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American Journal of Clinical Nutrition, Vol. 87, No. 3, 679-687, March 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Demographic, anthropometric, and lifestyle factors and dietary intakes in relation to daidzein-metabolizing phenotypes among premenopausal women in the United States1,2,3

Charlotte Atkinson, Katherine M Newton, Erin J Aiello Bowles, Mellissa Yong and Johanna W Lampe

1 From the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (CA, MY, and JWL); Department of Epidemiology, University of Washington, Seattle, WA (JWL and KMN); Group Health Center for Health Studies, Seattle, WA (KMN and EJAB)

2 Supported by the National Institutes of Health (R01CA97366 and U01CA63731).

3 Address reprint requests to JW Lampe, Cancer Prevention Program, Fred Hutchinson Cancer Research Center, PO Box 19024, M4-B402, Seattle, WA 98109. Email: jlampe{at}fhcrc.org.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: The soy isoflavone daidzein is metabolized to equol and O-desmethylangolensin (ODMA) by intestinal bacteria in {approx}30–50% and 80–90% of persons, respectively. Studies suggest beneficial health effects associated with daidzein-metabolizing phenotypes; thus, assessing their determinants is an important goal.

Objective: We evaluated relations between daidzein-metabolizing phenotypes and demographic, anthropometric, lifestyle, and dietary factors among premenopausal women in the United States.

Design: Two hundred women provided a first-void urine sample after a 3-d soy challenge and completed a health and demographics questionnaire, physical activity questionnaire, food-frequency questionnaire, and 3-d food record. Urine samples were measured for isoflavones by gas chromatography–mass spectrometry to determine daidzein-metabolizing phenotypes.

Results: Fifty-five (27.5%) and 182 (91%) women had detectable concentrations of urinary equol and ODMA (>87.5 ng/mL), respectively, and were classed as producers of these metabolites. Compared with nonproducers, equol producers were more likely (P ≤ 0.05) to be Hispanic or Latino, to be highly educated, and to have frequent constipation, and ODMA producers were taller and less likely to be Asian than white. Equol and ODMA producers reported higher overall physical activity than did nonproducers.

Conclusions: We observed associations between equol production and ethnicity, education, constipation, and physical activity and between ODMA production and race, height, and physical activity. Associations with race and ethnicity were based on small numbers of Asian and Hispanic or Latino women, and confirmation of these findings is needed. Few dietary factors, assessed with the use of either a food-frequency questionnaire or food record, were associated with daidzein-metabolizing phenotypes.

Key Words: Daidzein • equol • isoflavone • O-desmethylangolensin • phytoestrogen metabolism • premenopausal women


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Daidzein, a soy isoflavone, can be metabolized by intestinal bacteria to equol and O-desmethylangolensin (ODMA). However, studies have shown that only {approx}30–50% of humans produce equol (1-6) and {approx}80–90% produce ODMA (3, 6, 7). In vitro studies suggest that equol is more biologically active than is daidzein, which has led to substantial interest in equol production and human health (8, 9). The equol-producer phenotype per se also was associated with health outcomes. For example, in postmenopausal women in the United States, the ability to produce equol was favorably associated with breast cancer risk factors, including mammographic breast density and the ratio of urinary 2-hydroxyestrone to 16{alpha}-hydroxyestrone (7, 10). Equol production also was beneficially associated with risk factors for cardiovascular disease and prostate cancer (11-13). Few studies have investigated ODMA production and human health, but several studies with small sample sizes have found ODMA production to be associated with higher breast and bone densities and with urinary excretion of 2-hydroxyestrone (7, 10, 14).

Investigators have attempted to identify the bacteria involved in equol and ODMA production, and several candidate bacteria were identified (15-18). Recent work suggested a consortium of bacteria may be involved in equol production (19), and the bacteria responsible for equol production probably differ from the bacteria responsible for ODMA production (19, 20). However, it remains unclear why some, but not all, persons harbor equol- or ODMA-producing bacteria.

Few studies have assessed demographic, anthropometric, and other lifestyle factors in relation to daidzein-metabolizing phenotypes. In the United States, equol production was positively associated with education, and ODMA production was inversely associated with age, height, weight, and body mass index (in kg/m2) (10, 14, 21). In addition, Asians were less likely than whites to be ODMA producers, and former smokers were more likely than never smokers to be equol or ODMA producers. In contrast, in Korean American women, education was inversely associated with equol production (22).

Among the studies that have assessed diet in relation to equol production, some (1, 4, 23, 24), but not all (25-27), have reported positive associations between equol production and intakes of soy, animal meat, green tea, and a low-fat high-carbohydrate diet. In postmenopausal women, ODMA producers consumed more energy from carbohydrate and less energy from fat than did nonproducers (7). Intervention studies were not able to convert equol nonproducers into producers (28-31), and the daidzein-metabolizing phenotypes appear to be stable over time (12, 32), suggesting that early-life exposures may be important determinants of these phenotypes.

We hypothesized that factors which can influence the composition and activity of the resident intestinal microbiota, such as diet and early-life exposures, would be associated with equol and ODMA production. In a population of premenopausal women, we evaluated relations between daidzein-metabolizing phenotypes and demographic, anthropometric, and lifestyle factors, including physical activity. In addition, we investigated relations between daidzein-metabolizing phenotypes and dietary intakes, measured with the use of both a 3-d food record and a food-frequency questionnaire (FFQ).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
The study was designed to investigate a number of factors in relation to daidzein-metabolizing phenotypes, including demographics, anthropometrics, lifestyle factors, dietary intakes, and hormone-related outcomes. We report here the relations between daidzein-metabolizing phenotypes and demographic, anthropometric, lifestyle, and dietary factors.

Recruitment
Women were recruited from within Group Health (GH), a large integrated health plan in Washington State. Eligibility criteria were primarily established to include premenopausal women and to exclude women taking exogenous hormones and women who had taken antibiotics in the 3 mo before participation in the study. Women aged 40–45 y who had undergone a screening mammogram in the previous 10 mo were identified from the GH Breast Cancer Screening Program (33). GH databases, including pharmacy and hospital records, and self-report data from questionnaires completed at the time of each mammogram were used to exclude women who had filled >1 prescription for hormone therapy (HT) or oral contraceptives (OCs) within 18 mo of the sampling date; had a personal history of, or current, breast cancer; had breast implants; had a hysterectomy or oophorectomy; used tamoxifen or raloxifene; had a diagnosis of gastrointestinal disorders or surgeries in the 10 y before their mammogram; or received prescriptions for antibiotics, bisphosphonates, or corticosteroids within 3 mo of the sampling date. Women were recruited according to the Breast Imaging Reporting and Data System (BIRADS) density score (34) assigned to their most recent screening mammogram because one of the study outcomes was mammographic breast density. We aimed to recruit approximately even numbers of women with a BIRADS density score of 1 or 2 (combined as one group), 3, and 4. An initial contact letter was mailed to potential study participants and was followed up with a phone call to further screen potential participants for interest and eligibility. Some questions, such as those about personal history of breast cancer or current use of hormones, verified the selection of women based on the GH databases and confirmed that, for example, women had not started taking HT or OCs since being identified as a potential participant. On the basis of responses at the phone screening, women were ineligible to participate if they were allergic to soy beans or soy protein; had been diagnosed with Crohn disease or ulcerative colitis or had any part of their colon removed; had been diagnosed with breast cancer; were pregnant or planning to become pregnant; had a hysterectomy or any part of their ovaries removed; were perimenopausal (skipped ≥1 periods in the previous 12 mo, or had irregular bleeding patterns); were currently using HT or OCs, had used them for ≥1 mo in the past 12 mo, or had used them in the 6 mo before their screening mammogram; were currently taking antibiotics or had taken them for ≥1 mo in the previous 12 mo; and had ever taken tamoxifen or were currently taking raloxifene, bisphosphonates, or oral steroids.

A total of 1407 women were identified as potential participants from the GH Breast Cancer Screening Program. Of these women, 367 (26%) were found to be ineligible, 691 (49%) refused participation, and 146 (10%) were not able to be interviewed or scheduled for a clinic visit. A total of 203 women attended a study clinic visit. All study procedures were approved by the Institutional Review Boards of the Fred Hutchinson Cancer Research Center (FHCRC) and GH, and all study participants provided written informed consent.

Clinic visits and data collection
The study clinic visit was scheduled to occur during days 5 through 9 of the woman's menstrual cycle because blood and urine samples were collected for a variety of measures, including hormones. One hundred ninety-eight women (98%) attended the clinic visit during this time frame. Before their appointment, a health and demographics questionnaire, a physical activity questionnaire, and a FFQ were mailed to each participant, and each participant was asked to complete the questionnaires and bring them to the clinic visit. The 120-item FFQ, developed in 2001 by the FHCRC Nutrition Assessment Shared Resource for use in epidemiologic studies of diet and health (35), is an update of the FFQ used in the Women's Health Initiative (36) and the Selenium and Vitamin E Cancer Prevention Trial (37). The measurement characteristics of the earlier version of this instrument used in the Women's Health Initiative, compared with short-term dietary recall and recording methods, have been described (36). The FFQ asked for information on dietary intakes during the past 3 mo, and the physical activity questionnaire asked for information during 4 different time periods (see "Physical activity data"). During the clinic visit, participants’ weight, height, and waist and hip circumferences were measured. All participants were provided with a 3-d food record booklet and asked to record all food and drinks consumed for 3 consecutive days, preferably within 2 wk after their clinic visit. A serving size booklet also was provided, which contained pictures of some commonly consumed foods in different portion sizes, in addition to a ruler, a thickness guide, a serving spoon size guide, and tips on how to estimate servings. Completed food record booklets were mailed back to GH.

Dietary intake data
Dietary intake data from the 3-d food record were analyzed with the use of NUTRITION DATA SYSTEM FOR RESEARCH software [versions 35 and 36, with output in version 36 (June 2005); Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN]. The database for the FFQ was developed by the Nutrition Assessment Shared Resource at FHCRC and is based on the nutrient strings in the NUTRITION DATA SYSTEM FOR RESEARCH software. From the 3-d food records, we estimated servings per day of fruit (including juices); vegetables (including fried potatoes); grains [subdivided into refined (grain or grain-based products that do not contain any whole-grain ingredients), some whole grain (grain or grain-based products with a whole-grain ingredient that is not the first ingredient on the food label), and whole grain (grain or grain-based products with a whole-grain ingredient as the first ingredient on the food label]; meat (including red meat, game, poultry, cold cuts, and sausages); fish and shellfish; eggs; dairy foods (including cream); and tea and coffee. Servings per day were calculated, based on standardized serving sizes similar to those specified in the Dietary Guidelines for Americans (38). Fruit and vegetables were further classified into 67 botanical families (39). Servings per day of fruit and vegetables on the FFQ were assessed with the use of the 5-a-day method and the summation method (40). A number of questions were asked at the beginning of the FFQ on usual food choices and preparation methods, which allow a more refined analysis of intake of certain foods. These "adjustment" questions included questions, for example, on the usual type of milk (eg, whole milk, 2% milk, soy milk, etc) used on cereal, as a beverage, or in coffee or tea and on how often the skin on chicken is eaten. Further details of adjustment questions were published elsewhere (41). Responses to the adjustment questions on usual type of milk were used to determine whether soy milk was used on cereal, as a beverage, or in coffee or tea. We assigned soy-consumer status (yes or no) with responses to questions on intakes of 1) tofu, tempeh, and products such as tofu hot dogs, soy burgers, and tofu cheese; 2) miso soup; 3) milk on cereals (if usual milk on cereals was soy); 4) milk as a beverage (including latte, cappuccino, mocha, or hot chocolate; if usual milk as a beverage was soy); and 5) milk added to tea or coffee (if usual milk added was soy).

Physical activity data
A modified version of the Historical Leisure Activity Questionnaire (42) was used to assess household and recreational physical activity. The modified Historical Leisure Activity Questionnaire was self-administered and included household activities, such as gardening or yard work and light and heavy housecleaning, in addition to recreational activities. The questionnaire asked for information on duration and frequency of activities participated in during the past year and during 3 prior age periods: between age of onset of menstruation and 21 y, during ages 22–33 y, and during ages 34–45 y (43). The total number of years spent in each activity for the age periods between age of onset of menstruation and 21 y and during ages 34–45 y depended on age of onset of menstruation and age at clinic visit, respectively. If a woman participated in the activity >10 times during her lifetime, she provided an estimate of the number of years, months per year, and hours per week spent in each activity during each time period. The average number of hours per week spent in each activity was calculated and then multiplied by the units of metabolic equivalents (METs) specific to that activity to obtain a measure of energy expenditure in MET-hours per week (44). For each time period, the average weekly energy expenditure was determined by summing the weekly energy expenditure across all activities. In addition, we calculated average MET-hours per week across all time periods (ie, age at menstruation to current age). A previous study has shown that the modified physical activity questionnaire is reproducible and provides a useful measure of average lifetime physical activity (43).

Soy challenge urine sample collection
Women were phenotyped for equol- and ODMA-producer status with a soy challenge as described previously (22). Briefly, at their clinic visit, each woman was provided with a urine collection kit consisting of 3 soy protein bars (Revival Soy; Physicians Laboratories, Kernersville, NC; {approx}38 mg daidzein as aglycone equivalents per bar) or a 99-g (3.5-oz) bag of soy nuts (Genisoy Soy Nuts; Genisoy Food Co, Tulsa, OK; {approx}10 mg daidzein as aglycone equivalents per one-third bag), a urine collection container, an instruction sheet, a short questionnaire about the urine collection, and a prepaid mailer for returning the urine sample and questionnaire to FHCRC. Participants were asked to consume 1 soy bar (or one-third bag of soy nuts) on 3 consecutive days and to collect a first-void urine sample on the morning of the fourth day, ideally within 2 wk of their clinic visit. We previously showed that isoflavones are stable in urine at room temperature (21), and urine samples were returned to FHCRC through the US postal system. When received at the laboratory, samples were refrigerated, and then divided into aliquots and frozen at –20 °C.

Urine sample analyses
Urine samples (2 mL) were measured for isoflavonoids [equol, daidzein, genistein, ODMA, and dihydrodaidzein (DHD)] by gas chromatography–mass spectrometry as described elsewhere (7). With an initial urine volume of 2 mL and a final volume of 0.35 mL, the detection limit in urine was 87.5 ng/mL (equivalent to 362 nmol/L equol, 344 nmol/L daidzein, 324 nmol/L genistein, 339 nmol/L ODMA, and 342 nmol/L DHD). Equol and ODMA producers were defined as persons with detectable urinary concentrations of equol and ODMA, respectively. Daidzein concentrations < 100 ng/mL (equivalent to <394 nmol/L) were considered indicative of noncompliance with consuming the soy bars or soy nuts. The intraassay CVs for isoflavonoids in the quality control sample, measured in duplicate for each batch, were <9%. The interassay CVs were <13%.

Urinary creatinine concentrations were measured to ensure that urine samples were sufficiently concentrated (>80 mg/L; 0.71 mmol/L). Measurements were based on a kinetic modification of the Jaffe reaction with the use of the Roche Reagent for Creatinine (Roche Diagnostic Systems, Nutley, NJ) on a Roche Cobas Mira Plus chemistry analyzer.

Data analysis
Differences between producers and nonproducers of the daidzein metabolites in demographic and lifestyle factors were assessed with the use of unpaired t tests, chi-square analyses, and Fisher's exact tests. For the 3-d food record, mean intake of nutrients during the 3 d of data collection were calculated. Data for several nutrients were skewed, but log transformation did not improve the distributions for most nutrients; thus, comparisons between producers and nonproducers of the daidzein metabolites were made with the nonparametric Wilcoxon-Mann-Whitney test. Because of the large number of statistical comparisons (n = 54) on nutrient and food group data from the 3-d food record, we also present our findings adjusted for the false discovery rate (FDR). Analyses of botanical group data from the 3-d food record were limited to those groups that were consumed by ≥50 participants. Data for physical activity were skewed, and log transformation improved the distributions for these data. Comparisons of physical activity between producers and nonproducers of the daidzein metabolites were performed with a group mean comparison t test on the log-transformed mean values. Data were analyzed with the use of SAS (version 9.1; SAS Institute, Cary, NC) and STATA/SE (version 9.0; STATA Corp, College Station, TX). In unadjusted analyses, a P value of ≤0.05 was considered statistically significant, and in analyses adjusted for FDR a P value of ≤0.001 was considered statistically significant.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Of the 203 women who attended a clinic visit, 200 returned a soy challenge urine sample and of these women, 199 completed the health and demographics questionnaire, 199 completed the physical activity questionnaire, and 194 completed the 3-d food record. Physical activity data were incomplete for ≥1 time periods for 80 women (40%). All urine samples had creatinine concentrations > 200 mg/L and daidzein concentrations > 370 ng/mL. Data on the amount of the soy bar (or soy nuts, n = 5 participants) consumed on days 1, 2, and 3 were available for 190, 190, and 186 women, respectively. Of these women, 186 (98%), 184 (97%), and 181 (97%) reported that they had consumed at least one-half of the assigned portion (ie, one-half soy bar or one-sixth bag of soy nuts) on days 1, 2, and 3, respectively, of the 3-d soy challenge. Data on the length of time between sample collection and processing in the laboratory were available for 181 women; mean (±SD) time interval was 6.7 ± 8.4 d, and 165 (91%) samples were processed within 14 d of sample collection. No difference was observed in total isoflavone excretion (sum of equol, daidzein, genistein, ODMA, and DHD) between samples that were processed within and outside 14 d of collection (106 ± 55 and 123 ± 50 nmol/mL, respectively; P = 0.19).

The mean age for all study participants was 42.4 ± 1.3 y, and 88% were white. Fifty-five (27.5%) and 182 (91%) of the 200 women who returned a soy challenge urine sample were classed as equol and ODMA producers, respectively. Fifty (25%) women produced equol and ODMA, 5 (2.5%) produced equol but not ODMA, 132 (66%) did not produce equol but did produce ODMA, and 13 (6.5%) did not produce equol or ODMA. Equol producers were more likely than were nonproducers to be Hispanic or Latino, and differences were observed between equol producers and nonproducers in level of education. More equol producers than nonproducers were in the ≥17 y of education category, but fewer equol producers than nonproducers were in the 16 y of education category. In addition, equol producers were more likely than nonproducers to have frequent constipation (Table 1Go). ODMA producers were taller than nonproducers and were less likely to be Asian than white. The association between ODMA production and height remained after adjustment for race (data not shown). Women who did not know if they were born prematurely were less likely to be ODMA producers than women who were not born prematurely, but this finding was based on a small number of women. No other associations were statistically significant.


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  		TABLE 1 Study participant anthropometrics, demographics, and lifestyle factors by equol-producer and O-desmethylangolensin (ODMA)–producer status1

 
Compared with nonproducers, equol and ODMA producers had higher levels of physical activity, and these differences were significant for average MET-hours per week for total years (ie, age at menstruation to current age) (Table 2Go). Equol producers, compared with nonproducers, also had significantly higher levels of physical activity during the past year and between age 34 y and current age.


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  		TABLE 2 Physical activity during 4 time periods by equol-producer and O-desmethylangolensin (ODMA)–producer status1

 
Dietary intakes of specific nutrients according to the 3-d food record were not statistically significantly associated with either daidzein-metabolizing phenotype (Table 3Go). In unadjusted analyses, compared with nonproducers, equol producers consumed more servings per day of vegetables and eggs, and ODMA producers consumed more servings per day of fruit. However, when adjusted for the FDR, these findings were not statistically significant (P > 0.001). Analyses on botanical group data showed that equol producers, compared with nonproducers, consumed more servings per day of Rosaceae (eg, apples, pears, stone-fruits, strawberries, and raspberries) (0.77 ± 0.60 compared with 0.53 ± 0.58 servings/d; P = 0.005) and Labiatae (eg, basil, marjoram, oregano, rosemary, sage, and thyme) (0.014 ± 0.032 compared with 0.012 ± 0.040 servings/d; P = 0.05), and ODMA producers consumed fewer servings per day of Solanaceae (eg, potatoes, tomatoes, capsicum, and eggplant) than do nonproducers (0.88 ± 0.74 compared with 1.14 ± 0.52 servings/d; P = 0.02).


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  		TABLE 3 Daily nutrient and food group intakes by equol-producer and O-desmethylangolensin (ODMA)–producer status according to the 3-d food record1

 
According to the FFQ, in the past 3 mo ODMA producers, compared with nonproducers, consumed more servings per day of fruit, but this was only significant for data generated with the 5-a-day method (1.78 ± 1.23 compared with 1.43 ± 1.85 servings/d; P = 0.03) and not the summation method (1.95 ± 1.34 compared with 1.63 ± 1.78 servings/d; P = 0.09). The difference between equol producers and nonproducers in percentage of energy from carbohydrate was of borderline statistical significance [52.2% ± 7.3% compared with 49.5% ± 9.6%, respectively; P = 0.06 (Wilcoxon's test) and P = 0.04 (t test)]. Ninety-three (47%) women reported consuming any soy foods or beverages ≥1 time/mo, and 49 (25% of all women) reported consuming soy foods or beverages ≥1 time/wk, but no differences were observed between producers and nonproducers of either daidzein metabolite in the number of soy consumers (P > 0.05). No other differences in dietary intakes between producers and nonproducers of either daidzein metabolite were statistically significant (data not shown).

Study participants reported consuming meat or a meat-based product on ≥1 d of the 3-d food record (93%) and on the FFQ (96%). The proportion of producers and nonproducers of either daidzein metabolite among participants who did and did not consume meat or meat-based products was not different (P > 0.05; data not shown).

We conducted exploratory analyses on the 3-d food record data stratified according to frequency of constipation. There was evidence of an interaction between frequency of constipation and intake of dairy foods for ODMA-producer status (P = 0.05). Among participants with occasional constipation, ODMA producers (n = 62) consumed more servings per day of dairy foods than did nonproducers (n = 10) (2.3 ± 1.5 compared with 1.4 ± 1.1 servings/d, respectively; P = 0.05).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We observed associations between equol production and ethnicity, education, constipation, and physical activity and between ODMA production and race, height, and physical activity. Intakes of selected food groups and botanical groups also were associated with the daidzein-metabolizing phenotypes, although associations between equol production and intakes of vegetables and eggs and between ODMA production and intake of fruit were not significant when adjusted for the FDR. To our knowledge, this is the first study to have shown relations between equol production and constipation and ethnicity and between the daidzein-metabolizing phenotypes and physical activity. However, associations with race and ethnicity were based on small numbers of women who were Asian and who were Hispanic or Latino.

More equol producers than nonproducers had received ≥17 y of education, which is largely consistent with our previous study in a predominantly white population of men and women (21) and in the subset of white women from that study (22). The nature of the relation between equol production and education is unclear, although years of education may be serving as a marker for other exposures not captured in this study.

We observed a positive association between height and ODMA production, which is in contrast to our previous study among men and women in the United States (mean age: 39 y) in which an inverse association was observed (21). It was hypothesized that the intestinal microbiota may influence adult height (45), but the mechanism for such a relation is not fully understood.

Geographic differences in intestinal microbial populations were reported (46, 47), which could be due to differences in race or ethnicity and, in turn, could incorporate factors such as genetics, diet, hygiene, bacterial populations in the exogenous environment, cultural practices (eg, use of antimicrobials), or a combination of these factors. It was suggested that the prevalence of equol producers is higher in Asian populations than in Western populations (22, 26, 48), but we did not see an association between equol production and race. In agreement with our previous findings (21, 22), Asians were less likely than were whites to be ODMA producers. However, given the low numbers of Asian women in the current study, these findings should be interpreted with caution.

Equol producers were more likely than were nonproducers to have frequent constipation, which is somewhat in agreement with other reported studies of phytoestrogen metabolism (49, 50). There are several mechanisms by which gut transit time could influence the metabolites produced by intestinal bacteria, including affecting the location of and time available for metabolism of dietary compounds, and the composition and growth rate of the microbial community (51, 52). Physical activity has led to a faster gut transit time in some (53, 54), but not all (55), studies. Although we observed a positive association between equol production and constipation, self-reported physical activity was higher among equol producers than among nonproducers.

Diet clearly influences the intestinal microbiota (56), but, despite assessing diet with the use of both a 3-d food record and a FFQ, we observed few associations between diet and the daidzein-metabolizing phenotypes. According to the 3-d food record, there were positive associations between equol production and servings per day of vegetables and eggs and between ODMA production and servings per day of fruit, although these findings were not statistically significant when adjusted for the FDR. In contrast, other studies have reported associations between equol production and low-fat high-carbohydrate diets (1, 4) and intakes of soy (23, 24), plant protein (4), meat (23, 57), and caffeine (7). We know of no other studies that have assessed intakes of botanical groups in relation to daidzein-metabolizing phenotypes, and further studies are needed to confirm our findings. We also observed an interaction between frequency of constipation in adult life and intake of dairy foods for ODMA-producer status, but this finding also requires confirmation.

In adults, the ability to produce equol does not appear to be easily altered by dietary means (28-31). Recent data suggested a higher prevalence of equol producers among vegetarians (26). We did not specifically ask whether participants classed themselves as vegetarian, but the prevalence of producers and nonproducers of either daidzein metabolite was not different among meat eaters and non–meat eaters. However, most women consumed meat or meat-based products.

To our knowledge, this is the largest study to date of potential determinants of daidzein-metabolizing phenotypes in premenopausal women. The study was conducted in a well-characterized population of women who were recruited according to relatively stringent eligibility criteria. In contrast with our previous study among families in the United States (21), recruitment was not based on familial relations, which removes the potential for confounding because of such relations. In addition, the assessment of premenopausal women within a relatively tight age range provides an ideal population in which to study potential determinants of daidzein-metabolizing phenotypes, given that the effects of aging on the intestinal microbiota are not fully known and that older persons were less likely to be equol producers than were children or young-to-middle-aged adults in our previous study (21).

There are several limitations of our study. The assessment of potential determinants of ODMA production was limited because of the relatively small number of ODMA nonproducers. In addition, most women were white, well-educated, and recruited according to the BIRADS density score to achieve a wide range of densities in this premenopausal study population. As such, our findings may be generalizable only to similar populations of women. However, despite the selection of women based on their BIRADS density score, the proportion of equol and ODMA producers in this study was similar to that reported elsewhere in predominantly white populations (4, 7, 31, 58). The assessment of dietary intakes and physical activity is notoriously difficult (59, 60). Although we attempted to collect detailed dietary information with the use of 2 different methods, we may not have captured the relevant exposures, or time period of exposures, associated with the daidzein-metabolizing phenotypes. Dietary data were not collected at the time of phenotyping for equol and ODMA producer status, but this is unlikely to affect our findings, given that the phenotypes appear to be stable within persons over time (12, 32) and that, in general, it does not seem possible to change the phenotypes with dietary intervention (28-31). The physical activity measure provides only a crude estimate because it does not take into account occupational activity. Nonetheless, there are few occupational tasks that have an energy output that exceeds moderate activity (61). Because we asked about activities that spanned a wide period of time, including >20 y ago, some women were unable to remember this information. Thus, many women had missing data for some age periods and overall physical activity. Prior studies have shown that vigorous-intensity activity is more accurately recalled than moderate-intensity activity (42, 43, 62, 63), which may have led to some bias in our study because of nonresponse to certain items on the questionnaire.

In this population of premenopausal women, equol producers were more likely than were nonproducers to be Hispanic or Latino, highly educated, and to have frequent constipation, and ODMA producers were less likely to be Asian than white and to be taller than nonproducers. Given the small number of women in this study population who were Asian or Hispanic or Latino, the findings in relation to race and ethnicity need to be interpreted with caution. Equol and ODMA producers also reported higher overall physical activity than did nonproducers. Our study population consisted primarily of non-Hispanic white women, and further studies are needed to more fully examine the roles of race and ethnicity on the daidzein-metabolizing phenotypes. This is the first study to have shown a relation between constipation and equol production, and further studies also are needed to confirm this finding. Few dietary factors, when assessed with the use of both a FFQ and a food record, were associated with either daidzein-metabolizing phenotype. Thus, although we observed some significant associations between demographic, anthropometric, lifestyle, and dietary factors and the daidzein-metabolizing phenotypes, the underlying factors associated with these phenotypes remain elusive.


   ACKNOWLEDGMENTS
 
We thank Kelly Ehrlich, Kathy Plant, and the GH Department for screening interviews, clinic visits, and study coordination; Wendy Thomas for isoflavone analyses; JoAnn Prunty for creatinine analyses; and all of the study participants.

The author's responsibilities were as follows—CA: study design, data analysis and interpretation, and drafting of the manuscript; KMN: securing funding, study design, data interpretation, and significant advice and consultation about all aspects of the study; EJAB: data interpretation; MY: data analysis and interpretation; JWL: securing funding, study concept and design, data interpretation, and significant advice and consultation about all aspects of the study. All authors contributed to and approved the final version of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
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Received for publication July 6, 2007. Accepted for publication October 4, 2007.





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