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Cited by 132Bone health and osteoporosis researchBone health and treatmentsMenopause: Health Impacts and Treatments

Systematic review of the use of bone turnover markers for monitoring the response to osteoporosis treatment: the secondary prevention of fractures, and primary prevention of fractures in high-risk groups

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Burch, Jane, Rice, Stephen, Yang, Huiqin, Neilson, Aileen, Stirk, Lisa, Francis, Roger, Holloway, Paul, Selby, Peter +1 more·2014·Health Technology Assessment
DOI 10.3310/hta18110PMC PMC4781229
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Abstract

BACKGROUND: There is currently no standard practice for the monitoring of patients receiving treatment for osteoporosis. Repeated dual-energy X-ray absorptiometry (DXA) is commonly used for monitoring treatment response, but it has its limitations. Bone turnover markers have advantages over DXA as they are non-invasive, relatively cheap and can detect changes in bone turnover rates earlier. However, they do have disadvantages, particularly high within- and between-patient variability. The ability of bone turnover markers to identify treatment non-responders and predict future fracture risk has yet to be established. OBJECTIVES: We aimed to determine the clinical effectiveness, test accuracy, reliability, reproducibility and cost-effectiveness of bone turnover markers for monitoring the response to osteoporosis treatment. DATA SOURCES: We searched 12 electronic databases (including MEDLINE, EMBASE, The Cochrane Library and trials registries) without language restrictions from inception to March 2012. We hand-searched three relevant journals for the 12 months prior to May 2012, and websites of five test manufacturers and the US Food and Drug Administration (FDA). Reference lists of included studies and relevant reviews were also searched. REVIEW METHODS: A systematic review of test accuracy, clinical utility, reliability and reproducibility, and cost-effectiveness of two formation and two resorption bone turnover markers, in patients being treated for osteoporosis with any of bisphosphonate [alendronate (Fosamax, MSD), risedronate (Actonel, Warner Chilcott Company), zolendronate (Zometa, Novartis)], raloxifene (Evista, Eli Lilly and Company Ltd), strontium ranelate (Protelos, Servier Laboratories Ltd), denosumab (Prolia, Amgen Ltd) or teriparatide (Forsteo, Eli Lilly and Company Ltd), was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Given the breadth of the review question, a range of study designs and outcome measures were eligible. The development of a decision model was planned to determine the cost-effectiveness of bone turnover markers for informing changes in patient management if clinical effectiveness could be established. RESULTS: Forty-two studies (70 publications) met the inclusion criteria; none evaluated cost-effectiveness. Only five were randomised controlled trials (RCTs); these assessed only the impact of bone marker monitoring on aspects of adherence. No RCTs evaluated the effectiveness of bone turnover marker monitoring on treatment management. One trial suggested that feedback of a good response decreased non-persistence [hazard ratio (HR) 0.71, 95% confidence interval (CI) 0.53 to 0.95], and feedback of a poor response increased non-persistence (HR 2.22, 95% CI 1.27 to 3.89); it is not clear whether or not the trial recruited a population representative of that seen in clinical practice. Thirty-three studies reported results of some assessment of test accuracy, mostly correlations between changes in bone turnover and bone mineral density. Only four studies reported on intra- or interpatient reliability and reproducibility in treated patients. Overall, the results were inconsistent and inconclusive, owing to considerable clinical heterogeneity across the studies and the generally small sample sizes. As clinical effectiveness of bone turnover monitoring could not be established, a decision-analytic model was not developed. CONCLUSIONS: There was insufficient evidence to inform the choice of which bone turnover marker to use in routine clinical practice to monitor osteoporosis treatment response. The research priority is to identify the most promising treatment-test combinations for evaluation in subsequent, methodologically sound, RCTs. In order to determine whether or not bone turnover marker monitoring improves treatment management decisions, and ultimately impacts on patient outcomes in terms of reduced incidence of fracture, RCTs are required. Given the large number of potential patient population-treatment-test combinations, the most promising combinations would initially need to be identified in order to ensure that any RCTs focus on evaluating those strategies. As a result, the research priority is to identify these promising combinations, by either conducting small variability studies or initiating a patient registry to collect standardised data. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

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  • Biochemical markers may predict rapidity of bone loss in untreated patients.

    “Predict rapidity of bone loss in untreated patients”

    From Clinician’s Guide to Prevention and Treatment of Osteoporosis (2014) · cites this paper as [25]

    The Scientific summary (Background; page unavailable) says biochemical markers measure bone turnover, and the References list studies linking marker levels with bone loss, including rapid bone loss. The available passages do not establish that markers predict the rate of bone loss specifically in untreated patients; the review’s discussion centers on monitoring treatment response. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

  • Biochemical markers may predict extent of fracture risk reduction when repeated after 3-6 months of treatment with FDA-approved therapies.

    “Predict extent of fracture risk reduction when repeated after 3–6 months of treatment with FDA-approved therapies”

    From Clinician’s Guide to Prevention and Treatment of Osteoporosis (2014) · cites this paper as [25]

    The ‘Statement of principal findings’ (page not available in our copy) says that changes in bone turnover markers, usually measured 1–6 months after treatment began, may be associated with later fracture outcomes, but that too few studies were available for firm conclusions. The ‘Monitoring response to treatments for osteoporosis’ section reports that markers explained some fracture-risk reduction in studies of four specific drugs. The review does not establish that repeat testing at 3–6 months predicts the extent of reduction across FDA-approved therapies. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Biochemical markers may help determine adequacy of patient compliance and persistence with osteoporosis therapy.

    “Help determine adequacy of patient compliance and persistence with osteoporosis therapy”

    From Clinician’s Guide to Prevention and Treatment of Osteoporosis (2014) · cites this paper as [25]

    The review’s “Compliance and persistence” section describes a model in which a bone turnover marker result could lead to a conclusion that compliance or treatment response was inadequate, but notes that the model did not include test-accuracy data. “Measures of adherence” says that a marker-identified non-response could reflect non-compliance or non-persistence, among several other causes. The source therefore supports markers as a possible clue, not as a way to determine the adequacy of both compliance and persistence. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Biochemical markers of bone turnover may predict risk of fracture independently of bone density in untreated patients.

    “Biochemical markers of bone turnover may [25]:Predict risk of fracture independently of bone density in untreated patients”

    From Clinician’s Guide to Prevention and Treatment of Osteoporosis (2014) · cites this paper as [25]

    The Monitoring response to treatments for osteoporosis section says bone turnover markers might help assess future fracture risk compared with BMD measurements. But the review addresses treatment monitoring, not prediction in untreated patients, and its Scientific summary says their use as independent predictors of future fracture risk has yet to be established. The cited copy therefore supports the possibility, but not the claim's untreated-patient qualifier. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Biochemical markers may predict magnitude of BMD increases with FDA-approved therapies.

    “Predict magnitude of BMD increases with FDA-approved therapies”

    From Clinician’s Guide to Prevention and Treatment of Osteoporosis (2014) · cites this paper as [25]

    The Clinical Effectiveness and Cost-Effectiveness Evidence section reports correlations between changes in bone turnover markers and BMD, including a statistically significant but weak correlation during teriparatide treatment. It also says not all correlations were significant and cautions that their usefulness varies by population. The review does not establish that markers predict the magnitude of BMD increases across FDA-approved therapies generally. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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  102. ibandronic acid or ibandronate or boniva or bondronat or bonviva or bandrobon or bonat).af. (0) DOI: 10.3310/hta18110 HEALTH TECHNOLOGY ASSESSMENT 2014 VOL. 18 NO. 11 · DOI
  103. patients receiving an osteoporosis treatment but not one of the ones being evaluated in the review: 17 10,251,266,289,378-390
  104. non-RCTs that had fewer than 20 patients reaching the analysis stage of the study: 19 45,412-429 14. no outcomes of interest reported or sufficient data to calculate any: 85 31,46,51,52,59,100,102,103,105,124,138,214,221,225,227,228,231,240,430-496 Systematic review of the cost-effectiveness evidence 1. Examined health benefits only. 167
  105. Review article mentioned cost-effectiveness in the abstract, but reported only the characteristics and clinical effectiveness of bone turnover markers in the results; bibliography was scanned for relevant cost-effectiveness studies but none was cited. 497
  106. Jane Burch, Research Fellow (jane.burch@york.ac.uk). Eight years' experience in systematic reviews and systematic review methodology. Has worked on systematic reviews for NICE, the HTA programme and the NHS Cancer Screening Programmes. Will be responsible for all aspects of the clinical effectiveness review and co-ordinating the production of the final report.
  107. Stephen Rice, Research Fellow in Health Economics (stephen.rice@york.ac.uk). Over seven years' experience in economic evaluation and evidence synthesis. Will be responsible for the cost-effectiveness review, development of any cost-effectiveness model, and writing the economic sections of the report.
  108. Aileen Neilson, Research Fellow in Health Economics (aileen.neilson@york.ac.uk). Involved with various health outcomes research and economic evaluation studies within the National Health Service setting in the UK, and against a broader European context. Will assist with the cost-effectiveness review, development of any cost-effectiveness model, and writing the report.
  109. Huiqin Yang, Research Fellow (huiqin.yang@york.ac.uk). Six years' experience in health services research. Has worked on systematic reviews for NICE and the HTA programme. Will assist with all aspects of the clinical effectiveness review and the writing of the final report.
  110. Dr Paul Holloway (paul.holloway@imperial.ac.uk). Clinical and academic interest in metabolic bone disease since training as senior registrar and clinical lecturer in Oxford in 1980s. Has run a metabolic bone clinic at St Mary's since 2004 and is acting director of the St Mary's SAS for bone markers. Will provide advice and comments on the protocol and report.

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Authors
Burch, J., Rice, S., Yang, H., Neilson, A., Stirk, L., Francis, R., Holloway, P., Selby, P., & Craig, D.
Affiliations
  • University of York (GB)
  • Newcastle University (GB)
  • Imperial College Healthcare NHS Trust (GB)
  • Manchester Royal Infirmary (GB)
Corresponding author
Craig, Dawn
ORCID
Venue
Health Technology Assessment
Publisher
National Institute for Health and Care Research
Published
2014-02
Volume
18
Issue
11
Pages
1-180
ISSN
  • 1366-5278 (ISSN-L)
  • 2046-4924
DOI
10.3310/hta18110
PMC
PMC4781229
OpenAlex
W2058667774
Cited by
132
References
0
Citations by year
2026: 2 · 2025: 4 · 2024: 7 · 2023: 10 · 2022: 13 · 2021: 12 · 2020: 11 · 2019: 10 · 2018: 10 · 2017: 23 · 2016: 16 · 2015: 8 · 2014: 6
FWCI
3.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Health Technology Assessment (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Newcastle University ePrints (Newcastle Univesity)
Funders
National Institute for Health Research
Topics
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
  • Health Sciences › Medicine › Oncology › Bone health and treatments
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Menopause: Health Impacts and Treatments
Keywords
bone turnover markers, osteoporosis treatment monitoring, treatment response, fracture prevention, secondary fracture prevention, bone formation markers, bone resorption markers, bisphosphonates, raloxifene, strontium ranelate, denosumab, teriparatide, diagnostic accuracy, cost-effectiveness, medication adherence, treatment non-responders, fracture risk
MeSH terms
Bone Resorption, Humans, Osteoporosis, Primary Prevention, Absorptiometry, Photon, Bone Density, Bone Density Conservation Agents, Fractures, Bone, Secondary Prevention

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