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Departments of Epidemiology [L. M. H., S. E. H., D. S., G. A. C., W. C. W., D. J. H.], Nutrition [S. A. S-W., W. C. W.], Biostatistics [D. S.], and Cancer Cell Biology, [K. T. K.], Harvard School of Public Health, Boston, Massachusetts; Channing Laboratory, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts 02115 [S. E. H., K. T. K., G. A. C., W. C. W., D. J. H.]; and Harvard Center for Cancer Prevention, Boston, Massachusetts [G. A. C., D. J. H.]
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The oxidation of ethanol is catalyzed predominantly by the
ADHs3
, and to a lesser extent by cytochrome P450IIE1 and
H2O2-dependent catalase
(16)
. ADH is a zinc-containing cytosolic enzyme that
oxidizes short-chain alcohols to aldehydes, mainly formaldehyde and
acetaldehyde (17)
. Animal studies suggest that this is the
rate-limiting step of ethanol metabolism (18)
. ADH, a
dimeric protein consisting of two 40-kDa enzyme subunits, is found
primarily in the liver but also in other tissues, including the kidney,
lung, and gastric mucosa (16
, 17)
. There are a total of
five enzyme subunits (
, ß, 
, 
, and 
) encoded at five
separate gene loci: ADH1, ADH2, ADH3,
ADH4, and ADH5, respectively. The ADH enzymes are
divided into three classes determined by the preferential substrates of
the isoenzymes. The class I ADH enzymes, encoded by ADH1,
ADH2, and ADH3, are mainly involved in the
oxidation of ethanol and other small, aliphatic alcohols. Polymorphic
variants exist among the class I ADH genes, specifically
ADH2 and ADH3, and are known to produce enzymes
with distinct kinetic properties. These functional differences produce
variability in alcohol metabolic capacity between individuals (16
, 17)
.
At the ADH3 locus, the allele coding for the
1 allele differs from the
2 by two amino acids at positions 271 and 349
(19)
. The substitution of Gln for Arg at position 271 is
believed to affect enzyme kinetics by changing the NAD(H) dissociation
rate, producing a 2 subunit that is less
metabolically active (slower oxidizer) than 1
(19)
. Pharmacokinetic studies have shown that there is a
2.5-fold difference in Vmax, the
maximum achievable velocity of the enzyme, for ethanol oxidation
between the homodimeric 1 isoenzyme compared
with the homodimeric 2 isoenzyme
(16)
.
Epidemiological studies have investigated the ADH3 polymorphism as a potential modifier of alcohol-cancer relationships. In a recent case-control study, Harty et al. (20) found that fast oxidizers who consumed very high levels of alcohol (>56 drinks per week) have a 5.3-fold increased risk of oral-pharyngeal cancer (95% CI = 1.0–28.8) relative to slow and intermediate oxidizers who consumed similar levels of alcohol. The authors hypothesize that heavy drinkers who are homozygous for the fast oxidizing allele receive more exposure to the carcinogenic effects of acetaldehyde. However, this result was not confirmed in another study (21) . Freudenheim et al. (22) suggested an increased risk of breast cancer for homozygotes with the fast oxidizing allele compared with the intermediate and slow oxidizers among premenopausal women only (odds ratio = 2.3; 95% CI = 1.2–4.3). We investigated the ADH3 polymorphism as both a potential risk factor for breast cancer and a potential modifier of the association between alcohol consumption and breast cancer in a case-control study nested within the prospective NHS. To identify a potential mechanism, we assessed the relationships among ADH3 genotype, alcohol consumption, and plasma levels of steroid hormones in a subset of these women.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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In 1989–1990, blood samples were collected from 32,826 of the cohort members as described previously (12) . Each woman arranged to have her blood drawn and returned via overnight courier; 97% of the samples were received within 26 h of collection. The stability of estrogens in whole blood within this time period has been documented previously (23) . Upon arrival, the samples were centrifuged and aliquoted into plasma, buffy coat, and RBC components and archived in continuously monitored liquid nitrogen freezers. The subcohort for this nested case-control study consisted of women who returned a blood sample and were free of diagnosed cancer (excluding non-melanoma skin cancer) at the time of blood draw. Cases were women who had a confirmed diagnosis of breast cancer anytime after blood collection through May 31, 1994. During this period, 465 eligible cases of incident breast cancer were identified. The estimated follow-up rate for the sub-cohort, as a proportion of potential person-years of observation through 1994, is over 97%. Controls (n = 465) were randomly selected among women who had provided a blood sample and were matched to cases (1:1) on year of birth, menopausal status (postmenopausal versus not), postmenopausal hormone use, month of blood return, time of day of blood collection, and fasting status at blood draw. For postmenopausal cases who were not taking postmenopausal hormones at least 3 months prior to blood draw, a second matched control was randomly selected to increase the power for analyses with plasma steroid hormone levels. The total number of controls was 621.
Laboratory Techniques.
Blinded to case-control status, we used a slightly modified version of
the PCR-restriction fragment length polymorphism method
described by Hardy et al. (20)
to genotype at the
ADH3 locus; DNA samples were digested with NlaIII
after amplification rather than prior to amplification. In addition,
blinded quality controls (
10% of the sample size) were included and
were genotyped with 100% concordance.
Hormone Analysis.
Plasma hormone levels of estradiol, estrone, estrone sulfate,
testosterone, androstenedione, percentage of free estradiol,
bioavailable estradiol (i.e., unbound plus albumin-bound),
percentage of free bioavailable estradiol, DHEA, and DHEAS were assayed
in up to three separate batches (12)
. Estrone sulfate from
batches 1 and 2 and SHBG were assayed in the laboratory of Dr. C.
Longcope (University of Massachusetts Medical Center). The
Nichols Institute (San Juan Capistrano, CA) performed all other hormone
assays. Within-batch laboratory coefficients of variation were
13.6%, with the exception of SHBG, which was 21.9%. The total
number of postmenopausal controls with measured plasma hormone levels
was 307, with the exception of SHBG (n = 298).
Statistical Analysis.
We used conditional logistic regression to calculate RRs and 95% CIs
to assess the risk of breast cancer for the three different genotypes:
ADH31,1 (fast),
ADH31,2 (intermediate), and
ADH32,2 (slow). The following breast cancer
risk factors were adjusted for in the multivariate models to control
for any potential confounding: alcohol consumption (nondrinkers, >0 to
10, >10 g/day) body mass index (<22, 22–25, >25–29, >29
kg/m2), age of menarche (<12, 12–14, >14
years), the interaction between parity and age at first birth (0; 1–2
children, 24 years; >2 children, 24 years; 1–2 children, >24
years; >2 children, >24 years), weight gain since 18 years of age
(<10, 10–25, >25 kg), family history of breast cancer (yes/no), and
history of benign breast disease (yes/no). Among postmenopausal women,
duration of postmenopausal hormone use (none, <5, 
5 years) and
age at menopause (<48, 48 to <52, 52 years) were also adjusted for
in the multivariate models. Prospective information regarding breast
cancer risk factors were obtained from the 1976 baseline questionnaire,
subsequent biennial questionnaires, and a questionnaire completed at
blood draw. Menopausal status and use of postmenopausal hormones were
assessed at blood draw and updated until date of diagnosis for cases
and matched controls. The relationship between alcohol consumption and
risk of breast cancer within genotype was determined by including
interaction terms between each alcohol category (nondrinkers, >0 to
10, >10 g/day) and ADH3 genotype. Current alcohol
consumption was based on data collected prior to blood draw (1986
questionnaire); the 1990 questionnaire was used for individuals who did
not provide this information on the 1986 questionnaire. Nineteen
individuals were missing alcohol data from both questionnaires and were
excluded from the alcohol analysis. In addition, we assessed the
relationship between ADH3 genotype, alcohol consumption at
ages 18–22 (nondrinkers, <3, 3 drinks/week), and breast cancer
risk.
Separate analyses were conducted for premenopausal and postmenopausal women. For postmenopausal women, conditional logistic regression was used to compute RRs and 95% CIs. Because of the relatively small number of premenopausal women, unconditional logistic regression was used to compute RRs and 95% CIs controlling for the matching factors, body mass index, age of menarche, the interaction between parity and age of first birth, family history of breast cancer, and history of benign breast disease. For subgroup analyses with small sample sizes, conditional logistic regression did not provide stable estimates when adjusting for all breast cancer risk factors, but they did provide similar estimates to unconditional analyses when these covariates were not included in the models.
To test for trends and interactions, current alcohol consumption was modeled as a continuous variable derived by assigning the median alcohol values of the controls for each consumption category to all individuals in that category. The P for trend was based on the Wald test. Conditional logistic regression was used for the combined genotypes. Because of small sample sizes, unconditional logistic regression controlling for the matching factors, as well as the previously mentioned risk factors, was used for determining trend within each genotype. For alcohol-ADH3 interactions, ADH3 genotype was modeled as a three-category ordinal variable. Statistical significance of ADH3-alcohol interactions was determined by a likelihood ratio test comparing the model with the interaction term to the model with the main effects only.
ADH3 genotype was also assessed as a modifier of the relationship between alcohol and plasma hormone levels in the postmenopausal controls who were not using hormones at the time of blood draw. Women with hormone levels below detectable limits (n = 8) were assigned the lowest detectable level. Within each batch, hormone values >3 interquartile ranges were treated as outliers and excluded. Various hormone fractions were not assayed for all women because of an insufficient quantity of plasma. In addition, five women with missing alcohol data were excluded from all hormone analyses.
To adjust for potential confounders, we regressed alcohol consumption as well as the natural log of the hormone value on the following variables: body mass index (kg/m2), age (years), smoking (never, past, or current), and indicators for two of the three laboratory batches. These variables were selected because they were found to be associated with hormone levels in an overlapping subset of the NHS cohort (12) . Pearson correlation coefficients between the two sets of adjusted residuals were used to assess the linear association between alcohol consumption and the natural logarithm of plasma hormone levels for each ADH3 genotype. To test for trends in the relationship between alcohol consumption and plasma hormone levels across ADH3 genotypes, we regressed hormone levels on alcohol consumption (g/day), ADH3 genotype (ordinal), the interaction between alcohol and genotype [ADH3 ordered times alcohol consumption (g/day)], as well as the potential confounders listed above. The P for trend was based on the Wald test of the interaction term.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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10 g/day). Eighty-five percent of
cases and 85% of controls reported that they were Caucasian. The
ADH3 genotype distribution in this study population was
consistent with the previously reported estimates for Caucasians:
40–50% for the 2 allele and 50–60% for
the 1 allele (16)
. Among the
cases, the distribution of 1 homozygotes,
heterozygotes, and 2 homozygotes was 32.9,
51.4, and 15.7%, respectively. The distribution was similar for the
controls: 34.0, 48.3, and 17.7%. Both distributions were in
Hardy-Weinberg equilibrium (cases: 2
(df, 1) = 1.87, P = 0.17; controls:
2 (df, 1) = 0.07,
P = 0.79).
It has been suggested that gastric ADH3 activity differs among the
three genotypes: activity is highest among 1
homozygotes, followed by heterozygotes and 2
homozygotes (24)
. However, in some epidemiological
studies, the heterozygotes (intermediate oxidizers) and
2 homozygotes (slow oxidizers) have been
combined in the analysis (20, 21, 22)
. In this study, RRs were
computed for the slow and intermediate groups both separately and
combined. Regardless of whether the intermediate and slow groups were
collapsed, no overall association between ADH3 and breast
cancer risk was found. The adjusted RR for the combined slow and
intermediate oxidizers with the fast oxidizers was 1.0 (95% CI =
0.7–1.3). Compared with the fast oxidizers, the adjusted RRs were 0.9
(95% CI = 0.6–1.3) for slow oxidizers and 1.1 (95% CI =
0.8–1.4) for the intermediate oxidizers. When stratified on menopausal
status, there still was no association between ADH3 genotype
and breast cancer risk (Table 1)
. Because there is prior biological and epidemiological evidence to
suggest that ADH3 activity is distinct for each genotype, heterozygotes
were not combined with either homozygous group in subsequent analyses.
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10,
>10 g/day), no association between current daily alcohol consumption
and risk of breast cancer was observed among the overall study
population (RR = 1.1 for >10 g/day compared with none; 95%
CI = 0.7–1.6; P, test for trend = 0.94). On the
basis of the estimated RRs and 95% CIs comparing all combinations of
alcohol consumption and ADH3 genotype to the nondrinking
fast oxidizers, there was no clear evidence of effect modification by
ADH3 genotype [Table 2
; 2 (df, 1) =2.03; P = 0.15].
|
). There was no indication of an interaction between alcohol and
ADH3 genotype for breast cancer risk
[2 (df, 1) = 0.58;
P = 0.45].
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10 g/day compared with none (95% CI = 0.7–3.1); and
RR = 0.5 for >10 g/day compared with none (95% CI =
0.2–1.4)]. Furthermore, there was no evidence of an interaction
between alcohol and ADH3 genotype
[2 (df, 1) = 0.65l
P = 0.42].
A weak, nonsignificant association between alcohol consumption during
the ages of 18–22 and breast cancer risk was observed (RR = 1.2
for >3 drinks/week compared with none; 95% CI = 0.7–1.9;
P, test for trend = 0.30). This relationship was not
modified by ADH3 genotype [2
(df, 1) = 0.21; P = 0.65].
In addition, we assessed the correlations between current daily
alcohol consumption and plasma hormone levels among postmenopausal
controls who were not taking postmenopausal hormones within the 3
months before blood draw (Table 4)
. Weak correlations were observed with estrone sulfate
(r = 0.14; P = 0.02), bioavailable
estradiol (r = 0.14; P = 0.02),
percentage of bioavailable estradiol (r = 0.14;
P = 0.01), testosterone (r = -0.11;
P = 0.06), and SHBG (r = -0.13;
P = 0.03). When stratified by ADH3
genotype, the correlations for estrone sulfate, estradiol, free
estradiol, and DHEAS were statistically significant for the fast
oxidizers and more positive compared with the slow and intermediate
oxidizers. However, a significant trend in the relationship between
alcohol consumption and plasma hormone level based on oxidative
capacity (ADH3 genotype) was observed only for DHEAS
(P, test for trend = 0.01). In addition, a
statistically significant trend was observed for SHBG (P,
test for trend = 0.02), and a modest trend was observed for
testosterone (P, test for trend = 0.07). For SHBG and
testosterone, stronger inverse associations between alcohol consumption
and plasma hormone levels were observed for slow oxidizers compared
with fast oxidizers.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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1,
2) have been identified and well characterized
(16
, 17) . Although they differ by only one amino acid, the
ADH2 alleles have been shown to vary by 20-fold in the rate
of ethanol oxidation (16)
. More than 90% of Caucasians
have the slowest oxidizing ADH2 allele
(ß1), thus limiting statistical power to
investigate potential association(s) between variant ADH2
alleles and disease among Caucasians. The ADH3 alleles have
also been shown to have distinct kinetic properties, but not to the
same extent as the ADH2 alleles (16)
. The
2.5-fold variation in Vmax between
11 and
22 enzymes is thought
to contribute to differences in the rate of ethanol oxidation; however,
the ADH3 polymorphism has not been shown to have any clear
short-term effects on blood alcohol levels in human feeding studies
(16
, 25) . The prevalence of the variant ADH3
allele (2) is high in Caucasian populations
(40–50%) and lower in African-Americans (15%) and Asians (5%; Ref.
16
). Epidemiological studies have associated the
ADH3 polymorphism with the risk of alcoholism and alcoholic
liver cirrhosis, suggesting a functional role for variation in
ADH3 (26
, 27)
. In a case-control study with 134 premenopausal and 181 postmenopausal cases, Freudenheim et al. (22) observed an increased risk of breast cancer among premenopausal women for the fast oxidizing genotype, particularly among moderate to heavy drinkers. In our prospective study, we observed no increase in risk of breast cancer associated with the ADH3 genotype, regardless of alcohol consumption or menopausal status. The discrepancy could be attributed to different levels of average alcohol consumption among the two study populations or small sample sizes. Freudenheim et al. (22) had 260 premenopausal women (134 cases and 126 controls), and our study had 182 premenopausal women (88 cases and 94 controls).
One difficulty in the interpretation of our nested case-control study was the lack of a significant positive relationship of alcohol consumption with breast cancer risk, which was observed previously in the overall cohort (1) . In the pooled analysis of prospective cohort studies, the multivariate RR was 1.09 (95% CI = 1.04–1.13) for a 10 g/day increment in alcohol consumption (5) . When alcohol consumption is treated as a continuous variable, the multivariate RR for a 10 g/day increment of alcohol consumption in our nested case-control study is close to this estimate (RR = 1.08; 95% CI = 0.96–1.22). However, even with 465 cases, our study had inadequate power to detect this modest association.
In addition, we did not observe an association between past alcohol consumption (ages 18–22) and overall breast cancer risk. The majority of epidemiological studies have focused only on current alcohol consumption in relation to breast cancer risk (1, 2, 3, 4, 5) . There is some evidence to suggest that current alcohol consumption is more relevant to breast cancer risk than consumption in the distant past (28) .
A hypothesized mechanism for the relationship between alcohol consumption and breast cancer is an effect of alcohol on steroid hormone levels. To date, there have been eight published studies evaluating the association between alcohol intake and urine or plasma estrogen levels among European or Caucasian postmenopausal women (13) . Estrone sulfate, the most abundant estrogen in postmenopausal women, has been hypothesized to be a major source of estradiol in both malignant and normal breast tissue (29 , 30) . Hankinson et al. (12) investigated endogenous plasma hormone levels among 217 healthy, postmenopausal women, a subset of the NHS cohort oversampled for women with moderate alcohol consumption, and found a statistically significant correlation between alcohol consumption and estrone sulfate levels (r = 0.17; P = 0.02). A similar positive correlation was observed among the largely independent subset of women in this study. In addition, we observed weak positive correlations between alcohol consumption and bioavailable estradiol and percentage of bioavailable estradiol. When stratified by ADH3 genotype, there was no evidence of a trend in the relationship between alcohol consumption and hormone level dependent on oxidative capacity (ADH3 genotype) for any of these hormones. These data are compatible with the absence of an interaction between alcohol consumption, ADH3 genotype, and breast cancer risk in this study population.
Modest inverse associations between alcohol consumption and hormone
levels were observed with testosterone and SHBG. Madigan et
al. (31)
also observed a weak inverse association
between alcohol consumption and SHBG among postmenopausal women whose
alcohol consumption was 20 g/week. Obesity, a risk factor for breast
cancer in postmenopausal women, is strongly associated with decreased
SHBG (31
, 32)
. Increased tissue availability of estrogens
as a result of decreased SHBG is a potential mechanism for the
relationship between obesity, as well as alcohol consumption and risk
of postmenopausal breast cancer (8)
.
Statistically significant trends in the relationship between alcohol consumption and hormone level dependent on ADH3 genotype were observed for DHEAS and SHBG. For DHEAS, a modest positive correlation was observed among rapid oxidizers, and a modest inverse correlation was observed among slow oxidizers. SHBG was modestly inversely correlated with alcohol consumption among the slow and intermediate oxidizers only. Some researchers have suggested that SHBG serves as a vehicle for receptor binding and cell transport, so protein-bound estradiol may be an active component (33) . Testosterone had a similar relationship; inverse correlations were observed among the slow and intermediate oxidizers only. Heavy alcohol consumption reduces testosterone levels (34, 35, 36) . Human studies have shown that male alcoholics who abstain from drinking experience increases in testosterone levels (36) . These data suggest that among rapid oxidizers, fast clearance of alcohol from the bloodstream may mitigate the depression of androgen and SHBG levels associated with alcohol intake. However, studies in men and in women including a larger proportion of heavier drinkers are clearly needed.
In conclusion, these data suggest that variation at the ADH3 locus modestly influences the response of some hormones and SHBG to alcohol consumption, but this variation is neither independently associated with breast cancer nor a modifier of the association between alcohol and breast cancer risk among light to moderate drinkers. Similar to the data from the overall cohort and consistent with other epidemiological studies, we observed a modest association with increasing alcohol consumption and breast cancer risk in postmenopausal women (1, 2, 3, 4, 5) . This relationship was not significantly modified by ADH3 genotype. No association between alcohol and breast cancer was observed among premenopausal women; however, the number of premenopausal women in this study was small. The prospective design, relatively large number of incident cases, and high follow-up rate of our study strengthen the validity of our results. Although large studies with a higher proportion of heavier alcohol consumers would be necessary to exclude a modest interaction of alcohol, ADH3 genotype, and breast cancer risk, our study suggests that any such interaction is likely to be weak or nonexistent.
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Acknowledgments |
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Footnotes |
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1 Supported by NIH Grants CA40356, CA65725, and
CA49449. 
2 To whom requests for reprints should be
addressed, at Channing Laboratory, 181 Longwood Avenue, Boston, MA
02115. E-mail: lhines{at}hsph.harvard.edu
3 The abbreviations used are: ADH3,
alcohol dehydrogenase type 3; RR, relative risk; 95% CI, 95%
confidence interval; NHS, Nurses’ Healthy Study; DHEA,
dehydroepiandrosterone; DHEAS, dehydroepiandrosterone sulfate; SHBG,
sex hormone-binding globulin.
Received 3/ 8/00; revised 8/ 9/00; accepted 8/14/00.
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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1 and 2 subunits of human liver alcohol dehydrogenase. Eur. J. Biochem., 159: 215-218, 1986.[Medline]
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