The Journal of Nutrition
Sociodemographic and Lifestyle Variables and Their Relationship to
Nutritional Biomarkers: Findings from NHANES
Sociodemographic and Lifestyle Variables
Are Compound- and Class-Specific Correlates
of Urine Phytoestrogen Concentrations
in the U.S. Population1–3
Michael E. Rybak, Maya R. Sternberg, and Christine M. Pfeiffer*
National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA
the extent to which sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle variables (smoking, alcohol
consumption, BMI, physical activity, and dietary supplement use) were correlates of spot urine concentration for daidzein,
genistein, O-desmethylangolensin (DMA), equol, enterodiol, and enterolactone in the U.S. population aged $20 y (NHANES
for each sociodemographic and lifestyle variable. We used bivariate significance testing and covariate adjustment by use of
multiple regression models to identify influential variables and used b coefficients to estimate relative effects. Urine creatinine
significant (P < 0.05) associations with the sociodemographic and lifestyle variables that withstood covariate adjustment.
Smoking was a significant correlate of urine DMA and enterolactone, with concentrations at least 25% lower in smokers vs.
nonsmokers. Consumers of 1 daily alcoholic drink vs. none were estimated to have 18–21% lower urine equol and DMA con-
centrations. A 25% increase in BMI was associated with a 21% lower urine enterolactone concentration, and increasing physi-
cal activity was associated with a >6% higher urine enterolactone concentration. Dietary supplement use was not significantly
associated with any of the urine phytoestrogens. Overall, we found that relationships between sociodemographic and lifestyle
variables and urine phytoestrogen concentration were highly compound and class specific. J. Nutr. 143: 986S–994S, 2013.
Isoflavones and lignans are plant-derived dietary compounds
generally believed to be beneficial to human health (1). Soybeans
and soy-based products such as soy flour, soy milk, miso, tofu,
and tempeh are major dietary sources of isoflavones (2). Seeds
such as linseed, flaxseed, and sesame seeds are conspicuous
sources of lignans; however, most dietary consumption of
lignans originates from more ubiquitous, lower-concentration
sources such as seed oils, whole-grain cereals, beans, and other
fruit and vegetables (1). Isoflavones and lignans are commonly
referred to as phytoestrogens, a class of compounds capable of
some degree of direct or metabolite-mediated estrogenic activity
in the human body. The pseudoestrogenic behavior of these
compounds has been postulated as an antagonistic mechanism
that reduces the risk of hormone-dependent cancers such as
breast (3,4) and prostate (5,6) cancer, and may also have an
effect on other hormone-dependent conditions such as meno-
pausal symptoms (7). Phytoestrogens have also been studied in
the context of health conditions and diseases unrelated to their
phytoestrogenic activity, such as cardiovascular disease risk (8,9).
2Author disclosures: M. E. Rybak, M. R. Sternberg, and C. M. Pfeiffer, no
conflicts of interest.
3Supplemental Tables 1–3 and Supplemental Figure 1 are available from the
‘‘Online Supporting Material’’ link in the online posting of the article and from the
same link in the online table of contents at http://jn.nutrition.org.
1Published in a supplement to The Journal of Nutrition. An Extension of the Second
National Report on Biochemical Indicators of Diet and Nutrition in the U.S. Population.
The findings and conclusions in this report are those of the authors and do not
necessarily represent the official views or positions of the Centers for Disease Control
of Health and Human Services. The views expressed in these papers are not
necessarily those of the Supplement Coordinator or Guest Editors. The Supplement
and Prevention. Supplement Coordinator disclosures: Christine M. Pfeiffer had no
the Editor of The Journal of Nutrition has delegated supervision of both technical
conformity to the published regulations of The Journal of Nutrition and general
oversight of the scientific merit of each article. The Guest Editor for this supplement
was Kevin Schalinske. Guest Editor disclosure: Kevin Schalinske had no conflicts to
disclose. Publication costs for this supplement were defrayed in part by the payment
of page charges.Thispublicationmusttherefore beherebymarked"advertisement"in
accordance with 18 USC section 1734 solely to indicate this fact. The opinions
expressed in this publication are those of the authors and are not attributable to the
sponsors or the Publisher, Editor, or Editorial Board of The Journal of Nutrition.
* To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
ã 2013 American Society for Nutrition.
986S Manuscript received December 6, 2012. Initial review completed January 4, 2013. Revision accepted February 9, 2013.
First published online April 17, 2013; doi:10.3945/jn.112.172981.
by guest on November 3, 2015
Supplemental Material can be found at:
in particular the potential risk of developmental abnormalities in
infants from isoflavone exposure (10).
The NHANES is a program of continuous studies designed
and conducted by the CDC for the purpose of assessing the
health and nutritional status of the U.S. population (11). From
1999 to 2010, the NHANES data sets have included spot urine
concentration measurements for 6 phytoestrogens in study
participants aged $6 y: 2 plant isoflavones, daidzein and
genistein; 2 enterogenous daidzein metabolites, equol and
O-desmethylangolensin (DMA)4; and 2 enterolignans, enterolac-
tone and enterodiol (Supplemental Fig. 1). Urine and serum/plasma
isoflavone and lignan intake (12,13), and so their measurement
in cross-sectional studies such as the NHANES can go beyond
simply assessing exposure and provide insight on a population?s
dietary habits. Most recently, the NHANES urine phytoestro-
gen data for 2003–2006 were analyzed and presented in the
CDC?s Second National Report on Biochemical Indicators of
Diet and Nutrition in the U.S. Population 2012 (Second
Nutrition Report) (14,15), the latest in a series of publications
providing descriptive statistics on nutritional and diet-related
biological indicators as a tool for establishing reference levels, iden-
tifying disparities, tracking trends over time, and evaluating the
effectiveness of public health interventions. The NHANES urine
phytoestrogen data have been used in other similar descriptive
Select urine phytoestrogens in NHANES have been examined
in relation to specific variables such as dietary isoflavone intake
(18), dairy consumption (19), and serum lipids (20), but to the
best of our knowledge, no comprehensive analyses of the asso-
ciation of commonly studied sociodemographic and lifestyle
variables with urine phytoestrogens in the NHANES exist.
Peeters et al. (21) looked atthe variance in plasmaphytoestrogen
concentrations explained by geographic, sociodemographic,
lifestyle, and laboratory variables in 1414 subjects from the
European Prospective Investigation into Cancer and Nutrition
study. Kilkkinen et al. (22) studied various determinants of
serum enterolactone concentration in 2380 Finnish adults (aged
25–46 y). Chun et al. (18) reported data for urine daidzein,
DMA, equol, and genistein from NHANES 1999–2000 strati-
fied by sociodemographic and lifestyle variables in an attempted
validation of isoflavone intake; however, their reported values
are inconsistent with other analyses of the NHANES 1999–2000
data (14,16,17) and their reported sample size exceeds the number
of urine phytoestrogen observations in the NHANES 1999–2000
data set (23). Sociodemographic and lifestyle variables have also
been studied as determinants of biological phytoestrogen con-
centrations in smaller settings such as daidzein-metabolizing
phenotypes in U.S. (24) and Japanese (25) women. Of all these
studies, the work of Kilkkinen et al. (22) is the most
comparable example of a systematic study of sociodemo-
graphic and lifestyle variables in which modeling for covariate
effects was considered, albeit for a single biomarker (serum
The goal of our study was to assess the combined association
of specific sociodemographic [age, sex, race-ethnicity, education,
and poverty-income ratio (PIR)] and lifestyle variables (smok-
ing, alcohol consumption, BMI, physical activity, and dietary
supplement use) with urine phytoestrogen concentrations from
NHANES 2003–2006 as a logical extension of the Second
Nutrition Report. Similar analyses were also conducted for
companion publications in this supplement on water-soluble
(26) and fat-soluble (27) nutrients, trace elements (28), and
acrylamide (29). The common purpose of these analyses was
to better understand the demographic differentials in bio-
marker concentrations observed in the Second Nutrition
Report, as well as provide a foundation of knowledge to
researchers who develop predictive models or address specific
Participants and Methods
Survey design and participants. The NHANES collects cross-
sectional data on the health and nutritional status of the civilian
noninstitutionalized U.S. population (11). Since 1999, the National
Center for Health Statistics at the CDC has conducted NHANES as a
continuous survey with data released in 2-y cycles. The survey obtains a
stratified, multistage, probability sample designed to represent the U.S.
population on the basis of age, sex, and race-ethnicity. All respondents
gave their informed consent, and the NHANES protocol was reviewed
and approved by the National Center for Health Statistics Research
Ethics Review Board. Interview and examination response rates for each
survey period are publicly available (30).
Laboratory methods. Spot urine specimens from a one-third subset of
participants from the 2003–2004 NHANES cycle were analyzed for
urine phytoestrogens by use of HPLC-MS/MS with electrospray
ionization (31,32). Aliquots from the 2005–2006 NHANES cycle
were analyzed for the same analytes by use of HPLC-MS/MS with
atmospheric pressure ionization (33,34). Good agreement has been
demonstrated between results obtained by the 2 methods (33). All
reported results satisfied the requirements of a multi-rule quality control
Study variables. The following sociodemographic and lifestyle varia-
bles and categories were used in our analyses: sex (male, female), age
(20–39, 40–59, $60 y); race-ethnicity [Mexican American (MA), non-
Hispanic black (NHB), non-Hispanic white (NHW)], education (less than
high school, high school, more than high school), PIR [#1.85 (low), >1.85–
3.5 (medium), >3.5 (high)] (36,37), smoking [serum cotinine: #10 mg/L
(nonsmoker), >10 mg/L (smoker)] (38), alcohol consumption (average daily
numberof‘‘standard’’ drinks:none, >0 to<1drink/d,$1to <2drinks/d,$2
weight), $25.0 to <30.0 kg/m2(overweight), $30.0 kg/m2(obese)],
physical activity [metabolic equivalent task (MET)-min/wk from leisure-
time physical activity: none, >0 to <500, $500 to <1000, $1000 MET-
min/wk] (39),supplement use [reported takinga dietary supplement within
the past 30 d: yes (user), no (nonuser)]. The following variables were also
assessed: liver dysfunction [aspartate aminotransferase (AST) or alanine
aminotransferase (ALT) >70 U/L (impaired), AST and ALT #70 U/L
(normal)], because of its relationship with phytoestrogen metabolism; and
urine creatinine (continuous) because of its use in correcting for variable
dilution in spot urine samples. Sex, age, race-ethnicity, education, PIR,
alcohol consumption, and supplement use were self-reported by study
participants. BMI was determined by using height and weight measure-
ments performed by trained examiners. Laboratory methods for serum
ASTand ALT, serum cotinine, and urine creatinine are described elsewhere
Analytic sample. All mobile examination center–examined NHANES
2003–2004 and 2005–2006participants aged $20 y with at least 1 urine
phytoestrogen measurement were eligible for inclusion in our study.
Individuals who reported antibiotic use within the past 30 d were
excluded because of potential effects on enterogenous phytoestrogen
metabolism via gut microbiota. No study participants were excluded on
4Abbreviations used: ALT, alanine aminotransferase; AST, aspartate amino-
transferase; DMA, O-desmethylangolensin; MA, Mexican American; MET,
metabolic equivalent task; NHB, non-Hispanic black; NHW, non-Hispanic white;
PIR, poverty-income ratio.
Correlates of phytoestrogen concentration987S
by guest on November 3, 2015
the basis of health variables. On the basis of these criteria, data were
available for ~3000 participants (Supplemental Table 1).
Statistical analyses. A companion publication in this supplement by
Sternberg et al. (40) provides complete details of the statistical
approaches used in this analysis. Sternberg et al. also discuss the
approaches used in developing the multiple regression models due to
the limited df, such as the number of covariates considered, the forms
chosen for continuous covariates, and how interactions between
covariates were addressed.
Urine phytoestrogen distributions were highly right-skewed; log-
transformation corrected this and was used along with the calculation of
geometric means when parametric tests were performed. Spearman
correlations were used to explore bivariate associations between each
urine phytoestrogen and selected continuous variables. Bivariate asso-
ciations for categorical variables were explored by presenting the
geometric means and 95% CIs for each urine phytoestrogen across
the categories. Geometric means were compared across categories by
the use of Wald F tests. Simple linear regression was used to provide an
accompanying measure of the percentage of the total variability in the
urine phytoestrogen that is explained by a single covariate (model
Multiple linear regression was used to assess the impact of con-
founding and to determine whether significance persists after adjusting
for differences in key variables. In all cases, the dependent variable was
the natural log-transformation of the urine phytoestrogen concentration.
We used the independent variable as a continuous variable when possible.
Alcohol consumption, BMI, and physical activity were log-transformed
because these variables tend to be skewed to the right. The predictor
variables were arranged into 3 sets or ‘‘chunks’’: sociodemographic
variables (age, sex, race-ethnicity, educational level, PIR), lifestyle
variables (smoking, alcohol consumption, BMI, physical activity level,
and dietary supplement use), and urine creatinine. Independent variables
were tested in a hierarchical, chunk-wise fashion such that each chunk
of related variables was tested simultaneously to determine which
independent variables were related to the dependent variable. The
influence of each chunk was assessed by a Satterthwaite-adjusted F
chunk test. For each model the coefficient of multiple determination
(R2) was calculated to provide a measure of the percentage of the total
variability in the urine phytoestrogen concentration that the model
explains. Wald F P values indicated whether any single b coefficient was
significantly different from 0.
The results of 4 regression models were summarized for each urine
phytoestrogen: simple linear regression (model 1), multiple linear
regression with the sociodemographic chunk (model 2), multiple linear
regression with both sociodemographic and lifestyle chunks (model 3),
and multiple linear regression with the sociodemographic and lifestyle
chunks and urine creatinine (model 4). All variables were retained in all
models to allow for uniform presentation and comparison of results
across all urine phytoestrogens. The results from each of the models were
summarized by presenting the predicted percentage change in urine
phytoestrogen concentration with change in each covariate, holding all
other remaining covariates constant.
Descriptive information of the respondent characteristics in the
NHANES 2003–2004 and NHANES 2005–2006 samples for
urine phytoestrogens can be found in Supplemental Table 2.
Spearman correlation analyses were performed between urine
demographic variables, as well as urine creatinine concentra-
tions (Table 1). With the exception of creatinine, significant
correlations were weak (Spearman |r| < 0.2). Consistencies were
observed in some cases among phytoestrogen classes, namely
plant isoflavones (daidzein and genistein), daidzein metabolites
(equol and DMA), and enterolignans (enterodiol and enter-
olactone). Weak to moderate significant correlations (0.18 # |r| #
0.40) were observed for creatinine with all phytoestrogens.
Bivariate methods (model 1) were used to describe the
association of individual sociodemographic variables, lifestyle
variables, and urine creatinine concentrations with urine phytoes-
trogen biomarker concentrations (Table 2). Sociodemographic
andlifestyle variables weresignificantly (P < 0.05) related tourine
phytoestrogen concentrations in limited cases. Race-ethnicity had
very highly significant (P < 0.0001) associations with daidzein
metabolites (equol, DMA), and similarly significant associations
were also seen with alcohol consumption (P = 0.0031 for equol,
P < 0.0001 for DMA). Lifestyle variables resulting in highly
significant relationships included smoking status (enterolactone,
P = 0.0002) and physical activity (enterolactone, P = 0.0006).
Supplement use was the only variable that was not significantly
associated with biomarker status. Although significant relation-
ships were observed for several of the variables, the degree to
which they explained the variability observed (based on model r2
value) was miniscule (<2%).
Multiple regression models were used to determine the
percentage of biomarker variation explained by each chunk of
study variables (Supplemental Table 3). From 1 to 2% of the
observed variability in phytoestrogen biomarker concentration was
attributable to the sociodemographic variables (model 2). Except
for the plant isoflavones (daidzein, genistein), the addition of
lifestyle variables (model 3) further increased the amount of
variability in biomarker concentration explained, with the largest
increases observed for the mammalian (i.e., enterogenous) phytoes-
trogens enterolactone (4%), DMA, and equol (3%). Further
phytoestrogens and selected continuous sociodemographic and lifestyle variables in adults aged $20 y,
Spearman correlation coefficients describing bivariate associations between urine
VariableGenisteinDaidzein Equol DMAEnterodiolEnterolactone
Smoking (serum cotinine)
1Values exclude individuals who reported antibiotic use in the past 30 d. Sample sizes for urine phytoestrogens by variable are given in
Supplemental Table 1. *P , 0.05. DMA, O-desmethylangolensin; PIR, family poverty-income ratio.
2Calculated as the average daily number of ‘‘standard’’ drinks [i.e., (quantity 3 frequency)/365.25]; 1 drink ; 15 g ethanol.
3Calculated as total metabolic equivalent task minutes/wk on the basis of self-reported leisure-time physical activity.
by guest on November 3, 2015
Unadjusted phytoestrogen biomarker concentrations by sociodemographic and lifestyle variables in adults aged $20 y,
Variable GenisteinDaidzein EquolDMA EnterodiolEnterolactone
Less than high school
More than high school
.0 to ,1 drink/d
$1 to ,2 drinks/d
.0 to ,500 MET-min/wk
$500 to ,1000 MET-min/wk
28.9 (25.3, 33.0)
28.7 (25.4, 32.5)
29.7 (26.2, 33.6)
62.2 (53.5, 72.3)
62.9 (56.0, 70.6)
61.0 (52.7, 70.6)
8.62 (7.55, 9.84)
6.88 (6.00, 7.88)
6.97 (6.02, 8.07)
4.25 (3.57, 5.07)
4.57 (3.92, 5.33)
4.39 (3.64, 5.30)
39.2 (34.6, 44.5)
38.8 (32.3, 46.6)
38.9 (34.4, 44.0)
278 (239, 323)
285 (244, 334)
337 (296, 385)
31.9 (29.1, 35.1)
26.4 (23.6, 29.6)
68.1 (61.1, 76.0)
57.0 (51.2, 63.4)
7.88 (7.15, 8.68)
7.19 (6.40, 8.07)
4.26 (3.67, 4.96)
4.55 (3.99, 5.19)
40.5 (35.4, 46.4)
37.6 (33.5, 42.1)
302 (266, 343)
285 (247, 329)
27.9 (25.2, 31.0)
29.1 (23.7, 35.8)
28.3 (25.9, 31.0)
52.0 (47.0, 57.5)
69.0 (56.9, 83.8)
60.3 (54.5, 66.6)
5.19 (4.64, 5.81)
6.20 (5.44, 7.07)
8.12 (7.34, 8.99)
1.98 (1.62, 2.41)
4.71 (3.67, 6.05)
4.71 (4.20, 5.28)
38.4 (32.0, 46.0)
37.7 (33.1, 42.9)
38.9 (34.7, 43.6)
327 (277, 386)
285 (246, 329)
291 (259, 327)
32.5 (27.7, 38.1)
26.6 (23.0, 30.7)
29.1 (26.0, 32.5)
62.3 (50.9, 76.4)
58.4 (49.9, 68.3)
63.8 (56.6, 72.0)
6.66 (5.72, 7.74)
7.15 (6.03, 8.47)
7.98 (7.13, 8.92)
3.29 (2.46, 4.39)
4.01 (3.32, 4.84)
5.04 (4.37, 5.81)
34.9 (30.1, 40.3)
32.1 (27.3, 37.6)
43.9 (38.7, 49.7)
283 (236, 340)
256 (210, 312)
315 (280, 354)
30.7 (27.8, 33.9)
28.8 (24.3, 34.2)
28.3 (24.8, 32.3)
62.3 (55.3, 70.2)
61.1 (51.6, 72.4)
63.0 (53.8, 73.9)
6.60 (5.93, 7.34)
7.70 (6.57, 9.03)
8.18 (7.20, 9.31)
3.74 (3.00, 4.66)
4.07 (3.37, 4.91)
5.19 (4.39, 6.13)
31.9 (27.4, 37.1)
41.5 (37.1, 46.6)
43.7 (36.5, 52.4)
258 (218, 304)
291 (249, 341)
327 (289, 369)
29.6 (26.9, 32.5)
27.4 (24.3, 30.9)
64.5 (58.7, 70.9)
56.3 (47.3, 67.0)
7.78 (7.00, 8.65)
7.03 (6.28, 7.87)
5.02 (4.43, 5.69)
3.30 (2.64, 4.13)
40.6 (36.5, 45.2)
36.1 (32.0, 40.8)
337 (308, 370)
221 (182, 268)
29.6 (26.4, 33.2)
29.1 (25.4, 33.3)
25.7 (18.2, 36.2)
25.6 (18.0, 36.4)
67.5 (59.7, 76.4)
61.9 (53.9, 71.1)
52.5 (36.9, 74.7)
51.1 (37.0, 70.4)
7.18 (6.36, 8.10)
8.20 (7.38, 9.12)
6.68 (4.94, 9.04)
4.75 (3.49, 6.45)
5.15 (4.30, 6.15)
4.67 (4.01, 5.44)
3.45 (2.24, 5.32)
2.17 (1.50, 3.15)
34.8 (28.7, 42.3)
40.5 (35.3, 46.5)
43.5 (31.4, 60.3)
39.6 (29.9, 52.6)
273 (233, 320)
318 (281, 360)
284 (193, 418
213 (138, 329)
21.6 (11.2, 41.7)
29.3 (24.0, 35.7)
28.4 (25.1, 32.2)
29.4 (27.2, 31.8)
28.5 (15.6, 52.0)
60.5 (49.2, 74.4)
62.3 (55.6, 69.6)
65.6 (59.4, 72.4)
6.55 (3.01, 14.3)
7.33 (6.22, 8.64)
7.16 (6.37, 8.04)
8.07 (7.32, 8.90)
1.59 (0.780, 3.25)
4.52 (3.75, 5.45)
4.19 (3.51, 5.00)
4.73 (4.01, 5.57)
32.7 (17.5, 61.3)
40.1 (33.6, 47.9)
38.2 (33.0, 44.2)
39.8 (35.0, 45.1)
284 (139, 581)
342 (288, 407)
298 (261, 340)
253 (221, 290)
29.5 (26.5, 32.7)
25.5 (21.5, 30.3)
30.1 (24.0, 37.8)
30.6 (25.2, 37.2)
63.3 (55.4, 72.3)
57.2 (47.6, 68.9)
60.6 (48.8, 75.4)
64.6 (53.5, 78.0)
6.59 (5.61, 7.75)
7.39 (6.26, 8.72)
8.01 (6.75, 9.52)
8.65 (7.57, 9.89)
3.72 (3.06, 4.51)
4.62 (3.76, 5.68)
4.68 (3.50, 6.26)
4.82 (3.89, 5.98)
34.8 (28.9, 41.9)
35.3 (30.3, 41.1)
45.9 (36.8, 57.2)
43.7 (37.1, 51.6)
254 (217, 298)
250 (212, 295)
349 (278, 439)
354 (315, 398)
Correlates of phytoestrogen concentration 989S
by guest on November 3, 2015
addition of urine creatinine (model 4) had the greatest impact; the
model combining creatinine with sociodemographic and lifestyle
variables accounted for 8–17% of the variability in biomarker
b Coefficients from multiple regression models were used to
estimate the percentage change in biomarker concentrations
expected with changes in a given variable both before and after
adjusting for sociodemographic variables, lifestyle variables,
and urine creatinine concentration (Table 3; b coefficients
provided in Supplemental Table 3). Before any adjustments
(model 1), the largest difference was observed with smoking,
where DMA and enterolactone concentrations were estimated to
be at least 30% lower in smokers vs. nonsmokers. Sex had a
notable relationship with plant isoflavone concentrations, with
urine daidzein and genistein estimated to be at least 15% lower
in females than in males. Urine daidzein was estimated to be
14% lower in MAs vs. NHWs, and even larger differences were
observed for daidzein metabolites (236% for equol, 258% for
DMA). Urine equol concentrations were estimated to be 24%
lower in NHBs compared with NHWs. The consumption of
1 alcoholic drink/d was associated with lower urine concentra-
tions of daidzein metabolites (218% for equol and 229% for
DMA). PIR was a significant correlate of enterolignan and DMA
concentration; enterodiol and enterolactone were 17 and 14%
lower, respectively, and DMA was 21% lower with a 2-unit
decrease in PIR. Education was also a notable correlate of urine
DMA and enterodiol, with concentrations 24–27% lower in
individuals with a high school education or less. Urine enter-
olactone concentrations changed with lifestyle variables related
in BMI and 7% higher with increasing physical activity (750 vs.
150 MET-min/wk). With some exceptions, adjusting for socio-
demographic or sociodemographic and lifestyle variables generally
did not have a prominent effect on the estimated percentage
had a substantial influence on the percentage changes estimated
in the biological sociodemographic variables and often inverted
the direction of the relative biomarker difference observed. The
previously noted sex differences for daidzein and genistein were no
longer significant, and new significant differences appeared for
equol (19%), DMA (29%), and enterodiol (37%), with women
having higher concentrations than men. The previously noted
lower equol concentrations in NHBs compared with NHWs were
24% lower urinebiomarker concentrations compared with NHWs
for all other phytoestrogens.
In addition to the variables discussed above, we also assessed
liver dysfunction for its association with urine phytoestrogen
concentrations (data not shown). When added to the model that
included all sociodemographic variables, lifestyle variables, and
creatinine, liver dysfunction was not significantly associated
with any of the urine phytoestrogens.
In this investigation we studied a selection of frequently used
sociodemographic and lifestyle variables, as well as urine
creatinine, as correlates of 6 urine phytoestrogens in the adult
U.S. population. We found that the associations tended to be
specific to either a single compound or to a class of compounds
originating from a common precursor (e.g., daidzein metabo-
lites, enterolignans), or by a shared mechanism (e.g., mamma-
We found that smoking was a significant correlate of DMA
and enterolactone concentrations. Weak but significant negative
correlations with serum cotinine were observed for DMA (r =
20.08) and enterolactone (r = 20.12), and urine concentrations
were estimated to be at least 25% lower in smokers than in
nonsmokers, independent of adjustment for other sociodemo-
graphic variables, lifestyle variables, or urine creatinine. The
association of lower biological enterolactone concentrations
with smoking has been observed elsewhere. Kilkkinen et al. (22)
found that serum enterolactone concentrations in Finnish adults
were >26% higher in men and >28% higher in women who were
non- or former smokers vs. current smokers, but this association
did not remain significant in men after adjustment for other
variables (P = 0.28). Peeters et al. (21) observed in a subset of the
European Prospective Investigation into Cancer and Nutrition
study that smoking explained 2.0% of the total serum enter-
olactone variance (P < 0.05) and <0.3% of the total serum DMA
variance (P $ 0.05) in a model that included age, sex, BMI, and
alcohol as well as geographic and laboratory variables.
Alcohol consumption was significantly related to daidzein
metabolites. Urine equol and DMA were both negatively
correlated with alcohol consumption (r = 20.11 in both cases),
with urine concentrations estimated to be 18 and 21% lower
with the consumption of 1 alcoholic drink/d compared with
none, respectively (model 4). Bolca et al. (41) reported that
postmenopausal women with higher alcohol intakes were more
likely to be strong equol producers. This appears to contradict
our observations with equol; however, the presence of equol in
the urine is not an absolute indicator of equol production
Variable GenisteinDaidzeinEquol DMAEnterodiolEnterolactone
28.8 (26.1, 31.8)
29.2 (26.3, 32.5)
60.8 (55.4, 66.6)
63.7 (55.8, 72.8)
7.84 (6.90, 8.92)
7.17 (6.43, 8.00)
4.88 (4.26, 5.58)
3.94 (3.26, 4.76)
40.6 (36.3, 45.4)
37.3 (32.7, 42.5)
316 (282, 353)
271 (235, 312)
1Biomarker concentrations (mg/L) are expressed as geometric means with 95% CI in parentheses. Values exclude individuals who reported antibiotic use in the past 30 d. Sample
sizes for urine phytoestrogens by variable are given in Supplemental Table 1. SI (nmol/L) conversion factors are as follows: genistein, 33.70; daidzein, 33.93; equol, 34.13; DMA,
3.87; enterodiol, 3 3.31; enterolactone, 33.35. P values are based on Wald F test, which tests for significant differences in at least 1 of the means across a given variable. Values
for r2are based on model 1, simple linear regression, by using categories as shown. DMA, O-desmethylangolensin; MET-min, metabolic equivalent task minutes; PIR, family
poverty-income ratio (low: #1.85; medium: .1.85–3.5; high: .3.5).
2Based on serum cotinine concentration: smoker, .10 mg/L; nonsmoker, #10 mg/L.
3Categories by increasing alcohol consumption calculated as average daily number of ‘‘standard’’ drinks [i.e., (quantity 3 frequency)/365.25]; 1 drink ; 15 g ethanol.
4Underweight: ,18.5 kg/m2; normal weight: $18.5 to ,25.0 kg/m2; overweight: $25.0 to ,30.0 kg/m2; obese: $30.0 kg/m2.
5Based on reported use of a dietary supplement within the past 30 d.
by guest on November 3, 2015
and lifestyle variables through chunk-wise modeling using data for adults aged $20 y, NHANES 2003–20061
Estimated change in phytoestrogen biomarker concentration with change in covariable after adjusting for sociodemographic
VariableGenisteinDaidzein Equol DMAEnterodiol Enterolactone
Age, every 10-y increase
Sex: female vs. male
Race-ethnicity, NHB vs. NHW
Race-ethnicity, MA vs. NHW
PIR, every 2-unit decrease
Education, #HS vs. .HS
Smoking2, yes vs. no
Alcohol, 1 vs. 0 drink/d3
BMI, 25% increase
Physical activity4, 750 vs. 150 MET-min/wk
Supplement use5, yes vs. no
1Values are changes in percentages (%) and exclude individuals who reported antibiotic use in the past 30 d. Sample sizes for urine phytoestrogens by variable are given in
Supplemental Table 1. Model 1: simple linear regression; model 2: multiple linear regression adjusting for sociodemographic variables; model 3: multiple linear regression adjusting
for sociodemographic and lifestyle variables; model 4: multiple linear regression adjusting for sociodemographic and lifestyle variables and urine creatinine. Change in covariate
was carried out while holding any other variables in the model constant. *Different from zero, P , 0.05. DMA, O-desmethylangolensin; HS, high school; MA, Mexican American;
MET-min, metabolic equivalent task minutes; NHB, non-Hispanic black; NHW, non-Hispanic white; PIR: family poverty-income ratio.
2Based on serum cotinine concentration: yes (smoker), .10 mg/L; no (nonsmoker), #10 mg/L.
3Calculated as average daily number of ‘‘standard’’ drinks [i.e., (quantity 3 frequency)/365.25]; 1 drink ; 15 g ethanol.
4Calculated as total MET-min/wk on the basis of self-reported leisure-time physical activity.
5Based on reported use of any dietary supplement in the past 30 d.
Correlates of phytoestrogen concentration991S
by guest on November 3, 2015
because dietary exposure is also possible (19). We are not aware
of any direct reports of the effect of alcohol consumption on
DMA; however, a negative association has been reported for the
microbial O-demethylation of isoxanthohumol with alcohol
consumption in postmenopausal women (42), and it is plausible
that similar phenomena may partially explain the negative
association we observed with DMA. Alcohol consumption was
not a significant correlate of enterolignan concentrations in our
study or elsewhere (22), suggesting that the effects of alcohol on
gut metabolism are not straightforward.
We observed that PIR was significantly associated with urine
enterolignan concentrations. Both urine enterolactone and
enterodiol were positively correlated with PIR (r = 0.07 and
0.11, respectively; P < 0.05). Urine enterolignan concentrations
were 13–18% lower with every 2-unit decrease in PIR across all
models. We believe that this relationship is a consequence of
dietary patterns associated with PIR. Kerver et al. (43) showed in
U.S. adults (NHANES III) that PIR was positively correlated
with the percentage of individuals in a dietary pattern typified by
higher intakes of likely lignan sources (whole grains, fruit, and
vegetables) and negatively correlated with an antithetic dietary
pattern. Kilkkinen et al. (22) confirmed that positive relation-
ships exist between intake of whole grains, fruit, and vegetables
and serum enterolactone concentrations. In light of this, we
believe that the associations observed between urine enter-
olignans and PIR ostensibly point to a larger pattern of
sociodemographic and lifestyle characteristics that influence
healthy food choices and, in turn, urine enterolignan concen-
trations, particularly for enterolactone. We found that BMI
and physical activity were both significant correlates of urine
enterolactone concentrations in patterns consistent with a
healthy lifestyle. BMI was negatively correlated such that a
25% increase in BMI was associated with 21% lower enter-
olactone concentrations and physical activity was positively
correlated such that an increase in physical activity (750 vs.
150 MET-min/wk) was associated with >6% higher enter-
olactone concentrations after covariate adjustment (model 4).
Interestingly, dietary supplement use was not significantly
associated with any of the urine phytoestrogens measured.
Although dietary supplement use is quite common in the U.S.
population, we suspect that isoflavone and lignan exposure
through dietary supplement usage is actually quite low. Bailey
et al. (44) reported from NHANES 2003–2006 that the prev-
alence of dietary supplement usage (6SE) in the U.S. population
was396 1% for personsaged19–30y and increased to716 1%
for persons $71 y. However, if we consider only botanical
supplementuse—becauseisoflavones and/or lignans are less likely
to be constituents of other supplement types (multivitamin/
multimineral, amino acid)—the prevalence of usage was much
lower, ranging from 13 6 1% (19–30 y) to 20 6 1% (51–70 y).
In addition, most isoflavone and lignan supplements are
marketed on the basis of phytoestrogenic structure-function
claims (45) and their use is often targeted to specific populations
(e.g., peri- and postmenopausal women) and likely makes up
only a fraction of overall botanical dietary supplement use.
Finally, in the case of the isoflavone supplements, the amount of
isoflavones found in these supplements may be moderate or low
when compared with the amounts available in typical servings of
soy-based foods (46).
In addition to sociodemographic and lifestyle variables, we
included urine creatinine in our analyses due to its established
relationship with urine biomarker measurements. In a study in
22,245 participants from NHANES III (1988–1994), Barr et al.
(47) showed that sex, race-ethnicity, and age were significant
determinants of creatinine concentration. Creatinine concentra-
tions tended to be higher in men than in women and higher in
NHBs than in NHWs and decreased with increasing age for
adults ($20 y). We observed that urine creatinine was the most
prominent correlate of urine phytoestrogen concentrations out
of all variables studied. We found that urine creatinine, when
included in the model adjusted for sociodemographic and
lifestyle variables, was the strongest correlate of urine concen-
tration for all phytoestrogens.
In this article we have demonstrated to what extent com-
monly studied sociodemographic and lifestyle variables were
correlates of urine phytoestrogen concentrations in the U.S.
population (NHANES 2003–2006) and found that these rela-
tionships were mostoften compound or class specific. We believe
our study has 2 key strengths that make it a valuable addition to
the field of phytoestrogen research. First, it is a unique work in
that no other study has examined the relationship of as many
sociodemographic and lifestyle variables across as many
phytoestrogenic biomarkers in NHANES or in any comparable
representative population subset. Second, a standardized anal-
ysis approach (40) was used in our study that enables the
comparison of our findings with those presented for other
nutritional and dietary biomarkers from the same NHANES
period (26–29) and presents our data in a format consistent with
reference works such as the Second Nutrition Report (14,15).
We acknowledge that there are limitations to our study design
and that further study in the context of additional variables is
warranted. Although we have summarized general patterns of
urine phytoestrogen concentrations with respect to a selected set
of sociodemographic and lifestyle variables, a limited amount of
the total variability was explained (R2#4%), suggesting that
other important variables related to phytoestrogens exist.
Therefore, caution should be exercised in interpreting b coef-
ficients from these models because they provide limited model fit
and may be biased if an important variable has been omitted.
Urine creatinine provides a good example of this: 8–17% of the
variability observed was explained when urine creatinine was
included in the linear regression models adjusted for socio-
demographic and lifestyle variables compared with 1–4% when
it was not, and the b coefficients for race-ethnicity and BMI
showed dramatic changes when urine creatinine was added to the
model. Dietary intake is likely the most obvious determinant of
biomarker status not included in our analyses. We did not include
dietary intake in this study for 2 reasons. First, isoflavone and
lignan intake data are not readily available for the NHANES
2003–2006 and calculating this from the available dietary intake
data would be a significant undertaking. Second, our analysis was
designed to examine how the concentrations of urine phytoes-
trogens were associated with selected variables after adjusting for
sociodemographic and lifestyle variables, and in that context
isoflavone and lignan intakes would serve more so as outcome
variables as opposed to covariates. We also did not study pre-
analytical and physiologic variables (e.g., fasting, time of speci-
men collection, renal function, inflammation); however, these
have been studied separately in an accompanying article (48).
Nonetheless, our study of sociodemographic and lifestyle varia-
bles as correlates of urine phytoestrogen concentration does serve
as a valuable first step in identifying covariates that, together with
significant preanalytical variables, should be considered in future
studies examining dietary intake or chronic disease risk.
The authors acknowledge technical assistance from Bridgette
Haynes and Yi Pan and contributions from the following
by guest on November 3, 2015
laboratory members: Daniel L. Parker, Donna J. LaVoie, and
Carissa D. Powers. C.M.P. and M.R.S. designed the overall
research project with input from M.E.R.; M.R.S. performed all
statistical analyses; and M.E.R. wrote the initial draft of the
manuscript, which was modified based on feedback from all
coauthors, and had primary responsibility for its content. All
authors read and approved the final manuscript.
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