METHODS: Collaborating investigators from 15 prospective studies provided individual-participant records (from predominantly men of white European ancestry) on blood or toenail selenium concentrations and prostate cancer risk. Odds ratios of prostate cancer by selenium concentration were estimated using multivariable-adjusted conditional logistic regression. All statistical tests were two-sided.
RESULTS: Blood selenium was not associated with the risk of total prostate cancer (multivariable-adjusted odds ratio [OR] per 80 percentile increase = 1.01, 95% confidence interval [CI] = 0.83 to 1.23, based on 4527 case patients and 6021 control subjects). However, there was heterogeneity by disease aggressiveness (ie, advanced stage and/or prostate cancer death, Pheterogeneity = .01), with high blood selenium associated with a lower risk of aggressive disease (OR = 0.43, 95% CI = 0.21 to 0.87) but not with nonaggressive disease. Nail selenium was inversely associated with total prostate cancer (OR = 0.29, 95% CI = 0.22 to 0.40, Ptrend < .001, based on 1970 case patients and 2086 control subjects), including both nonaggressive (OR = 0.33, 95% CI = 0.22 to 0.50) and aggressive disease (OR = 0.18, 95% CI = 0.11 to 0.31, Pheterogeneity = .08).
CONCLUSIONS: Nail, but not blood, selenium concentration is inversely associated with risk of total prostate cancer, possibly because nails are a more reliable marker of long-term selenium exposure. Both blood and nail selenium concentrations are associated with a reduced risk of aggressive disease, which warrants further investigation.
OBJECTIVE: To examine whether men with low concentrations of circulating free testosterone have a reduced risk of prostate cancer.
DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual participant data from 20 prospective studies including 6933 prostate cancer cases, diagnosed on average 6.8 yr after blood collection, and 12 088 controls in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) of incident overall prostate cancer and subtypes by stage and grade, using conditional logistic regression, based on study-specific tenths of calculated free testosterone concentration.
RESULTS AND LIMITATIONS: Men in the lowest tenth of free testosterone concentration had a lower risk of overall prostate cancer (OR=0.77, 95% confidence interval [CI] 0.69-0.86; p<0.001) compared with men with higher concentrations (2nd-10th tenths of the distribution). Heterogeneity was present by tumour grade (phet=0.01), with a lower risk of low-grade disease (OR=0.76, 95% CI 0.67-0.88) and a nonsignificantly higher risk of high-grade disease (OR=1.56, 95% CI 0.95-2.57). There was no evidence of heterogeneity by tumour stage. The observational design is a limitation.
CONCLUSIONS: Men with low circulating free testosterone may have a lower risk of overall prostate cancer; this may be due to a direct biological effect, or detection bias. Further research is needed to explore the apparent differential association by tumour grade.
PATIENT SUMMARY: In this study, we looked at circulating testosterone levels and risk of developing prostate cancer, finding that men with low testosterone had a lower risk of prostate cancer.
METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.
CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.