Oestrogen exposure and breast cancer risk

Epidemiological and experimental evidence implicates oestrogens in the aetiology of breast cancer. Most established risk factors for breast cancer in humans probably act through hormone-related pathways, and increased concentrations of circulating oestrogens have been found to be strongly associated with increased risk for breast cancer in postmenopausal women. This article explores the evidence for the hypothesis that oestrogen exposure is a major determinant of risk for breast cancer. We review recent data on oestrogens and breast cancer risk, consider oestrogen-related risk factors and examine possible mechanisms that might account for the effects of oestrogen. Finally, we discuss how these advances might influence strategies for reducing the incidence of breast cancer

Lifestyle factors and prostate-specific antigen (PSA) testing in UK Biobank: Implications for epidemiological research

AbstractBackgroundThe central role of prostate-specific antigen (PSA) testing in the diagnosis of prostate cancer leads to the possibility that observational studies that report associations between risk factors and prostate cancer could be affected by detection bias. This study aims to investigate whether reported risk factors for prostate cancer are associated with PSA testing in a large middle-aged population-based cohort in the UK.MethodsThe cross-sectional association between a wide range of sociodemographic, lifestyle, dietary and health characteristics with PSA testing was examined in 212,039 men aged 40–69 years in UK Biobank.ResultsA total of 62,022 (29%) men reported they had ever had a PSA test. A wide range of factors was associated with a higher likelihood of PSA testing including age, height, education level, family history of prostate cancer, black ethnic origin, not being in paid/self-employment, living with a wife or partner, having had a vasectomy, being diagnosed with cancer or hypertension and having a high dietary intake of cereal, cooked and salad/raw vegetables, fresh fruit and tea. Conversely, socioeconomic deprivation, Asian ethnic origin, current smoking, low alcohol intake, high body-mass index, high coffee consumption and being diagnosed with diabetes, heart disease or stroke were associated with a lower likelihood of PSA testing.ConclusionsA variety of sociodemographic, lifestyle and health-related characteristics are associated with PSA testing, suggesting that observed associations of some of these traits with risk for prostate cancer in epidemiological studies may be, at least partially, due to detection bias

The relationship between lipoprotein A and other lipids with prostate cancer risk:A multivariable Mendelian randomisation study

BACKGROUND: Numerous epidemiological studies have investigated the role of blood lipids in prostate cancer (PCa) risk, though findings remain inconclusive to date. The ongoing research has mainly involved observational studies, which are often prone to confounding. This study aimed to identify the relationship between genetically predicted blood lipid concentrations and PCa. METHODS AND FINDINGS: Data for low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides (TG), apolipoprotein A (apoA) and B (apoB), lipoprotein A (Lp(a)), and PCa were acquired from genome-wide association studies in UK Biobank and the PRACTICAL consortium, respectively. We used a two-sample summary-level Mendelian randomisation (MR) approach with both univariable and multivariable (MVMR) models and utilised a variety of robust methods and sensitivity analyses to assess the possibility of MR assumptions violation. No association was observed between genetically predicted concentrations of HDL, TG, apoA and apoB, and PCa risk. Genetically predicted LDL concentration was positively associated with total PCa in the univariable analysis, but adjustment for HDL, TG, and Lp(a) led to a null association. Genetically predicted concentration of Lp(a) was associated with higher total PCa risk in the univariable (OR(weighted median) per standard deviation (SD) = 1.091; 95% CI 1.028 to 1.157; P = 0.004) and MVMR analyses after adjustment for the other lipid traits (OR(IVW) per SD = 1.068; 95% CI 1.005 to 1.134; P = 0.034). Genetically predicted Lp(a) was also associated with advanced (MVMR OR(IVW) per SD = 1.078; 95% CI 0.999 to 1.163; P = 0.055) and early age onset PCa (MVMR OR(IVW) per SD = 1.150; 95% CI 1.015,1.303; P = 0.028). Although multiple estimation methods were utilised to minimise the effect of pleiotropy, the presence of any unmeasured pleiotropy cannot be excluded and may limit our findings. CONCLUSIONS: We observed that genetically predicted Lp(a) concentrations were associated with an increased PCa risk. Future studies are required to understand the underlying biological pathways of this finding, as it may inform PCa prevention through Lp(a)-lowering strategies

Examination of potential novel biochemical factors in relation to prostate cancer incidence and mortality in UK Biobank

Background: Although prostate cancer is a leading cause of cancer death, its aetiology is not well understood. We aimed to identify novel biochemical factors for prostate cancer incidence and mortality in UK Biobank. Methods: A range of cardiovascular, bone, joint, diabetes, renal and liver-related biomarkers were measured in baseline blood samples collected from up to 211,754 men at recruitment and in a subsample 5 years later. Participants were followed-up via linkage to health administrative datasets to identify prostate cancer cases. Hazard ratios (HRs) and 95% confidence intervals were calculated using multivariable-adjusted Cox regression corrected for regression dilution bias. Multiple testing was accounted for by using a false discovery rate controlling procedure. Results: After an average follow-up of 6.9 years, 5763 prostate cancer cases and 331 prostate cancer deaths were ascertained. Prostate cancer incidence was positively associated with circulating vitamin D, urea and phosphate concentrations and inversely associated with glucose, total protein and aspartate aminotransferase. Phosphate and cystatin-C were the only biomarkers positively and inversely, respectively, associated with risk in analyses excluding the first 4 years of follow-up. There was little evidence of associations with prostate cancer death. Conclusion: We found novel associations of several biomarkers with prostate cancer incidence. Future research will examine associations by tumour characteristics.</p

Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk:A Mendelian randomization analysis

BACKGROUND The associations of adiposity with aggressive prostate cancer risk are unclear. Using two-sample Mendelian randomization, we assessed the association of metabolically unfavourable adiposity (UFA), favourable adiposity (FA) and for comparison body mass index (BMI), with prostate cancer, including aggressive prostate cancer. METHODS We examined the association of these genetically predicted adiposity-related traits with risk of prostate cancer overall, aggressive and early onset disease using outcome summary statistics from the PRACTICAL consortium (including 15,167 aggressive cases). RESULTS In inverse-variance weighted models, there was little evidence that genetically predicted one standard deviation higher UFA, FA and BMI were associated with aggressive prostate cancer [OR: 0.85 (95% CI:0.61-1.19), 0.80 (0.53-1.23) and 0.97 (0.88-1.08), respectively]; these associations were largely consistent in sensitivity analyses accounting for horizontal pleiotropy. There was no strong evidence that genetically determined UFA, FA or BMI were associated with overall prostate cancer or early age of onset prostate cancer. CONCLUSIONS We did not find differences in the associations of UFA and FA with prostate cancer risk, which suggest that adiposity is unlikely to influence prostate cancer via the metabolic factors assessed; however, these did not cover some aspects related to metabolic health that may link obesity with aggressive prostate cancer, which should be explored in future studies

Predicting non-native insect impact: focusing on the trees to see the forest

Non-native organisms have invaded novel ecosystems for centuries, yet we have only a limited understanding of why their impacts vary widely from minor to severe. Predicting the impact of non-established or newly detected species could help focus biosecurity measures on species with the highest potential to cause widespread damage. However, predictive models require an understanding of potential drivers of impact and the appropriate level at which these drivers should be evaluated. Here, we used non-native, specialist herbivorous insects of forest ecosystems to test which factors drive impact and if there were differences based on whether they used woody angiosperms or conifers as hosts. We identified convergent and divergent patterns between the two host types indicating fundamental similarities and differences in their interactions with non-native insects. Evolutionary divergence time between native and novel hosts was a significant driver of insect impact for both host types but was modulated by different factors in the two systems. Beetles in the subfamily Scolytinae posed the highest risk to woody angiosperms, and different host traits influenced impact of specialists on conifers and woody angiosperms. Tree wood density was a significant predictor of host impact for woody angiosperms with intermediate densities (0.5–0.6 mg/mm3) associated with highest risk, whereas risk of impact was highest for conifers that coupled shade tolerance with drought intolerance. These results underscore the importance of identifying the relevant levels of biological organization and ecological interactions needed to develop accurate risk models for species that may arrive in novel ecosystems