Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Background: Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD–risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable. Methods: Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I 2 statistics. Results: BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I 2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD–risk association (1.51 (1.41, 1.61); I 2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I 2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV–risk association (1.44 (1.34, 1.54); I 2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I 2 = 0%, P = 0.36, respectively). Conclusions: When volumetric MD–breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable

Number of risky lifestyle behaviors and breast cancer risk

Background Lifestyle factors are associated with overall breast cancer risk, but less is known about their associations, alone or jointly, with risk of specific breast cancer subtypes. Methods We conducted a case–control subjects study nested within a cohort of women who participated in the Norwegian Breast Cancer Screening Program during 2006–2014 to examine associations between risky lifestyle factors and breast cancer risk. In all, 4402 breast cancer cases subjects with information on risk factors and hormone receptor status were identified. Conditional logistic regression was used to estimate odds ratios (ORs), with 95% confidence intervals (CIs), in relation to five risky lifestyle factors: body mass index (BMI) of 25 kg/m² or greater, three or more glasses of alcoholic beverages per week, ever smoking, fewer than four hours of physical activity per week, and ever use of menopausal hormone therapy. Analyses were adjusted for education, age at menarche, number of pregnancies, and menopausal status. All statistical tests were two-sided. Results Compared with women with no risky lifestyle behaviors, those with five had 85% (OR = 1.85, 95% CI = 1.42 to 2.42, Ptrend  .18 for all). Conclusions Number of risky lifestyle factors was positively associated with increased risk for luminal A–like and luminal B–like HER2-positive breast cancer

Volumetric Mammographic Density, Age-Related Decline, and Breast Cancer Risk Factors in a National Breast Cancer Screening Program.

Background: Volumetric mammographic density (VMD) measures can be obtained automatically, but it is not clear how these relate to breast cancer risk factors.Methods: The cohort consisted of 46,428 women (ages 49-71 years) who participated in BreastScreen Norway between 2007 and 2014 and had information on VMD and breast cancer risk factors. We estimated means of percent and absolute VMD associated with age, menopausal status, body mass index (BMI), and other factors.Results: The associations between VMD and most breast cancer risk factors were modest, although highly significant. BMI was positively associated with absolute VMD, whereas inversely associated with percent VMD. Percent VMD was inversely associated with a 5-year older age at screening in premenopausal and postmenopausal women (-0.18% vs. -0.08% for percent VMD and -0.11 cm3 vs. -0.03 cm3 for absolute VMD). This difference was largest among postmenopausal women with BMI < 25 kg/m2 (P for interaction with percent VMD < 0.0001), never users of postmenopausal hormone therapy (P for interaction < 0.0001), and premenopausal women with a family history of breast cancer (P for interaction with absolute VMD = 0.054).Conclusions: VMD is associated with several breast cancer risk factors, the strongest being BMI, where the direction of the association differs for percent and absolute VMD. The inverse association with age appears modified by menopausal status and other breast cancer risk factors.Impact: Because VMD methods are becoming widely available in screening and clinical settings, the association between VMD measures and breast cancer risk factors should be investigated further in longitudinal studies. Cancer Epidemiol Biomarkers Prev; 27(9); 1065-74. ©2018 AACR

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Abstract Background Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD–risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable. Methods Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I 2 statistics. Results BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I 2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD–risk association (1.51 (1.41, 1.61); I 2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I 2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV–risk association (1.44 (1.34, 1.54); I 2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I 2 = 0%, P = 0.36, respectively). Conclusions When volumetric MD–breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable

Physical activity and body mass shape quality of life trajectories in mid-age women

To determine the combined longitudinal effect of body mass index (BMI) and physical activity (PA) on health-related quality of life (HrQoL), using the SF-6D (SF-36) utility measure.Five waves of self-reported data from the 1946-51 cohort (n=5,200; data collection, 2001-2013) of the Australian Longitudinal Study on Women's Health were used. Mixed effect models were employed to address the objective.Women with high PA experienced higher HrQoL regardless of BMI group, however, for those healthy or overweight, there was a very small decline in HrQoL over time. Women reporting no PA levels experienced the lowest baseline mean SF-6D score within each BMI group, with decreasing trajectories over the follow-up period. The rate of decline was greatest in women with obesity. Within each BMI group, there was a large, increasing gap in HrQoL between those who reported no and low PA over time. Women with obesity and high PA experienced similar HrQoL trajectories to women with normal weight or overweight with low PA levels. Overweight women with moderate PA experienced similar HrQoL to those with low PA but normal weight.PA may mitigate the adverse effect of overweight and obesity on HrQoL at mid-life, at higher activity levels. Implications for public health: PA benefits HrQoL regardless of body mass, with larger gains for those currently not physically active. Moderate to high PA may mitigate the effect of overweight and obesity

Incidence of breast cancer subtypes in immigrant and non-immigrant women in Norway

Background Breast cancer incidence differs between non-immigrants and immigrants from low- and middle-income countries. This study investigates whether immigrants also have different subtype-specific incidences. Methods We used national health registries in Norway and calculated subtype-specific incidence rate ratios (IRRs) for invasive breast cancer among women aged 20–75 and 20–49 years between 2005 and 2015. Immigrant groups were classified by country of birth broadly defined based on WHO regional groupings. Subtype was defined using estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (HER2) status as luminal A-like (ER+ PR+ HER2-), luminal B-like/HER2- (ER+ PR- HER2-), luminal B-like/HER2+ (ER+ PR any HER2+), HER2+ (ER-PR-HER2+) and triple-negative breast cancer (TNBC) (ER-PR-HER2-). Results Compared to non-immigrants, incidence of the luminal A-like subtype was lower in immigrants from Sub-Saharan Africa (IRR 0.43 95% CI 0.28–0.66), South East Asia (IRR 0.63 95% CI 0.51–0.79), South Asia (IRR 0.67 95% CI 0.52–0.86) and Eastern Europe (IRR 0.86 95% CI 0.76–0.99). Immigrants from South Asia had higher rates of HER2 + tumors (IRR 2.02 95% CI 1.26–3.23). The rates of TNBC tended to be similar regardless of region of birth, except that women from South East Asia had an IRR of 0.54 (95% CI 0.32–0.91). Conclusions Women from Eastern Europe, Sub-Saharan Africa and Asia had different subtype-specific incidences compared to women from high-income countries (including non-immigrants). These differences in tumor characteristics between immigrant groups should be taken into consideration when planning preventive or screening strategies

Stage‑specific survival has improved for young breast cancer patients since 2000: but not equally

Purpose The stage-specific survival of young breast cancer patients has improved, likely due to diagnostic and treatment advances. We addressed whether survival improvements have reached all socioeconomic groups in a country with universal health care and national treatment guidelines. Methods Using Norwegian registry data, we assessed stage-specific breast cancer survival by education and income level of 7501 patients (2317 localized, 4457 regional, 233 distant and 494 unknown stage) aged 30–48 years at diagnosis during 2000–2015. Using flexible parametric models and national life tables, we compared excess mortality up to 12 years from diagnosis and 5-year relative survival trends, by education and income as measures of socioeconomic status (SES). Results Throughout 2000–2015, regional and distant stage 5-year relative survival improved steadily for patients with high education and high income (high SES), but not for patients with low education and low income (low SES). Regional stage 5-year relative survival improved from 85 to 94% for high SES patients (9% change; 95% confidence interval: 6, 13%), but remained at 84% for low SES patients (0% change; − 12, 12%). Distant stage 5-year relative survival improved from 22 to 58% for high SES patients (36% change; 24, 49%), but remained at 11% for low SES patients (0% change; − 19, 19%). Conclusions Regional and distant stage breast cancer survival has improved markedly for high SES patients, but there has been little survival gain for low SES patients. Socioeconomic status matters for the stage-specific survival of young breast cancer patients, even with universal health care

Alcohol, Physical Activity, Smoking, and Breast Cancer Subtypes in a Large, Nested Case-Control Study from the Norwegian Breast Cancer Screening Program.

Background: To what extent alcohol, smoking, and physical activity are associated with the various subtypes of breast cancer is not clear. We took advantage of a large population-based screening cohort to determine whether these risk factors also increase the risk of the poor prognosis subtypes.Methods: We conducted a matched case-control study nested within the Norwegian Breast Cancer Screening Program during 2006-2014. A total of 4,402 breast cancer cases with risk factor and receptor data were identified. Five controls were matched to each case on year of birth and year of screening. Conditional logistic regression was used to estimate ORs of breast cancer subtypes adjusted for potential confounders.Results: There were 2,761 luminal A-like, 709 luminal B-like HER2-negative, 367 luminal B-like HER2-positive, 204 HER2-positive, and 361 triple-negative cancers. Current alcohol consumption was associated with breast cancer risk overall [OR 1.26; 95% confidence interval (CI), 1.09-1.45] comparing 6+ glasses a week to never drinkers. However, this risk increase was found only for luminal A-like breast cancer. Smoking 20+ cigarettes a day was associated with an OR of 1.41 (95% CI, 1.06-1.89) overall, with significant trends for luminal A-like and luminal B-like HER2-negative cancer. Current physical activity (4+ hours/week compared with none) was associated with 15% decreased risk of luminal A-like cancer, but not clearly with other subtypes.Conclusions: In this large study, alcohol, smoking, and physical activity were predominantly associated with luminal A-like breast cancer.Impact: Alcohol, smoking, and physical activity were associated with luminal A-like breast cancer subtype. Cancer Epidemiol Biomarkers Prev; 26(12); 1736-44. ©2017 AACR