van Leeuwen et al. status was unknown; therefore, these were excluded from the analyses. Across regions, participant groups did not differ signiﬁcantly with respect to age, sex, and smoking status. Statistical analysis of differences in gene expres- sion between current, former, and never- smokers revealed CYP1B1 expression levels to be signiﬁcantly different between current and former smokers (p = 0.029) and between cur- rent and never-smokers (p < 0.001). Because of the apparent confounding effect of smoking, we further investigated gene expression in non- smokers (i.e., never and former smokers only), changing the size of the total population to 319 individuals. Per region, at least 29 individ- uals remained in the analyses; therefore, we consider the populations still of adequate size in terms of power. A map of Flanders with bar charts of the average gene expressions among habitants per region is shown in Figure 1. Compared with the total population average, subjects with the most distinct gene expression proﬁles live in Olen (expressions well above the population average) as well as in the fruit culti- vation region and in Gent (both with expres- sions well below the population average). In a one-way ANOVA analysis with a post hoc Bonferroni test, all genes appeared to signiﬁ- cantly differ in expression between inhabitants from two or more regions (p < 0.003), except for DGAT2 (p = 0.06). Based on the individual gene expression as well as the mean/sum of all gene expressions, inhabitants from Olen show the most signiﬁcant differences compared with subjects living in the fruit cultivation region (Fruit) and Gent (p < 0.001). We performed Pearson correlation analyses to investigate asso- ciations between individual gene expression (i.e., gene expression values per study partici- pant regardless of region of inhabitance) and blood and urinary measures of biomarkers of exposure, early biological effect, and tumor markers. These analyses were carried out for the total nonsmoking population (composed of never and former smokers) or separately for female and male participants. Significant correlations between gene expression and blood and urinary measures of biomarkers of exposure are presented in Table 6 (all cur- rently nonsmoking individuals, females only, and males only). Discussion The FLEHS project was initiated by the Flemish government in 2001, with the plan to use the forthcoming study results in envi- ronmental risk assessment and environmental health policy making. Flanders typically com- prises a range of environmental burdens such as urban areas, regions with dense traffic, intensive agriculture, and industry. In the pre- sent cross-sectional study, we investigated the expression of eight key genes in peripheral blood sampled from the adult Flanders popu- lation by means of quantitative PCR. Except for DGAT2, all gene expression levels differed significantly between current nonsmoking (i.e., never-smoking and for- merly smoking) inhabitants from two or more regions. Based on individual gene expression as well as the mean of all gene expressions, the most deviating region is Olen, because its inhabitants returned the most signiﬁcant dif- ferences compared with inhabitants of the other regions, in particular Harbor, Fruit, Gent, and Rural (Figure 1). Many significant correlations of gene expression with endogenous levels of relevant environmental carcinogens were observed among all currently nonsmoking individuals; Cd in blood or urine, PCBs, dioxins and furans, hexachlorobenzene (HCB), p,p´-DDE, t,t-MA, and 1-OH-pyrene (Table 6). The majority of these associations have not yet been described in literature, whereas reported biological functions of these gene expressions have been linked to carcinogenesis. All but one of these correlations were positive. The only negative correlation was found between CYP1B1 expression levels and urinary levels of t,t-MA. Up to now, CYP1B1 gene expression in vivo has not been reported to be inﬂuenced by benzene or its metabolites such as t,t-MA. CYP1B1 gene expression is known to be inducible by dioxin and PAH. This was not demonstrated in this population, possibly because of heterogeneity of the general popu- lation for blood levels of this environmental carcinogen. SOD2 expression correlated signif- icantly with blood or urine measures of three exposure markers: PCBs, p,p´-DDE, and Cd in urine. Acknowledging SOD2 for its func- tion in oxidant scavenging, these correlations indicate elevated oxidative stress as a result from these exposures. Although this associa- tion has been reported extensively for Cd exposure (Bertin and Averbeck 2006), it has not for exposure to PCBs and DDT or DDE, although these compounds are metabolized through oxidative processes. ATF4 expression levels showed a signiﬁcant correlation with uri- nary levels of 1-OH-pyrene, a metabolite well known for representing PAH exposure. Although not previously reported, this might indicate that PAH exposure influences tran- scription. Furthermore, ATF4 expression cor- related with DNA strand breaks, expressed as COMET P90. ATF4 is also known as CREB2, a member of CREB family, which is known as a key regulator in the control of cellular gene expression, regulating cell cycle and growth factor genes of which aberrant expression is observed in certain cancers (Cheng et al. 2007). MAPK14 gene expression returned cor- relations with most of the environmental car- cinogens; PCBs, HCB, p,p´-DDE, and Cd in blood as well as in urine. Upon environmental carcinogenic exposure, reported for PCB-47, MAPK14 functions as a mediator of COX2 gene expression (Bezdecny et al. 2007). In turn, COX2 expression is known to be deregulated in certain tumors. Considering the substrates of Table 5. Measurements [mean (range)] of early biological effect markers and tumor markers by region of residence. Cells with MN (no.) MN ‰a 8-OH-dGb COMET (P90)c COMET (median)c PSAd CEAd p53e 1522 Antwerp 8.48 (1.8–20.0) 9.41 (1.9–21.9) 15.37 (5.1–32.8) 8.90 (5.4–15.5) 1.75 (0.1–5.5) 1.48 (0.3–4.9) 2.65 (0.4–22.2) 41.1 (0.01–748.0) Harbor 5.54 (0.8–15.2) 5.99 (0.8–15.9) 14.93 (5.9–23.0) 12.97 (6.7–21.7) 3.85 (0.4–7.5) 1.47 (0.3–3.9) 1.96 (0.4–4.8) 176.6 (0.01–1378.0) Fruit 7.72 (0.3–14.5) 8.45 (3.0–15.4) 17.21 (7.3–32.4) 10.06 (6.6–13.6) 2.83 (1.2–4.3) 1.12 (0.3–4.8) 2.03 (0.3–5.6) 84.5 (0.01–804.0) Olen 6.70 (1.5–16.4) 7.23 (1.5–18.6) 15.15 (5.7–40.3) 8.82 (5.0–13.5) 1.65 (0.0–4.3) 1.27 (0.2–4.1) 2.45 (0.4–8.7) 71.6 (0.1–1396.0) Gent 7.02 (0.9–18.3) 7.75 (0.9–20.4) 15.76 (7.2–29.6) 8.43 (5.4–11.5) 2.33 (0.5–7.6) 2.06 (0.3–27.1) 2.37 (0.5–15.5) 86.9 (0.01–1531.0) Incinerator 9.41 (0.0–35.9) 10.57 (0.0–38.9) 18.87 (3.7–57.2) 8.06 (3.7–13.7) 1.79 (0.0–3.5) 0.95 (0.2–4.4) 2.66 (0.6–13.8) 71.9 (0.01–1327.0) Rural 8.71 (1.0–22.7) 9.14 (1.0–28.2) 15.02 (3.7–42.2) 8.63 (5.4–12.0) 2.29 (0.4–4.5) 1.20 (0.0–3.8) 2.61 (0.6–14.0) 95.9 (0.01–1620.0) Canal 7.62 (1.8–28.8) 8.84 (2.3–45.9) 15.79 (7.9–24.5) 8.58 (5.9–12.1) 1.95 (0.1–4.4) 1.32 (0.4–4.0) 1.63 (0.7–4.0) 35.9 (0.01–507.0) Total 7.76 (0.0–35.9) 8.63 (0.0–45.9) 15.95 (3.7–57.2) 8.82 (3.7–21.7) 2.14 (0.0–7.6) 1.40 (0.0–27.1) 2.39 (0.3–22.2) 76.6 (0.01–1620.0) Fruit, fruit cultivation region. Individuals for whom smoking status was not known or reported are not included. aNumber of micronuclei per 1,000 binucleated cells. bμg/g creatinine. cCOMET was not measured in all study participants, but it was measured in at least 10 individuals per region; COMET P90 and COMET median in % DNA in the comet tail. dng/mL. epg/mL. VOLUME 116 | NUMBER 11 | November 2008 • Environmental Health Perspectives