who had DMA% of 64.2% (difference 7.2%; p < 0.001). When we repeated the analysis with urine creatinine as a continuous variable, the p-value was the same (< 0.001). The results of the multivariate analysis are presented in Table 4, using step-up methods to explore the findings. Covariates included in all models were age, sex, housing, educa- tion, skin lesion status, total urinary arsenic, and BMI. The first model for InAs (InAs1) included creatinine and lycopene. In the uni- variate analysis (Table 3), high creatinine was associated with a lower InAs%, with a differ- ence of 7.8%. With the covariates and lyco- pene in the model, the size of this difference is reduced from 7.8% to 4.6%, and lycopene becomes the more important variable both in terms of the difference in magnitude (4.9%) and p-values (0.03 vs. 0.08, Table 4). The association with creatinine was further reduced when riboflavin was added to the model. T a b l e 4 a l s o p r e s e n t s f i v e m o d e l s (MMA1–MMA5) for MMA. In the univari- ate analysis, there were eight factors that had strong relationships with MMA% (p ≤ 0.02). In the multivariate models, animal fat remains important in all models, with high animal fat intake related to a high MMA%. To a lesser extent, high plasma retinol was also associated with high MMA%, whereas high serum sele- nium was associated with a lower MMA%. Two DMA models are given in Table 4; the strongest relationship was with urinary creati- nine, with a high urine creatinine being asso- ciated with a high DMA%. Discussion Diet and arsenic methylation, and blood micronutrients and arsenic methylation, have previously been assessed separately (Hall et al. 2009; Heck et al. 2007, 2009; Huang et al. 2009; Steinmaus et al. 2005). We believe this is the first comprehensive study of arse- nic methylation including both dietary and micronutrient variables, along with urinary creatinine, considered together in the same analysis. New findings to emerge from this work include the fact that urinary creatinine was the strongest predictor of DMA%, with a high urinary creatinine being associated with more InAs being fully methylated to DMA. Once urinary creatinine was taken into account, no nutritional factor was a significant predictor of DMA%. A high urinary creatinine was associated in the opposite direction with InAs%, which had a lower percentage in urine when creatinine was high. This association for InAs% decreased when lycopene and ribo- flavin were included in the model, with lyco- pene becoming the strongest predictor variable in that a high lycopene concentration was associated with a low InAs%. One previous study suggested that low plasma lycopene was associated with reduced arsenic methylation Metabolite InAs% MMA% DMA% Model InAs% models InAs1 MMA% models MMA1 InAs2 InAs3 MMA2 MMA3 MMA4 MMA5 DMA% models DMA1 DMA2 Covariates in all models were age (continuous variable), sex, housing, education, skin lesion status, total urinary arsenic (as a continuous variable), and BMI (as a continuous variable). Environmental Health Perspectives • volume 119 | number 9 | September 2011 Diet, micronutrients, and arsenic methylation Table 3. Association between dietary variables, serum levels of micronutrients, and urinary creatinine as assessed by comparing the mean arsenic metabolite percent between the lowest (T1) and highest (T3) tertiles of each factor. Difference p-Valuea The table is limited to variables with p < 0.20. ap-Value from the t-test of the mean difference. Table 4. Results of multivariate analysis of nutritional factors measured in diet (d), plasma (p), serum (s), and urine (u) with indicators of arsenic methylation InAs%, MMA%, and DMA%. The beta coefficients are for the highest tertile of each nutritional factor with the lowest tertile as the reference category. Nutritional factor Urine creatinine Plasma lycopene Dietary riboflavin Serum selenium Serum folate Plasma vitamin B6 Dietary calcium Plasma lutein-zeaxanthine Dietary animal fat Plasma retinol Plasma homocysteine Dietary retinol Dietary animal protein Serum folate Urine creatinine Serum selenium Plasma alpha-tocopherol Plasma beta-cryptoxanthine Plasma vitamin B6 Plasma methionine Dietary phosphorus Dietary carbohydrate Urine creatinine Dietary riboflavin Plasma lycopene Plasma lutein-zeaxanthine T1 27.7 26.3 20.9 19.6 20.4 23.5 23.0 23.9 7.0 7.3 7.3 7.3 7.0 9.6 7.2 9.2 8.0 7.3 7.7 7.8 7.3 8.6 64.2 70.5 65.4 65.9 Nutritional factor Creatinine (u) Lycopene (p) Creatinine (u) Lycopene (p) Riboflavin (d) Creatinine (u) Lycopene (p) Riboflavin (d) Selenium (s) Animal fat (d) Retinol (p) Animal fat (d) Retinol (p) Homocysteine (p) Animal fat (d) Retinol (p) Homocysteine (p) Folate (s) Animal fat (d) Retinol (p) Selenium (s) Animal fat (d) Retinol (p) Folate (s) Selenium (s) Creatinine (u) Riboflavin (d) Creatinine (u) Riboflavin (d) Lycopene (p) Beta coefficient –4.6 –4.9 –4.1 –6.2 5.9 –3.4 –5.3 4.6 2.5 2.0 1.8 1.9 1.7 1.9 1.9 1.6 1.1 –2.1 2.3 2.1 –2.2 2.5 2.0 –1.1 –2.3 5.4 –3.3 5.7 –3.8 3.9 T3 19.8 20.5 25.2 23.7 24.3 20.4 25.9 21.0 9.3 9.2 9.2 9.1 8.7 8.0 8.7 7.7 9.1 8.4 8.7 8.8 8.1 7.8 71.4 65.9 69.6 69.9 Lower CI limit –11.0 –9.4 –9.0 –11.2 0.9 –8.0 –10.2 –0.4 –2.6 0.8 0.5 0.6 0.4 0.6 0.5 0.2 –0.4 –3.6 0.9 0.7 –3.6 1.1 0.6 –2.6 –3.8 0.3 –8.0 0.2 –8.9 –1.2 –7.8 –5.9 4.3 4.1 3.9 –3.1 2.9 –2.9 2.3 1.9 1.9 1.8 1.7 –1.7 1.5 –1.5 1.1 1.1 1.0 0.9 0.9 –0.8 7.2 –4.6 4.3 4.0 Upper CI limit 1.8 –0.4 0.8 –1.2 10.9 1.2 –0.4 9.6 7.6 3.3 3.1 3.2 3.1 3.3 3.3 3.0 2.6 –0.6 3.7 3.5 –0.8 3.9 3.4 0.4 –0.9 10.5 1.4 11.2 1.3 9.0 0.001 0.01 0.04 0.03 0.04 0.14 0.19 0.14 0.001 0.001 0.002 0.004 0.003 0.007 0.004 0.02 0.07 0.07 0.11 0.11 0.15 0.15 0.001 0.03 0.07 0.06 p-Value 0.08 0.03 0.14 0.02 0.02 0.20 0.04 0.07 0.35 0.003 0.005 0.01 0.02 0.01 0.003 0.03 0.14 0.03 0.001 0.002 0.001 0.001 0.006 0.15 0.002 0.04 0.17 0.04 0.15 0.13 1311