Abstract
Background
Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer.
Methods
Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression. For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium.
Results
In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD = 1.09: 95% CI 1.07, 1.11), aggressive (1.09: 1.03, 1.16) and possibly early-onset disease (1.11: 1.00, 1.24); associations were similar in MR analyses (OR per 1 SD = 1.07: 1.00, 1.15; 1.10: 1.01, 1.20; and 1.13; 0.98, 1.30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1.06: 1.02, 1.11) and IGFBP-3 (1.08: 1.04, 1.11) had higher risks of overall prostate cancer, whereas higher IGFBP-1 was associated with a lower risk (0.95: 0.91, 0.99); these associations were attenuated following adjustment for IGF-I.
Conclusions
These findings support the role of IGF-I in the development of prostate cancer, including for aggressive disease.
Keywords: Insulin-like growth factor-I, prostate cancer, aggressive prostate cancer, prospective analysis, Mendelian randomization, international consortia
Key Messages.
We used observational and genetic data from international consortia to investigate the associations of circulating insulin-like growth factors (IGF-I, IGF-II) and their binding proteins (IGFBP-1,-2,-3) with overall, aggressive and early-onset prostate cancer.
Our findings support the role of IGF-I in the development of prostate cancer, including aggressive disease.
Our results suggest the need for more research on the modifiable determinants of IGF-I, and whether interventions to lower IGF-I might reduce the risk of prostate cancer.
Introduction
Prostate cancer is the second most common cancer in men worldwide and a leading cause of cancer death.1 Insulin-like growth factors (IGFs) are important growth-promoting peptides that act through the IGF-I receptor.2,3 IGF-I and IGF-II are mainly produced by the liver and circulate in the bloodstream, but they are also produced in local tissues where they function in a paracrine/autocrine manner.3 The majority of both of these growth factors circulate bound to IGF proteins (IGFBPs),2,4 which extend the half-life of the IGFs and modulate IGF signalling.2,4 Higher IGF-I signalling increases cell survival and decreases apoptosis, increasing the probability of carcinogenesis.4,5 Circulating IGF-I concentrations are positively associated with risks of several cancers, particularly prostate, breast and colorectal cancer.6,7
The Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group (EHNBPCCG) is a pooled individual participant nested case-control dataset of prospective studies of hormonal and nutritional factors and prostate cancer risk, which previously reported positive associations of IGF-I, IGF-II, IGFBP-2 and IGFBP-3 with overall prostate cancer risk and an inverse association with IGFBP-1.8 However, in this previous study it was unclear whether IGF-II or the IGFBPs are associated with prostate cancer independently of IGF-I, and the analyses of associations with aggressive disease subtypes were underpowered to provide strong evidence of an effect.8 The EHNBPCCG dataset has since been expanded to include more than double the number of prostate cancer cases (up to 17 000 prostate cancer cases, including 2300 aggressive cases).
In blood-based observational analyses it is difficult to rule out the possibility of biases including residual confounding or reverse causality. Mendelian randomization (MR) uses germline genetic variants as proxies of putative risk factors and estimates their associations with disease risk. These germline genetic variants are randomly allocated and fixed at conception, and therefore MR is less likely to be affected by these biases and so is potentially a more robust method for causal inference.9 In order to appraise causality for IGF-I, we carried out two-sample MR analyses using instruments identified from UK Biobank and genetic data from the PRACTICAL consortium.10–12 Using these genetic datasets, we also ran colocalization analyses to investigate whether the IGF1 gene region and prostate cancer share the same genetic signal to exclude the possibility of confounding by linkage disequilibrium.13
Using these two international consortia and UK Biobank, we aimed to assess the associations of circulating IGF-I with overall, aggressive and early-onset prostate cancer risk, using observational and genetic methods. The analysis of very large datasets can provide more robust risk estimates, and the integration of evidence from these different epidemiological approaches can strengthen the basis for causal inference.14 We additionally report observational associations of IGF-II and IGFBPs-1,-2,-3 with overall, aggressive and early-onset subtypes.
Methods
Endogenous hormones, nutritional biomarkers and Prostate Cancer Collaborative Group
Data collection and study designs
Individual participant data were available from up to 20 prospective studies with IGF-I (17 009 cases), IGF-II (4466 cases), IGFBP-1 (4491 cases), IGFBP-2 (3776 cases) and IGFBP-3 (9113 cases) measurements. Participating studies are listed in Supplementary Table S1 and further details of data collection and processing are provided in the Supplementary material. Matching criteria are shown in Supplementary Table S2. Assay details and hormone measurement data are provided in Supplementary Table S3.
Data processing and outcomes
Disease definitions were as defined by the PRACTICAL consortium.10,11 Aggressive prostate cancer was categorized as ‘yes’ for any of the following: disease metastases at diagnosis (M1), Gleason score 8+ (or equivalent), prostate cancer death (defined as death from prostate cancer) or prostate-specific antigen (PSA) >100 ng/mL. Early-onset prostate cancer was defined as a diagnosis aged ≤55 years. Further details of the disease characterization can be found in the Supplementary Methods.
Statistical analysis
Conditional logistic regression was used to estimate prostate cancer risk by circulating concentrations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3. Analyses were conditioned on the study-specific matching variables and adjusted for age at blood collection, body mass index (BMI), height, smoking status, alcohol consumption, racial or ethnic group, education, married/cohabiting and diabetes status. Biomarkers were standardized by study and entered into the model as continuous variables, so each increment represents 1 study-specific SD increase in biomarker concentration. For categorical analyses, biomarkers were categorized into study-specific fifths with cut-points determined in controls.15 Further details are available in the Supplementary Methods.
Further analyses
We examined heterogeneity in the associations of each biomarker with prostate cancer by participant characteristics, with subgroups defined a priori based on the availability of data and previous analyses using this dataset8,16; heterogeneity in the associations by study was also examined (Supplementary Methods). We additionally investigated unadjusted matched associations, associations in tenths, and estimates per 80th percentile increase. Associations were also examined following mutual adjustment for other biomarkers (IGF-I, IGF-II, IGFBP-1,-2,-3, free and total testosterone and sex hormone-binding globulin [SHBG]), and we tested for interactions between these biomarkers; further details are available in the Supplementary Methods. Stratified analyses and associations in tenths were not investigated for early-onset disease due to the limited number of cases.
Mendelian randomization analysis
Genetic instruments for hormone concentrations
Single nucleotide polymorphisms (SNPs) associated with circulating IGF-I concentrations were identified from a publicly available genome-wide association study (GWAS) based on 158 444 male UK Biobank participants of White British ancestry (P <5 x 10–8 significance threshold).17 We pruned SNPs by a linkage disequilibrium threshold of r2<0.001, based on the lowest P-value.
Genetic associations with prostate cancer
SNP associations for prostate cancer were obtained from the PRACTICAL and GAME-ON/ELLIPSE consortia,10,11 which currently do not include UK Biobank data. Individual studies included in these consortia are detailed in Conti et al.12 and Schumacher et al.10 Associations with overall prostate cancer risk were generated from 85 554 prostate cancer cases and 91 972 controls,12 with aggressive disease from 15 167 cases and 58 308 controls and with early-onset disease from 6988 cases and 44 256 controls,10 all of White European ancestry.
Statistical analysis
The MR estimation for hormones was conducted using the inverse-variance weighted (IVW) method.18 We additionally calculated the I2 statistic to assess measurement error in SNP-exposure associations,19 the F statistic to assess instrument strength,20,21 Cochran’s Q statistic to test for heterogeneity between the MR estimates for each SNP22 and PhenoScanner was used to assess pleiotropy of the genetic instruments.23 As sensitivity analyses, we used the MR residual sum and outlier (MR-PRESSO), MR robust adjusted profile score (MR-RAPS) and leave-one-out analyses to investigate the role of SNP outliers.24 To assess pleiotropy, we used the weighted median, MR-Egger and the MR-Egger intercept.25 We also used the contamination mixture method, which assumes a normal distribution of valid instruments around the true causal value, and invalid instruments are normally distributed around zero in order to account for potentially pleiotropic variants.26 To rule out reverse causality, analyses were repeated after applying Steiger filtering which excludes variants with larger effects on prostate cancer risk than on IGF-I.27
The associations of the IGF-I cis-SNP, defined as the lead SNP on the biomarker gene coding region identified from the exposure datasets, with prostate cancer were investigated using the Wald ratio. This cis-SNP is less likely than trans-SNPs to be affected by horizontal pleiotropy.28
Colocalization analysis
Colocalization was used to investigate whether the associations of variation in the IGF1 gene region with both circulating IGF-I concentration and prostate cancer risk, share the same genetic signal or whether the associations identified by our MR analysis may be confounded by linkage disequilibrium.13 Analyses were conducted for a 75-kb region surrounding the lead IGF-I cis-SNP (rs5742653) using the UK Biobank and PRACTICAL datasets.12,17 Colocalization was assessed using three approaches: conventional colocalization,13 which tests for the presence of a single shared genetic signal; as well as the sum of single effects (SuSiE) regression framework29; and conditional iterative colocalization.30 The latter two methods allow for the possibility of multiple independent (but partially correlated) causal variants in proximity.31 We created colocalization plots using LocusCompareR32 and a z-z locus plot.33 We considered a posterior probability of a shared causal variant (PP4) of >0.7 as being consistent with evidence of colocalization between IGF-I and prostate cancer.13 Further details of the colocalization analysis are available in the Supplementary Methods.
Details of statistical software and packages used are available in the Supplementary Methods. All tests of significance were two-sided, and P-values <0.05 were considered statistically significant.
Results
Study and participant characteristics in the observational analyses
A total of 20 studies, contributing up to 17 009 cases and 37 243 controls, were included in this analysis. Prostate cancer was classified as aggressive in 2332 cases and early-onset disease in 607 cases. Study participants were 91.3% of White ethnicity (Table 1). Men who were diagnosed with overall prostate cancer were taller and had a lower BMI than their matched controls (Table 1).
Table 1.
Characteristics of prostate cancer cases and controls in the EHNBPCCG participants
| Controls | Cases |
|||
|---|---|---|---|---|
| Overall | Aggressivea | Early-onsetb | ||
| N | 37 243 | 17 009 | 2332 | 607 |
| Age (years), mean (SD) | 61.4 (7.7) | 60.7 (8.0) | 61.2 (7.9) | 47.1 (5.3) |
| Height (cm), mean (SD) | 174.9 (7.0) | 175.3 (7.1) | 175.2 (7.3) | 177.3 (6.9) |
| BMI (kg/m2), mean (SD) | 27.4 (4.1) | 26.8 (3.6) | 26.9 (3.9) | 26.3 (3.6) |
| PSA at blood collection (ng/mL), mean (IQR) | 0.9 (1.2) | 2.4 (3.3) | 2.9 (5.7) | 1.9 (2.8) |
| Time from blood collection to diagnosis, mean (SD) | – | 6.7 (5.4) | 8.0 (6.3) | 5.6 (5.0) |
| Age at diagnosis, mean (SD) | – | 67.5 (6.5) | 67.3 (6.2) | 52.7 (2.4) |
| Racial/ethnic group, N (%) | ||||
| White | 33 988 (91.3) | 15 617 (91.8) | 2217 (95.1) | 532 (87.6) |
| Black | 1145 (3.1) | 505 (3.0) | 53 (2.3) | 30 (4.9) |
| East Asian | 336 (0.9) | 146 (0.9) | 8 (0.3) | 3 (0.5) |
| Other | 707 (1.9) | 266 (1.6) | 22 (0.9) | 11 (1.8) |
| Not known | 1067 (2.9) | 475 (2.8) | 32 (1.4) | 31 (5.1) |
| Smoking status, N (%) | ||||
| Never | 14 985 (40.2) | 6791 (39.9) | 804 (34.5) | 305 (50.2) |
| Ex | 16 511 (44.3) | 7300 (42.9) | 1000 (42.9) | 170 (28.0) |
| Current | 5203 (14.0) | 2533 (14.9) | 491 (21.1) | 127 (20.9) |
| Not known | 544 (1.5) | 385 (2.3) | 37 (1.6) | 5 (0.8) |
| Alcohol consumption (g ethanol/day), N (%) | ||||
| Non-drinker | 2851 (7.7) | 1806 (10.6) | 264 (11.3) | 52 (8.6) |
| <10 | 9073 (24.4) | 4535 (26.7) | 649 (27.8) | 162 (26.7) |
| 10 + | 21 385 (57.4) | 9171 (53.9) | 1284 (55.1) | 346 (57.0) |
| Not known | 3934 (10.6) | 1497 (8.8) | 135 (5.8) | 47 (7.7) |
| Diabetes status, N (%) | ||||
| Yes | 2921 (7.8) | 864 (5.1) | 127 (5.4) | 12 (2.0) |
| No | 31 707 (85.1) | 14 847 (87.3) | 2052 (88.0) | 533 (87.8) |
| Not known | 2615 (7.0) | 1298 (7.6) | 153 (6.6) | 62 (10.2) |
| Married/cohabiting, N (%) | ||||
| Yes | 9478 (25.4) | 6810 (40.0) | 1157 (49.6) | 235 (38.7) |
| No | 1407 (3.8) | 922 (5.4) | 149 (6.4) | 40 (6.6) |
| Not known | 26 358 (70.8) | 9277 (54.5) | 1026 (44.0) | 332 (54.7) |
BMI, body mass index; EHNBPCCG, Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group; IQR, interquartile range; PSA, prostate-specific antigen; SD, standard deviation.
Aggressive disease was defined as Gleason Score 8+, death from prostate cancer, metastatic disease or PSA >100 ng/mL.
Onset defined as diagnosed aged ≤55 years.
Prostate cancer characteristics by study are displayed in Supplementary Table S4. Mean age at blood collection for each study ranged from 33.8 to 76.8 years (overall mean = 61.2 years, SD = 7.8 years). Cases were diagnosed on average 6.7 years (SD = 5.4) after blood collection, and the average age at diagnosis was 67.5 years (SD = 6.5) (Table 1). Aggressive disease was diagnosed on average 8.0 years after blood collection (SD = 6.3) (Table 1). Partial correlations between biomarkers ranged from r = −0.004 (PSA and IGF-II) to r = 0.54 (IGF-II and IGFBP-2) (Supplementary Table S5).
IGF-I
In observational analyses, higher IGF-I was related to dose-dependent elevated risks of overall (OR per 1 SD increment = 1.09: 95% CI 1.07, 1.11; P <0.0001) and aggressive prostate cancer (1.09: 1.03, 1.16; P = 0.01), and there was a suggestive association with early-onset disease (1.11: 1.00, 1.24; P = 0.05).
In MR analyses, higher IGF-I was associated with increased risks of overall and aggressive disease (OR per genetically predicted 1-SD increment = 1.07: 1.00, 1.15; P = 0.05; and 1.10: 1.01, 1.20; P = 0.04, respectively) and was positively related to risk of early-onset disease (1.13: 0.98, 1.30; P = 0.08) (Figure 1). The MR sensitivity analyses were generally directionally consistent with IGF-I, although the confidence intervals were wider (Table 2).
Figure 1.
Risks of overall, aggressive* and early-onset† prostate cancer by study-specific fifths of biomarker concentrations (observational only) and 1 SD increment (observational and Mendelian randomization). Estimates are from logistic regression conditioned on the matching variables and adjusted for age, BMI, height, alcohol intake, smoking status, marital status, education status, racial/ethnic group and diabetes status. The position of each square indicates the magnitude of the odds ratio, and the area of the square is proportional to the inverse of the variance of the log odds ratio. The length of the horizontal line through the square indicates the 95% confidence interval. MR risk estimates are estimated using the inverse-variance weighted method for the full instrument methods and the Wald ratio in the cis-SNP analyses. Ptrend represents 1-SD increase in biomarker concentration. *Aggressive cancer defined as Gleason grade 8+, or prostate cancer death, or metastases or PSA >100 ng/mL. †Early-onset defined as diagnosed ≤55 years. BMI, body mass index; CI, confidence interval; IGF, insulin-like growth factor; IGFBP, insulin-like growth factor-binding protein; OR, odds ratio; PSA, prostate-specific antigen; SD, standard deviation; MR, Mendelian randomization; SNP, single nucleotide polymorphism
Table 2.
Mendelian randomization estimates between genetically predicted circulating IGF-I concentrations and overall, aggressive and early-onset prostate cancer
| Overall prostate cancer |
Aggressive prostate cancera |
Early-onset prostate cancerb |
||||||
|---|---|---|---|---|---|---|---|---|
| (85 554 cases, 91 972 controls) |
(15 167 cases, 58 308 controls) |
(6988 cases, 44 256 controls) |
||||||
| Variance explained | N SNPs | OR per 1-SD increment (95% CI) | P-value | OR per 1-SD increment (95% CI) | P-value | OR per 1-SD increment (95% CI) | P-value | |
| IGF-I (SD = 5.4 nmol/L) | ||||||||
| Inverse-variance weighted | 8.7% | 154 | 1.07 (1.00, 1.15) | 0.05 | 1.10 (1.01, 1.20) | 0.04 | 1.13 (0.98, 1.30) | 0.08 |
| Weighted median | 1.01 (0.95, 1.08) | 0.71 | 1.03 (0.91, 1.16) | 0.63 | 1.07 (0.90, 1.29) | 0.44 | ||
| MR-Egger | 1.00 (0.85, 1.17) | 0.99 | 1.01 (0.83, 1.24) | 0.90 | 0.98 (0.71, 1.35) | 0.89 | ||
| MR-Egger intercept | 0.73 | 0.38 | 0.31 | |||||
| MR-RAPS | 1.04 (0.98, 1.12) | 0.22 | 1.11 (1.00, 1.22) | 0.04 | 1.11 (0.96, 1.28) | 0.16 | ||
| MR-PRESSO | 1.06 (1.00, 1.12) | 0.05 | 1.08 (0.99, 1.18) | 0.08 | 1.10 (0.97, 1.25) | 0.13 | ||
| Contamination mixture | 1.01 (0.90, 1.06) | 0.73 | 1.32 (1.17, 1.45) | 0.0005 | 1.13 (0.96, 1.42) | 0.16 | ||
| cis-SNP (rs5742653) | 0.2% | 1 | 1.45 (1.16, 1.83) | 0.001 | 1.45 (0.96, 2.19) | 0.08 | 2.11 (1.16, 3.83) | 0.01 |
SD estimates based on UK Biobank males.
CI, confidence interval; IGF-I, insulin-like growth factor-I; MR, Mendelian randomization; OR, odds ratio; PRESSO, pleiotropy residual sum and outlier; PSA, prostate-specific antigen; RAPS, robust adjusted profile score; SD, standard deviation; SNP, single nucleotide polymorphism.
Aggressive disease was defined as Gleason Score 8+, death from prostate cancer, metastatic disease or PSA >100 ng/mL.
Early-onset defined as diagnosed aged ≤55 years.
The associations with prostate cancer risk were also directionally consistent when IGF-I was proxied by the cis-SNP (rs5742653) (1.45: 1.16, 1.83; P = 0.001; 1.45: 0.96–2.19; P = 0.08; and 2.11: 1.16, 3.83; P = 0.01, for overall, aggressive and early-onset disease, respectively) (Figure 1). Both SuSiE and conditional iterative analyses indicated multiple independent shared causal variants for IGF-I and overall prostate cancer (maximum PP4 >0.99 using SuSiE and PP4 = 0.72 using conditional iterative regression) (Supplementary Table S6 and Supplementary Figures S1 and S2).
IGF-II and IGFBPs-1–3
In observational analyses, men with higher circulating IGF-II and IGFBP-3 had an elevated risk of overall prostate cancer (OR per 1-SD increment = 1.06: 95% CI 1.02, 1.11; P = 0.01; and 1.08: 1.04, 1.11; P <0.0001, respectively). IGFBP-1 was inversely associated with overall prostate cancer (0.95: 0.91, 0.99; P = 0.03), and IGFBP-2 was not associated with prostate cancer risk (0.98: 0.93, 1.03; P = 0.46) (Figure 1). These biomarkers were not associated with aggressive or early-onset disease (Figure 1).
Further analyses—observational analysis
Associations of IGF-I with overall and aggressive prostate cancer were generally consistent by subgroups and secondary outcomes (Figures 2 and 3). The OR for prostate cancer death was 1.08 for IGF-I (1.00, 1.17) (Figure 2). There was some evidence of larger magnitudes of associations with overall prostate cancer for men with a family history of prostate cancer (1.19: 1.09, 1.29) than for men without (1.07: 1.03, 1.11; Phet = 0.02) (Figure 2).
Figure 2.
Odds ratio (95% CIs) for overall prostate cancer per study-specific 1-SD increment of IGF-I concentration by subgroup in the EHNBPCCG. Estimates are from logistic regression conditioned on the matching variables and adjusted for age, BMI, height, alcohol intake, smoking status, marital status, education status, racial/ethnic group and diabetes status. The position of each square indicates the magnitude of the odds ratio, and the area of the square is proportional to the inverse of the variance of the log odds ratio. The length of the horizontal line through the square indicates the 95% confidence interval. Tests for heterogeneity for case-defined factors were obtained by fitting separate models for each subgroup and assuming independence of the ORs using a method analogous to a meta-analysis. Tests for heterogeneity for non-case-defined factors were assessed with a χ2 test of interaction between subgroup and the binary variable. *Aggressive cancer defined as Gleason grade 8+, or prostate cancer death, or metastases or PSA >100 ng/mL. †Localized defined as TNM stage <T2 with no reported lymph node involvement or metastases or stage I; other localized stage if TNM stage T2 with no reported lymph node involvement or metastases, stage II, or equivalent; advanced stage if they were TNM stage T3 or T4 and/or N1+ and/or M1, stage III–IV or equivalent. ‡ Low grade defined as Gleason score was <7 or equivalent (i.e. extent of differentiation good, moderate); medium grade if Gleason score was 7 (i.e. poorly differentiated); high grade if the Gleason score was ≥8 or equivalent (i.e. undifferentiated). BMI, body mass index; CI, confidence interval; EHNBPCCG, Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group; IGF-I, insulin-like growth factor-I; OR, odds ratio; PSA, prostate-specific antigen; SD, standard deviation; TNM, tumour, node, metastases
Figure 3.
Odds ratio (95% CIs) for aggressive* prostate cancer per study-specific 1-SD increment of IGF-I concentration by subgroup in the EHNBPCCG. Estimates are from logistic regression conditioned on the matching variables and adjusted for age, BMI, height, alcohol intake, smoking status, marital status, education status, racial/ethnic group and diabetes status. The position of each square indicates the magnitude of the odds ratio, and the area of the square is proportional to the inverse of the variance of the log odds ratio. The length of the horizontal line through the square indicates the 95% confidence interval. Tests for heterogeneity for case-defined factors were obtained by fitting separate models for each subgroup and assuming independence of the ORs using a method analogous to a meta-analysis. Tests for heterogeneity for non-case-defined factors were assessed with a χ2 test of interaction between subgroup and the binary variable. *Aggressive cancer defined as Gleason grade 8+, or prostate cancer death, or metastases or PSA >100 ng/mL. †Localized/other localized defined as TNM stage <T2 with no reported lymph node involvement or metastases or stage I, or TNM stage T2 with no reported lymph node involvement or metastases, stage II, or equivalent; advanced stage if they were TNM stage T3 or T4 and/or N1+ and/or M1, stage III–IV or equivalent. ‡Low/medium grade defined as Gleason score was <8 or equivalent (i.e. extent of differentiation good, moderate, poor); high grade if the Gleason score was ≥8 or equivalent (i.e. undifferentiated). BMI, body mass index; CI, confidence interval; EHNBPCCG, Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group; IGF, insulin-like growth factor-I; OR, odds ratio; PSA, prostate-specific antigen; SD, standard deviation; TNM, tumour, node, metastases
The associations of IGF-II and IGFBPs with prostate cancer risk were broadly similar by subgroups (Supplementary Figures S3–S10). There was evidence of heterogeneity in the association of IGFBP-2 with overall prostate cancer by BMI (Phet = 0.0007); for men whose BMI was <25 kg/m2 at baseline, IGFBP-2 was inversely associated with prostate cancer (0.89: 0.83, 0.96), and the OR for men with BMI 30+ was 1.19 (0.99, 1.42) (Supplementary Figure S7). IGFBP-2 was also inversely associated with aggressive disease risk for men whose BMI was <25 kg/m2 (0.78: 0.66, 0.94), but not for men who had a higher BMI (Phet = 0.01) (Supplementary Figure S8).
Associations with overall and aggressive prostate cancer by study are available in Supplementary Figures S11–S20. There was some evidence of heterogeneity by study in the associations of IGF-I with aggressive disease (Phet = 0.02) (Supplementary Figure S12), and IGF-II and IGFBP-2 with overall prostate cancer risk (Phet = 0.0001 and 0.02, respectively) (Supplementary Figures S13 and S17). Associations were broadly similar to the primary analyses in unadjusted matched analyses (Supplementary Figure S21), using study-specific tenths (Supplementary Figure S22) and per 80 percentile increase (Supplementary Table S7). Following mutual adjustment for IGF-I, the associations of IGF-II and IGFBP-1 with risk were attenuated to the null (Supplementary Table S8). For IGF-I and IGFBP-3, mutual adjustment slightly attenuated the associations with overall prostate cancer risk, but both these associations remained (Supplementary Table S8).
There was some evidence of interactions in the associations of IGF-II, IGFBP-1 and IGFBP-2 concentrations with prostate cancer risk by total testosterone concentrations; men with total testosterone concentrations above the study-specific median showed evidence of a positive relationship for IGF-II and an inverse association for IGFBP-1, whereas these associations were null for men with lower total testosterone concentrations (Phet = 0.03 and 0.02, respectively) (Supplementary Table S9). Only men with lower total testosterone concentrations had a positive association between IGFBP-2 and overall prostate cancer (Phet = 0.01). For aggressive disease, the OR for IGFBP-2 was 1.27 for men with lower total testosterone concentrations (1.00, 1.62), and in men with higher total testosterone there was an inverse relationship of IGFBP-2 with aggressive disease (0.75: 0.60, 0.93; Phet <0.01), although the number of aggressive cases was limited (N = 443) (Supplementary Table S10).
Further analyses—mendelian randomization
There was no strong evidence of measurement error in the genetic instruments for IGF-I (I2 = 0.99) and all SNPs had an F statistic >10.20 There was significant heterogeneity in the MR estimates for the SNPs with overall prostate cancer, and for aggressive and early-onset disease (Cochran’s Q P <0.001). Full MR results are found in Supplementary Table S11. Forest plots of single-SNP results are available in Supplementary Figures S23–25, leave-one-out plots are available in Supplementary Figures S26–28 and MR scatterplots are available in Supplementary Figure S29. Outliers identified by MR-PRESSO are available in Supplementary Table S12. Following Steiger filtering, the results were slightly attenuated (Supplementary Table S13). Using PhenoScanner, 430 traits were identified as being linked to genetically predicted IGF-I, including height and measures of adiposity (Supplementary Figure S30). Higher concentrations of IGF-I instrumented by the cis-SNP (rs5742653) were associated with increased peak expiratory flow (P <5 x 10–8).
Discussion
This is the first study that has applied both observational and genetic approaches using data from large international consortia to investigate the associations of IGF-I with prostate cancer risk. Our results support a role of circulating IGF-I in the development of prostate cancer, including aggressive disease. In observational analyses, IGF-II and IGFBPs-1 and -3 were also associated with overall prostate cancer risk, but these associations were attenuated following adjustment for IGF-I.
Genetic analyses may be more informative than observational analyses about the direct role of the exposure on the outcome. The weaker findings from genetic analyses from the multi-SNP (cis and trans) instrument, compared with the cis-SNP may be related to associations of some of the trans-SNPs with other components of the IGF signalling pathway such as the IGFBPs.34 For the lead cis-SNP MR we observed larger magnitude effects, which likely indicates stronger biological plausibility of a direct role for IGF-I and a reduced role of horizontal pleiotropy,35 and may also be due to the possible role of local IGF1 expression in the prostate tissue. Moreover, colocalization analyses showed strong evidence of a shared genetic cause at the IGF1 gene for IGF-I concentrations and risk for prostate cancer, indicating that our findings are unlikely to be due to confounding by linkage disequilibrium.
In our observational analyses, IGF-II, IGFBP-1 and IGFBP-3 were positively associated with overall prostate cancer, but we were underpowered to detect associations with aggressive or early-onset disease. Following further adjustment for IGF-I, the associations with overall disease were attenuated although IGFBP-3 remained significantly associated with overall prostate cancer. These results suggest that the observed associations may be at least partially due to the correlations of these biomarkers with IGF-I. Analogous genetic approaches such as multivariable MR may be useful in exploring the direct and indirect effects of these biomarkers on prostate cancer risk.36
These analyses have several strengths. This is the largest collection of observational and genetic data on hormones and prostate cancer risk available, representing almost all the available data worldwide. This large sample size maximizes power to assess associations robustly and enabled us to investigate associations across subgroups. Further, by incorporating observational and genetic methods, we were able to use different lines of evidence for a more robust investigation towards causal inference.14
This study had a number of limitations. IGFs and IGFBPs are also produced locally as well as by the liver, which may affect prostate cancer risk independently of circulating concentrations.2,4 Consequently, the predictive value of circulating IGF-I as an indicator of intra-prostatic IGF signalling remains incompletely understood,4 and future research including measured intra-prostatic IGF-I and IGF-I receptor expression may help to clarify this. Our analyses relied on single biomarker measurements, and although these biomarkers have good reproducibility over a 1 to 5 year period (intraclass correlation coefficients 0.60–0.90 for IGF-I and IGFBP-1,-2,-3),37–39 this would be expected to lead to underestimates of risk in the observational analyses.40 Although associations were generally consistent by subgroup, the number of statistical tests in these analyses increased the possibility of false-positives. Assay methods used to measure the biomarkers varied by study, and some IGF biomarkers are more difficult to measure than others (for example, IGF-II); measurement error would be expected to be non-differential and therefore tend to bias associations towards the null. As in the standard approach for MR, effect estimates were calculated on the same scale as for the observational analyses, and this scaling‐up results in some imprecision with wide confidence intervals in the associations; the concordance of the directions of the associations is therefore particularly important. Wider confidence intervals in MR sensitivity analyses may relate to lower power for some of these methods.41
Conclusion
In conclusion, the findings from these analyses using observational and genetic data from large-scale international consortia are supportive of a role of IGF-I in the aetiology of prostate cancer. For the first time we show evidence that IGF-I is important for aggressive, clinically relevant disease. These findings support the need for more research on the modifiable determinants of IGF-I, and on whether interventions to lower IGF-I might reduce the risk of prostate cancer.
PRACTICAL, CRUK, BPC3, CAPS and PEGASUS consortia investigators
Principal Investigators from the PRACTICAL [http://practical.icr.ac.uk/], CRUK, BPC3, CAPS, PEGASUS consortia: Rosalind A Eeles,1,2 Christopher A Haiman,3 Zsofia Kote-Jarai,1 Fredrick R Schumacher,4,5 Sara Benlloch,1,6 Ali Amin Al Olama,6,7 Kenneth R Muir,8 Sonja I Berndt,9 David V Conti,3 Fredrik Wiklund,10 Stephen Chanock,9 Ying Wang,11 Catherine M Tangen,12 Jyotsna Batra,13,14 Judith A Clements,13,14 APCB BioResource (Australian Prostate Cancer BioResource),15,14 Henrik Grönberg,10 Nora Pashayan,16,17 Johanna Schleutker,18,19 Demetrius Albanes,9 Stephanie Weinstein,9 Alicja Wolk,20 Catharine M L West,21 Lorelei A Mucci,22 Géraldine Cancel-Tassin,23,24 Stella Koutros,9 Karina Dalsgaard Sørensen,25,26 Eli Marie Grindedal,27 David E Neal,28,29,30 Freddie C Hamdy,31,32 Jenny L Donovan,33 Ruth C Travis,34 Robert J Hamilton,35,36 Sue Ann Ingles,37 Barry S Rosenstein,38 Yong-Jie Lu,39 Graham G Giles,40,41,42 Robert J MacInnis,40,41 Adam S Kibel,43 Ana Vega,44,45,46 Manolis Kogevinas,47,48,49,50 Kathryn L Penney,51 Jong Y Park,52 Janet L Stanford,53,54 Cezary Cybulski,55 Børge G Nordestgaard,56,57 Sune F Nielsen,56,57 Hermann Brenner,58,59,60 Christiane Maier,61 Jeri Kim,62 Esther M John,63 Manuel R Teixeira,64,65,66 Susan L Neuhausen,67 Kim De Ruyck,68 Azad Razack,69 Lisa F Newcomb,53,70 Davor Lessel,71 Radka Kaneva,72 Nawaid Usmani,73,74 Frank Claessens,75 Paul A Townsend,76,77 Jose Esteban Castelao,78 Monique J Roobol,79 Florence Menegaux,80 Kay-Tee Khaw,81 Lisa Cannon-Albright,82,83 Hardev Pandha,77 Stephen N Thibodeau,84 David J Hunter,85 Peter Kraft,86 William J Blot87,88 and Elio Riboli89
1Institute of Cancer Research, London, UK
2Royal Marsden NHS Foundation Trust, London, UK
3Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
4Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
5Seidman Cancer Center, University Hospitals, Cleveland, OH, USA
6Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
7University of Cambridge, Department of Clinical Neurosciences, Stroke Research Group, Cambridge, UK
8Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
9Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
10Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
11Department of Population Science, American Cancer Society, Atlanta, GA, USA
12SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
13Australian Prostate Cancer Research Centre-QLD, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
14Translational Research Institute, Brisbane, QLD, Australia
15Australian Prostate Cancer Research Centre-QLD, Queensland University of Technology, Brisbane; Prostate Cancer Research Program, Monash University, Melbourne; Dame Roma Mitchell Cancer Centre, University of Adelaide, Adelaide; Chris O'Brien Lifehouse, Royal Prince Alfred Hospital, Camperdown: Australia
16Department of Applied Health Research, University College London, London, UK
17Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
18Institute of Biomedicine, University of Turku, Finland
19Department of Medical Genetics, Turku University Hospital, Turku, Finland
20Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
21Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, Christie Hospital NHS Foundation Trust, Manchester, UK
22Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
23CeRePP, Tenon Hospital, Paris, France
24Sorbonne Universite, GRC n°5, AP-HP, Tenon Hospital,Paris, France
25Department of Molecular Medicine, Aarhus University Hospital, Aarhus N, Denmark
26Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
27Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
28Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
29University of Cambridge, Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
30Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
31Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
32Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
33Population Health Sciences, Bristol Medical School, University of Bristol, UK
34Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
35Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, Canada
36Dept. of Surgery (Urology), University of Toronto, Toronto, ON, Canada
37Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
38Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
39Centre for Cancer Biomarker and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
40Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
41Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
42Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
43Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, USA
44Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, Spain
45Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
46Centro de Investigación en Red de Enfermedades Raras, Madrid, Spain
47ISGlobal, Barcelona, Spain
48IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
49Universitat Pompeu Fabra , Barcelona, Spain
50CIBER Epidemiología y Salud Pública, Madrid, Spain
51Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
52Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
53Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
54Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
55International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
56Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
57Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
58Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
59German Cancer Consortium, German Cancer Research Center Heidelberg, Germany
60Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
61Humangenetik Tuebingen, Tuebingen, Germany
62University of Texas M.D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, TX, USA
63Departments of Epidemiology & Population Health and of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
64Department of Genetics, Portuguese Oncology Institute of Porto, Porto, Portugal
65Biomedical Sciences Institute, University of Porto, Porto, Portugal
66Cancer Genetics Group, IPO-Porto Research Center, Portuguese Oncology Institute of Porto, Porto, Portugal
67Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
8Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, , Ghent, Belgium
69Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
70Department of Urology, University of Washington, Seattle, WA, USA
71Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
72Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
73Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, Canada
74Division of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, Canada
75Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
76Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester, UK
77University of Surrey, Guildford, UK
78Genetic Oncology Unit, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur, Vigo (Pontevedra), Spain
79Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
80Exposome and Heredity, Faculté de Médecine, Université Paris-Saclay, Villejuif, France
81Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
82Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
83George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
84Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
85Nuffield Department of Population Health, University of Oxford, Oxford, UK
86Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
87Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
88International Epidemiology Institute, Rockville, MD, USA
89Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Funding and acknowledgements information for the PRACTICAL consortium, CRUK, BPC3, CAPS and PEGASUS are in the Supplementary material, available as Supplementary data at IJE online.
Ethics approval
Each individual study obtained ethical approval, therefore additional ethical approval for this study was not required.
Supplementary Material
Acknowledgements
We thank all participants, researchers and support staff who made the study possible. CHDS would like to acknowledge the support of Alice Whittemore and David Feldman. CLUE thank Kathy J Helzlsouer for her contributions to the cohort and thank the staff and participants of the CLUE study for their important contributions. Cancer data were provided by the Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health, with funding from the State of Maryland and the Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data are also supported by the Cooperative Agreement NU58DP006333, funded by the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. The authors would like to thank Paul Appleby for past contributions to establishing the EHNBPCCG dataset and related coding. We additionally thank investigators from BUPA, CHS, EPIC Norfolk and MMAS for contributing data. DISCLAIMER: where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.
Conflict of interest
None declared.
Contributor Information
Eleanor L Watts, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Aurora Perez-Cornago, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Georgina K Fensom, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Karl Smith-Byrne, Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
Urwah Noor, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Colm D Andrews, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Marc J Gunter, Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France.
Michael V Holmes, Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
Richard M Martin, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK; National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and Weston NHS Foundation Trust and University of Bristol, Bristol, UK.
Konstantinos K Tsilidis, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Demetrius Albanes, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Aurelio Barricarte, Group of Epidemiology of Cancer and Other Chronic Diseases, Navarra Public Health Institute, Pamplona, Spain; Group of Epidemiology of Cancer and Other Chronic Diseases, Navarra Institute for Health Research (IdiSNA), Pamplona, Spain; CIBER Epidemiology and Public Health CIBERESP, Madrid, Spain.
H Bas Bueno-de-Mesquita, Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Utrecht, The Netherlands.
Barbara A Cohn, Child Health and Development Studies, Public Health Institute, Berkeley, CA, USA.
Melanie Deschasaux-Tanguy, Sorbonne Paris Nord University, Nutritional Epidemiology Research Team, Epidemiology and Statistics Research Center, University of Paris, Bobigny, France.
Niki L Dimou, Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France.
Luigi Ferrucci, National Institute on Aging, Baltimore, MD, USA.
Leon Flicker, WA Centre for Health & Ageing, Medical School, University of Western Australia, Perth, WA, Australia; Western Australian Centre for Health and Ageing, University of Western Australia, Perth, WA, Australia.
Neal D Freedman, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Graham G Giles, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia.
Edward L Giovannucci, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Christopher A Haiman, Department of Preventive Medicine, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
Graham J Hankey, WA Centre for Health & Ageing, Medical School, University of Western Australia, Perth, WA, Australia.
Jeffrey M P Holly, IGFs & Metabolic Endocrinology Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Jiaqi Huang, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Wen-Yi Huang, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Lauren M Hurwitz, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Rudolf Kaaks, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Tatsuhiko Kubo, Department of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Loic Le Marchand, University of Hawaii, Cancer Center, Honolulu, HI, USA.
Robert J MacInnis, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia.
Satu Männistö, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
E Jeffrey Metter, Department of Neurology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
Kazuya Mikami, Departmemt of Urology, Japanese Red Cross Kyoto Daiichi Hospital, Kyoto, Japan.
Lorelei A Mucci, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Anja W Olsen, Department of Public Health, Aarhus University, Aarhus, Denmark; Danish Cancer Society, Research Center, Copenhagen, Denmark.
Kotaro Ozasa, Departmemt of Epidemiology, Radiation Effects Research Foundation, Hiroshima, Japan.
Domenico Palli, Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network, Florence, Italy.
Kathryn L Penney, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Elizabeth A Platz, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Michael N Pollak, Departments of Medicine and Oncology, McGill University, Montreal, QC, Canada.
Monique J Roobol, Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Catherine A Schaefer, Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Jeannette M Schenk, Cancer Prevention Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Pär Stattin, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Akiko Tamakoshi, Department of Public Health, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Elin Thysell, Department of Medical Biosciences, Umeå University, Umeå, Sweden.
Chiaojung Jillian Tsai, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Mathilde Touvier, Sorbonne Paris Nord University, Nutritional Epidemiology Research Team, Epidemiology and Statistics Research Center, University of Paris, Bobigny, France.
Stephen K Van Den Eeden, Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA; Department of Urology, University of California San Francisco, San Francisco, CA, USA.
Elisabete Weiderpass, Director’s Office, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Stephanie J Weinstein, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Lynne R Wilkens, University of Hawaii, Cancer Center, Honolulu, HI, USA.
Bu B Yeap, WA Centre for Health & Ageing, Medical School, University of Western Australia, Perth, WA, Australia; Department of Endocrinology and Diabetes, Fiona Stanley Hospital, Perth, WA, Australia.
Naomi E Allen, Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK; UK Biobank Ltd, Stockport, UK.
Timothy J Key, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Ruth C Travis, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
The PRACTICAL Consortium, CRUK, BPC3, CAPS, PEGASUS:
Rosalind A Eeles, Christopher A Haiman, Zsofia Kote-Jarai, Fredrick R Schumacher, Sara Benlloch, Ali Amin Al Olama, Kenneth R Muir, Sonja I Berndt, David V Conti, Fredrik Wiklund, Stephen Chanock, Ying Wang, Catherine M Tangen, Jyotsna Batra, and Judith A Clements
Data availability
Studies pooled by the EHNBPCCG are not owned by the writing group and so are not available from this consortium. Individual studies may be contacted to request access to their data. PRACTICAL consortium data are available upon request, see [http://practical.icr.ac.uk/blog/] for further details.
Supplementary data
Supplementary data are available at IJE online.
Author contributions
Author contributions are available as a Supplementary file at IJE online.
Funding
This work was supported by Cancer Research UK (grant numbers C8221/A19170 and C8221/A29017) to fund the centralized pooling, checking and data analysis. E.L.W. was supported by the Nuffield Department of Population Health Early Career Research Fellowship. A.P-C. is supported by a Cancer Research UK Population Research Fellowship (C60192/A28516) and by the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (2019/1953). A.T.B.C. was supported in part by the Intramural Research Program of the National Institutes of Health and the National Cancer Institute. CLUE is funded by the American Institute for Cancer Research, National Institutes of Health Grants (IU01AG18033 and IU01CA86308). H.I.M.S. was supported by research grants from the National Health and Medical Research Council of Australia. J.A.C.C. was supported by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. M.E.C. is supported by the US National Institutes of Health (Grant U01 CA164973). R.M.M. was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. R.M.M. was also supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Studies pooled by the EHNBPCCG are not owned by the writing group and so are not available from this consortium. Individual studies may be contacted to request access to their data. PRACTICAL consortium data are available upon request, see [http://practical.icr.ac.uk/blog/] for further details.