A large study of androgen receptor germline variants and their relation to sex hormone levels and prostate cancer risk. Results from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

Background: Androgens are key regulators of prostate gland maintenance and prostate cancer growth, and androgen deprivation therapy has been the mainstay of treatment for advanced prostate cancer for many years. A long-standing hypothesis has been that inherited variation in the androgen receptor (AR) gene plays a role in prostate cancer initiation. However, studies to date have been inconclusive and often suffered from small sample sizes. Objective and Methods: We investigated the association of AR sequence variants with circulating sex hormone levels and prostate cancer risk in 6058 prostate cancer cases and 6725 controls of Caucasian origin within the Breast and Prostate Cancer Cohort Consortium. We genotyped a highly polymorphic CAG microsatellite in exon 1 and six haplotype tagging single nucleotide polymorphisms and tested each genetic variant for association with prostate cancer risk and with sex steroid levels. Results: We observed no association between AR genetic variants and prostate cancer risk. However, there was a strong association between longer CAG repeats and higher levels of testosterone (P = 4.73 × 10−5) and estradiol (P = 0.0002), although the amount of variance explained was small (0.4 and 0.7%, respectively). Conclusions: This study is the largest to date investigating AR sequence variants, sex steroid levels, and prostate cancer risk. Although we observed no association between AR sequence variants and prostate cancer risk, our results support earlier findings of a relation between the number of CAG repeats and circulating levels of testosterone and estradiol.

Development and validation of a risk score predicting substantial weight gain over 5 years in middle-aged European men and women.

BACKGROUND Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. METHODS We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). RESULTS Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively. CONCLUSION The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.