The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium

ORIGINAL PAPER The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium Neil M. Davies 1,2 •Tom R. Gaunt 1,2 •Sarah J. Lewis 1,2 •Jeff Holly 3 •Jenny L. Donovan 1 • Freddie C. Hamdy 4 •John P. Kemp 2,5 •Rosalind Eeles 6,7 •Doug Easton 8 •Zsofia Kote-Jarai 6 • Ali Amin Al Olama 8 •Sara Benlloch 8 •Kenneth Muir 9 •Graham G. Giles 10,11 •Fredrik Wiklund 12 • Henrik Gronberg 12 •Christopher A. Haiman 13 •Johanna Schleutker 14,15 •Børge G. Nordestgaard 16 • Ruth C. Travis 17 •David Neal 18,19 •Nora Pashayan 20,41 •Kay-Tee Khaw 21 •Janet L. Stanford 22,23 • William J. Blot 24 •Stephen Thibodeau 25 •Christiane Maier 26,27 •Adam S. Kibel 28,29 •Cezary Cybulski 30 • Lisa Cannon-Albright 31 •Hermann Brenner 32,33,34 •Jong Park 35 •Radka Kaneva 36 •Jyotsna Batra 37 • Manuel R. Teixeira 38,39 •Hardev Pandha 40 •PRACTICAL consortium •Mark Lathrop 42,43 • George Davey Smith 1,2 •Richard M. Martin 1,2,44 Received: 9 April 2015 / Accepted: 12 August 2015 / Published online: 19 September 2015 ÓThe Author(s) 2015. This article is published with open access at Springerlink.com Abstract Background Epidemiological studies suggest a potential role for obesity and determinants of adult stature in prostate cancer risk and mortality, but the relationships described in the literature are complex. To address uncertainty over the causal nature of previous observational findings, we investigated associations of heightand adiposity-related genetic variants with prostate cancer risk and mortality. Methods We conducted a case–control study based on 20,848 prostate cancers and 20,214 controls of European ancestry from 22 studies in the PRACTICAL consortium. We constructed genetic risk scores that summed each man’s number of height and BMI increasing alleles across multiple single nucleotide polymorphisms robustly associated with each phenotype from published genome-wide association studies. Results The genetic risk scores explained 6.31 and 1.46 % of the variability in height and BMI, respectively. There was only weak evidence that genetic variants previously associated with increased BMI were associated with a lower prostate cancer risk (odds ratio per standard deviation PRACTICAL consortium is provided in appendix section. Electronic supplementary material The online version of this article (doi:10.1007/s10552-015-0654-9) contains supplementary material, which is available to authorized users. &Richard M. Martin [email protected] Neil M. Davies [email protected] 1 School of Social and Community Medicine, University of Bristol, Bristol, UK 2 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK 3 School of Clinical Sciences, University of Bristol, Bristol BS10 5NB, UK 4 Nuffield Department of Surgery, University of Oxford, Oxford, UK 5 University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia 6 The Institute of Cancer Research, London SM2 5NG, UK 7 The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK 8 Strangeways Laboratory, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge, UK 9 Institute of Population Health, University of Manchester, Manchester, UK 10 Cancer Epidemiology Centre, The Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC, Australia 11 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia 123 Cancer Causes Control (2015) 26:1603–1616 DOI 10.1007/s10552-015-0654-9 increase in BMI genetic score 0.98; 95 % CI 0.96, 1.00; p=0.07). Genetic variants associated with increased height were not associated with prostate cancer incidence (OR 0.99; 95 % CI 0.97, 1.01; p=0.23), but were associated with an increase (OR 1.13; 95 % CI 1.08, 1.20) in prostate cancer mortality among low-grade disease (pheterogeneity, low vs. high grade \0.001). Genetic variants associated with increased BMI were associated with an increase (OR 1.08; 95 % CI 1.03, 1.14) in all-cause mortality among men with low-grade disease (pheterogeneity =0.03). Conclusions We found little evidence of a substantial effect of genetically elevated height or BMI on prostate cancer risk, suggesting that previously reported observational associations may reflect common environmental determinants of height or BMI and prostate cancer risk. Genetically elevated height and BMI were associated with increased mortality (prostate cancer-specific and all-cause, respectively) in men with low-grade disease, a potentially informative but novel finding that requires replication. Keywords Height Body mass index Prostate cancer  Mendelian randomization Single nucleotide polymorphisms Instrumental variables analysis Introduction Prostate cancer is the most common male cancer in Europe and North America, but the robust identification of potentially modifiable risk factors has proven elusive [1]. Epidemiological studies suggest a potential role for obesity [2–5] and determinants of adult stature [6], but the relationships described in the literature are complex [7–9]. Inverse associations have generally been observed between adiposity and localized prostate cancer, but associations are largely positive with advanced or high-grade [2,10] and fatal [3] cancer and may vary in direction depending on whether obesity was observed in early or middle to late adulthood [4]. Adult stature is generally positively associated with prostate cancer, although associations may be stronger for fatal [11] or highcompared with low-grade disease [6]. The explanation for these associations is unclear. Observations regarding obesity could be due to confounding by common causes of both obesity and prostate cancer (e.g., calorie and dietary fat intake) [12]; the mitogenic hormones insulin and insulin-like growth factor-I [13,14]; delayed detection in obese men [8,9]; or a real biological effect [15]. Observed height associations could reflect early-life environmental (e.g., fetal, dietary, social, hormones, and psychological circumstances) or shared genetic contributions to stature and prostate cancer risk [16–18]. Genetic epidemiological studies are less susceptible to confounding than observational epidemiology. This is because conditional on population structure, genetic variants are more likely to be independent of later environment and lifestyle factors [19]; they are also unlikely to be affected by reverse causation. Thus, the existence of genetic variation in obesity and height can provide robust evidence about how associations of phenotypes, in this case obesity and height, with diseases arise [15]. We previously reported that a single nucleotide polymorphism (SNP) associated with obesity (FTO rs9939609-A) was inversely associated with lowgrade prostate cancer (odds ratio, OR 0.90 per A allele; 95 % CI 0.81, 0.99; p=0.03), but positively associated with high12 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden 13 Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA 14 Department of Medical Biochemistry and Genetics, University of Turku, Turku, Finland 15 Institute of Biomedical Technology/BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland 16 Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 75, 2730 Herlev, Denmark 17 Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK 18 Surgical Oncology (Uro-Oncology: S4), University of Cambridge, Addenbrooke’s Hospital, Hills Road, Box 279, Cambridge, UK 19 Li Ka Shing Centre, Cancer Research UK Cambridge Research Institute, Cambridge, UK 20 Strangeways Laboratory, Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Worts Causeway, Cambridge, UK 21 Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, UK 22 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 23 Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA 24 International Epidemiology Institute, 1455 Research Blvd., Suite 550, Rockville, MD 20850, USA 25 Mayo Clinic, Rochester, MN, USA 26 Department of Urology, University Hospital Ulm, Ulm, Germany 27 Institute of Human Genetics, University Hospital Ulm, Ulm, Germany 1604 Cancer Causes Control (2015) 26:1603–1616 123 grade cancer (OR 1.16; 0.99, 1.37; p=0.07) [15]. These data suggest that the comparable observational associations between adiposity phenotypes and prostate cancer outcomes are not confounded. However, the evidence for these effects was weak, originating from a single study of moderate size (1,550 cases) using only a single variant, and there is no evidence we are aware of linking genetic variation in height with prostate cancer. The results, therefore, require confirmation and extension in larger datasets, using heightand additional adiposity-related genetic variants. Our aim was to use genetic variation in height and body mass index (BMI) as unconfounded exposures to investigate the causal associations of obesity and stature with prostate cancer risk and outcomes (Mendelian randomization [20]). Instead of the single-variant, single-sample approach used previously, we employ a more powerful two-sample, multiple-variant approach [21,22] that combines several polymorphisms (based on confirmed genetic variant-intermediate phenotype associations [23,24]) into genetic risk scores in order to explain more of the variance in BMI and height exposures and thus increase power and avoid weak instrument bias [21]. Methods Participants in this study were men of European genotypic ancestry from 22 independent studies contributing to the international PRACTICAL Consortium (PRostate cancer AssoCiation group To Investigate Cancer-Associated aLterations in the genome, http://www.practical.ccge. medschl.cam.ac.uk)[25,26]. The individual studies are described at http://www.nature.com/ng/journal/v45/n4/ extref/ng.2560-S1.pdf, with summary data in Table 1.Ofthe studies within the PRACTICAL Consortium at the time of data extraction, we excluded the EPIC-Norfolk, CAPS, and SEARCH studies (involving 3,005 cases and 2,825 controls), because they were included in the genome-wide studies that originally detected the height and BMI genetic variants [23, 24]. Cancers were categorized as low grade (Gleason score B6) or high grade (Gleason score C7) and localized (T1 or T2 on TNM staging, or if not available, ‘‘localized’’ on SEER staging) or advanced (T3 or T4 on TNM staging, or if not available, ‘‘regional’’ or ‘‘distant’’ on SEER staging). All studies met the appropriate ethical criteria for each country in accordance with the principles embodied in the Declaration of Helsinki. Genotyping Genotyping was carried out using an Illumina Custom Infinium genotyping array (iCOGS), designed for the Collaborative Oncological Gene-environment Study (COGS), and consisted of 211,155 SNPs (details at http:// ec.europa.eu/research/health/medical-research/cancer/fp7projects/cogs_en.html)[25,26]. This array was devised to evaluate genetic variants for associations with breast, ovarian, and prostate cancer; 68,638 were specifically chosen for their potential relevance to prostate cancer. The remaining 125,877 SNPs measured by the array were chosen for 28 Brigham and Women’s Hospital/Dana-Farber Cancer Institute, 45 Francis Street-ASB II-3, Boston, MA 02115, USA 29 Washington University, St. Louis, Missouri 30 International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland 31 Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA 32 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany 33 Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany 34 German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany 35 Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, FL, USA 36 Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University Sofia, 2 Zdrave St, 1431 Sofia, Bulgaria 37 Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia 38 Department of Genetics, Portuguese Oncology Institute, Porto, Portugal 39 Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal 40 The University of Surrey, Guildford, Surrey GU2 7XH, UK 41 Department of Applied Health Research, University College London, 1-19 Torrington Place, London WC1E 7HB, UK 42 Commissariat a `l’Energie Atomique, Center National de Ge ´notypage, Evry, France 43 McGill University-Ge ´nome Que ´bec Innovation Centre, Montreal, Canada 44 Bristol Nutrition Biomedical Research Unit, National Institute for Health Research, Bristol, UK Cancer Causes Control (2015) 26:1603–1616 1605 123 relevance to other cancers and common SNPs which had been previously associated with any trait. Participants with low call rates (\95 %) and high or low heterozygosity (p\1910 -5 ) were excluded; 201,598 SNPs passed quality control for the European ancestry samples. We used these genotypic data to impute 2.6 million SNPs based on the HapMap 2 CEU reference panel and using IMPUTE2 software [27]. We excluded poorly imputed SNPs (R 2 \0.3). Constructing genetic risk scores for BMI and height We constructed genetic risk scores [21] for height and BMI using 179 and 32 variants, respectively, previously reported in genome-wide association studies (GWAS) to be associated with height [23] and BMI [24]. We used allele dosages from the imputation to construct the genetic risk score. The dosages code each SNP continuously from 0 to 2, and the dosages across all SNPs are summed to estimate Table 1 Clinical characteristics of the men in each of the studies contributing to the PRACTICAL consortium (n=41,062) Study Country nMean % Controls Cases Age at diagnosis (years) PSA at diagnosis (ng/ml) Screen detected b (%) Family history prostate cancer Gleason score 8–10 Advanced stage (T3 or T4) Distant spread (SEER) CPCS1 Denmark 2,771 848 69.5 48.0 0.0 8.2 35.0 – – CPCS2 Denmark 1,009 265 64.9 36.0 0.0 14.7 10.6 – – EPIC Europe a 1,079 722 64.9 19.7 0.0 – 3.6 3.8 0.9 ESTHER Germany 318 313 65.5 58.7 61.9 8.9 9.1 26.4 3.4 FHCRC USA 730 761 59.7 16.1 – 21.7 10.4 – 2.6 IPO-Porto Portugal 66 183 59.3 8.3 82.8 20.0 15.8 64.5 0.0 MAYO USA 488 767 65.2 15.5 73.7 29.1 33.0 44.4 0.5 MCCS Australia 1,169 1,698 58.5 136.6 – 23.4 11.0 14.0 0.8 MEC USA 829 819 69.5 – – 13.0 36.0 – 2.8 MOFFITT USA 100 412 64.9 7.3 0.0 22.9 11.2 3.5 0.5 PCMUS Bulgaria 140 151 69.3 32.5 21.2 5.3 29.8 42.4 18.5 PPF-UNIS UK 176 244 68.9 32.0 – 25.3 10.9 25.7 9.0 Poland Poland 359 438 67.7 40.2 0.0 10.6 14.0 36.8 2.8 ProMPT UK 1 166 66.3 33.0 0.0 34.6 18.9 32.7 7.8 ProtecT UK 1,474 1,542 62.8 9.6 100.0 7.9 5.7 11.3 0.4 QLD/ ProsCan Australia 87 186 61.3 6.7 – 36.2 4.0 0.0 0.0 STHMI Sweden 2,224 2,002 66.2 – – 20.2 10.2 14.2 1.6 TAMPERE Finland 2,413 2,753 68.2 69.1 46.8 – 15.4 21.0 7.3 UKGPCS UK 4,182 4,549 63.8 83.9 28.9 23.4 17.2 32.9 10.7 ULM Germany 354 601 63.8 19.1 – 44.9 15.5 39.9 1.1 UTAH USA 245 440 62.6 – – 51.4 16.1 – 4.7 WUGS USA 0 988 60.8 6.2 – 42.3 7.9 24.2 0.1 Studies: Copenhagen Prostate Cancer Study 1 (CPCS1); Copenhagen Prostate Cancer Study 2 (CPCS2); European Prospective Investigation Into Cancer and Nutrition (EPIC); Epidemiological investigations of the chances of preventing, recognizing early and optimally treating chronic diseases in an elderly population (ESTHER); Fred Hutchinson Cancer Research Center (FHCRC); Portuguese Oncology Institute, Porto (IPOPorto); Mayo Clinic (MAYO); Melbourne Collaborative Cohort Study (MCCS); Multiethnic Cohort Study (MEC); The Moffitt Group (MOFFITT); Prostate Cancer study Medical University Sofia (PCMUS); Prostate Project Foundation-Postgraduate Medical School, Surrey (PPFUNIS); The Poland Group (Poland); Prostate cancer: Mechanisms of progression and Treatment (ProMPT); Prostate testing for cancer and Treatment (ProtecT); Retrospective Queensland Study (QLD) and the Prostate Cancer Supportive Care and Patient Outcomes Project (ProsCan); Stockholm 1 (STHMI); Finnish Genetic Predisposition to Prostate Cancer Study (TAMPERE); U.K. Genetic Prostate Cancer Study and The Prostate Cancer Research Foundation Study (UKGPCS); Familial Prostate Cancer Study Ulm (ULM); UTAH Study (UTAH); Washington University Genetics Study (WUGS) a Germany, Greece, Italy, Netherlands, Spain, Sweden, Oxford b Studies with 0 % screen detected are entirely based on clinically detected cases, and studies with no information about method of detection have a missing value; 12,231 individuals have information of method of detection 1606 Cancer Causes Control (2015) 26:1603–1616 123 the number of height or BMI increasing risk alleles per man. Each genetic variant was given a weight equal to the effect of the variant on height or BMI reported by the previous GWASs [23,24]. The genetic risk score is therefore a weighted sum of the estimated number of risk alleles across several genotypes, which can improve the precision of the results compared to an unweighted score [21]. Supplementary Tables 1 and 2 provide details of the variants used and weights assigned. Statistical analysis We estimated associations of the genetic risk scores with measured height and BMI using linear regression based on 1,270 men without prostate cancer [i.e., prostate-specific antigen (PSA) level \3.0 ng/ml or men with a raised PSA but who were biopsy negative] from the ProtecT population-based study [15,28], one of the PRACTICAL studies with the relevant phenotypic data in a well-defined control group. We computed Fstatistics and R 2 values (the proportion of variation in height and BMI explained by the genetic risk score) from the linear regression to evaluate the strength of the genetic risk score instruments in a population of men at increased risk of cancer. We had 82 and 78 % power to detect an odds ratio of 1.12 and 1.25 for the effects of height and BMI on prostate cancer risk, assuming a sample size of 41,062 and that the genetic risk scores explained 6.31 and 1.46 % of the variation in height and BMI, respectively [29]. We investigated associations of the phenotypes (height and BMI) and the genetic risk scores (for height and BMI) with measured covariables in the ProtecT cases to assess whether the scores were likely to be independent of potential environmental confounding factors and to assess the potential for pleiotropy (genetic confounding). We included the following potential confounders: diabetes; occupation (managerial vs. nonmanagerial); exercise (strenuous; moderate or strenuous, vs. light); alcohol intake (three or more drinks a week vs. two or less); smoking (passive, current, or ex-smoker vs. never); diagnostic PSA level; and age at recruitment. We investigated whether the scores predicted circulating insulin-like growth factor (IGF-I) levels (a potential mechanism linking size with prostate cancer [13,14]) and benign prostatic hyperplasia (a potential cause of detection bias [30]). We assessed the relationship of the height and BMI genetic risk scores with prostate cancer risk, stage, and grade across all 22 eligible studies contributing to PRACTICAL using logistic regression to compute ORs, with robust standard errors to account for within-study clustering. The genetic risk score was standardized to mean zero and standard deviation one, and the ORs were parameterized as the change in outcome per standard deviation increase in genetic risk score. In a secondary analysis, we also computed ORs comparing the highest versus the lowest quintile of each genetic risk score to illustrate the differences in outcomes between the extremes of the BMI or height allele score distributions. This reduced form, the association of the instrument (the genetic risk score) with the outcome, is a valid test of the direction of the effect of a phenotype on an outcome [31,32]. We investigated between-study heterogeneity by estimating the logistic regressions individually for each study and using the Stata metan command to estimate the I 2 statistic assuming a fixed-effect model. As we found little evidence of heterogeneity, we report the ORs from the logistic regression analyses conducted across the 22 included studies. We calculated ORs for all prostate cancers and then separately for localized versus advanced and low-grade (Gleason score B6) versus high-grade (Gleason score C7) cancers. Among men with prostate cancer (case-only analysis), we estimated associations of the standardized height and BMI weighted genetic risk scores with all-cause and prostate cancer-specific mortality using Cox proportional hazards regression, with age at diagnosis as the start date and age at death or final follow-up timepoint as the exit date, with standard errors clustered by study (there was no evidence that the proportional hazards assumption was violated). We tested for heterogeneity in association of the genetic risk scores with localized versus advanced and lowversus high-grade prostate cancer risk using a multivariate logistic regression. We tested for heterogeneity in the association of the genetic risk scores and survival of patients with localized versus advanced and low versus high grade using the test proposed by Altman and Bland [33]. Sensitivity analyses We assessed the potential for pleiotropy, since it is possible that variants identified in the genome-wide scans are not specific for height or BMI and have effects on the prostate cancer outcomes independent of their effects on the exposures (height or BMI) [34]. If the no-effect modification assumption holds, similar instrumental variable estimates acquired using independent instruments would provide suggestive evidence against an influence of pleiotropic effects, as it is unlikely that they have shared pleiotropy [21,35]. Therefore, as a sensitivity analysis we tested for evidence of heterogeneity across different SNPs for each of our baseline results which differed from the null. We generated two independent genetic instruments for BMI using (1) rs1558902 in FTO, the individual SNP with the largest effect size in the meta-analysis of GWASs for BMI [24] and (2) a weighted allelic score constructed from the remaining BMI-associated SNPs. We randomly Cancer Causes Control (2015) 26:1603–1616 1607 123 split the height allele score into two independent weighted scores containing 89 and 90 SNPs (for details of the SNPs in each score see Supplementary Table 3). The height SNPs were in linkage equilibrium, and hence, these scores were statistically independent. We estimated the association of each instrument with prostate cancer and tested for heterogeneity [33]. The top eight principal components that reflect the population’s genetic structure were estimated and included as covariates in adjusted regression models to account for confounding by population stratification. We also report the associations of the genetic risk scores with survival additionally adjusted for PSA level, grade, and stage. We ran all statistical analyses in Stata version 13.1 (StataCorp LP, 2014, College Station, TX). Results Our sample consisted of 20,848 cases and 20,214 controls of European genetic descent, with genotypic data from the iCOGs array that had passed quality control and was not included in the GIANT consortium used to generate the genetic risk scores (EPIC-Norfolk, CAPS, and SEARCH studies) (Table 1). The percentage of high-grade cancers reported varied between studies (3.6–35.0 %), as did the proportion of advanced stage cancers (3.5–64.5 %). The case-only survival analysis was based on 15,491 men, because 5,357 of the 20,848 men with prostate cancer did not have age at entry or exit in the dataset. Associations of genetic risk scores with measured height and BMI in ProtecT Associations of the weighted genetic risk scores with height and BMI in the ProtecT sub-sample are shown in Table 2. The results with the unweighted score were similar, but less precise (results not shown). The genetic risk scores explained 6.31 and 1.46 % of the variability in height and BMI, respectively, consistent with previous studies [23,24], which suggest that the genetic risk scores are strong instruments for the phenotypes. Associations of genetic risk scores with potential confounders in ProtecT Taller men were more likely to have managerial jobs, have lower PSA levels, and have joined the ProtecT study at a younger age (Table 3), but there was little evidence that the height genetic risk score was associated with any of the confounders except benign hypertrophy of the prostate (all pvalues [0.05). Heavier men were more likely to have diabetes; be inactive; drink fewer than 3 drinks a week; be a nonsmoker; and have lower IGF-I levels (Table 3), but we found little evidence that the BMI genetic risk score was associated with any of the potential confounders (all pvalues [0.05). Association of the genetic risk scores and prostate cancer risk and mortality Associations of the genetic risk scores for height and BMI with prostate cancer risk are shown in Table 4, with the study-specific estimates in Supplementary Figures 1–10. There was little consistent evidence that the genetic risk score for height was associated with prostate cancer, although there was weak evidence of an inverse association with advanced prostate cancer [OR, per standard deviation increase in height genetic score 0.96; 95 % CI 0.93, 0.99, p=0.01; pheterogeneity, advanced vs. localized 0.05]. There was weak evidence that the genetic risk score for BMI was associated with a reduced prostate cancer risk (OR per standard deviation increase in BMI genetic score 0.98; 95 % CI 0.96, 1.00; p=0.07), but little evidence of variation by stage or grade (pheterogeneity 0.64 and 0.13, respectively). The height genetic risk score was associated with an increase in prostate cancer-specific mortality among men with low-grade disease (OR per standard deviation increase in the height score 1.13; 95 % CI 1.08, 1.20, pheterogeneity, low vs. high grade \0.001), but there was little evidence of associations with all-cause mortality (Table 5). The BMI genetic risk score was associated with higher allcause mortality among low-grade disease (OR per standard deviation increase in the BMI score 1.08; 95 % CI 1.03, 1.14, pheterogeneity low vs. high grade =0.03), but there was little evidence of associations with prostate cancerspecific mortality. Table 2 Association of weighted height and BMI genetic risk scores with measured height and weight in 907 controls in ProtecT [28] nMean difference 95 % CI r 2 (%) F-statistic Lower limit Upper limit Height 907 0.26 0.20 0.33 6.31 67.6 BMI 901 0.12 0.06 0.19 1.46 13.6 To allow direct comparison of effect sizes, BMI and height phenotypic measurements and the genetic risk scores were normalized to mean zero and standard deviation one 1608 Cancer Causes Control (2015) 26:1603–1616 123 Table 3 Odds ratio or change in continuous variable covariates per standard deviation change in either height and BMI (phenotypes) or genetic risk scores for height and BMI (instruments) in the ProtecT study cases [28] nObserved phenotype a Genetic risk scores a Effect estimate Confidence interval b pvalue Effect estimate Confidence interval b pvalue Lower Upper Lower Upper Standardized height Odds ratio c Odds ratio c Binary variables Diabetes 726 0.91 0.64 1.30 0.62 0.94 0.70 1.25 0.66 Managerial occupation 818 1.21 1.06 1.40 0.006 0.91 0.79 1.04 0.17 Strenuous exercise 621 1.13 0.96 1.33 0.13 1.03 0.87 1.21 0.75 Moderate or strenuous exercise 621 1.15 0.96 1.37 0.12 1.01 0.85 1.20 0.90 C3 drinks in the last week 820 1.13 0.98 1.30 0.09 1.05 0.91 1.22 0.47 Passive smoker 752 1.03 0.89 1.19 0.72 1.00 0.86 1.16 0.99 Ever smoker 780 1.10 0.95 1.27 0.21 1.08 0.93 1.25 0.33 Current smoker 552 1.09 0.89 1.35 0.40 1.21 0.97 1.51 0.08 Benign hypertrophy of the prostate 704 0.77 0.58 1.02 0.07 1.38 1.01 1.88 0.05 Regression coefficient c Regression coefficient c Continuous variables PSA (ng/ml) 828 -1.15 -2.01 -0.29 0.009 -0.31 -1.32 0.70 0.55 IGF-I (ng/ml) 718 1.80 -2.23 5.83 0.38 -2.53 -6.31 1.25 0.19 Age (years) 1,109 -0.53 -0.83 -0.24 \0.001 0.07 -0.23 0.38 0.64 Standardized BMI Odds ratio c Odds ratio c Binary variables Diabetes 724 1.90 1.45 2.48 \0.001 1.16 0.86 1.57 0.33 Managerial occupation 813 0.96 0.83 1.10 0.54 0.91 0.79 1.05 0.20 Strenuous exercise 617 0.91 0.77 1.08 0.28 1.05 0.89 1.23 0.57 Moderate or strenuous exercise 617 0.80 0.66 0.96 0.02 1.01 0.85 1.20 0.94 C3 drinks in the last week 814 0.87 0.75 1.01 0.07 0.90 0.78 1.04 0.17 Passive smoker 748 1.12 0.97 1.30 0.13 0.97 0.84 1.12 0.65 Ever smoker 776 1.09 0.93 1.27 0.29 1.09 0.94 1.27 0.25 Current smoker 548 0.71 0.54 0.94 0.02 1.18 0.95 1.48 0.13 Benign hypertrophy of the prostate 700 0.92 0.69 1.23 0.56 0.94 0.71 1.25 0.66 Regression coefficient c Regression coefficient c Continuous variables PSA (ng/ml) 822 -0.25 -1.55 1.05 0.70 -0.21 -0.96 0.54 0.58 IGF-I (ng/ml) 714 -5.38 -9.12 -1.64 0.005 1.77 -1.97 5.51 0.35 Age (years) 1,101 -0.28 -0.60 0.04 0.08 -0.04 -0.33 0.25 0.79 a Observed phenotypes and genetic risk scores normalized to mean zero and standard deviation one b Robust standard errors c Odds ratio or change in continuous variable per standard deviation change in height and BMI (phenotype or genetic risk score) Cancer Causes Control (2015) 26:1603–1616 1609 123 Sensitivity analysis Prostate cancer risk There was little evidence that men with height variants with larger effects on the height phenotype were more or less likely to be diagnosed with prostate cancer (r 2 =0.0071) (Fig. 1; see Supplementary Table 4 for associations of each of the height variants with prostate cancer risk). There was some evidence that BMI variants with the largest effect on BMI were most strongly inversely associated with prostate cancer (r 2 =0.0231) (Fig. 2; Supplementary Table 5 for associations of each of the BMI variants with prostate cancer risk). We found little evidence of heterogeneity in the effect of BMI proxied by independent instruments based on independent genetic scores made up of different sets of SNPs. Individuals with more BMI increasing FTO alleles were less likely to be diagnosed with prostate cancer (OR per BMI increasing allele rs1558902-A 0.97; 95 % CI 0.94, 1.01, p=0.10). In line with this, the allele score based on the remaining 31 BMI SNPs was also inversely associated with prostate cancer (OR per standard deviation increase in BMI genetic score excluding FTO 0.99; 95 % CI 0.97, 1.01, p=0.33; pvalue for heterogeneity between the two independent instruments =0.38). All-cause mortality There was little evidence that the two sets of independent height or BMI allele scores were associated with an increased risk of all-cause mortality in men diagnosed with prostate cancer (see Supplementary Table 10 for associations of all 179 height SNPs and all-cause mortality and Supplementary Table 7 for associations of each of the 32 BMI SNPs with all-cause mortality and prostate cancerspecific mortality). Prostate cancer-specific mortality Both sets of independent height allele scores were associated with an increased risk of prostate cancer-specific mortality in men diagnosed with low-grade prostate cancer (hazard ratio per one standard deviation increase in the first height allele score 1.10; 95 % CI 1.03, 1.19, p=0.008; and in the second height allele score 1.09; 95 % CI 1.05, 1.13, p\0.001; pvalue for heterogeneity =0.86; see Table 4 Odds ratio of prostate cancer per one standard deviation change in height or BMI genetic score nUnadjusted Adjusted a Odds ratio c Confidence interval b pvalue Odds ratio c Confidence interval b pvalue pheterogeneity d Lower Upper Lower Upper Height Controls 20,214 1.00 – – – 1.00 – – – – All prostate cancers 20,848 0.96 0.91 1.01 0.12 0.99 0.97 1.01 0.23 Localized prostate cancer 12,975 0.96 0.88 1.03 0.27 1.00 0.98 1.02 0.72 0.05 Advanced prostate cancer 4,325 0.90 0.83 0.98 0.02 0.96 0.93 0.99 0.01 Low-grade prostate cancer 8,784 0.96 0.90 1.02 0.20 0.99 0.96 1.01 0.30 0.55 High-grade prostate cancer 8,230 0.97 0.92 1.02 0.26 1.00 0.98 1.02 0.85 BMI Controls 20,214 1.00 – – – 1.00 – – – – All prostate cancers 20,848 0.98 0.96 1.01 0.15 0.98 0.96 1.00 0.07 Localized prostate cancer 12,975 0.98 0.96 1.00 0.10 0.98 0.96 1.00 0.05 0.64 Advanced prostate cancer 4,325 1.01 0.97 1.05 0.69 1.01 0.97 1.05 0.62 Low-grade prostate cancer 8,784 0.98 0.94 1.02 0.25 0.97 0.94 1.00 0.09 0.13 High-grade prostate cancer 8,230 1.00 0.97 1.02 0.69 1.00 0.98 1.01 0.65 a Adjusted for the eight principal components of population stratification b Based in robust standard errors to account for within-study clustering c Change in odds ratio per standard deviation change in height and BMI genetic risk score (standardized to mean zero standard deviation one) d Localized versus advanced, or highversus low-grade using multivariate logistic regression 1610 Cancer Causes Control (2015) 26:1603–1616 123 Supplementary Table 8 for the association of prostate cancer-specific mortality and each of the 179 height SNPs). None of the BMI independent instruments or individual SNPs were associated with prostate cancer-specific mortality (Supplementary Table 9). Further adjusting the associations of the genetic risk scores and survival for PSA level, grade, and stage made no substantial differences to the results (Supplementary Table 10). Discussion We found weak evidence that genetically elevated BMI was associated with a reduced risk of prostate cancer, but that genetically elevated height was not associated with prostate cancer risk. The height and BMI allele scores were positively associated with prostate cancer-specific and allcause mortality, respectively, but only among men with low-grade disease (pheterogeneity, lowvs. high-grade prostate cancer \0.05). Although evidence for these associations was relatively weak, the inverse relationship of BMI with prostate cancer risk is in line with both observational data [8] and our previous genetic study [15]. The latter report showed an inverse relationship of a single obesity-related SNP (FTO rs9939609) with overalland low-grade prostate cancer in ProtecT, a much smaller population-based sample of 1,550 screen-detected prostate cancers and 1,815 controls [15]. We found inverse associations of a related SNP in FTO (rs1558902, which is in linkage disequilibrium with Table 5 Hazard ratio of all-cause and prostate cancer-specific mortality among men with prostate cancer per one standard change in height or BMI genetic score Number of participants Number of failures Years at risk (1000s) Unadjusted Adjusted a Hazard ratio c Confidence interval b pvalue Hazard ratio c Confidence interval b pvalue pheterogeneity d Lower Upper Lower Upper All-cause mortality Height All cases 14,649 3,591 105 1.02 0.97 1.08 0.47 1.00 0.96 1.04 0.88 Localized 8,553 1,447 65 1.01 0.93 1.09 0.81 1.00 0.93 1.07 0.97 0.20 Advanced 3,435 1,332 25 1.08 0.98 1.18 0.11 1.07 0.99 1.14 0.07 Low grade 5,684 905 43 1.04 0.97 1.11 0.32 1.02 0.95 1.09 0.57 0.80 High grade 5,892 1,365 36 1.02 0.97 1.08 0.36 1.01 0.96 1.06 0.71 BMI All cases 14,649 3,591 105 1.02 0.99 1.05 0.18 1.02 0.99 1.05 0.23 Localized 8,553 1,447 65 1.04 0.99 1.10 0.09 1.04 0.99 1.10 0.09 0.28 Advanced 3,435 1,332 25 1.01 0.98 1.04 0.50 1.01 0.98 1.05 0.59 Low grade 5,684 905 43 1.09 1.04 1.15 0.001 1.08 1.03 1.14 0.002 0.03 High grade 5,892 1,365 36 1.00 0.96 1.05 0.89 1.00 0.95 1.05 0.98 Prostate cancer-specific mortality Height All cases 14,649 1,483 105 1.02 0.98 1.06 0.44 1.00 0.97 1.04 0.87 Localized 8,553 363 65 0.98 0.91 1.07 0.72 0.99 0.91 1.08 0.79 0.29 Advanced 3,435 745 25 1.05 1.00 1.10 0.06 1.04 1.00 1.09 0.07 Low grade 5,684 188 43 1.13 1.06 1.21 \0.001 1.13 1.08 1.20 \0.001 \0.001 High grade 5,892 678 36 0.97 0.93 1.02 0.20 0.97 0.93 1.01 0.19 BMI All cases 14,649 1,483 105 0.99 0.96 1.03 0.76 1.00 0.96 1.04 0.94 Localized 8,553 363 65 0.95 0.88 1.03 0.22 0.95 0.87 1.05 0.31 0.09 Advanced 3,435 745 25 1.04 0.98 1.10 0.18 1.05 0.99 1.10 0.11 Low grade 5,684 188 43 0.95 0.89 1.01 0.08 0.95 0.88 1.01 0.12 0.03 High grade 5,892 678 36 1.05 0.99 1.11 0.12 1.05 0.98 1.13 0.14 a Adjusted for the first eight principal components of population stratification b Based in robust standard errors to account for within-study clustering c Change in hazard ratio per standard deviation change in height and BMI genetic risk score (standardized to mean zero standard deviation one) d Localized versus advanced, or highversus low-grade using Bland–Altman tests Cancer Causes Control (2015) 26:1603–1616 1611 123