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Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC Jan 3, 2013.
Published in final edited form as:
PMCID: PMC3535829
NIHMSID: NIHMS428039

Aflatoxin B1 Exposure, Hepatitis B Virus Infection and Hepatocellular Carcinoma in Taiwan

Abstract

To evaluate the role of aflatoxin B1 (AFB1) exposure on risk of hepatocellular carcinoma (HCC), a case-control study nested within a community-based cohort was conducted. Baseline blood and urine samples were used to determine the level of AFB1-albumin adducts and urinary AFB1 metabolites. Conditional logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the effect of AFB1 exposure on risk of HCC. The adjusted-ORs (95%CIs) were 1.54 (1.01–2.36) and 1.76 (1.18–2.58), respectively, for those with AFB1-albumin adducts and urinary AFB1 metabolites levels above the mean compared to those with levels below the mean. When compared to subjects in the lowest quartile of urinary AFB1 metabolites, there was an increase in risk of HCC, with adjusted ORs (95% CIs) of 0.57 (0.14–2.43), 1.43 (0.32–6.42) and 4.91 (1.18–20.48; Ptrend=0.02), respectively, among noncarriers of hepatitis B virus (HBV) infection. The adjusted OR (95%CI) was 7.49 (5.13–10.93) for carriers of HBsAg compared to noncarriers, regardless of AFB1 status. The ORs (95%CI) were 10.38 (5.73–18.82) and 15.13 (7.83–29.25), for carriers of HBsAg with levels of AFB1-albumin adducts and urinary AFB1 metabolites above the mean, respectively. The combined effect of aflatoxin exposure and HBV infection did not differ by duration of follow-up. Consistent with our previous study with fewer subjects, these data demonstrate that AFB1 exposure is a risk factor for HCC risk. However, in this larger study, the effect of combined AFB1 exposure and HBV infection is more consistent with an additive than a multiplicative model.

Keywords: Aflatoxins, AFB1-albumin adducts, Hepatitis B virus, Hepatocellular carcinoma, Urinary aflatoxin metabolites

Introduction

In Taiwan, primary hepatocellular carcinoma (HCC) is the leading cause of cancer death for males and the second for females. Epidemiological evidence suggests that dietary exposure to aflatoxin B1 (AFB1) and chronic infection with hepatitis B virus (HBV) are major risk factors for HCC (13).

The biotransformation of AFB1, primarily in the liver, is intimately linked with its toxic and carcinogenic effects (4). AFB1 undergoes an initial two-election oxidation, yielding an active intermediate, AFB1-exo-8,9-epoxide, which is later detoxified through a variety of metabolic processes (5). Epoxidation of AFB1 to the exo-8,9-epoxide is a critical step in the genotoxic pathway of this carcinogen. The exo-epoxide is highly unstable and binds with DNA to form the predominant trans-8,9-dihydro-8-(N7-guanyl)-9-hydroxy-AFB1 (AFB1-N7-Gua) adduct, a pro-mutagenic DNA lesion (5, 6). In addition to formation of the 8,9-oxide, AFB1 also undergoes metabolism to yield stable urinary metabolites which are poorer substrates for epoxidation, such as AFM1, AFQ1, and AFP1 (5). The reactive epoxides are detoxified by glutathione S-transferase (GST)-mediated conjugation (7).

Biomarkers of AFB1 exposure include AFB1-DNA, serum AFB1-albumin and urinary AFB1 metabolites such as AFM1, and AFB1-mercapturic acid (5). In general, AFB1-albumin adducts have been recognized as a long-term marker of AFB1 exposure. Because albumin adducts are as long-lived as albumin, which has half-life of 21 days in humans, they provide information on the accumulated exposure over a period 2–3 months (8, 9). Measurement of the adduct levels in urine, however, provides a non-invasive means of estimating the levels of AFB1 exposure.

We have developed antibody-based methods for measurement of AFB1 exposure including AFB1-albumin and urinary AFB1 metabolites and demonstrated that dietary exposure to AFB1 increased HCC risk among the Taiwanese population using a case-control study nested within a community-based Cancer Screening Program (CSP) cohort (10, 11). Our previous study with a total of three to four years of follow-up found that in HBV-infected males there was an adjusted odds ratio (OR) of 2.8 (95%CI=0.9–9.0) for detectable compared with non-detectable AFB1-albumin adducts and 5.5 (95%CI=1.3–23.4) for high compared with low levels of AFB1 metabolites in urine (11). There are limited data on the effect of AFB1 exposure among non-carriers of HBsAg due to the small number of HCC cases without chronic HBV infection. The specific aims of this study, which encompasses larger numbers of subjects as a result of the continued follow-up of the cohort, were to investigate the role of AFB1 exposure, as assessed by AFB1-albumin adducts as well as urinary excretion AFB1 metabolites, in HCC risk using data and biospecimens collected in the CSP cohort (10, 11).

Materials and methods

Study cohort

Subjects are from the community-based CSP cohortrecruited in Taiwan. This study was approved by Columbia University’s Institutional Review Board as well as the research ethics committee of the College of Public Health, National Taiwan University, Taipei, Taiwan. Written informed consent was obtained from all subjects, and strict quality controls and safeguards were used to protect confidentiality. The cohort characteristics and methods of screening and follow up have been described in detail previously (10, 11). Briefly, individuals who were between 30 and 64 years old and lived in seven townships in Taiwan, three located on Penghu Islets that has the highest HCC incidence rates in Taiwan, and the other four from Taiwan Island, were recruited between July 1990 and June 1992. A total of 12,020 males and 11,923 females were enrolled. Participants were personally interviewed based on a structured questionnaire regarding epidemiological information and donated a 20 ml fasting blood sample as well as a spot urine at recruitment. Biospecimens were transported on dry ice to a central laboratory at the National Taiwan University and stored at −70°C. Epidemiological information collected at baseline included socio-demographic characteristics, habits of alcohol intake and cigarette smoking, health history and family history of cancers. Habitual cigarette smoking was defined as having smoked more than four days a week for at least six months. Information about duration and intensity was also obtained. Habitual alcohol intake was defined as drinking alcohol-containing products more than four days a week for at least six months.

At enrollment, blood samples were tested in Taiwan for serological markers, including alanine transaminase (ALT), aspartate transaminase (AST), α-fetoprotein (AFP), hepatitis B surface antigen (HBsAg), and antibody against hepatitis C virus (anti-HCV). HBsAg was tested by radioimmunoassay (Abbott Laboratories, North Chicago, IL, USA). Anti-HCV and AFP were tested by enzyme immunoassay using commercial kits (Abbott Laboratories, North Chicago, IL, USA). Both ALT and AST levels were determined with a serum chemistry autoanalyzer (Hitachi Model 736; Hitachi Co., Tokyo, Japan) using commercial reagents (Biomerieux, Mercy I’Etoile France). Anti-HCV and AFP were assayed in all males and females who resided in Hu-Hsi and Pai-Hsa on the Penghu Islets. The other assays were carried out on samples from all participants. Any participant who had an elevated level of ALT (≥45 IU/ml), AST (≥40 IU/ml), or AFP (≥20 ng/ml), was positive for HBsAg or anti-HCV or had a family history of HCC or liver cirrhosis among first degree relatives was referred for upper abdominal ultrasonography examination. Suspected HCC cases were referred to teaching medical centers for confirmatory diagnosis by computerized tomography, digital subtracted angiogram, aspiration cytology and pathological examination. The criteria for HCC diagnosis included: a histo-pathological examination; a positive lesion detected by at least two different imaging techniques (abdominal ultrasonography, angiogram or computed tomography); or by one imaging technique and a serum AFP level >400 ng/ml.

Intensive follow-up, including abdominal ultrasonographic screening and serum AFP level determination every three months, was carried out on those with ultrasonographic images indicative of liver cirrhosis whereas others were examined annually. Any suspected HCC cases identified during follow-up were referred for confirmatory diagnosis as described above. Ninty-seven cases were diagnosed by histopathological examination, 146 cases by at least 2 different imaging techniques (abdominal ultrasonography, angiogram, computed tomography, or magnetic resonance imaging) and 74 cases by one imaging technique and α-fetoprotein ≥400 ng/ml.

Study subjects

Between February 1991 and June 2004, a total of 241 cases were newly diagnosed with HCC. Cases were primarily identified through linkage to the national cancer registry and death certification systems. Of these, 11 cases were excluded because of non-availability of stored blood. A total of 1052 controls were randomly selected from cohort subjects who were not affected with HCC through the follow-up period by matching to each case by age (±5 years), gender, residential township and date of recruitment (±3 months). The number of matched controls per case varied depending on the number of eligible controls with available specimens and ranged from 2–6. Data on AFB1 biomarkers were available from our prior study on 56 cases and 220 controls (11). None of these controls became a case during the additional follow-up. Baseline blood (174 cases and 832 controls) and urine (142 cases and 684 controls) samples from new subjects were available and shipped to Columbia University on dry ice for determination of AFB1-albumin adducts as well as urinary AFB1 metabolites.

AFB1-albumin adducts in blood and AFB1 metabolites in urine

Additional samples were assayed for albumin adducts by ELISA as previous described (11). Briefly, 50 μl of albumin extracts, equivalent to 200 μg albumin, were added to plates previously coated with 3 ng of AFB1 epoxide-modified human serum albumin (HAS). Polyclonal antiserum #7 was used at 1:2×105 dilution and the secondary antiserum, goat anti-rabbit IgG-alkaline phosphatase conjugate, was used at 1:750 dilution. Concentrations of AFB1-albumin adducts were determined using a standard curve of serially diluted AFB1 epoxide-modified HSA that had been enzymatically digested. Samples with less than 20% inhibition were considered undetectable and assigned a value of 0.01 fmol/μg Two controls samples were analyzed with each batch of test samples, a pooled sample of plasma from non-smoking U.S. subjects and a positive control of serum from a rat treated with 1.5 mg AFB1. The coefficient of variation of the mean of two controls was 20% (N=13).

Urines were assayed essentially as described previously (11). Seppak urine extracts (50 μl) were added to plates previously coated with 3 ng of AFB1 epoxide-modified bovine serum albumin. AF8E11 was used at 1:1500 dilution and the secondary antiserum, goat anti-mouse alkaline phosphatase was used at 1:1000 dilution. AF8E11 mainly reacts with AFB1, but there is significant cross-reactivity with a number of aflatoxin derivatives, including AFB2, AFM1, AFG1, and AFP1 (12). Concentrations of urinary metabolites were determined using a standard curve of serially diluted AFB1. Samples with less than 20% inhibition were considered undetectable and assigned a value of 1 fmol/ml. A pooled sample of urine from 5 controls was used as a quality control and analyzed with each batch of test samples. The coefficient of variation was 10% (N=5).

Statistical methods

The χ2 test was used to examine differences in the distributions of variables between cases and controls. Levels of AFB1-albumin adducts, and urinary AFB1 metabolites were natural log-transformed to normalize the distribution. All statistical analysis used data after log-transformation. The cut point values were based on the distribution of log-transformed data among controls, however, for ease of interpretation, we present the values as arithmetic data. The distributions of the two biomarkers are higher in the more recently assayed samples compared to prior data. Initially in the data analysis, we used different cutoff values for the two batches of aflatoxin biomarker assay. However, the results were not significantly different between these two rounds. In the final data analysis, we combined both batches and adjusted for the batch effect on the biomarkers’ values by creating a new variable indicating round 1 or 2. Pearson partial correlation coefficient was used to determine the correlation between AFB1-albumin adducts and urinary AFB1 metabolites adjusted by age and gender. To examine the independent and combined effects of the levels of AFB1 exposure on HCC among subjects with and without chronic HBV infection, the levels of biomarkers were analyzed as a categorical variable rather than a continuous variable. Subjects were divided into different exposure groups: those with biomarker levels above the mean value (59.8 fmol/mg for AFB1-albumin adducts and 55.2 fmol/ml for urinary AFB1 metabolites) for all controls samples versus those below the mean. To evaluate the dose-response relationships between biomarkers and HCC risk, subjects were divided into quartiles based on control values (<26.9, ≥26.9 to <43.5, ≥43.5 to <71.35, ≥71.35 fmol/mg for AFB1-albumin adducts and <25.7, ≥25.7 to <48.5, ≥48.5 to <77.35, ≥77.35 fmol/ml for urinary AFB1 metabolites). HBsAg, anti-HCV, assay batch, smoking, alcohol consumption, and BMI-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were derived from conditional logistic regression models stratified on the matching factors to estimate the association between levels of AFB1 exposure and HCC risk. The test for trend of adjusted ORs across strata was based on Wald’s test with consecutive scores 1, 2, 3, and 4 assigned to the 1st, 2nd, 3rd, and 4th quartile of biomarker. All analyses were performed with SAS software 9.0 SAS Institute, Cary, NC). All statistical tests were based on two-tailed probability.

Results

The sociodemographic characteristics of HCC cases and controls among all subjects and those with data on both biomarkers are given in Table 1. The distributions of characteristics were similar for both groups. There were no differences in mean ages and BMI between cases and controls. Significantly more controls (64.4%) were negative for both HBV and HCV biomarkers compared to cases (15.0%). The distributions of habitual smoking were similar for cases and controls (50.0% and 45.3%, respectively) but there was a significant difference in frequency of alcohol consumption (22.8% and 17.0%, respectively).

Table 1
Sociodemographic characteristics of HCC cases and controls

The mean levels of AFB1-albumin adducts were not statistically significantly different between cases and controls (56.5±53.5 versus 59.8±59.5 fmol/mg, respectively; p=0.45, Table 2). The mean level of urinary AFB1 metabolites however, was significantly higher in cases than in controls (71.3±54.9 versus 55.2±41.9 fmol/ml, respectively, p<0.0001). There was a weak linear correlation between levels of AFB1-albumin adducts and urinary AFB1 metabolites, with a partial Pearson correlation coefficient of 0.15 (p<0.0001) among controls (data not shown). The association of AFB1 exposure with HCC risk is given in Table 2. AFB1-albumin adducts were detectable in more than 93% of subjects. After multivariable adjustment, the OR for those with AFB1-albumin adducts levels above the mean compared to those with levels below the mean was 1.54 (95%CI=1.01–2.36). When AFB1-albumin adduct levels were divided into quartiles based on control values, there was a nonstatistically significant increased risk for those in the higher quartiles, with adjusted of ORs (95%CI) of 1.11 (0.69–1.83), 0.18 (0.69–2.03), and 1.47 (0.83–2.58), for the 2nd, 3rd, and 4th quartile relative to the lowest, respectively (Ptrend=0.19). The adjusted OR for those with urinary AFB1 metabolite levels above the mean compared to those with levels below the mean was 1.76 (95%CI=1.18–2.58). When urinary AFB1 metabolite levels were divided into quartiles, HCC risk increased with adjusted ORs (95%CI) of 0.98 (0.54–1.78), 0.95 (0.55–1.69) and 1.99 (1.18–3.36) for subjects with AFB1 metabolites in the 2nd, 3rd, and 4th quartile, respectively, compared with those in the lowest quartile (Ptrend=0.008).

Table 2
Biomarkers of AFB1 exposure and risk of HCC

To assess the effect of HBV infection on the relationship between biomarkers and HCC, subjects were stratified into two groups based on HBsAg status (Table 3). The effect of AFB1 exposure, comparing subjects with AFB1-albumin adduct levels above the mean versus below the mean, was not different between carriers and noncarriers of HBsAg (OR=1.43, 95%CI=0.76–2.71 and OR=1.65, 95%CI=0.63–4.33, respectively). Similarly, among carriers of HBsAg, the ORs (95%CI) were 0.75 (0.38–1.46), 1.14 (0.53–2.46), and 1.46 (0.66–3.24), respectively, for adduct levels in the 2nd, 3rd, and 4th quartile of adducts compared with those in the lowest quartile (Ptrend=0.33). The corresponding ORs (95%CI) were 1.87 (0.67–5.21), 1.03 (0.32–3.31), and 1.70 (0.46–6.28) (Ptrend=0.64) among noncarriers.

Table 3
Biomarkers of AFB1 exposure and risk of HCC by HBsAg status

The OR for those with urinary AFB1 metabolite levels above the mean compared to those with levels below the mean was 4.29 (95%CI=1.43–12.85) among noncarriers. There was a statistically significant dose-response relationship between urinary AFB1 metabolites and HCC with ORs (95%CI) of 0.57 (0.14–2.43), 1.43 (0.32–6.42), and 4.91 (1.18–20.48, Ptrend=0.02), respectively, for adduct levels in the 2nd, 3rd, and 4th quartile of adducts among noncarriers compared with those in the lowest quartile. There were no significant associations among carriers of HBsAg.

The combined effect of HBV infection and AFB1 exposure is shown in Table 4. Adjusted OR (95%CI) was 7.49 (5.13–10.93) for carriers of HBsAg compared to noncarriers, regardless of AFB1 status. The ORs (95%CI) were 10.38 (5.73–18.82) and 15.13 (7.83–29.25) for HBsAg carriers with levels of albumin adduct and urinary metabolites above the mean compared to non carriers with values below the mean, respectively. Values were similar when cases were stratified into those diagnosed within 3 years of (N=83) or more than 3 years after (N=147) sample collection (Table 4). Carriers of HBsAg and with both albumin adducts and urinary metabolites levels above the mean had a significantly increased HCC risk (OR=17.29, 95%CI=6.98–42.82) compared to non carriers and with both biomarkers below the mean (Ptrend<0.0001) (Table 4). The combined effect of HBV or AFB1 did not significantly differ based on the age of onset of HCC. The OR for HBsAg carriers with levels of albumin adduct above the mean compared to non carriers with values below the mean were 11.94 (5.00–12.85) and 10.87 (5.32–22.20) for cases diagnosed with HCC at ages under and above 50, respectively (data not shown). The corresponding ORs (95%CI) were 15.94 (5.00–28.82) and 16.76 (7.63–36.81) for HBsAg carriers with levels of urinary metabolites above the mean compared to non carriers with values below the mean (data not shown).

Table 4
The combined effect of HBV infection and AFB 1 exposure and risk of HCC

Discussion

In this study, with a longer follow-up period and more HCC cases than our prior study in this cohort, we applied two biological markers of AFB1 exposure, AFB1-albumin adducts and urinary AFB1 metabolites, to investigate the relationship between AFB1, HBV infection and HCC. Consistent with our previous study that included 56 cases diagnosed with HCC within four years of follow-up (11), we found a statistically significant elevated risk of HCC with increasing level of AFB1 exposure. This is also in agreement with another nested case-control study of 18,244 men in Shanghai, the People’s Republic of China, among whom there were 22 incident cases of liver cancer. Analysis of urine samples banked 1–4 years prior to diagnosis gave a relative risk (RRs) of 2.4 (95%CI=1.0–5.9) for any of the AFB1 metabolites and 4.9 (95%CI=1.5–16.3) for detectable AFB1-N7-guanine adducts (13). A subsequent follow-up study from this cohort showed that the presence of any urinary AFB1 biomarker significantly predicted liver cancer (RR=5.0; 95%CI=2.1–11.8), with a RR of 9.1 (95%CI=2.9–29.2) for the presence of AFB1-N7-Guanine adducts (14). Although Gan et al (15) used a different assay for measurement of AFB1-albumin adducts, we used their data correlating adducts to chronic intake AFB1 to estimate median intake of AFB1 from median levels of albumin adducts among our controls. These estimations suggest that about 30 ug/day increases risk of developing HCC 1.5-fold.

Our previous report on this cohort, with urinary AFB1 metabolites data on 38 HCC cases and AFB1-albumin data on 52 cases, reported among HBsAg carriers an 111-fold increased risk of HCC with high urinary AFB1 metabolites and 70-fold for detectable AFB1-albumin adducts compared to those with low/non-detectable levels and negative for HBsAg (11). A synergistic interaction between the presence of urinary AFB1 biomarkers and HBV seropositive status was also observed in the cohort study conducted in China with 50 HCC cases (RR=59.4, 95%CI=16.6–212.0) (14). In the present study, with a much larger sample size (230 cases with AFB1-albumin adduct and 198 with AFB1 metabolites data), we did not observe a significant synergistic interaction between HBV infection and AFB1 exposure among cases diagnosed with HCC between 1 to 13 years after sample collection. Stratifying subjects by years between sample collection and diagnosis did not alter these results. This result is in contrast to our earlier study due to the larger number of cases (N=83) diagnosed within 3 years as a result of the more complete case identification. Thus, the primary reason for these discrepant results may be the small sample sizes in prior studies. To date, there are no other data on the long-term combined effect of AFB1 with HBV on HCC.

It is difficult to accurately assess AFB1 exposure at the individual level either by questions about consumption of foods suspected to be major sources of exposure or by direct analysis of foods. Epidemiological studies using urine and blood samples banked several years prior to diagnosis have shown that the presence of AFB1 metabolites in urine is associated with a significantly evaluated risk of HCC (11, 13, 14). In general, AFB1-albumin adducts have been recognized as a somewhat longer-term marker of AFB1 exposure. Because albumin adducts are as long-lived as albumin, which has half-life of 21 days in humans, they provide information on the accumulated exposure over a period of 2–3 months. The long-term stability of human AFB1-albumin adducts has been confirmed (16) as well as there significant correlation (r=0.69) with AFB1 intake (15). Studies in rats showed that AFB1-albumin adducts reflect the formation of DNA damage occurring in the liver (17). In humans, AFB1-albumin adducts are correlated with the prevalence of a specific codon 249 mutation in p53 in liver tumors (18). With the larger sample size in the present study, we are able to observe a statistically significant increased HCC risk with an OR of 1.54 (95%CI=1.01–2.36) among subjects with AFB1-albumin adducts level above the mean compared to those with level of AFB1-albumin adducts below the mean, although the test of a dose-response effect did not reach statistic significance.

Urinary AFB1 metabolites also provide a reliable assessment of dietary AFB1 intake (19, 20) with a correlation of 0.65 found between total dietary AFB1 intake and urinary AFM1 excretion (20). Both nested case-control studies of HCC including ours (11) and the one in China (13, 14) suggest that measurement of urinary AFB1 metabolites is also a useful biomarker to evaluate the HCC risk. Our present results suggest that both AFB1 biomarkers are valuable biomarkers for predicting HCC risk even in cases diagnosed more than 13 years after blood and urine collection.

In our study, levels of AFB1-albumin adducts and urinary AFB1 metabolites show only a weak although statistically significant linear correlation. This is not unexpected since there is complex balance of cytochrome P450-, epoxide hydrolase-, and glutathione S-transferase-mediated metabolism of AFB1 that results in various metabolites including the reactive epoxide that is responsible for albumin adduct formation (21).

Although the formation of AFB1-guanine adducts through interaction between AFB1–8,9- epoxide and hepatic DNA has been shown to be critical for the carcinogenesis induced by AFB1 (22), exposure to AFB1 can also induce oxidative DNA damage in liver tissue (23, 24). We previously observed positive correlations between urinary AFB1 metabolites and biomarkers of oxidative damage (8-oxodeoxyguanosine and 15-F2t-isoprostane) in urine (25, 26), suggesting that levels of urinary AFB1 metabolites might partly represent the amount of oxidative damage produced by exposure to AFB1. An elevated level of urinary 15-F2t-isoprostane was also associated with an increased risk of HCC (25).

Previously, the concept of a causative role for AFB1 in human liver was not universally accepted because of the presence of endemic HBV in high risk populations (27). In the present study, we found urinary AFB1 metabolites independently, and significantly associated with increased risk of HCC. In prior studies (11, 13, 14), the small number of cases without chronic HBV infection limited ability to evaluate the effect of AFB1 exposure on HCC risk among this subgroup. In the larger present study, we observed a statistically significant individual effect of AFB1 exposure on HCC risk among noncarriers with an OR of 4.29 for those with urinary AFB1 metabolites above versus below the mean. This provides evidence that not only HBV carriers but also noncarriers are vulnerable to the carcinogenic effect of AFB1. It is unclear why the effect of AFB1 on risk of HCC was somewhat weaker among carriers of HBV than noncarriers. With respect to AFB1-albumin, in cases with serious liver disease, such as liver cirrhosis and hepatitis virus infection, the turnover of albumin might be much more variable impacting on adduct formation. In addition, HCV infection might also impact liver function and our data analysis may not fully adjust for this. Unfortunately, we do not have sufficient HCV-infected cases to stratify by HCV status.

A limitation of the study is that biomarker samples were assayed in two batches 12 years apart. The distributions of two biomarkers are higher in the more recently assayed samples compared to prior data. Due to sample availability, we could not reanalyze the prior samples. In the data analysis, we adjusted for the batch of aflatoxin biomarker assay effect on the biomarker values. Since samples were run in case-control sets and conditional logistic regression models were used to analyze data, cases and their controls are still comparable. Another limitation is the smaller number of subjects with urine compared to plasma. However, in their distributions by gender, age, BMI, hepatitis virus infection, smoking status and alcohol consumption, subjects with available data on both biomarkers were similar to the total study population.

In summary, we found a significant positive association between AFB1 exposure, measured as AFB1-albumin adducts and urinary AFB1 metabolites, and risk of HCC. But we no longer observed a synergistic interaction between HBV infection and AFB1 exposure on HCC risk. The effect of combined AFB1 exposure and HBV infection is more consistent with an additive than a multiplicative model.

Acknowledgments

This work was supported by NIH grants RO1ES05116 and P30ES009089.

Abbreviations

AFB1
aflatoxin B1
Anti-HCV
antibodies against hepatitis C virus
BMI
body mass index
CSP
Cancer Screening Program
CI
confidence interval
ELISA
enzyme-linked immunosorbent assay
HBsAg
hepatitis B virus surface antigen
HBV
hepatitis B virus
HCC
hepatocellular carcinoma
OR
odds ratio

Appendix

Using the mean value of AFB1-albumin adducts among controls and figure 1 from Gan et al (15), we converted adduct levels to dietary intake of AFB1, resulting in a value of approximately 30 μg/day. As shown in Table 2, this level of intake corresponds to an OR of 1.5. However, Gan et al used an RIA with a different antibody than used in this paper which may lead to differences in quantitative values of adduct levels and thus, errors in the estimation.

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