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

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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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.

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Correspondence to Helena Johansson.

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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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Keywords

  • Marker of bone turnover
  • Fracture
  • Serum procollagen type I N propeptide
  • Serum carboxy-terminal cross-linking telopeptide of type I collagen
  • Meta-analysis