Hospitalier Universitaire de Québec, Quebec, QC, Canada). Fatty acid analysis in erythrocyte mem- branes. The fatty acid composition of phos- pholipids in erythrocyte membranes was also determined at the Centre de Recherche sur les Maladies Lipidiques. Briefly, total lipids were extracted with a chloroform/methanol mix- ture, and the phospholipid fraction was iso- lated by thin-layer chromatography (Shaikh and Downar 1981). Fatty acids were trans- methylated (Lepage and Roy 1988), and the resulting fatty acid methyl esters were analyzed by capillary-column gas chromatography on a Hewlett-Packard 5890 series II gas chromato- graph (Hewlett-Packard, Mississauga, ON, Canada) equipped with a fused silica column (DB-23; 30 m × 0.25 mm inner diameter × 0.25 μ m thickness; Agilent Technologies, Mississauga, ON, Canada) and a flame ion- ization detector. Concentrations of individual fatty acids in membrane phospholipids were expressed as a percentage of total fatty acids (weight basis). The sum of eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) content of erythrocytes was used as a surrogate of marine food con- sumption. This sum has been referred to as the omega-3 index and has been shown to be inversely associated with risk for CHD mor- tality (Harris and Von Schacky 2004). Genotyping. A subsample of 659 partici- pants gave their consent to the genotyping measures. Variants of rs662, rs854560, and rs705379 were originally analyzed using Sequenom technology at McGill University and Génome Québec Innovation Centre (Montréal, QC, Canada). One SNP failed (rs705379), and the call rate was > 99.7% for the remaining two SNPs. The error rate of this technology is < 0.5% according to duplicate and control samples tested at the Génome Québec Innovation Centre. The rs705379 SNP was reanalyzed with the TaqMan tech- nology at the same center with success and a call rate of 99.9%. TaqMan technology has an average error rate of 0.1%. All genotypes were in Hardy–Weinberg equilibrium. Statistical analyses. To meet normality assumption, body mass index (BMI), fat mass, blood mercury and lead levels, plasma HDL-C and LDL-C concentrations, EPA content of erythrocyte membranes, PON1 activity, and food consumption rates were log transformed, whereas an inverse transformation was retained for blood selenium levels. Transformations were selected based on both normality test (Kolmogorov–Smirnov) and bivariate relation- ships between variables. We used the BoxCox macro developed by Michael Friendly to achieve the best transformation (http://www. datavis.ca/sasmac/boxcox.html). Pearson’s cor- relation coefficients were used to investigate crude relationships between PON1 activity CI, confidence interval. aIncludes all cardiovascular diseases except hypertension. Environmental Health Perspectives • volume 119 | number 8 | August 2011 MeHg and plasma paraoxonase activity in Inuit (kilounits per liter) and blood concentrations of mercury (nanomoles per liter), selenium (micromoles per liter) and lead (micromoles per liter), age (years), BMI (kilograms per square meter), fat mass (kilograms), HDL-C (millimoles per liter), LDL-C (millimoles per liter), marine food consumption (grams per day), and n-3 PUFAs (percent). Student’s t-tests or analyses of variance were used to test differences in mean PON1 activity according to sex, alcohol consumption (daily, weekly, monthly, never, or yearly), daily cigarette con- sumption (0, 1–10, 11–24, ≥ 25), genotypic variables (rs705379, rs662, rs854560), sta- tin intake, cardiovascular diseases, diabetes, and dyslipidemia (for disease definitions, see Château-Degat et al. 2010). We subsequently conducted multiple linear regression analyses to examine the relation between blood mercury level and plasma PON1 activity, while control- ling for several potential confounding factors. Interactions terms (mercury × PON1 variants, mercury × selenium, mercury × n-3 PUFAs) were included in initial models. Generalized estimating equations techniques were used to take into account the clustering structure of the data (household was the sampling unit). Continuous explanatory variables were also centered and scaled for multicollinearity and interpretive purposes. Furthermore, because of a considerable number of missing values for gene variants (27% of participants), multiple linear regression analysis was rerun after using multiple imputation methods on genotypic variables. The Kolmogorov–Smirnov test of normality was conducted on regression residu- als. Contrasts were used to test a linear combi- nation of mercury quartiles coefficients (trend test). Because of the complex design sampling and to achieve precision in the parameter estimates, statistical tests were weighted and bootstrapped (balanced repeated replication). All analyses were performed using SUDAAN software (version 10.0.1; Research Triangle Institute, Research Triangle Park, NC, USA) and SAS software (version 9.2; SAS Institute Inc., Cary, NC, USA). We selected an α-level of 0.05 for hypothesis testing. Results Table 1 lists selected characteristics of our study group, which is composed of 896 Inuit Table 1. Selected characteristics of Inuit adults participating to the Nunavik Health Survey, 2004. Characteristic Age (years) Weight (kg) Height (cm) BMI (kg/m2) Fat mass (kg) EPA (%) DHA (%) HDL-C (mmol/L) LDL-C (mmol/L) Blood lead (μmol/L) Consumption (g/day) Marine mammal fat Marine mammal meat Wild fish No. of cigarettes/day (%) 0 1–10 11–24 ≥ 25 Frequency of alcohol consumption (%) Daily Weekly Monthly Never or yearly Statin intake (%) Yes No Cardiovascular diseasea (%) Yes No Diabetes (%) Yes No Dyslipidemia (%) Yes No n 405 380 390 380 379 405 405 402 402 405 346 347 347 100 83 113 58 26 83 82 161 34 371 70 335 12 393 35 370 Men Mean (95% CI) 36.5 (35.9–37.0) 74.4 (72.8–76.0) 166.1 (165.5–166.7) 26.9 (26.4–27.4) 16.7 (15.7–17.6) 1.5 (1.4–1.6) 4.9 (4.8–5.1) 1.5 (1.4–1.6) 2.8 (2.7–2.9) 0.27 (0.25–0.29) 4.4 (3.4–5.4) 25.9 (21.2–30.5) 59.8 (52.2–67.4) 27.2 (23.4–31.4) 24.5 (20.4–29.0) 32.4 (27.8–37.3) 15.9 (12.7–19.8) 7.5 (4.4–10.2) 23.1 (19.3–27.3) 24.0 (19.8–28.7) 45.5 (40.6–50.4) 7.8 (5.9–10.2) 92.2 (89.8–94.1) 16.6 (13.7–19.9) 83.4 (80.1–86.3) 3.0 (1.8–4.9) 97.0 (95.2–98.2) 8.0 (6.1–10.4) 92.0 (89.6–93.9) n 491 438 447 438 438 491 491 490 490 491 401 404 404 96 174 122 42 34 74 95 227 36 455 94 397 20 455 36 455 Women Mean (95% CI) 36.7 (36.2–37.2) 65.8 (64.5–67.1) 153.9 (153.6–154.4) 27.7 (27.2–28.2) 22.3 (21.3–23.2) 1.7 (1.6–1.8) 5.7 (5.6–5.8) 1.8 (1.7–1.9) 2.7 (2.6–2.8) 0.21 (0.20–0.23) 4.9 (3.7–6.1) 18.5 (15.4–21.6) 51.1 (44.5–57.7) 21.6 (18.5–25.2) 40.6 (36.2–45.1) 27.8 (24.0–32.0) 10.0 (7.8–12.8) 8.3 (6.0–11.3) 18.7 (15.5–22.3) 21.9 (18.6–25.6) 51.2 (47.2–55.2) 7.1 (5.3–9.4) 92.9 (90.6–94.7) 19.7 (16.7–23.0) 80.3 (77.0–83.3) 3.88 (2.6–5.7) 96.1 (94.3–97.4) 7.1 (5.3–9.4) 92.9 (90.7–94.7) 1079