Vesper et al. reported in other study populations (Bjellaas et al. 2007; Chevolleau et al. 2007; Hartmann et al. 2008; Heudorf et al. 2008; Kütting et al. 2008; Olesen et al. 2008; Vesper et al. 2007, 2008; Wilson et al. 2008; Wirfält et al. 2008). The mean and median levels reported in these study populations for nonsmokers differ, rang­ ing between 19 pmol/g and 47 pmol/g for HbAA and between 17 pmol/g and 49 pmol/g for HbGA. These differences in mean and median levels across different studies may be explained by differences in the demographics of the populations, differences in analytical methods used in these studies, and different acrylamide exposures in the different study populations. One recent study, investigating acrylamide exposure in groups of adults 41–60 years of age from nine European countries and using the same analytical method, found median levels among nonsmoking groups ranging between 35 pmol/g and 65 pmol/g for HbAA and 27 pmol/g and 58 pmol/g for HbGA, with the Dutch and British groups having the highest median levels (Vesper et al. 2008). The median HbAA and HbGA levels of the comparable age group in this study are similar to those found in the Dutch group (HbAA: 48.6 pmol/g hemoglobin; HbGA: 49.6 pmol/g hemoglobin). Several investigations have attempted to compute a daily intake of acrylamide from measured levels of hemoglobin adducts (Fennell et al. 2005). In general, these are roughly comparable with intakes based on dietary estimates (Dybing et al. 2005; FDA 2006; Mucci and Wilson 2008; Svensson et al. 2003; World Health Organization 2002). Using the computation method of Fennell et al. (2005), our adduct data predict a non­ smoking overall population mean intake of 0.8 µg/kg body weight/day, which is broadly similar to some of these other estimates. Our findings indicate that secondhand smoke can increase HbAA and HbGA levels. Therefore, exposure from food may not be the only rel­ evant exposure source in nonsmokers. Some limitations are inherent in these types of cal­ culations, including using high­dose acute kinetic studies to compute low­dose chronic exposure estimates and difficulty in accurately accounting for additional (nonadduct) and variable pathways of acrylamide metabolism and elimination. Although such estimates pro­ vide an approximate magnitude of acrylamide exposure, further studies are needed to assess the applicability of this model for chronic exposures and for estimating subtle differences between subpopulations or exposure sources. Because acrylamide mutagenicity depends on its conversion to glycidamide, it is of inter­ est to determine HbGA levels and to assess the relationship between HbAA and HbGA. Therefore, we applied a multiple regression analysis to predict HbGA levels based on 282 HbAA levels, cotinine, sex, and BSA. A 10% increase in HbAA levels was associated with an HbGA increase ranging between 7.55% and 9.65% for different population subgroups. This increase was similar for smokers and nonsmokers. Because of the high intersub­ ject variability in acrylamide metabolism, this relationship is applicable only for population groups and cannot be applied to individuals. The reasons for negative associations between BSA and HbAA levels in nonsmok­ ers are not fully understood. Reported asso­ ciations between body mass index and HbAA have been inconsistent (Vesper et al. 2008; Wilson et al. 2008). On the basis of obser­ vations in animal studies, Osterman­Golkar et al. (2003) suggested that increased body mass leads to an increase in blood volume and the amount of hemoglobin available for adduct formation, and thus to a dilution of the adducts at similar intake doses. This dilution effect also affects serum cotinine lev­ els, complicating further assessments of the impact of smoking on HbAA levels, as shown in our model for smokers, where increased BSA levels tend to attenuate the effect of serum cotinine on HbAA. The study population is representative of U.S. noninstitutionalized individuals, and the study results may not be applicable to institu­ tionalized individuals or to other countries. A large number of statistical comparisons have been made in the analysis of the data, and some findings may be the result of random chance. The point estimates of biomarker exposure are estimates of the various subpopulation exposures to acrylamide and not estimates of individual­ ized exposures or specific exposure sources. 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