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Abstract

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

To assess the influence of bone turnover on bone density and fracture risk, we measured serum levels of osteocalcin (OC), bone alkaline phosphatase (BAP), and carboxy-terminal propeptide of type I procollagen (PICP), as well as 24-h urine levels of cross-linked N-telopeptides of type I collagen (NTx) and the free pyridinium cross-links, pyridinoline (Pyd) and deoxypyridinoline (Dpd), among 351 subjects recruited from an age-stratified random sample of Rochester, Minnesota women. PICP, NTx, and Dpd were negatively associated with age among the 138 premenopausal women. All of the biochemical markers were positively associated with age among the 213 postmenopausal women, and the prevalence of elevated turnover (>1 standard deviation [SD] above the premenopausal mean) varied from 9% (PICP) to 42% (Pyd). After adjusting for age, most of the markers were negatively correlated with bone mineral density (BMD) of the hip, spine, or forearm as measured by dual-energy X-ray absorptiometry, and women with osteoporosis were more likely to have high bone turnover. A history of osteoporotic fractures of the hip, spine, or distal forearm was associated with reduced hip BMD and with elevated Pyd. After adjusting for lower BMD and increased bone resorption, reduced bone formation as assessed by OC was also associated with prior osteoporotic fractures. These data indicate that a substantial subset of elderly women has elevated bone turnover, which appears to adversely influence BMD and fracture risk. Combined biochemical and BMD screening may provide better prediction of future fracture risk than BMD alone.


INTRODUCTION

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

It is generally understood that fractures result from a complex interplay between bone strength (assessed in vivo as bone mineral density [BMD]) and the skeletal loads imposed by the activities of daily living or by trauma, especially from falls.(1,2) More recently, it has been recognized that an increased rate of bone turnover may be an additional risk factor for fractures(3) because it exacerbates bone loss,(4) because it leads to perforation of trabeculae and loss of structural elements of bone,(5) or because it further reduces bone strength by enlarging the remodeling space.(3,6) It is well documented that bone turnover rises around the time of menopause(7) and continues to increase with age thereafter.(8–11) However, the age-specific prevalence of elevated bone turnover has not been well defined in a population-based sample of women. More importantly, the influence of bone turnover on fracture risk in the community remains uncertain. A significant effect is anticipated from the observation that high bone turnover predicted hip fracture risk among elderly French women(12) and from detailed studies showing an adverse effect of increased turnover on vertebral fracture risk that was independent of bone density.(3) Consequently, we measured bone turnover in 351 subjects recruited from an age-stratified random sample of women and assessed the relationship of bone turnover to bone density and to the risk of osteoporotic fractures.

MATERIALS AND METHODS

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

Study subjects

Subjects were recruited from an age-stratified random sample of Rochester, Minnesota women that was selected using the medical records linkage system of the Rochester Epidemiology Project.(13) Over half of the Rochester population is identified annually in this system and the majority are seen in any 3-year period. Thus, the enumerated population (those women seen in 1990 ± 1 year) approximates the underlying population of the community, including both free-living and institutionalized individuals. Altogether, 938 women aged 20 years and over were approached for this study but 126 were ineligible (89 were demented and could not give informed consent; 11 were pregnant; 9 were radiation workers; 8 were participants in an ongoing clinical trial of osteoporosis prophylaxis; and 9 died before they could be contacted). Of the 812 eligible women, 351 (43%) participated and provided full study data. There were approximately 50 women per decade aged 20–29 years to age 80 years and over. These included 138 premenopausal women (mean age ± SD, 35.0 ± 8.6 years; range, 21–54 years) and 213 postmenopausal women (mean, 67.8 ± 13.2 years; range, 34–94 years). Among postmenopausal women, the mean time since menopause was 22.1 ± 12.3 years. Forty-seven of the postmenopausal women were on estrogen replacement therapy (ERT) at the time of the study (37 on oral conjugated estrogens, 9 on transdermal estrogen, and 1 on an oral contraceptive). Because ERT influences both bone turnover(14,15) and fracture risk,(16) these women were considered separately in some analyses. All subjects were interviewed in accordance with a standard protocol in order to collect clinical data, including a history of fracture which was verified by review of each subject's complete (inpatient and outpatient) medical records in the community. “Osteoporotic” fractures included clinically recognized fractures of the hip, spine, or distal forearm that resulted from minimal or moderate trauma (e.g., a fall from standing height or less) among women 35 years of age or older.

Bone densitometry

Bone mineral density (BMD) in grams per square centimeter was determined for the lumbar spine (L2–L4 in anteroposterior [AP] projection), proximal femur (total), and wrist (total) using dual-energy X-ray absorptiometry (DXA) with the Hologic QDR2000 instrument (Hologic, Waltham, MA, U.S.A.) using software version 5.40. The coefficients of variation (CV) for the spine, hip, and forearm BMD measurements were 0.6, 1.8, and 0.8%, respectively. BMD at each site was categorized into three groups according to World Health Organization criteria as normal, low, or osteoporotic(17) relative to young normal means and standard deviations (SDs). These means (and SDs) for BMD of the spine, hip, and forearm were 1.10 ± 0.13, 0.94 ± 0.12, and 0.56 ± 0.04 g/cm2, respectively, as judged from all premenopausal study subjects combined. Thus, by this classification, osteoporosis was defined as BMD below 0.78 g/cm2 in the spine, 0.63 g/cm2 in the hip, and 0.45 g/cm2 in the forearm.

Biochemical markers

Bone turnover was measured using biochemical assays that are indices of bone formation or bone resorption.(7) Bone formation was assessed by measurement of serum levels of osteocalcin (OC) in nanograms per milliliter, bone alkaline phosphatase (BAP) isoenzyme in units per liter, and carboxy-terminal propeptide of type I collagen (PICP) in nanograms per milliliter. Serum OC was measured by radioimmunoassay (RIA) using antibody G12 with intra-assay CV of 4.4%.(8) Serum BAP was measured by enzyme-linked immunosorbent assays (ELISA) with intra-assay CV of 6.3%.(18) Serum PICP was also measured by ELISA (Prolagen C, Metra Biosystems, Inc., Mountain View, CA, U.S.A.) with intra-assay CV of 4.7%. Bone resorption was evaluated by the measurement of 24-h urine levels of the free pyridinium cross-links, pyridinoline (Pyd) and deoxypyridinoline (Dpd), as well as cross-linked N-telopeptides (NTx) of type I collagen, all assessed in units of nanomoles per liter of glomerular filtrate (nmol/l GF) since this represents the more precise correction for alterations in renal function. However, mean levels for NTx, Pyd, and Dpd were also expressed as nanomoles per nanomole Cr, as is conventionally done. Results were similar regardless of how the markers were expressed. The glomerular filtration rate was assessed by creatinine clearance. Free Pyd and free Dpd were measured by ELISA (Pyrilinks and Pyrilinks-D kits, Metra Biosystems, Inc.) with intra-assay CVs of 8.7 and 5.4%, respectively. NTx was also measured by ELISA (Osteomark, Ostex International, Seattle, WA, U.S.A.) with intra-assay CV of 7.6%. In addition, we calculated an uncoupling index (Pyd Z score minus OC Z score) as suggested by Eastell and colleagues.(19)

Statistical analysis

The relationship of biochemical markers of bone turnover with age and with spine, hip, and forearm BMD was studied using Spearman correlation coefficients, and partial correlation coefficients which were adjusted for age. The correlations were done separately for premenopausal women and for postmenopausal women. The postmenopausal women currently on ERT and those not on ERT were also considered separately in some analyses. A detailed examination of the influence of ERT on bone turnover is presented elsewhere.(15) The increase in prevalence of elevated biochemical markers of bone turnover by category of BMD among postmenopausal women not currently on ERT was tested using logistic regression, where elevated biochemical marker was treated as the endpoint, and category of BMD (osteoporotic, low, normal) and age were the independent variables.

Multiple logistic regression was used to model predictors of a history of osteoporotic fracture. Osteoporotic fracture was the endpoint, while age, BMD (spine, hip, and wrist), the biochemical markers (OC, BAP, PICP, NTx, Pyd, and Dpd), and estrogen replacement status (ever vs. never) were the potential predictors. Variables were selected in a stepwise fashion, entering only variables that were significant at the 0.10 level after adjusting for the other variables in the model. Interactions and higher ordered terms were investigated. All postmenopausal women were used for this analysis. Kendall's tau-a was used to compare logistic models when various biochemical markers where substituted one for another.

RESULTS

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

Among premenopausal women, all of the biochemical markers of bone turnover correlated negatively with age (Fig. 1), but this result was statistically significant only for serum PICP and urinary NTx and Dpd (Table 1). Each of the markers was positively correlated with age when the postmenopausal women were considered as a group, but some of the correlations were no longer statistically significant when the postmenopausal women on ERT were considered separately from those not on therapy (Table 1). These data are illustrated in Fig. 1.

Table Table 1. Correlation of Biochemical Markers* of Bone Turnover with Age and with Bone Mineral Density (BMD, g/cm2) at Various Skeletal Sites After Adjusting for Age Among Rochester, Minnesota Women
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Figure FIG. 1. Regression of biochemical markers of bone turnover on age for premenopausal women, postmenopausal women on estrogen replacement therapy (Post/ERT), and postmenopausal women not on ERT (Post/no ERT) for serum levels of osteocalcin (OC, ng/ml), bone alkaline phosphatase (BAP, U/l), and carboxy-terminal propeptide of type I collagen (PICP, ng/ml) and 24-h urine levels of cross-linked N-telopeptides of type I collagen (NTx, nmol/l GF), free pyridinoline (Pyd, nmol/l GF), and free deoxypyridinoline (Dpd, nmol/l GF).

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To determine the age-specific prevalence of high bone turnover among the postmenopausal women, we arbitrarily defined elevated bone turnover as values more than 1 SD above mean levels for the premenopausal women. Premenopausal mean levels (±SD) were 4.9 ± 1.4 ng/ml for OC, 17.4 ± 5.7 U/l for BAP, 73.7 ± 27.0 ng/ml for PICP, 2.2 ± 1.0 nmol/l GF for NTx, 2.7 ± 0.7 nmol/l GF for Pyd (deleting one outlier), and 0.4 ± 0.1 nmol/l GF for Dpd. Consequently, the cut-off levels that defined “high” turnover were 6.3, 23.1, 100.7, 3.1, 3.4, and 0.5, respectively, for the six markers. If expressed conventionally as nanomoles per nanomole Cr, the mean levels for the urinary markers were 31.3 ± 14.3 for NTx, 38.6 ± 9.6 for Pyd, and 5.8 ± 1.4 nmol/mmol Cr for Dpd. The cut-off levels for these latter values were 45.6, 48.2, and 7.2, respectively. As might be expected from the data already shown in Fig. 1, mean levels of the bone turnover markers were lower among the postmenopausal women on ERT (Table 2). Among the postmenopausal women not on ERT, the prevalence of high turnover increased with age as also shown in Table 2. Overall, the proportion of postmenopausal women with elevated serum OC levels rose from about 18% among women under 60 years of age to 70% at age 80 years and over. Likewise, among all postmenopausal women combined, the prevalence of high urinary Pyd rose from 13% at age <60 years to 84% among women 80 years old and older. The age-adjusted (to 1990 U.S. white women ≥50 years of age) prevalence of high turnover among postmenopausal women 50 years of age or over was 35% as assessed by OC, 37% by BAP, 9% by PICP, 25% by NTx, 42% by Pyd, and 35% by Dpd.

Table Table 2. Distribution of Biochemical Markers* of Bone Turnover Among 213 Postmenopausal Rochester, Minnesota Women
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After adjusting for age, there was a statistically significant negative correlation of NTx with BMD at the hip and spine among the premenopausal women, while serum OC was negatively associated with BMD of the spine (Table 1). Among the postmenopausal women who were not on ERT, NTx and to a lesser extent OC, BAP, and PICP were negatively correlated with BMD at each of the three skeletal sites. Among those on estrogen, urinary NTx and Dpd were negatively correlated with BMD of the hip and serum BAP with BMD of the spine and forearm. However, NTx was the best predictor of age-adjusted BMD at each of the three sites when all of the markers were allowed to compete in multiple regression models, both for the premenopausal women and for the postmenopausal women who were not on estrogen. For the postmenopausal women on ERT, urinary Dpd was the best predictor of hip BMD, while BAP was best for BMD of the spine and forearm. The association of the different markers with BMD of the hip is illustrated in Fig. 2.

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Figure FIG. 2. Regression of biochemical markers of bone turnover on bone mineral density (BMD, g/cm2) of the proximal femur (hip) for premenopausal women, postmenopausal women on estrogen replacement therapy (Post/ERT), and postmenopausal women not on ERT (Post/no ERT) for serum levels of osteocalcin (OC, ng/ml, deleting two outliers), bone alkaline phosphatase (BAP, U/l), and carboxy-terminal propeptide of type I collagen (PICP, ng/ml) and 24-h urine levels of cross-linked N-telopeptides of type I collagen (NTx, nmol/l GF, deleting three outliers), free pyridinoline (Pyd, nmol/l GF, deleting one outlier), and free deoxypyridinoline (Dpd, nmol/l GF).

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Among the 47 postmenopausal women on ERT, only 12 had osteoporosis of the spine, hip, or forearm by World Health Organization criteria, but they were younger than the postmenopausal who were not on ERT (mean, 61.0 vs. 69.7 years). For the 166 postmenopausal women in the latter group, 89 had osteoporosis at one or more of these skeletal sites, and there was generally a greater prevalence of elevated markers among the women with osteoporosis than those with low bone mass or normal BMD (Table 3). This trend was statistically significant in 11 of the 18 combinations made up by the six markers and the three skeletal sites where BMD was assessed. Within a given BMD category, however, bone turnover was generally higher among the oldest women; two-thirds of the time there was a greater prevalence of elevated bone turnover among the women 70 years of age and over compared with women younger than 70 years old (data not shown). Thus, high bone turnover was associated both with increasing age and with decreasing bone density, and there was no significant interaction between the two.

Table Table 3. Proportion (%) with Elevated (>1 SD Above Premenopausal Mean) Biochemical Markers* of Bone Turnover by Category of Bone Mineral Density (BMD) Among 166 Postmenopausal Rochester, Minnesota Women Who Were Not on Estrogen Replacement Therapy
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Altogether, 45 postmenopausal women had experienced one or more osteoporotic fractures of the hip, spine, or distal forearm. In univariate analyses, prior osteoporotic fractures were significantly associated with age (age and age2) and with BMD measured at the hip, spine, and forearm. However, hip BMD was the strongest independent predictor of fracture in a multivariate assessment that included age and the other BMD assessments (age-adjusted odds ratio per 1 SD decrease in hip BMD, 3.2; 95% confidence interval [CI], 1.9–5.5). Similarly, all of the biochemical markers of bone turnover were positively associated with fracture history univariately, except PICP, which was negatively associated. However, the relationship was statistically significant only for NTx (p = 0.006), Pyd (p < 0.001), and Dpd (p = 0.007). After adjusting for age, only Pyd was an independent predictor of fracture risk among the biochemical markers (odds ratio per 1 SD increase in Pyd, 1.5; 95% CI, 1.0–2.2). There was no significant association between fractures and a history of estrogen replacement therapy so this variable was not considered further.

In a multivariate analysis, Pyd was the only measure of bone resorption that independently predicted a history of osteoporotic fractures. Although not statistically significant, the other two measures of bone resorption (Dpd and NTx) remained positively associated with fracture risk, while all three measures of bone formation (OC, BAP, and PICP) were negatively associated. Indeed, the negative association of OC with fracture risk was enhanced by simultaneously adjusting for elevated bone resorption, as assessed by Pyd, and vice versa. These relationships are shown in Table 4. The OC term in the model was statistically significant (p = 0.022) but, when Pyd and OC were replaced by an uncoupling index (Pyd Z score minus OC Z score), the combined term was a highly significant independent predictor of fracture risk (p = 0.002). The different markers were correlated, however, and it made little difference to the predictive power of the model which ones were used. For example, OC was correlated with BAP (r = 0.46; p < 0.001). If BAP were substituted for OC, the model Kendall tau-a changed only from 0.205 to 0.193. Likewise, Pyd was correlated with Dpd (r = 0.77; p < 0.001) and with NTx (r = 0.56; p < 0.001); replacing Pyd with Dpd or NTx reduced the model tau-a to 0.197 or 0.184. The predictive power of the model was not improved by substituting the uncoupling index for the markers themselves (tau-a = 0.204). Age was not an independent predictor of fracture history in this model. Fractures due to severe trauma (e.g., motor vehicle accidents and falls from heights) were not influenced by BMD nor the markers of bone turnover.

Table Table 4. Multiple Logistic Regression Model of Predictors of Osteoporotic Fractures* Among 213 Postmenopausal Rochester, Minnesota Women
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DISCUSSION

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

This community-based study demonstrates that biochemical markers of bone turnover, both of “formation” and “resorption,” continue to increase with age following the menopause. This was previously demonstrated in healthy volunteers for OC, BAP, PICP, NTx, and the pyridinium cross-links.(8–11,18,20–24) In the postmenopausal women here, all of the markers were positively correlated with age. After adjusting for age, the markers were negatively correlated with bone density at the hip, spine, and forearm in postmenopausal women, which is also consistent with earlier reports.(8,11,23–26) All of the markers displayed this tendency, but NTx demonstrated the strongest relationship and was the only one independently associated with BMD at each site in multivariate models that included age and the other markers among premenopausal women and among all postmenopausal women combined. However, it is premature to conclude from these data that one marker is to be preferred over another since the different markers performed similarly in most analyses. Indeed, among the postmenopausal women on ERT, Dpd was the best predictor of bone density at the hip. These results are similar to those of Garnero and colleagues, who found in a study of 432 healthy women 1–40 years after menopause that OC, BAP, PICP, NTx, and type I collagen cross-linked C-telopeptide (ICTP) performed fairly comparably in predicting BMD of the hip, spine, radius, and total body.(11) Likewise, when comparing premenopausal women with postmenopausal women whose spinal BMD was more than 2 SD below the young normal mean, all of a panel of markers of bone formation, except for PICP, and all of the markers for bone resorption, except for ICTP, were elevated among the postmenopausal women with low bone density.(27)

It is not clear what level of increased bone turnover might be of concern among older women since the rate of bone loss is directly correlated with the bone turnover rate.(11,14,25,28–33) Others have defined high turnover as ≥2 SD above premenopausal means(12) and, by this criterion, prevalence rates among postmenopausal women would be lower than those reported here, i.e., 21, 14, 1, 12, 20, and 11% for OC, BAP, PICP, NTx, Pyd, and Dpd, respectively. However, additional efforts will be needed to achieve consensus on appropriate normal values, optimal cut-off levels, and the best assays to use. Moreover, it is likely that the gap between bone resorption and subsequent bone formation is most important.(19) This notion is reinforced by our observation that, after adjusting for the influence of elevated bone resorption as assessed by urinary free Pyd, fracture risk was associated with reduced bone formation as measured by serum OC. It also remains to be determined whether or not these relationships are influenced by the mechanism underlying the elevated bone turnover. Thus, estrogen deficiency dominates immediately following the menopause,(28,29,31,34–37) and the postmenopausal women in this cohort who were on ERT had lower levels of bone turnover. However, the increasing levels of turnover with aging, especially among the oldest women, are probably better accounted for by secondary hyperparathyroidism.(8,9,15,38–41)

Despite this potential heterogeneity in pathophysiology, we found, as predicted by Riggs and colleagues,(3) that elevated bone turnover was associated with a history of osteoporotic fractures of the spine, hip, and forearm at all ages. On average, the most recent fracture in each subject occurred 10.6 years (median, 8.3 years; range 3 months to 45 years) prior to study, so acute changes in these markers as a consequence of the fracture(42,43) should not have distorted our results. Others have reported a 27% increase in serum OC among women and men who had a distal forearm fracture 3–12 months earlier.(43) Likewise, an 11–22% increase in OC has been observed in women with postmenopausal vertebral fractures,(19,44) along with 26% higher levels of free Dpd.(19) Urinary Pyd and Dpd were also about 40% greater among elderly women with hip fractures, but OC levels were 20% less,(45) as seen also by others.(46,47) Among the postmenopausal women with a history of one or more osteoporotic fractures in this study, mean levels of OC, BAP, NTx, Pyd, and Dpd were 13, 6, 39, 28, and 16% higher, respectively, than among postmenopausal women without such a history. Mean levels of PICP were 3% lower among those with a history of fracture.

In a multivariate analysis, elevated levels of biochemical markers of bone resorption were associated with an increased risk of fracture, even after adjusting for bone density. This observation is somewhat at variance with the results of a Swedish study, where fracture risk was associated with reduced serum levels of PICP and ICTP.(48) Our findings are more consistent with a Danish study that demonstrated greater bone loss and increased fracture risk among perimenopausal women with high bone turnover as assessed by serum alkaline phosphatase, urinary calcium, and hydroxyproline.(4) After 15 years, the perimenopausal women who were fast bone losers (>3%/year) had a 2-fold increased risk of fracture.(32) Our data indicate that a similar phenomenon applies to older women as well since high levels of Pyd were associated with a comparable increase in the odds of fracture of 2.0 after adjustment for hip BMD. This is also consistent with other results.(12,49) For example, among French women ≥75 years of age, levels of ICTP that were 2 SD above the premenopausal mean were associated with a 1.7-fold increase in hip fracture risk after adjusting for hip BMD and gait speed.(12) Likewise, a high Dpd level was associated with a 1.6-fold increase in risk, but OC, BAP, and NTx were not independently associated with hip fractures in that study.

Bone density later in life, and thus fracture risk, is influenced about equally by peak bone mass and the subsequent rate of bone loss.(50) A substantial subset of elderly women has elevated bone turnover. We have estimated, for example that 25% of postmenopausal women have urinary NTx levels more than 1 SD above the mean for premenopausal women. In the present analysis, elevated bone turnover appeared to have a negative influence on BMD. Even after adjusting for bone density, however, elevated levels of bone turnover were independently associated with fracture risk among postmenopausal women, with an indication that increased bone resorption was especially important when accompanied by low levels of bone formation. Since biochemical markers are able to estimate the rate of bone formation and resorption, and perhaps provide unique information about rapid, perforative bone loss, combined biochemical and BMD screening may provide a better prediction of the future risk of osteoporosis and fractures than BMD alone as has been suggested by Christiansen.(51) Indeed, in one study, elderly women with osteoporotic levels of hip BMD and elevated bone resorption by ICTP had a 5-fold greater risk of hip fracture than women with either risk factor alone.(12) This notion needs to be evaluated in additional prospective studies to determine the optimal protocol for assessment and to demonstrate that this approach can improve the management of individual patients.

Acknowledgements

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

We thank Mrs. Veronica L. Gathje and Mrs. Margaret F. Holets for help with data collection, Mrs. Carol A. McAlister for performing the assays, Mrs. Cindy Crowson for assistance with data analysis and Mrs. Mary Roberts for help in preparing the manuscript. This work was supported by research grant AR27065 from the National Institute of Arthritis, Musculoskeletal and Skin Diseases, United States Public Health Service.

REFERENCES

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