OBJECTIVE: The study objective was to investigate whether underlying associations exist between prostate cancer risk and male self-reported body size and shape.
METHODS: Data were collected from a large case-control study of men (1928 cases and 2043 controls) using self-administered questionnaires. Data from self-reported pictograms of perceived body size relating to three decades of life (20's, 30's and 40's) were recorded and analysed, including the pattern of change. The associations of self-identified body shape with prostate cancer risk were also explored.
RESULTS: Self-reported body size for men in their 20's, 30's and 40's did not appear to be associated with prostate cancer risk. More than half of the subjects reported an increase in self-reported body size throughout these three decades of life. Furthermore, no association was observed between self-reported body size changes and prostate cancer risk. Using 'symmetrical' body shape as a reference group, subjects with an 'apple' shape showed a significant 27% reduction in risk (Odds ratio = 0.73, 95% C.I. 0.57-0.92).
CONCLUSIONS: Change in self-reported body size throughout early to mid-adulthood in males is not a significant risk factor for the development of prostate cancer. Body shape indicative of body fat distribution suggested that an 'apple' body shape was protective and inversely associated with prostate cancer risk when compared with 'symmetrical' shape. Further studies which investigate prostate cancer risk and possible relationships with genetic factors known to influence body shape may shed further light on any underlying associations.
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 development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).
RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.
CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.