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

Organic pollutants in sea-surface microlayer and aerosol in thecoastal environment of Leghorn—(Tyrrhenian Sea)

The levels of dissolved and particle-associated n-alkanes, alkylbenzenes, phthalates, PAHs, anionic surfactants and surfactant fluorescent organic matter ŽSFOM. were measured in sea-surface microlayer ŽSML. and sub-surface water ŽSSL. samples collected in the Leghorn marine environment in September and October 1999. Nine stations, located in the Leghorn harbour and at increasing distances from the Port, were sampled three times on the same day. At all the stations, SML concentrations of the selected organic compounds were significantly higher than SSL values and the enrichment factors ŽEFsSML concentrationrSSL concentration. were greater in the particulate phase than in the dissolved phase. SML concentrations varied greatly among the sampling sites, the highest levels Žn-alkanes 3674 mgrl, phthalates 177 mgrl, total PAHs 226 mgrl. being found in the particulate phase in the Leghorn harbour. To improve the knowledge on pollutant exchanges between sea-surface waters and atmosphere, the validity of spray drop adsorption model ŽSDAM. was verified for SFOM, surface-active agents, such as phthalates, and compounds which can interact with SFOM, such as n-alkanes and PAHs. q2001 Elsevier Science B.V. All rights reserved

SUBSTANTIA-NIGRA REGULATES ACTION of ANTIEPILEPTIC DRUGS

ESCOLA PAULISTA MED SCH,DEPT NEUROL & NEUROSURG,EXPTL NEUROL LAB,BR-04023 São Paulo,SP,BRAZILESCOLA PAULISTA MED SCH,DEPT NEUROL & NEUROSURG,EXPTL NEUROL LAB,BR-04023 São Paulo,SP,BRAZILWeb of Scienc

Cigarette smoking and risk of breast cancer in a New Zealand multi-ethnic case-control study.

BACKGROUND: The association between breast cancer and tobacco smoke is currently unclear. The aim of this study was to assess the effect of smoking behaviours on the risk of breast cancer among three ethnic groups of New Zealand women. METHODS: A population-based case-control study was conducted including breast cancer cases registered on the New Zealand Cancer Registry between 2005 and 2007. Controls were matched by ethnicity and 5-year age-group. Logistic regression was used to estimate the association between breast cancer and smoking at different time points across the lifecourse, for each ethnic group. Estimated odds ratios (OR) were adjusted for established risk factors. RESULTS: The study comprised 1,799 cases (302 Māori, 70 Pacific, 1,427 non-Māori/non-Pacific) and 2,540 controls (746 Māori, 191 Pacific, 1,603 non-Māori/non-Pacific). There was no clear association between smoking and breast cancer for non-Māori/non-Pacific women, although non-Māori/non-Pacific ex-smokers had statistically significant increased risk of breast cancer when smoking duration was 20 years or more, and this remained significant in the fully adjusted model (OR 1.31, 95% CI 1.03 to 1.66). Māori showed more consistent increased risk of breast cancer with increasing duration among current smokers (<20 years OR 1.61, 95% CI 0.55 to 4.74; 20+ years OR 2.03, 95% CI 1.29 to 3.22). There was a clear pattern of shorter duration since smoking cessation being associated with increased likelihood of breast cancer, and this was apparent for all ethnic groups. CONCLUSION: There was no clear pattern for cigarette smoking and breast cancer incidence in non-Māori/non-Pacific women, but increased risks were observed for Māori and Pacific women. These findings suggest that lowering the prevalence of smoking, especially among Māori and Pacific women, could be important for reducing breast cancer incidence

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008