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Computed tomography-based paravertebral muscle density predicts subsequent vertebral fracture risks independently of bone mineral density in postmenopausal women following percutaneous vertebral augmentation

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Abstract

Background

The risk of subsequent vertebral fractures (SVF) after the primary vertebral fracture cannot be explained by lower bone mineral density (BMD) alone. Computed tomography (CT) measurements of paravertebral muscle density (PMD) are recognized radiographic markers used to predict physical function, fragile fractures.

Aims

This study aims to investigate the relationship between PMD and the risk of SVF in cohorts of postmenopausal women, and to determine if combining both PMD and BMD measures derived from CT can improve the accuracy of predicting SVF.

Methods

This study enrolled 305 postmenopausal women between the ages of 50 and 88 for 3 years of follow-up studies. Trabecular attenuation (Hounsfield units, HU) was measured at L1 level and muscle attenuation of paravertebral muscle at L3 level on preoperative lumbar CT scans to determine the L1 BMD and L3 PMD. Kaplan–Meier analysis was applied to evaluate SVF-free survival. The hazard ratios (HRs) of PMD for SVF events were estimated with the Cox proportional hazards model. The predictive values of L1 BMD and L3 PMD for SVF were quantified using the Receiver-Operating Characteristic (ROC) curve.

Result

During the 3 years of follow-up studies, 88 patients (28.9%) suffered an SVF. ROC curve analysis demonstrated that an L3 PMD threshold of 32 HU had a sensitivity of 89.8% and a specificity of 62% for the prediction of SVF. Kaplan–Meier analysis showed that L3 PMD ≤ 32 HU was significantly associated with lower SVF-free survival (p < 0.001; log-rank test). After adjusting for age, BMI, diabetes, postoperative osteoporosis treatment, handgrip strength, L1 BMD, multivariate analyses also indicated a persistent modest effect of L3 PMD on SVF-free survival. The area under the ROC curve of L3 PMD and L1 BMD, combined to predict the risk of SVF, was 0.790, which was significantly higher than the value for L1 BMD alone (0.735). L3 PMD and L1 BMD significantly improved the accuracy of SVF risk prediction compared with L1 BMD alone, which was confirmed by reclassification improvement measures. The inclusion of handgrip strength and postoperative osteoporosis treatment in the model further improved SVF prediction accuracy, and PMD remained significant in the model.

Conclusion

Decreased L3 PMD is an independent risk predictor of SVF. Combined CT-based L1 BMD and L3 PMD can significantly improve the accuracy of predicting the risk of SVF in postmenopausal women who have suffered prior osteoporotic vertebral fractures.

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References

  1. Zhu RS, Kan SL, Ning GZ et al (2019) Which is the best treatment of osteoporotic vertebral compression fractures: balloon kyphoplasty, percutaneous vertebroplasty, or non-surgical treatment? A Bayesian network meta-analysis. Osteoporos Int 30:287–298. https://doi.org/10.1007/s00198-018-4804-2

    Article  PubMed  Google Scholar 

  2. Lee HJ, Park J, Lee IW et al (2019) Clinical, Radiographic, and morphometric risk factors for adjacent and remote vertebral compression fractures over a minimum follow-up of 4 years after percutaneous vertebroplasty for osteoporotic vertebral compression fractures: novel three-dimensional voxel-based morphometric analysis. World neurosurgery 125:e146–e157. https://doi.org/10.1016/j.wneu.2019.01.020

    Article  PubMed  Google Scholar 

  3. Camacho PM, Petak SM, Binkley N et al (2020) American association of clinical endocrinologists/American college of endocrinology clinical practice guidelines for the diagnosis and treatment of postmenopausal osteoporosis—2020 update. Endocr Pract 26:1–46. https://doi.org/10.4158/GL-2020-0524SUPPL

    Article  PubMed  Google Scholar 

  4. Malik AT, Retchin S, Phillips FM et al (2020) Declining trend in osteoporosis management and screening following vertebral compression fractures—a national analysis of commercial insurance and medicare advantage beneficiaries. Spine J 20:538–546. https://doi.org/10.1016/j.spinee.2019.10.020

    Article  PubMed  Google Scholar 

  5. Hinde K, Maingard J, Hirsch JA et al (2020) Mortality outcomes of vertebral augmentation (vertebroplasty and/or balloon kyphoplasty) for osteoporotic vertebral compression fractures: a systematic review and meta-analysis. Radiology 295:96–103. https://doi.org/10.1148/radiol.2020191294

    Article  PubMed  Google Scholar 

  6. Jang S, Graffy PM, Ziemlewicz TJ et al (2019) Opportunistic osteoporosis screening at routine abdominal and thoracic ct: normative L1 trabecular attenuation values in more than 20000 adults. Radiology 291:360–367. https://doi.org/10.1148/radiol.2019181648

    Article  PubMed  Google Scholar 

  7. Cheng X, Zhao K, Zha X et al (2021) Opportunistic screening using low-dose CT and the prevalence of osteoporosis in china: a nationwide. Multicenter Study J Bone Miner Res 36:427–435. https://doi.org/10.1002/jbmr.4187

    Article  PubMed  CAS  Google Scholar 

  8. Wang H, Zou D, Sun Z et al (2020) Hounsfield unit for assessing vertebral bone quality and asymmetrical vertebral degeneration in degenerative lumbar scoliosis. Spine (Phila Pa 1976) 45:1559–1566

    Article  Google Scholar 

  9. Graffy PM, Lee SJ, Ziemlewicz TJ et al (2017) Prevalence of vertebral compression fractures on routine ct scans according to l1 trabecular attenuation: determining relevant thresholds for opportunistic osteoporosis screening. AJR Am J Roentgenol 209:491–496. https://doi.org/10.2214/AJR.17.17853

    Article  PubMed  Google Scholar 

  10. Martin L, Birdsell L, Macdonald N et al (2013) Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 31:1539–1547. https://doi.org/10.1200/JCO.2012.45.2722

    Article  PubMed  Google Scholar 

  11. Aubrey J, Esfandiari N, Baracos VE et al (2014) Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol (Oxf) 210:489–497. https://doi.org/10.1111/apha.12224

    Article  CAS  Google Scholar 

  12. Anderson DE, Quinn E, Parker E et al (2016) Associations of computed tomography-based trunk muscle size and density with balance and falls in older adults. J Gerontol A Biol Sci Med Sci 71:811–816. https://doi.org/10.1093/gerona/glv185

    Article  PubMed  Google Scholar 

  13. Wang L, Yin L, Zhao Y et al (2020) Muscle density, but not size, correlates well with muscle strength and physical performance. J Am Med Dir Assoc 22:751-759.e2. https://doi.org/10.1016/j.jamda.2020.06.052

    Article  PubMed  Google Scholar 

  14. Hirschfeld HP, Kinsella R, Duque G (2017) Osteosarcopenia: where bone, muscle, and fat collide. Osteoporos Int 28:2781–2790. https://doi.org/10.1007/s00198-017-4151-8

    Article  PubMed  CAS  Google Scholar 

  15. Scott D, Seibel M, Cumming R et al (2019) Does combined osteopenia/osteoporosis and sarcopenia confer greater risk of falls and fracture than either condition alone in older men? The concord health and ageing in men project. J Gerontol A Biol Sci Med Sci 74:827–834. https://doi.org/10.1093/gerona/gly162

    Article  PubMed  CAS  Google Scholar 

  16. Yin L, Xu Z, Wang L et al (2020) Associations of muscle size and density with proximal femur bone in a community dwelling older population. Front Endocrinol 11:503. https://doi.org/10.3389/fendo.2020.00503

    Article  Google Scholar 

  17. Lang T, Cauley JA, Tylavsky F et al (2010) Computed tomographic measurements of thigh muscle cross-sectional area and attenuation coefficient predict hip fracture: the health, aging, and body composition study. J Bone Miner Res 25:513–519. https://doi.org/10.1359/jbmr.090807

    Article  PubMed  Google Scholar 

  18. Newman AB, Kupelian V, Visser M et al (2006) Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. J Gerontol A Biol Sci 61:72–77. https://doi.org/10.1093/gerona/61.1.72

    Article  Google Scholar 

  19. Wang L, Yin L, Zhao Y et al (2020) Muscle density discriminates hip fracture better than CTXA hip areal bone mineral density. J Cachexia Sarcopenia Muscle 11:1799–1812. https://doi.org/10.1002/jcsm.12616

    Article  PubMed  PubMed Central  Google Scholar 

  20. Mourtzakis M, Prado CM, Lieffers JR et al (2008) A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab 33:997–1006. https://doi.org/10.1139/H08-075

    Article  PubMed  Google Scholar 

  21. Di Monaco M, Castiglioni C, De Toma E et al (2014) Handgrip strength but not appendicular lean mass is an independent predictor of functional outcome in hip—fracture women: a short-term prospective study. Arch Phys Med Rehabil 95:1719–1724. https://doi.org/10.1016/j.apmr.2014.04.003

    Article  PubMed  Google Scholar 

  22. Genant HK, Wu CY, van Kuijk C et al (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 8:1137–1148. https://doi.org/10.1002/jbmr.5650080915

    Article  PubMed  CAS  Google Scholar 

  23. Bozdogan H (2000) Akaike’s information criterion and recent developments in information complexity. J Math Psychol 44:62–91. https://doi.org/10.1006/jmps.1999.1277

    Article  PubMed  CAS  Google Scholar 

  24. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  PubMed  CAS  Google Scholar 

  25. Kennedy KF, Pencina MJ (2010) A sas macro to compute added predictive ability of new markers predicting a dichotomous outcome. In: SouthEeast SAS users group annual meeting proceedings 2010. http://analytics.ncsu.edu/sesug/2010/SDA07.Kennedy.pdf.

  26. Pencina MJ, D’Agostino RB, Pencina KM et al (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481. https://doi.org/10.1093/aje/kws207

    Article  PubMed  PubMed Central  Google Scholar 

  27. Che H, Breuil V, Cortet B et al (2019) Vertebral fractures cascade: potential causes and risk factors. Osteoporos Int 30:555–563. https://doi.org/10.1007/s00198-018-4793-1

    Article  PubMed  CAS  Google Scholar 

  28. Kobayashi T, Kaneko M, Narukawa M (2020) Influence of prevalent vertebral fracture on the correlation between change in lumbar spine bone mineral density and risk of new vertebral fracture: a meta-analysis of randomized clinical trials. Clin Drug Investig 40:15–23. https://doi.org/10.1007/s40261-019-00868-4

    Article  PubMed  Google Scholar 

  29. Kaufman JM, Palacios S, Silverman S et al (2013) An evaluation of the fracture risk assessment tool (FRAX®) as an indicator of treatment efficacy: the effects of bazedoxifene and raloxifene on vertebral, nonvertebral, and all clinical fractures as a function of baseline fracture risk assessed by FRAX®. Osteoporos Int 24:2561–2569. https://doi.org/10.1007/s00198-013-2341-6

    Article  PubMed  CAS  Google Scholar 

  30. Pickhardt PJ, Pooler BD, Lauder T et al (2013) Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158:588–595. https://doi.org/10.7326/0003-4819-158-8-201304160-00003

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lee SJ, Graffy PM, Zea RD et al (2018) Future osteoporotic fracture risk related to lumbar vertebral trabecular attenuation measured at routine body CT. J Bone Miner Res 33:860–867. https://doi.org/10.1002/jbmr.3383

    Article  PubMed  CAS  Google Scholar 

  32. Zhang SB, Xu HW, Yi YY et al (2021) Evaluation of the use of CT attenuation for the prediction of subsequent vertebral fracture in patients with osteoporosis. Pain Physician 24:E493-e500

    PubMed  Google Scholar 

  33. Guizelini PC, de Aguiar RA, Denadai BS et al (2018) Effect of resistance training on muscle strength and rate of force development in healthy older adults: a systematic review and meta-analysis. Exp Gerontol 102:51–58. https://doi.org/10.1016/j.exger.2017.11.020

    Article  PubMed  Google Scholar 

  34. Peng X, Li X, Xu Z et al (2020) Age-related fatty infiltration of lumbar paraspinal muscles: a normative reference database study in 516 Chinese females. Quant Imaging Med Surg 10:1590–1601. https://doi.org/10.21037/qims-19-835

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lorbergs AL, Allaire BT, Yang L et al (2019) A Longitudinal study of trunk muscle properties and severity of thoracic kyphosis in women and men: the Framingham study. J Gerontol A Biol Sci Med Sci 74:420–427. https://doi.org/10.1093/gerona/gly056

    Article  PubMed  Google Scholar 

  36. Huang CWC, Tseng IJ, Yang SW et al (2019) Lumbar muscle volume in postmenopausal women with osteoporotic compression fractures: quantitative measurement using MRI. Eur Radiol 29:4999–5006. https://doi.org/10.1007/s00330-019-06034-w

    Article  PubMed  Google Scholar 

  37. Wang WF, Lin CW, Xie CN et al (2019) The association between sarcopenia and osteoporotic vertebral compression refractures. Osteoporos Int 30:2459–2467. https://doi.org/10.1007/s00198-019-05144-x

    Article  PubMed  CAS  Google Scholar 

  38. Scott D, Chandrasekara SD, Laslett LL et al (2016) Associations of sarcopenic obesity and dynapenic obesity with bone mineral density and incident fractures over 5–10 years in community-dwelling older adults. Calcif Tissue Int 99:30–42. https://doi.org/10.1007/s00223-016-0123-9

    Article  PubMed  CAS  Google Scholar 

  39. Zhang SB, Chen H, Xu HW et al (2020) Association between handgrip strength and subsequent vertebral-fracture risk following percutaneous vertebral augmentation. J Bone Miner Metab 39:186–192. https://doi.org/10.1007/s00774-020-01131-z

    Article  PubMed  Google Scholar 

  40. Kirk B, Feehan J, Lombardi G et al (2020) Muscle, bone, and fat crosstalk: the biological role of myokines, osteokines, and adipokines. Curr Osteoporos Rep 18:388–400. https://doi.org/10.1007/s11914-020-00599-y

    Article  PubMed  Google Scholar 

  41. Michaud M, Balardy L, Moulis G et al (2013) Proinflammatory cytokines, aging, and age-related diseases. J Am Med Dir Assoc 14:877–882. https://doi.org/10.1016/j.jamda.2013.05.009

    Article  PubMed  Google Scholar 

  42. Frank-Wilson AW, Farthing JP, Chilibeck PD et al (2016) Lower leg muscle density is independently associated with fall status in community-dwelling older adults. Osteoporos Int 27:2231–2240. https://doi.org/10.1007/s00198-016-3514-x

    Article  PubMed  CAS  Google Scholar 

  43. Bassani T, Casaroli G, Galbusera F (2019) Dependence of lumbar loads on spinopelvic sagittal alignment: An evaluation based on musculoskeletal modeling. PLoS ONE 14:e0207997. https://doi.org/10.1371/journal.pone.0207997

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Kanis JA, Harvey NC, Cooper C et al (2016) A systematic review of intervention thresholds based on FRAX: a report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation. Arch Osteoporos 11:25. https://doi.org/10.1007/s11657-016-0278-z

    Article  PubMed  PubMed Central  Google Scholar 

  45. Harvey NC, Odén A, Orwoll E et al (2018) Measures of physical performance and muscle strength as predictors of fracture risk independent of FRAX, falls, and aBMD: a meta-analysis of the osteoporotic fractures in men (MrOS) study. J Bone Miner Res 33:2150–2157. https://doi.org/10.1002/jbmr.3556

    Article  PubMed  Google Scholar 

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Acknowledgements

Primary Funding Source: This work was funded by the Special Program for Research on Healthy Aging of Shanghai Municipal Health Commission (2020YJZX0116), and Science and Technology project of Jiangxi Provincial Health Commission (202140997).

Funding

The work of Shan-Jin Wang was supported by The Special Program for Research on Healthy Aging of Shanghai Municipal Health Commission under Grant 2020YJZX0116, and Science and Technology Project of Jiangxi Provincial Health Commission under Grant 202140997.

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SJW initiated the idea and did the data analysis. SBZ wrote the assay. YYY and HWX supervised and reviewed the manuscript. HC and XYF gathered the data and helped with the data analysis. All authors read and approved the final manuscript.

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Correspondence to Shan-Jin Wang.

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Zhang, SB., Chen, H., Xu, HW. et al. Computed tomography-based paravertebral muscle density predicts subsequent vertebral fracture risks independently of bone mineral density in postmenopausal women following percutaneous vertebral augmentation. Aging Clin Exp Res 34, 2797–2805 (2022). https://doi.org/10.1007/s40520-022-02218-5

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