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Body compositions differently contribute to BMD in different age and gender: a pilot study by QCT

Archives of Osteoporosis volume 14, Article number: 31 (2019) Cite this article

Abstract

Summary

The study was to investigate the correlation between body compositions and bone mineral density (BMD) and to evaluate the body composition contribution to BMD. In male, LM showed positive effect on BMD. In female, SAT showed positive, and FM and F/L showed negative effect on BMD.

Purpose

The purpose of the study was to investigate the correlation between body compositions and bone mineral density (BMD) performed by quantitative computed tomography (QCT), and to evaluate the body composition contribution to BMD.

Methods

Three hundred ninety-four participants, including 122 male (31%) and 272 female (69%), were divided into groups by gender, age, and BMD. BMD and body compositions [including fat mass (FM), lean mass (LM), bone mass/lean mass ratio (B/L), fat mass/lean mass ratio (F/L), total adipose tissue (TAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT)] were retrospectively compared among groups using one-way ANOVA or t test. A stepwise multivariate analysis was used to evaluate the body composition contribution to BMD and produced models.

Results

In male, BMD got decreased with age (P < 0.05). LM increased before 30–49 years, then decreased (P < 0.05). TAT and SAT decreased with age (P < 0.05). LM in OP group was lower than those in the other two groups (P < 0.05). Through stepwise multivariate analysis, LM firstly got into model 1 (M1, β = 0.589). In female, BMD, LM TAT, and VAT were increased before 30–49 years, then decreased (P < 0.05). FM and F/L increased with age (P < 0.05). SAT decreased with age (P < 0.05). FM and F/L in OP group were higher than those in other groups. LM, B/L, TAT, and SAT in the OP group were lower than those in the other groups (P < 0.05). SAT entered the M1 with a maximum β value (β = 0.584).

Conclusions

BMD and body compositions displayed different characteristics with age. In male, LM showed positive effect on BMD. In female, SAT showed positive, and FM and F/L showed negative effect on BMD.

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References

  1. Hahn MH, Won YY (2016) Bone mineral density and fatty degeneration of thigh muscles measured by computed tomography in hip fracture patients. J Bone Metab 23(4):215–221

    Article  Google Scholar 

  2. Nielson CM, Srikanth P, Orwoll ES (2012) Obesity and fracture in men and women: an epidemiologic perspective. J Bone Miner Res 27(1):1–10. https://doi.org/10.1002/jbmr.1486.

    Article  PubMed  Google Scholar 

  3. Gnudi S, Sitta E, Fiumi N (2007) Relationship between body composition and bone mineral density in women with and without osteoporosis: relative contribution of lean and fat mass. J Bone Miner Metab 25(5):326–332

    Article  Google Scholar 

  4. Ahn SH, Lee SH, Kim H, Kim BJ, Koh JM (2014) Different relationships between body compositions and bone mineral density according to gender and age in Korean populations (KNHANES 2008-2010). J Clin Endocrinol Metab 99(10):3811–3820

    Article  CAS  Google Scholar 

  5. Tucker G, Metcalfe A, Pearce C, Need AG, Dick IM, Prince RL, Nordin BE (2007) The importance of calculating absolute rather than relative fracture risk. Bone 41(6):937–941

    Article  Google Scholar 

  6. Sheu Y, Marshall LM, Holton KF, Caserotti P, Boudreau RM, Strotmeyer ES, Cawthon PM, Cauley JA (2013) Abdominal body composition measured by quantitative computed tomography and risk of non-spine fractures: the osteoporotic fractures in men (MrOS) study. Osteoporos Int 24(8):2231–2241

    Article  CAS  Google Scholar 

  7. Engelke K, Adams JE, Armbrecht G, Augat P, Bogado CE, Bouxsein ML, Felsenberg D, Ito M, Prevrhal S, Hans DB, Lewiecki EM (2008) Clinical use of quantitative computed tomography and peripheral quantitative computed tomography in the management of osteoporosis in adults: the 2007 ISCD Official Positions. J Clin Densitom 11(1):123–162. https://doi.org/10.1016/j.jocd.2007.12.010

    Article  PubMed  Google Scholar 

  8. Li N, Li XM, Xu L, Sun WJ, Cheng XG, Tian W (2013) Comparison of QCT and DXA: osteoporosis detection rates in postmenopausal women. Int J Endocrinol 2013:895474

    PubMed  PubMed Central  Google Scholar 

  9. Marcus RL, Addison O, Kidde JP, Dibble LE, Lastayo PC (2010) Skeletal muscle fat infiltration: impact of age, inactivity, and exercise. J Nutr Health Aging 14(5):362–366

    Article  CAS  Google Scholar 

  10. Li GW, Tang GY, Liu Y, Tang RB, Peng YF, Li W (2012) MR spectroscopy and micro-CT in evaluation of osteoporosis model in rabbits: comparison with histopathology. Eur Radiol 22(4):923–929

    Article  Google Scholar 

  11. Zhu J, Zhang L, Wu X, Xiong Z, Qiu Y, Hua T, Tang G (2017) Reduction of longitudinal vertebral blood perfusion and its likely causes: a quantitative dynamic contrast-enhanced MR imaging study of a rat osteoporosis model. Radiology 282(2):369–380

    Article  Google Scholar 

  12. Seeman E, Delmas PD (2006) Bone quality—the material and structural basis of bone strength and fragility. N Engl J Med 354(21):2250–2261

    Article  CAS  Google Scholar 

  13. D'Antona G, Pellegrino MA, Carlizzi CN, Bottinelli R (2007) Deterioration of contractile properties of muscle fibres in elderly subjects is modulated by the level of physical activity. Eur J Appl Physiol 100(5):603–611

    Article  Google Scholar 

  14. Ahedi H, Aitken D, Scott D, Blizzard L, Cicuttini F, Jones G (2014) The association between hip muscle cross-sectional area, muscle strength, and bone mineral density. Calcif Tissue Int 95(1):64–72

    Article  CAS  Google Scholar 

  15. Kaji H (2013) Linkage between muscle and bone: common catabolic signals resulting in osteoporosis and sarcopenia. Curr Opin Clin Nutr Metab Care 16(3):272–277

    Article  Google Scholar 

  16. Karasik D, Cohen-Zinder M (2012) The genetic pleiotropy of musculoskeletal aging. Front Physiol 3:303

    Article  Google Scholar 

  17. Baldelli S, Lettieri Barbato D, Tatulli G, Aquilano K, Ciriolo MR (2014) The role of nNOS and PGC-1alpha in skeletal muscle cells. J Cell Sci 127(Pt 22):4813–4820

    Article  Google Scholar 

  18. Handschin C, Spiegelman BM (2011) PGC-1 coactivators and the regulation of skeletal muscle fiber-type determination. Cell Metab 13(4):351

    Article  CAS  Google Scholar 

  19. Terracciano C, Celi M, Lecce D, Baldi J, Rastelli E, Lena E, Massa R, Tarantino U (2013) Differential features of muscle fiber atrophy in osteoporosis and osteoarthritis. Osteoporos Int 24(3):1095–1100

    Article  CAS  Google Scholar 

  20. Kotwal N, Upreti V, Nachankar A, Hari Kumar KVS, Prospective A (2018) Observational study of osteoporosis in men. Indian J Endocrinol Metab 22(1):62–66

    Article  CAS  Google Scholar 

  21. Chung W, Lee J, Ryu OH (2014) Is the negative relationship between obesity and bone mineral content greater for older women? J Bone Miner Metab 32(5):505–513

    Article  CAS  Google Scholar 

  22. Wang L, Wang W, Xu L, Cheng X, Ma Y, Liu D, Guo Z, Su Y, Wang Q (2013) Relation of visceral and subcutaneous adipose tissue to bone mineral density in Chinese women. Int J Endocrinol 2013:378632

    PubMed  PubMed Central  Google Scholar 

  23. Glass NA, Torner JC, Letuchy EM, Burns TL, Janz KF, Eichenberger Gilmore JM, Schlechte JA, Levy SM (2018) Does visceral or subcutaneous fat influence peripheral cortical bone strength during adolescence? A longitudinal study. J Bone Miner Res 33(4):580–588

    Article  CAS  Google Scholar 

  24. Lee SH, Kim TS, Choi Y, Lorenzo J (2008) Osteoimmunology: cytokines and the skeletal system. BMB Rep 41(7):495–510

    Article  CAS  Google Scholar 

  25. Mantzoros CS, Magkos F, Brinkoetter M, Sienkiewicz E, Dardeno TA, Kim SY, Hamnvik OP, Koniaris A (2011) Leptin in human physiology and pathophysiology. Am J Physiol Endocrinol Metab 301(4):E567–E584

    Article  CAS  Google Scholar 

  26. Lee I, Cho J, Jin Y, Ha C, Kim T, Kang H (2016) Body fat and physical activity modulate the association between sarcopenia and osteoporosis in elderly Korean women. J Sports Sci Med 15(3):477–482

    PubMed  PubMed Central  Google Scholar 

  27. Dominici M, Le Blanc K, Mueller I, Slaper-Cortenbach I, Marini F, Krause D, Deans R, Keating A, Prockop D, Horwitz E (2006) Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. Cytotherapy 8(4):315–317

    Article  CAS  Google Scholar 

  28. Santos VRD, Christofaro DGD, Gomes IC, Junior IFF, Gobbo LA (2018) Relationship between obesity, sarcopenia, sarcopenic obesity, and bone mineral density in elderly subjects aged 80 years and over. Rev Bras Ortop 53(3):300–305

    Article  Google Scholar 

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Funding

The authors are supported by grants from the Project of Shanghai Shen Kang Hospital Development Center (No. SHDC22015026, 16CR4029A) and Shanghai Municipal Science and Technology Commission (No 16410722200).

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Author notes

  1. Xueli Zhang and Ting Hua contributed equally to this study and should be considered co-first authors.

Authors and Affiliations

  1. Department of Radiology, Shanghai TenthPeople’s Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, China

    Xueli Zhang, Ting Hua, Jingqi Zhu, Kun Peng, Sifeng Kang, Tingting Xu, Jian Hu & Guangyu Tang

  2. Department of Radiology, Tongren Hospital, Shanghai Jiaotong University School of Medical, Shanghai, 200336, China

    Jun Yang

Authors
  1. Xueli Zhang
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  2. Ting Hua
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  4. Kun Peng
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  5. Jun Yang
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  6. Sifeng Kang
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  8. Jian Hu
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  9. Guangyu Tang
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Corresponding author

Correspondence to Guangyu Tang.

Ethics declarations

The studies have been approved by the appropriate institutional and national research ethics committee and have been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Ethics committee of our institution approved the study, and informed consent was obtained from all individual participants included in the study.

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Zhang, X., Hua, T., Zhu, J. et al. Body compositions differently contribute to BMD in different age and gender: a pilot study by QCT. Arch Osteoporos 14, 31 (2019). https://doi.org/10.1007/s11657-019-0574-5

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