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Physical Fitness Components Are Bone Mineral Density Predictors in Adulthood: Cross-sectional Study

Abstract

Background: Health-related physical fitness (HRPF) attributes are considered important markers beneficial to various health outcomes. However, the literature is divergent regarding HRPF and bone health in adulthood, especially due to the end of the second and beginning of the third decades of life when the peak bone mass period occurs.

Objective: To analyze which HRPF variables are areal bone mineral density (aBMD) predictors in adult males and females.

Methods: This study evaluated 137 healthy young adults aged 18-25 years (50% males). Dual-energy X-ray absorptiometry (DXA) was used to estimate fat mass and lean mass and aBMD, hand grip strength test, sit-ups test, flexibility test, lower limb muscle strength and 20-meter run were used to evaluate physical fitness. Multiple linear regression using the backward method was used to analyze bone mineral density predictors by sex.

Results: HRPF indicators showed correlations from R = 0.28 in the right femoral neck aBMD to R = 0.61 in the upper limbs aBMD in males; in females, correlations from R = 0.27 in total body aBMD to R = 0.68 in the lower limbs aBMD were found. In males, body mass and HRPF indicators were aBMD predictors with HRPF indicators explaining variance from R²=0.214 in the lumbar spine to R²=0.497 in the upper limbs, and in females, with the exception of the lumbar spine, variance from R²=0.237 in the right femoral neck aBMD to R²=0.442 in the lower limbs aBMD was found.

Conclusion: Health-related physical fitness components were able to predict aBMD in different anatomical regions in young adults, especially muscle strength and cardiorespiratory fitness indicators for males, while only lean mass and fat mass for females.

References
1.
Chen F, Su Q, Tu Y, Zhang J, Chen X, Zhao T . Maximal muscle strength and body composition are associated with bone mineral density in chinese adult males. Medicine (Baltimore). 2020; 99(6):e19050. PMC: 7015573. DOI: 10.1097/MD.0000000000019050. View

2.
Xiao P, Cui A, Hsu C, Peng R, Jiang N, Xu X . Global, regional prevalence, and risk factors of osteoporosis according to the World Health Organization diagnostic criteria: a systematic review and meta-analysis. Osteoporos Int. 2022; 33(10):2137-2153. DOI: 10.1007/s00198-022-06454-3. View

3.
Mukaka M . Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2013; 24(3):69-71. PMC: 3576830. View

4.
Aziziyeh R, Amin M, Habib M, Garcia Perlaza J, Szafranski K, McTavish R . The burden of osteoporosis in four Latin American countries: Brazil, Mexico, Colombia, and Argentina. J Med Econ. 2019; 22(7):638-644. DOI: 10.1080/13696998.2019.1590843. View

5.
Shigaki G, L Barbosa C, Batista M, Romanzini C, Goncalves E, Serassuelo Junior H . Tracking of health-related physical fitness between childhood and adulthood. Am J Hum Biol. 2019; 32(4):e23381. DOI: 10.1002/ajhb.23381. View