Soy isoflavones decrease fibroglandular breast tissue measured by magnetic resonance imaging in premenopausal women: A 2-year randomized double-blind placebo controlled clinical trial.

BACKGROUND & AIMS: Populations consuming soy have reduced risk for breast cancer, but the mechanisms are unclear. We tested the hypothesis that soy isoflavones, which have ovarian hormone-like effects, can reduce fibroglandular breast tissue (FGBT, 'breast density'), a strong risk marker for breast cancer. METHODS: Premenopausal women (age 30-42 years) were randomized to consume isoflavones (136.6 mg as aglycone equivalents, n = 99) or placebo (n = 98) for 5 days per week up to 2 years, and changes in breast composition measured by magnetic resonance imaging at baseline and yearly intervals were compared after square root transformation using linear mixed effects regression models. RESULTS: By intention-to-treat analyses (n = 194), regression coefficients (ß estimates) of the interaction of time and isoflavone treatment were -0.238 (P = 0.06) and -0.258 (P < 0.05) before and after BMI adjustment, respectively for FGBT, 0.620 (P < 0.05) and 0.248 (P = 0.160), respectively for fatty breast tissue (FBT), and -0.155 (P < 0.05) and -0.107 (P < 0.05), respectively for FGBT as percent of total breast (FGBT%). ß Estimates for interaction of treatment with serum calcium were -2.705 for FBT, and 0.588 for FGBT% (P < 0.05, before but not after BMI adjustment). BMI (not transformed) was related to the interaction of treatment with time (ß = 0.298) or with calcium (ß = -1.248) (P < 0.05). Urinary excretion of isoflavones in adherent subjects (n = 135) significantly predicted these changes in breast composition. Based on the modeling results, after an average of 1.2, 2.2 and 3.3 years of supplementation, a mean decrease of FGBT by 5.3, 12.1, and 19.3 cc, respectively, and a mean decrease of FGBT% by 1.37, 2.43, and 3.50%, respectively, were estimated for isoflavone exposure compared to placebo treatment. Subjects with maximum isoflavone excretion were estimated to have 38 cc less FGBT (or ∼3.13% less FGBT%) than subjects without isoflavone excretion. Decrease in FGBT and FGBT% was more precise with daidzein than genistein. CONCLUSIONS: Soy isoflavones can induce a time- and concentration-dependent decrease in FGBT, a biomarker for breast cancer risk, in premenopausal women, and moderate effects of calcium on BMI and breast fat, suggesting a beneficial effect of soy consumption. TRIAL REGISTRATION: www. CLINICALTRIALS: gov identifier: NCT00204490. TRIAL REGISTRATION: www. CLINICALTRIALS: gov identifier: NCT00204490.

Novel effects of phytoestrogenic soy isoflavones on serum calcium and chloride in premenopausal women: A 2-year double-blind, randomized, placebo-controlled study.

BACKGROUND: Soy phytoestrogens are potential alternatives to postmenopausal hormone replacement therapy (HRT). Adverse effects of HRT such as myocardial infarction, stroke, and pulmonary embolism are mediated by calcium-induced signaling. OBJECTIVE: To determine whether soy isoflavones affect serum calcium in healthy female subjects. DESIGN: In a double-blind trial, 197 premenopausal women were randomly assigned to either isoflavone (N = 99) or placebo pills (N = 98) 5 days per week for up to 2 years, plus prenatal vitamins. Isoflavone pills contained 60 mg genistein, 60 mg daidzein and 16.6 mg glycitein (expressed as aglycone equivalents). All pills contained 15 mg riboflavin as an adherence marker. Blood chemistries and urinary daidzein, genistein and riboflavin were measured multiple times during the luteal phase before and during treatment. RESULTS: Analysis of the adherent population (N = 83 per group), revealed significantly strong associations between urinary levels of isoflavones and serum concentrations of calcium (regression coefficients 0.082 for daidzein and 0.229 for genistein, all P < 0.01) and chloride (regression coefficient, -1.537 for genistein, P < 0.0001), mediated in part by albumin. The effects amounted to mean changes of +0.24 mg/dL for calcium and -1.45 mEq/L for chloride, with each visit for subjects excreting the most vs. the least amounts of isoflavones. These associations were not evident in the intention-to-treat analysis (N = 197) that did not assess expected variations in isoflavone levels within and between subjects from metabolism and adherence. CONCLUSIONS: These novel and strong effects of soy isoflavones on calcium homeostasis have important implications for long term effects of these natural substances on cardiovascular diseases.

Similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm.

Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm.

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R(2) = 0.93) and %-density (R(2) = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.