of data: A.W. Roddam, P. Appleby, H.B. Carter, C. Chen, N.S. Weiss, S. Rinaldi, R. Kaaks, J.A.M.J.L. Janssen, M.F. Wildhagen, E.A. Platz, E. Giovannucci, C. Schaefer, C.P. Quesenberry, J.H. Vogelman, G.G. Giles, P. Stattin, M. Johansson, J.M. Chan, P. Gann, S.E. Oliver, J.M. Holly, F. Meyer, I. Bairati.

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