Associations between estimated acrylamide intakes, and ... - Nature

European Journal of Clinical Nutrition (2008) 62, 314–323

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ORIGINAL ARTICLE

Associations between estimated acrylamide intakes, and hemoglobin AA adducts in a sample from ¨ Diet and Cancer cohort the Malmo ¨ rnqvist2, A Axmon3 and L Hagmar3,{ E Wirfa¨lt1, B Paulsson2, M To 1 Department of Clinical Sciences in Malmo¨, Lund University, Malmo¨, Sweden; 2Department of Environmental Chemistry, Stockholm University, Malmo¨, Sweden and 3Division of Occupational and Environmental Medicine and Psychiatric Epidemiology, Lund University Hospital, Malmo¨, Sweden

Objective: To examine the coherence of estimated intakes of acrylamide (AA) from foods, with hemoglobin (Hb) AA adduct levels, an objective marker of environmental AA exposure. Design: A cross-sectional study. Setting: The Malmo¨ Diet and Cancer study, a large population-based prospective cohort (n ¼ 28 098) in the south of Sweden. Subjects: A sample of non-smoking (n ¼ 70) and smoking (n ¼ 72) women and men selected to obtain large variation in Hb AA adducts. Methods: Self-reported data on the usual consumption of foods were combined with published data on the AA content in Swedish foods. The Hb AA adduct levels were determined by a modified Edman degradation method. Linear regression and correlation analysis examined associations between estimated AA intakes, and Hb AA adducts. Results: In randomly selected individuals (n ¼ 40), the estimated median AA intake was 28 mg per day. In linear regression models, adjusting for sex, significant associations were seen in non-smokers between Hb AA adducts and estimated AA from foods (P ¼ 0.006). In smokers both AA from foods (P ¼ 0.006) and the calculated amount of tobacco consumed (P ¼ 0.003) were significantly associated with Hb AA adducts. Positive partial correlations between dietary AA estimates and Hb AA adducts were seen in smoking men (r ¼ 0.37) and women (r ¼ 0.59), and in non-smoking men (r ¼ 0.60), but not in non-smoking women. Conclusions: This study suggests that both diet and tobacco are important sources of the environmental AA exposure, although the lack of correlations in non-smoking women cast doubt on the validity of dietary AA intake estimates used in cancer epidemiology, or suggest that unrecognized factors may influence the internal dose measure of AA exposure. Sponsorship: Funded by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS); the European Commission (Contract no FOOD-CT-2003–506820, HEATOX); The Swedish Cancer Society; The Swedish Research Council; and The City of Malmo¨.

European Journal of Clinical Nutrition (2008) 62, 314–323; doi:10.1038/sj.ejcn.1602704; published online 14 March 2007 Keywords: acrylamide; diet; hemoglobin adducts; epidemiology

Introduction Correspondence: Dr E Wirfa¨lt, Department of Clinical Sciences in Malmo¨, Lund University, Nutrition Epidemiology, Malmo¨ Diet and Cancer study, CRC entrance 72 UMAS, SE-20502, Malmo¨, Sweden. E-mail: [email protected] { deceased. Contributors: All authors have contributed substantially to the interpretation of findings and to the write-up of the paper. LH initiated and designed the project; EW conducted all statistical analyses and wrote most of the paper; BP and MT were responsible for the biochemical analyses; and AA provided statistical support and advice. Received 8 December 2006; revised 19 January 2007; accepted 22 January 2007; published online 14 March 2007

The toxicological effects of acrylamide (AA), a chemical used for industrial production of plastics and other polymers, are well known. In 1994, the International Agency for Cancer Research classified AA as ‘probably carcinogenic to humans’ (IARC, 1994). Previously, drinking water and tobacco were considered the main sources of exposure to the general population. However, a few years ago, research at Stockholm University revealed that AA is formed when foods are heated at high temperatures, and that certain carbohydrate-rich

Estimated AA-intakes and Hb AA-adducts E Wirfa¨lt et al

315 foods, particularly potatoes, could contain high levels of AA (Tareke et al., 2000, 2002). This was later confirmed by others (Roseń and Hellena¨s, 2002; World Health Organisation, 2002). To date a number of reports on the AA content of foods have been published, databases of the AA content in foods have been established, and researchers have estimated the average exposures to dietary AA in different populations (Dybing et al., 2005). Encouraged by the reports and the ensuing debate, the food industry has adjusted the manufacturing procedures to reduce the AA content of some marketed food products (CIAA, 2005). Using different models for the extrapolation of risk from cancer tests in rats, the cancer risks from exposure to AA ¨ rnqvist et al., 1998). The risk figures have been estimated (To indicate that the absolute cancer risk associated with dietary AA is not negligible for the population, although the cancer risk is low for the individual (Hagmar et al., 2005). Epidemiological studies have estimated the dietary AA intakes by combining published data on AA content in foods and self-reported data on dietary habits, and examined the cancer risk at various sites associated with the estimated AA intakes (Mucci et al., 2003, 2004, 2005, 2006; Pelucchi et al., 2003, 2004), but none of these studies have indicated increased cancer risks associated with AA intakes. However, the epidemiological studies have been criticized for not having enough statistical power to confirm the relatively low-risk enhancements of AA in the diet expected from the ¨ rnqvist, 2003). Moreover, it is animal studies (Hagmar and To not clear if the intake estimates of AA in published studies are valid reflections of the true dietary exposures. To our knowledge, only a couple of attempts have been made to compare the accuracy of the estimated dietary AA intakes against objective markers of AA exposure (Hagmar et al., ¨ tting et al., 2005). 2005; Ku Monitoring of hemoglobin (Hb) adducts is an established methodology for measuring the internal dose after exposure ¨ rnqvist et al., 2002). This methodolto reactive chemicals (To ogy has been applied in studies of AA exposure in for ¨ rnqvist example, occupational environments (reviewed by To et al., 2006). The level of Hb adducts reflects the dose in blood during the last months (as the lifespan of erythrocytes ¨ Diet and Cancer (MDC) is a large is ca. 120 days). The Malmo population-based prospective cohort study in the south of Sweden (Berglund et al., 1993). We have in a previous study (Hagmar et al., 2005) described the variation in AA adduct levels in Hb, by examining erythrocyte samples from the biobank of the MDC cohort. Individuals (n ¼ 142) were selected on the basis of a dietary history methodology to obtain the highest possible variation in the dietary exposure of AA. The internal dose of AA in non-smokers (n ¼ 70) was shown to vary by a factor up to five (Hagmar et al., 2005). The present study is a continuation of the study indicated above. The aim was to compare the measured Hb AA adduct levels (in the erythrocyte samples) with estimated AA intakes from foods in the subgroup of individuals from the MDC cohort. Dietary intake estimates were obtained by combining

self-reported data on the usual food consumption with published data on the AA contribution from Swedish foods. The associations between estimated AA intakes from specific food groups, the sum of estimated AA intakes from all food groups, and Hb AA adduct levels, were examined separately in smokers and in non-smokers. In smokers, the associations between the amount of tobacco consumed and Hb AA adduct levels were also examined. The distributions of, and correlations between, estimated AA intakes and of Hb AA adducts were also examined separately in smoking and nonsmoking men and women.

Methods MDC cohort The baseline examinations of the population-based MDC cohort (n ¼ 28 098) were conducted from March 1991 until October 1996 (Berglund et al., 1993). Eligible participants were men in the age range 46–73 years, and women in the ¨ (the third age-range 45–73 years, living in the city of Malmo largest city of Sweden) and with Swedish reading and writing skills. The Ethical committee at Lund University approved the study. Participants visited the study center twice. During the first visit, the study procedures and questionnaires were explained, direct measurements made and blood samples collected. Two weeks later, the questionnaires completed at home were reviewed and the diet history interview conducted.

Dietary data in the MDC cohort Information on ‘usual’ dietary habits was collected through a modified diet history method: combining a 7-day menu book and a 168-item diet history questionnaire (both completed at home) with a 1-h interview (Callmer et al., 1993). In the menu book, participants recorded cooked meals, cold beverages (i.e., milk, juice, soft drinks, water and alcoholic beverages), drugs, natural remedies, and dietary supplements during 7 consecutive days. In the diet history, questionnaire information was recorded about foods consumed regularly and with low day-to-day variation (i.e., hot beverages, sandwiches, edible fats, breakfast cereals, yoghurt, milk, fruits, cakes, candies and snacks). Participants reported the exact consumption frequencies, and a booklet of photographs aided in the estimation of portion sizes. The reference period of the questionnaire was the preceding year. The dietary interview focused on details about food preparation and on portion sizes recorded in the menu book. The reported average intakes of foods (grams per day) were translated into energy and nutrients by applying these data to the MDC food and nutrient database that originates from PC KOST version 2/93 of the Swedish National Food Administration. The current study examined food sources of AA (see below), total energy intakes (MJ), the percent contribution of fat, carbohydrates and protein to total European Journal of Clinical Nutrition


316 Table 1 Baseline characteristics in a sample of men (n ¼ 70) and women (n ¼ 72) by smoking status from the Malmo¨ Diet and Cancer cohort 1991–1996 Men Smokers n ¼ 35

Women Non-smokers n ¼ 35

Smokers n ¼ 35

Non-smokers n ¼ 37

Mean s.d. Age (years) BMI (kg/m2) Total energy intake (MJ) Dietary fat (% energy) Dietary protein (% energy) Dietary carbohydrate (% energy) Dietary fibre (g/4.19 MJ)

56 26 13.4 39 15 41 7.1

6.2 3.2 3.1 6.3 2.4 6.6 2.5

59 28 13.4 38 15 43 9.7

8.4 4.4 2.7 7.6 2.2 5.7 4.6

53 25 10.5 39 15 43 8.2

7.1 4.6 2.1 5.4 3.1 6.5 2.6

56 25 10.5 37 14 45 10.0

7.9 3.0 2.1 6.3 2.2 6.6 3.1

Abbreviations: BMI, body mass index, s.d., standard deviation.

energy (% energy), and dietary fibre intakes (grams per 4.19 MJ). The reproducibility and concurrent validity of the MDC diet methodology was previously examined with 18 days of food records during 1 year as the reference (Riboli et al., 1997). The validation coefficients of nutrient intakes were generally higher than those found in comparable methods in other populations (Willett, 1998). Misreporting of intake is an ongoing controversy in studies using self-report instruments to collect dietary information in epidemiological studies. Black et al. (1991) and Goldberg et al. (1991) have suggested that the ratio between energy intake (EI) and basal metabolic rate (BMR) obtained from the equations recommended by World Health Organization (FAO/WHO/UNU, 1985), may be used to identify individuals that report too low or too high habitual EIs. A ratio of 1.35 has been suggested as the lower cutoff for reasonable habitual EIs. This study excluded individuals below the lower cutoff (potential ‘under-reporters’) and those above the 99th percentile of the EI–BMR ratio (potential ‘overreporters’). Consequently, a total of 7128 men and 10 176 women were considered as the recruitment basis for the present study.

this stratum; one with high consumption of snacks and one that ranked high on both coffee and fried potato. Third, five individuals were selected at random among those that did not consume any of the four foods in each of the four gender and smoking status strata (‘low group’). Seventy-two study participants were smokers and 70 non-smokers. Information on age and sex was obtained through the unique person identification number, and smoking status through a standardized lifestyle and socio-economic questionnaire (Table 1). Current smokers were contrasted against non-smokers (also including former smokers that had not smoked for the last year). Trained staff conducted anthropometrical measurements with participants in light in-door clothing. The body mass index (BMI) was computed from weight (kg) divided by the height (m) squared. Smoking women were somewhat younger than both smoking men and non-smoking men and women, whereas BMI on average was higher among non-smoking men. As expected, EIs were higher in men. The dietary composition of non-smoking women appeared to be lower in percent energy from fat and higher in carbohydrates, and higher in energy adjusted fibre intakes, as compared with the other study groups.

Study population In order to achieve a high variation in dietary AA exposure, a study population of 142 individuals was identified from three broad exposure categories for AA: random (n ¼ 40), high (n ¼ 82) and low (n ¼ 20). These study participants were selected from four strata defined by gender and smoking status (smokers versus non-smokers). First, 10 individuals from each of the four strata of gender and smoking status were selected at random (‘random group’). Second, in each of the four strata, the five individuals with the highest consumption of four foods with high content of AA (i.e., coffee, crisp bread, deep- and pan-fried potatoes, and snacks) were identified, and included in the study population (‘high group’). Owing to the difficulties in finding female smokers with high consumption of only one of these food groups, two more subjects were added in the study population from

Estimation of AA intakes from foods Information on the AA content in Swedish foods was obtained from Svensson et al. (2003) that list 10 food items/groups as the major dietary sources. The amounts of AA in mg per kg food were as follows (median values): potato crisps 980, French fries 410, fried potato 300, cookies/ biscuits/wafers 230, crisp bread/thin unleavened bread 135, bread 40, breakfast cereals 100, tortilla crisps 150, pop corn 390 and coffee 25. Later a Danish study (Granby and Fagt, 2004) indicated that the AA value for coffee reported by Svensson et al. (2003) was too high as a general estimate. This has recently been confirmed by researchers at the Swedish National Food Administration, who now give 12 mg per kg as a better general estimate of the AA content in Swedish coffee (personal communication K-E Hellena¨s, Sw. NFA). This figure was adopted in this study. The food items were compared

European Journal of Clinical Nutrition


317 with the food codes of the MDC food database. Direct correspondence was observed only for three items, that is, French fries, fried potatoes and coffee. Breakfast cereals have six different food codes in the MDC database, soft bread has four food codes and three codes identify crisp bread. On the other hand only one MDC code is used for potato crisps, tortilla crisps and popcorn and only two codes are found in the MDC database for all types of cakes and cookies (i.e., high- and low-fat items). To harmonize the information, ‘raw’ data was examined in a subsample of the MDC cohort (i.e., individuals that joined the MDC study during the spring of 1993). Seventy-two percent of the reported snacks during that period were potato crisps; 22% tortilla crisps and 5% pop corn. Similarly, 40% of the high-fat cakes and cookies corresponded to the category cookies/biscuits/wafers (according to Svensson et al. (2003)) and 60% to Danish pastries or different filled cakes, whereas the low-fat cakes and cookies mainly were soft wheat bread and buns. As a consequence, and for the purpose of this paper, we were able to identify and estimate the AA intakes for eight food groups: French fries, fried potatoes, breakfast cereal, coffee, bread, crisp bread, snacks and cakes and cookies. The snacks and the cakes and cookies food groups were weighted according to the percent distribution in the ‘raw’ data. The median values of the AA content of the corresponding food items (mg per kg food) as described above were multiplied with the amounts of foods consumed by each individual. Thus the estimated AA intakes from the each of the eight food groups are expressed as mg per day. The total sum of AA from all eight food groups was also calculated.

Tobacco consumption Information on current tobacco habits was in the MDC study collected separately for cigarettes, cigars and pipe tobacco in the standardized lifestyle and socio-economic questionnaire. The number of smoked cigarettes and cigars was converted to grams of tobacco consumed per day (one cigarette ¼ 1 g, and one cigar ¼ 3 g), and the total amount of tobacco (g per day) was computed as the sum of these three types of tobacco.

¨ rnqvist et al., 1986) with using the N-alkyl Edman method (To modifications for measurement of AA adducts as described by Bergmark (1997). Globin, precipitated from lysed erythrocytes, was incubated with pentafluorophenyl isothiocyanate. The modified N-terminal valines were detached as fluorinated derivatives, and isolated by extraction. The quantification of the derivatives of the AA adducts was performed by gas chromatography–tandem mass spectrometry (GC–MS/MS) in the negative ion/chemical ionization mode, using a corresponding deuterated analyte as internal standard and through comparison with a calibration curve. The limit of detection was estimated to about 0.006 nmol/g globin and the coefficient of variation was less than 10%.

Statistical analysis Associations between variables were assessed using Pearson’s and partial correlation coefficients and linear regression. To reduce skewness continuous variables were log transformed before analysis. Results relating to estimated AA intakes from foods were adjusted for total energy, dietary interviewer, and method version. Linear regression analyses were also adjusted for age and sex. When both the predictor and outcome variable in linear regression are log transformed, the interpretation of the regression coefficient (b) is not straightforward. However, using the formula 100 (eb ln(1.1)1), the change in the average value of the outcome for every 10% increase in the predictor may be estimated (Vittinghoff et al., 2005). P-values in two-sided tests were considered statistically significant if smaller than 0.05. The SPSS package (version 11.0) was used for all statistical computations (SPSS, 2001).

Results The estimated dietary AA intakes of men and women are in Figure 1 (non-smokers) and Figure 2 (smokers) plotted separately for individuals from the different exposure

MDC biobank Non-fasting blood samples had been collected and separated, and the erythrocyte fractions were stored at 801C. The MDC biological bank is in good condition. The quality control program has been evaluated and has not revealed any quality problems of blood components (Pero et al., 1998).

Hb adducts for AA The erythrocyte fractions of the blood bank samples were analyzed with regard to Hb adducts from AA (cf. Hagmar et al. (2005)). Adducts to N-terminal valines were measured

Figure 1 Estimated AA intake from food (mg/day) among nonsmoking subjects in the three exposure categories low, random and high. Filled circles represent men and unfilled circles represent women.



318

Figure 2 Estimated AA intake from food (mg/day) among smokers in the three exposure categories low, random and high. Filled circles represent men and unfilled circles represent women.

Figure 3 Relation between estimated dietary AA intakes from foods (mg/day) and Hb adducts from AA (nmol/g) in non-smoking men and women. Filled circles represent men and unfilled circles represent women.

categories (low, random and high intake). In all three exposure categories, the ranges of estimated intakes appear similar in men and women. The intake ranges also appear similar in non-smokers versus smokers. The wide ranges of intakes in the ‘high’ categories are contrasted against the narrow ranges of the ‘low’ categories. In the total group of individuals the estimated dietary AA intakes ranged from 3.5 to 189 mg/day, with a median of 45 mg/day. Similarly, the medians and intake ranges of the estimated AA in the different exposure categories were: low 9.2, 3.5–22 (n ¼ 20); random 25, 10–62 (n ¼ 40); high 69, 19–189 (n ¼ 82) mg/day. The ‘crude’ associations between measured Hb AA adducts and estimated dietary AA intakes and the calculated amounts of consumed tobacco, respectively, are presented in Figures 3, 4, and 5 separately for men and women, and smokers and non-smokers. The range in the Hb AA adduct levels in the non-smoking group was 0.02–0.10 nmol/g globin and in the smokers the range was 0.03–0.43 nmol/g globin. Positive Pearson’s correlation were observed between Hb AA adducts and estimated AA intakes from all foods in European Journal of Clinical Nutrition

Figure 4 Relation between estimated dietary AA intakes from foods (mg/day) and Hb adducts from AA (nmol/g) in smoking men and women. Filled circles represent men and unfilled circles represent women.

Figure 5 Relation between calculated amount of consumed tobacco (g/day) and Hb adducts from AA (nmol/g) in smoking men and women. Filled circles represent men and unfilled circles represent women.

smokers (r ¼ 0.36, P ¼ 0.002) and in non-smokers (r ¼ 0.43, Po0.001). In smokers, the corresponding correlation coefficient for the calculated amount of tobacco was also positive (r ¼ 0.47, Po0.001). Linear regression revealed that, among non-smokers, each 10% increase in the estimated AA intake through food consumption would result in a 1.68% increase in the average measured AA (P ¼ 0.006), and that this factor explained 13% of the variation in the measured AA (Table 2). Among smokers, estimated AA intake through food consumption and tobacco use were evaluated separately as well as in multivariate models. Each 10% increase in estimated AA intake through food consumption resulted in an 4.38% increase in the average measured Hb AA adducts (Po0.001) when tobacco use was not considered, and in an 2.54% increase (P ¼ 0.006) when tobacco use was also included in the model. The change in average measured AA adducts based on a 10% increase in tobacco use was similar when AA intake through food consumption was disregarded (2.95%;


319 Table 2 Linear regression (using log-transformed variables) modelsa predicting the levels of Hb AA-adducts (nmol/g globin) associated with estimated AA intakes from foods, and the amount of tobacco in smokers, in smokers and non-smokers from a sub-sample of the Malmo¨ Diet and Cancer cohort 1991–1996 AA contributors

Adjusted

Foods

Tobacco

RSQ

Changeb

P-value

Changeb

P-value

Smokers (n ¼ 72) AA all foodsc Amount of tobacco AA all foods with tobaccoc

0.25 0.26 0.32

4.38 — 2.54

o0.001 — 0.006

— 2.95 2.24

— o0.001 0.003

Non-smokers (n ¼ 70) AA-all foodsc

0.13

1.68

0.006

—

—

Abbreviation: AA, acrylamide. a All models are adjusted for age and sex. b Percentage increase in the average value of the outcome per 10% increase in the predictor. c Also adjusted for total energy, diet method, and dietary interviewer.

Po0.001) or taken into consideration (2.24%; P ¼ 0.003). Together, estimated AA intake through food consumption and tobacco use explained 32% of the variation in measured AA. The distributions of the estimated AA intakes from all foods and specific food groups, the calculated amounts of tobacco consumed, as well as the Hb AA adduct levels are shown in Table 3, separately for men and women and different smoking status groups. For instance, bread, crisp bread and cake and cookies contributed to estimated AA intakes in most individuals in all four smoking-gender groups. Less than 25% of the smoking and non-smoking men and women were exposed to AA from French fries, but this was a slightly more common AA source in smoking women. Snacks appear to contribute to AA intake in less than half the samples, and coffee to be a less important source in non-smoking women than in the other groups. Both Pearsons and partial correlation coefficients between the sum of AA intake from all foods and AA adducts were positive in smoking women and non-smoking men (Table 3). The corresponding partial correlation (adjusted for total energy, dietary interviewer and method version) was also positive in smoking men. However, in non-smoking women no correlation was seen between AA intake from all foods and AA adducts. Also, in smokers Pearson’s correlation between the amount of tobacco used and AA adducts were positive in men (r ¼ 0.46, P ¼ 0.007) and in women (r ¼ 0.59, Po0.001).

Discussion This pilot study of a subsample from the large populationbased MDC cohort, utilizing the extensive database and accompanying biobank, had the unique opportunity to investigate the coherence between estimated intakes of AA from self-reported dietary information, and Hb AA adducts

as an objective marker of AA exposure. The findings indicate that both diet and tobacco are important sources of the environmental AA exposure. As expected, and similarly to other studies (Bergmark, 1997; Schettgen et al., 2004), the association between tobacco use and measured AA adducts was in this study strong in smokers. Positive associations between Hb adducts and estimated AA intakes from foods were observed in non-smoking men and smoking men and women. Tobacco smoking and occupational exposure are known major environmental sources of AA. Although cosmetics, drinking water, food packaging materials and passive tobacco smoking are potential AA sources, it has been concluded that these factors are negligible compared to diet for the internal burden of AA in humans (European Commission, 1998, 2002; European Union, 2002). Given the sample selection, and the limited sample size that may have contributed to weakened associations, this report should be regarded as a pilot study that demonstrates some obstacles associated with the use of large databases and biobanks in secondary data analysis.

Previous studies of AA exposure and Hb adducts During continuous exposure, the Hb-adduct levels in humans reflect the dose of AA in blood over the last months ¨ rnqvist et al., 2002). In animal experiments, dietary (To intake of AA has been shown to increase the AA adduct ¨ m et al., 2005), and to level in Hb (Tareke et al., 2000; Vikstro give reliable quantitative measures of the internal dose of ¨ rnqvist et al. (2006). Exposure-related AA; reviewed by To increases in Hb AA adducts have been shown in workers with occupational exposure to AA (Bergmark et al., 1993; Hagmar et al., 2001; Jones et al., 2006), and in tobacco smokers (Bergmark, 1997; Schettgen et al., 2004). An human study with oral exposure (in aqueous solution) at the level of high occupational exposures showed a clearcut relationship between exposure and the increased level of the Hb AA European Journal of Clinical Nutrition


320 Table 3 Estimated acrylamide intakes (mg per day) from foods, amounts of tobacco consumed (g per day), and measured Hb adduct levels of AA (nmol per gram globin), in a sub-sample (n ¼ 142) from the Malmo¨ Diet and Cancer cohort 1991–1996 by gender and smoking status Smoking-gender groups

AA-contributors

Mean

Median

25th

75th

Correlations between AA contributors and Hb AA adducts Partiala

Pearson r (P-value)

mg/day Smokers

Non-smokers

Men (n ¼ 35)

Hb AA adducts Amount of tobacco AA from all foods Snacks Coffee Fried potato French fries Breakfast cereal Bread Crisp bread Cake, cookies

0.14 20 53 8 15 8 10 o1 5 4 3

0.14 20 46 0 11 6 0 0 5 1 2

0.09 8 22 0 4 0 0 0 3 o1 o1

0.18 25 79 1 22 13 0 0 7 5 4

— 0.46 0.22 0.30 0.26 0.009 0.06 0.46 0.03 0.13 0.15

— (0.007) (0.21) (0.08) (0.13) (40.5) (40.5) (0.005) (40.5) (0.45) (0.38)

— — 0.37 0.36 0.35 0.04 0.13 0.47 0.04 0.18 0.17

— — (0.04) (0.04) (0.05) (40.5) (0.47) (0.007) (40.5) (0.33) (0.35)

Women (n ¼ 37)

Hb AA adducts Amount of tobacco AA from all foods Snacks Coffee Fried potato French fries Breakfast Cereal Bread Crisp bread Cake, cookies

0.17 14 53 14 14 7 9 o1 3 3 3

0.16 15 52 0 7 0 0 0 3. 1 3

0.11 10 27 0 5 0 0 0 1 o1 1

0.21 20 83 6 16 8 15 o1 5 2 4

— 0.59 0.52 0.27 0.51 0.11 0.26 0.18 0.05 0.32 0.27

— (o0.001) (0.001) (0.11) (0.001) (40.5) (0.12) (0.28) (40.5) (0.06) (0.11)

— — 0.59 0.30 0.49 0.09 0.23 0.09 0.03 0.32 0.18

— — (o0.001) (0.08) (0.004) (40.5) (0.18) (40.5) (40.5) (0.06) (0.30)

Men (n ¼ 35)

Hb AA adducts AA from all foods Snacks Coffee Fried potato French fries Breakfast cereal Bread Crisp bread Cake, cookies

0.04 57 13 10 13 8 1 5 4 4

0.04 53 0 7 8 0 0 4 1 3

0.03 16 0 1 0 0 0 3 1 1

0.06 85 8 26 16 0 1 6 4 5

— 0.66 0.21 0.48 0.39 0.48 0.32 0.02 0.30 0.31

— (o0.001) (0.22) (0.003) (0.02) (0.003) (0.06) (40.5) (0.08) (0.07)

— 0.60 0.15 0.42 0.31 0.39 0.37 0.08 0.30 0.26

— (o0.001) (0.41) (0.02) (0.08) (0.03) (0.04) (40.5) (0.10) (0.15)

Women (n ¼ 35)

Hb AA adducts AA from all foods Snacks Coffee Fried potato French fries Breakfast cereal Bread Crisp bread Cake, cookies

0.04 46 12 9 8 5 o1 4 4 4

0.04 38 0 5 4 0 0 3 2 3

0.03 19 0 1 0 0 0 2 o1 1

0.05 63 3 7 6 0 o1 5 3 5

— 0.07 0.09 0.16 0.17 0.30 0.04 0.22 0.05 0.44

— (40.5) (40.5) (0.37) (40.33) (0.08) (40.5) (0.22) (40.5) (0.008)

— 0.05 0.15 0.18 0.20 0.30 0.02 0.27 0.03 0.44

— (40.5) (0.40) (0.33) (0.27) (0.10) (40.5) (0.14) (40.5) (0.01)

Abbreviation: AA, acrylamide. a Adjusted for total energy, dietary interviewer and method version.

adduct (Fennell et al., 2005). The result from a pilot study where AA Hb adducts was measured after a dietary experiment (using potato crisps) in humans was not conclusive (Vesper et al., 2005), but ongoing studies with AA intake over longer periods of time show positive correlations (unpublished results). Urinary metabolites, which have a shorter time of turnover are advantageous for measuring uptake and excretion of AA in short-time human experiments with AA containing foods (Bjellaas et al., 2005; Fuhr et al., 2006). European Journal of Clinical Nutrition

Distribution of estimated dietary AA intakes The study participants were selected from different strata (i.e., selected at random, or among those with no or high, reported intakes of specific foods) in order to potentially maximize differences in dietary AA exposure. As a result, the proportion of participants with high-dietary intakes of AA was most likely larger compared to the overall cohort. However, when examining the study participants selected at random separately, both the estimated mean intakes of AA


321 and the mean values of Hb AA adducts were lower, and in line with exposures observed in other studies (Svensson ¨ rnqvist et al., 2006). The distributions of et al., 2003; To estimated intakes (as indicated by Table 3) suggest that fried potato (in non-smoking men) and French fries (in smoking women) were foods of great importance for the observed significant associations with Hb AA adducts. In contrast, non-smoking women had the lower estimated mean AA intakes from these foods, but instead bread, crisp-bread and cakes and cookies were the major contributors. As the estimated intakes of the latter food groups were similar across gender and smoking status groups, it is plausible that these foods were associated with substantial misclassification of AA intakes in all gender smoking status groups. Misclassification of AA intakes may have been less common among consumers of fried potatoes and French fries. In addition, the AA contribution by coffee seems larger in smoking men and women and in non-smoking men, than in non-smoking women.

Factors contributing to weak associations Several factors could contribute to the results of this study. The lack of significant associations between dietary AA estimates and Hb AA adducts in non-smoking women primarily suggests that there are unrecognized factors associated with the exposure of AA. First, the limited sample size may likely have contributed to attenuated associations when smoking-gender subgroups were examined separately. In linear regression models, separately for smokers and nonsmokers (adjusted for sex), associations between both estimated dietary intakes of AA and calculated amounts of tobacco were clearly related to Hb AA adducts. The larger study samples (72 smokers and 70 non-smokers) undoubtedly contributed to the statistically significant associations of the regression models. Secondly, in the MDC study (similar to many other epidemiological studies) dietary data was not collected with AA intakes as the major study aim. Some food groups lacked sufficient detail (e.g. cakes and cookies) and only crude AA estimates for these food groups could be made from this information. Third, information on the AA content of foods overall is insufficient. We used published data on the AA content of some foods, because a common food database on AA in Swedish foods is not available. Recent analyses at the Swedish National Food Administration (K-E Hellena¨s, personal communication) indicates that the current AA levels of foods are similar to those measured in 2002 (Svensson et al., 2003), despite the efforts of the food industry to lower the AA content of foods. This suggests that the AA levels in the different foods, published by Svensson et al. (2003), should approximately be similar to the AA levels in foods during 1990s, when the MDC cohort was established. Data for AA in home cooked foods are not available, which particularly is a limitation as a feeding study indicated that the AA content

in home cooked foods could be much higher than in similar ¨ rgel et al., 2002). Moreover, the AA commercial products (So content may vary between for example, different brands of the same food, suggesting that the true dietary variation may be underestimated (by studies like ours) when only one published source is used to estimate intakes. In addition, it was recently shown that the AA formed during cooking evaporates (Eriksson et al., 2007). Although, airborne AA from food preparation under non-occupational conditions is expected to give only minor contribution to the total background AA exposure, it could potentially contribute to exposure misclassification of AA in certain individuals. Fourth, not yet recognized differences in bioavailability of AA between food types or inter-individual differences in metabolic rates, may result in variations between exposure/ intake and internal dose. For instance, studies of Hb adducts indicate that at very low AA exposures (e.g. non-smokers) the metabolism of AA to glycidamide, the epoxy metabolite of AA, is relatively higher compared to that of individuals with somewhat higher AA exposures (e.g. smokers and exposed workers) (Schettgen et al., 2004; Boettcher et al., 2005; Vesper et al., 2005). These metabolic differences may potentially result in inter-individual variations in the internal dose measured as Hb AA adducts, despite similar food intake, and thus influence the observed associations. Such factors could also complicate the interpretation in epidemiological studies (e.g. estimating cancer risks), because glycidamide is mutagenic and assumed to be the cancer risk increasing agent in AA exposure (Rice, 2005; Manjanatha et al., 2006).

Other dietary studies We are not aware of any published studies of controlled dietary experiments in humans that have measured all dietary quantities with known content of AA, and examined the dose–response effect of AA intakes from total diet on Hb AA adduct levels. The double portion technique, where a second portion is collected of everything the participant eats during a given time period and analyzed for its content of AA would (although expensive and cumbersome) be an alternative design for a study of the dose–response effect (Abdulla et al., 1981; Lightowler and Davies, 2002). We have identified one small pilot study that (similar to our study) assessed selfreport dietary intakes of AA (but used a 40 item questionnaire) during the preceding 3 months, and compared these with Hb adduct levels of AA (n ¼ 10 women, including one ¨ tting et al., 2005). The observed non significant smoker) (Ku correlations were considered to indicate low validity of the dietary questionnaire rather than no real association between diet and the internal AA exposure.

The need for bio banks in epidemiology The finding of AA as a food carcinogen has lead to epidemiological studies for assessment of the human cancer risk of dietary AA (Mucci et al., 2003, 2004, 2005, 2006; European Journal of Clinical Nutrition


322 Pelucchi et al., 2003, 2004). A crucial cornerstone of these evaluations has been the quantitative assessment of dietary AA intake based on a combination of published data on AA content in foods and self-reported data on dietary habits. None of these studies have to date indicated increased cancer risk associated with AA intakes. However, there are several prerequisites to enable the use of epidemiology for such an assessment. The first prerequisite is a reasonably large interindividual variation in AA exposure. As we already have shown in a previous analysis of this material, the interindividual variation of AA adducts in non-smoking subjects varies by a factor up to five (Hagmar et al., 2005). This variation must be considered to be low and a hinder for conclusive cancer epidemiology. Moreover, quantitative estimates of dietary AA intake have to be accurate. The present study is the first in which estimates of AA intake from foods are compared to an objective biomarker of AA. Among smokers, the explained variance associated with foods was 25% (32% for foods and tobacco), whereas among non-smokers the explained variance was 13% (Table 2). Even if using a dietary assessment methodology of high relative validity, we could not explain most of the variation in AA intake. This will inevitably lead to an exposure misclassification that will restrict the possibility to use case–control studies to evaluate low or moderate enhancements of AA associated cancer risks. Animal-based risk models applied on human exposure to AA result in that only minor differences of the cancer risks can be expected to occur between groups of individuals with the highest and the lowest exposures, ¨ rnqrespectively (Dybing and Sanner, 2003; Hagmar and To vist, 2003; Vainio, 2003). Thus, our data support the view that for conclusive AA epidemiology, cohorts with biobanks are needed that enable individual assessment of exposure biomarkers, such as Hb adducts. Until such studies have been performed we have to rely on animal toxicology for human cancer risk assessments.

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