Abstract
Hemoglobin adducts of acrylamide (HbAA) and glycidamide (HbGA) have been measured as biomarkers of acrylamide exposure and metabolism in a nationally representative sample of the US population in the NHANES 2003–2004. We assessed the association of sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle variables (smoking, alcohol consumption, BMI, physical activity, and dietary supplement use) with these biomarkers in US adults (≥20 y). We used bivariate and multiple regression models and assessed the magnitude of an estimated change in biomarker level with change in a covariable for 2 biomarkers of acrylamide exposure.
Smoking was strongly and significantly correlated with HbAA and HbGA levels (rs=0.51 and 0.42, respectively), with biomarker concentrations being 126% and 101% higher in smokers compared to nonsmokers after adjusting for sociodemographic and lifestyle covariates. Age was moderately and significantly correlated with both biomarkers (rs=−0.21 and −0.22, respectively). BMI (rs=−0.11) and alcohol consumption (rs=0.13) were weakly yet significantly correlated with HbAA levels only. The estimated percent change in biomarker concentration was ≤20% for all variables other than smoking after adjusting for sociodemographic and lifestyle covariates. Using multiple regression models, the sociodemographic variables explained 9% and 7%, while the sociodemographic and lifestyle variables together explained 46% and 25% of the variability in HbAA and HbGA, respectively, showing the importance of considering and adequately controlling for these variables in future studies. Our findings will be useful in the design and analysis of future studies that assess and evaluate exposure to acrylamide and its metabolism to glycidamide.
Keywords: NHANES, acrylamide, glycidamide, hemoglobin adducts, education, poverty income ratio, smoking, alcohol consumption, BMI, physical activity, supplement use
INTRODUCTION
Acrylamide occurs in a wide range of food products that are commonly consumed by a large portion of the population. It is formed in food during frying or baking from the reaction of reducing sugars such as glucose with the amino acid asparagine via the Maillard reaction (1, 2, 3). Among different food products, potato chips, French fries and some baked goods were found to contain high amounts of acrylamide (4, 5). Dietary intake is considered one major source of acrylamide exposure in the general population. Exposure to acrylamide is of concern, because acrylamide is a suspected human carcinogen and a potentially endocrine disrupting chemical (6, 7, 8, 9). These concerns initiated a wide range of research to assess intake, possible health risks, and to minimize acrylamide content in foods (10, 11, 12, 13).
Acrylamide is metabolized in the liver to its epoxide, glycidamide, which is primarily mediated by cytochrome P450 2E1 (14). Glycidamide forms DNA adducts and thus is considered genotoxic (15, 16, 17). Therefore, knowledge about exposure to acrylamide and its metabolite is important to assess and interpret potential health effects.
Adducts of acrylamide (HbAA)4 and glycidamide (HbGA) with hemoglobin, so called hemoglobin adducts, have been successfully used as biomarkers to assess acrylamide exposure in humans (18, 19, 20). These adducts reflect the time weighted exposure of acrylamide in a person over the past 4 mo.
CDC’s Second National Report on Biochemical Indicators of Diet and Nutrition in the US Population provides a descriptive analysis of the HbAA and HbGA levels of Americans by age, sex, and race-ethnicity for data from the NHANES 2003–2004. These analyses however, cannot explain why there are differences in these exposure biomarker levels among demographic subgroups (21).
Dietary intake and smoking are generally understood to be the 2 important determinants of HbAA and HbGA biomarker concentrations in blood. Several studies have investigated the relationship between dietary and smoking exposure and these biomarkers (20, 22, 24, 25, 26, 27, 28). However, to our knowledge, no studies have systematically examined the association of a panel of sociodemographic and lifestyle variables on HbAA and HbGA levels in the US population. The objective of our analysis was to assess the association of 10 selected sociodemographic and lifestyle variables with these biomarkers.
SUBJECTS AND METHODS
Survey design and participants
The NHANES collects cross-sectional data on the health and nutrition status of the civilian non-institutionalized US population (29). Participants in NHANES 2003–2004, aged ≥20 y (n = 4152) who had a stored blood specimen available for analysis of HbAA and HbGA constituted the study sample. All respondents gave their informed consent, and the NHANES protocol was reviewed and approved by the NCHS Research Ethics Review Board.
Laboratory methods
HbAA and HbGA were measured in EDTA whole blood as described previously (27, 30). The detection limits for HbAA and HbGA adducts were 3 and 4 pmol/g of Hb, respectively. The inter-day imprecision (n = 20 d) of this method, expressed as percent CV, was on average 13% for HbAA and 19% for HbGA, determined with 3 blood pools.
Sociodemographic and lifestyle variables
For bivariate analyses, we categorized the variables as follows: age (20–39 y, 40–59 y, and ≥60 y); race-ethnicity (non-Hispanic white [NHW], non-Hispanic black [NHB], and Mexican American [MA]); education (<high school, high school, and >high school); family poverty income ratio (PIR; 0–1.85 [low], >1.85–3.5 [medium], and >3.5 [high], (31)); smoking status (serum cotinine ≤10 μg/L [nonsmoker], >10 μg/L [smoker], (32)); alcohol consumption (average daily number of “standard” drinks [1 drink ≈ 15 g ethanol ]; no drinks, <1 drink (not 0), 1-<2 drinks, ≥2 drinks); BMI (kg/m2) (<18.5 [underweight], ≥18.5 and <25.0 [normal], ≥25.0 and <30.0 [overweight], ≥30.0 [obese], (33)); physical activity (calculated as total metabolic equivalent task (MET)-min/wk from self-reported leisure time physical activities; no leisure time physical activities, 0–<500, 500–<1000, ≥1000 MET-min/wk); supplement use (reported taking a dietary supplement within the past 30 d; yes [user], no [non-user]).
Statistical analyses
As we used the same statistical methods for the series of papers presented in this supplement, the reader is referred to Sternberg et al. (34) for a detailed description of the methods and for a discussion of compromises taken in developing the multiple regression model due to the limited degrees of freedom, such as the number of covariates considered, the chosen form of continuous covariates, and the consideration of interactions between covariates. Bivariate associations for categorical variables were assessed by calculating the geometric means and 95% CI for each category and Spearman correlations for selected continuous variables. Linear regression analyses were used to assess the confounding effects and to determine whether statistical significance persisted after adjusting for differences in key variables. HbAA and HbGA data were log transformed based on the distribution of the biomarker. The covariates were arranged into 2 chunks: sociodemographic variables (age, sex, race-ethnicity, education level, and PIR) and lifestyle variables (smoking, alcohol consumption, BMI, physical activity, and dietary supplement use) and entered hierarchically. We summarized the results of each model and showed the magnitude of association by presenting the percent change in biomarker concentrations with change in each covariate holding all other remaining covariates constant for each model. All estimates were weighted to account for the unequal probabilities of selection and adjustment for non-response. Two-sided P-values were flagged as statistically significant if P <0.05.
RESULTS
HbAA and HbGA data were available for 4093 and 4152 NHANES participants, respectively. The study population was estimated to consist of 48% men and 71% nonsmokers. The proportion of NHW, NHB and MA was 72%, 11% and 8%, respectively (Table 1).
Table 1.
Descriptive information for the adult US population aged ≥20 y by sociodemographic and lifestyle variables, NHANES 2003–2004
| Variable | Category | Estimate1 | Sample size2 | |
|---|---|---|---|---|
| Acrylamide hemoglobin adduct | Glycidamide hemoglobin adduct | |||
| Age, y | 20–39 | 38.8 | 1406 | 1446 |
| 40–59 | 38.5 | 1164 | 1177 | |
| ≥60 | 22.7 | 1523 | 1529 | |
| Sex | Male | 48.0 | 1974 | 2008 |
| Female | 52.1 | 2119 | 2144 | |
| Race-ethnicity | Mexican American | 7.76 | 862 | 867 |
| Non-Hispanic black | 11.2 | 748 | 787 | |
| Non-Hispanic white | 72.1 | 2182 | 2203 | |
| Education | <High school | 30 | 1213 | 1229 |
| High school | 25 | 1039 | 1047 | |
| >High school | 45 | 1834 | 1870 | |
| PIR3 | Low (0–1.85) | 31.4 | 1664 | 1702 |
| Middle (>1.85–3.5) | 27.6 | 1012 | 1016 | |
| High (>3.5) | 41.1 | 1192 | 1207 | |
| Supplement use4 | No | 70.2 | 2106 | 2183 |
| Yes | 29.8 | 1924 | 1960 | |
| Smoking status5 | No | 71.2 | 2987 | 3013 |
| Yes | 28.9 | 1067 | 1098 | |
| Alcohol consumption6 | No drinks | 30.6 | 1413 | 1596 |
| <1 (not 0) | 56.6 | 1954 | 909 | |
| 1–<2 | 7.6 | 237 | 493 | |
| ≥2 | 5.2 | 184 | 989 | |
| BMI7 | Underweight | 1.73 | 57 | 56 |
| Normal weight | 32.0 | 1217 | 1225 | |
| Overweight | 34.1 | 1431 | 1438 | |
| Obese | 32.2 | 1303 | 1347 | |
| Physical activity8 | None reported | 33.0 | 1588 | 1596 |
| 0–<500 | 25.0 | 894 | 909 | |
| 500–<1000 | 13.6 | 483 | 493 | |
| ≥1000 | 28.4 | 964 | 989 | |
Estimates provided are percent (%)
Values represent unweighted sample sizes
PIR, family poverty income ratio; low: 0–1.85; medium: >1.85–3.5; high: >3.5
“Supplement user” defined as participant who reported taking a dietary supplement within the past 30 d
Alcohol consumption: calculated as average daily number of “standard” drinks [(quantity × frequency)/365.25]; 1 drink ≈ 15 g ethanol
BMI (kg/m2) definitions: underweight: <18.5; normal weight: 18.5–<25; overweight: 25–<30; and obese: ≥30
Physical activity: calculated as total metabolic equivalent task (MET)-min/wk from self-reported leisure time physical activities
Spearman correlation coefficients showed that smoking was strongly and significantly correlated with HbAA (rs = 0.51) and HbGA (rs = 0.42), while other variables showed no or only weak correlations (|rs| ≤0.2, P <0.05) (Table 2). Using bivariate methods (model 1), all sociodemographic variables were significantly but weakly associated with HbAA and all but sex and PIR were significantly but weakly associated with HbGA (Table 3). Values for both biomarkers were lower among persons with higher age, educational level and PIR. NHB had higher HbAA and lower HbGA values compared to the other 2 race-ethnic groups and men had higher HbAA values compared to women. Most lifestyle variables except for smoking were either weakly or not associated with biomarkers of acrylamide exposure (Table 4). Smoking was associated with at least 100% higher hemoglobin adduct values compared to not smoking; supplement use with ~20% higher values for both biomarkers; and increasing consumption of alcohol with higher levels of HbAA. Physical activity was not associated with either marker of acrylamide exposure, while higher BMI was associated with lower levels of HbAA only.
Table 2.
Spearman correlation coefficients describing bivariate associations between hemoglobin adducts of acrylamide and glycidamide and selected continuous sociodemographic and lifestyle variables for adults ≥20 y, NHANES 2003–2004
| Variable | Acrylamide hemoglobin adduct | Glycidamide hemoglobin adduct |
|---|---|---|
| Age | −0.21* | −0.22* |
| PIR1 | −0.06* | −0.07* |
| Smoking | 0.51* | 0.42* |
| Alcohol consumption | 0.13* | −0.03 |
| BMI | −0.11* | 0.00 |
| Physical activity | 0.02 | −0.04 |
Significant correlation; P <0.05
PIR, family poverty income ratio
Table 3.
Unadjusted biomarker levels by sociodemographic variable categories for adults ≥20 y, NHANES 2003–20041
| Variable | Acrylamide hemoglobin adduct pmol/g Hb |
Glycidamide hemoglobin adduct pmol/g Hb |
|
|---|---|---|---|
| Age, y | 20–39 | 68.5 (64.1 – 73.3) | 65.0 (61.4 – 68.9) |
| 40–59 | 64.0 (59.9 – 68.4) | 60.1 (56.8 – 63.5) | |
| ≥60 | 50.1 (47.9 – 52.3) | 45.5 (42.8 – 48.3) | |
| P-value2 | <0.0001 | <0.0001 | |
| r2 (%)3 | 4 | 4 | |
| Sex | Men | 65.9 (61.5 – 70.5) | 58.6 (55.7 – 61.7) |
| Women | 58.9 (55.7 – 62.2) | 57.8 (54.4 – 61.4) | |
| P-value | <0.0001 | 0.54 | |
| r2 (%) | 1 | < 1 | |
| Race-ethnicity4 | MA | 62.7 (59.3 – 66.3) | 62.9 (59.0 – 67.0) |
| NHB | 66.5 (58.0 – 76.2) | 52.9 (49.4 – 56.7) | |
| NHW | 63.1 (59.2 – 67.3) | 60.0 (56.1 – 64.1) | |
| P-value | <0.0001 | <0.0001 | |
| r2 (%) | 2 | 1 | |
| Education | <High school | 65.0 (58.9 – 71.6) | 59.0 (54.4 – 63.9) |
| High school | 69.0 (62.7 – 75.9) | 64.3 (59.6 – 69.4) | |
| >High school | 58.1 (55.1 – 61.3) | 55.2 (52.1 – 58.5) | |
| P-value | 0.0004 | 0.0009 | |
| r2 (%) | 2 | 1 | |
| PIR5 | Low | 66.7 (62.7 – 70.9) | 60.3 (56.9 – 63.9) |
| Medium | 61.5 (57.7 – 65.6) | 59.2 (56.8 – 61.8) | |
| High | 59.2 (55.9 – 62.8) | 56.4 (53.3 – 59.6) | |
| P-value | <0.0001 | 0.06 | |
| r2 (%) | 1 | < 1 | |
Values represent geometric means (95% CI)
P-value based on Wald F test, which tests whether at least 1 of the means across the categories is significantly different
r2 based on model 1, simple linear regression, using categories as shown
MA, Mexican American; NHB, non-Hispanic black; NHW, non-Hispanic white
PIR, family poverty income ratio; low: 0–1.85; medium: >1.85–3.5; high: >3.5
Table 4.
Unadjusted biomarker levels by lifestyle variable categories for adults ≥20 y, NHANES 2003–20041
| Variable | Acrylamide hemoglobin adduct pmol/g Hb |
Glycidamide hemoglobin adduct pmol/g Hb |
|
|---|---|---|---|
| Supplement use2 | No | 57.4 (54.8 – 60.1) | 54.5 (51.5 – 57.5) |
| Yes | 68.4 (63.3 – 73.9) | 63.1 (59.9 – 66.4) | |
| P-value3 | <0.0001 | <0.0001 | |
| r2 (%)4 | 2 | 1 | |
| Smoking5 | No | 48.2 (46.4 – 50.1) | 47.7 (45.5 – 49.9) |
| Yes | 114 (104 – 125) | 93.7 (86.7 – 101) | |
| P-value | <0.0001 | <0.0001 | |
| r2 (%) | 42 | 20 | |
| Alcohol consumption6 | No drinks | 54.4 (50.7 – 58.4) | 54.9 (51.4 – 58.7) |
| <1 (not 0) | 63.0 (59.4 – 66.7) | 59.7 (56.5 – 63.0) | |
| 1–<2 | 79.1 (71.4 – 87.6) | 64.2 (56.2 – 73.4) | |
| ≥2 | 84.4 (70.1 – 102) | 55.8 (45.9 – 67.9) | |
| P-value | <0.0001 | 0.0245 | |
| r2 (%) | 4 | 4 | |
| BMI7 | Underweight | 80.4 (58.5 – 110) | 55.7 (39.2 – 79.0) |
| Normal | 67.4 (62.8 – 72.3) | 59.1 (55.4 – 63.0) | |
| Overweight | 60.9 (56.9 – 65.1) | 57.3 (52.9 – 62.1) | |
| Obese | 58.1 (54.5 – 61.9) | 58.7 (55.7 – 61.7) | |
| P-value | 0.0002 | 0.89 | |
| r2 (%) | 1 | 0 | |
| Physical activity8 | None reported | 64.1 (59.2 – 69.5) | 59.5 (55.2 – 64.2) |
| 0–<500 | 61.5 (58.2 – 64.9) | 60.4 (56.6 – 64.4) | |
| 500–<1000 | 58.8 (54.0 – 63.9) | 55.2 (49.7 – 61.4) | |
| ≥1000 | 61.8 (57.9 – 66.1) | 56.5 (52.8 – 60.5) | |
| P-value | 0.23 | 0.06 | |
| r2 (%) | < 1 | < 1 | |
Values represent geometric means (95% CI)
“Supplement user” defined as participant who reported taking any dietary supplement within the past 30 d
P-value based on Wald F test, which tests whether at least one of the means across the categories is significantly different
r2 based on model 1, simple linear regression, using categories as shown
“Smoker” defined by serum cotinine concentration >10 μg/L; ≤10 μg/L defined as “nonsmoker”
Alcohol consumption (drinks/d): calculated as average daily number of “standard” drinks [(quantity × frequency)/365.25]; 1 drink ≈ 15 g ethanol
BMI (kg/m2) definitions: underweight: <18.5; normal weight: 18.5–<25; overweight: 25–<30; and obese: ≥30
Physical activity: calculated as total metabolic equivalent task (MET)-min/wk from self-reported leisure time physical activities
Using multiple regression models, the sociodemographic variables (model 2) explained 9% of the variability in biomarker values for HbAA and 7% for HbGA (Supplemental Table 1). Together, the sociodemographic and lifestyle variables (model 3) explained 46% and 25% of the HbAA and HbGA variability, respectively. In all models, age, smoking, supplement use, and BMI remained significantly associated with HbAA levels after adjusting for sociodemographic and lifestyle variables (Supplemental Table 1). Age, smoking, and race (NHB vs. NHW only) remained significantly associated with HbGA levels. Sex and alcohol consumption were significantly associated with HbGA after controlling for both sociodemographic and lifestyle variables.
Because the log transformations may obscure the interpretation of the beta coefficients, we estimated the percent change in biomarker levels associated with each covariable (Fig. 1 and Supplemental Table 2). Smoking had a strong association with both biomarkers, with estimated biomarker levels in smokers being ~100% higher for HbGA and ~130% higher for HbAA compared to nonsmokers after adjusting for sociodemographic and lifestyle variables. All other estimated percent changes were comparatively moderate (≤20%) and generally not consistent between HbAA and HbGA, except for age which was associated with slightly but significantly lower HbAA (3%) and HbGA (6%) values with every 10 y increase (model 3).
Figure 1.
Estimated change in hemoglobin adduct of acrylamide and glycidamide with change in sociodemographic and lifestyle variables for adults ≥20 y, NHANES 2003–2004
Model 1 represents simple linear regression, model 2 represents multiple linear regression with sociodemographic factors, and model 3 represents multiple linear regression with sociodemographic and lifestyle factors; changes derived from a linear regression model while holding any other variables in the model constant; asterisk accompanying the percent change indicates significance (P <0.05).
NHB, non-Hispanic black; NHW, non-Hispanic white; HS, high school; PIR, poverty income ratio.
DISCUSSION
Among the 10 sociodemographic and lifestyle variables investigated in this representative sample of US adults, smoking showed a strong association with both biomarkers of acrylamide exposures and remained a significant variable in all regression models. Smokers had at least twice the HbAA and HbGA levels compared to nonsmokers which is consistent with other studies (27, 35). Among the sociodemographic variables, we found age to be significantly negatively associated with HbAA and HbGA levels in all models, suggesting that acrylamide exposure as well as metabolism may change with age.
In our full regression model, race (NHB vs. NHW only) and sex were significant correlates for HbGA but not for HbAA, suggesting differences in acrylamide metabolism rather than in acrylamide exposure. Different polymorphisms for CYP 2E1 and their impact on acrylamide metabolism have been described (36, 37, 38). However, no information is available about the occurrence and frequency of these polymorphisms in this study population. Similarly, sex differences in the pharmacologic activity of CYP 2E1 seem to exist (39, 40), but the impact of these differences on acrylamide is not fully understood.
Our analysis found that BMI was significantly negatively associated with HbAA levels, which is consistent with a previous study (35). The reason for the negative association is not fully understood and requires further investigation.
Alcohol consumption was significantly negatively associated with HbGA values in our analysis only after adjusting for sociodemographic and lifestyle variables, which appears consistent with observations in other study populations (30, 41). Alcohol induces CYP 2E1, which metabolizes alcohol as well as acrylamide (42). The negative association could be explained with a competitive effect of alcohol and acrylamide as substrate.
While sociodemographic variables alone explained only 9% of the variability in HbAA and 7% for HbGA, the full regression model 3 which also included lifestyle variables explained 46% of HbAA and 25% of HbGA variability. These findings seem consistent with a previous study that investigated the association of food intake on biomarker levels using data from the same NHANES survey, but included children as well (43).
To our knowledge, this is the first study that examined the association and cumulative effects of demographic, socioeconomic, and lifestyle variables on HbAA and HbGA levels. By applying a systematic modeling approach, we were able to assess the magnitude of an estimated change in biomarker level with change in covariable across biomarkers. Furthermore, the findings for the acrylamide biomarkers can also be compared to findings for other classes of diet and nutrition biomarkers reported in accompanying papers in this supplement. The use of a chunk test in our modeling approach maintains the interpretation of the beta coefficients and P-values and therefore is less likely to yield false positive findings that are associated with automatic variable selection methods like backwards elimination.
The limitations in our study are the lack of testing for interactions between individual variables, which was not performed due to limitations in degrees of freedom. We did not study biological variables or how dietary intake is associated with HbAA and HbGA or interacts with variables included in our analysis. However, dietary acrylamide intake estimates were found to correlate only weakly with HbAA and HbGA (20, 22, 25). This could be explained with the high variability in acrylamide content within food groups, the different exposure periods covered by food intake questionnaires and biomarkers, and incomplete acrylamide data in foods. Our analysis was designed to examine associations of HbAA and HbGA with selected variables after adjusting for sociodemographic and lifestyle variables. In this context, acrylamide intake would be considered an outcome variable as opposed to a covariate.
In summary, we conclude that smoking, age and BMI were the 3 variables that were related with HbAA and that smoking, race and alcohol consumption were important correlates for HbGA. The results from this descriptive modeling analysis will help future data analyses that set out to build predictive models to answer specific research questions.
Supplementary Material
Acknowledgments
The authors acknowledge technical assistance from Bridgette Haynes and Yi Pan and contributions from the following laboratory members: Christina Waters, Judith Heitz, Antoinette Smith, Ashley Ribera, Marcela Muresan, Ashley Tippins, Cindy Tse, and Monir Clark. C.M.P and M.R.S. designed the overall research project with input from H.W.V.; H.W.V. and M.R.S. conducted most of the research; M.R.S. analyzed most of the data; and H.W.V. wrote the initial draft, which was modified after feedback from all coauthors, and had primary responsibility for content. All authors read and approved the final manuscript.
Footnotes
No specific sources of financial support. The findings and conclusions in this report are those of the authors and do not necessarily represent the official views or positions of the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry or the Department of Health and Human Services.
Author disclosures: HW Vesper, MR Sternberg, T Frame, CM Pfeiffer, no conflicts of interest.
Supplemental Tables 1–2 are available from the “Online Supporting Material” link in the online posting of the article and from the same link in the online table of contents at http://jn.nutrition.org.
Abbreviations used: HbAA, hemoglobin adduct of acrylamide; HbGA hemoglobin adduct of glycidamide; NCHS, National Center for Health Statistics; PIR, poverty income ratio.
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