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Isoflavones in children and adults consuming soy
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ABB Archives of Biochemistry and Biophysics 476 (2008) 161–170 www.elsevier.com/locate/yabbi
Isoflavones in children and adults consuming soy q Adrian A. Franke a,*, Brunhild M. Halm a,b, Leslie A. Ashburn a b
a Cancer Research Center of Hawai‘i, Natural Products & Cancer Biology Program, 1236 Lauhala Street, Honolulu, HI 96813, USA Cancer Research Center of Hawai‘i, Cancer Prevention & Control Program, and Kapi‘olani Medical Center for Women and Children, USA
Received 5 December 2007, and in revised form 5 February 2008 Available online 14 February 2008
Abstract Soy and their isoflavones (IFLs) are believed to protect against breast cancer, particularly when exposure occurs during childhood. Little is known about the bioavailability of IFLs in children and how this is affected by oral antibiotics (OABX). We measured IFLs by LC/MS and found that the urinary IFL excretion rate (UIER) reflects circulating IFLs accurately when area-under-curve (AUC) and identical time intervals are used (r = 0.93; p < 0.001). UIER in children and adults was determined when healthy and when on OABX by collecting urine in pairs of baseline and overnight specimen before and after consuming soy nuts, respectively. Compared to when healthy, children on OABX showed significantly decreased UIER but adults on OABX showed increased UIER (p < 0.05). All 37 healthy children showed significantly higher UIERs compared to all 34 healthy adults. UIER is an adequate surrogate for determining IFL bioavailability and for measuring soy or IFL exposure in epidemiologic and other studies. Ó 2008 Elsevier Inc. All rights reserved. Keywords: Oral antibiotics; Isoflavones; Bioavailability; Soy; Adults; Children; Urine
The effects of soy intake against chronic diseases including breast, prostate, and colorectal cancer, osteoporosis and cardiovascular disorders, as well as menopausal symptoms, are much investigated but not always consistent [1–7]. However, in two large epidemiologic studies strong preventive effects were observed against breast cancer later in life when soy was consumed at young age [8,9]. The health effects caused by the ability of the gut flora to produce the DE1 metabolites equol (EQ) or O-desmethylangolensin
q
Supported in part by Physicians Pharmaceuticals, Inc. and NIH Grant RR020890 (there are no conflicts of interest). * Corresponding author. Fax: +1 808 586 2970. E-mail address: [email protected] (A.A. Franke). 1 Abbreviations used: AUC, area-under-curve; BLU, baseline urine; BW, body weight; DE, daidzein; DHDE, dihydrodaidzein; DHGE, dihydrogenistein; DMA, desmethylangolensin; EQ, equol; GE, genistein; GLYE, glycitein; HPLC, high pressure liquid chromatography; IFL(s), isoflavone(s); M, metabolites (DHDE + DHGE + EQ + DMA); NM, non-metabolites (DE + GE + GLYE); OABX, oral antibiotics; ONU, overnight urine; total IFLs, all isoflavonoids (DE + GE + GLYE + DHDE + DHGE + EQ + DMA); UIER, urinary isoflavone excretion rate (nmol/h or nmol/h/kg BW). 0003-9861/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.abb.2008.02.009
(DMA) by intestinal bacteria are so far unclear and inconsistent [10–14]. Our recent findings on higher IFL bioavailability in children [15] and on cardioprotective effects independent from lipid profiles [16] strengthen the hypothesis that IFLs play an important role in the biological effects of soy intake. IFL exposure occurs mainly by the diet through intake of soy products which typically contain a total of 0.01%– 0.3% IFLs composed mainly of glycosides of genistein (GE), daidzein (DE), and glycitein (GLYE) (Table 1) [14,17–19]. Orally administered IFLs are believed to be efficiently absorbed after cleavage of the glycosides, which occurs mainly by intestinal bacteria [20–25]. In previous research, urinary or plasma IFLs were found to be reliable biomarkers for soy consumption [26–32], and urinary appearance of IFLs reflect plasma values accurately [21,33]. Given the widespread use of antibiotics, there is an urgent need to investigate the influence of OABX on the bioavailability of micronutrients. The UIER was hypothesized to change due to intestinal microflora alteration by age and OABX. We reported recently that healthy children
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Table 1 Structures and codes of isoflavonoids analyzed (a) Unmetabolized isoflavonoids
R7
O 3
R6 R5
4'
O
OH
Aglucon
Code
R5
R6
R7
Conjugates in soy foods
Code
Genistein
GE
OH
H
OH
7-O-(600 -O- Acetyl)-glucoside of daidzein of genistein of glycitein
D-Ac G-Ac GLY-Ac
R7
O H3C
6'' O
O
HO HO
O HO
Glycitein
GLYE
H
OH
OCH3
7-O-(600 -O- Malonyl)-glucoside of daidzein of genistein of glycitein
D-Mal G-Mal GLY-Mal
O
O
HO
6'' O
O
HO HO
O HO
Daidzein
DE
H
H
OH
7-O-glucoside of daidzein of genistein of glycitein
D G GLY
6'' HO
O
HO
O
HO HO (b) Metabolized isoflavonoids by the gut flora
HO
HO
O
OH
CH3
3
R5
Dihydrodaidzein Dihydrogenistein Equol
R4
R5
4'
O
OH
Code
R4
R5
DHDE DHGE EQ
=O =O H
H OH H
take up more IFLs from soy than healthy adults [15] but found that IFL uptake decreases in children while taking OABX [34]. We hypothesized that this prior result could be explained by the differences in the gut environment which is sterile at birth and develops a microbiota gradually over several years until an adult type flora is established [15,35]. Whether adults on OABX react similar to children is a focus of our ongoing studies. Expanding our initial findings, we aimed to compare the urinary IFL appearance in children (ages 4–17) to that of adults (ages 18 years and older), and to study how UIERs may be affected during oral antibiotic therapy for bacterial infec-
OH
Code O-Desmethylangolensin
DMA
tions. In addition, the usefulness of UIER to reflect circulating isoflavones was reevaluated with a novel approach. Materials and methods Intervention protocol to assess the role of antibiotics on urinary isoflavonoids Participants in this study fell into two categories. The first category was adults (ages P 18 years) and children (ages 4–17 years) requiring oral antibiotic therapy for a bacterial infection unrelated to this project. All participants were selected for this study by their family physician (one of the authors) who had good experience with their compliance from
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170 previous encounters, or they were recruited from the Cancer Research Center of Hawaii by word of mouth and flyers. They began the study after they were on OABX for at least 3 days and then repeated the protocol again at least 4 weeks later when healthy and off OABX. The second category was children (ages 4–17 years) and adults (ages P 18 years) who had not been on any antibiotics prior to participating in this study. Some were siblings and parents of the OABX participants, and others were recruited from the Cancer Research Center of Hawaii and the Honolulu Waldorf School by word of mouth and flyers. All participants had to be able to consume soy nuts and to collect an overnight urine (ONU) sample. A total of 99 participants of both genders were included in the analysis including 37 children and 34 adults not taking OABX (Tables 2a and 2b), and 16 children and 12 adults taking OABX (Tables 3a and 3b). Two hundred and twenty-eight were approached to participate in the study. Eighty of these declined. Forty-nine participants had to be excluded: three voluntarily withdrew, 12 had high baseline urinary IFL excretion sug-
Table 2a Characteristics of participants not taking OABX
Number Age (years) BW (kg) Male Female Asian Non-Asian Mexican/Hispanic Mixed Asian/Caucasian Asian/Pacific islanders Asian/Pacific islanders/ Caucasian Other Healthy ONU collection (h)
Children (no OABX)
Adults (no OABX)
Total
37 10.4 ± 3.9 38.9 ± 18.4 12 (32%) 25 (68%) 7 (19%) 13 (35%) 0 (0%) 17 (46%) 10 (59%) 0 (0%) 4 (24%)
34 41.0 ± 9.6 63.02 ± 18.8 7 (21%) 27 (79%) 7 (21%) 23 (68%) 0 (0%) 4 (12%) 1 (25%) 1 (25%) 0 (0%)
71 25.0 ± 17.0 50.4 ± 22.1 19 (27%) 52 (73%) 14 (20%) 36 (51%) 0 (0%) 21 (30%) 11 (52%) 1 (5%) 4 (19%)
3 (18%) 12.3 ± 0.7
2 (50%) 12.2 ± 0.9
5 (24%) 12.2 ± 0.8
Means ± standard deviation; sum of % may differ from 100 due to rounding. Table 2b Characteristics of participants taking OABX
Number Age (years) BW (kg) Male Female Asian Non-Asian Mexican/Hispanic Mixed Asian/Caucasian Asian/Pacific islanders Asian/Pacific islanders/ Caucasian Other OABX ONU collection (h) Healthy ONU collection (h) No. of days after OABX when control sample was collected
Children (OABX)
Adults (OABX)
Total
16 11.3 ± 3.6 44.3 ± 11.5 5 (31%) 11 (69%) 1 (6%) 11 (69%) 0 (0%) 4 (25%) 2 (50%) 0 (0%) 0 (0%)
12 37.8 ± 9.12 64.53 ± 12.2 3 (25%) 9 (75%) 3 (25%) 6 (50%) 1 (8%) 2 (17%) 1 (50%) 0 (0%) 0 (0%)
28 22.6 ± 14.8 53.0 ± 17.2 8 (29%) 20 (71%) 4 (14%) 17 (61%) 1 (4%) 6 (21%) 3 (50%) 0 (0%) 0 (0%)
2 (50%) 12.3 ± 0.9 11.8 ± 1.1 66.6 ± 31.7
1 (50%) 11.4 ± 2.1 11.9 ± 0.5 60.1 ± 28.1
3 (50%) 11.9 ± 1.3 11.7 ± 3.8 63.2 ± 29.7
Means ± standard deviation; sum of % may differ from 100 due to rounding.
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gesting soy consumption before the start of the study, 24 gave incomplete urine collections as shown by low creatinine values or lack of a spot urine, one took soy protein powder instead of soy nuts, one had a serious and life-threatening condition during the time of participation, and eight had diarrhea, other digestive problems or gave insufficient information for analysis. Subjects were excluded if they were on medication for any preexisting condition, or if they had gastrointestinal disease, as well as kidney or liver problems. Other exclusion criteria were vomiting and/or diarrhea during the study period and an allergy to soy. (None of the 28 OABX participants reported serious side effect from taking OABX. Only one reported slight dizziness.) We also excluded children who were brought up in a complex social structure, i.e. where the parents shared legal custody. If the children spent half of their time with each parent, there could be a problem in communication between households, which would affect children taking the antibiotics as prescribed, as well as parents reporting any side effects. One family consisting of one adult and two children (all without OABX) each repeated the study, and averaged values have been used. Five of the OABX participants reported in this study repeated the protocol since they required a second course of antibiotic treatment. The second course of antibiotics for four children and one adult was started 84 (mean) and 37 days after the first antibiotic course was completed, respectively. A total of 107 data pairs are considered in this report, including the repeat OABX subjects, since eliminating them from the analysis did not change the final results significantly. Adults started the study 8.0 ± 3.0 days after beginning their OABX course, or having completed an average of 83% of the OABX, with 100% of participants taking them as prescribed. Urine collections during children’s antibiotic treatment were performed 7.79 ± 1.84 days after the start of the antibiotic regimen. Children had completed an average of 78% of the full OABX course with 81% complying with the entire course as prescribed up to the study day. Compliance and completion of the antibiotic course were verified at time of specimen collection by interviews. Tables 2 and 3, a and b, respectively, detail the ages and weight of the participants, the infections that led to antibiotic therapy, and the types of antibiotics prescribed. If the participants were taking OABX, a couple of days was allowed to pass before approaching the participants about scheduling their urine collections, so that they would have some time to improve their health condition and to maximize antibiotic exposure. The details of the study and methods for collecting urine were explained to all of the participants and the children’s parents before commencing the study. If they had been taking OABX, participants began the study protocol after at least 3 consecutive days of antibiotic therapy, and if possible, on the second-tolast day of the therapy (Tables 3a and 3b). The University of Hawaii Committee on Human Studies and the Institutional Review Board approved the study protocol and all consent and assent forms. All adult participants signed consent forms, and parents signed a consent form for their children, and children 7 years of age and above provided their own additional assent separately from their parents. Five children and 17 adult participants received 15 g of soy nuts, whereas all remaining participants received body weight (BW) adjusted soy nuts at doses of 15 g nuts per 54.4 kg BW. In the course of this study, the dosing was changed towards adjusting for BW. We report here the results of all participants, because the final data did not change significantly when non-BW-adjusted participants were removed from the analysis. All participants received supplies for their urine collection, a worksheet, and a background information questionnaire to complete. At 18:00 they emptied their bladder into a small container provided to them (baseline urine (BLU)), and immediately thereafter consumed the soy food. They were asked to collect all urine voids for 12 h in large containers, including when they got up the next morning (ONU) at approximately 06:00. They were also instructed to chill the urine in the coolers provided and to avoid all other soy products, including supplements with soy or IFLs, from the time they woke up on the study day until the next day. The urine was transported on ice to our laboratory where ONU was mixed and weighed. BLU and ONU were stored in 2 mL aliquots at 20 °C until analyzed. Urine containers included small amounts of boric and ascorbic acid as preservatives [36].
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Table 3a OABX use in childrena Diagnosis leading to OABX therapy
Name of OABX
Single dose amount
Dosing frequency (times per day)
Days on OABX on study day
Total days of prescribed OABX
No. of days after OABX control sample collected
Sinusitis Sinusitis Skin infection Skin infection* Ear infection Sinusitis Scarlet fever
Amoxicillin Cefdinir Clindamycin Clindamycin Cefdinir Cefdinir Amoxicillin; Zithromax
1 2 4 4 1 1 2 1
10 10 9 8 10 9 10
10 14 14 14 10 10 10
102 42 100 51 36 40 47
Urinary tract infection
Trimethoprim/ sulfamethoxazole Cefdinir Cefdinir Cefdinir Zithromax
500 mg 300 mg 300 mg 300 mg 460 mg 400 mg 600 mg day 1–8; then 200 mg day 9; then 100 mg days 10–13 60/300 mg
2
7
10
33
300 mg 600 mg 600 mg 500 mg day 1, then 250 mg days 2–5 600 mg days 1–2; then 160 mg/800 mg days 3–11
1 1 1 1
6 9 6 5
10 10 10 5
50 128 101 57
3 2
9
11
91
300 mg 250 mg 200 mg 875/125 mg 600 mg 300 mg 300 mg
2 3 2 2 1 1 1
6 7 8 5 9 5 7
10 7 10 10 10 5 10
44 47 44 90 131 57 41
Ear infection Ear infection* Respiratory infection Respiratory infection* Skin infection
Streptococcal pharyngitis Ear infection Sinusitis Sinusitis Ear infection Streptococcal pharyngitis Sinusitis* a *
Clindamycin; then trimethoprim/ sulfamethoxazole Amoxicillin Amoxicillin Amoxicillin Amoxicillin clavulanate Cefdinir Cefdinir Cefdinir
Sixteen subjects, four repeat therapies indicated by italics. Repeated treatment of participants 3, 8, 9 and 16 in the sequence shown.
Table 3b OABX use in adultsa Diagnosis leading to OABX therapy
Name of OABX
Single dose amount
Dosing frequency (times per day)
Days on OABX on study day
Total days of prescribed OABX
No. of days after OABX control sample collected
Streptococcal pharyngitis Sinusitis Bronchitis Pharyngitis Bronchitis* Bronchitis
Amoxicillin Amoxicillin Moxifloxacin Azithromycin Doxycycline Azithromycin
1 1 1 1 2 1
7 10 7 3 7 5
10 10 7 5 7 5
48 32 50 110 65 103
Staphylococcal skin infection
2
7
10
37
Sinusitis Streptococcal pharyngitis Ear infection Sinusitis Dental abscess
Trimethoprim/ sulfamethoxazole Biaxin Cephalexin Ciprofloxacin Ciprofloxacin Clindamycin
750 mg 500 mg 500 mg 500 mg 100 mg 500 mg day 1, then 250 mg days 2–5 160 mg/800 mg
1 4 2 2 4
13 9 4 12 10
14 10 10 14 14
64 35 50 104 33
Tonsillitis
Cefdinir
1000 mg 500 mg 500 mg 500 mg 300 mg days 1–2, then 150 mg days 3–12 300 mg
1
10
10
50
a *
Twelve subjects, one repeat therapy indicated by italics. Repeated treatment of participant 4 in the sequence shown.
On the worksheet, participants recorded all foods consumed the day of the first urine collection, including before and after having eaten the study soy food. Dietary data were recorded to monitor soy intake other than the study food. Participants noted the time, the soy nuts for the study were consumed (just a few minutes after providing the spot urine) and the time of the final urine collection in the morning, typically between 05:00 and
07:00. In addition, OABX subjects provided information about the condition that led to antibiotic therapy; the name, dosage and duration of therapy; compliance with taking the antibiotics as prescribed; and side effects of therapy, if any. OABX participants repeated this protocol when healthy, i.e. 4–6 weeks after antibiotic treatment was terminated and no other health condition occurred in the meantime.
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170 Soy foods used in the study were soy nuts (DrSoy Nutrition, Irvine, CA; Physicians Pharmaceuticals, Inc., Kernersville, NC). The IFL composition and content of the nuts were determined by high pressure liquid chromatography (HPLC) with photo diode array detection without hydrolysis; flavone was used as internal standard. This method has been validated in a comparison with LC/MS based assays [37]. To account for variations in IFL content by crop and manufacturing batches, we measured IFL in each batch received. For the majority of participants (45 children and 32 adults) the serving sizes were controlled to provide BW-adjusted IFL dosages (15 g nuts per 54.4 kg BW). This led to the following IFL doses (mg aglycon units per 54.4 kg BW): daidzin (8.0 ± 1.1), glycitin (0.1 ± 0.1), genistin (8.9 ± 1.6), daidzin–malonate (1.1 ± 0.4), glycitin–malonate (0.2 ± 0.1), genistin–malonate (1.7 ± 0.6), daidzin–acetate (6.2 ± 1.4), glycitin–acetate (0.2 ± 0.1), genistin–acetate (6.1 ± 1.0), DE (0.4 ± 0.2), GLYE (0.0 ± 0.0), GE (0.5 ± 0.2); computation revealed total DE (15.6 ± 1.9), total GLYE (0.5 ± 0.2), total GE (17.2 ± 2.4), and all isoflavonoids (total IFLs) (33.4 ± 4.2). Relative to total IFLs, glucosides were 51%, malonyl-glucosides 9%, acetyl-glucosides 37%, and aglucons 3%, equivalent to 47% total DE, 2% total GLYE, and 51% total GE. It is important to note that participants providing collections during antibiotic treatment and again when healthy were given soy foods from the same batch for both collections. Consequently, IFL doses were identical within each participant.
Intervention protocol to assess the correlation between plasma and urine values Eight male and six female health professionals, in good health, from the Cancer Research Center of Hawaii between the ages of 38 and 63 years consumed 10 g of roasted soy nuts equivalent to a total of 22.1 mg IFL aglycon as determined by HPLC (see above). Three to six hours after soy intake, blood was collected in green-top vacutainers (Li-heparin), and plasma was prepared after centrifugation followed by storage at 80 °C until analyzed. This was repeated 4–9 h after soy intake. All urine samples were collected between the two blood draws, after each participant emptied their bladder shortly after the first blood draw. All times and weights of urine were recorded. Urine aliquots were stored at 80 °C until analyzed. Final urinary excretion rates were not adjusted to BW since the BW of all individuals was fairly similar. The University of Hawaii Committee on Human Studies approved the study protocol and all consent forms which were signed by all participants.
Isoflavonoid analysis from plasma and urine DE, GE, GLYE, EQ, dyhyrdodaidzein (DHDE), dyhydrogenistein (DHGE), and DMA were analyzed from plasma or urine by HPLC with electrospray ionization (negative mode) ion trap mass spectrometry [38–40]. In brief, triply 13C labeled internal standards of DE, GE, EQ, and DMA (obtained from the University of St. Andrews, UK) were added to each specimen hydrolyzed with glucuronidase and sulfatase (Roche Applied Sciences, Indianapolis, IN) followed by repeated phase separation with diethyl ether [20]. The combined ether fractions were dried under nitrogen and redissolved in a 1:1 mixture of acetonitrile/sodium acetate buffer (0.2 M, pH 5). A volume of 5–20 lL of this extract were analyzed with a Surveyor TSQ QuantumUltra triple quadrupole system (ThermoFisher, San Jose, CA) equipped with a Gemini C18 reversed phase column (150 2.0; 5 lm) coupled to a Gemini C18 (4 2.0; 5lm) direct-connect guard column (Phenomenex, Torrance, CA). Elution was performed at 200 lL/min with a mixture of methanol/acetonitrile/water/0.1% aq. formic acid at 15/15/70/0 which was changed linearly over 6 min to 40/40/10/10, held at this ratio for 1 min until changed in 0.1 min back to 15/15/70/0 for equilibration. The general MS conditions were as follows: source, ESI; ion polarity, negative; spray voltage, 4000 V; sheath and auxiliary and ion sweep gas, nitrogen; sheath gas pressure, 30 arbitrary units; auxiliary gas pressure, 10 arbitrary units; ion sweep gas pressure, 10 arbitrary units; ion transfer capillary temperature, 250 °C; scan type, high resolution selected reaction monitoring (HRSRM); collision gas, argon; collision gas pressure, 1.0 mm Torr. The transitions (m/z) used for quantitation were for DE from 253.0
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to 223.0, 208.0, and 132.0, for DHDE from 255.0 to 149.0, for 13C3-DE from 256.0 to 226.0, 210.0, and 132.9, for GLYE from 283.1 to 268.0 and 240.0, for DHGE from 271.1 to 165.0, for 13C3-GE from 271.9 to 160.4, 135.0, and 134.1, for EQ from 241.1 to 135.0, 121.0, and 119.0, for 13C3EQ from 244.1 to 120.0, for GE from 269.1 to 159.1, 133.0, and 132.0, for DMA from 257.1 to 136.0, 109.0, and 108.0, for 13C3-DMA from 260.1 to 109.0. Limits of quantitation for all analytes using 1.8 mL urine were 2.5 nM except for DHDE and DHGE (1.5 nM) and DMA and EQ (5.0 nM). Between-day coefficients of variation ranged 4–12% (DE), 5– 18% (GE), and 3–14% (GLYE), and 5–15% for all metabolites. Urinary creatinine was determined with a Roche–Cobas MiraPlus clinical auto analyzer using a kit from Randox Laboratories (Crumlin, UK) that is based on a kinetic modification of the Jaffe´ reaction. Betweenday coefficients of variation were 4–7%.
Calculation of hourly UIER We measured urine weights in gram and used this as a surrogate for volume determination, although the density of urine is known to be slightly higher than 1. However, this inaccuracy seems acceptable considering the relatively larger measurement errors connected with urinary volume determinations, and also considering that urine collections per se bear inherent inaccuracies. The amount of IFLs at baseline present in the ONU collection, although small in all samples, was subtracted from the IFL amount in the ONU sample in order to adjust for background IFLs in the ONU sample. Since the time of previous void of BLU was unknown, its expression in hourly units was not readily available. Hourly units could, however, be calculated by multiplying the creatinine/hour value as available from ONU with the known nmol/creatinine value of BLU separately for each subject. This seemed adequate due to the relatively constant creatinine excretion within an individual [41,42]. We thereby obtained the UIER of BLU in nmol/hour units and determined the amount that this UIER contributed to the measured ONU amount (see below) as follows: the known elimination half time of IFLs (average T1/2 = 8 h [22]) and the trapezoid method were applied to calculate AUC values in order to arrive at absolute BLU amounts present in the 12-hour period of ONU collection [21] that, according to our protocol, immediately followed the BLU collection. The ONU concentration determined by LC/MS in nmol/mL units was multiplied by the weight of the ONU in grams to arrive at absolute amounts (nmol) in the collected urine specimen (the density of urine was assumed to be 1; see above). This IFL amount was adjusted if ONU collections deviated from the 12-hour urinary collection period, based on factors that consider the typical exponential elimination pattern of IFLs [21–23] and convert amounts collected for longer or shorter times than 12 h into exactly 12 h as described in detail previously [15,34]. The IFL amount contributed by BLU (see above) was then subtracted from the ONU amount followed by dividing by 12 (for the 12 h of collection) and by the kg BW of the subject to arrive at the 12-hour collection-time, BW, and baseline adjusted UIER of ONU (mol/hour/kg BW). Plasma AUC values were calculated by the trapezoid method as described by us in detail recently [21].
Statistical analysis Paired and unpaired t-tests as well as z-tests were performed using Excel 2004 for the Macintosh (Microsoft Inc., Redmond, WA). These tests were performed with the original UIER values, as well as with log transformed values to take non-normality into consideration. Because results with the latter data led to similar results, we present here t-tests based on the untransformed data.
Results Isoflavone values in plasma and urine of 14 healthy adults were better correlated when AUC for plasma and
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Table 4 Isoflavone values in plasma and urine and their correlationa Using concentration (plasma) and excretion rate (urine)
Timeb at first collection (h)
Plasma level at first collection (nM)
Timeb at second collection (h)
Plasma level at first collection (nM)
Urine excretion ratec (nmol/h)
Correlations coefficientd
Median (range)
6.0 (3.0–7.0)
349 (188–966)
7.1 (4.3–8.9)
356 (238–817)
1538 (551–3947)
0.68
Using area-undercurve values
Time from first to second collection (h)
Plasma value from first to second collection (nM h)
Urine excretion from first to second collection (nmol/h h)
Median (range)
6.0–7.1 (3.0–8.9)
408 (221–1701)
1827 (634–7002)
0.93*
a
Data from eight males and four females, all health professionals. Time after soy intake. c From all urine collected between first and second collection after bladder was emptied at first collection. d Between plasma and urine values by linear regression. e Calculated by the trapezoid method from plasma concentrations at the first and second collection [21] and by using absolute excretion amounts for urine calculated from the first to the second collection. * p < 0.001 according to z-test. b
urine was used (r = 0.93, p < 0.001; Table 4), instead of UIER for urine, a time-based unit (nmol/h), and IFL levels for plasma, a non-time-based unit (nmol/L). Children had higher UIERs than adults when all were healthy, whether they had previously taken OABX or not (Table 5). After combining all available data from the 99 participants when they were healthy, results reached significance (p < 0.001 by unpaired t-test) for DE (+40%), GE (+53%), non-metabolites (NM) (+42%), and total IFLs (+26%) (Table 5), similar to our previous findings [15]. However, changes of UIER as a function of OABX were opposite in children and adults which again, agrees with our initial reports [15,34]. During OABX use in this extended study, children (n = 16) showed a trend towards lower UIERs compared to when healthy, which reached significance (p < 0.05 by paired t-test) for DE (20%), GE (40%), NM (21%), and total IFLs (25%) (Table 6a). In contrast, the 12 adults we investigated showed the opposite pattern, namely a trend towards higher UIERs
Table 5 Mean UIER in 53 children and 46 healthy adults
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
Children (nmol/h/kg)
Adults (nmol/h/kg)
Children versus adults
p
27.1 9.2 1.2 1.4 4.3 2.1 1.1 36.8 5.0 39.6
19.3 6.1 6.1 0.7 4.4 1.4 0.3 25.8 6.2 31.3
+40% +53% 80% +85% 3% +51% +207% +42% 19% +26%
<0.002 <0.002 0.44 0.15 0.95 0.36 0.35 <0.001 0.36 0.032
p = significance of difference between adults and children by unpaired t-test. M = metabolites, NM = non-metabolites, Total IFLs = sum of all isoflavonoids tested.
Table 6a Mean UIER of individual IFLs in 16 children during oral antibiotic therapy (OABX) and at least 4 weeks later when healthy
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
OABX (nmol/h/kg)
Healthy (nmol/h/kg)
OABX versus healthy
p
26.1 6.9 1.0 0.1 0.3 1.0 0.4 34.0 1.6 35.6
32.7 11.3 3.7 2.7 2.7 4.6 4.0 43.1 4.7 47.7
20% 40% 74% 95% 89% 78% 90% 21% 65% 25%
0.009 0.003 0.15 0.16 0.19 0.07 0.17 0.003 0.07 0.002
during OABX use compared to when healthy which reached significance (p < 0.05 by paired t-test) for DE (+87%), NM (+81%), and total IFLs (+74%), while GE showed a borderline significant increase of +54%
Table 6b Mean UIER of individual IFLs in 12 adults during oral antibiotic therapy (OABX) and at least 4 weeks later when healthy [43]
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
OABX (nmol/h/kg)
Healthy (nmol/h/kg)
OABX versus healthy
p
35.2 6.8 0.5 0.3 4.9 3.8 0.1 42.6 9.1 51.5
18.9 4.4 0.3 0.1 3.0 2.7 0.2 23.6 6.0 29.6
+87% +54% +37% +154% +63% +42% 42% +81% +52% +74%
0.016 0.056 0.22 0.32 0.12 0.84 0.17 0.02 0.17 0.01
p = significance of difference between adults and children by paired t-test. M = metabolites, NM = non-metabolites, Total IFLs = sum of all isoflavonoids tested.
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(p = 0.056; Table 6b) [43]. This overall pattern did not change markedly when considering exclusively BWadjusted versus non-BW-adjusted soy nut doses for adults, after removing the five OABX and three non-OABX participants with repeated collections, nor when using logged values to consider non-normality. An exception was GE in adults, which improved the p value from 0.056 to 0.021 with logged values (data not shown). Type of antibiotic, type of illness, gender, age and ethnicity of the participant did not show any correlation to UIERs neither while taking antibiotics nor again when healthy. Discussion Benefits of using urine to measure IFLs include its noninvasiveness compared with blood draws, as well as the ability to collect highly concentrated urine (particularly ONU) in large amounts, which leads to low quantitation limits. Also, urine can be obtained by participants themselves without medical supervision, in private, and most importantly, can be accumulated over many hours (even days) reflecting exposures over much longer time periods compared to data from blood, which only reflects one given point in time per collection. In addition, the accuracy of urine collection can be examined by comparing the measured creatinine amount with established data for each gender and age group (Table 7) [41,42]. The correlation between IFL values in urine and blood are much improved by using AUC units for both matrices instead of UIER for urine, a time based unit, and IFL level for plasma, a non-time-based unit (Table 4). This is largely due to the time domain being accurately considered by using the identical time intervals for the AUCs of both matrices, which is particularly important for plasma, because levels change markedly over a given time period. Our current results are in excellent agreement with several previous independent reports on UIERs reflecting IFL plasma concentrations accurately [21,29,32, 33,44,45]. Therefore, UIERs can be used as a reliable surrogate to determine circulating IFLs and thereby assess IFL bioavailability. Since bioavailability is defined based on circulating levels, we refer here to ‘apparent bioavailability’ when using urinary excretion data. In the present study, we generated highly accurate urinary data in a novel approach by adjusting UIER in ONU to exactly 12 h, by subtracting IFL amounts that were present from baseline exposures, and by adjusting for subjects’ BW. In this way, urine collection times deviating from the required 12 h, and presence of urinary IFLs unrelated to the soy exposure from this study, were adequately considered. In addition, the completeness of urine collection was checked (Table 7), and some participants were excluded when their measured creatinine amount was >30% lower than the expected theoretical amount. Urinary excretion expressed relative to time (hour) is more accurate than expressed relative to creatinine [33], because the latter depends mostly on muscle mass which can change largely depending on BW, gender, and age
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Table 7 Daily creatinine excretion per kg of body weight as a function of age and gender Age (years)
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Urinary creatinine (mg/kg/d) Females
Males
14.5 15.7 15.7 17.9 17.9 17.9 18.9 18.9 18.9 18.9 18.9 20.9 20.9 20.9 20.9 22.0 22.0 20.4 20.4 17.6 17.6 14.9 14.9 12.9 12.9 11.8 11.8 10.7 10.7 8.4 8.4
15.2 17.1 17.1 19.3 19.3 19.3 20.7 20.7 20.7 20.7 20.7 23.3 23.3 23.3 23.3 24.0 24.0 21.9 21.9 19.7 19.7 19.3 19.3 16.9 16.9 14.2 14.2 11.7 11.7 9.4 9.4
Ages 3–17 = Remer et al., 2002 [41]. Ages 20–95 = Kampmann et al., 1974 [42] except ages 20–25 where we suggest to use 22 mg/kg/d for women and 24 mg/kg/d for men to adjust the original Kampmann data (19.7 mg/kg/d for women and 23.8 mg/kg/d for men) to the more recently determined Remer values (20.9 mg/kg/d and 23.3 mg/kg/d for 17 year old girls and boys, respectively).
[41,46]. This is particularly relevant in growing children, not only due to marked changes of muscle mass in absolute terms, but also after adjustment for BW [41,42,47,48]. Urinary excretions adjusted solely for creatinine underestimate true excretion in heavier individuals—for example, in males versus females, or in older children versus younger children—and can lead to erroneous results [49]. Collection of urine over a 24-hour period or longer would be ideal if collected correctly, but is often difficult or impossible to perform in human studies. Collections of that duration bear inherent risks of missed samples or inclusion of other confounders. A good compromise is the collection of overnight urine, which is relatively easy to do for participants in the privacy of their homes, resulting in very high compliance [18,50] and which leads to a concentrated matrix, making analysis easier. We recommend this particularly for research with children after they reach bladder control, due to the ease for parents to supervise their children in following the protocol.
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The lack of conversion to time-based urinary excretion rates might explain the lower UIE in older than in younger soy-exposed children [35] or the loss of differences in urinary sex steroid excretion in females of different reproductive ages [51]. Therefore, as a general rule, it is best to have the collection time interval and the volume of the urine specimen available. The second choice is to convert a creatinine based excretion value into a time-based excretion (in hours) by multiplying the former with the creatinine/hour ratio as determined from any timed urine collection for a given subject since the creatinine excretion remains quite constant within a subject. The third choice is to multiply the nmol/mg creatinine value with the established mg-creatinine/kg BW/day ratio as shown in Table 7 [41,42]. We suggest using 22 mg/kg/d and 24 mg/kg/d for 20–30 yearold women and men, respectively, to adjust the original data (19.7 mg/kg/d for women and 23.8 mg/kg/d for men) [42] to the more recent values (20.9 mg/kg/d and 23.3 mg/kg/d for 17 year old girls and boys, respectively) [41]. The higher apparent IFL bioavailability in healthy children (n = 37) relative to healthy adults (n = 34) we found in this extension of our initial study [34] is in excellent agreement with our previous reports on healthy minors, such as infants [52], (pre)pubertal girls [53], and schoolaged children [34]. These findings suggest a higher systemic IFL exposure in children versus adults at the same relative soy dose. When considering that children eat generally much more per kg BW, the IFL exposure in children is probably up to twofold higher relative to adults. This could result in children experiencing more benefits from the health effects of soy [8,9]. We believe that the IFL exposure after soy intake will stay below levels that would give rise to concern regarding adverse effects. Toxic activity is usually observed at much higher IFL levels, and adverse effects have not been reported in populations with high soy intake. In this soy intervention study, we found that the overwhelming majority of the children investigated showed consistently lower UIERs on OABX compared to when healthy. This finding confirms our very recent report from eleven minors [34] whether all data were combined or treated separately (data not shown). In contrast, the adults investigated behaved opposite to children by showing higher UIERs during OABX therapy compared to when healthy whether the soy dose was BW adjusted or not. This is in agreement with one of our earlier studies in one individual (not included in the current analysis) when he was on OABX therapy for one day only [21]. It is noteworthy that the two adults who were excluded from the present study because of diarrhea while on OABX showed lower UIERs. This is in agreement with our earlier results of lower IFL bioavailability in one healthy volunteer who developed diarrhea due to treatment with a polyethylene glycol based laxative plus oral neomycin and erythromycin [21] and can be explained by the decreased time available for hydrolysis to the bioavailable IFL aglucons. We believe that our general findings in children and adults can be
explained by the changes in gut bacteria caused by OABX, or to unknown factors connected with the disease that led to OABX therapy. Soy products such as the nuts used in this study contain their IFLs mainly as glycosides [14,17– 19] which are hydrolyzed after ingestion to the bioavailable aglucons mainly by intestinal bacteria [20–25]. However, gut bacteria do also further metabolize the ‘free’ bioavailable IFL aglucons to further breakdown products such as EQ, DMA, p-ethylphenol, and other non-specific phenolic agents [54]. Although definitive evidence is lacking, our seemingly paradoxical observation in adults versus children could be due to OABX in children reducing the IFL-hydrolyzing gut bacteria significantly (and possibly also the IFL aglycon degrading flora), which consequently leads to the IFLs not becoming bioavailable. Vice versa, OABX in adults could possibly preferentially reduce the IFL aglycon degrading gut bacteria that are involved in IFL degradation to unknown and non-specific metabolites, while the bacteria that hydrolyze IFL glycosides are comparably little affected; thereby the time during which bioavailable IFL aglucons can be absorbed is increased. Our hypothesis could at least in part be tested by using IFL aglucons such as in fermented soy products [37]. The subjects who repeated the protocol showed almost super-imposable UIER patterns with or without OABX which indicates good repeatability. All participants of this study were treated with broad spectrum OABX, but no correlation between specific types of antibiotics used, type of illness, gender, age and ethnicity of the participant and UIER changes was observed. This might be due to the overall small sample size and/or inter-individual differences and the small numbers in each ethnic group. Similarly, our protocol did not allow for detailed evaluation of IFL metabolites, because a 12-hour urine sampling after soy exposure is insufficient to allow for efficient metabolite formation by the gut bacteria. With this pilot study, we were unable to differentiate effects due to the bacterial infection, the antibiotics, or both. To further investigate the role of inflammation and infection on the IFL metabolism in humans, we recently initiated studies with participants suffering from viral or other acute and chronic infections not requiring antibiotics. The human intestinal tract is very different from animals, and since it contains many species that cannot be cultured using present techniques, in-vitro and animal studies can only provide a framework for understanding gut biota-related processes. Intervention studies in humans seem more likely to shed light on these processes including IFL metabolism. The present intervention was performed with highly compliant subjects, most of them who were known from previous encounters. It specifically included only urine specimen that were collected correctly as determined by comparisons with expected theoretical creatinine values, and it showed statistically significant results despite the relative small numbers of subjects. One of the major concerns in human studies involving OABX is compliance with taking the medication as prescribed and completing the study protocol correctly. We selected
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170
the participants carefully, with the majority recruited through their family physician (one of the authors) who had good experience with their compliance. We also monitored the OABX use by questionnaire and personal interviews. In addition, most of our participants had dosing only once or twice a day (Tables 3a and 3b) and fewer doses per day is known to lead to good compliance with antibiotic therapy [55,56]. In summary, our present and recent reports indicate that urine is extremely useful, and often superior, to values from blood for the determination of soy and/or IFL exposure and can be used as a reliable surrogate for the estimation of soy intake and systemic IFL exposure. The urinary data reported in this paper are very accurate due to the novel approach of adjusting the UIER in ONU to exactly 12 h, by subtracting amounts that were present at baseline, and by adjusting for subjects’ BW. Results of this extended study replicate our previous results on higher apparent IFL bioavailability in children relative to adults and on decreased and increased UIER by OABX in children and adults, respectively. Future studies in adults and children with increased sample sizes and extended protocols will be helpful to develop a deeper understanding of the role of the gut flora, disease state, OABX use, and other factors on metabolism and bioavailability of dietary agents in humans and their connection to potential health effects. Acknowledgments We thank Laurie Custer, B.S., for performing the chemical analyses, the local Waldorf School, Bibiana Potter, Bonnie Ozaki-James, and Beth Allingham for recruiting participants, Sandra, M., Hebshi, M.S., for study coordination, and Dr. T. Remer, Research Institute of Child Nutrition, Dortmnund/Germany, for helpful discussions. Support from Physicians Pharmaceuticals, Inc., Kernersville, NC, and DrSoy Nutrition, Irvine, CA, for this research is acknowledged. Adrian A. Franke, Ph.D., the principal investigator of this study, was responsible for the design and overall performance of the study; he carried out data interpretation, statistical analyses, and wrote the manuscript. Brunhild M. Halm, M.D., Ph.D., assisted in the design and overall performance of the study, recruited study participants, was available for medical advice, supervised personnel, conducted data interpretations, and assisted in writing the manuscript. Leslie A. Ashburn, M.A., was a study coordinator and assisted in manuscript preparation, statistical analyses, as well as matters related to the institutional review board. No authors have a conflict of interest. References [1] M. Messina, V. Persky, K.D.R. Setchell, S. Barnes, Nutr. Cancer 21 (1994) 113–131. [2] S. Barnes, C. Grubbs, K.D.R. Setchell, J. Carlson, in: W. Pariza, U. Aeschbacher, J.S. Felton, S. Sato (Eds.), Mutagens and Carcinogens in the Diet, Wiley-Liss, New York, 1990, pp. 239–254.
169
[3] C.A. Lamartiniere, J. Moore, M. Holland, S. Barnes, Proc. Soc. Exp. Biol. Med. 208 (1995) 120–123. [4] W.B. Murrill, N.M. Brown, J.-X. Zhang, P.A. Manzolillo, S. Barnes, C.A. Lamartiniere, Carcinogenesis 17 (1996) 1451–1457. [5] M. Pollard, P.H. Luckert, Nutr. Cancer 28 (1997) 41–45. [6] J.-X. Zhang, G. Hallmans, M. Landstrom, A. Bergh, J.E. Damber, P. Aman, H. Adlercreutz, Cancer Lett. 114 (1997) 313–314. [7] F.M. Uckun, W.E. Evans, C.J. Forsyth, K.G. Waddick, L.T. Ahlgren, L.M. Chelstrom, A. Burkhardt, J. Bolen, D.E. Myers, Science 267 (1995) 886–891. [8] X.O. Shu, F. Jin, Q. Dai, W. Wen, J.D. Potter, L.H. Kushi, Z. Ruan, Y.T. Gao, W. Zheng, Cancer Epidemiol. Biomarkers Prev. 10 (2001) 483–488. [9] A.H. Wu, P. Wan, J. Hankin, C.C. Tseng, M.C. Yu, M.C. Pike, Carcinogenesis 23 (2002) 1491–1496. [10] C. Atkinson, C.L. Frankenfeld, J.W. Lampe, Exp. Biol. Med. (Maywood) 230 (2005) 155–170. [11] C.L. Frankenfeld, A. McTiernan, E.J. Aiello, W.K. Thomas, K. LaCroix, J. Schramm, S.M. Schwartz, V.L. Holt, J.W. Lampe, Cancer Epidemiol. Biomarkers Prev. 13 (2004) 1156– 1162. [12] C.L. Frankenfeld, A. McTiernan, W.K. Thomas, K. LaCroix, L. McVarish, V.L. Holt, S.M. Schwartz, J.W. Lampe, Maturitas 53 (2006) 315–324. [13] K.D.R. Setchell, S.P. Borriello, P. Hulme, D.N. Kirk, M. Axelson, Am. J. Clin. Nutr. 40 (1984) 569–578. [14] P.L. Horn-Ross, M. Lee, E.M. John, J. Koo, Cancer Causes Control 11 (2000) 299–302. [15] B.M. Halm, L.A. Ashburn, A.A. Franke, Br. J. Nutr. 98 (2007) 998– 1005. [16] S.E. Walker, M.R. Adams, A.A. Franke, T.C. Register, Atherosclerosis 196 (2008) 106–113. [17] S.T. Umphress, S.P. Murphy, A.A. Franke, L.J. Custer, C.L. Blitz, J. Food Comp. Anal. 18 (2005) 533–550. [18] A.A. Franke, L.J. Custer, C.M. Cerna, K. Narala, Proc. Soc. Exp. Biol. Med. 208 (1995) 18–26. [19] H. Adlercreutz, W. Mazur, Ann. Med. 29 (1997) 95–120. [20] A.A. Franke, L.J. Custer, W. Wang, S.J. Shi, Proc. Soc. Exp. Biol. Med. 217 (1998) 263–273. [21] A.A. Franke, L.J. Custer, S.A. Hundahl, Nutr. Cancer 50 (2) (2004) 141–154. [22] K.D. Setchell, N.M. Brown, P.B. Desai, L. Zimmer-Nechimias, B. Wolfe, A.S. Jakate, V. Creutzinger, J.E. Heubi, J. Nutr. 133 (2003) 1027–1035. [23] L. Zubik, M. Meydani, Am. J. Clin. Nutr. 77 (2003) 1459–1465. [24] M. Richelle, S. Pridmore-Merten, S. Bodenstab, M. Enslen, E.A. Offord, J. Nutr. 132 (2002) 2587–2592. [25] P.C.H. Hollman, Pharm. Biol. 42 (2004) 74–83. [26] A. Seow, C.-Y. Shi, A.A. Franke, J. Hankin, H.-P. Lee, M.C. Yu, Cancer Epidemiol. Biomarkers Prev. 7 (1998) 135–140. [27] Z. Chen, W. Zheng, L.J. Custer, Q. Dai, X.-O. Shu, F. Jin, A.A. Franke, Nutr. Cancer 33 (1999) 82–89. [28] C. Atkinson, H.E. Skor, E.D. Fitzgibbons, D. Scholes, C. Chen, K. Wahala, S.M. Schwartz, J.W. Lampe, Cancer Epidemiol. Biomarkers Prev. 11 (2002) 253–260. [29] P.B. Grace, J.I. Taylor, Y.L. Low, R.N. Luben, A.A. Mulligan, N.P. Botting, M. Dowsett, A.A. Welch, K.T. Khaw, N.J. Wareham, N.E. Day, S.A. Bingham, Cancer Epidemiol. Biomarkers Prev. 13 (2004) 698–708. [30] A.H. Wu, M.C. Yu, C.C. Tseng, N.C. Twaddle, D.R. Doerge, Carcinogenesis 25 (2004) 77–81. [31] P. Fanti, T.J. Stephenson, I.M. Kaariainen, B. Rezkalla, Y. Tsukamoto, T. Morishita, M. Nomura, A.A. Franke, Nephrol. Dial. Transplant. 18 (2003) 1862–1868. [32] M.R. Ritchie, M.S. Morton, N. Deighton, A. Blake, J.H. Cummings, Br. J. Nutr. 91 (2004) 447–457. [33] A.A. Franke, B.M. Halm, L.J. Custer, Y. Tatsumura, S.M. Hebshi, Am. J. Clin. Nutr. 84 (2006) 406–413.
170
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[34] B.M. Halm, A.A. Franke, L.A. Ashburn, S.M. Hebshi, L.R. Wilkens, Nutr. Cancer 60 (2008) 14–22. [35] L. Hoey, I.R. Rowland, A.S. Lloyd, D.B. Clarke, H. Wiseman, Br. J. Nutr. 91 (2004) 607–616. [36] A.A. Franke, L.J. Custer, J. Chromatogr. B 662 (1994) 47– 60. [37] A.A. Franke, J.H. Hankin, M.C. Yu, G. Maskarinec, S.H. Low, L.J. Custer, J. Agric. Food Chem. 47 (1999) 977–986. [38] R.M. Blair, S.E. Appt, A.A. Franke, T.B. Clarkson, J. Nutr. 133 (2003) 2262–2267. [39] A.A. Franke, L.J. Custer, L.R. Wilkens, L. Le Marchand, A.N.S. Nomura, M.T. Goodman, L.N. Kolonel, J. Chromatogr. B 777 (2002) 43–57. [40] G. Maskarinec, S.M. Hebshi, L.J. Custer, A.A. Franke, Eur. J. Cancer Prev. 17 (2008) 67–70. [41] T. Remer, A. Neubert, C. Maser-Gluth, Am. J. Clin. Nutr. 75 (2002) 561–569. [42] J. Kampmann, K. Siersbaek-Nielsen, M. Kristensen, J.M. Hansen, Acta Med. Scand. 196 (1974) 517–520. [43] A.A. Franke, B.M. Halm, L.A. Ashburn, Nutr. Cancer, in press. [44] Y. Arai, M. Uehara, M. Kimira, S. Watanabe, Soy Prot. Res. Jpn. 4 (2001) 135–141.
[45] P. Fanti, R. Asmis, T.J. Stephenson, B.P. Sawaya, A.A. Franke, Nephrol. Dial. Transplant. (2006). [46] S.J. Fomon (Ed.), Nutrition of Normal Infants, Mosby, St. Louis, 1993. [47] H. Kesteloot, J.V. Joossens, J. Hum. Hypertens. 10 (1996) 245–249. [48] D.B. Barr, L.C. Wilder, S.P. Caudill, A.J. Gonzalez, L.L. Needham, J.L. Pirkle, Environ. Health Perspect. 113 (2005) 192–200. [49] N. Knudsen, E. Christiansen, M. Brandt-Christensen, B. Nygaard, H. Perrild, Eur. J. Clin. Nutr. 54 (2000) 361–363. [50] G. Maskarinec, S. Singh, L. Meng, A.A. Franke, Cancer Epidemiol. Biomarkers Prev. 7 (1998) 613–619. [51] V. Hall Moran, H.L. Leathard, J. Coley, J. Endocrinol. 170 (2001) 157–164. [52] A.A. Franke, B.M. Halm, L.J. Custer, Y. Tatsumura, S.M. Hebshi, Am. J. Clin. Nutr. 84 (2006) 406–413. [53] G. Maskarinec, Y. Morimoto, R. Novotny, F.J. Nordt, F.Z. Stanczyk, A.A. Franke, Nutr. Cancer 52 (2005) 22–28. [54] R.A. King, in: G.S. Gilani, J.J.B. Anderson (Eds.), Phytoestrogens and Health, AOCS Press, Illinois, 2002, pp. 209–234. [55] P. Kardas, J. Antimicrob. Chemother. 49 (2002) 897–903. [56] E. Ramalle-Gomara, R. Bermejo-Ascorbe, R. Alonso Marin, I. Marino Alejo, M.I. Saenz de Cabezon Bustinduy, C. Villaro Amilburu, Aten Primaria 24 (1999) 364–367.
ABB Archives of Biochemistry and Biophysics 476 (2008) 161–170 www.elsevier.com/locate/yabbi
Isoflavones in children and adults consuming soy q Adrian A. Franke a,*, Brunhild M. Halm a,b, Leslie A. Ashburn a b
a Cancer Research Center of Hawai‘i, Natural Products & Cancer Biology Program, 1236 Lauhala Street, Honolulu, HI 96813, USA Cancer Research Center of Hawai‘i, Cancer Prevention & Control Program, and Kapi‘olani Medical Center for Women and Children, USA
Received 5 December 2007, and in revised form 5 February 2008 Available online 14 February 2008
Abstract Soy and their isoflavones (IFLs) are believed to protect against breast cancer, particularly when exposure occurs during childhood. Little is known about the bioavailability of IFLs in children and how this is affected by oral antibiotics (OABX). We measured IFLs by LC/MS and found that the urinary IFL excretion rate (UIER) reflects circulating IFLs accurately when area-under-curve (AUC) and identical time intervals are used (r = 0.93; p < 0.001). UIER in children and adults was determined when healthy and when on OABX by collecting urine in pairs of baseline and overnight specimen before and after consuming soy nuts, respectively. Compared to when healthy, children on OABX showed significantly decreased UIER but adults on OABX showed increased UIER (p < 0.05). All 37 healthy children showed significantly higher UIERs compared to all 34 healthy adults. UIER is an adequate surrogate for determining IFL bioavailability and for measuring soy or IFL exposure in epidemiologic and other studies. Ó 2008 Elsevier Inc. All rights reserved. Keywords: Oral antibiotics; Isoflavones; Bioavailability; Soy; Adults; Children; Urine
The effects of soy intake against chronic diseases including breast, prostate, and colorectal cancer, osteoporosis and cardiovascular disorders, as well as menopausal symptoms, are much investigated but not always consistent [1–7]. However, in two large epidemiologic studies strong preventive effects were observed against breast cancer later in life when soy was consumed at young age [8,9]. The health effects caused by the ability of the gut flora to produce the DE1 metabolites equol (EQ) or O-desmethylangolensin
q
Supported in part by Physicians Pharmaceuticals, Inc. and NIH Grant RR020890 (there are no conflicts of interest). * Corresponding author. Fax: +1 808 586 2970. E-mail address: [email protected] (A.A. Franke). 1 Abbreviations used: AUC, area-under-curve; BLU, baseline urine; BW, body weight; DE, daidzein; DHDE, dihydrodaidzein; DHGE, dihydrogenistein; DMA, desmethylangolensin; EQ, equol; GE, genistein; GLYE, glycitein; HPLC, high pressure liquid chromatography; IFL(s), isoflavone(s); M, metabolites (DHDE + DHGE + EQ + DMA); NM, non-metabolites (DE + GE + GLYE); OABX, oral antibiotics; ONU, overnight urine; total IFLs, all isoflavonoids (DE + GE + GLYE + DHDE + DHGE + EQ + DMA); UIER, urinary isoflavone excretion rate (nmol/h or nmol/h/kg BW). 0003-9861/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.abb.2008.02.009
(DMA) by intestinal bacteria are so far unclear and inconsistent [10–14]. Our recent findings on higher IFL bioavailability in children [15] and on cardioprotective effects independent from lipid profiles [16] strengthen the hypothesis that IFLs play an important role in the biological effects of soy intake. IFL exposure occurs mainly by the diet through intake of soy products which typically contain a total of 0.01%– 0.3% IFLs composed mainly of glycosides of genistein (GE), daidzein (DE), and glycitein (GLYE) (Table 1) [14,17–19]. Orally administered IFLs are believed to be efficiently absorbed after cleavage of the glycosides, which occurs mainly by intestinal bacteria [20–25]. In previous research, urinary or plasma IFLs were found to be reliable biomarkers for soy consumption [26–32], and urinary appearance of IFLs reflect plasma values accurately [21,33]. Given the widespread use of antibiotics, there is an urgent need to investigate the influence of OABX on the bioavailability of micronutrients. The UIER was hypothesized to change due to intestinal microflora alteration by age and OABX. We reported recently that healthy children
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Table 1 Structures and codes of isoflavonoids analyzed (a) Unmetabolized isoflavonoids
R7
O 3
R6 R5
4'
O
OH
Aglucon
Code
R5
R6
R7
Conjugates in soy foods
Code
Genistein
GE
OH
H
OH
7-O-(600 -O- Acetyl)-glucoside of daidzein of genistein of glycitein
D-Ac G-Ac GLY-Ac
R7
O H3C
6'' O
O
HO HO
O HO
Glycitein
GLYE
H
OH
OCH3
7-O-(600 -O- Malonyl)-glucoside of daidzein of genistein of glycitein
D-Mal G-Mal GLY-Mal
O
O
HO
6'' O
O
HO HO
O HO
Daidzein
DE
H
H
OH
7-O-glucoside of daidzein of genistein of glycitein
D G GLY
6'' HO
O
HO
O
HO HO (b) Metabolized isoflavonoids by the gut flora
HO
HO
O
OH
CH3
3
R5
Dihydrodaidzein Dihydrogenistein Equol
R4
R5
4'
O
OH
Code
R4
R5
DHDE DHGE EQ
=O =O H
H OH H
take up more IFLs from soy than healthy adults [15] but found that IFL uptake decreases in children while taking OABX [34]. We hypothesized that this prior result could be explained by the differences in the gut environment which is sterile at birth and develops a microbiota gradually over several years until an adult type flora is established [15,35]. Whether adults on OABX react similar to children is a focus of our ongoing studies. Expanding our initial findings, we aimed to compare the urinary IFL appearance in children (ages 4–17) to that of adults (ages 18 years and older), and to study how UIERs may be affected during oral antibiotic therapy for bacterial infec-
OH
Code O-Desmethylangolensin
DMA
tions. In addition, the usefulness of UIER to reflect circulating isoflavones was reevaluated with a novel approach. Materials and methods Intervention protocol to assess the role of antibiotics on urinary isoflavonoids Participants in this study fell into two categories. The first category was adults (ages P 18 years) and children (ages 4–17 years) requiring oral antibiotic therapy for a bacterial infection unrelated to this project. All participants were selected for this study by their family physician (one of the authors) who had good experience with their compliance from
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170 previous encounters, or they were recruited from the Cancer Research Center of Hawaii by word of mouth and flyers. They began the study after they were on OABX for at least 3 days and then repeated the protocol again at least 4 weeks later when healthy and off OABX. The second category was children (ages 4–17 years) and adults (ages P 18 years) who had not been on any antibiotics prior to participating in this study. Some were siblings and parents of the OABX participants, and others were recruited from the Cancer Research Center of Hawaii and the Honolulu Waldorf School by word of mouth and flyers. All participants had to be able to consume soy nuts and to collect an overnight urine (ONU) sample. A total of 99 participants of both genders were included in the analysis including 37 children and 34 adults not taking OABX (Tables 2a and 2b), and 16 children and 12 adults taking OABX (Tables 3a and 3b). Two hundred and twenty-eight were approached to participate in the study. Eighty of these declined. Forty-nine participants had to be excluded: three voluntarily withdrew, 12 had high baseline urinary IFL excretion sug-
Table 2a Characteristics of participants not taking OABX
Number Age (years) BW (kg) Male Female Asian Non-Asian Mexican/Hispanic Mixed Asian/Caucasian Asian/Pacific islanders Asian/Pacific islanders/ Caucasian Other Healthy ONU collection (h)
Children (no OABX)
Adults (no OABX)
Total
37 10.4 ± 3.9 38.9 ± 18.4 12 (32%) 25 (68%) 7 (19%) 13 (35%) 0 (0%) 17 (46%) 10 (59%) 0 (0%) 4 (24%)
34 41.0 ± 9.6 63.02 ± 18.8 7 (21%) 27 (79%) 7 (21%) 23 (68%) 0 (0%) 4 (12%) 1 (25%) 1 (25%) 0 (0%)
71 25.0 ± 17.0 50.4 ± 22.1 19 (27%) 52 (73%) 14 (20%) 36 (51%) 0 (0%) 21 (30%) 11 (52%) 1 (5%) 4 (19%)
3 (18%) 12.3 ± 0.7
2 (50%) 12.2 ± 0.9
5 (24%) 12.2 ± 0.8
Means ± standard deviation; sum of % may differ from 100 due to rounding. Table 2b Characteristics of participants taking OABX
Number Age (years) BW (kg) Male Female Asian Non-Asian Mexican/Hispanic Mixed Asian/Caucasian Asian/Pacific islanders Asian/Pacific islanders/ Caucasian Other OABX ONU collection (h) Healthy ONU collection (h) No. of days after OABX when control sample was collected
Children (OABX)
Adults (OABX)
Total
16 11.3 ± 3.6 44.3 ± 11.5 5 (31%) 11 (69%) 1 (6%) 11 (69%) 0 (0%) 4 (25%) 2 (50%) 0 (0%) 0 (0%)
12 37.8 ± 9.12 64.53 ± 12.2 3 (25%) 9 (75%) 3 (25%) 6 (50%) 1 (8%) 2 (17%) 1 (50%) 0 (0%) 0 (0%)
28 22.6 ± 14.8 53.0 ± 17.2 8 (29%) 20 (71%) 4 (14%) 17 (61%) 1 (4%) 6 (21%) 3 (50%) 0 (0%) 0 (0%)
2 (50%) 12.3 ± 0.9 11.8 ± 1.1 66.6 ± 31.7
1 (50%) 11.4 ± 2.1 11.9 ± 0.5 60.1 ± 28.1
3 (50%) 11.9 ± 1.3 11.7 ± 3.8 63.2 ± 29.7
Means ± standard deviation; sum of % may differ from 100 due to rounding.
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gesting soy consumption before the start of the study, 24 gave incomplete urine collections as shown by low creatinine values or lack of a spot urine, one took soy protein powder instead of soy nuts, one had a serious and life-threatening condition during the time of participation, and eight had diarrhea, other digestive problems or gave insufficient information for analysis. Subjects were excluded if they were on medication for any preexisting condition, or if they had gastrointestinal disease, as well as kidney or liver problems. Other exclusion criteria were vomiting and/or diarrhea during the study period and an allergy to soy. (None of the 28 OABX participants reported serious side effect from taking OABX. Only one reported slight dizziness.) We also excluded children who were brought up in a complex social structure, i.e. where the parents shared legal custody. If the children spent half of their time with each parent, there could be a problem in communication between households, which would affect children taking the antibiotics as prescribed, as well as parents reporting any side effects. One family consisting of one adult and two children (all without OABX) each repeated the study, and averaged values have been used. Five of the OABX participants reported in this study repeated the protocol since they required a second course of antibiotic treatment. The second course of antibiotics for four children and one adult was started 84 (mean) and 37 days after the first antibiotic course was completed, respectively. A total of 107 data pairs are considered in this report, including the repeat OABX subjects, since eliminating them from the analysis did not change the final results significantly. Adults started the study 8.0 ± 3.0 days after beginning their OABX course, or having completed an average of 83% of the OABX, with 100% of participants taking them as prescribed. Urine collections during children’s antibiotic treatment were performed 7.79 ± 1.84 days after the start of the antibiotic regimen. Children had completed an average of 78% of the full OABX course with 81% complying with the entire course as prescribed up to the study day. Compliance and completion of the antibiotic course were verified at time of specimen collection by interviews. Tables 2 and 3, a and b, respectively, detail the ages and weight of the participants, the infections that led to antibiotic therapy, and the types of antibiotics prescribed. If the participants were taking OABX, a couple of days was allowed to pass before approaching the participants about scheduling their urine collections, so that they would have some time to improve their health condition and to maximize antibiotic exposure. The details of the study and methods for collecting urine were explained to all of the participants and the children’s parents before commencing the study. If they had been taking OABX, participants began the study protocol after at least 3 consecutive days of antibiotic therapy, and if possible, on the second-tolast day of the therapy (Tables 3a and 3b). The University of Hawaii Committee on Human Studies and the Institutional Review Board approved the study protocol and all consent and assent forms. All adult participants signed consent forms, and parents signed a consent form for their children, and children 7 years of age and above provided their own additional assent separately from their parents. Five children and 17 adult participants received 15 g of soy nuts, whereas all remaining participants received body weight (BW) adjusted soy nuts at doses of 15 g nuts per 54.4 kg BW. In the course of this study, the dosing was changed towards adjusting for BW. We report here the results of all participants, because the final data did not change significantly when non-BW-adjusted participants were removed from the analysis. All participants received supplies for their urine collection, a worksheet, and a background information questionnaire to complete. At 18:00 they emptied their bladder into a small container provided to them (baseline urine (BLU)), and immediately thereafter consumed the soy food. They were asked to collect all urine voids for 12 h in large containers, including when they got up the next morning (ONU) at approximately 06:00. They were also instructed to chill the urine in the coolers provided and to avoid all other soy products, including supplements with soy or IFLs, from the time they woke up on the study day until the next day. The urine was transported on ice to our laboratory where ONU was mixed and weighed. BLU and ONU were stored in 2 mL aliquots at 20 °C until analyzed. Urine containers included small amounts of boric and ascorbic acid as preservatives [36].
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Table 3a OABX use in childrena Diagnosis leading to OABX therapy
Name of OABX
Single dose amount
Dosing frequency (times per day)
Days on OABX on study day
Total days of prescribed OABX
No. of days after OABX control sample collected
Sinusitis Sinusitis Skin infection Skin infection* Ear infection Sinusitis Scarlet fever
Amoxicillin Cefdinir Clindamycin Clindamycin Cefdinir Cefdinir Amoxicillin; Zithromax
1 2 4 4 1 1 2 1
10 10 9 8 10 9 10
10 14 14 14 10 10 10
102 42 100 51 36 40 47
Urinary tract infection
Trimethoprim/ sulfamethoxazole Cefdinir Cefdinir Cefdinir Zithromax
500 mg 300 mg 300 mg 300 mg 460 mg 400 mg 600 mg day 1–8; then 200 mg day 9; then 100 mg days 10–13 60/300 mg
2
7
10
33
300 mg 600 mg 600 mg 500 mg day 1, then 250 mg days 2–5 600 mg days 1–2; then 160 mg/800 mg days 3–11
1 1 1 1
6 9 6 5
10 10 10 5
50 128 101 57
3 2
9
11
91
300 mg 250 mg 200 mg 875/125 mg 600 mg 300 mg 300 mg
2 3 2 2 1 1 1
6 7 8 5 9 5 7
10 7 10 10 10 5 10
44 47 44 90 131 57 41
Ear infection Ear infection* Respiratory infection Respiratory infection* Skin infection
Streptococcal pharyngitis Ear infection Sinusitis Sinusitis Ear infection Streptococcal pharyngitis Sinusitis* a *
Clindamycin; then trimethoprim/ sulfamethoxazole Amoxicillin Amoxicillin Amoxicillin Amoxicillin clavulanate Cefdinir Cefdinir Cefdinir
Sixteen subjects, four repeat therapies indicated by italics. Repeated treatment of participants 3, 8, 9 and 16 in the sequence shown.
Table 3b OABX use in adultsa Diagnosis leading to OABX therapy
Name of OABX
Single dose amount
Dosing frequency (times per day)
Days on OABX on study day
Total days of prescribed OABX
No. of days after OABX control sample collected
Streptococcal pharyngitis Sinusitis Bronchitis Pharyngitis Bronchitis* Bronchitis
Amoxicillin Amoxicillin Moxifloxacin Azithromycin Doxycycline Azithromycin
1 1 1 1 2 1
7 10 7 3 7 5
10 10 7 5 7 5
48 32 50 110 65 103
Staphylococcal skin infection
2
7
10
37
Sinusitis Streptococcal pharyngitis Ear infection Sinusitis Dental abscess
Trimethoprim/ sulfamethoxazole Biaxin Cephalexin Ciprofloxacin Ciprofloxacin Clindamycin
750 mg 500 mg 500 mg 500 mg 100 mg 500 mg day 1, then 250 mg days 2–5 160 mg/800 mg
1 4 2 2 4
13 9 4 12 10
14 10 10 14 14
64 35 50 104 33
Tonsillitis
Cefdinir
1000 mg 500 mg 500 mg 500 mg 300 mg days 1–2, then 150 mg days 3–12 300 mg
1
10
10
50
a *
Twelve subjects, one repeat therapy indicated by italics. Repeated treatment of participant 4 in the sequence shown.
On the worksheet, participants recorded all foods consumed the day of the first urine collection, including before and after having eaten the study soy food. Dietary data were recorded to monitor soy intake other than the study food. Participants noted the time, the soy nuts for the study were consumed (just a few minutes after providing the spot urine) and the time of the final urine collection in the morning, typically between 05:00 and
07:00. In addition, OABX subjects provided information about the condition that led to antibiotic therapy; the name, dosage and duration of therapy; compliance with taking the antibiotics as prescribed; and side effects of therapy, if any. OABX participants repeated this protocol when healthy, i.e. 4–6 weeks after antibiotic treatment was terminated and no other health condition occurred in the meantime.
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170 Soy foods used in the study were soy nuts (DrSoy Nutrition, Irvine, CA; Physicians Pharmaceuticals, Inc., Kernersville, NC). The IFL composition and content of the nuts were determined by high pressure liquid chromatography (HPLC) with photo diode array detection without hydrolysis; flavone was used as internal standard. This method has been validated in a comparison with LC/MS based assays [37]. To account for variations in IFL content by crop and manufacturing batches, we measured IFL in each batch received. For the majority of participants (45 children and 32 adults) the serving sizes were controlled to provide BW-adjusted IFL dosages (15 g nuts per 54.4 kg BW). This led to the following IFL doses (mg aglycon units per 54.4 kg BW): daidzin (8.0 ± 1.1), glycitin (0.1 ± 0.1), genistin (8.9 ± 1.6), daidzin–malonate (1.1 ± 0.4), glycitin–malonate (0.2 ± 0.1), genistin–malonate (1.7 ± 0.6), daidzin–acetate (6.2 ± 1.4), glycitin–acetate (0.2 ± 0.1), genistin–acetate (6.1 ± 1.0), DE (0.4 ± 0.2), GLYE (0.0 ± 0.0), GE (0.5 ± 0.2); computation revealed total DE (15.6 ± 1.9), total GLYE (0.5 ± 0.2), total GE (17.2 ± 2.4), and all isoflavonoids (total IFLs) (33.4 ± 4.2). Relative to total IFLs, glucosides were 51%, malonyl-glucosides 9%, acetyl-glucosides 37%, and aglucons 3%, equivalent to 47% total DE, 2% total GLYE, and 51% total GE. It is important to note that participants providing collections during antibiotic treatment and again when healthy were given soy foods from the same batch for both collections. Consequently, IFL doses were identical within each participant.
Intervention protocol to assess the correlation between plasma and urine values Eight male and six female health professionals, in good health, from the Cancer Research Center of Hawaii between the ages of 38 and 63 years consumed 10 g of roasted soy nuts equivalent to a total of 22.1 mg IFL aglycon as determined by HPLC (see above). Three to six hours after soy intake, blood was collected in green-top vacutainers (Li-heparin), and plasma was prepared after centrifugation followed by storage at 80 °C until analyzed. This was repeated 4–9 h after soy intake. All urine samples were collected between the two blood draws, after each participant emptied their bladder shortly after the first blood draw. All times and weights of urine were recorded. Urine aliquots were stored at 80 °C until analyzed. Final urinary excretion rates were not adjusted to BW since the BW of all individuals was fairly similar. The University of Hawaii Committee on Human Studies approved the study protocol and all consent forms which were signed by all participants.
Isoflavonoid analysis from plasma and urine DE, GE, GLYE, EQ, dyhyrdodaidzein (DHDE), dyhydrogenistein (DHGE), and DMA were analyzed from plasma or urine by HPLC with electrospray ionization (negative mode) ion trap mass spectrometry [38–40]. In brief, triply 13C labeled internal standards of DE, GE, EQ, and DMA (obtained from the University of St. Andrews, UK) were added to each specimen hydrolyzed with glucuronidase and sulfatase (Roche Applied Sciences, Indianapolis, IN) followed by repeated phase separation with diethyl ether [20]. The combined ether fractions were dried under nitrogen and redissolved in a 1:1 mixture of acetonitrile/sodium acetate buffer (0.2 M, pH 5). A volume of 5–20 lL of this extract were analyzed with a Surveyor TSQ QuantumUltra triple quadrupole system (ThermoFisher, San Jose, CA) equipped with a Gemini C18 reversed phase column (150 2.0; 5 lm) coupled to a Gemini C18 (4 2.0; 5lm) direct-connect guard column (Phenomenex, Torrance, CA). Elution was performed at 200 lL/min with a mixture of methanol/acetonitrile/water/0.1% aq. formic acid at 15/15/70/0 which was changed linearly over 6 min to 40/40/10/10, held at this ratio for 1 min until changed in 0.1 min back to 15/15/70/0 for equilibration. The general MS conditions were as follows: source, ESI; ion polarity, negative; spray voltage, 4000 V; sheath and auxiliary and ion sweep gas, nitrogen; sheath gas pressure, 30 arbitrary units; auxiliary gas pressure, 10 arbitrary units; ion sweep gas pressure, 10 arbitrary units; ion transfer capillary temperature, 250 °C; scan type, high resolution selected reaction monitoring (HRSRM); collision gas, argon; collision gas pressure, 1.0 mm Torr. The transitions (m/z) used for quantitation were for DE from 253.0
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to 223.0, 208.0, and 132.0, for DHDE from 255.0 to 149.0, for 13C3-DE from 256.0 to 226.0, 210.0, and 132.9, for GLYE from 283.1 to 268.0 and 240.0, for DHGE from 271.1 to 165.0, for 13C3-GE from 271.9 to 160.4, 135.0, and 134.1, for EQ from 241.1 to 135.0, 121.0, and 119.0, for 13C3EQ from 244.1 to 120.0, for GE from 269.1 to 159.1, 133.0, and 132.0, for DMA from 257.1 to 136.0, 109.0, and 108.0, for 13C3-DMA from 260.1 to 109.0. Limits of quantitation for all analytes using 1.8 mL urine were 2.5 nM except for DHDE and DHGE (1.5 nM) and DMA and EQ (5.0 nM). Between-day coefficients of variation ranged 4–12% (DE), 5– 18% (GE), and 3–14% (GLYE), and 5–15% for all metabolites. Urinary creatinine was determined with a Roche–Cobas MiraPlus clinical auto analyzer using a kit from Randox Laboratories (Crumlin, UK) that is based on a kinetic modification of the Jaffe´ reaction. Betweenday coefficients of variation were 4–7%.
Calculation of hourly UIER We measured urine weights in gram and used this as a surrogate for volume determination, although the density of urine is known to be slightly higher than 1. However, this inaccuracy seems acceptable considering the relatively larger measurement errors connected with urinary volume determinations, and also considering that urine collections per se bear inherent inaccuracies. The amount of IFLs at baseline present in the ONU collection, although small in all samples, was subtracted from the IFL amount in the ONU sample in order to adjust for background IFLs in the ONU sample. Since the time of previous void of BLU was unknown, its expression in hourly units was not readily available. Hourly units could, however, be calculated by multiplying the creatinine/hour value as available from ONU with the known nmol/creatinine value of BLU separately for each subject. This seemed adequate due to the relatively constant creatinine excretion within an individual [41,42]. We thereby obtained the UIER of BLU in nmol/hour units and determined the amount that this UIER contributed to the measured ONU amount (see below) as follows: the known elimination half time of IFLs (average T1/2 = 8 h [22]) and the trapezoid method were applied to calculate AUC values in order to arrive at absolute BLU amounts present in the 12-hour period of ONU collection [21] that, according to our protocol, immediately followed the BLU collection. The ONU concentration determined by LC/MS in nmol/mL units was multiplied by the weight of the ONU in grams to arrive at absolute amounts (nmol) in the collected urine specimen (the density of urine was assumed to be 1; see above). This IFL amount was adjusted if ONU collections deviated from the 12-hour urinary collection period, based on factors that consider the typical exponential elimination pattern of IFLs [21–23] and convert amounts collected for longer or shorter times than 12 h into exactly 12 h as described in detail previously [15,34]. The IFL amount contributed by BLU (see above) was then subtracted from the ONU amount followed by dividing by 12 (for the 12 h of collection) and by the kg BW of the subject to arrive at the 12-hour collection-time, BW, and baseline adjusted UIER of ONU (mol/hour/kg BW). Plasma AUC values were calculated by the trapezoid method as described by us in detail recently [21].
Statistical analysis Paired and unpaired t-tests as well as z-tests were performed using Excel 2004 for the Macintosh (Microsoft Inc., Redmond, WA). These tests were performed with the original UIER values, as well as with log transformed values to take non-normality into consideration. Because results with the latter data led to similar results, we present here t-tests based on the untransformed data.
Results Isoflavone values in plasma and urine of 14 healthy adults were better correlated when AUC for plasma and
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Table 4 Isoflavone values in plasma and urine and their correlationa Using concentration (plasma) and excretion rate (urine)
Timeb at first collection (h)
Plasma level at first collection (nM)
Timeb at second collection (h)
Plasma level at first collection (nM)
Urine excretion ratec (nmol/h)
Correlations coefficientd
Median (range)
6.0 (3.0–7.0)
349 (188–966)
7.1 (4.3–8.9)
356 (238–817)
1538 (551–3947)
0.68
Using area-undercurve values
Time from first to second collection (h)
Plasma value from first to second collection (nM h)
Urine excretion from first to second collection (nmol/h h)
Median (range)
6.0–7.1 (3.0–8.9)
408 (221–1701)
1827 (634–7002)
0.93*
a
Data from eight males and four females, all health professionals. Time after soy intake. c From all urine collected between first and second collection after bladder was emptied at first collection. d Between plasma and urine values by linear regression. e Calculated by the trapezoid method from plasma concentrations at the first and second collection [21] and by using absolute excretion amounts for urine calculated from the first to the second collection. * p < 0.001 according to z-test. b
urine was used (r = 0.93, p < 0.001; Table 4), instead of UIER for urine, a time-based unit (nmol/h), and IFL levels for plasma, a non-time-based unit (nmol/L). Children had higher UIERs than adults when all were healthy, whether they had previously taken OABX or not (Table 5). After combining all available data from the 99 participants when they were healthy, results reached significance (p < 0.001 by unpaired t-test) for DE (+40%), GE (+53%), non-metabolites (NM) (+42%), and total IFLs (+26%) (Table 5), similar to our previous findings [15]. However, changes of UIER as a function of OABX were opposite in children and adults which again, agrees with our initial reports [15,34]. During OABX use in this extended study, children (n = 16) showed a trend towards lower UIERs compared to when healthy, which reached significance (p < 0.05 by paired t-test) for DE (20%), GE (40%), NM (21%), and total IFLs (25%) (Table 6a). In contrast, the 12 adults we investigated showed the opposite pattern, namely a trend towards higher UIERs
Table 5 Mean UIER in 53 children and 46 healthy adults
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
Children (nmol/h/kg)
Adults (nmol/h/kg)
Children versus adults
p
27.1 9.2 1.2 1.4 4.3 2.1 1.1 36.8 5.0 39.6
19.3 6.1 6.1 0.7 4.4 1.4 0.3 25.8 6.2 31.3
+40% +53% 80% +85% 3% +51% +207% +42% 19% +26%
<0.002 <0.002 0.44 0.15 0.95 0.36 0.35 <0.001 0.36 0.032
p = significance of difference between adults and children by unpaired t-test. M = metabolites, NM = non-metabolites, Total IFLs = sum of all isoflavonoids tested.
Table 6a Mean UIER of individual IFLs in 16 children during oral antibiotic therapy (OABX) and at least 4 weeks later when healthy
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
OABX (nmol/h/kg)
Healthy (nmol/h/kg)
OABX versus healthy
p
26.1 6.9 1.0 0.1 0.3 1.0 0.4 34.0 1.6 35.6
32.7 11.3 3.7 2.7 2.7 4.6 4.0 43.1 4.7 47.7
20% 40% 74% 95% 89% 78% 90% 21% 65% 25%
0.009 0.003 0.15 0.16 0.19 0.07 0.17 0.003 0.07 0.002
during OABX use compared to when healthy which reached significance (p < 0.05 by paired t-test) for DE (+87%), NM (+81%), and total IFLs (+74%), while GE showed a borderline significant increase of +54%
Table 6b Mean UIER of individual IFLs in 12 adults during oral antibiotic therapy (OABX) and at least 4 weeks later when healthy [43]
DE GE GLYE EQ DMA DHDE DHGE NM M Total IFLs
OABX (nmol/h/kg)
Healthy (nmol/h/kg)
OABX versus healthy
p
35.2 6.8 0.5 0.3 4.9 3.8 0.1 42.6 9.1 51.5
18.9 4.4 0.3 0.1 3.0 2.7 0.2 23.6 6.0 29.6
+87% +54% +37% +154% +63% +42% 42% +81% +52% +74%
0.016 0.056 0.22 0.32 0.12 0.84 0.17 0.02 0.17 0.01
p = significance of difference between adults and children by paired t-test. M = metabolites, NM = non-metabolites, Total IFLs = sum of all isoflavonoids tested.
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(p = 0.056; Table 6b) [43]. This overall pattern did not change markedly when considering exclusively BWadjusted versus non-BW-adjusted soy nut doses for adults, after removing the five OABX and three non-OABX participants with repeated collections, nor when using logged values to consider non-normality. An exception was GE in adults, which improved the p value from 0.056 to 0.021 with logged values (data not shown). Type of antibiotic, type of illness, gender, age and ethnicity of the participant did not show any correlation to UIERs neither while taking antibiotics nor again when healthy. Discussion Benefits of using urine to measure IFLs include its noninvasiveness compared with blood draws, as well as the ability to collect highly concentrated urine (particularly ONU) in large amounts, which leads to low quantitation limits. Also, urine can be obtained by participants themselves without medical supervision, in private, and most importantly, can be accumulated over many hours (even days) reflecting exposures over much longer time periods compared to data from blood, which only reflects one given point in time per collection. In addition, the accuracy of urine collection can be examined by comparing the measured creatinine amount with established data for each gender and age group (Table 7) [41,42]. The correlation between IFL values in urine and blood are much improved by using AUC units for both matrices instead of UIER for urine, a time based unit, and IFL level for plasma, a non-time-based unit (Table 4). This is largely due to the time domain being accurately considered by using the identical time intervals for the AUCs of both matrices, which is particularly important for plasma, because levels change markedly over a given time period. Our current results are in excellent agreement with several previous independent reports on UIERs reflecting IFL plasma concentrations accurately [21,29,32, 33,44,45]. Therefore, UIERs can be used as a reliable surrogate to determine circulating IFLs and thereby assess IFL bioavailability. Since bioavailability is defined based on circulating levels, we refer here to ‘apparent bioavailability’ when using urinary excretion data. In the present study, we generated highly accurate urinary data in a novel approach by adjusting UIER in ONU to exactly 12 h, by subtracting IFL amounts that were present from baseline exposures, and by adjusting for subjects’ BW. In this way, urine collection times deviating from the required 12 h, and presence of urinary IFLs unrelated to the soy exposure from this study, were adequately considered. In addition, the completeness of urine collection was checked (Table 7), and some participants were excluded when their measured creatinine amount was >30% lower than the expected theoretical amount. Urinary excretion expressed relative to time (hour) is more accurate than expressed relative to creatinine [33], because the latter depends mostly on muscle mass which can change largely depending on BW, gender, and age
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Table 7 Daily creatinine excretion per kg of body weight as a function of age and gender Age (years)
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Urinary creatinine (mg/kg/d) Females
Males
14.5 15.7 15.7 17.9 17.9 17.9 18.9 18.9 18.9 18.9 18.9 20.9 20.9 20.9 20.9 22.0 22.0 20.4 20.4 17.6 17.6 14.9 14.9 12.9 12.9 11.8 11.8 10.7 10.7 8.4 8.4
15.2 17.1 17.1 19.3 19.3 19.3 20.7 20.7 20.7 20.7 20.7 23.3 23.3 23.3 23.3 24.0 24.0 21.9 21.9 19.7 19.7 19.3 19.3 16.9 16.9 14.2 14.2 11.7 11.7 9.4 9.4
Ages 3–17 = Remer et al., 2002 [41]. Ages 20–95 = Kampmann et al., 1974 [42] except ages 20–25 where we suggest to use 22 mg/kg/d for women and 24 mg/kg/d for men to adjust the original Kampmann data (19.7 mg/kg/d for women and 23.8 mg/kg/d for men) to the more recently determined Remer values (20.9 mg/kg/d and 23.3 mg/kg/d for 17 year old girls and boys, respectively).
[41,46]. This is particularly relevant in growing children, not only due to marked changes of muscle mass in absolute terms, but also after adjustment for BW [41,42,47,48]. Urinary excretions adjusted solely for creatinine underestimate true excretion in heavier individuals—for example, in males versus females, or in older children versus younger children—and can lead to erroneous results [49]. Collection of urine over a 24-hour period or longer would be ideal if collected correctly, but is often difficult or impossible to perform in human studies. Collections of that duration bear inherent risks of missed samples or inclusion of other confounders. A good compromise is the collection of overnight urine, which is relatively easy to do for participants in the privacy of their homes, resulting in very high compliance [18,50] and which leads to a concentrated matrix, making analysis easier. We recommend this particularly for research with children after they reach bladder control, due to the ease for parents to supervise their children in following the protocol.
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The lack of conversion to time-based urinary excretion rates might explain the lower UIE in older than in younger soy-exposed children [35] or the loss of differences in urinary sex steroid excretion in females of different reproductive ages [51]. Therefore, as a general rule, it is best to have the collection time interval and the volume of the urine specimen available. The second choice is to convert a creatinine based excretion value into a time-based excretion (in hours) by multiplying the former with the creatinine/hour ratio as determined from any timed urine collection for a given subject since the creatinine excretion remains quite constant within a subject. The third choice is to multiply the nmol/mg creatinine value with the established mg-creatinine/kg BW/day ratio as shown in Table 7 [41,42]. We suggest using 22 mg/kg/d and 24 mg/kg/d for 20–30 yearold women and men, respectively, to adjust the original data (19.7 mg/kg/d for women and 23.8 mg/kg/d for men) [42] to the more recent values (20.9 mg/kg/d and 23.3 mg/kg/d for 17 year old girls and boys, respectively) [41]. The higher apparent IFL bioavailability in healthy children (n = 37) relative to healthy adults (n = 34) we found in this extension of our initial study [34] is in excellent agreement with our previous reports on healthy minors, such as infants [52], (pre)pubertal girls [53], and schoolaged children [34]. These findings suggest a higher systemic IFL exposure in children versus adults at the same relative soy dose. When considering that children eat generally much more per kg BW, the IFL exposure in children is probably up to twofold higher relative to adults. This could result in children experiencing more benefits from the health effects of soy [8,9]. We believe that the IFL exposure after soy intake will stay below levels that would give rise to concern regarding adverse effects. Toxic activity is usually observed at much higher IFL levels, and adverse effects have not been reported in populations with high soy intake. In this soy intervention study, we found that the overwhelming majority of the children investigated showed consistently lower UIERs on OABX compared to when healthy. This finding confirms our very recent report from eleven minors [34] whether all data were combined or treated separately (data not shown). In contrast, the adults investigated behaved opposite to children by showing higher UIERs during OABX therapy compared to when healthy whether the soy dose was BW adjusted or not. This is in agreement with one of our earlier studies in one individual (not included in the current analysis) when he was on OABX therapy for one day only [21]. It is noteworthy that the two adults who were excluded from the present study because of diarrhea while on OABX showed lower UIERs. This is in agreement with our earlier results of lower IFL bioavailability in one healthy volunteer who developed diarrhea due to treatment with a polyethylene glycol based laxative plus oral neomycin and erythromycin [21] and can be explained by the decreased time available for hydrolysis to the bioavailable IFL aglucons. We believe that our general findings in children and adults can be
explained by the changes in gut bacteria caused by OABX, or to unknown factors connected with the disease that led to OABX therapy. Soy products such as the nuts used in this study contain their IFLs mainly as glycosides [14,17– 19] which are hydrolyzed after ingestion to the bioavailable aglucons mainly by intestinal bacteria [20–25]. However, gut bacteria do also further metabolize the ‘free’ bioavailable IFL aglucons to further breakdown products such as EQ, DMA, p-ethylphenol, and other non-specific phenolic agents [54]. Although definitive evidence is lacking, our seemingly paradoxical observation in adults versus children could be due to OABX in children reducing the IFL-hydrolyzing gut bacteria significantly (and possibly also the IFL aglycon degrading flora), which consequently leads to the IFLs not becoming bioavailable. Vice versa, OABX in adults could possibly preferentially reduce the IFL aglycon degrading gut bacteria that are involved in IFL degradation to unknown and non-specific metabolites, while the bacteria that hydrolyze IFL glycosides are comparably little affected; thereby the time during which bioavailable IFL aglucons can be absorbed is increased. Our hypothesis could at least in part be tested by using IFL aglucons such as in fermented soy products [37]. The subjects who repeated the protocol showed almost super-imposable UIER patterns with or without OABX which indicates good repeatability. All participants of this study were treated with broad spectrum OABX, but no correlation between specific types of antibiotics used, type of illness, gender, age and ethnicity of the participant and UIER changes was observed. This might be due to the overall small sample size and/or inter-individual differences and the small numbers in each ethnic group. Similarly, our protocol did not allow for detailed evaluation of IFL metabolites, because a 12-hour urine sampling after soy exposure is insufficient to allow for efficient metabolite formation by the gut bacteria. With this pilot study, we were unable to differentiate effects due to the bacterial infection, the antibiotics, or both. To further investigate the role of inflammation and infection on the IFL metabolism in humans, we recently initiated studies with participants suffering from viral or other acute and chronic infections not requiring antibiotics. The human intestinal tract is very different from animals, and since it contains many species that cannot be cultured using present techniques, in-vitro and animal studies can only provide a framework for understanding gut biota-related processes. Intervention studies in humans seem more likely to shed light on these processes including IFL metabolism. The present intervention was performed with highly compliant subjects, most of them who were known from previous encounters. It specifically included only urine specimen that were collected correctly as determined by comparisons with expected theoretical creatinine values, and it showed statistically significant results despite the relative small numbers of subjects. One of the major concerns in human studies involving OABX is compliance with taking the medication as prescribed and completing the study protocol correctly. We selected
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the participants carefully, with the majority recruited through their family physician (one of the authors) who had good experience with their compliance. We also monitored the OABX use by questionnaire and personal interviews. In addition, most of our participants had dosing only once or twice a day (Tables 3a and 3b) and fewer doses per day is known to lead to good compliance with antibiotic therapy [55,56]. In summary, our present and recent reports indicate that urine is extremely useful, and often superior, to values from blood for the determination of soy and/or IFL exposure and can be used as a reliable surrogate for the estimation of soy intake and systemic IFL exposure. The urinary data reported in this paper are very accurate due to the novel approach of adjusting the UIER in ONU to exactly 12 h, by subtracting amounts that were present at baseline, and by adjusting for subjects’ BW. Results of this extended study replicate our previous results on higher apparent IFL bioavailability in children relative to adults and on decreased and increased UIER by OABX in children and adults, respectively. Future studies in adults and children with increased sample sizes and extended protocols will be helpful to develop a deeper understanding of the role of the gut flora, disease state, OABX use, and other factors on metabolism and bioavailability of dietary agents in humans and their connection to potential health effects. Acknowledgments We thank Laurie Custer, B.S., for performing the chemical analyses, the local Waldorf School, Bibiana Potter, Bonnie Ozaki-James, and Beth Allingham for recruiting participants, Sandra, M., Hebshi, M.S., for study coordination, and Dr. T. Remer, Research Institute of Child Nutrition, Dortmnund/Germany, for helpful discussions. Support from Physicians Pharmaceuticals, Inc., Kernersville, NC, and DrSoy Nutrition, Irvine, CA, for this research is acknowledged. Adrian A. Franke, Ph.D., the principal investigator of this study, was responsible for the design and overall performance of the study; he carried out data interpretation, statistical analyses, and wrote the manuscript. Brunhild M. Halm, M.D., Ph.D., assisted in the design and overall performance of the study, recruited study participants, was available for medical advice, supervised personnel, conducted data interpretations, and assisted in writing the manuscript. Leslie A. Ashburn, M.A., was a study coordinator and assisted in manuscript preparation, statistical analyses, as well as matters related to the institutional review board. No authors have a conflict of interest. References [1] M. Messina, V. Persky, K.D.R. Setchell, S. Barnes, Nutr. Cancer 21 (1994) 113–131. [2] S. Barnes, C. Grubbs, K.D.R. Setchell, J. Carlson, in: W. Pariza, U. Aeschbacher, J.S. Felton, S. Sato (Eds.), Mutagens and Carcinogens in the Diet, Wiley-Liss, New York, 1990, pp. 239–254.
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[3] C.A. Lamartiniere, J. Moore, M. Holland, S. Barnes, Proc. Soc. Exp. Biol. Med. 208 (1995) 120–123. [4] W.B. Murrill, N.M. Brown, J.-X. Zhang, P.A. Manzolillo, S. Barnes, C.A. Lamartiniere, Carcinogenesis 17 (1996) 1451–1457. [5] M. Pollard, P.H. Luckert, Nutr. Cancer 28 (1997) 41–45. [6] J.-X. Zhang, G. Hallmans, M. Landstrom, A. Bergh, J.E. Damber, P. Aman, H. Adlercreutz, Cancer Lett. 114 (1997) 313–314. [7] F.M. Uckun, W.E. Evans, C.J. Forsyth, K.G. Waddick, L.T. Ahlgren, L.M. Chelstrom, A. Burkhardt, J. Bolen, D.E. Myers, Science 267 (1995) 886–891. [8] X.O. Shu, F. Jin, Q. Dai, W. Wen, J.D. Potter, L.H. Kushi, Z. Ruan, Y.T. Gao, W. Zheng, Cancer Epidemiol. Biomarkers Prev. 10 (2001) 483–488. [9] A.H. Wu, P. Wan, J. Hankin, C.C. Tseng, M.C. Yu, M.C. Pike, Carcinogenesis 23 (2002) 1491–1496. [10] C. Atkinson, C.L. Frankenfeld, J.W. Lampe, Exp. Biol. Med. (Maywood) 230 (2005) 155–170. [11] C.L. Frankenfeld, A. McTiernan, E.J. Aiello, W.K. Thomas, K. LaCroix, J. Schramm, S.M. Schwartz, V.L. Holt, J.W. Lampe, Cancer Epidemiol. Biomarkers Prev. 13 (2004) 1156– 1162. [12] C.L. Frankenfeld, A. McTiernan, W.K. Thomas, K. LaCroix, L. McVarish, V.L. Holt, S.M. Schwartz, J.W. Lampe, Maturitas 53 (2006) 315–324. [13] K.D.R. Setchell, S.P. Borriello, P. Hulme, D.N. Kirk, M. Axelson, Am. J. Clin. Nutr. 40 (1984) 569–578. [14] P.L. Horn-Ross, M. Lee, E.M. John, J. Koo, Cancer Causes Control 11 (2000) 299–302. [15] B.M. Halm, L.A. Ashburn, A.A. Franke, Br. J. Nutr. 98 (2007) 998– 1005. [16] S.E. Walker, M.R. Adams, A.A. Franke, T.C. Register, Atherosclerosis 196 (2008) 106–113. [17] S.T. Umphress, S.P. Murphy, A.A. Franke, L.J. Custer, C.L. Blitz, J. Food Comp. Anal. 18 (2005) 533–550. [18] A.A. Franke, L.J. Custer, C.M. Cerna, K. Narala, Proc. Soc. Exp. Biol. Med. 208 (1995) 18–26. [19] H. Adlercreutz, W. Mazur, Ann. Med. 29 (1997) 95–120. [20] A.A. Franke, L.J. Custer, W. Wang, S.J. Shi, Proc. Soc. Exp. Biol. Med. 217 (1998) 263–273. [21] A.A. Franke, L.J. Custer, S.A. Hundahl, Nutr. Cancer 50 (2) (2004) 141–154. [22] K.D. Setchell, N.M. Brown, P.B. Desai, L. Zimmer-Nechimias, B. Wolfe, A.S. Jakate, V. Creutzinger, J.E. Heubi, J. Nutr. 133 (2003) 1027–1035. [23] L. Zubik, M. Meydani, Am. J. Clin. Nutr. 77 (2003) 1459–1465. [24] M. Richelle, S. Pridmore-Merten, S. Bodenstab, M. Enslen, E.A. Offord, J. Nutr. 132 (2002) 2587–2592. [25] P.C.H. Hollman, Pharm. Biol. 42 (2004) 74–83. [26] A. Seow, C.-Y. Shi, A.A. Franke, J. Hankin, H.-P. Lee, M.C. Yu, Cancer Epidemiol. Biomarkers Prev. 7 (1998) 135–140. [27] Z. Chen, W. Zheng, L.J. Custer, Q. Dai, X.-O. Shu, F. Jin, A.A. Franke, Nutr. Cancer 33 (1999) 82–89. [28] C. Atkinson, H.E. Skor, E.D. Fitzgibbons, D. Scholes, C. Chen, K. Wahala, S.M. Schwartz, J.W. Lampe, Cancer Epidemiol. Biomarkers Prev. 11 (2002) 253–260. [29] P.B. Grace, J.I. Taylor, Y.L. Low, R.N. Luben, A.A. Mulligan, N.P. Botting, M. Dowsett, A.A. Welch, K.T. Khaw, N.J. Wareham, N.E. Day, S.A. Bingham, Cancer Epidemiol. Biomarkers Prev. 13 (2004) 698–708. [30] A.H. Wu, M.C. Yu, C.C. Tseng, N.C. Twaddle, D.R. Doerge, Carcinogenesis 25 (2004) 77–81. [31] P. Fanti, T.J. Stephenson, I.M. Kaariainen, B. Rezkalla, Y. Tsukamoto, T. Morishita, M. Nomura, A.A. Franke, Nephrol. Dial. Transplant. 18 (2003) 1862–1868. [32] M.R. Ritchie, M.S. Morton, N. Deighton, A. Blake, J.H. Cummings, Br. J. Nutr. 91 (2004) 447–457. [33] A.A. Franke, B.M. Halm, L.J. Custer, Y. Tatsumura, S.M. Hebshi, Am. J. Clin. Nutr. 84 (2006) 406–413.
170
A.A. Franke et al. / Archives of Biochemistry and Biophysics 476 (2008) 161–170
[34] B.M. Halm, A.A. Franke, L.A. Ashburn, S.M. Hebshi, L.R. Wilkens, Nutr. Cancer 60 (2008) 14–22. [35] L. Hoey, I.R. Rowland, A.S. Lloyd, D.B. Clarke, H. Wiseman, Br. J. Nutr. 91 (2004) 607–616. [36] A.A. Franke, L.J. Custer, J. Chromatogr. B 662 (1994) 47– 60. [37] A.A. Franke, J.H. Hankin, M.C. Yu, G. Maskarinec, S.H. Low, L.J. Custer, J. Agric. Food Chem. 47 (1999) 977–986. [38] R.M. Blair, S.E. Appt, A.A. Franke, T.B. Clarkson, J. Nutr. 133 (2003) 2262–2267. [39] A.A. Franke, L.J. Custer, L.R. Wilkens, L. Le Marchand, A.N.S. Nomura, M.T. Goodman, L.N. Kolonel, J. Chromatogr. B 777 (2002) 43–57. [40] G. Maskarinec, S.M. Hebshi, L.J. Custer, A.A. Franke, Eur. J. Cancer Prev. 17 (2008) 67–70. [41] T. Remer, A. Neubert, C. Maser-Gluth, Am. J. Clin. Nutr. 75 (2002) 561–569. [42] J. Kampmann, K. Siersbaek-Nielsen, M. Kristensen, J.M. Hansen, Acta Med. Scand. 196 (1974) 517–520. [43] A.A. Franke, B.M. Halm, L.A. Ashburn, Nutr. Cancer, in press. [44] Y. Arai, M. Uehara, M. Kimira, S. Watanabe, Soy Prot. Res. Jpn. 4 (2001) 135–141.
[45] P. Fanti, R. Asmis, T.J. Stephenson, B.P. Sawaya, A.A. Franke, Nephrol. Dial. Transplant. (2006). [46] S.J. Fomon (Ed.), Nutrition of Normal Infants, Mosby, St. Louis, 1993. [47] H. Kesteloot, J.V. Joossens, J. Hum. Hypertens. 10 (1996) 245–249. [48] D.B. Barr, L.C. Wilder, S.P. Caudill, A.J. Gonzalez, L.L. Needham, J.L. Pirkle, Environ. Health Perspect. 113 (2005) 192–200. [49] N. Knudsen, E. Christiansen, M. Brandt-Christensen, B. Nygaard, H. Perrild, Eur. J. Clin. Nutr. 54 (2000) 361–363. [50] G. Maskarinec, S. Singh, L. Meng, A.A. Franke, Cancer Epidemiol. Biomarkers Prev. 7 (1998) 613–619. [51] V. Hall Moran, H.L. Leathard, J. Coley, J. Endocrinol. 170 (2001) 157–164. [52] A.A. Franke, B.M. Halm, L.J. Custer, Y. Tatsumura, S.M. Hebshi, Am. J. Clin. Nutr. 84 (2006) 406–413. [53] G. Maskarinec, Y. Morimoto, R. Novotny, F.J. Nordt, F.Z. Stanczyk, A.A. Franke, Nutr. Cancer 52 (2005) 22–28. [54] R.A. King, in: G.S. Gilani, J.J.B. Anderson (Eds.), Phytoestrogens and Health, AOCS Press, Illinois, 2002, pp. 209–234. [55] P. Kardas, J. Antimicrob. Chemother. 49 (2002) 897–903. [56] E. Ramalle-Gomara, R. Bermejo-Ascorbe, R. Alonso Marin, I. Marino Alejo, M.I. Saenz de Cabezon Bustinduy, C. Villaro Amilburu, Aten Primaria 24 (1999) 364–367.