Ayotte et al. adults between 18 and 74 years of age; the mean age of the group was 36.6 years. We noted a BMI value exceeding 30 kg/m2 in 26.0% of men and 31.1% of women. More than two-thirds of the participants were cur- rent smokers, and their average consumption was 15.7 cigarettes/day for men and 12.5 ciga- rettes/day for women. The vast majority of men (92.8%) and women (84.3%) declared drinking alcohol. More than 90% of men and women declared consuming marine mam- mal meat and wild fish, whereas about 70% reported eating marine mammal fat. Daily wild fish consumption was more than double that of marine mammal meat and exceeded by an order of magnitude the consumption of marine mammal fat. According to the medi- cal files, about 8% of men and 7% of women had dyslipidemia and were taking hypo- lipemic drugs (statins) at the time of the study. Cardiovascular diseases were affecting 17% of men and 20% of women, whereas only 3% of men and 4% of women had diabetes. Figure 1A presents the frequency distri- bution of plasma PON1 activity. The geo- metric mean was 10.7 kU/L, with values ranging from 4.3 to 24.6 kU/L. Frequency distributions of blood mercury concentra- tions (Figure 1B) and blood selenium concen- trations (Figure 1C) were markedly skewed to the right. Geometric mean values were 53.2 nmol/L (range, 0.4–720) and 3.8 mμol/L (range, 1.5–23.0) for blood mercury and blood selenium concentrations, respectively. We first tested crude correlations between PON1 activity and mercury and selenium concentrations. PON1 activity was positively correlated to both blood selenium (Pearson’s r = 0.14; p < 0.001) and mercury (r = 0.08; p = 0.024) concentrations. In addition, PON1 activity was positively correlated with the sum of EPA and DHA content of erythrocyte membranes (r = 0.17; p < 0.001), HDL-C (r = 0.29; p < 0.001), LDL-C (r = 0.12; p < 0.001), and marine mammal fat consump- tion (r = 0.10; p = 0.008). In bivariate analy- ses (Table 2), women displayed higher PON1 activity than did men (p < 0.001). The three GM = 10.7 95% CI = 10.5–10.9 n = 896 120 100 y 80 ue60 q F 40 20 0 1080 SNPs of known functionality in the PON1 gene were all significantly associated with PON1 activity (p < 0.001). Alcohol consump- tion was positively associated with PON1 activity (p = 0.046). Participants with diabetes had a lower mean PON1 activity than did those without the disease (p = 0.017). We used multivariate models to examine the relation between PON1 activity and blood concentrations of mercury and selenium, while taking into account several potential confounders, including PON1 gene variants previously shown to influence PON1 activ- ity or expression (Table 3). In a model that explained 31.4% of the variance in PON1 activity (n = 651), blood selenium concen- tration remained positively associated with PON1 activity (p < 0.001), whereas, in con- trast with the bivariate analysis, blood mer- cury concentration was inversely associated with the activity of the enzyme (p < 0.001). Also in contrast with the results of the bivari- ate analysis, age was inversely associated with PON1 activity in the multivariate model (p < 0.001). Sex was not associated with PON1 activity, and we therefore excluded it from the final model. We obtained a simi- lar model for the entire population sample (n = 896) after multiple imputation of geno- types (data not shown). Further adjustment of the multivariate model for blood lead level, fat mass, or tradi- tional food intakes did not modify the relation between blood mercury level and PON1 activ- ity [see Supplemental Material, models 1–3 (doi:10.1289/ehp.1003296)]. We tested the interaction terms between mercury and sele- nium, mercury and n-3 PUFAs, and mercury and PON1 gene variants [see Supplemental M a t e r i a l , m o d e l s 4 – 6 ( d o i : 1 0 . 1 2 8 9 / ehp.1003296)]. None of these interaction terms were statistically significant (p > 0.1). Figure 2 presents the relation between blood mercury concentrations (expressed in quartiles) and adjusted geometric mean PON1 activity. Mean activity values decreased in a concentration-dependent fashion with increasing blood mercury concentrations (p for trend < 0.001). The geometric mean activity corresponding to the highest blood mercury quartile (> 110 nmol/L; 9.8 kU/L) was 13% lower (p < 0.001) than that of the lowest blood mercury quartile (≤ 30 nmol/L; 11.3 kU/L). Discussion In this cross-sectional study, we examined the relationship between blood mercury levels and plasma PON1 activity in a representative sample of the Inuit population of Nunavik, which has been shown to be highly exposed to MeHg mainly through their consump- tion of marine mammal meat, kidney, and liver (Fontaine et al. 2008). After control- ling for several potential confounding fac- tors, we found that increasing blood mercury concentrations were inversely associated with the activity of this enzyme, which protects HDL and LDL from oxidation. Previous evidence for such a relationship had been obtained from in vitro studies in which liver microsomes were exposed to various concen- trations of the organomercurial compound (Gonzalvo et al. 1997). To our knowledge, this is the first study to report an apparent inhibitory effect of MeHg exposure on PON1 activity in a human population. Although blood mercury levels showed a weak positive correlation with PON1 activi- ties in bivariate analyses, a multivariate analy- sis that included several covariates revealed a negative association of blood mercury levels with PON1 activities. This can be explained by the intercorrelations of age, blood sele- nium concentrations, plasma HDL levels, and the proportion of EPA plus DHA in red blood cell membranes (data not shown), in addition to these variables being linked to blood mercury levels and PON1 activities. Despite these multiple intercorrelations, we avoided collinearity problems in multivariate models by centering and scaling continuous explanatory variables. We noted that in addition to blood mer- cury concentrations, age was also inversely related to PON1 activity, as previously GM = 53.2 95% CI = 49.6–57.0 n = 896 GM = 3.8 95% CI = 3.7–3.9 n = 896 300 y 200 F 100 0 0 e e u u q q 300 y 200 F 100 0 c c c n n n e e e r r r ovlume 119 | number 8 | August 2011 • Environmental Health Perspectives 6 10 16 20 26 PON1 activity (kU/L) 200 400 600 Blood mercury (nmol/L) 5 10 15 20 25 Blood selenium (µmol/L) Figure 1. Frequency distributions of PON1 activity (A), blood mercury concentrations (B), and blood selenium concentrations (C) in Inuit adults from Nunavik, 2004. Abbreviations: CI, confidence interval; GM, geometric mean.