Correspondence potentially sparing SAM for methylation of other substrates such as As. We are currently testing this hypothesis in a randomized controlled trial of creatine supplementation. In addition, as Basu et al. (2011) noted, and as we have previously reported (Gamble and Liu 2005), one implication of the observed association between uCr and As methylation capacity is that urinary As should not be expressed per gram creatinine to correct for urine concentration. Rather, uCr should be included as a covariate in regression models. One concerning aspect of the study by Basu et al. (2011) is the handling of blood samples used for nutrient measurements. As noted by Basu et al. and in a previous pub- lication on these same participants (Chung et al. 2006), the blood samples were stored in an ice chest in the field for up to 24 hr before processing. This 24-hr delay can be problematic for some nutrients, especially folate, which is extremely sensitive to oxida- tive degradation (Drammeh et al. 2008). Basu et al. (2011) reported that in univariate analy- ses, they observed higher urinary percentages of InAs in individuals with higher serum folate concentrations. This finding is contrary to our previous findings that folate facilitates As methylation (Gamble et al. 2005, 2006, 2007; Hall et al. 2007, 2009). This discrep- ancy might be explained by differences in sample processing. Basu et al. (2011) also reported associa- tions between dietary intake of several nutri- ents (assessed using a modified 24-hr recall) and As methylation capacity. One of the most critical and widely discussed issues in nutri- tional epidemiology is the method used to adjust for total energy intake (TEI) (Willett et al. 1997). The main reasons to adjust for TEI are to a) adjust for potential confound- ing by TEI, b) remove extraneous variation in nutrient intakes that is due only to their correlation with TEI, and c) simulate a dietary intervention. What is often most relevant is diet composition, or nutrient intake in rela- tion to TEI (Willett et al. 1997). Several methods are available to adjust for TEI, and the best approach can vary depending on the nutrient and question of interest. Basu et al. (2011) adjusted for TEI by dividing each nutrient intake by TEI (nutrient density method). While this approach is appealing because of its simplicity, in reality it can cre- ate a complex variable (Willett and Stampfer 1998). For example, when TEI is related to the outcome of interest, the use of nutri- ent densities can actually induce confound- ing in the opposite direction. Although we cannot determine from Basu et al.’s article whether TEI measured by the 24-hr recall was associated with As methylation, in theory, an association seems plausible. Also, because their statistical analysis tested for associations A 146 between multiple nutrients and urinary As metabolites, it is best to acknowledge that some of the statistically significant associations might be due to chance alone. The authors declare that they have no actual or potential competing financial interests. Department of Environmental Mary V. Gamble Health Sciences Mailman School of Public Health Columbia University New York, New York E-mail: Mvg7@columbia.edu Megan N. Hall Department of Epidemiology Mailman School of Public Health Columbia University New York, New York E-mail: Mh2825@columbia.edu Ref Rencs Ahsan H, Chen Y, Kibriya MG, Slavkovich V, Parvez F, Jasmine F, et al. 2007. Arsenic metabolism, genetic susceptibility, and risk of premalignant skin lesions in Bangladesh. Cancer Epidemiol Biomarkers Prev 16(6):1270–1278. Basu A, Mitra S, Chung J, Guha Mazumder DN, Ghose N, Kalman DA, et al. 2011. Creatinine, diet, micronutrients, and arsenic methylation in West Bengal, India. Environ Health Perspect 119:1308–1313. Brosnan JT, da Silva RP, Brosnan ME. 2011. The metabolic burden of creatine synthesis. Amino Acids 40(5):1325–1331. Chung JS, Haque R, Guha Mazumder DN, Moore LE, Ghosh N, Samanta S, et al. 2006. Blood concentrations of methio- nine, selenium, beta-carotene, and other micronutrients in a case-control study of arsenic-induced skin lesions in West Bengal, India. Environ Res 101(2):230–237. Drammeh BS, Schleicher RL, Pfeiffer CM, Jain RB, Zhang M, Nguyen PH. 2008. Effects of delayed sample processing and freezing on serum concentrations of selected nutri- tional indicators. Clin Chem 54(11):1883–1891. Gamble MV, Liu X. 2005. Urinary creatinine and arsenic metabolism [Letter]. Environ Health Perspect 113:A442. Gamble MV, Liu X, Ahsan H, Pilsner R, Ilievski V, Slavkovich V, et al. 2005. Folate, homocysteine, and arsenic metabolism in arsenic-exposed individuals in Bangladesh. Environ Health Perspect 113:1683–1688. Gamble MV, Liu X, Ahsan H, Pilsner JR, Ilievski V, Slavkovich V, et al. 2006. Folate and arsenic metabolism: a double-blind, placebo-controlled folic acid-supplementation trial in Bangladesh. Am J Clin Nutr 84(5):1093–1101. Gamble MV, Liu X, Slavkovich V, Pilsner JR, Ilievski V, Factor- Litvak P, et al. 2007. Folic acid supplementation lowers blood arsenic. Am J Clin Nutr 86(4):1202–1209. Hall M, Gamble M, Slavkovich V, Liu X, Levy D, Cheng Z, et al. 2007. Determinants of arsenic metabolism: blood arsenic metabolites, plasma folate, cobalamin, and homocysteine concentrations in maternal–newborn pairs. Environ Health Perspect 115:1503–1509. Hall MN, Liu X, Slavkovich V, Ilievski V, Pilsner JR, Alam S, et al. 2009. Folate, cobalamin, cysteine, homocysteine, and arsenic metabolism among children in Bangladesh. Environ Health Perspect 117:825–831. Mudd SH, Poole JR. 1975. Labile methyl balances for nor- mal humans on various dietary regimens. Metabolism 24(6):721–735. Schalinske KL, Steele RD. 1989. Quantitation of carbon flow through the hepatic folate-dependent one-carbon pool in rats. Arch Biochem Biophys 271(1):49–55. Willett WC, Howe GR, Kushi LH. 1997. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 65(4 suppl):1220S–1228S. Willett W, Stampfer M. 1998. Implications of total energy intake for epidemiologic analysis. In: Nutritional Epidemiology (Willett W, ed). 2nd ed. Oxford University Press:New York, 273–301. Relationship of Creatinine and Nutrition with Arsenic Metabolism: Smith et al. Respond http://dx.doi.org/10.1289/ehp.1104807R We thank Hall and Gamble for their interest in our findings. In our article (Basu et al. 2011), we cited their earlier results concerning urinary dimethylarsinic acid (DMA) and creatinine concentrations, noting that in 2005 they reported a strong correlation between urinary creatinine and the percentage of DMA (DMA%) (Gamble et al. 2005). We also noted rather similar findings from another group working in Bangladesh (Nermell et al. 2008), so together with our findings in India, there are three separate studies that have reported this association (Basu et al. 2011; Gamble et al 2005; Nermell et al 2008). Our results highlight the strength of the relationship between urinary creatinine and urinary DMA%, which was stronger than the relationship between DMA% and any of the 19 dietary factors and 16 blood micro- nutrients that we investigated. Gamble and Hall state that they had pre- viously reported (Gamble and Liu 2005) that one implication of the observed asso- ciation between urinary creatinine and arse- nic methylation was that urinary As should not be expressed as per gram creatinine to correct for urine concentrations. This is not quite what they said, although they did point out risks in expressing urinary arsenic per gram of creatinine. However, in that letter (Gamble and Liu 2005) and again in this one, they state that urinary creatinine should be included as a covariate in regres- sion models. We do not agree with this recommendation. Steinmaus et al. (2009) reported a spurious relationship between low-level arsenic concentrations and diabetes in the United States after including urinary creatinine in a regression model. They noted that urinary creatinine concentrations may change as a consequence of diabetes, which is known to affect renal function, and that it is not appropriate to adjust for a factor that is a consequence of the disease being studied. We also do not agree with Gamble and Hall’s criticism of our blood sample handling, which they appear to have raised because our folate findings were different from theirs (e.g., Gamble et al. 2005). For our study (Basu et al. 2011), blood samples were collected in remote villages, and on occasion they had to be stored on ice for up to 24 hr. Gamble and Hall state that “this 24-hr delay can be prob- lematic for some nutrients, especially folate, which is extremely sensitive to oxidative degradation.” In support of this statement they cite Drammeh et al. (2008), who found reductions in serum folate concentrations ovlume 120 | number 4 | April 2012 • Environmental Health Perspectives