SEARCH

SEARCH BY CITATION

Abstract

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

The ability of biochemical markers to predict the rate of postmenopausal bone loss is still controversial. To investigate this issue further, baseline levels of a panel of specific and sensitive biochemical bone markers were correlated to the rate of change of forearm bone mineral density (BMD) assessed by four measurements over a 4-year period using dual-energy X-ray absorptiometry in a large population-based prospective cohort of 305 women aged 50–88 years (mean 64 years), 1–38 years postmenopausal. In the whole population, higher baseline levels of bone formation (serum osteocalcin and serum type I collagen N-terminal propeptide) and bone resorption markers (urinary N-telopeptides; urinary and serum C-telopeptides) were significantly associated with faster BMD loss (r = −0.19 to −0.30, p < 0.001), independently of age. In women within 5 years of menopause that have the highest rate of bone loss, the predictive value of bone markers was increased with correlation coefficients reaching 0.53. Women with an abnormally high bone turnover, i.e., with levels of bone markers at baseline 2 SD above the mean of premenopausal women, had a rate of bone loss that was 2- to 6-fold higher than women with a low turnover (p = 0.01–0.0001) according to the marker. When the population was categorized according to quartiles of bone markers at baseline, a similar relationship between increased levels of bone markers and faster rate of bone loss was found (p = 0.008–0.0001). In the logistic regression model, the odds-ratio of fast bone loss, defined as the rate of bone loss in the upper tertile of the population, was increased by 1.8- to 3.2-fold for levels of biochemical markers in the high turnover group compared with levels within the premenopausal range, with, however, a limited value for identifying individual fast bone losers. We conclude that increased levels of some of the new biochemical markers of bone turnover are associated with greater radial bone loss. Because increased bone loss is associated with an increased risk of fracture, bone turnover markers may be useful to improve the prediction of the risk of osteoporosis in postmenopausal women.


INTRODUCTION

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

ONE OF THE MAIN CHALLENGES of preventing osteoporosis is identifying patients at risk for subsequent fracture. Population studies have clearly shown that a low bone mineral density (BMD), which can be accurately and precisely measured by dual-energy X-ray absorptiometry (DXA), is able to predict osteoporotic fractures.1–3 Riis et al.4 recently reported that in women within 3 years of menopause, a fast rate of bone loss assessed by nine repeated BMD measurements at the radius over 2 years, increases the risk of fractures assessed over a 15-year follow-up period, independently, and to a similar extent as BMD level. These data strongly suggest that a low peak BMD and a fast rate of postmenopausal bone loss are both important and independent predictors of osteoporotic fractures. Therefore, a test that identifies women who are going to lose bone most rapidly in the future would be a valuable tool for assessing the risk of fracture in combination with a BMD measurement.

It has been suggested that biochemical markers of bone turnover may be used to identify fast bone losers. Several cross-sectional studies indicate that bone turnover rate assessed by markers increases after the menopause and that high bone turnover is sustained long after the menopause.5 Levels of biochemical markers in postmenopausal women correlate negatively with BMD measured at several skeletal sites. The correlation between bone markers and BMD becomes much stronger with advancing age, so that in women more than 30 years after the menopause bone turnover accounts for 40–50% of the variance of BMD of the whole skeleton.6 These cross-sectional data suggest that a sustained increase of bone turnover in postmenopausal women induces a higher rate of bone loss and therefore an increased risk of osteoporosis. Prospective studies, which are required to confirm the usefulness of bone markers to predict rate of loss, suffer from methodological issues mainly because of their short-term follow-up. Indeed, when the rate of bone loss is assessed by annual measurement of BMD at the spine, hip, or radius over 2 years, the amount of bone loss is on the same order of the magnitude as the precision error of repeated measurements in a single individual.7 This technical limitation impairs a valid assessment of the relationship between bone turnover and the subsequent rate of bone loss in individual postmenopausal women and probably explains the conflicting results that have been published.8–13 In addition to their short-term follow-up period, most of the previous longitudinal studies used the nonprecise single-photon absorptiometry8,10 or dual-photon absorptiometry13 to measure changes in BMD, and some of them used conventional and non–bone-specific biochemical markers such as urinary hydroxyproline or total alkaline phosphatase.11 Two studies have found a moderate to strong relationship between modern markers and bone loss measured by DXA, but both were retrospective.14,15

To examine further the potential utility of bone markers to identify fast bone losers, we analyzed the value of baseline levels of specific bone markers, including a new serum assay for type I collagen C-telopeptides (CTX), to predict rate of forearm bone loss assessed by yearly BMD measurements over 4 years using DXA in a large cohort of healthy untreated and well characterized postmenopausal women.

MATERIALS AND METHODS

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

Subjects

Postmenopausal women are part of the OFELY cohort which is a prospective study of the determinants of bone loss. The cohort of this study comprises 1039 healthy volunteers 31–89 years of age from a large health insurance company (Mutuelle Générale de l'Education Nationale). Among the 672 postmenopausal women (menopause was defined as an absence of menses for at least 12 months) recruited at baseline, 75 had withdrawn during the 4-year follow-up study for personal reasons and 4 died. Of the remaining 593 volunteers, 11 women were subsequently excluded because of a disease known to influence calcium metabolism: primary hyperparathyroidism (n = 4), hyperthyroidism (n = 4), and Paget's disease of bone (n = 3). Two hundred and seventy-seven women who had received the following treatment that might affect bone metabolism were also excluded: tamoxifen (n = 12), fluoride (n = 16), bisphosphonate (n = 22), calcitonin (n = 4), intermittent doses of corticosteroids (n = 7), thyroxine therapy for hypothyroidism (n = 33), anabolic steroid (n = 1), and estrogen replacement therapy (n = 182). The study was performed on the remaining 305 healthy postmenopausal women from 50 to 88 years of age who were all followed for 4 years. None of these women had taken estrogen during the 4-year period of the study and all had stopped treatment for at least 12 months before the baseline visit. This study was approved by the local ethical committee, and informed consent was obtained from all participants.

BMD measurements

BMD was measured at the mid- and distal radius using DXA on a QDR 2000 device (Hologic, Inc., Waltham, MA, U.S.A.). The mid-area is composed mainly (>95%) of cortical bone, and the distal area comprises both cortical (about 75%) and trabecular (about 25%) bone. For technical reasons, valid assessement of the axial BMD rate of change was not obtained in this cohort. The short-term in vivo precision of DXA was 1.2%, and 0.6% for the mid- and distal radius, respectively. Measurements were performed at baseline, year 2, year 3, and year 4 (four consecutive measurements) by the same technician. Long-term precision of DXA over the 4-year study was calculated using the mean of the SEM for individual regression analyses of BMD against time.16 These were found to be 0.51 ± 0.34%/year and 0.40 ± 0.29%/year for the mid- and distal radius, respectively.

Markers of bone turnover

For each woman, blood, first (FMV) and second (SMV) morning void, and 24 h urine samples were collected at the baseline visit. Blood samples were collected between 8:00 a.m. and 9:30 a.m. after an overnight fast for all women. Serum and urine samples were stored frozen at −80°C until assayed.

Bone formation markers:

Serum osteocalcin was measured with a human immunoradiometric assay, which uses two monoclonal antibodies recognizing, respectively, the 5–13 and 43–49 sequence of the molecule, and purified intact human bone osteocalcin as a standard (ELSA-OST-NAT™; CIS BioInternational, Cedex, Saclay, France). This assay measures only the intact molecule and not the N-midfragment.17 The intra- and interassay coefficients of variation (CV) are typically below 10%, and the sensitivity is 0.3 ng/ml.17

Serum bone alkaline phosphatase was measured with an immunoradiometric assay using two monoclonal antibodies directed against the human bone isoenzyme and bone alkaline phosphatase purified from human SAOS-2 osteosarcoma cells as a standard (Ostase™; Hybritech, Inc., San Diego, CA, U.S.A.). This assay cross-reacts by only 16% with the circulating liver isoenzyme. The intra- and interassay CVs are <10%.18

Serum C-terminal propeptide of type I collagen (PICP) was measured by a two-site enzyme-linked immunoassay (ELISA) (Prolagen-C; MetraBiosystems, Palo Alto, CA, U.S.A.). The intra- and interassay CVs are below 8%.19

Serum intact N-terminal propeptide of type I collagen (PINP) was measured by a new radioimmunoassay (Intact PINP; Farmos Diagnostica, Uppsala, Sweden).20 The intra-and interassay CV of PINP immunoassay are between 3.1% and 9.3%.20

Bone resorption markers:

Urinary type I collagen N-telopeptide breakdown products (urinary NTX) were measured by ELISA (Osteomark; Ostex International, Inc., Seattle, WA, U.S.A.).21 The intra- and interassay CVs are <10%.6

Urinary CTX breakdown products were measured by an ELISA (CrossLaps ELISA; CIS BioInternational, Gif/Yvette, France) based on an immobilized synthetic peptide with an amino acid sequence specific for a part of the C-telopeptide of the α1 chain of type I collagen (Glu-Lys-Ala-His-βAsp-Gly-Gly-Arg) (CrossLaps antigen; CIS BioInternational).22 The intra- and interassay CVs are <10% and 13%, respectively.23

Serum type I collagen C-telopeptide breakdown products (serum CTX) were measured by a two site ELISA (Serum Crosslaps one step; CIS BioInternational) using monoclonal antibodies raised against an amino acid sequence specific for a part of the C-telopeptide of the α1 chain of type I collagen (Glu-Lys-Ala-His-βAsp-Gly-Gly-Arg).24 Intra- and interassay CVs are <5% and 8%, respectively.24

Urinary NTX and CTX data obtained from FMV and SMV samples were corrected by the urinary creatinine (Cr) concentration measured by a standard colorimetric method.

Statistical analyses

In calculating the rate of change in BMD, it was assumed that the expected change is linear over the 4-year follow-up period. BMD was regressed on time to yield a rate of change in BMD for each subject. For 297 out of the 305 women (97%), 4 individual values of BMD were available at baseline, year 2, year 3, and year 4. Four women had three consecutive BMD data (baseline, year 3 and year 4) and four women were assessed only at baseline and year 4. Under this model, the annual percentage of change in BMD for each subject was then derived by dividing the regression slope by the intercept at time 0. We also calculated the absolute rate of BMD change by using raw values of the regression slope. Preliminary analyses showed that both expressions of the rate of change gave similar results and thus data are presented using the annual percentage change. Significance of the rate of change versus 0 was assessed by one group Student's t-test. Relationships between annual percentage change in BMD, baseline bone marker levels, age, and years since menopause were assessed by linear regression analyses after logarithmic transformation of bone marker data. The mean ± 2 SD of premenopausal controls was used as a cut-off limit between low and high turnover groups. The premenopausal range was obtained from the values of 134 healthy premenopausal women, 35–55 years of age drawn from the same OFELY cohort and described elsewhere.6 Differences in rate of BMD change between high and low turnover groups were assessed by unpaired Student's t-test. Tests for linear trend in mean rate of BMD change across quartiles of each marker were also performed. Women were divided into tertiles of rates of BMD change to determine the sensitivity, specificity, positive predictive value, and negative predictive value of low and high bone turnover for rapid bone loss, i.e., in the upper tertile of the population. The increased risk of fast bone loss associated with high turnover was estimated by odds-ratio obtained from conditional logistic regression adjusted for age and baseline BMD. All analyses were adjusted for age and performed using the Statistical Analysis Software (SAS, Cary, NC, U.S.A.).

RESULTS

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

Bone turnover and BMD at baseline

Baseline relevant characteristics of postmenopausal women are shown in Table 1. All biochemical markers of both bone formation and bone resorption correlated significantly with each other with, however, stronger association between serum osteocalcin, serum PINP, urinary CTX, urinary NTX, and serum CTX (r = 0.54–0.76) than with serum BAP and serum PICP (r = 0.31–0.59) (Table 2). BMD at the mid- and distal radius correlated negatively with age (r = −0.48 to −0.51, for mid- and distal radius, respectively, p < 0.001) and years since menopause (r = −0.45 to −0.48, p < 0.001). In contrast, biochemical markers of bone turnover did not correlate with age or years since menopause except slightly for serum PICP and serum PINP (r = −0.22 to −0.25 for PICP and r = −0.28 to −0.30, p < 0.001 for PINP with age and years since menopause, respectively). Biochemical markers of bone turnover were negatively correlated with both mid- and distal radius BMD except serum PICP, serum PINP, and serum BAP (Table 2). At baseline, 24, 28, 11, 14, 43, 29, and 22% of postmenopausal women were classified as high bone turnover, i.e., with levels 2 SD above the premenopausal range, according to serum osteocalcin, serum BAP, serum PICP, serum PINP, urinary NTX, urinary CTX (SMV sample) and serum CTX, respectively.

Table Table 1..  Baseline Characteristics of the 305 Postmenopausal Women
Thumbnail image of
Table Table 2..  Matrix of Correlations Between Markers of Bone Turnover and Forearm BMD at Baseline in 305 Healthy Postmenopausal Women
Thumbnail image of

Longitudinal changes of forearm BMD

The distribution of the rates of BMD change for the whole postmenopausal population is slightly skewed to the left (skewness coefficient: mean ± SE; −0.75 ± 0.14 and −0.69 ± 0.14 and for mid- and distal radius, respectively) but without any evidence of bimodal pattern. The mean and SD annual percentage of change in BMD for the whole postmenopausal population was −0.224% and 0.867%/year and −0.592% and 0.957%/year for mid- and distal radius, respectively (p < 0.0001 vs. 0 for both) with high correlation between the rates of change at these two skeletal sites (r = 0.64, p < 0.001). Mid-but not distal radius BMD changes correlated significantly with years since menopause (r = 0.22, p = 0.004) with highest bone loss in early postmenopausal women (data not shown).

Prediction of forearm bone loss by biochemical markers of bone turnover

Baseline levels of all bone markers except serum BAP correlated significantly with the rate of BMD changes at both mid- and distal radius, high levels of markers being associated with faster bone loss (Table 3). The regression and correlation coefficients were consistently higher in early postmenopausal women when the rate of bone loss is faster (Table 3). For urinary resorption markers, correlation was similar for all types of urine samples (Table 3).

Table Table 3..  Regression Analysis Between Baseline Bone Marker Levels and Age-Adjusted Rate of Forearm BMD Loss Over 4 Years in Healthy Untreated Postmenopausal Women
Thumbnail image of

The average rate of mid- and distal radius BMD loss increased significantly with increasing quartiles of baseline bone marker levels except for serum BAP and serum PICP (Table 4). Postmenopausal women with high turnover at baseline, i.e., with bone marker levels above the upper limit of the premenopausal range, lost from 4- to 6-fold more bone at mid radius and from 1.8- to 2-fold more bone at distal radius than women with low turnover according to the marker considered (Fig. 1).

thumbnail image

Figure FIG. 1.. Midradius BMD change over 4 years in 305 healthy postmenopausal women with low and high bone turnover at baseline. High turnover was defined as bone marker levels above the upper limit of the premenopausal range. p values refer to the difference in the rate of bone loss between the two groups of bone turnover. Bars represent the mean ± SEM.

Download figure to PowerPoint

Table Table 4..  Rate of Forearm BMD Change According to Quartiles of Bone Marker Levels at Baseline in 305 Healthy Postmenopausal Women
Thumbnail image of

The odds ratio for fast bone loss among women with marker levels above the upper limit of the premenopausal range compared with those with levels below this cutpoint ranged from 1.8 to 3.2 (Table 5). The sensitivity, specificity, positive predictive value, and negative predictive value are shown on Table 5. Although the sensitivity is low ranging from 16% to 59%, the specificity is high and ranged from 66% to 93%. From a clinical standpoint, the positive predictive value, which describes the probability of excessive bone loss among women with elevated marker, and the negative predictive value, which is the probability of not having excessive bone loss among women with normal markers, are probably more important than the sensitivity and the specificity. With a pretest probability of excessive bone loss of 33%, the positive predictive value ranged from 44% to 56%, and the negative predictive value was between 68 and 76% (Table 5).

Table Table 5..  Odds Ratio (OR), Sensitivity (Sens), Specificity (Spec), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) of High and Low Bone Turnover for Identification of Fast Bone Losers (Highest Tertile of Rate of Loss)
Thumbnail image of

DISCUSSION

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

In this large cohort of healthy untreated postmenopausal women, we found that increased levels of specific and sensitive biochemical markers of bone turnover were associated with a faster rate of bone loss assessed prospectively at the forearm over 4 years. Women with high levels of bone markers, i.e., above the upper limit of the premenopausal range, lost from 2- to 6-fold more bone than women with normal levels of markers, although the value of bone markers to predict the rate of bone loss at the individual level was quite limited.

When investigating the association between a predictive test and the outcome, it is of crucial importance to have an accurate estimation of both parameters. To ensure a reliable estimation of bone turnover rate, we used the most specific and sensitive biochemical markers measured on standardized serum and urine samples. An accurate estimation of the rate of bone loss is certainly the main challenge in such studies. To reduce the intraindividual variation of the rate of loss, we performed four consecutive BMD measurements using the most precise technique available over a 4-year period. According to Nguyen et al.,25 increasing time of follow-up or frequency of BMD measurements would only slightly improve the precision of the rate of loss at the individual level.

In this context of optimal assessment of bone turnover and rate of BMD change, we found highly significant correlation between baseline levels of biochemical markers and rate of bone loss, indicating that the higher the bone turnover, the faster the rate of postmenopausal bone loss in the following years. The correlation coefficients observed for serum osteocalcin compared well with those previously reported by Johansen et al.9 in a 2-year study performed in women within 3 years of menopause and more recently by Slemenda et al.26 in a cohort of postmenopausal women of similar age (mean 66 years), whereas this is the first study reporting strong predictive value of serum PINP. For resorption markers, we found a very similar predictive value for urinary NTX, urinary CTX, and serum CTX with correlation coefficients close to those reported by Uebelhart et al.10 between urinary total deoxypyridinoline excretion and the rate of forearm bone loss over 2 years in younger women. However, we did not confirm a better prediction from FMV and SMV samples compared with 24 h urine samples,10 suggesting that all these types of urine sampling are adequate to assess the bone resorption rate. The discrepancy between these two studies could be related to the difference in the bone resorption markers investigated, i.e., the total urinary excretion of pyridinoline compared with peptide-bound cross-links, although diurnal variation for these two forms of type I collagen fragments appears similar.27,28 It should be pointed out, however, that from a practical point of view FMV or SMV samples would be more convenient than 24 h collection. Regression and correlation coefficients were highest in early postmenopausal women, i.e., populations that have been investigated in most of the previous studies. This could partly be related to a greater between individual variability in bone loss rates at this period of life and also to an increased precision in the estimation of the rate of loss at the individual level in those early postmenopausal women because of a higher rate of bone loss than in older women.

To examine further the predictive value of biochemical markers, we also analyzed differences in the rate of loss per subgroups of marker levels at baseline. We found a strong relationship between increased quartiles of bone markers and a faster rate of bone loss, with a difference between extreme quartiles of about 2% over the 4 years of follow-up. However, a more clinically meaningful strategy would be to use a single specific cut-off of bone marker levels to identify future fast bone losers. Although additional efforts will be needed to achieve consensus on optimal cut-off levels, we believe that one of them could be the upper limit of the premenopausal range since healthy estrogen-repleted premenopausal women do not lose bone. Indeed we found that women with high turnover lost bone at midradius from 4- to 6-fold more rapidly and had an odds ratio for fast bone loss of 2–3 compared with low turnover individuals, although odds ratios are likely to overestimate the relative risk because the prevalence of fast bone loss is common. We found very similar results for all markers, including those that were not significantly associated with bone loss in regression analyses, i.e., serum BAP and PICP. In agreement with these findings, Ross and Knowlton,15 recently reported that women in the highest tertile of serum osteocalcin, BAP, and free pyridinoline (i.e., a cut-off that identifies a proportion of women similar to the upper limit of the premenopausal range) had also a 2- to 4-fold increased risk of being fast bone losers at the calcaneus. However, since the positive predictive value of markers predicting a high rate of bone loss was only 50%, it appears that bone markers have limited value to identify fast bone losers.

Taking into account both correlation and subgroups analysis, our data strongly support the view that increased levels of biochemical markers of bone turnover are associated with a faster rate of bone loss on a population basis. However, our data, including the rather low correlation coefficients and the limited sensitivity and positive predictive value, also suggest that measurement of bone markers does not allow an accurate prediction of the amount of bone that will be lost in a single patient. In such analyses, repeated DXA measurements is usually considered as the gold standard to estimate the rate of loss, which obviously is far from being perfect. For example, although osteoporosis is believed to be a generalized disease, the correlation coefficients between rate of loss at various skeletal sites—excluding different sites within the same bone—usually do not exceed 0.3–0.4 and may even be lower and not significant.13 This indicates that estimating the rate of loss of a specific skeletal site from rate of loss at another site using DXA in both situations would be not more efficient than using bone markers. Because levels of bone markers reflect whole body turnover, it also seems reasonable to believe that biochemical markers may be more predictive of total body bone loss than radius bone loss whose turnover represents only a minor contribution to marker levels. Thus, instead of considering biochemical markers as a surrogate for sequential bone mass measurements for prognostic purposes, bone marker should be considered as a prognostic test per se. This view is indeed strongly supported by two prospective studies showing that increased bone turnover is a strong predictor of future hip fracture independently of BMD in older women from France29 and The Netherlands,30,31 although we cannot exclude the possibility that this may not apply to other populations.

The strengths of this study included its prospective design, large number of subjects, long duration of follow-up, multiple BMD measurements, and use of state-of-the-art resorption and formation markers. Nonetheless, our study has several limitations. We measured bone loss only at the radius and not at other skeletal sites, including the spine and the hip. However, BMD measurement of the radius has a low precision error and is therefore suitable for longitudinal assessment of bone loss. In addition, radius BMD appears to predict osteoporotic fractures as well as spine and hip measurements as suggested by a recent meta-analysis32 with, however, the exception of hip BMD to predict hip fracture. We excluded about half of the cohort because of various bone metabolic diseases and treatments to analyze the predictive value of markers in a group of healthy untreated postmenopausal women. This may explain why the rate of bone loss in this healthy population is slightly lower that that previously reported in other cohorts. Thus, our results may not be generalizable to other populations such as those treated with antiresorptive treatments.

In summary, we found that the newly developed biochemical markers of bone turnover, including assays for serum intact PINP and serum CTX, are predictive of forearm rate of loss measured by DXA over 4 years with, however, quite a low predictive value at the individual level. Further studies are required to determine if bone markers are also predictive of rate of loss at clinically relevant skeletal sites such as spine and hip.

Acknowledgements

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

We thank O. Borel, A. Bourgeaud, C. Boulu-Chataigner, S. Jaisse, C. Valverde, and B. Vey-Marty for excellent technical assistance. This work was supported by a contract INSERM-MSD-Chibret (OFELY Study).

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  • 1
    Cummings SR, Nevitt MC, Bowner WS, Stone K, Fox KM, Ensrud KE, Cauley J, Black D, Vogt TM 1995 Risk factors for hip fracture in white women N Engl J Med 332:767773.
  • 2
    Melton LJ, Atkinson EJ, O'Fallon WM, Wahner HW, Riggs BL 1993 Long-term fracture prediction by bone mineral density assessed at different skeletal sites J Bone Miner Res 8:12271233.
  • 3
    Cummings SR, Black DM, Nevitt MC, Browner W, Cauley J, Ensrud K, Genant HK, Palermo L, Scott J, Vogt T 1993 Bone density at various sites for prediction of hip fractures Lancet 341:7275.
  • 4
    Riis BJ, Hansen MA, Jensen AM, Overgaard K, Christiansen C 1996 Low bone mass and fast rate of bone loss at menopause: Equal risk factors for future fracture: A 15 year follow-up study Bone 19:912.
  • 5
    Delmas PD, Stenner D, Wahner HW, Mann KG, Riggs BL 1983 Increase in serum bone gamma carboxyglutamic acid protein with aging in women J Clin Invest 71:13161421.
  • 6
    Garnero P, Sornay-Rendu E, Chapuy MC, Delmas PD 1996 Increased bone turnover in late postmenopausal women is a major determinant of osteoporosis J Bone Miner Res 3:337349.
  • 7
    Eastell R 1996 Assessment of bone density and bone loss Osteoporos Int 6:S36S37.
  • 8
    Christiansen C, Riis BJ, Rodbro P 1987 Prediction of rapid bone loss in postmenopausal women Lancet 1:11051108.
  • 9
    Johansen JS, Riis BJ, Delmas PD, Christiansen C 1988 Plasma BGP: An indicator of spontaneous bone loss and effect of estrogen treatment in postmenopausal women Eur J Clin Invest 18:191195.
  • 10
    Uebelhart D, Schlemmer A, Johansen J, Gineyts E, Christiansen C, Delmas PD 1991 Effect of menopause and hormone replacement therapy on the urinary excretion of pyridinium crosslinks J Clin Endocrinol Metab 72:367373.
  • 11
    Hansen MA, Kirsten O, Riss BJ, Christiansen C 1991 Role of peak bone mass and bone loss in postmenopausal osteoporosis: 12 years study BMJ 303:961964.
  • 12
    Pouillès JM, Trémollières F, Ribot C 1993 Is it possible to identify women with accelerated vertebral bone loss? Results of a longitudinal study in ninety-two recently postmenopausal women Rev Rheum (Engl) 60:186191.
  • 13
    Keen RW, Nguyen T, Sobnack R, Perry LA, Thompson PW, Spector TD 1996 Can biochemical markers predict bone loss at the hip and spine? A 4-year prospective study of 141 early postmenopausal women Osteoporos Int 6:399406.
  • 14
    Dresner-Pollak R, Parker RA, Poku M, Thompson J, Seibel MJ, Greenspan SL 1996 Biochemical markers of bone turnover reflect femoral bone loss in elderly women Calcif Tissue Int 59:328333.
  • 15
    Ross PD, Knowlton W 1998 Rapid bone loss is associated with increased levels of biochemical markers J Bone Miner Res 13:297302.
  • 16
    Schlesselman JJ 1973 Planning a longitudinal study. II. Frequency of measurement and study duration J Chron Dis 26:561570.
  • 17
    Garnero P, Grimaux M, Seguin P, Delmas PD 1994 Characterization of immunoreactive forms of human osteocalcin generated in vivo and in vitro J Bone Miner Res 9:255264.
  • 18
    Garnero P, Delmas PD 1993 Assessment of the serum levels of bone alkaline phosphatase with a new immunoradiometric assay in patients with metabolic bone disease J Clin Endocrinol Metab 77:10461053.
  • 19
    Winterbottom N, Vernon S, Freeman K, Daniloff G, Seyedin S 1993 A serum immunoassay for the C-terminal propeptide of type I collagen J Bone Miner Res 8(Suppl 1):S341.
  • 20
    Melkko J, Kauppila S, Niemi S, Risteli L, Haukipuro K, Jukkola A, Risteli J 1996 Immunoassay for intact amino-terminal propeptide of human type I procollagen Clin Chem 46:947954.
  • 21
    Hanson DA, Weiss MAE, Bollen AM, Maslan SL, Singer FR, Eyre DR 1992 A specific immunoassay for monitoring human bone resorption: quantitation of type I collagen cross-linked N-telopeptides in urine J Bone Miner Res 7:12511258.
  • 22
    Bonde M, Qvist P, Fledelius C, Riis BJ, Christiansen C 1994 Immunoassay for quantifying type I collagen degradation products in urine evaluated Clin Chem 40:20222025.
  • 23
    Garnero P, Gineyts E, Riou JP, Delmas PD 1994 Assessment of bone resorption with a new marker of collagen degradation in patients with metabolic bone diseases J Clin Endocrinol Metab 79:780785.
  • 24
    Rosenquist C, Fledelius C, Christgau S, Pedersen BJ, Bonde M, Qvist P, Christiansen C 1998 Serum Crosslaps one step ELISA. First application of monoclonal antibodies for measurement in serum of bone-related degradation products from C-terminal telopeptides of type I collagen Clin Chem 44:22812289.
  • 25
    Nguyen TV, Sambrook PN, Eisman JA 1997 Sources of variability in bone mineral density measurements: Implications for study design and analysis of bone loss J Bone Miner Res 12:124135.
  • 26
    Slemenda C, Longcope C, Peacock M, Hui S, Johnston CC 1996 Sex steroids, bone mass, and bone loss: A prospective study of pre-, peri-, and postmenopausal women J Clin Invest 97:1421.
  • 27
    Bollen A-M, Martin MD, Leroux BG, Eyre DR 1995 Circadian variation in urinary excretion of bone collagen crosslinks J Bone Miner Res 10:18851889.
  • 28
    Oshima H, Kushida K, Takahashi M 1998 Circadian variation of urinary type I collagen crosslinked C-telopeptide and free and peptide bound forms of pyridinium crosslinks Bone 22:7378.
  • 29
    Garnero P, Hausherr E, Chapuy M-C, Marcelli C, Grandjean H, Muller C, Cormier C, Breart G, Meunier PJ, Delmas PD 1996 Markers of bone resorption predict hip fracture risk in elderly women: The EPIDOS Prospective Study J Bone Miner Res 11:15311538.
  • 30
    van Daele PLA, Seibel MJ, Burger H, Hofman A, Grobbee DE, van Leeuwen JP, Birkenhager JC, Pols HA 1996 Case-control analysis of bone resorption markers, disability, and hip fracture: the Rotterdam study BMJ 312:482483.
  • 31
    van Daele PLA, Seibel MJ, Burger H, De Laet CEDH, van Leeuwen JPTM, Hofman A, Birkenhäger JC, Pols HAP 1996 Evidence for uncoupling of bone formation and bone resorption in women with hip fractures: A prospective study Osteoporos Int 6(Suppl 1):199.
  • 32
    Marshall D, Johnell O, Wedel H 1996 Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures BMJ 312:12541259.