Fracture Risk Assessment in Chronic Kidney Disease, Prospective Testing Under Real World Environments (FRACTURE): a prospective study
© West et al; licensee BioMed Central Ltd. 2010
Received: 10 March 2010
Accepted: 20 August 2010
Published: 20 August 2010
Chronic kidney disease (CKD) is associated with an increased risk of fracture. Decreased bone mass and disruption of microarchitecture occur early in the course of CKD and worsens with the progressive decline in renal function so that at the time of initiation of dialysis at least 50% of patients have had a fracture. Despite the excess fracture risk, and the associated increases in morbidity and mortality, little is known about the factors that are associated with an increase in fracture risk. Our study aims to identify prognostic factors for bone loss and fractures in patients with stages 3 to 5 CKD.
This prospective study aims to enroll two hundred and sixty men and women with stages 3 to 5 CKD. Subjects will be followed for 24 months and we will examine the ability of: 1) bone mineral density by dual x-ray absorptiometry at the spine, hip, and radius; 2) volumetric bone density by high resolution peripheral quantitated computed tomography at the radius and tibia; 3) serum markers of bone turnover; 4) bone formation rate by bone biopsy; and 5) muscle strength and balance to predict spine and non-spine fractures, identified by self-report and/or vertebral morphometry. All measurements will be obtained at baseline, at 12 and at 24 months with the exception of bone biopsy, which will be measured once at 12 months. Subjects will be contacted every 4 months to determine if there have been incident fractures or falls.
This study is one of the first that aims to identify risk factors for fracture in early stage CKD patients. Ultimately, by identifying risk factors for fracture and targeting treatments in this group-before the initiation of renal replacement therapy - we will reduce the burden of disease due to fractures among patients with CKD.
Stages of Chronic Kidney Disease .
Glomerular Filtration Rate (GFR); mL/min/1.73 m2
Kidney damage with normal or increased GFR
Kidney damage with mild decrease in GFR
60 to 89
Moderate decrease in GFR
30 to 59
Severe decrease in GFR
15 to 29
<15 or dialysis
The prevalence of osteoporotic hip and vertebral fractures among dialysis patients exceeds that of the general population, with profound effects on morbidity and mortality [4, 5, 7–11]. A recent retrospective study  reported that the incidence of hip fractures in a hemodialysis population was 17.4 times greater and occurred at a younger age compared to the general population (16 and 13 years younger among men and women, respectively). Furthermore, patients with CKD stage 5 who require renal replacement therapy (also known as end stage renal disease (ESRD)) had a one-year mortality rate of 64% following a hip fracture compared with 15 to 20% in the general population.
The first step to decreasing the morbidity and mortality associated with fractures in patients with CKD is to identify those patients at high risk, in order to direct appropriate preventative strategies. While most research has focused on identifying prognostic factors for fracture among ESRD patients, it excludes the earlier stages of CKD and also has several pragmatic limitations. For example, dialysis patients are frequently reluctant to participate in studies due to the required additional time commitments outside of regularly scheduled dialysis sessions. As well, assessments may be invasive, and may not have direct patient benefit. As a result, most bone related studies in ESRD involve a select population comprising small numbers of relatively young, healthy patients, with limited clinical assessments [7, 8, 13–18]. This limits the generalizability of these studies. Finally, because of differences in mineral metabolism with decreasing renal function, the risk factors for fracture in patients with ESRD may not be the same as the risk factors for fracture in patients with earlier stages of CKD.
The etiology of fractures in patients with CKD is multifactorial. Factors that may play a role include decreases in the quantity of bone or osteoporosis which can be identified by bone mineral density (BMD) testing, and alterations in the quality of bone. Bone quality can be indirectly assessed by markers of bone turnover and peripheral quantitated computed tomography (pQCT) and directly assessed with bone biopsy. Other risk factors for fracture in patients with CKD may be poor nutrition, inactivity, and an increased risk of falling due to myopathy and peripheral neuropathy . As a result, there are no recommendations or standard methods of practice to assess fracture risk. Indeed most clinicians caring for patients with CKD do not utilize BMD testing, measurement of bone turnover markers, pQCT or bone biopsy to assess fracture risk. Bone biopsy, in particular, is not used in clinical practice to assess fracture risk. Further, those clinicians who do order the occasional noninvasive test to assess fracture risk in patients with CKD may not know how to confidently interpret the results. The FRACTURE study (Fracture Risk Assessment in Chronic Kidney Disease, Prospective Testing Under Real World Environments) has been designed to expand the current knowledge of prognostic factors for fracture in ESRD to the CKD stage 3 to 5 population and address some of the existing knowledge gaps in the identification, diagnosis, and interpretation of bone disease and fracture risk in CKD.
The aim of this study is to prospectively identify prognostic factors for bone loss and fractures in patients with stages 3 to 5 CKD.
We will test four primary hypotheses in patients with CKD stages 3-5:
1. BMD at cortical sites (the mid radius) will be associated with fractures.
2. Volumetric BMD at the forearm by High Resolution-pQCT (HR-pQCT) will be associated with fractures.
3. There will be a positive correlation between the neuromuscular testing (i.e., the timed up and go) and presence of fractures.
4. There will be a positive association between fractures and the presence of either: adynamic bone disease, or hyperparathyroid bone disease, as defined by the bone formation rate (BFR).
Study Design and Setting
This is a 24-month prospective cohort study of 260 men and women attending outpatient renal clinics at the University Health Network (UHN), and at St. Michael's Hospital in Toronto, Ontario, Canada. Other sites will be recruited as necessary. Subjects will undergo all testing (described below) upon study entry and then at 12 and 24 months with the exception of bone biopsy, which will be performed only once at 12 months. Subjects who progress to ESRD will have assessments at the time of dialysis initiation or transplantation, and then 12 months later. Subjects will also be contacted by telephone every 4 months, at which time they will be asked about the occurrence of falls and fractures since their last study contact.
This study has been reviewed by the local Research Ethics Boards, and has received ethics approval at all participating institutions.
Inclusion Criteria: Men and women aged 18 years and older, with a glomerular filtration rate of ≤60 mL/min/1.73 m2 at study entry are eligible for recruitment.
Exclusion Criteria: Patients who are unable to give informed consent, unable to have spinal radiographs or BMD measurements; those taking bisphosphonates, calcitonin, hormone replacement therapy, or the oral contraceptive pill are excluded. Subjects allergic to tetracycline are excluded from the bone biopsy component of the study.
All assessments (below), with the exception of bone biopsy, will be obtained at a single site (UHN) using standard protocols.
i. Bone Mineral Density
BMD will be measured at the lumbar spine (L1 to L4), the total hip, femoral neck and proximal radius using a bone densitometer (Hologic). To reduce variability, BMD will be measured using a standard protocol (including regular use of a phantom spine). BMD tests will be reported according to the International Society for Clinical Densitometry (ISCD) protocol by a single ISCD certified clinician blinded to the study hypotheses.
ii. High Resolution Peripheral Quantitative Computed Tomography
HR-pQCT measurements will be obtained at the non-dominant radius (non-weight bearing site), and also at the tibia (weight bearing site) using the Xtreme CT device (Scanco Medical AG, Basserdorf, Switzerland). Measurements that can be obtained from HR-pQCT include: total volumetric density, cortical volumetric density, trabecular volumetric density, cortical thickness, trabecular thickness, trabecular separation, and trabecular number. The reproducibility of density measurements is <0.3% CV (phantom) .
iii. Markers of Bone Turnover
Blood samples will be collected, and then centrifuged at 4°C for 15 minutes. Serum will be aliquotted and immediately stored in microtubes at -80°C for future measurement of bone turnover markers and bone cytokines.
iv. Bone biopsy
Bone histomorphometry will be performed in a subgroup of 50 subjects. To ensure adequate power, 25 subjects with confirmed prevalent fractures will be included, as well as 25 without confirmed fracture. We will obtain a bone biopsy of the anterior superior iliac crest at 12 month follow up only.
Subjects will be given tetracycline labels administered over ~2 week period as follows: Two days of tetracycline (250 mg, by mouth, four times per day), and then given a 12 day intermission (days 3-14). On day 15, subjects will be given the same tetracycline (250 mg, by mouth, four times per day) for 4 days (days 15-18). Anterior iliac crest bone biopsies will be obtained one to two days later. By labeling twice with tetracycline standard histomorphometry can determine the mineral apposition rate and mineralizing perimeter for the two separate labeling periods and thereby calculate the BFR.
Bone biopsy technique:
The iliac biopsy will be obtained following the protocol of Meunier , using a Bordier trephine, 8 mm in internal diameter, to avoid fracture and compression of the core of bone and minimize sampling error. The procedure is performed under local anesthesia, supplemented by parenteral sedation if needed to achieve proper muscle relaxation.
Samples will be processed at 4°C according to standardized techniques. The primary histomorphometric variable to be studied is the BFR. The classification of bone disease associated with ESRD is based on the BFR and it has been purported that either an increased BFR (e.g. >500 μm/mm tissue area/day; as seen in hyperparathyroidism) or a decreased BFR (e.g. <108 μm/mm tissue area/day; as seen in adynamic bone disease), may be the strongest predictor of fractures. We will determine if the BFR is a prognostic factor for fracture by first classifying turnover into low, normal or high, based on the reference values noted above.
v. Muscle Strength, Balance and Falls
Muscle strength and balance will be assessed using three tests: The Timed Up and Go (TUG), Hand Grip, and in a subgroup of subjects, the 6-Minute Walk Test (6MW). These tests allow determination of muscle strength and balance across a wide range of functional abilities, has been validated in populations with renal disease and has been shown to be associated with fractures in cross sectional studies of patients with ESRD .
Primary Outcome Variable
i. Spine and Nonspine Fractures
Subjects will be questioned about a history of non-spine fractures at baseline and every 4 months during the follow up. Fractures will be classified as either traumatic or "low trauma" (osteoporotic) based on World Health Organization criteria; an osteoporotic fracture is defined as a fracture from one's standing height or less. Data from a prospective study demonstrate that self-report of fractures is accurate for osteoporotic fractures . Subjects will have anterior and lateral radiographs of the thoracic and lumbar spine at study entry to evaluate the presence of baseline (prevalent) vertebral fractures and then at 12 and 24 months to assess the development of new (incident) vertebral fractures. All radiographs will be obtained and reviewed using a standardized methodology, vertebral morphometry , by a muscular-skeletal radiologist with expertise in the radiographic diagnosis of vertebral fractures.
Withdrawal from Study
If requested, subjects will be withdrawn from the study without prejudice, as documented and explained at the time of consenting.
i. Sample Size Estimations
Based on our prior experience with recruiting subjects with ESRD, and the natural history of CKD the study anticipates recruitment of 260 subjects over 2 years. It is estimated that of these 260 subjects, 60 will have renal transplants, progress to ESRD or die, leaving 200 subjects. The power calculations were based on comparisons of mean baseline values between 30% of patients with prevalent fractures and 70% without prevalent fractures. This 30% is a conservative estimate given prior data that demonstrates that 50% of CKD patients have sustained a fracture prior to starting dialysis [2–5], and data from the Canadian Multicentre Osteoporosis Study demonstrates that among a population based sample of healthy men and women 50 years and older 38% of men and 41% of women have had a fracture . A sample size of 200 subjects is adequate to meet all our hypotheses. Sample size calculations for each of the primary hypotheses are detailed below:
1. BMD at cortical sites (the mid radius) will be associated with fractures
Based on a review of the literature, a well accepted minimal clinically important difference (MCID) in BMD between fractured and non-fractured patients is 3%, which is associated with standard deviations (SD) that range from 4.5% to 6.8% [25–32]. We will have 81% (at a SD of 6.8%) to 99% power (at a SD of 4.5%) to detect a 3% difference in mid radius BMD.
2. Volumetric BMD at the forearm by HR-pQCT will be associated with fractures
The limited published data suggest that an MCID in volumetric BMD ranges from 1 to 3 with a SD that varies from 1 to 2 . The most conservative assumption is a MCID of 1 with an SD 2. We will have 89% power to detect this MCID.
3. There will be a positive correlation between the "TUG" and presence of fractures
Our data demonstrates a MCID of 5 seconds that can be associated with a SD of 10 to 12 seconds. We will have 89 to 76% power to detect this difference.
4. There will be a positive association between fractures and the presence of either: adynamic bone disease, or hyperparathyroid bone disease, as defined by the BFR
Power calculations for this hypothesis were based on the assumption that of the 60 patients with prevalent fractures (30% of 200) just under half (40%) would agree to biopsy (25 subjects) and we would recruit another 25 non-fractured subjects for a total of 50. If we assume that 85% of patients with fracture would have an abnormal BFR, we have 80% power to detect an abnormal BFR rate of 45% or lower in the non-fractured group. We anticipate that approximately 45% of subjects will have incident fractures in the two-year follow-up period.
ii. Statistical Analyses
We will use linear regression models to assess relationships, with BMD, volumetric BMD (by HR-pQCT), TUG and BFR (from bone biopsy) as predictor variables and presence or absence of prevalent fracture as the main outcome variable. Unadjusted analyses will be performed to determine the association between each predictor variable of interest and fracture outcome. Those with a positive association will be adjusted for potentially confounding covariates such as gender, age, weight, serum calcium, diabetes, and intact serum PTH. Goodness-of-fit will be evaluated for all models by inspecting plots of residuals, as well as use of Hosmer and Lemeshow Goodness of fit test; variables will be transformed as necessary. Our primary analyses will not be stratified by gender as earlier work by our group has not demonstrated a significant difference in fractures by gender among patients with renal disease. However supplementary analyses will be performed separately by gender and by the presence of type II diabetes. We will consider a p value of less than 0.05 statistically significant and we will not adjust for multiple comparisons. Our study statistician will perform our analyses using STATA version 10 (STATA Corp. College Stn. TX) and R 2.7 (R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria)
Anticipated Challenges and Solutions
We have anticipated some potential challenges and have offered corresponding solutions:
1. Subjects drop out after enrollment
Based on chart review of the Renal Management Clinic at the UHN, approximately 15 to 20% of subjects progress to ESRD, receive a transplant, transfer care or die every year, with very few patients lost to follow up. To account for these potential "drop outs" after enrollment, we will recruit 260 subjects, in anticipation that 200 will continue to be followed.
2. Unable to recruit patients from single site
The clinic at UHN has a potential pool of over 500 subjects, however, we have established collaborative links with St. Michael's Hospital in Toronto as well as with other Toronto area institutions, which will assist us in recruiting and meeting our sample size goals.
3. Failure to obtain adequate samples for bone biopsy component
It is possible, due to the invasive nature of the bone biopsy procedure that few patients will consent. To maximize the number of biopsies and to limit drop outs, the protocol was designed so that we obtain a single bone biopsy at one year follow up, rather than obtaining repeat bone biopsy samples. Note that there is a paucity of data on bone histomorphometry and stages of CKD - thus at a minimum our findings will serve as pilot data for a larger study.
This study is designed to identify risk factors for fracture in early stage CKD patients. To our knowledge, no other prospective studies in Canada have been conducted to determine fracture risk factors in patients with stage 3-5 CKD, not yet on renal replacement therapy. Ultimately, by identifying risk factors for fracture and targeting treatments in this group - before initiating renal replacement therapy - we will reduce the burden of disease due to fractures among patients with ESRD.
This study is funded by The Physicians' Services Incorporated Foundation, The Kidney Foundation of Canada, and the Canadian Institutes of Health Research.
- Eknoyan G, Lameire N, Barsoum R, Eckardt KU, Levin A, Levin N, Locatelli F, MacLeod A, Vanholder R, Walker R, Wang H: The burden of kidney disease: improving global outcomes. Kidney Int. 2004, 66 (4): 1310-1314. 10.1111/j.1523-1755.2004.00894.x.View ArticlePubMedGoogle Scholar
- Jamal SA, Gilbert J, Gordon C, Bauer DC: Cortical pQCT measures are associated with fractures in dialysis patients. J Bone Miner Res. 2006, 21 (4): 543-548. 10.1359/jbmr.060105.View ArticlePubMedGoogle Scholar
- Jamal SA, Leiter RE, Jassal V, Hamilton CJ, Bauer DC: Impaired muscle strength is associated with fractures in hemodialysis patients. Osteoporos Int. 2006, 17 (9): 1390-1397. 10.1007/s00198-006-0133-y.View ArticlePubMedGoogle Scholar
- Malluche HH, Faugere MC: Renal osteodystrophy. N Engl J Med. 1989, 321 (5): 317-319. 10.1056/NEJM198908033210509.View ArticlePubMedGoogle Scholar
- Lindberg JS, Moe SM: Osteoporosis in end-state renal disease. Semin Nephrol. 1999, 19 (2): 115-122.PubMedGoogle Scholar
- Cummings SR, Kelsey JL, Nevitt MC, O'Dowd KJ: Epidemiology of osteoporosis and osteoporotic fractures. Epidemiol Rev. 1985, 7: 178-208.PubMedGoogle Scholar
- Atsumi K, Kushida K, Yamazaki K, Shimizu S, Ohmura A, Inoue T: Risk factors for vertebral fractures in renal osteodystrophy. Am J Kidney Dis. 1999, 33 (2): 287-293. 10.1016/S0272-6386(99)70302-1.View ArticlePubMedGoogle Scholar
- Chan YL, Furlong TJ, Cornish CJ, Posen S: Dialysis osteodystrophy. A study involving 94 patients. Medicine (Baltimore). 1985, 64 (5): 296-309.View ArticleGoogle Scholar
- Schaab PC, Murphy G, Tzamaloukas AH, Hays MB, Merlin TL, Eisenberg B, Avasthi PS, Worrell RV: Femoral neck fractures in patients receiving long-term dialysis. Clin Orthop Relat Res. 1990, 224-231. 260
- Vonesh EF, Moran J: Mortality in end-stage renal disease: a reassessment of differences between patients treated with hemodialysis and peritoneal dialysis. J Am Soc Nephrol. 1999, 10 (2): 354-365.PubMedGoogle Scholar
- Eknoyan G: Cardiovascular mortality and morbidity in dialysis patients. Miner Electrolyte Metab. 1999, 25 (1-2): 100-104. 10.1159/000057429.View ArticlePubMedGoogle Scholar
- Coco M, Rush H: Increased incidence of hip fractures in dialysis patients with low serum parathyroid hormone. Am J Kidney Dis. 2000, 36 (6): 1115-1121. 10.1053/ajkd.2000.19812.View ArticlePubMedGoogle Scholar
- Fontaine MA, Albert A, Dubois B, Saint-Remy A, Rorive G: Fracture and bone mineral density in hemodialysis patients. Clin Nephrol. 2000, 54 (3): 218-226.PubMedGoogle Scholar
- National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002, 39 (2 Suppl 1): S1-266.Google Scholar
- Inaba M, Okuno S, Kumeda Y, Yamakawa T, Ishimura E, Nishizawa Y: Increased incidence of vertebral fracture in older female hemodialyzed patients with type 2 diabetes mellitus. Calcif Tissue Int. 2005, 76 (4): 256-260. 10.1007/s00223-004-0094-0.View ArticlePubMedGoogle Scholar
- Urena P, Bernard-Poenaru O, Ostertag A, Baudoin C, Cohen-Solal M, Cantor T, de Vernejoul MC: Bone mineral density, biochemical markers and skeletal fractures in haemodialysis patients. Nephrol Dial Transplant. 2003, 18 (11): 2325-2331. 10.1093/ndt/gfg403.View ArticlePubMedGoogle Scholar
- Yamaguchi T, Kanno E, Tsubota J, Shiomi T, Nakai M, Hattori S: Retrospective study on the usefulness of radius and lumbar bone density in the separation of hemodialysis patients with fractures from those without fractures. Bone. 1996, 19 (5): 549-555. 10.1016/S8756-3282(96)00246-3.View ArticlePubMedGoogle Scholar
- Kaji H, Suzuki M, Yano S, Sugimoto T, Chihara K, Hattori S, Sekita K: Risk factors for hip fracture in hemodialysis patients. Am J Nephrol. 2002, 22 (4): 325-331. 10.1159/000065222.View ArticlePubMedGoogle Scholar
- Sakhaee K, Gonzalez GB: Update on renal osteodystrophy: pathogenesis and clinical management. Am J Med Sci. 1999, 317 (4): 251-260. 10.1097/00000441-199904000-00008.View ArticlePubMedGoogle Scholar
- Boutroy S, Bouxsein ML, Munoz F, Delmas PD: In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab. 2005, 90 (12): 6508-6515. 10.1210/jc.2005-1258.View ArticlePubMedGoogle Scholar
- Meunier P, Bressot C: Endocrine Influences on bone cells and bone remodeling evaluated by clinical histomorphometry. 1983, Anonymous New York: Raven Press, 455-Google Scholar
- Nevitt MC, Cummings SR, Browner WS, Seeley DG, Cauley JA, Vogt TM, Black DM: The accuracy of self-report of fractures in elderly women: evidence from a prospective study. Am J Epidemiol. 1992, 135 (5): 490-499.PubMedGoogle Scholar
- Black DM, Palermo L, Nevitt MC, Genant HK, Christensen L, Cummings SR: Defining incident vertebral deformity: a prospective comparison of several approaches. The Study of Osteoporotic Fractures Research Group. J Bone Miner Res. 1999, 14 (1): 90-101. 10.1359/jbmr.19220.127.116.11.View ArticlePubMedGoogle Scholar
- Adachi JD, Loannidis G, Berger C, Joseph L, Papaioannou A, Pickard L, Papadimitropoulos EA, Hopman W, Poliquin S, Prior JC, Hanley DA, Olszynski WP, Anastassiades T, Brown JP, Murray T, Jackson SA, Tenenhouse A, Canadian Multicentre Osteoporosis Study (CaMos) Research Group: The influence of osteoporotic fractures on health-related quality of life in community-dwelling men and women across Canada. Osteoporos Int. 2001, 12 (11): 903-908. 10.1007/s001980170017.View ArticlePubMedGoogle Scholar
- Cummings SR, Palermo L, Browner W, Marcus R, Wallace R, Pearson J, Blackwell T, Eckert S, Black D: Monitoring osteoporosis therapy with bone densitometry: misleading changes and regression to the mean. Fracture Intervention Trial Research Group. JAMA. 2000, 283 (10): 1318-1321. 10.1001/jama.283.10.1318.View ArticlePubMedGoogle Scholar
- Cummings SR, Bates D, Black DM: Clinical use of bone densitometry: scientific review. JAMA. 2002, 288 (15): 1889-1897. 10.1001/jama.288.15.1889.View ArticlePubMedGoogle Scholar
- Bates DW, Black DM, Cummings SR: Clinical use of bone densitometry: clinical applications. JAMA. 2002, 288 (15): 1898-1900. 10.1001/jama.288.15.1898.View ArticlePubMedGoogle Scholar
- Cummings SR, Black D: Bone mass measurements and risk of fracture in Caucasian women: a review of findings from prospective studies. Am J Med. 1995, 98 (2A): 24S-28S. 10.1016/S0002-9343(05)80041-5.View ArticlePubMedGoogle Scholar
- Cummings SR, Black DM, Thompson DE, Applegate WB, Barrett-Connor E, Musliner TA, Palermo L, Prineas R, Rubin SM, Scott JC, Vogt T, Wallace R, Yates AJ, LaCroix AZ: Effect of alendronate on risk of fracture in women with low bone density but without vertebral fractures: results from the Fracture Intervention Trial. JAMA. 1998, 280 (24): 2077-2082. 10.1001/jama.280.24.2077.View ArticlePubMedGoogle Scholar
- Black DM, Greenspan SL, Ensrud KE, Palermo L, McGowan JA, Lang TF, Garnero P, Bouxsein ML, Bilezikian JP, Rosen CJ, PaTH Study Investigators: The effects of parathyroid hormone and alendronate alone or in combination in postmenopausal osteoporosis. N Engl J Med. 2003, 349 (13): 1207-1215. 10.1056/NEJMoa031975.View ArticlePubMedGoogle Scholar
- Black DM, Reiss TF, Nevitt MC, Cauley J, Karpf D, Cummings SR: Design of the Fracture Intervention Trial. Osteoporos Int. 1993, 3 (Suppl 3): S29-39. 10.1007/BF01623005.View ArticlePubMedGoogle Scholar
- Ettinger B, Black DM, Mitlak BH, Knickerbocker RK, Nickelsen T, Genant HK, Christiansen C, Delmas PD, Zanchetta JR, Stakkestad J, Gluer CC, Krueger K, Cohen FJ, Eckert S, Ensrud KE, Avioli LV, Lips P, Cummings SR: Reduction of vertebral fracture risk in postmenopausal women with osteoporosis treated with raloxifene: results from a 3-year randomized clinical trial. Multiple Outcomes of Raloxifene Evaluation (MORE) Investigators. JAMA. 1999, 282 (7): 637-645. 10.1001/jama.282.7.637.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2369/11/17/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.