![[Feedback]](/icons/banner/feedbackACT.gif){kind=icon}
![[For Subscribers]](/icons/banner/subscriberACT.gif){kind=icon}
![[Archive]](/icons/banner/archiveACT.gif){kind=icon}
![[Search]](/icons/banner/searchACT.gif){kind=icon}
![[Contents]](/icons/banner/tocACT.gif){kind=icon}
|
|
|
|
|
||
,2
* Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104; Department of Obstetrics and Gynecology, University of Michigan Health Sciences System, Ann Arbor, MI 48104; ** Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA 01655; Division of Epidemiology, Department of Public Health Sciences, University of California School of Medicine, Davis; and Department of Population Health and Reproduction, University of California, Davis, CA 95616
2 To whom correspondence should be addressed. E-mail: mfsowers{at}umich.edu.
 |
ABSTRACT |
|---|
 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
-hydroxyestrone (16-OHE1) in a racially diverse population. With a cross-sectional study design, urine samples from 1881 African-American, Caucasian, Chinese, Japanese, and Hispanic women, aged 42–52 y, from the Study of Women's Health Across the Nation (SWAN) were assayed by EIA for 2-OHE1 and 16-OHE1. Dietary factors and beverages were measured using a modified Block FFQ. Dietary fiber, vegetable and fruit servings, Brassica vegetables, polyphenols, coffee, caffeine, green and black tea, and total alcohol and wine were related to metabolite values using multiple variable regression analyses. In adjusted analyses, 2-OHE1 concentrations were significantly associated with race/ethnicity, weight, smoking, and consumption of hydroxybenzoic acid, anthocyanidins, wine, and caffeine (P < 0.05). Regression models incorporating these variables explained 19–20% of the variation in 2-OHE1 concentrations. Regression models for 16-OHE1, which explained 16–17% of the variability, included race/ethnicity, smoking, caffeine, total dietary fiber, and fiber from fruits and vegetables as variables. These associations may reflect why increased consumption of polyphenol-containing foods and fruit as well as decreased smoking, caffeine intake, and body size would be consistent with hypothesized benefits and risks for selected health outcomes.
KEY WORDS: • estrogen metabolism • polyphenols • isoflavones • caffeine • body size
Lifestyle practices, including dietary and beverage consumption, can influence estrogen metabolism as reflected in the urinary excretion of 2-hydroxyestrone (2-OHE1)3 and 16-hydroxyestrone (16-OHE1). Understanding the contribution of lifestyle factors to estrogen catabolism is important because 16-hydroxyestrogens appear to retain substantial estrogenic activity through covalent binding to the estrogen receptor (1) and histone proteins (2). Further, the 2-hydroxyestrogens may act as antioxidants (3). A growing appreciation of the potential physiological effect of these metabolites has developed, leading investigators to evaluate their contribution to variation in breast cancer frequency (4), bone mineral density levels (5), and oxidized LDL cholesterol concentration, an important contributor to the atherogenic process (6).
It is unclear whether race/ethnic differences in estrogen metabolism are independent of the contribution of lifestyle variation (7). For example, the prevalence of smoking behavior has marked race/ethnic variation (8), and smoking may increase induction of the 2-hydroxylation metabolic pathway (7). Physical activity patterns are also differentially reported by race/ethnic groups, and intense physical activity is associated with an increased likelihood of 2-hydroxylation of estrogen metabolites (9).
The type of dietary macronutrient intake may influence excreted estrogen metabolites. For example, chimpanzees whose dietary fat intake was increased from 15 to 65% of total energy intake had increased 16-OHE1 and decreased 2-OHE1 excretion (10). In some (11), but not all studies (12), women who were switched from a diet of 40 to 25% of energy from fat had less excretion of 16-estrogen metabolites. A high-protein diet may enhance 2-hydroxylation of estradiol (13). Consumption of cruciferous (Brassica) vegetables such as cabbage, Brussels sprouts, and broccoli may be associated with the downregulation of the cytochrome P450 (CYP)1A1 enzyme (14) which is associated with 2-hydroxylation of estrone or alteration of the 2:16 hydroxyestrone ratio (15). Selected flavonoids in citrus fruits (i.e., naringenin, kaempferol, limogen, and quercetin) may also affect the CYP450 system enzymes, which are important in estrogen metabolism (16).
We examined the relation of diet and lifestyle factors to urinary 2-OHE1 and 16-OHE1 concentrations in 1881 middle-aged African-American, Caucasian, Chinese, Hispanic, and Japanese women living in the United States as the first stage in determining how behaviors are related to estrogen metabolism in a manner that may affect health outcomes. The following questions were addressed: 1) Are intakes of food products such as servings of fruits and vegetables, caffeine and beverages, macronutrients (i.e., fiber) or polyphenols (including subgroups such as isoflavones) associated with 2-OHE1 or 16-OHE1 concentrations? 2) Are measures of body size, cigarette smoke exposure, or physical activity associated with 2-OHE1 concentrations or with 16-OHE1 concentrations? 3) Do differences in lifestyle factors explain the racial/ethnic variation in 2-OHE1 or 16-OHE1 concentrations?
As a secondary activity, we described race/ethnic variation in dietary food and nutrient intake.
|
SUBJECTS AND METHODS |
|---|
|
TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
This report includes data from 1881 (430 African-American, 945 Caucasian, 213 Chinese, 71 Hispanic, 222 Japanese) pre- and perimenopausal women from the Study of Women's Health Across the Nation (SWAN), a community-based, longitudinal study of the menopausal transition (17). Participants were enrolled at 7 clinical sites in Boston MA, Chicago IL, the Detroit area of MI, Los Angeles CA, Hudson County NJ, Oakland CA, and Pittsburgh PA. Study eligibility criteria for enrollment in the SWAN longitudinal cohort were as follows: age 42–52 y; intact uterus and at least 1 ovary; no current use of estrogens or other medications known to affect ovarian function at baseline; at least 1 menstrual period in the 3 mo before enrollment; and self-identification as a member of 1 of the 5 eligible racial/ethnic groups. Each site enrolled a Caucasian sample; additionally, each site enrolled a sample of non-Caucasian women, including African-American women in Boston, Chicago, the Detroit area, and Pittsburgh, and Japanese, Chinese, and Hispanic women in Los Angeles, Oakland, and New Jersey, respectively. Institutional Review Board approval for the study protocol and informed consent were obtained at each study site (University of Michigan; Massachusetts General Hospital; Rush University; University of California, Davis; University of California, Los Angeles; New Jersey Medical School; University of Pittsburgh).
Measures
Dietary data were obtained from a modified Block interviewer-assisted FFQ (18), administered in 4 languages (English, Spanish, Chinese, or Japanese) that obtained "usual" dietary pattern for the previous year. The English-language FFQ version contained a 103-item core food list. The Hispanic, Chinese, and Japanese ethnic group versions included the 103-item core food list plus 9, 12, and 16 additional culturally specific foods, respectively. Serving sizes were reported with the assistance of food models. Daily dietary intake values for energy, dietary protein, fat, carbohydrates, dietary fiber (which is expressed as total daily intakes and source-specific intakes, including fiber from beans, grains, fruits, and vegetables), vegetable and fruit servings, and servings of coffee, wine, beer, liquor, green tea, and black tea were taken from the FFQ and its associated dietary database (18). The FFQ was administered to participants throughout the study year, minimizing the likelihood of a seasonal effect.
Brassica vegetable servings and antioxidant fruit and vegetable servings were calculated from the raw food frequency data. Cabbage, broccoli, cauliflower, Brussels sprouts, and kale were grouped as the most commonly consumed members of the Brassica family of vegetables. The antioxidant vegetable group included kale, spinach, Brussels sprouts, alfalfa sprouts, broccoli, beets, red bell pepper, onion, corn, and eggplant. The antioxidant fruit group included prunes, raisins, blueberries, blackberries, strawberries, raspberries, plums, oranges, red grapes, cherries, kiwi, and pink grapefruit. The results for these food groups are reported as numbers of servings per day.
Polyphenol values from Manach et al. (19) were assigned to foods and beverages by using the midpoint of the published range (in mg of the particular polyphenol/serving) for an average daily serving estimated from the FFQ (Appendix 1). Some food or beverage items on the FFQ could contribute to more than one polyphenol subgroup; thus, decision rules were developed for their assignment. For example, the FFQ groups all berries together as a single item; however, only blueberries contribute hydroxycinnamic acid. Because blueberry consumption is limited, berries were not assigned to the hydroxyannamic acid subgroup. In contrast, several types of berries assessed with the FFQ (strawberries, blackberries and raspberries) include hydroxybenzoic acids; therefore, the mean number of berry servings was assigned a daily hydroxybenzoic acid intake value. Isoflavone values included daily genistein and daidzein intakes that had been estimated previously from the FFQ and database using information from Reinli (20). USDA tables were used as the reference source for the hot chili peppers included in the Spanish version of the FFQ.
|
Total alcohol consumption per day was categorized as none, <7.1 g (<0.25 oz), 7.1–14.2 g (0.25–0.50 oz), and at least 14.2 g (0.50 oz). Wine consumption was evaluated separately and categorized as none, no more than 0.50 serving/d, and >0.50 serving/d.
Other lifestyle variables. Weight (kg) was measured using balance beam scales, and height (m) was measured using stadiometers. Data on active and passive smoke exposure were obtained from a self-administered questionnaire incorporating American Thoracic Society questions (21) and validated questions on passive exposure (22), respectively. Number of cigarettes per day was categorized as 0, 1–9 (less than a half-pack), 10–19 (at least a half-pack but less than a pack), and 20+ (at least a pack). Total weekly hours of passive smoke exposure at home, work, or in other public/social settings was categorized into quartiles for nonzero values, with a 5th category for no exposure. Baseline leisure physical activity was based on the frequency, intensity, and duration of the 2 sports or exercise activities reported by the participant as occurring in the year before assessment (23). Possible physical activity scores ranged from 1 to 5.
Estrogen metabolites.
The estrogen metabolites 2-OHE1 and 16-OHE1 were assayed in urine collected during a first morning void at the first annual follow-up visit (n = 1729); however, annual core specimen collection protocols at 2 of the sites did not include urine. To obtain this type of sample at these 2 sites, urine samples (n = 152) collected as part of the protocol for the SWAN Daily Hormone SubStudy (DHS) were selected and assayed. DHS urine samples were collected during the early follicular stage of the menstrual cycle to be consistent with the timing of samples collected at the annual visit.
2-OHE1 and 16-OHE1 were assayed by EIA (ESTRAMETTM) in triplicate (24); values were corrected for creatinine. The inter- and intra-assay CV were <10% for each analyte over the assay range of 2.09–139.6 µmol/L. Urinary forms of 2-OHE1 and 16-OHE1 are found as 3-glucuronides or mixtures of 3- and 16-glucuronides and sulfates. The estrogen metabolites were deconjugated from glucuronic acid and sulfate to achieve recognition by the monoclonal antibodies using a mixture of ß-glucuronidase and arylsulfatase enzyme isolated from the snail Helix pomatia.
Serum estradiol (E2) concentrations were measured with a modified, off-line ACS:180 (E2-6) immunoassay (Bayer Diagnostics). Inter- and intra-assay CV were 10.6 and 6.4%, respectively, over the assay range of 73.42 to 1835.5 pmol/L. Because the blood was drawn during the early follicular phase of the menstrual cycle, the assay range was calibrated to address the lower E2 values. Less than 1% of the study participants had transitioned to menopause at the first annual follow-up visit. Because E2 is a source of the metabolites of interest, early follicular E2 values were included in modeling, serving as a proxy for menopause status.
Statistical approach
Analyses were conducted using SAS Version 8.0 statistical software. Diet information came from the baseline administration of the FFQ. Other data were collected at the first follow-up visit because urine samples for analyses of the estrogen metabolites were available only from the first follow-up visit. Data were excluded from this report for any of the following reasons: loss from the baseline enrollment of 3302 women due to attrition (n = 453) or surgical menopause (n = 9), use of exogenous reproductive hormone therapy at the first follow-up visit (n = 212), missing estrogen metabolite data because the protocol at 2 sites did not include urine collection (n = 593), missing serum E2 levels (n = 9), missing baseline dietary data (n = 99), and missing number of cigarettes smoked (n = 14) or passive smoke exposure hours (n = 50). The final analytical sample included 1881 participants: 430 African-American, 945 Caucasian, 71 Hispanic, 213 Chinese, and 222 Japanese women.
Means ± SE were calculated for continuous variables, including 2-OHE1 and 16-OHE1, serum E2, weight, and age. Dietary variable distributions were expressed as medians and interquartile ranges (IQR = difference between the 75th and the 25th percentiles) to account for the highly skewed nature of the distributions of these variables. Continuous variables were log-transformed, as appropriate, to satisfy the normality assumption in parametric testing. In multivariate regression, explanatory factors were categorized to accommodate the nonlinear associations with hormone metabolites.
ANOVA with F-tests were used to evaluate potential differences between race/ethnic groups. Differences in dietary data that were not normally distributed were evaluated using the Kruskal-Wallis test. Analyses of covariance (ANCOVA) were used to determine the associations between dietary and lifestyle variables and the estrogen metabolites. Partial Spearman correlations were estimated to describe relations between the dietary variables and the estrogen metabolites. Dietary variables were evaluated singly rather than simultaneously to avoid collinearity.
In models with 2-OHE1 as the outcome variable, serum E2, weight, age, race/ethnicity, and kilojoules of energy (dietary models only) were included as covariates; in models with 16-OHE1 as the outcome variable, the covariates were serum E2, age, race/ethnicity, and kilojoules of energy (dietary models only). Unadjusted and adjusted analyses showed that 16-OHE1 was not related to weight. Interactions between weight and E2, weight and race/ethnic group, and dietary factors were evaluated, but a significant interaction was observed only between race/ethnicity and weight. Therefore, only the model evaluating the association between weight and 2-OHE1 included that interaction term.
P-values from the correlations used to characterize statistical significance in initial variable selection for multivariate modeling were two-sided at < 0.10; 95% CI were used to describe associations from ANCOVA models.
|
RESULTS |
|---|
|
TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
-OHE1 levels varied among race/ethnic groups (Table 1). The concentration of 2-OHE1 in Caucasian women was 55.6 µmol/L, which was significantly higher than those of Japanese, Chinese, and Hispanic women. African-American women had the highest 16-OHE1 concentration (34.9 µmol/L), which was significantly higher than all other race/ethnic groups. Hispanic and Japanese women had the lowest concentrations of both 2-OHE1 (37.3 and 37.8 µmol/L, respectively) and 16-OHE1 (both with 21.3 µmol/L). The serum E2 concentration was significantly lower in Japanese women (231.3 pmol/L) than in Caucasian women (272.4 pmol/L).
|
The total polyphenol profile of Japanese and Chinese women was markedly different from that of Hispanic, Caucasian, and African-American women (Table 2). This was driven by the increased consumption of monomeric flavanols and isoflavones (including genistein and daidzein). Further, the median consumption of isoflavones was twice as great in Japanese women compared with Chinese women [previously reported by Huang et al. (25)]. Hispanic women consumed fewer foods with hydroxybenzoic acids and anthocyanidins. Additionally, race/ethnic differences were notable in consumption of the macronutrients (energy in kilojoules, protein, fat, carbohydrates), and amounts of fiber, fruits and vegetables, and polyphenol subgroups shown in Table 2.
|
|
67.1 mg caffeine/d (31.8 µmol/L), the lowest quartile of intake. The association with wine was not monotonic, with the highest 2-OHE1 concentration in the middle category; this association remained significant with multivariate adjustment. Coffee intakes and green tea consumption were not related to 2-OHE1 levels after adjustment for covariates. Foods rich in hydroxybenzoic acids (i.e., berries) and anthocyanidins (i.e., berries and cabbage) comprised the 2 polyphenol subgroups whose consumption was significantly and positively associated with 2-OHE1.
16-OHE1 metabolite concentrations.
Smoking was associated with 16-hydroxyestrone concentrations in models adjusted for E2 concentrations, age, and race (Table 3). Nonsmokers had lower mean 16-OHE1 levels (21.6 µmol/L) compared with all levels of smokers (26.5 µmol/L for 1–9 cigarettes/d, 27.2 µmol/L for 10–19 cigarettes/d, and 24.8 µmol/L for 20 or more cigarettes/d). Caffeine consumption (excluding caffeine from coffee) was positively related to 16-OHE1 concentrations with the highest level of caffeine consumption (
319 mg/d) showing the highest metabolite levels (Table 3). Total dietary fiber [results not shown, ß = –0.16 (95% CI –0.24, –0.09)], as well as fiber estimated from vegetables and fruits, was significantly inversely associated with 16-OHE1.
|
DISCUSSION |
|---|
|
TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
-hydroxyestrone but not the 2:16 hydroxyestrone ratio because, biologically, the metabolites have potentially distinct roles, including antioxidant and receptor ligand binding capacities (4,5,26). The 16-hydroxylated metabolite is well-defined by its ability to bind with and engage in signal transduction through the estrogen receptor (27). In contrast, the role of the 2-hydroxylated metabolite is less well-defined. African-American women had a significantly lower 2:16 hydroxyestrone ratio than Caucasian women, consistent with other reports (28,29). We found that Chinese and Japanese women were relatively similar to each other but different from Caucasian women as also reported by Aldercruetz et al. (30). Importantly, the association of metabolites with race/ethnicity existed independently of body size, smoking behavior, and dietary consumption, which might influence estrogen metabolism. It remains to be determined whether these race/ethnic differences in estrogen metabolites are related to a differential presentation of disease frequencies and symptoms by race/ethnic group. Most research on these disease associations occurs in Asian populations, but our data suggest the importance of also including African-American women in future studies.
Body size, reflected in weight (results are similar with and without adjustment for height), can influence the estrogen metabolite pool through increased aromatization of androgens to estrogens by adipocytes (31). Coker et al. (29) reported that obese women were more likely to have lower 2:16-hydroxyestrone ratios than nonobese women. We found that obese women were more likely to have lower 2-OHE1, a relation we cannot explain. Only a modest rise was observed in 16-OHE1 with increased weight and this was not significant. This suggests that the observation by Coker et al. (29) was, in large measure, due to lower 2-hydroxyestrone concentrations. Because of the importance of body size to the estrogen metabolite pool, physical activity might also be an important mediator. Intense physical activity is associated with an increased likelihood of 2-hydroxylation of estrogen metabolites (32,33). In unadjusted analyses, there was a modest positive increase in 2-OHE1 with increasing physical activity score and no association with 16-OHE1; however, these associations were not significant when adjusted for weight and race/ethnicity.
Nonsmokers had lower 2-OHE1 and 16-OHE1 concentrations than smokers with no dose-response among the smokers. This is in contrast to the report of Jernström et al. (34) on premenopausal women in which smokers and nonsmokers (or number of cigarette packs smoked) did not differ after adjustment for age, ethnicity, menstrual status, and oral contraceptive use (34). Because smokers are generally 10–15 pounds (4.5–6.8 kg) lighter than nonsmokers (35), we considered an interaction between smoking status and body size in relation to the estrogen metabolites but there was no significant interaction.
Our adjusted analyses indicated that caffeine (from chocolate, tea, and cola beverages but excluding caffeine from coffee), was associated with both estrogen metabolites; in contrast, Jernström et al. (34) reported that higher coffee consumption was significantly associated with higher levels of 2-OHE1 and nonsignificantly negatively associated with 16-OHE1 (34). We speculate that the CYP450 enzymes responsible for the conversion of estrone to the metabolites of interest (CYP1A1 and CYP1B1) may be induced by increased caffeine intake, which is consistent with the long-time practice of using caffeine as a challenge test for enzyme systems (36,37).
An alcohol-induced rise in estrogens is a consequence of alcohol catabolism in the liver (38). Interestingly, this rise was particularly noted around the periovulatory and luteal phase of the menstrual cycle (39). We did not see an association between alcohol consumption and the estrogen metabolites in the follicular phase in our adjusted analyses. Potentially, we might have seen a pattern had the urine samples for this study been collected in the luteal phase of the menstrual cycle. Wine consumption, however, was modestly associated with 2-OHE1 levels in adjusted analyses. Red wine, but not white wine, was reported to negatively affect the activity of the CYP450 enzyme aromatase, suppressing the conversion of androgens to estrogens (40).
This study is distinctive in examining classes of carboxylic acid derivatives of compounds with a phenolic ring system (including hydroxybenzoic and hydroxycinnamic acids). Hydroxybenzoic acids occur primarily as glycosides in which hydroxycinnamic acids appear as simple esters. In these chemical classes, the phenolic hydroxyl moiety may be stoichiometrically positioned in the estrogen receptor to mimic the C3-hydroxyl location on the steroid A ring, which is responsible for activation of the receptor (41). Other heterocyclic phenols, widely designated as phytoestrogens, have structures analogous to human estrogens and bind to the estrogen receptors and ß to stimulate transcriptional activity (42,43). We evaluated specific estimates of genistein and diadzein, the main compounds of the isoflavone class of phytoestrogens (44) found in soybeans and soy-derived foods such as tempeh, miso, and tofu. There was no association with genistein and diadzein, consistent with the report by Martini et al. (45) but in contrast to Xu et al. (46) who reported that moderate soy protein consumption was associated with decreased amounts of 16-OHE1.
Within this study, food products thought to have antioxidant activity, including hydroxybenzoic acid and anthocyanidins, were more likely to be related to higher levels of 2-OHE1. Polyphenols were positively associated with variation in 2-OHE1 concentrations, whereas fiber intakes were negatively associated with 16-OHE1. It is almost axiomatic that a high-fiber diet diminishes estrogen absorption from the intestinal lumen (47) and that binding of unconjugated estrogens to fiber in the gut impedes their reabsorption (48). Increased fiber intake has been associated with less bioavailable estradiol (49). Dietary fiber intake and plasma estradiol concentrations were negatively correlated in a study of premenopausal Asian immigrant and Caucasian women (50). Types of fiber have different effects, and the effect may or may not reflect the contribution of other dietary constituents. For example, significant reductions in serum estrogen concentrations were reported in premenopausal women supplemented with wheat bran, but not in those supplemented with oat or corn bran, all of which doubled dietary fiber consumption without reducing fat intake (47).
The indole content of Brassica vegetables (including Brussels sprouts, broccoli, cabbage, kale, turnips, collards and cauliflower) was studied as a protective mechanism against breast cancer (51). However, not all studies, including those with an intercountry orientation, identified a protective effect with Brassica vegetable consumption for breast cancer (52,53). One explanation may be the amount of Brassica vegetable consumed. The mean consumption in our study was less than one-third serving per day, which is probably insufficient to provide detectable protection.
The capabilities and limitations of this study must be considered. Food intakes, estimated from the FFQ, may be imprecisely recalled by respondents with respect to serving size and frequency of consumption, resulting in misclassification. Errors can arise from limitations in existing food composition databases for polyphenols, missing food sources, and failure to account for the variation generated by soil content in which crops are grown or the processing/cooking to which the foods are subjected (20,54). Although this study included a single urine sample for metabolite assessment, measures of 2-OHE1 and the 16-OHE1 have a high within-woman correlation of 0.85. SWAN also incorporated strategies to minimize the effect of measurement error, including collection of urine within d 2–5 of the onset of menses when estrogen production was lowest and the ratio of estradiol to estrone was most consistent.
In summary, this study confirms the importance of race/ethnicity in characterizing estrogen metabolism among middle-aged women. The current data demonstrate that selected self-reported fiber, antioxidant, and polyphenol intake, as well as weight, smoking, and caffeine intake were associated with the urinary concentrations of estrogen metabolites. Further, there appeared to be more variation in 2-OHE1 explained by environmental factors than 16-OHE1. This assessment suggests that it may be beneficial to increase consumption of polyphenol-containing foods and fruit as well as to decrease smoking, caffeine intake, and body size; this would be consistent with hypothesized benefits and risks for specific health outcomes.
|
FOOTNOTES |
|---|
3 Abbreviations used: ANCOVA, analyses of covariance; E2, estradiol; CYP450, cytochrome P450; IQR, interquartile range; 2-OHE1, 2-hydroxyestrone; 16-OHE1, 16-hydroxyestrone; SWAN, Study of Women's Health Across the Nation.
Manuscript received 22 November 2005. Initial review completed 5 January 2006. Revision accepted 19 March 2006.
|
LITERATURE CITED |
|---|
|
TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
1. Swaneck GE, Fishman J. Covalent binding of the endogenous estrogen 16-hydroxyestrone to estradiol receptor in human breast cancer cells: characterization and intranuclear localization. Proc Natl Acad Sci U S A. 1988;85:7831–35.
2. Yu SC, Fishman J. Interaction of histones with estrogens. Covalent adduct formation with 16-hydroxyestrone. Biochemistry. 1985;24:8017–21.[Medline]
3. Bradlow HL, Hershcopf RE, Fishman JF. Oestradiol 16 alpha-hydroxylase: a risk marker for breast cancer. Cancer Surv. 1986;5:573–83.[Medline]
4. Wellejus A, Olsen A, Tjonneland A, Thomsen BL, Overvad K, Loft S. Urinary hydroxyestrogens and breast cancer risk among postmenopausal women: a prospective study. Cancer Epidemiol Biomarkers Prev. 2005;14:2137–42.
5. Lim SK, Won YJ, Lee JH, Kwon SH, Lee EJ, Kim KR, Lee HC, Huh KB, Chung BC. Altered hydroxylation of estrogen in patients with postmenopausal osteopenia. J Clin Endocrinol Metab. 1997;82:1001–6.
6. Seeger H, Mueck AO, Lippert TH. Effect of estradiol metabolites on the susceptibility of low density lipoprotein to oxidation. Life Sci. 1997;61:865–8.[Medline]
7. Michnovicz JJ, Hershcopf RJ, Naganuma H, Bradlow HL, Fishman J. Increased 2-hydroxylation of estradiol as a possible mechanism for the anti-estrogenic effect of cigarette smoking. N Engl J Med. 1986;315:1305–9.[Abstract]
8. Gold EB, Bromberger J, Crawford S, Samuels S, Greendale GA, Harlow SD, Skurnick J. Factors associated with age at natural menopause in a multiethnic sample of midlife women. Am J Epidemiol. 2001;153:865–74.
9. Matthews CE, Fowke JH, Dai Q, Bradlow HL, Jin F, Shu XO, Gao YT, Longcope C, Hebert JR, Zheng W. Physical activity, body size, and estrogen metabolism in women. Cancer Causes Control. 2004;15:473–81.[Medline]
10. Musey PI, Collins DC, Bradlow HL, Gould KG, Preedy JR. Effect of diet on oxidation of 17-beta estradiol in vivo. J Clin Endocrinol Metab. 1987;65:792–5.[Abstract]
11. Longcope C, Gorbach S, Goldin B, Woods M, Dwyer J, Morrill A, Warram J. The effect of a low fat diet on estrogen metabolism. J Clin Endocrinol Metab. 1987;64:1246–50.[Abstract]
12. Pasagian-Macaulay A, Meilahn EN, Bradlow HL, Sepkovic DW, Buhari AM, Simkin-Silverman L, Wing RR, Kuller LH. Urinary markers of estrogen metabolism 2- and 16-hydroxylation in premenopausal women. Steroids. 1996;61:461–67.[Medline]
13. Anderson KE, Kappas A, Conney AH, Bradlow HL, Fishman J. The influence of dietary protein and carbohydrate on the principal oxidative biotransformations of estradiol in normal subjects. J Clin Endocrinol Metab. 1984;59:103–7.[Abstract]
14. Anderson KE. Influence of diet and nutrition on clinical pharmacokinetics. Clin Pharmacokinet. 1988;14:325–46.[Medline]
15. Kall MA, Vang O, Clausen J. Effects of dietary broccoli on human drug metabolising activity. Cancer Lett. 1997;114:169–70.[Medline]
16. Schubert W, Cullberg G, Edgar B, Hedner T. Inhibition of 17 beta-estradiol metabolism by grapefruit juice in ovariectomized women. Maturitas. 1994;20:155–63.[Medline]
17. Sowers MF, Crawford S, Sternfeld B, Morgenstein D, Gold E, Greendale G, Evans D, Neer R, Matthews K, et al. Design, survey sampling and recruitment methods of SWAN: a multi-center, multi-ethnic, community-based cohort study of women and the menopausal transition. In: Lobo RA, Kelsey J, Marcus R, editors. Menopause: biology and pathobiology. San Diego: Academic Press; 2000. p.175–88.
18. Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J Epidemiol. 1986;124:453–69.
19. Manach C, Scalbert A, Morand C, Rémésy C, Jiménez L. Polyphenols: food sources and bioavailability. Am J Clin Nutr. 2004;79:727–47.
20. Reinli K, Block G. Phytoestrogen content of foods—a compendium of literature values. Nutr Cancer. 1996;26:123–48.[Medline]
21. Ferris BG. Epidemiology Standardization Project (American Thoracic Society). Am Rev Respir Dis. 1978;118:1–20.[Medline]
22. Coghlin J, Hammond SK, Gann PH. Development of epidemiologic tools for measuring environmental tobacco smoke exposure. Am J Epidemiol. 1989;130:696–704.
23. Sternfeld B, Ainsworth BE, Quesenbury CP Jr. Physical activity patterns in a diverse population of women. Prev Med. 1999;28:313–23.[Medline]
24. Klug TL, Bradlow HL, Sepkovic DW. Monoclonal antibody-based enzyme immunoassay for simultaneous quantitation of 2- and 16-hydroxyestrone in urine. Steroids. 1994;59:648–55.[Medline]
25. Huang MH, Schocken M, Block G, Sowers MF, Gold E, Sternfeld B, Seeman T, Greendale G. Variation in nutrient intakes by ethnicity: results from the Study of Women's Health Across the Nation (SWAN). Menopause. 2002;9:309–19.[Medline]
26. Westerlind KC, Gibson KJ, Wolfe P. The effect of diurnal and menstrual cyclicity and menopausal status on estrogen metabolites: implications for disease-risk assessment. Steroids. 1999;64:233–43.[Medline]
27. Gupta M, McDougal A, Safe S. Estrogenic and antiestrogenic activities of 16- and 2-hydroxy metabolites of 17ß-estradiol in MCF-7 and T47D human breast cancer cells. J Steroid Biochem Mol Biol. 1998;67:413–9.[Medline]
28. Taioli E, Garte SJ, Trachman J, Garbers S, Sepkovic DW, Osborne MP, Mehl S, Bradlow HL. Ethnic differences in estrogen metabolism in healthy women [letter]. J Natl Cancer Inst. 1996;88:617.
29. Coker AL, Crane MM, Sticca RP, Sepkovic DW. Re: Ethnic differences in estrogen metabolism in healthy women. J Natl Cancer Inst. 1997;89:89–90.
30. Adlercreutz H, Gorbach SL, Goldin BR, Woods MN, Dwyer JT, Hamalainen E. Estrogen metabolism and excretion in Oriental and Caucasian women. J Natl Cancer Inst. 1994;86:1076–82.
31. Azziz R. Reproductive endocrinologic alterations in female asymptomatic obesity. Fertil Steril. 1989;52:703–25.[Medline]
32. De Crée C, Ball P, Seidlitz B, Van Kranenburg G, Geurten P, Keizer HA. Effects of a training program on resting plasma 2-hydroxycatecholestrogen levels in eumenorrheic women. J Appl Physiol. 1997;83:1551–6.
33. Snow RC, Barbieri RL, Frisch RE. Estrogen 2-hydroxylase oxidation and menstrual function among elite oarswomen. J Clin Endocrinol Metab. 1989;69:369–76.[Abstract]
34. Jernström H, Klug TL, Sepkovic DW, Bradlow HL, Narod SA. Predictors of the plasma ratio of 2-hydroxyestrone to 16-hydroxyestrone among pre-menopausal, nulliparous women from four ethnic groups. Carcinogenesis. 2003;24:991–1005.
35. Klesges RC, Klesges LM, Meyers AW. Relationship of smoking status, energy balance, and body weight: analysis of the Second National Health and Nutrition Examination Survey. J Consult Clin Psychol. 1991;59:899–905.[Medline]
36. Ayalogu EO, Snelling J, Lewis DF, Talwar S, Clifford MN, Ioannides C. Induction of hepatic CYP1A2 by the oral administration of caffeine to rats: lack of association with the Ah locus. Biochim Biophys Acta. 1995;1272:89–94.[Medline]
37. Goasduff T, Dréano Y, Guillois B, Ménez JF, Berthou F. Induction of liver and kidney CYP1A1/1A2 by caffeine in rat. Biochem Pharmacol. 1996;52:1915–9.[Medline]
38. Gill J. The effects of moderate alcohol consumption on female hormone levels and reproductive function. Alcohol Alcohol. 2000;35:417–23.
39. Reichman ME, Judd JT, Longcope C, Schatzkin A, Clevidence BA, Nair PP, Cambell WS, Taylor PR. Effects of moderate alcohol consumption on plasma and urinary hormone concentrations in premenopausal women. J Natl Cancer Inst. 1993;85:722–7.
40. Eng ET, Williams D, Mandava U, Kirma N, Tekmal RR, Chen S. Suppression of aromatase (estrogen synthetase) by red wine phytochemicals. Breast Cancer Res Treat. 2001;67:133–46.[Medline]
41. Labaree DC, Zhang JX, Harris HA, O'Connor C, Reynolds TY, Hochberg RB. Synthesis and evaluation of B-, C-, and D-ring-substituted estradiol carboxylic acid esters as locally active estrogens. J Med Chem. 2003;46:1886–904.[Medline]
42. Nikov GN, Hopkins NE, Boue S, Alworth WL. Interactions of dietary estrogens with human estrogen receptors and the effect on estrogen receptor-estrogen response element complex formation. Environ Health Perspect. 2000;108:867–72.[Medline]
43. Cassidy A. Potential tissue selectivity of dietary phytoestrogens and estrogens. Curr Opin Lipidol. 1999;10:47–52.[Medline]
44. Messina MJ. Legumes and soybeans: overview of their nutritional profiles and health effects. Am J Clin Nutr. 1999;70: 3suppl:439S–50.
45. Martini MC, Dancisak BB, Haggans CJ, Thomas W, Slavin JL. Effects of soy intake on sex hormone metabolism in premenopausal women. Nutr Cancer. 1999;34:133–9.[Medline]
46. Xu X, Duncan AM, Merz BE, Kurzer MS. Effects of soy isoflavones on estrogen and phytoestrogen metabolism in premenopausal women. Cancer Epidemiol Biomarkers Prev. 1998;7:1101–8.[Abstract]
47. Rose DP, Goldman M, Connolly JM, Strong LE. High-fiber diet reduces serum estrogen concentrations in premenopausal women. Am J Clin Nutr. 1991;54:520–5.
48. Shultz TD, Howie BJ. In vitro binding of steroid hormones by natural and purified fibers. Nutr Cancer. 1986;8:141–7.[Medline]
49. Rock CL, Flatt SW, Thomson CA, Stefanick ML, Newman VA, Jones LA, Natarajan L, Ritenbaugh C, Hollenbach KA, et al. Effects of a high-fiber, low-fat diet intervention on serum concentrations or reproductive steroid hormones in women with a history of breast cancer. J Clin Oncol. 2004;22:2379–87.
50. Goldin BR, Adlercreutz MD, Gorbach MD, Woods MN, Dwyer JT, Conlon T, Bohn E, Gershoff SN. The relationship between estrogen levels and diets of Caucasian American and Oriental immigrant women. Am J Clin Nutr. 1986;44:945–53.
51. Fowke JH, Longcope C, Hebert JR. Brassica vegetable consumption shifts estrogen metabolism in healthy postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2000;9:773–79.
52. Graham S, Marshall J, Mettlin C, Rzepka T, Nemoto T, Byers T. Diet in the epidemiology of breast cancer. Am J Epidemiol. 1982;116:68–75.
53. Franceschi S, Parpinel M, LaVecchia C, Favero A, Talamini R, Negri E. Role of different types of vegetables and fruit in the prevention of cancer of the colon, rectum, and breast. Epidemiology. 1998;9:338–41.[Medline]
54. Pillow PC, Duphorne CM, Chang S, Contois JH, Strom SS, Spitz MR, Hursting SD. Development of a database for assessing dietary phytoestrogen intake. Nutr Cancer. 1999;33:3–19.[Medline]
This article has been cited by other articles:
|
|
 |
|
  E. V Bandera, L. H Kushi, D. F Moore, D. M Gifkins, and M. L McCullough Association between dietary fiber and endometrial cancer: a dose-response meta-analysis Am. J. Clinical Nutrition, December 1, 2007; 86(6): 1730 - 1737. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. M. Tomey, M. R. Sowers, X. Li, D. S. McConnell, S. Crawford, E. B. Gold, B. Lasley, and J. F. Randolph Jr Dietary Fat Subgroups, Zinc, and Vegetable Components Are Related to Urine F2a-Isoprostane Concentration, a Measure of Oxidative Stress, in Midlife Women J. Nutr., November 1, 2007; 137(11): 2412 - 2419. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. Greenlee, C. Atkinson, F. Z. Stanczyk, and J. W. Lampe A Pilot and Feasibility Study on the Effects of Naturopathic Botanical and Dietary Interventions on Sex Steroid Hormone Metabolism in Premenopausal Women Cancer Epidemiol. Biomarkers Prev., August 1, 2007; 16(8): 1601 - 1609. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. L. Campbell, K. C. Westerlind, V. J. Harber, G. J. Bell, J. R. Mackey, and K. S. Courneya Effects of Aerobic Exercise Training on Estrogen Metabolism in Premenopausal Women: A Randomized Controlled Trial Cancer Epidemiol. Biomarkers Prev., April 1, 2007; 16(4): 731 - 739. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Mahabir, D. J. Baer, L. L. Johnson, T. J. Hartman, J. F. Dorgan, W. S. Campbell, B. A. Clevidence, and P. R. Taylor Usefulness of Body Mass Index as a Sufficient Adiposity Measurement for Sex Hormone Concentration Associations in Postmenopausal Women Cancer Epidemiol. Biomarkers Prev., December 1, 2006; 15(12): 2502 - 2507. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
