Skip to main content

C-reactive protein and fracture risk: an updated systematic review and meta-analysis of cohort studies through the use of both frequentist and Bayesian approaches

Abstract

Summary

This systematic review and meta-analysis were conducted on all eligible cohort studies to evaluate the association between high-sensitivity C-reactive protein (hs-CRP) and osteoporotic fracture risk. Both frequentist and Bayesian approaches were employed for the meta-analysis. We found that high tertiles of hs-CRP were significantly associated with increased fracture risk.

Introduction

The association between the inflammatory marker CRP and osteoporotic fracture has remained uncertain. In this study, we conducted a systematic review and meta-analysis to examine the association of serum hs-CRP and fracture risk.

Methods

We performed a systematic literature search of relevant databases, including PubMed, Embase, and MEDLINE publications from January 1950 through April 2020. Three reviewers independently performed the study selection, quality assessment, and data abstraction. Frequentist and Bayesian hierarchical random-effects models were used separately for the analysis. Statistical heterogeneity was assessed using Higgin’s I2 and Cochran’s Q statistic, and publication bias was examined using funnel plots and rank correlation tests.

Results

Fourteen cohort studies that reported t fracture outcomes were eligible for the systematic review. Only ten studies (n = 29,741) qualified for meta-analysis. In the frequentist approach, the RR for fracture in a comparison of the top tertile group to the bottom tertile group of hs-CRP was 1.54 (1.18, 2.01). The estimated risk of fracture remained significant in all sensitivity and subgroup analyses. Higgin’s I2 (30.52%) and Cochran’s Q statistic (p < 0.01) suggested there was moderate heterogeneity for the meta-analysis. In the Bayesian approach, the pooled RR was 1.60 (95% CI (1.07–2.49)), and the probabilities that the high level of hs-CRP increased fracture risk by more than 0%, 10%, and 20% were 99%, 98%, and 93%, respectively.

Conclusion

A high level of hs-CRP is associated with a significantly increased risk of osteoporotic fracture.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Abbreviations

BMD:

Bone mineral density

CI:

Confidence interval

CRP:

C-reactive protein

HR:

Hazard ratio

MOOSE:

Meta-analysis of observational studies in epidemiology

OR:

Odds ratio

PRISMA:

Preferred Reporting Items for Systematic Review and Meta-analyses

RR:

Relative risk

REDCap:

Research Electronic Data Capture

References

  1. Dong Y, Zhao R, Wang C, Guo T (2018) Tuina for osteoporosis. Medicine (Baltimore) 97. https://doi.org/10.1097/MD.0000000000009974

  2. Johnell O, Kanis JA (2006) An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 17:1726–1733. https://doi.org/10.1007/s00198-006-0172-4

    Article  CAS  Google Scholar 

  3. Sözen T, Özışık L, Başaran NÇ (2017) An overview and management of osteoporosis. Eur J Rheumatol 4:46–56. https://doi.org/10.5152/eurjrheum.2016.048

    Article  PubMed  Google Scholar 

  4. Clarke B (2008) Normal Bone anatomy and physiology. Clin J Am Soc Nephrol 3:S131–S139. https://doi.org/10.2215/CJN.04151206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Harmer D, Falank C, Reagan MR (2019) Interleukin-6 interweaves the bone marrow microenvironment, bone loss, and multiple myeloma. Front Endocrinol 9. https://doi.org/10.3389/fendo.2018.00788

  6. Hs L, Yh P, Sk K (2016) Relationship between serum inflammatory marker and bone mineral density in healthy adults. J Bone Metab 23:27–33. https://doi.org/10.11005/jbm.2016.23.1.27

    Article  Google Scholar 

  7. Stojanović D, Bůžková P, Mukamal KJ et al (2018) Soluble inflammatory markers and risk of incident fractures in older adults: the Cardiovascular Health Study. J Bone Miner Res 33:221–228. https://doi.org/10.1002/jbmr.3301

    Article  PubMed  Google Scholar 

  8. Cauley JA, Barbour KE, Harrison SL et al (2016) Inflammatory markers and the risk of hip and vertebral fractures in men: the osteoporotic fractures in men (MrOS). J Bone Miner Res Off J Am Soc Bone Miner Res 31:2129–2138. https://doi.org/10.1002/jbmr.2905

    Article  CAS  Google Scholar 

  9. Cauley JA, Danielson ME, Boudreau RM et al (2007) Inflammatory markers and incident fracture risk in older men and women: the Health Aging and Body Composition Study. J Bone Miner Res Off J Am Soc Bone Miner Res 22:1088–1095. https://doi.org/10.1359/jbmr.070409

    Article  CAS  Google Scholar 

  10. Ganesan K, Teklehaimanot S, Tran T-H et al (2005) Relationship of C-reactive protein and bone mineral density in community-dwelling elderly females. J Natl Med Assoc 97:329–333

    PubMed  PubMed Central  Google Scholar 

  11. de Pablo P, Cooper MS, Buckley CD (2012) Association between bone mineral density and C-reactive protein in a large population-based sample. Arthritis Rheum 64:2624–2631. https://doi.org/10.1002/art.34474

    Article  CAS  PubMed  Google Scholar 

  12. Apalset EM, Gjesdal CG, Ueland PM et al (2014) Interferon gamma (IFN-γ)-mediated inflammation and the kynurenine pathway in relation to risk of hip fractures: the Hordaland Health Study. Osteoporos Int 25:2067–2075. https://doi.org/10.1007/s00198-014-2720-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Berglundh S, Malmgren L, Luthman H et al (2015) C-reactive protein, bone loss, fracture, and mortality in elderly women: a longitudinal study in the OPRA cohort. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 26:727–735. https://doi.org/10.1007/s00198-014-2951-7

    Article  CAS  Google Scholar 

  14. Wu Z-J, He J-L, Wei R-Q et al (2015) C-reactive protein and risk of fracture: a systematic review and dose-response meta-analysis of prospective cohort studies. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 26:49–57. https://doi.org/10.1007/s00198-014-2826-y

    Article  CAS  Google Scholar 

  15. Hind K, Pearce M, Birrell F (2017) Total and visceral adiposity are associated with prevalent vertebral fracture in women but not men at age 62 years: the Newcastle Thousand Families Study. J Bone Miner Res Off J Am Soc Bone Miner Res 32:1109–1115. https://doi.org/10.1002/jbmr.3085

    Article  CAS  Google Scholar 

  16. Dahl K, Ahmed LA, Joakimsen RM et al (2015) High-sensitivity C-reactive protein is an independent risk factor for non-vertebral fractures in women and men: the Tromsø Study. Bone 72:65–70. https://doi.org/10.1016/j.bone.2014.11.012

    Article  CAS  PubMed  Google Scholar 

  17. Stroup DF, Berlin JA, Morton SC et al (2000) Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 283:2008–2012. https://doi.org/10.1001/jama.283.15.2008

    Article  CAS  PubMed  Google Scholar 

  18. Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151(264–269):W64. https://doi.org/10.7326/0003-4819-151-4-200908180-00135

    Article  Google Scholar 

  19. Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76:378–382. https://doi.org/10.1037/h0031619

    Article  Google Scholar 

  20. Pasco JA, Kotowicz MA, Henry MJ et al (2006) High-sensitivity C-reactive protein and fracture risk in elderly women. JAMA 296:1349–1355. https://doi.org/10.1001/jama.296.11.1353

    Article  Google Scholar 

  21. Schett G, Kiechl S, Weger S et al (2006) High-sensitivity C-reactive protein and risk of nontraumatic fractures in the Bruneck study. Arch Intern Med 166:2495–2501. https://doi.org/10.1001/archinte.166.22.2495

    Article  CAS  PubMed  Google Scholar 

  22. Nakamura K, Saito T, Kobayashi R et al (2011) C-reactive protein predicts incident fracture in community-dwelling elderly Japanese women: the Muramatsu study. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 22:2145–2150. https://doi.org/10.1007/s00198-010-1425-9

    Article  CAS  Google Scholar 

  23. Ahmadi-Abhari S, Luben RN, Wareham NJ, Khaw K-T (2013) C-reactive protein and fracture risk: European prospective investigation into Cancer Norfolk Study. Bone 56:67–72. https://doi.org/10.1016/j.bone.2013.05.009

    Article  CAS  PubMed  Google Scholar 

  24. Eriksson AL, Movérare-Skrtic S, Ljunggren Ö et al (2014) High-sensitivity CRP Is an independent risk factor for all fractures and vertebral fractures in elderly men: the MrOS Sweden Study. J Bone Miner Res 29:418–423. https://doi.org/10.1002/jbmr.2037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ishii S, Cauley JA, Greendale GA et al (2013) C-Reactive protein, bone strength, and nine-year fracture risk: data from the Study of Women’s Health Across the Nation (SWAN). J Bone Miner Res Off J Am Soc Bone Miner Res 28. https://doi.org/10.1002/jbmr.1915

  26. Oei L, Campos-Obando N, Dehghan A et al (2014) Dissecting the relationship between high-sensitivity serum C-reactive protein and increased fracture risk: the Rotterdam Study. Osteoporos Int J Establ Result Coop Eur Found Osteoporos Natl Osteoporos Found USA 25:1247–1254. https://doi.org/10.1007/s00198-013-2578-0

    Article  CAS  Google Scholar 

  27. Wells G, Shea B, O’Connell D, et al (2000) The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis

  28. McCormack JP, Allan GM (2010) Measuring hsCRP—an important part of a comprehensive risk profile or a clinically redundant practice? PLoS Med 7:e1000196. https://doi.org/10.1371/journal.pmed.1000196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ressing M, Blettner M, Klug SJ (2010) Data analysis of epidemiological studies. Dtsch Arztebl Int 107:187–192. https://doi.org/10.3238/arztebl.2010.0187

    Article  PubMed  PubMed Central  Google Scholar 

  30. Nurminen M (1995) To use or not to use the odds ratio in epidemiologic analyses? Eur J Epidemiol 11:365–371. https://doi.org/10.1007/BF01721219

    Article  CAS  PubMed  Google Scholar 

  31. Petrie A, Bulman JS, Osborn JF (2003) Further statistics in dentistry Part 8: Systematic reviews and meta-analyses. Br Dent J 194:73–78. https://doi.org/10.1038/sj.bdj.4809877

    Article  CAS  PubMed  Google Scholar 

  32. Hemingway H, Henriksson M, Chen R et al (2010) The effectiveness and cost-effectiveness of biomarkers for the prioritisation of patients awaiting coronary revascularisation: a systematic review and decision model. Health Technol Assess Winch Engl 14(1–151):iii–iv. https://doi.org/10.3310/hta14090

    Article  Google Scholar 

  33. Danesh J, Collins R, Appleby P, Peto R (1998) Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 279:1477–1482. https://doi.org/10.1001/jama.279.18.1477

    Article  CAS  PubMed  Google Scholar 

  34. Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539–1558. https://doi.org/10.1002/sim.1186

    Article  Google Scholar 

  35. Furukawa TA, Guyatt GH, Griffith LE (2002) Can we individualize the “number needed to treat”? An empirical study of summary effect measures in meta-analyses. Int J Epidemiol 31:72–76. https://doi.org/10.1093/ije/31.1.72

    Article  PubMed  Google Scholar 

  36. Sproston NR, Ashworth JJ (2018) Role of C-reactive protein at sites of inflammation and infection. Front Immunol 9:754. https://doi.org/10.3389/fimmu.2018.00754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Colón-Emeric CS, Saag KG (2006) Osteoporotic fractures in older adults. Best Pract Res Clin Rheumatol 20:695–706. https://doi.org/10.1016/j.berh.2006.04.004

    Article  PubMed  PubMed Central  Google Scholar 

  38. Slaats J, ten Oever J, van de Veerdonk FL, Netea MG (2016) IL-1β/IL-6/CRP and IL-18/ferritin: Distinct inflammatory programs in infections. PLoS Pathog 12. https://doi.org/10.1371/journal.ppat.1005973

  39. Kohli SS, Kohli VS (2011) Role of RANKL–RANK/osteoprotegerin molecular complex in bone remodeling and its immunopathologic implications. Indian J Endocrinol Metab 15:175–181. https://doi.org/10.4103/2230-8210.83401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Boyce BF, Xing L (2008) Functions of RANKL/RANK/OPG in bone modeling and remodeling. Arch Biochem Biophys 473:139–146. https://doi.org/10.1016/j.abb.2008.03.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moschen AR, Kaser A, Enrich B et al (2005) The RANKL/OPG system is activated in inflammatory bowel disease and relates to the state of bone loss. Gut 54:479–487. https://doi.org/10.1136/gut.2004.044370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gowen M (1950) Mundy GR (1986) Actions of recombinant interleukin 1, interleukin 2, and interferon-gamma on bone resorption in vitro. J Immunol Baltim Md 136:2478–2482

    Google Scholar 

  43. Ansar W, Ghosh S (2013) C-reactive protein and the biology of disease. Immunol Res 56:131–142. https://doi.org/10.1007/s12026-013-8384-0

    Article  CAS  PubMed  Google Scholar 

  44. Mortensen JH, Manon-Jensen T, Jensen MD et al (2017) Ulcerative colitis, Crohn’s disease, and irritable bowel syndrome have different profiles of extracellular matrix turnover, which also reflects disease activity in Crohn’s disease. PLoS One 12:e0185855. https://doi.org/10.1371/journal.pone.0185855

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Q. Wu.

Ethics declarations

Conflicts of interest

None.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 40 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mun, H., Liu, B., Pham, T.H.A. et al. C-reactive protein and fracture risk: an updated systematic review and meta-analysis of cohort studies through the use of both frequentist and Bayesian approaches. Osteoporos Int 32, 425–435 (2021). https://doi.org/10.1007/s00198-020-05623-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-020-05623-6

Keywords

  • Bayesian meta-analysis
  • C-reactive protein
  • Fracture
  • Meta-analysis
  • Osteoporosis
  • Systematic review