Skip to main content
SpringerLink
Log in

Are there effects of age, gender, height, and body fat on the functional muscle-bone unit in children and adults?

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

The aim was to describe the effect of age, gender, height, different stages of human life, and body fat on the functional muscle-bone unit. All these factors had a significant effect on the functional muscle-bone unit and should be addressed when assessing functional muscle-bone unit in children and adults.

Introduction

For the clinical evaluation of the functional muscle-bone unit, it was proposed to evaluate the adaptation of the bone to the acting forces. A frequently used parameter for this is the total body less head bone mineral content (TBLH-BMC) determined by dual-energy X-ray absorptiometry (DXA) in relation to the lean body mass (LBM by DXA). LBM correlates highly with muscle mass. Therefore, LBM is a surrogate parameter for the muscular forces acting in everyday life. The aim of the study was to describe the effect of age and gender on the TBLH-BMC for LBM and to evaluate the impact of other factors, such as height, different stages of human life, and of body fat.

Methods

As part of the National Health and Nutrition Examination Survey (NHANES) study, between the years 1999–2006 whole-body DXA scans on randomly selected Americans from 8 years of age were carried out. From all eligible DXA scans (1999–2004), three major US ethnic groups were evaluated (non-Hispanic Whites, non-Hispanic Blacks, and Mexican Americans) for further statistical analysis.

Results

For the statistical analysis, the DXA scans of 8190 non-Hispanic White children and adults (3903 female), of 4931 non-Hispanic Black children and adults (2250 female) and 5421 of Mexican-American children and adults (2424 female) were eligible. Age, gender, body height, and especially body fat had a significant effect on the functional muscle-bone unit.

Conclusions

When assessing TBLH-BMC for LBM in children and adults, the effects of age, gender, body fat, and body height should be addressed. These effects were analyzed for the first time in such a large cohort.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

BMC:

Bone mineral content

CDC:

Centre for Disease Control and Prevention

DXA:

Dual-energy X-ray absorptiometry

FM:

Fat mass

LBM:

Lean body mass

LOESS:

locally weighted scatterplot smoothing

NHANES:

National Health and Nutrition Examination Survey

TBLH:

Total body less head

References

  1. Bikle DD, Tahimic C, Chang W, Wang Y, Philippou A, Barton ER (2015) Role of IGF-I signaling in muscle bone interactions. Bone 80:79–88. https://doi.org/10.1016/j.bone.2015.04.036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Carson JA, Manolagas SC (2015) Effects of sex steroids on bones and muscles: similarities, parallels, and putative interactions in health and disease. Bone 80:67–78. https://doi.org/10.1016/j.bone.2015.04.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sartori R, Sandri M (2015) BMPs and the muscle-bone connection. Bone 80:37–42. https://doi.org/10.1016/j.bone.2015.05.023

    Article  CAS  PubMed  Google Scholar 

  4. Houweling P, Kulkarni RN, Baldock PA (2015) Neuronal control of bone and muscle. Bone 80:95–100. https://doi.org/10.1016/j.bone.2015.05.006

    Article  CAS  PubMed  Google Scholar 

  5. Wolff J (2010) Das Gesetz der Transformation der Knochen, 1st edn. Pro Business, Berlin

  6. Frost HM (2003) Bone’s mechanostat: a 2003 update. Anat Rec A Discov Mol Cell Evol Biol 275(2):1081–1101. https://doi.org/10.1002/ar.a.10119

    Article  PubMed  Google Scholar 

  7. Schönau E, Werhahn E, Schiedermaier U et al (1996) Influence of muscle strength on bone strength during childhood and adolescence. Horm Res 45(Suppl 1):63–66

    PubMed  Google Scholar 

  8. Goodman CA, Hornberger TA, Robling AG (2015) Bone and skeletal muscle: key players in mechanotransduction and potential overlapping mechanisms. Bone 80:24–36. https://doi.org/10.1016/j.bone.2015.04.014

    Article  PubMed  PubMed Central  Google Scholar 

  9. Schoenau E, Neu CM, Beck B, Manz F, Rauch F (2002) Bone mineral content per muscle cross-sectional area as an index of the functional muscle-bone unit. J Bone Miner Res 17(6):1095–1101. https://doi.org/10.1359/jbmr.2002.17.6.1095

    Article  PubMed  Google Scholar 

  10. Crabtree NJ, Kibirige MS, Fordham JN, Banks LM, Muntoni F, Chinn D, Boivin CM, Shaw NJ (2004) The relationship between lean body mass and bone mineral content in paediatric health and disease. Bone 35(4):965–972. https://doi.org/10.1016/j.bone.2004.06.009

    Article  CAS  PubMed  Google Scholar 

  11. Schoenau E (2005) The “functional muscle-bone unit”: a two-step diagnostic algorithm in pediatric bone disease. Pediatr Nephrol 20(3):356–359. https://doi.org/10.1007/s00467-004-1744-1

    Article  PubMed  Google Scholar 

  12. Ferretti JL, Capozza RF, Cointry GR, García SL, Plotkin H, Alvarez Filgueira ML, Zanchetta JR (1998) Gender-related differences in the relationship between densitometric values of whole-body bone mineral content and lean body mass in humans between 2 and 87 years of age. Bone 22(6):683–690. https://doi.org/10.1016/S8756-3282(98)00046-5

    Article  CAS  PubMed  Google Scholar 

  13. Heymsfield SB, Smith R, Aulet M, Bensen B, Lichtman S, Wang J, Pierson RN Jr (1990) Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr 52(2):214–218

    Article  CAS  PubMed  Google Scholar 

  14. Crabtree NJ, Högler W, Cooper MS, Shaw NJ (2013) Diagnostic evaluation of bone densitometric size adjustment techniques in children with and without low trauma fractures. Osteoporos Int 24(7):2015–2024. https://doi.org/10.1007/s00198-012-2263-8

    Article  CAS  PubMed  Google Scholar 

  15. Duran I, Schütz F, Hamacher S, Semler O, Stark C, Schulze J, Rittweger J, Schoenau E (2017) The functional muscle-bone unit in children with cerebral palsy. Osteoporos Int 28(7):2081–2093. https://doi.org/10.1007/s00198-017-4023-2

    Article  CAS  PubMed  Google Scholar 

  16. Fricke O, Schoenau E (2007) The ‘functional muscle-bone unit’: probing the relevance of mechanical signals for bone development in children and adolescents. Growth Hormon IGF Res 17(1):1–9. https://doi.org/10.1016/j.ghir.2006.10.004

    Article  Google Scholar 

  17. Cure-Cure C, Capozza RF, Cointry GR, Meta M, Cure-Ramírez P, Ferretti JL (2005) Reference charts for the relationships between dual-energy X-ray absorptiometry-assessed bone mineral content and lean mass in 3,063 healthy men and premenopausal and postmenopausal women. Osteoporos Int 16(12):2095–2106. https://doi.org/10.1007/s00198-005-2007-0

    Article  PubMed  Google Scholar 

  18. Capozza RF, Cure-Cure C, Cointry GR, Meta M, Cure P, Rittweger J, Ferretti JL (2008) Association between low lean body mass and osteoporotic fractures after menopause. Menopause 15(5):905–913. https://doi.org/10.1097/gme.0b013e318164ee85

    Article  PubMed  Google Scholar 

  19. CDC National Health and Nutrition Examination Survey. https://www.cdc.gov/nchs/nhanes/about_nhanes.htm

  20. Curtin LR, Mohadjer LK, Dohrmann SM et al (2012) The National Health and Nutrition Examination Survey: sample design, 1999-2006. Vital Health Stat 2(155):1–39

    Google Scholar 

  21. Crabtree NJ, Arabi A, Bachrach LK, Fewtrell M, el-Hajj Fuleihan G, Kecskemethy HH, Jaworski M, Gordon CM, International Society for Clinical Densitometry (2014) Dual-energy X-ray absorptiometry interpretation and reporting in children and adolescents: the revised 2013 ISCD pediatric official positions. J Clin Densitom 17(2):225–242. https://doi.org/10.1016/j.jocd.2014.01.003

    Article  PubMed  Google Scholar 

  22. Schoeller DA, Tylavsky FA, Baer DJ, Chumlea WC, Earthman CP, Fuerst T, Harris TB, Heymsfield SB, Horlick M, Lohman TG, Lukaski HC, Shepherd J, Siervogel RM, Borrud LG (2005) QDR 4500A dual-energy X-ray absorptiometer underestimates fat mass in comparison with criterion methods in adults. Am J Clin Nutr 81(5):1018–1025

    Article  CAS  PubMed  Google Scholar 

  23. Kelly TL, Wilson KE, Heymsfield SB (2009) Dual energy X-ray absorptiometry body composition reference values from NHANES. PLoS One 4(9):e7038. https://doi.org/10.1371/journal.pone.0007038

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kuczmarski RJ (2002) 2000 CDC growth charts for the United States: methods and development. Vital and health statistics. Series 11, data from the National Health Survey, no. 246. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville

  25. Cole TJ, Green PJ (1992) Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med 11(10):1305–1319

    Article  CAS  PubMed  Google Scholar 

  26. Royston P, Wright EM (2000) Goodness-of-fit statistics for age-specific reference intervals. Statist Med 19(21):2943–2962. https://doi.org/10.1002/1097-0258(20001115)19:21<2943:AID-SIM559>3.0.CO;2-5

    Article  CAS  Google Scholar 

  27. van Buuren S, Fredriks M (2001) Worm plot: a simple diagnostic device for modelling growth reference curves. Stat Med 20(8):1259–1277. https://doi.org/10.1002/sim.746

    Article  PubMed  Google Scholar 

  28. Jacoby WG (2000) Loess. Elect Stud 19(4):577–613. https://doi.org/10.1016/S0261-3794(99)00028-1

    Article  Google Scholar 

  29. National Center for Health Statistics (2013) National health and nutrition examination survey: Analytic guidelines, 1999–2010. DHHS publication, no. 2013–1361. U.S. Department of Health and Human Services Centers for Disease Control and Prevention National Center for Health Statistics, Hyattsville Maryland

  30. Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape (with discussion). J Royal Statistical Soc C 54(3):507–554. https://doi.org/10.1111/j.1467-9876.2005.00510.x

    Article  Google Scholar 

  31. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K (2014) Mediation: R package for causal mediation analysis. J Stat Soft 59(5). https://doi.org/10.18637/jss.v059.i05

  32. Capozza RF, Cointry GR, Cure-Ramírez P et al (2004) A DXA study of muscle-bone relationships in the whole body and limbs of 2512 normal men and pre- and post-menopausal women. Bone 35(1):283–295. https://doi.org/10.1016/j.bone.2004.03.010

    Article  CAS  PubMed  Google Scholar 

  33. Schiessl H, Frost HM, Jee WSS (1998) Estrogen and bone-muscle strength and mass relationships. Bone 22(1):1–6. https://doi.org/10.1016/S8756-3282(97)00223-8

    Article  CAS  PubMed  Google Scholar 

  34. Galea GL, Price JS, Lanyon LE (2013) Estrogen receptors’ roles in the control of mechanically adaptive bone (re)modeling. Bonekey Rep 2:413. https://doi.org/10.1038/bonekey.2013.147

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chen L, Nelson DR, Zhao Y et al. Relationship between muscle mass and muscle strength, and the impact of comorbidities: a population-based, cross-sectional study of older adults in the United States

  36. Hannam K, Deere KC, Hartley A, al-Sari UA, Clark EM, Fraser WD, Tobias JH (2016) Habitual levels of higher, but not medium or low, impact physical activity are positively related to lower limb bone strength in older women: findings from a population-based study using accelerometers to classify impact magnitude. Osteoporos Int 28:2813–2822. https://doi.org/10.1007/s00198-016-3863-5

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bjørnerem Å, Bui QM, Ghasem-Zadeh A, Hopper JL, Zebaze R, Seeman E (2013) Fracture risk and height: an association partly accounted for by cortical porosity of relatively thinner cortices. J Bone Miner Res 28(9):2017–2026. https://doi.org/10.1002/jbmr.1934

    Article  PubMed  Google Scholar 

  38. Mueller SM, Herter-Aeberli I, Cepeda-Lopez AC, Flück M, Jung HH, Toigo M (2017) The effect of body composition and serum inflammatory markers on the functional muscle-bone unit in premenopausal women. Int J Obes 41(8):1203–1206. https://doi.org/10.1038/ijo.2017.100

    Article  CAS  Google Scholar 

  39. Kawai M, de Paula FJA, Rosen CJ (2012) New insights into osteoporosis: the bone-fat connection. J Intern Med 272(4):317–329. https://doi.org/10.1111/j.1365-2796.2012.02564.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Aloia JF, Vaswani A, Ma R, Flaster E (1995) To what extent is bone mass determined by fat-free or fat mass? Am J Clin Nutr 61(5):1110–1114

    Article  CAS  PubMed  Google Scholar 

  41. Ackerman A, Thornton JC, Wang J, Pierson RN, Horlick M (2006) Sex difference in the effect of puberty on the relationship between fat mass and bone mass in 926 healthy subjects, 6 to 18 years old. Obesity (Silver Spring) 14(5):819–825. https://doi.org/10.1038/oby.2006.95

    Article  Google Scholar 

  42. Berenson AB, Breitkopf CR, Newman JL, Rahman M (2009) Contribution of fat-free mass and fat mass to bone mineral density among reproductive-aged women of white, black, and Hispanic race/ethnicity. J Clin Densitom 12(2):200–206. https://doi.org/10.1016/j.jocd.2009.01.002

    Article  PubMed  PubMed Central  Google Scholar 

  43. Liu P-Y, Ilich JZ, Brummel-Smith K, Ghosh S (2014) New insight into fat, muscle and bone relationship in women: determining the threshold at which body fat assumes negative relationship with bone mineral density. Int J Prev Med 5(11):1452–1463

    PubMed  PubMed Central  Google Scholar 

  44. Rico H, Revilla M, Villa LF, del Buergo MA, Ruiz-Contreras D (1994) Determinants of total-body and regional bone mineral content and density in postpubertal normal women. Metab Clin Exp 43(2):263–266

    Article  CAS  PubMed  Google Scholar 

  45. Zhu K, Briffa K, Smith A, Mountain J, Briggs AM, Lye S, Pennell C, Straker L, Walsh JP (2014) Gender differences in the relationships between lean body mass, fat mass and peak bone mass in young adults. Osteoporos Int 25(5):1563–1570. https://doi.org/10.1007/s00198-014-2665-x

    Article  CAS  PubMed  Google Scholar 

  46. Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6):1173–1182

    Article  CAS  PubMed  Google Scholar 

  47. Zhao L-J, Liu Y-J, Liu P-Y, Hamilton J, Recker RR, Deng HW (2007) Relationship of obesity with osteoporosis. J Clin Endocrinol Metab 92(5):1640–1646. https://doi.org/10.1210/jc.2006-0572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Heaney RP (1995) Bone mass, the mechanostat, and ethnic differences. J Clin Endocrinol Metab 80(8):2289–2290. https://doi.org/10.1210/jcem.80.8.7629221

    CAS  PubMed  Google Scholar 

  49. Shepherd JA, Lu Y, Wilson K, Fuerst T, Genant H, Hangartner TN, Wilson C, Hans D, Leib ES, International Society for Clinical Densitometry Committee on Standards of Bone Measurement (2006) Cross-calibration and minimum precision standards for dual-energy X-ray absorptiometry: the 2005 ISCD Official Positions. J Clin Densitom 9(1):31–36. https://doi.org/10.1016/j.jocd.2006.05.005

    Article  PubMed  Google Scholar 

  50. Bauer DC, Glüer CC, Cauley JA, Vogt TM, Ensrud KE, Genant HK, Black DM (1997) Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. A prospective study. Study of Osteoporotic Fractures Research Group. Arch Intern Med 157(6):629–634

    Article  CAS  PubMed  Google Scholar 

  51. National Center for Health Statistics NCHS Data Brief, Number 288, October 2017

  52. Siris ES, Adler R, Bilezikian J, Bolognese M, Dawson-Hughes B, Favus MJ, Harris ST, Jan de Beur SM, Khosla S, Lane NE, Lindsay R, Nana AD, Orwoll ES, Saag K, Silverman S, Watts NB (2014) The clinical diagnosis of osteoporosis: a position statement from the National Bone Health Alliance Working Group. Osteoporos Int 25(5):1439–1443. https://doi.org/10.1007/s00198-014-2655-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Cointry GR, Capozza RF, Ferretti SE, Meta MD, Feldman S, Capiglioni R, Reina P, Fracalossi NM, Ulla MR, Cure-Cure C, Ferretti JL (2005) Absorptiometric assessment of muscle-bone relationships in humans: reference, validation, and application studies. J Bone Miner Metab 23(S1):109–114. https://doi.org/10.1007/BF03026334

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Prevention and Rehabilitation, UniReha, University of Cologne, Lindenburger Allee 44, 50931, Cologne, Germany

    I. Duran & E. Schoenau

  2. Children’s and Adolescents’ Hospital, University of Cologne, Cologne, Germany

    K. Martakis, C. Stark, O. Semler & E. Schoenau

  3. Department of International Health, School CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands

    K. Martakis

  4. Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany

    S. Hamacher

  5. Cologne Centre for Musculoskeletal Biomechanics, University of Cologne, Cologne, Germany

    C. Stark

  6. Center for Rare Skeletal Diseases in Childhood, University of Cologne, Cologne, Germany

    O. Semler

Authors
  1. I. Duran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Martakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Hamacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Stark
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. O. Semler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. Schoenau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Duran.

Ethics declarations

Conflicts of interest

None.

Electronic supplementary material

ESM 1

ᅟ (DOCX 12.6 kb)

ESM 2

ᅟ (DOCX 12.6 kb)

ESM 3

ᅟ (DOCX 13.6 kb)

ESM 4

ᅟ (DOCX 13.5 kb)

eFigure 1

. Age, gender, height, and body fat percentage effects on TBLH-BMC for LBM (non-Hispanic White). Lines indicate 3rd, 50th, and 97th centiles of the age-related distribution of TBLH-BMC for LBM (figure on the top). Data from the non-Hispanic White NHANES population (1999–2004) are shown. The DXA scans of 8190 non-Hispanic White children and adults (3903 females) were eligible. (GIF 94 kb)

High resolution image (TIFF 839 kb)

eFigure 2

. Age, gender, height, and body fat percentage effects on TBLH-BMC for LBM (non-Hispanic Black). Lines indicate 3rd, 50th, and 97th centiles of the age-related distribution of TBLH-BMC for LBM (figure on the top). Data from the non-Hispanic Black NHANES population (1999–2004) are shown. The DXA scans of 4931 non-Hispanic Black children and adults (2250 female) were eligible. (GIF 93 kb)

High resolution image (TIFF 836 kb)

eFigure 3

. Age, gender, height, and body fat percentage effects on TBLH-BMC for LBM (Mexican American). Lines indicate 3rd, 50th, and 97th centiles of the age-related distribution of TBLH-BMC for LBM (figure on the top). Data from the Mexican American NHANES population (1999–2004) are shown. The DXA scans of 5421 of Mexican-American children and adults (2424 female) were eligible. (GIF 93 kb)

High resolution image (TIFF 840 kb)

eFigure 4

Body height effect on TBLH-BMC for LBM. Each dot indicates a single proband (only NHANES population 2005–2006). The LOESS regression curve is depicted. Data from the “non-Hispanic White” NHANES population (1999–2004 and 2005–2006) are shown. The DXA scans of 2349 non-Hispanic White children and adults (1139 females) were eligible. (GIF 66 kb)

High resolution image (TIFF 641 kb)

eFigure 5

Body fat percentage effect on TBLH-BMC for LBM. Each dot indicates a single proband (only NHANES population 2005–2006). The LOESS regression curve is depicted. Data from the “non-Hispanic White” NHANES population (1999–2004 and 2005–2006) are shown. (GIF 70 kb)

High resolution image (TIFF 651 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duran, I., Martakis, K., Hamacher, S. et al. Are there effects of age, gender, height, and body fat on the functional muscle-bone unit in children and adults?. Osteoporos Int 29, 1069–1079 (2018). https://doi.org/10.1007/s00198-018-4401-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-018-4401-4

Keywords

Search

Navigation