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Keywords:

  • estrogen metabolism;
  • postmenopausal osteoporosis;
  • risk factors

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Because lifelong exposure to estrogen is a strong determinant of bone mass, we asked whether metabolic conversion of estrogen to either inactive or active metabolites would reflect postmenopausal bone mineral density (BMD) and rate of bone loss. Biochemical markers of inactive estrogen metabolites, urinary 2-hydroxyestrogen (2OHE1) and 2-methoxyestrogen (2MeOE1), and active metabolites, urinary 16α-hydroxyestrone (16αOHE1), estradiol (E2), and estriol (E3), were determined in 71 untreated, healthy postmenopausal women (age, 47-59 years) followed prospectively for 1 year. Urinary 2MeOE1 was correlated negatively with baseline vertebral (anteroposterior [AP] projection, r = −0.23 and p < 0.05; lateral view, r = −0.27 and p < 0.05) and proximal femur bone density measured by dual-energy X-ray absorptiometry (DXA; total, r = −0.38 and p < 0.01; neck, r = −0.28 and p = 0.02; trochanter, r = −0.44 and p < 0.01). BMDs of women in the lowest quartile of urinary 2MeOE1 (<15 ng/g) were significantly higher than those in the highest quartile at all skeletal sites (p < 0.05). Likewise, women in the lowest quartile of urinary 2OHE1/16αOHE1 ratio (<1.6) did not experience bone loss after 1 year, in contrast to women in the higher quartiles. We propose that the rate of inactivation of estrogens through 2-hydroxylation may contribute to postmenopausal osteoporosis.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

THE CRITICAL role of estrogen in the control of skeletal health is shown by the rapid bone loss after cessation of ovarian function(1) and by the preventative effect of estrogen replacement therapy on bone loss(2) and fracture incidence.(3,4) Lifelong estrogen exposure also is a very important determinant of bone density in premenopausal women.(5,6) Therefore, it is likely that the extent and duration of residual estrogen production may affect bone loss after menopause. This premise is supported by observations that estrogen deficiency is a strong, independent determinant of postmenopausal bone loss in addition to aging(7,8) and by the observed correlation between total urine estrogen excretion and bone loss.(9) However, studies that attempted to correlate serum estrogen levels with bone loss or osteoporotic fractures have provided controversial results. (10–13) Nonetheless, recent large-scale cross-sectional surveys lend support to the notion that serum estradiol (E2) is involved critically in age-dependent bone loss not only in women but also in men.(14,15)

Because secretion of E2 by residual ovarian activity is minimal after menopause, most circulating estrogen in postmenopausal women originates from conversion of androstenedione, an adrenal androgen, to estrone (E1) by the enzyme aromatase.(16,17) E1 can be then reversibly oxidized to E2. Although peripheral conversion from androgens occurs primarily in the adipose tissue,(18) a cytochrome P450 aromatase activity also is present in osteoblasts, thus providing a direct, local source of active estrogen for bone.(19) Thus, serum estrogen concentrations may not appropriately reflect total estrogen bioactivity in postmenopausal women. This problem is compounded by the existence of different catabolic pathways that convert E1 to many intermediate metabolites of various estrogenic activities. The major metabolic pathway of E1 is irreversible hydroxylation at either the C-16α or the C-2 position.(20,21) The products of these two competitive pathways essentially are opposite in their biological properties. The C-16α hydroxylation leads to the formation of 16α-hydroxyestrone (16OHE1) and estriol (E3), which retain proestrogenic activity,(22) whereas the 2-hydroxyestrogens (2OHE1 and 2-methoxyestrogens [2-MeOE1]) virtually are devoid of estrogenic activity.(23) Because these two irreversible pathways largely are mutually exclusive, it is reasonable to believe that changes in activity of one pathway relative to the other would shift the estrogen balance toward either a proestrogenic or an antiestrogenic state.(24) This is supported by studies showing that smoking(25,26) and treatment with cimetidine(27) alter the extent of 2-hydroxylation of E2 in women resulting in either antiestrogenic or proestrogenic consequences, respectively.

Accordingly, one would predict that the final products of E1 hydroxylation and the reciprocal activity of the two catabolic pathways may condition postmenopausal bone loss. In support of this hypothesis is a recent study by Lim and coworkers,(28) indicating a relationship between the pattern of estrogen metabolism and postmenopausal bone density, although methodological problems cast some doubts about these results.(29) Nonetheless, Westerlind et al.(30) recently have reported that 16αOHE1 has selective estrogen agonist activity in bone, whereas 2OHE1 is devoid of estrogenic activity in ovariectomized rats. The present study shows that the oxidative metabolism of estrogens is not only an independent determinant of postmenopausal bone density, but the extent of 2-hydroxylation of E2 is related directly to changes in bone density in women after the menopause.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Experimental subjects

Subjects for this study were recruited among women living in the St. Louis, MO metropolitan area through local advertising. They were followed in the Clinic of the Bone Health Program of Barnes-Jewish Hospital, North Campus, at Washington University Medical Center. The protocol was approved by the Barnes-Jewish Hospital Institutional Review Board. All participants were menopausal, either natural or surgically induced, younger than 60 years, and had a lumbar spine bone density higher than −1 SD of age-matched normal women. Thus, our patient population represented early postmenopausal women with normal bone density. Subjects with any conditions known to interfere with bone and mineral metabolism (hypercortisolemia, thyroid diseases, malabsorption, diabetes mellitus, prolonged immobilization, or hyperparathyroidism) or with a history of renal or liver diseases or malignancy were excluded. Other exclusion criteria included, alcohol abuse or intake of hormones or drugs known to interfere with bone and mineral metabolism (corticosteroids, antiepileptics, or loop diuretics) and with estrogen hydroxylation, that is, estrogens, progestins, cimetidine, thyroid hormone, monooxygenase inhibitors, and drugs known to affect cytochrome P450 activities. Current smoking also was an exclusion criterion, but past smokers (discontinued 1 year or more before enrollment) were allowed into the study. Seventy-one women who met the inclusion and exclusion criteria were enrolled and gave their written informed consent to participate in this study.

Clinical, dietary, and anthropometric data

Dietary calcium and vitamin D intake were calculated from a 7-day diet recall, as detailed previously.(31) Based on the dietary analysis, intakes of calcium and vitamin D were adjusted to 1200-1500 mg and 400-800 IU, respectively, by dietary changes and/or supplementations. Diet history also included intake of vegetables such as cabbage, cauliflower, brussels sprouts, broccoli, and kale. These vegetables contain high levels of phytochemicals, including indole-3-carbinol, which are known to preferentially induce phase I cytochrome P450's leading to enhanced 2-hydroxylation of estrogens.(32) Women consuming more than one serving per day of these vegetables were excluded from the study. Alcohol intake was expressed as the number of alcoholic drink-equivalents consumed in a week. A drink-equivalent was 28 ml of a heavy alcoholic beverage, one glass of wine (112 ml), or one 336-ml can of beer. The extent of previous smoking was expressed in pack-years, as the number of 20 cigarette packs smoked per day multiplied by years of smoking. Physical activity was expressed as a numerical score, defined as sedentary (sitting or lying most of the day, score 1), moderately active (on feet more than half a day, score 2), and very active (engaging in regular physical exercises, score 3).(31)

Reproductive history was assessed by a number of variables, including age at menarche, age at menopause, year since menopause (YSM; difference between age at baseline bone mineral density [BMD] and age at menopause), number of births, number of pregnancies, history of menstrual irregularities, months of lactation, years of use of birth control pills, and menopausal symptoms. Family history of osteoporosis was coded as the numbers of blood relatives who were diagnosed as osteoporotic, had fragility fractures without secondary causes, or developed kyphosis with age. Body mass index (BMI) was calculated as the weight in kilograms, divided by the square of the height in meters. As a measure of gynoid fat distribution, we calculated the ratio between waist circumference, taken at the umbilical level, and hip circumference, measured 6 inches below the anterior superior iliac spine (waist-hip ratio).

Biochemistry

Routine biochemical tests were performed at baseline and at the end of the study on morning fasting blood (glucose, creatinine [Cr], calcium, phosphorus, albumin, and alkaline phosphatase) and on 24-h urine collections for calcium, Cr, and estrogen metabolites. All these parameters were within the normal range in all our study subjects. Blood and urine biochemical parameters were measured by standard autoanalyzer techniques. Urine estrogen metabolites were measured using the ESTRAMET immunoassay kits (Immuna Care Corp., Bethlehem, PA, U.S.A.). The ESTRAMET series of test kits are monoclonal antibody-based competitive enzyme immunoassays for estrogen metabolites in microtiter plate format. The antibodies and urinary assays for 2-hydroxyestrogen and 16α-hydroxyestrogens have been described.(33) The monoclonal antibody to 2-hydroxyestrogens recognizes the 2-hydroxy forms of E2, E1, and E3 equivalently. Similarly, the monoclonal antibody to 2-methoxyestrogens recognizes the 2-methoxy forms of estrogen metabolites equivalently and exhibits less than 0.1% cross-reactivity with any other estrogen, including 2-hydroxyestrogens. The monoclonal antibody to E3 exhibited less than 2% cross-reactivity with any other estrogen. All urinary estrogen assays were performed according to methods described previously.(33) Briefly, urine samples were incubated with enzymes that deconjugated estrogen metabolite sulfates and glucuronides to their respective free forms. The amount of estrogen metabolite in the enzymic hydrolysate was determined by competition between deconjugated estrogen in the hydrolysate and estrogen-labeled alkaline phosphatase for binding to specific monoclonal antibodies attached to the microtiter plate. Greater than 90% of the metabolites in the urine exist as glucuronides and are recovered totally by this method. The inter- and intra-assay coefficients of variability for these enzyme-linked immunoassays are less than 9% and 13%, respectively.(33)

BMD

BMD was measured by dual-energy X-ray absorptiometry (DXA) using the Hologic QDR-2000 densitometer (Hologic, Inc., Waltham, MA, U.S.A.) at the lumbar spine and proximal femur. Spine scans were performed in both anteroposterior (AP) and lateral projections. For the AP projection, BMD was averaged from L2 through L4, whereas L2 and L3 were included in the calculation of lateral spine BMD. The nondominant hip was used for the proximal femur scans. Values calculated on the femoral neck, trochanteric area, and total femoral region were used in the data analysis. The CVs of this technique using the QDR-2000 densitometer were 1.12% and 1.27% at the lumbar spine (AP) and total proximal femur, respectively, calculated from three repeated measurements in 10 subjects. BMD obtained as grams per square centimeter also was normalized to age and sex normal values using the National Health and Nutrition Examination Survey (N-HANES) database(34) and expressed as Z score. After baseline assessment, the subjects returned at 6 months and 12 months for repeat BMD measurement. Rate of change of BMD was calculated as the slope of the regression line interpolating the three measurements against time and expressed as percent changes per year. Changes at 1 year also were calculated as percent of the initial value. Based on the precision of the machine, we considered a loss or gain of BMD as significant when the change was greater than 3.2% at the lumbar spine or 3.6% and at the total proximal femur, calculated as 2 × √2 × CV (%).

Statistical analysis

Results are expressed as mean ± SD, unless specified otherwise. Baseline BMD and rates of BMD changes, calculated as explained previously, were regressed against all the other variables, including age, YSM, BMI, hip-waist ratio, physical activity, use of birth control pills, parity, months of lactation, menstrual irregularities, calcium supplementation, family history of osteoporosis, smoking, ethanol, and 24-h urine calcium/Cr. Both simple and multiple correlation models were applied. In the latter case, Pearson partial correlation coefficients were calculated. Differences in baseline or rate of change of BMD among quartiles of estrogen metabolites were tested using Tukey's multiple t-test. Data were managed and analyzed using Excel 97 (Microsoft Corp., Redmond, WA, U.S.A.) and Statgraphic Plus 3.0 (Manugistic, Inc., Rockville, MD, U.S.A.)

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

The clinical features of the study population are indicated in Table 1, along with the baseline estrogen metabolites excretion. The average menopausal age was just above 7 years, thus meeting our objective of enrolling early postmenopausal women. There was a relatively high prevalence of positive family history of osteoporosis (44%) and of ex-smokers (46.4%), although enrollment criteria precluded entry to current smokers. Baseline BMD and annualized rates of bone density changes during the 12-month observation period are given in Table 2. The average BMD at baseline, normalized for age and sex against a normal population (Z score), was very close to zero at the lumbar spine (both AP and lateral projections). Negative average Z scores were obtained at all proximal femur regions, though none of these values were significantly different than 0 (one-sample t-test). Thus, the population enrolled in this observational study can be considered normal as far as BMD. Considering the CV of the DXA, only 15% of the study population exhibited significant bone loss (>3.2% at 1 year at the spine), and rates of BMD changes were insignificant taken as an average at all sites (Table 2). There were no differences in age or YSM between bone losers and nonlosers at any sites. The same results also were obtained when changes were calculated on the BMD value at 1 year as a percentage of the initial value (not shown).

Table Table 1.. Clinical Characteristics and Urinary Metabolites at Baseline
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Table Table 2.. Baseline and 1-Year Changes in BMD in 71 Healthy Postmenopausal Women
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Baseline levels of urine estrogen metabolites also fell within relatively large ranges (Table 1), perhaps reflecting the diverse postmenopausal ages of each individual enrolled. Among all the variables studied, the two 2-hydroxylated compounds, but not 16αOHE1, were correlated negatively with BMI and waist-hip ratio (r = −0.24 and r = −0.28, respectively, for 2MeOE1; p < 0.05). Conversely, the ratio of 2OHE1 and 2MeOE1 over 16αOHE1 were correlated with past smoking and alcohol intake (r = 0.50 and r = 0.25, respectively, for 2MeOE1/16αOHE1 ratio; p < 0.01). No correlations were found using either simple of partial correlation analyses between age, YSM, history of menstrual irregularity, and any urine estrogen metabolite.

By simple correlation analysis we found that urine estrogen metabolites, including 16αOHE1 and E2 were, in general, associated negatively with baseline BMD with different degrees of variability (Table 3). Of all the metabolites, 2MeOE1 showed consistent and significant negative correlations with BMD at all sites, including total femur and lateral spine (Fig. 1), with the exception of the AP spine projection, where such correlation barely missed the significance level (p = 0.06). Correlations between any metabolites and baseline, age-adjusted BMD were stronger at the trochanter, total femur, and lateral spine, and weaker at the AP spine (Table 3). All these correlations held after correcting for all the variables that were found to affect urine estrogen metabolites using partial correlation analyses (not shown). On the other hand, there was no association between BMD and the ratio of either 2-hydroxylated or 2-methoxylated estrogens over 16αOHE1 (Table 3). The correlations between rates of bone density changes and baseline urine metabolites were weak and not statistically significant (Table 2). However, the trend was negative for 2-hydroxylated metabolites and positive for 16αOHE1. Even in this case, partial correlation analysis did not significantly change the results.

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Figure FIG. 1. Correlation between baseline BMD and urine 2MeOE1. BMD measured at baseline at the lumbar spine, AP projection (filled circles and solid line) and proximal femur, total area (open circles and dotted line) is plotted as a function of urine 2MeOE1/Cr excretion at baseline. Regression coefficients are r = −0.23 (p = 0.06) for the spine, and r = −0.37 (p < 0.002) for the proximal femur.

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Table Table 3.. Simple Correlation (r) Between Urine Estrogen Metabolites and BMD in Healthy Postmenopausal Women Followed for 1 Year
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To test whether estrogen metabolites could distinguish women with low bone density and/or with highest rates of bone loss, we stratified the study population by quartiles of urinary estrogens and compared baseline BMD and rates of BMD changes among the groups. As shown in Fig. 2, after adjustment for waist-hip ratio, BMI, YSM, smoking, and total urine estrogen metabolites, women in the lowest quartile of urine 2MeOE1/Cr (<15 ng/g) had significantly higher BMD at all vertebral and proximal femoral sites. In general, the higher the quartile of 2MeOE1, the lower the baseline BMD, although at most skeletal sites only the first quartile was significantly different than all the other groups. Average baseline BMDs were not consistently different among the second, third, and fourth quartile groups. Similar results were obtained with urine 2OHE1 as a stratification variable (not shown). Likewise, the ratio of 2OHE1 over 16αOHE1 allowed us to distinguish subjects with different degrees of BMD changes. Accordingly, subjects in the lowest quartile of 2OHE1/16αOHE1 ratio (<1.6), that is, those with low 2-hydroxylated estrogens and high 16αOHE1 levels, had the most favorable bone density changes; that is, they did not lose bone within the 1-year observation, compared with all the other groups (Fig. 3). Segregation of bone nonlosers with 2OHE1/16αOHE1 ratio was evident at the spine rather than at the proximal femur, consistent with the minimal bone density changes observed at the latter site during the 1-year observation period.

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Figure FIG. 2. BMD at baseline at different sites of the lumbar spine and proximal femur stratified by quartiles of urine 2MeOE1. In each group, quartiles are from left (lowest) to right (highest), and the values represent the averages corrected for YSM, BMI, waist-hip ratio, smoking, and total urine estrogen metabolite excretion. Asterisk indicates statistical difference (p < 0.05) compared with the first quartile (Tukey's multiple t test).

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Figure FIG. 3. Percentage of yearly changes of BMD (BMD) at different sites of the lumbar spine and proximal femur stratified by quartiles of urine 2OHE1/16αOHE1 ratio. In each group, quartiles are from left (lowest) to right (highest), and the values represent the averages corrected for YSM, BMI, waist-hip ratio, smoking, and total urine estrogen metabolite excretion. Asterisk indicates statistical difference (p < 0.05) compared with the first quartile (Tukey's multiple t test).

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DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

We found that urinary excretion of catechol estrogens, that is, 2OHE1 and 2MeOE1, were correlated negatively with BMD in healthy, untreated, early postmenopausal women and that subjects who did not experience bone loss during the 1-year study were those in the lowest quartile of urine 2OHE1/16αOHE1 ratio (<1.6). These results suggest that the extent of estrogen 2-hydroxylation is an important contributor of postmenopausal bone density.

The physiological relevance of estrogen hydroxylation pathways has been stressed by a number of studies. For example, smoking, a putative risk factor for osteoporosis, was found to increase significantly 2-hydroxylation of estrogen with concomitant decrease in 16α-hydroxylation resulting in elevated urinary 2OHE1/E3.(25,26) We also found that waist-to-hip ratio and BMI were correlated negatively with 2-hydroxylated estrogen metabolites, suggesting that body mass and a gynoid fat distribution may favor the active metabolic pathway. Although the biological basis of this phenomenon remains obscure, it is consistent with the known positive effect of body mass on bone density,(35,36) and the negative effects of total body fat on 2-hydroxylation of estrogens.(37,38) Therefore, the many factors that modulate the synthesis of these metabolites could selectively influence estrogen target tissues such as bone.

Concordant conclusions emerge from studies in postmenopausal osteoporosis, a consequence of estrogen deficiency. Lim and coworkers recently reported low 16αOHE1 and high 2OHE1 urine excretion in Korean postmenopausal women with osteopenia compared with normal subjects.(28) We observed the same negative correlation between 2-hydroxyestrogen and BMD, at both the spine and the proximal femur in our cohort of healthy postmenopausal white women, although we could not confirm the positive correlation of 16αOHE1 and bone density reported by Lim et al.(28) In our subjects, all urinary estrogen metabolites were associated negatively with bone density at baseline. However, the similarities and differences between the two studies should be interpreted cautiously in light of the established differences in drug metabolism, diet, and other lifestyle factors between whites and Orientals.(39) In addition, methodological problems in the study of Lim and coworkers may have led to an overestimation of serum estrogen metabolite levels, thus confounding the outcome.(29) That estrogen metabolism may be a common denominator between two of the most common conditions affecting a woman's health is further supported by the observation of a 2.5-fold higher risk of breast cancer in postmenopausal women in the highest quartile of BMD compared with women below the upper quartile.(40) In addition, a recent case-control study on breast cancer indicated that postmenopausal women with 2OHE1/16αOHE1 ratio lower than 1.91 and 1.38 had 9 and 32 times higher risk of breast cancer, respectively, than subjects with a ratio above 1.91.(41) Therefore, women in the lowest quartile of estrogen 2-hydroxylation are protected from bone loss, but this may be associated with a higher risk of breast cancer.

In our study, the 2OHE1/16αOHE1 ratio proved to be a good index of rates of BMD changes in the lumbar spine, as indicated by the finding that women with urine 2OHE1/16αOHE1 ratio < 1.6 (first quartile) appeared to be protected from early postmenopausal bone loss at this site. However, the predictive value of the ratio seems to be weaker at the proximal femur. A positive correlation between total urine estrogen glucuronides and rate of change of forearm BMD has been observed by others,(9) although the effect of single estrogen metabolites on bone density was not examined in that study. Therefore, our results show for the first time in a prospective fashion that enhanced 2-hydroxylation translates into accelerated bone loss after menopause, at least at the spine. Thus, the extent of activation of each pathway conditions in a reciprocal fashion the changes in bone density that may occur within a certain period after menopause. Again, our results do not allow us to extend unambiguously this conclusion to the proximal femur. On the other hand, the negative correlation between metabolites of both pathways and BMD at baseline—the result of a lifetime balance between bone accretion and resorption—may reflect a general negative effect of estrogen catabolism on bone remodeling. In other words, increased clearance of estrogen and estrogen metabolites may reduce a lifetime exposure to active estrogen, a critical factor for bone mass buildup.(5,6)

Several lines of evidence indicate that 2-hydroxyestrogens are not only devoid of peripheral estrogenic activity but may serve as natural antiestrogens. For example, 2-hydroxylation products inhibit proliferation of estrogen-sensitive breast cancer cells(42,43) and stimulate the synthesis of sex hormone binding globulin, thus causing less free ligand to be available to target organs.(44) Furthermore, although they bind to estrogen receptors, 2-catechol estrogens do not induce transactivation of the transforming growth factor β (TGF-β) promoter, an index of estrogenic activity.(45) Others also reported menstrual irregularities in young women with high 2-hydroxy estrogens,(37,46) and interference by 2OHE1 with secretion of gonadotropins and prolactin in postmenopausal women treated with estrogen.(47) On the other hand, 16αOHE1 and E3, both products of E1 16α-hydroxylation, are potent uterotropic substances.(22) More recent data seem to suggest that 16αOHE1 may have bone-selective estrogen agonist activity. In ovariectomized rats, Westerlind et al.(30) obtained an equivalent effect on bone density with either 16αOHE1 or 17β-estradiol, whereas the effect of 16αOHE1 on the uterus and mammary gland were significantly less than that of 17β-estradiol.

A possible limitation of this study is constituted by the fact that no significant bone loss was detected in the majority of our population of healthy, early postmenopausal women. Simple dietary adjustments or voluntary intake of calcium supplements may have helped prevent significant bone loss in most of these women, despite estrogen deficiency. Another possible explanation may be related to the relatively high BMI in our study population. A large body size may have protected many of the women in the cohort from bone loss. In addition, large body fat is known to reduce the reproducibility of DXA measurements. It also could be argued that if the 2-hydroxylation pathway reflects bone loss, a stronger predictive value of these metabolites may emerge when bone loss is more accentuated, and that the exclusion of osteoporotic subjects may have precluded detection of such a correlation. Longer observation periods in more osteopenic women may provide this type of information, but such an approach would appear ethically questionable.

Our findings may have broader clinical relevance, because the results obtained suggest that specifically altering estrogen hydroxylation to the estrogenic pathways, by whatever means, may reduce the risk for postmenopausal bone loss. This paradigm can be thought of as a form of endogenous estrogen receptor modulation and could represent a novel direction for drug targeting. One could hypothesize that treatment of postmenopausal women with specific inhibitors of cytochrome P450's responsible for 2-hydroxylation of estrogen(48) might result in higher circulating levels of active estrogens and lead to reduction of the rate of bone loss. As a corollary, the possibility that the new, nonsteroidal selective estrogen receptor modulators may alter estrogen metabolism should be considered in defining their mechanism of action.

We conclude that estrogen status and possibly urinary end products of E1 hydroxylation contribute to bone density in postmenopausal women.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

This work was supported in part by Small Business Innovation Research contracts N43-DK-1-2274 and N44-DK-3-2274, the National Institutes of Health, and by Immuna Care Corp.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES
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