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Epidemiologic evidence linking environmental exposure to polycyclic aromatic hydrocarbons (PAH) with breast cancer is limited. Measurement of DNA adducts formed by aromatic compounds, including PAH, has been carried in breast tissue samples and white blood cells from women with breast cancer and different kinds of controls. However, these studies provide inconsistent results and bias cannot be ruled out. During the 7-year follow-up period, 305 women were diagnosed with first primary breast cancer in the EPICSpain cohort and were compared with a sample of 149 women without breast cancer at recruitment, using a case-cohort approach. Aromatic adducts to DNA from leukocytes collected at recruitment were measured by means of the 32P-post-labelling technique. The relative risk and 95% confidence interval (CI), adjusted by relevant confounders, were estimated by a modified version of Cox proportional hazards model. There was a significant increased risk for developing breast cancer when DNA adduct concentrations were doubled, with adjusted RR of 1.61 (95% CI 1.29-2.01). The increase in breast cancer risk was observed both for pre- and postmenopausal women. There was a significant interaction with tobacco smoking and body mass index, with higher effect of DNA adducts on breast cancer risk among smokers and women with normal weight. The results from our study support the hypothesis that factors leading to higher levels of aromatic DNA adducts in white blood cells may be involved in development of breast cancer. [ABSTRACT FROM AUTHOR]

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. [ABSTRACT FROM AUTHOR]