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A Meta-Analysis of Reference Markers of Bone Turnover for Prediction of Fracture

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Abstract

The aim of this report was to summarize the clinical performance of two reference bone turnover markers (BTMs) in the prediction of fracture risk. We used an updated systematic review to examine the performance characteristics of serum procollagen type I N propeptide (s-PINP) and serum C-terminal cross-linking telopeptide of type I collagen (s-CTX) in fracture risk prediction in untreated individuals in prospective cohort studies. We excluded cross-sectional studies. Ten potentially eligible publications were identified and six included in the meta-analysis. There was a significant association between s-PINP and the risk of fracture. The hazard ratio per SD increase in s-PINP (gradient of risk [GR]) was 1.23 (95 % CI 1.09–1.39) for men and women combined unadjusted for bone mineral density. There was also a significant association between s-CTX and risk of fracture, GR = 1.18 (95 % CI 1.05–1.34) unadjusted for bone mineral density. For the outcome of hip fracture, the association between s-CTX and risk of fracture was slightly higher, 1.23 (95 % CI 1.04–1.47). Thus, there is a modest but significant association between BTMs and risk of future fractures.

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References

  1. Vasikaran S, Eastell R, Bruyere O, Foldes AJ, Garnero P, Griesmacher A, McClung M, Morris HA, Silverman S, Trenti T, Wahl DA, Cooper C, Kanis JA (2011) Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards. Osteoporos Int 22(2):391–420

    Article  PubMed  CAS  Google Scholar 

  2. Bergmann P, Body JJ, Boonen S, Boutsen Y, Devogelaer JP, Goemaere S, Kaufman JM, Reginster JY, Gangji V (2009) Evidence-based guidelines for the use of biochemical markers of bone turnover in the selection and monitoring of bisphosphonate treatment in osteoporosis: a consensus document of the Belgian Bone Club. Int J Clin Pract 63(1):19–26

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  3. Brown JP, Albert C, Nassar BA, Adachi JD, Cole D, Davison KS, Dooley KC, Don-Wauchope A, Douville P, Hanley DA, Jamal SA, Josse R, Kaiser S, Krahn J, Krause R, Kremer R, Lepage R, Letendre E, Morin S, Ooi DS, Papaioaonnou A, Ste-Marie LG (2009) Bone turnover markers in the management of postmenopausal osteoporosis. Clin Biochem 42(10–11):929–942

    Article  PubMed  CAS  Google Scholar 

  4. Szulc P, Delmas PD (2008) Biochemical markers of bone turnover: potential use in the investigation and management of postmenopausal osteoporosis. Osteoporos Int 19(12):1683–1704

    Article  PubMed  CAS  Google Scholar 

  5. Vasikaran SD (2008) Utility of biochemical markers of bone turnover and bone mineral density in management of osteoporosis. Crit Rev Clin Lab Sci 45(2):221–258

    Article  PubMed  CAS  Google Scholar 

  6. Hannon R, Eastell R (2000) Preanalytical variability of biochemical markers of bone turnover. Osteoporos Int 11(Suppl 6):S30–S44

    Article  PubMed  Google Scholar 

  7. Gerdhem P, Ivaska KK, Alatalo SL, Halleen JM, Hellman J, Isaksson A, Pettersson K, Vaananen HK, Akesson K, Obrant KJ (2004) Biochemical markers of bone metabolism and prediction of fracture in elderly women. J Bone Miner Res 19(3):386–393

    Article  PubMed  CAS  Google Scholar 

  8. Nelson HD, Morris CD, Kraemer DF, Mahon S, Carney N, Nygren PM, Helfand M (2001) Osteoporosis in postmenopausal women: diagnosis and monitoring. Evid Rep Technol Assess (Summ) 28:1–2

    Google Scholar 

  9. Civitelli R, Armamento-Villareal R, Napoli N (2009) Bone turnover markers: understanding their value in clinical trials and clinical practice. Osteoporos Int 20(6):843–851

    Article  PubMed  CAS  Google Scholar 

  10. Leeming DJ, Alexandersen P, Karsdal MA, Qvist P, Schaller S, Tanko LB (2006) An update on biomarkers of bone turnover and their utility in biomedical research and clinical practice. Eur J Clin Pharmacol 62(10):781–792

    Article  PubMed  CAS  Google Scholar 

  11. Bauer D (2001) Biochemical markers of bone turnover: the Study of Osteoporotic Fracture. In: Eastell R, Baumann M, Hoyle N, Wieczorek L (eds) Bone markers—biochemical and clinical perspectives. Martin Dunitz, London, pp 219–223

    Google Scholar 

  12. Szulc P, Montella A, Delmas PD (2008) High bone turnover is associated with accelerated bone loss but not with increased fracture risk in men aged 50 and over: the prospective MINOS study. Ann Rheum Dis 67(9):1249–1255

    Article  PubMed  CAS  Google Scholar 

  13. Yoshimura N, Muraki S, Oka H, Kawaguchi H, Nakamura K, Akune T (2011) Biochemical markers of bone turnover as predictors of osteoporosis and osteoporotic fractures in men and women: 10-year follow-up of the Taiji cohort. Mod Rheumatol 21(6):608–620

    Article  PubMed  CAS  Google Scholar 

  14. Ivaska KK, Gerdhem P, Vaananen HK, Akesson K, Obrant KJ (2010) Bone turnover markers and prediction of fracture: a prospective follow-up study of 1040 elderly women for a mean of 9 years. J Bone Miner Res 25(2):393–403

    Article  PubMed  CAS  Google Scholar 

  15. Bauer DC, Garnero P, Harrison SL, Cauley JA, Eastell R, Ensrud KE, Orwoll E (2009) Biochemical markers of bone turnover, hip bone loss, and fracture in older men: the MrOS study. J Bone Miner Res 24(12):2032–2038

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  16. Garnero P, Sornay-Rendu E, Claustrat B, Delmas PD (2000) Biochemical markers of bone turnover, endogenous hormones and the risk of fractures in postmenopausal women: the OFELY study. J Bone Miner Res 15(8):1526–1536

    Article  PubMed  CAS  Google Scholar 

  17. Meier C, Nguyen TV, Center JR, Seibel MJ, Eisman JA (2005) Bone resorption and osteoporotic fractures in elderly men: the Dubbo Osteoporosis Epidemiology Study. J Bone Miner Res 20(4):579–587

    Article  PubMed  Google Scholar 

  18. Chapurlat RD, Garnero P, Breart G, Meunier PJ, Delmas PD (2000) Serum type I collagen breakdown product (serum CTX) predicts hip fracture risk in elderly women: the EPIDOS study. Bone 27(2):283–286

    Article  PubMed  CAS  Google Scholar 

  19. Dobnig H, Piswanger-Solkner JC, Obermayer-Pietsch B, Tiran A, Strele A, Maier E, Maritschnegg P, Riedmuller G, Brueck C, Fahrleitner-Pammer A (2007) Hip and nonvertebral fracture prediction in nursing home patients: role of bone ultrasound and bone marker measurements. J Clin Endocrinol Metab 92(5):1678–1686

    Article  PubMed  CAS  Google Scholar 

  20. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327(7414):557–560

    Article  PubMed Central  PubMed  Google Scholar 

  21. Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312(7041):1254–1259

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  22. Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, Eisman JA, Fujiwara S, Kroger H, Mellstrom D, Meunier PJ, Melton LJ 3rd, O’Neill T, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Predictive value of BMD for hip and other fractures. J Bone Miner Res 20(7):1185–1194

    Article  PubMed  Google Scholar 

  23. Kanis JA, Oden A, Johnell O, Johansson H, De Laet C, Brown J, Burckhardt P, Cooper C, Christiansen C, Cummings S, Eisman JA, Fujiwara S, Gluer C, Goltzman D, Hans D, Krieg MA, La Croix A, McCloskey E, Mellstrom D, Melton LJ 3rd, Pols H, Reeve J, Sanders K, Schott AM, Silman A, Torgerson D, van Staa T, Watts NB, Yoshimura N (2007) The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int 18(8):1033–1046

    Article  PubMed  CAS  Google Scholar 

  24. Akesson K, Ljunghall S, Jonsson B, Sernbo I, Johnell O, Gardsell P, Obrant KJ (1995) Assessment of biochemical markers of bone metabolism in relation to the occurrence of fracture: a retrospective and prospective population-based study of women. J Bone Miner Res 10(11):1823–1829

    Article  PubMed  CAS  Google Scholar 

  25. Vergnaud P, Garnero P, Meunier PJ, Breart G, Kamihagi K, Delmas PD (1997) Undercarboxylated osteocalcin measured with a specific immunoassay predicts hip fracture in elderly women: the EPIDOS Study. J Clin Endocrinol Metab 82(3):719–724

    PubMed  CAS  Google Scholar 

  26. Garnero P, Cloos P, Sornay-Rendu E, Qvist P, Delmas PD (2002) Type I collagen racemization and isomerization and the risk of fracture in postmenopausal women: the OFELY prospective study. J Bone Miner Res 17(5):826–833

    Article  PubMed  CAS  Google Scholar 

  27. Luukinen H, Kakonen SM, Pettersson K, Koski K, Laippala P, Lovgren T, Kivela SL, Vaananen HK (2000) Strong prediction of fractures among older adults by the ratio of carboxylated to total serum osteocalcin. J Bone Miner Res 15(12):2473–2478

    Article  PubMed  CAS  Google Scholar 

  28. Johansson H, Oden A, Kanis J, McCloskey E, Lorentzon M, Ljunggren O, Karlsson MK, Thorsby PM, Tivesten A, Barrett-Connor E, Ohlsson C, Mellstrom D (2012) Low serum vitamin D is associated with increased mortality in elderly men: MrOS Sweden. Osteoporos Int 23(3):991–999

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

H. J. was supported by an ESCEO-AMGEN Osteoporosis Fellowship Award. Amgen had no input into the analysis plan or the writing of this report.

Author information

Authors and Affiliations

  1. WHO Collaborating Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Beech Hill Road, Sheffield, S10 2RX, UK

    Helena Johansson, Anders Odén, John A. Kanis & Eugene V. McCloskey

  2. School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, 5000, Australia

    Howard A. Morris

  3. The MRC Epidemiology Resource Centre, Southampton General Hospital, University of Southampton, Southampton, SO16 6YD, UK

    Cyrus Cooper

  4. NIHR Musculoskeletal Biomedical Research Unit, Institute of Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK

    Cyrus Cooper

  5. Department of Core Clinical Pathology and Biochemistry, PathWest Laboratory Medicine, Royal Perth Hospital, Perth, WA, Australia

    Samuel Vasikaran

  6. School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, WA, Australia

    Samuel Vasikaran

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  1. Helena Johansson
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  2. Anders Odén
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  3. John A. Kanis
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  4. Eugene V. McCloskey
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  5. Howard A. Morris
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  6. Cyrus Cooper
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  7. Samuel Vasikaran
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Consortia

IFCC-IOF Joint Working Group on Standardisation of Biochemical Markers of Bone Turnover

Corresponding author

Correspondence to Helena Johansson.

Additional information

A complete list of members of the IFCC-IOF Joint Working Group on Standardisation of Biochemical Markers of Bone Turnover appears in the ‘‘Appendix’’.

The authors state that they have no conflicts of interest with regard to this manuscript.

Appendix

Appendix

Joint Working Group members: H. A. Morris (chair), C. Cooper (co-chair), S. Vasikaran, J. A. Kanis, C. Biegelmayer, E. Cavalier, E. Eriksen, A. Griesmacher, K. Makris, S. Niem, B. Ofenloch-Haehnle, H. Pham.

C. Biegelmayer (Medical School, University of Vienna, Vienna, Austria; Christian.Bieglmayer@meduniwien.ac.at); E. Cavalier ( University of Liège, CHU Sart-Tilman, Domaine du Sart-Tilman, B-4000 Liège, Belgium; etienne.cavalier@chu.ulg.ac.be); E. Eriksen (Endokrinologisk avdeling, Oslo universitetssykehus, Norway; e.f.eriksen@medisin.uio.no); A. Griesmacher (Zentralinstitut für Medizinische und Chemische Labordiagnostik, LKH-Universitätskliniken Innsbruck, 6020 Innsbruck, Austria, Andrea.Griesmacher@uki.at); K. Makris (Clinical Biochemistry Department, KAT General Hospital, 14651, Athens, Greece; kostas.makris.km@gmail.com; S. Niemi (Orion Diagnostica Oy, 90220 Oulu, Finland; seija.niemi@oriondiagnostica.fi); B. Ofenloch-Haehnle (Roche Diagnostics GmbH, 82377 Penzberg, Germany; beatus.ofenloch-haehnle@roche.com); H. Pham (Immunodiagnostic Systems (IDS), Boldon, UK; heather.pham@idsplc.com).

From HR between quartiles to GR

If the hazard function of a type of event is exp(α + β · x), where the risk variable X has a normal distribution with mean 0 and standard deviation 1, then the value of the hazard function of an individual chosen by random in the interval a to b of X is\( \begin{aligned} & \exp (\alpha ) \cdot \int\limits_{a}^{b} {\exp (\beta \cdot x) \cdot \exp ( - x^{2} /2)/\sqrt {2 \cdot \pi } dx} /(\varPhi (b) - \varPhi (a)) \\ & \quad = { \exp }(\alpha + \beta^{2} /2) \cdot \int\limits_{a}^{b} {{ \exp }( - (x - \beta )^{2} /2)/\sqrt {2 \cdot \pi } dx} /(\varPhi (b) - \varPhi (a)) \\ & \quad {\text{ = exp}}(\alpha + \beta^{2} /2) \cdot (\varPhi (b - \beta ) - \varPhi (a - \beta ))/(\varPhi (b) - \varPhi (a)) \\ \end{aligned} \)

If we consider four quartiles of the distribution of BTMs, Q1, Q2, Q3, and Q4, then the limits a and b will be −∝, −0.67458, 0, 0.67458, and ∝.

Thus, the following relationships are fulfilled:

$$ \begin{aligned} {\text{Q}}_{ 4} /\left( {{\text{Q}}_{ 1} + {\text{ Q}}_{ 2} + {\text{ Q}}_{ 3} } \right) \, & = \, \left[ { 1- \varphi \left( {0. 6 7 4 5 8 { }{-} \, \beta } \right)} \right]/[ 3\cdot \varphi \left( {0. 6 7 4 5 8 { }{-} \, \beta } \right)] \\ {\text{Q}}_{ 4} /{\text{Q}}_{ 1} & = \, \left[ { 1- \varphi \left( {0. 6 7 4 5 8 { }{-} \, \beta } \right)} \right]/\varphi \left( {{-}0. 6 7 4 5 8 { }{-} \, \beta } \right) \\ {\text{Q}}_{ 3} /{\text{Q}}_{ 1} & = \, \left[ {\varphi \left( {0. 6 7 4 5 8 { }{-} \, \beta } \right) \, {-} \, \varphi \left( { - \beta } \right)} \right]/\varphi \left( {{-}0. 6 7 4 5 8 { }{-} \, \beta } \right) \\ {\text{Q}}_{ 2} /{\text{Q}}_{ 1} & = \, \left[ {\varphi \left( { - \beta } \right) \, {-} \, \varphi \left( {{-}0. 6 7 4 5 8 { }{-} \, \beta } \right)} \right]/\varphi \left( {{-}0. 6 7 4 5 8 { }{-} \, \beta } \right) \\ \end{aligned} $$

If such a quotient is put equal to a number R, then it is not trivial to solve b (with a possible exception for the first quotient). One of two possibilities is to use an iterative procedure. Let us consider

$$ {\text{Q}}_{ 3} /{\text{Q}}_{ 1} = R. $$

Then we get

$$ \varphi \left( {0. 6 7 4 5 8 { }{-} \, \beta } \right) \, {-} \, \varphi \left( {{-}\beta } \right) \, {-}R \cdot \varphi \, \left( {{-}0. 6 7 4 5 8 { }{-} \, \beta } \right) \, = \, 0 $$

We have the general relationship f(x) ≈ f(x 0) + f′(x 0) · (x − x 0). To solve f(x) = Z, we use the relationship

$$ x = x_{0} + \, \left[ {Z{-}f\left( {x_{0} } \right)} \right]/f\prime \left( {x_{0} } \right) $$

repeatedly. In the special case considered, we have Z = 0. The derivative of φ(0.67458 − β), for example, is −exp[−(0.67458 − β)2/2]/(√2 · π).

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Johansson, H., Odén, A., Kanis, J.A. et al. A Meta-Analysis of Reference Markers of Bone Turnover for Prediction of Fracture. Calcif Tissue Int 94, 560–567 (2014). https://doi.org/10.1007/s00223-014-9842-y

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