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Background: After adjusting for age and body mass index (BMI), mammographic measures—dense area (DA), percent dense area (PDA), and nondense area (NDA)—are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time. Methods: We collected 4,320 mammograms (age range, 24–83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1–14). DA, PDA, and NDA were measured using the Cumulus software and normalized using the Box–Cox method. Correlations in the normalized risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modeling of Gompertz curves. Results: Mean normalized DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid-60s onward. Mean normalized NDA increased nonlinearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalized DA were 0.94, 0.93, 0.91, 0.91, and 0.91 for mammograms taken 2, 4, 6, 8, and 10 years apart, respectively. Similar correlations were estimated for the age- and BMI-adjusted normalized PDA and NDA. Conclusions: The mammographic measures that predict breast cancer risk are highly correlated over time. Impact: This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g., early 40s or even younger) and recommended appropriate screening and prevention strategies. Cancer Epidemiol Biomarkers Prev; 26(4); 651–60. ©2017 AACR.

Background Case–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers. Method We conducted a nested case–control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC). Results For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85–2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07). Conclusion The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes. Electronic supplementary material The online version of this article (10.1186/s13058-018-1081-0) contains supplementary material, which is available to authorized users.

Mammographic density measurements are associated with risk of breast cancer. Few studies have investigated the concurrent associations of mammographic dense and nondense areas, body mass index (weight (kg)/height (m)(2)), and ages at mammogram and diagnosis with breast cancer risk. We conducted a matched, case-control study nested within the Melbourne Collaborative Cohort Study (cohort recruitment in 1990-1994 and follow-up until 2007) to estimate the associations between these factors and breast cancer risk under alternative causal models. Mammographic dense area was positively associated with risk, and the strength of this association was only slightly influenced by the choice of the causal model (relative risk per 1 standard deviation = 1.50, 95% confidence interval: 1.32, 1.70). Mammographic nondense area was inversely associated with risk under the assumption that fat in the body and fat in the breast cause breast cancer through independent mechanisms (relative risk per 1 standard deviation = 0.75, 95% confidence interval: 0.65, 0.86), whereas it was not associated with risk under the assumption that they are both proxies of adiposity. Knowledge about the biological mechanisms regulating the role played by mammographic nondense area and body fat on breast cancer risk is essential to better estimate their impacts on individual risk.

Background: Adult weight is positively associated with postmenopausal breast cancer but few studies have investigated whether there are associations with weight and body mass index (BMI) in early adulthood, or subsequent weight change. Methods: A total of 14,441 postmenopausal women from the Melbourne Collaborative Cohort Study (MCCS) were followed for 16.5 years (mean) and 668 incident breast cancers were identified. Hazard ratios (HRs) were estimated using Cox regression. Results: Weight and BMI at 18 to 21 years were not associated with risk of any type of breast cancer and there was no variation by age. Women with the greatest increase in weight and BMI had higher risk at older ages [HR per 5 kg/m2 gain in BMI = 1.24; 95% confidence interval (CI), 1.11–1.40], although the test for homogeneity by age was not significant. At older ages, the association was stronger for progesterone (PR) positive disease compared with PR negative disease (HR per 5 kg/m2 gain in BMI, 1.43; 95% CI, 1.23–1.66; test for homogeneity by PR status, P < 0.01) and for diseases that were positive for both estrogen (ER) and PR (HR per 5 kg/m2 gain in BMI, 1.45; 95% CI, 1.24–1.69; test for homogeneity by ER/PR status, P = 0.02). HRs were also greater for HER2− and luminal A tumors, but the P values for homogeneity by tumor subgroups were not significant. Conclusion: Early adulthood weight is not associated with risk of postmenopausal breast cancer. Greater weight gain during adulthood might be associated with increased risk for older women (>69 years) and this association might vary by tumor hormone receptor status. Impact: Further studies need to investigate the impact of increase in weight during adulthood on postmenopausal breast cancer risk and the potential variation by age or tumor characteristics. Cancer Epidemiol Biomarkers Prev; 22(8); 1409–16. ©2013 AACR.

Background In a previous paper, we had assumed that the risk of screen-detected breast cancer mostly reflects inherent risk, and the risk of whether a breast cancer is interval versus screen-detected mostly reflects risk of masking. We found that inherent risk was predicted by body mass index (BMI) and dense area (DA) or percent dense area (PDA), but not by non-dense area (NDA). Masking, however, was best predicted by PDA but not BMI. In this study, we aimed to investigate if these associations vary by tumor characteristics and mode of detection. Methods We conducted a case-control study nested within the Melbourne Collaborative Cohort Study of 244 screen-detected cases matched to 700 controls and 148 interval cases matched to 446 controls. DA, NDA and PDA were measured using the Cumulus software. Tumor characteristics included size, grade, lymph node involvement, and ER, PR, and HER2 status. Conditional and unconditional logistic regression were applied as appropriate to estimate the Odds per Adjusted Standard Deviation (OPERA) adjusted for age and BMI, allowing the association with BMI to be a function of age at diagnosis. Results For screen-detected cancer, both DA and PDA were associated to an increased risk of tumors of large size (OPERA ~ 1.6) and positive lymph node involvement (OPERA ~ 1.8); no association was observed for BMI and NDA. For risk of interval versus screen-detected breast cancer, the association with risk for any of the three mammographic measures did not vary by tumor characteristics; an association was observed for BMI for positive lymph nodes (OPERA ~ 0.6). No associations were observed for tumor grade and ER, PR and HER2 status of tumor. Conclusions Both DA and PDA were predictors of inherent risk of larger breast tumors and positive nodal status, whereas for each of the three mammographic density measures the association with risk of masking did not vary by tumor characteristics. This might raise the hypothesis that the risk of breast tumours with poorer prognosis, such as larger and node positive tumours, is intrinsically associated with increased mammographic density and not through delay of diagnosis due to masking.

Background Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking. Methods We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases (192 small tumors (

Background: Epidemiologic studies have consistently reported positive associations between obesity and colon cancer risk for men, but the evidence is less consistent for women. Few studies have investigated effects of weight change on colon cancer risk. Methods: Using the Melbourne Collaborative Cohort Study, which recruited men and women mostly in 40 to 69 years of age, we investigated associations between weight and body mass index (BMI) at age 18 years and at study entry and weight change since age 18 years and colon cancer. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox regression. Results: During follow-up of 16,188 men and 23,438 women for 14 years on average, we ascertained 569 incident colon cancers. Weight and BMI at study entry were positively associated with colon cancer risk for men [HR, 1.12 (95% CI, 1.04-1.21) per 5-kg increment; HR, 1.39 (95% CI, 1.12-1.71) per 5 kg/m2], but not women. Risk of colon cancer was not associated with weight or BMI at age 18 years. Adult weight change was positively associated with colon cancer risk for men (HR, 1.11 per 5-kg increment; 95% CI, 1.03-1.20), but not women (HR, 1.00; 95% CI, 0.94-1.07). Men who gained ≥20 kg from age 18 had an increased risk of colon cancer compared with men whose weight was stable (HR, 1.47; 95% CI, 0.94-2.31). Conclusion: Weight gain during adult life increases men's risk of colon cancer. Impact: Avoiding excessive weight gain might help reduce colon cancer risk for men. Cancer Epidemiol Biomarkers Prev; 19(11); 2978–86. ©2010 AACR.

Epidemiologic studies have consistently reported that endogenous steroid hormone levels are associated with postmenopausal breast cancer risk, but little is known on the associations by tumor grade, hormone receptor status, or age at diagnosis.We performed a case-cohort study of naturally postmenopausal women within the Melbourne Collaborative Cohort Study that included a random sample of 857 women and 197 breast cancer cases diagnosed during a mean of 9.2 years of follow-up. Concentrations of total estradiol, estrone sulfate, testosterone, DHEA sulfate, androstenedione, and sex hormone binding globulin were measured in plasma collected at baseline before diagnosis; free estradiol plasma concentration was calculated. Cox regression was used to estimate associations adjusted for known and potential confounders.The HR for breast cancer comparing fourth and first quartiles was 1.44 [95% confidence interval (95% CI), 0.89-2.35] for total estradiol, 1.75 (95% CI, 1.06, 2.89) for free estradiol, 2.05 (95% CI, 1.24-3.37) for estrone sulfate, 1.25 (95% CI, 0.78-2.01) for testosterone, 1.41 (95% CI, 0.88-2.27) for DHEA sulfate, 1.49 (95% CI, 0.91-2.44) for androstenedione, and 0.33 (95% CI, 0.19-0.55) for sex hormone binding globulin. These associations did not differ by tumor grade and estrogen receptor/progesterone receptor status (all test for heterogeneity, P > 0.05). Risks associated with estrogen and androgen levels were stronger at older ages (test for interaction across age groups, P = 0.59 for total estradiol and P = 0.01 for testosterone).Our prospective study confirms earlier findings and suggests that the associations of endogenous hormones with postmenopausal breast cancer risk are independent of tumor grade, and hormone receptor status and might increase in strength with age. Cancer Epidemiol Biomarkers Prev; 19(2); 492–502

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

BACKGROUND: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures. METHODS: We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status. RESULTS: Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07). CONCLUSION: We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland. IMPACT: We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. This study was supported by Research Grant FIS PI060386 from Spain’s Health Research Fund (Fondo de Investigación Sanitaria); the EPY 1306/06 Collaboration Agreement between Astra-Zeneca and the Instituto de Salud Carlos III; and a grant from the Spanish Federation of Breast Cancer (FECMA). Sí

BACKGROUND: Evidence is emerging that prudent/healthy dietary patterns might be associated with a reduced risk of breast cancer. METHODS: Using data from the prospective Melbourne Collaborative Cohort Study, we applied principal factor analysis to 124 foods and beverages to identify dietary patterns and estimated their association with breast cancer risk overall and by tumour characteristics using Cox regression. RESULTS: During an average of 14.1 years of follow-up of 20 967 women participants, 815 invasive breast cancers were diagnosed. Among the four dietary factors that we identified, only that characterised by high consumption of fruit and salad was associated with a reduced risk, with stronger associations observed for tumours not expressing oestrogen (ER) and progesterone receptors (PR). Compared with women in the lowest quintile of the factor score, the hazard ratio for women in the highest quintile was 0.92 (95% confidence interval (CI)=0.70-1.21; test for trend, P=0.5) for ER-positive or PR-positive tumours and 0.48 (95% CI=0.26-0.86; test for trend, P=0.002) for ER-negative and PR-negative tumours (test for homogeneity, P=0.01). CONCLUSION: Our study provides additional support for the hypothesis that a dietary pattern rich in fruit and salad might protect against invasive breast cancer and that the effect might be stronger for ER- and PR-negative tumours.

Background/Objectives: Little is known about nutritional factors that influence circulating concentrations of steroid hormones, which are consistently associated with risk of breast cancer for postmenopausal women. We aimed to investigate the association between consumption of animal products and the plasma concentrations of steroid hormones and sex hormone-binding globulin (SHBG). Subjects/Methods: Cross-sectional analysis was conducted on plasma from 766 naturally postmenopausal women. We measured plasma concentrations of steroid hormones and SHBG, and estimated dietary intakes using a 121-item food frequency questionnaire. Log-transformed values of hormone concentrations were regressed on quartiles of intake of meat and dairy products among food items, and fats, proteins and cholesterol among nutrient intake. Results: Total red and fresh red meat consumption was negatively associated with SHBG levels (P for trend¼0.04 and o0.01, respectively). Mean SHBG concentrations were B8% and 13% lower for women in the highest quartile compared with the lowest quartile of total red and fresh red meat consumption, respectively. Positive associations were observed between dairy product consumption and total and free estradiol concentrations (P for trend¼0.02 and 0.03, respectively). Mean concentrations of total and free estradiol were 15 and 14% higher for women in the highest quartile of dairy product consumption than for those in the lowest quartile, respectively. No associations were observed with consumption of processed meat, chicken, fish, eggs, cholesterol, fats or protein. Conclusions: Our study suggests that greater consumption of total red and fresh red meat and dairy products might influence circulating concentrations of SHBG and estradiol, respectively. Confirmation and further investigation is required. European Journal of Clinical Nutrition (2010) 64, 176-183; doi:10.1038/ejcn.2009.129; published online 11 November 2009

Recent studies report that certain dietary patterns, especially those high in red and processed meats, are associated with prostate cancer risk. We prospectively investigated associations between empirically derived dietary patterns and prostate cancer risk using the Melbourne Collaborative Cohort Study. We followed 14,627 men of ages 34 to 75 years for an average of 13.6 years, and identified 1,018 incident prostate cancers. Factor analysis of the 121-item food frequency questionnaire identified four factors with eigenvalues >2 that explained 67% of the total variance. Using Cox proportional hazard models, we found no association between any dietary pattern and prostate cancer risk overall (all Ptrend ≥ 0.2). The hazard ratios for quartiles of the dietary pattern scores ranged from 0.87 to 1.14 and all 95% confidence intervals included 1. The analyses by aggressiveness, Gleason score groups, and age at diagnosis did not show any association (all Ptrend > 0.07). (Cancer Epidemiol Biomarkers Prev 2009;18(11):3126–9)

Steroid hormones are associated with the risk of postmenopausal breast cancer and evidence suggests that increased concentrations of oestrogens from peripheral aromatisation in adipose tissue partly explains the association between body mass index (BMI) and risk of postmenopausal breast cancer. This study examined the associations between circulating concentrations of steroid hormones and anthropometric measurements in a sample of naturally postmenopausal women from the Melbourne Collaborative Cohort Study, not using hormone replacement therapy. We measured plasma concentration of total oestradiol, oestrone sulphate, dehydroepiandrosterone sulphate, androstenedione, testosterone and sex hormone binding globulin (SHBG) and calculated concentration of free oestradiol. Body measurements included height, weight, BMI, waist circumference, fat mass and fat-free mass, the last two estimated by bioelectrical impedance analysis. BMI was positively associated with both oestrogens and androgens and negatively with SHBG. Fat mass was the principal measure responsible for the association observed between body size and total oestradiol. The associations between oestrone sulphate and androgens and body size were mainly with waist circumference. The associations between oestrogens and body size were close to null for the first 6 years since menopause and became positive thereafter. Our results are compatible with the hypothesis that after the menopause excess fat mass increases oestrogen concentrations through the peripheral aromatisation of androgens in adipose tissue. This effect requires around 6 years to be detectable by way of circulating steroid hormone levels. Refereed/Peer-reviewed