, 1997 and Diver et al., 2003). Multivariable analyses were done to estimate the effect of each exposure variable independently from potential confounders

if at least 10 observations per included dummy variable were available. All general determinants and exposure variables presented in Table 2 and the summary variable ‘any occupational exposure’ were treated as potential confounders. The dietary exposure variables presented in Table 4 were not, in order to prevent overcorrection. First, each of the potential confounders was added to the model separately. Subsequently, confounders that changed the crude beta with at least 10% were added to the model selleck screening library simultaneously. The 10% rule was not applied when the crude effect estimates of exposure variables were very weak (betas between − 1.0 and 1.0 pg/ml EEQ, − 1.0 and 1.0 × 10− 1 ng/ml AEQs, and − 5.0 and 5.0 pg/g lipid TEQ were considered weak effect estimates with regard to the need to adjust for potential confounders); in these cases, we only adjusted for confounders if this resulted

in substantially stronger effect estimates. A similar data analyses strategy was used to assess associations between specific variables and internal dioxin levels measured by the DR CALUX®. One hundred and eight men (80%) participated and provided plasma samples and interview data. The time of blood draw varied between 8:00 am and 8:30 pm. The mean, minimum, and maximum EEQs, AEQs, and TEQs measured in the total population are shown in Table 1. Plasma total lipid levels of the subset of men who were selected for the DR CALUX® measurements varied between 4.1 Selumetinib research buy and 8.5 g/l. Effect estimates for plasma EEQ and AEQ are displayed in Table 2, Table 3 and Table 4. The regression coefficients (beta) with 95% confidence intervals (95%CI) reflect the mean differences in EEQs and AEQs between the variable categories.

The corresponding intercepts varied between 12.8 and 16.2 pg/ml EEQ and 9.9 and 12.6 × 10− 1 ng/ml AEQ and were somewhat higher MTMR9 than the population means presented in Table 1 due to adjustment for time of the blood draw. As shown in Table 2, the four men of non-European origin (Turkish (n = 1), Asian (n = 2), and Latin-American (n = 1)), had 3.1 (95%CI 0.1–6.2) × 10− 1 ng/ml higher plasma AEQs compared to European Caucasian men, indicating an approximately 30% higher total plasma androgenic activity. In addition, men over 44 years of age seemed to have somewhat higher plasma AEQs compared to men younger than 40: beta 1.3 (95%CI − 0.2–2.7) × 10− 1 ng/ml. Smoking 10 or more cigarettes per day and drinking a minimum of 20 glasses of alcohol per week were associated with increases in plasma AEQs as well: beta 1.9 (95%CI 0.1–3.6) × 10− 1 ng/ml and beta 1.4 (95%CI 0.2–3.1) × 10− 1 ng/ml, respectively. Men who used prescriptive drugs were found to have 1.