Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 [95% confidence interval (CI) 4.84–5.29] for men of European ancestry to 3.74 [95% CI 3.36–4.17] for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher [95% CI 2.14–2.22], and men of East Asian ancestry 0.73-times lower [95% CI 0.71–0.76], than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Purpose A previous-day recall (PDR) may be a less error prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12–17 years) and adults (18–71 years). Methods In a 7-day study, participants completed three PDRs, wore two activity monitors, and completed measures of social desirability and body mass index (BMI). PDR measures of active and sedentary time was contrasted against an accelerometer (ActiGraph) by comparing both to a valid reference measure (activPAL) using measurement error modeling and traditional validation approaches. Results Age- and gender-specific mixed models comparing PDR to activPAL indicated: (1) a strong linear relationship between measures for sedentary (regression slope = β1=0.80 to 1.13) and active time (β1=0.64 to 1.09); (2) person-specific bias was lower than random error; and (3) correlations were high (Sedentary: r = 0.60 to 0.81; Active: r = 0.52 to 0.80). Reporting errors were not associated with BMI or social desirability. Models comparing ActiGraph to activPAL indicated: (1) a weaker linear relationship between measures for sedentary (β1=0.63 to 0.73) and active time (β1=0.61 to 0.72); (2) person-specific bias was slightly larger than random error; and (3) correlations were high (Sedentary: r = 0.68 to 0.77; Active: r = 0.57 to 0.79). Conclusions Correlations between the PDR and activPAL were high, systematic reporting errors were low, and the validity of the PDR was comparable to the ActiGraph. PDRs may have value in studies of physical activity and health, particularly those interested in measuring the specific type, location, and purpose of activity-related behaviors.

Overactive bladder (OAB) is a highly prevalent symptom condition that affects millions of US men and women. Not only can the symptoms of OAB be very bothersome, but OAB can have significant detrimental effects on many aspects of individuals’ lives, representing a particularly impactful health burden to quality of life and productivity. Estimates of the individual and societal costs for the management of OAB continue to rise, particularly as effective treatments remain elusive. As such, OAB represents a significant public health burden to the USA.

Prostate cancer incidence is 1.6-fold higher in African Americans than in other populations. The risk factors that drive this disparity are unknown and potentially consist of social, environmental, and genetic influences. To investigate the genetic basis of prostate cancer in men of African ancestry, we performed a genome-wide association meta-analysis using two-sided statistical tests in 10 202 case subjects and 10 810 control subjects. We identified novel signals on chromosomes 13q34 and 22q12, with the risk-associated alleles found only in men of African ancestry (13q34: rs75823044, risk allele frequency = 2.2%, odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.37 to 1.76, P = 6.10 × 10−12; 22q12.1: rs78554043, risk allele frequency = 1.5%, OR = 1.62, 95% CI = 1.39 to 1.89, P = 7.50 × 10−10). At 13q34, the signal is located 5’ of the gene IRS2 and 3’ of a long noncoding RNA, while at 22q12 the candidate functional allele is a missense variant in the CHEK2 gene. These findings provide further support for the role of ancestry-specific germline variation in contributing to population differences in prostate cancer risk.

BACKGROUND.Prior studies suggest that obese men have lower prostate‐specific antigen (PSA) levels than leaner men. Caucasian (CA) men also may have lower PSA levels than African‐American (AA) men, but the relevance of body size to racial disparities in PSA levels is unclear. The association between body mass index (BMI) and height on PSA and percentage of free PSA (%fPSA) was investigated within AA and CA men without a prior prostate cancer diagnosis.METHODS.AA (n = 150) and CA (n = 149) men of similar socioeconomic status completed an extensive in‐person interview and donated blood. PSA and %fPSA levels were compared across race, BMI, and height categories after adjusting for age and other factors.RESULTS.PSA levels decreased with increasing BMI (PSA = .72, .69, .67, .59 ng/mL for BMI 18.5 to <25, 25 to <30, 30 to <35, and ≥35, respectively; Ptrend = .18), and trends were significant among men less than age 60 years (PSA = .81, .76, .66, .59, respectively; Ptrend = .02). fPSA also significantly decreased with BMI among men <60 years (Ptrend = .04). In contrast, %fPSA was not associated with BMI. However, %fPSA increased 27% across height categories (Ptrend = .02). PSA levels were significantly lower among CA men (PSAAA = 0.87, PSACA = 0.63 ng/mL; P < .01), whereas %fPSA levels did not differ by race. Also, associations between body size and PSA or %fPSA did not significantly differ by race, and adjustment for BMI and height had no effect on the racial disparity in PSA (PSAAA = 0.87, PSACA = 0.63 ng/mL; P < .01).CONCLUSIONS.The data suggest that race, BMI, and height are independently associated with PSA and %fPSA levels. Cancer 2006. © 2006 American Cancer Society.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10−180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

African American (AA) race/ethnicity, lower body mass index (BMI), and higher insulin-like growth factor 1 (IGF-1) levels are associated with premenopausal breast cancer risk. This cross-sectional analysis investigated whether BMI or BMI at age 21 years contribute to racial differences in IGF-1, IGF-2, IGFBP-3, or free IGF-1. Participants included 816 white and 821 AA women between ages 40 and 79 years across a wide BMI range (18.5–40 kg/m2). Compared with white women, AA women had higher mean IGF-1 (146.3 vs. 134.4 ng/ml) and free IGF-1 (0.145 vs. 0.127) levels, and lower IGF-2 (1633.0 vs. 1769.3 ng/ml) and IGFBP-3 (3663.3 vs. 3842.5 ng/ml) levels (all p<0.01; adjusted for age, height, BMI, BMI at age 21, and menopause status). Regardless of race, IGF-1 and free IGF-1 levels sharply rose as BMI increased to 22–24 kg/m2, then declined thereafter, while IGF-2 and IGFBP-3 levels tended to rise with BMI. In contrast, BMI at age 21 was inversely associated with all IGF levels, but only among white women (p-interaction = 0.01). With the decline in IGF-1 with BMI at age 21 among whites, racial differences in IGF-1 significantly increased among women who were obese in early adulthood. In summary, BMI was associated with IGF-1 levels regardless of race/ethnicity, while obesity during childhood or young adulthood may have a greater impact on IGF-1 levels among white women. The effects of obesity throughout life on the IGF axis and racial differences in breast cancer risk require study.

Genome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these GWAS loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI GWAS loci identified in EA studies in 29,151 AAs from the Population Architecture using Genomics and Epidemiology (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8 × 10(-5). Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r(2) > 0.5) with the most significant SNP in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6 × 10(-8)) and DHX34 (rs4802349, p = 1.2 × 10(-7)), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci.