The journal of nutrition, health & aging

, Volume 23, Issue 1, pp 79–83 | Cite as

Relationship between Within-Visit Blood Pressure Variability and Skeletal Muscle Mass

  • Kwang-il KimEmail author
  • M.-G. Kang
  • S.-J. Yoon
  • J.-Y. Choi
  • S.-W. Kim
  • C.-H. Kim


Sarcopenia, defined as loss of skeletal muscle mass and function with age, is an important health issue in aging society. We tried to investigate the relationship between blood pressure variability and skeletal muscle mass in nation-wide large population cohort. This cross-sectional study was based on data acquired in the Korea National Health and Nutrition Examination Survey (KNHANES), conducted from 2009 to 2011 by the Korean Centers for Disease Control & Prevention. We included 14,481 participants (age ≥ 20 years, male 6,302) for the analysis who had both blood pressure and whole-body dual energy X-ray absorptiometry (DXA) scan data. As an intra-individual within-visit blood pressure variability index, we calculated standard deviation (SD), coefficient of variation (CV), and maximum minus minimum BP difference (MMD) of systolic and diastolic blood pressure, which was measured 3 times. Appendicular skeletal muscle mass (ASM) was the sum of lean masses of both arms and legs. We adjusted ASM by body mass index. Significant inverse relationship was observed between blood pressure variability index (SD, CV, and MMD) and adjusted ASM. Blood pressure variability index were significantly higher in the lowest ASM quintile group both in male and female participants (p<0.001). In multivariate analysis, blood pressure variability index were significantly associated with ASM, even after adjusting confounding factors (p<0.001). In conclusion, hemodynamic influence may play an important role in the development of sarcopenia.

Key words

Blood pressure variability cohort skeletal muscle mass 


  1. 1.
    Janssen I. Influence of sarcopenia on the development of physical disability: the Cardiovascular Health Study. J Am Geriatr Soc 2006;54:56–62.CrossRefGoogle Scholar
  2. 2.
    Bendall MJ, Bassey EJ, Pearson MB. Factors affecting walking speed of elderly people. Age Ageing 1989;18:327–32.CrossRefGoogle Scholar
  3. 3.
    Atkins JL, Whincup PH, Morris RW, Lennon LT, Papacosta O, Wannamethee SG. Sarcopenic obesity and risk of cardiovascular disease and mortality: a populationbased cohort study of older men. J Am Geriatr Soc 2014;62:253–60.CrossRefGoogle Scholar
  4. 4.
    Narici MV, Maffulli N. Sarcopenia: characteristics, mechanisms and functional significance. Br Med Bull 2010;95:139–59.CrossRefGoogle Scholar
  5. 5.
    Prior SJ, Ryan AS, Blumenthal JB, Watson JM, Katzel LI, Goldberg AP. Sarcopenia Is Associated With Lower Skeletal Muscle Capillarization and Exercise Capacity in Older Adults. J Gerontol A Biol Sci Med Sci 2016;71:1096–101.CrossRefGoogle Scholar
  6. 6.
    Muntner P, Whittle J, Lynch AI et al. Visit-to-Visit Variability of Blood Pressure and Coronary Heart Disease, Stroke, Heart Failure, and Mortality: A Cohort Study. Ann Intern Med 2015;163:329–38.CrossRefGoogle Scholar
  7. 7.
    Stevens SL, Wood S, Koshiaris C et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ 2016;354:i4098.CrossRefGoogle Scholar
  8. 8.
    Diaz KM, Tanner RM, Falzon L et al. Visit-to-visit variability of blood pressure and cardiovascular disease and all-cause mortality: a systematic review and meta-analysis. Hypertension 2014;64:965–82.CrossRefGoogle Scholar
  9. 9.
    Kim KI, Kim CH. Treating hypertension to reduce cardiovascular risk: a Korean perspective. Clin Ther 2012;34:1559–68.CrossRefGoogle Scholar
  10. 10.
    Studenski SA, Peters KW, Alley DE et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014;69:547–58.CrossRefGoogle Scholar
  11. 11.
    World Health Organization Western Pacific Region. International Association for the Study of Obesity Task Force: The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Health Communications Australia: Sydney, Australia, 2000, pp 15–21.Google Scholar
  12. 12.
    Schutte R, Thijs L, Liu YP et al. Within-subject blood pressure level—not variability- -predicts fatal and nonfatal outcomes in a general population. Hypertension 2012;60:1138–47.CrossRefGoogle Scholar
  13. 13.
    Grassi G, Seravalle G, Maloberti A et al. Within-visit BP variability, cardiovascular risk factors, and BP control in central and eastern Europe: findings from the BP-CARE study. J Hypertens 2015;33:2250–6.CrossRefGoogle Scholar
  14. 14.
    Rothwell PM, Howard SC, Dolan E et al. Effects of beta blockers and calciumchannel blockers on within-individual variability in blood pressure and risk of stroke. Lancet Neurol 2010;9:469–80.CrossRefGoogle Scholar
  15. 15.
    Shin JH, Shin J, Kim BK et al. Within-visit blood pressure variability: relevant factors in the general population. J Hum Hypertens 2013;27:328–34.CrossRefGoogle Scholar
  16. 16.
    Yano Y, Vongpatanasin W, Ayers C et al. Regional Fat Distribution and Blood Pressure Level and Variability: The Dallas Heart Study. Hypertension 2016;68:576–83.CrossRefGoogle Scholar

Copyright information

© Serdi and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  • Kwang-il Kim
    • 1
    Email author
  • M.-G. Kang
    • 1
  • S.-J. Yoon
    • 2
  • J.-Y. Choi
    • 1
  • S.-W. Kim
    • 1
  • C.-H. Kim
    • 1
  1. 1.Department of Internal Medicine, Seoul National University College of MedicineSeoul National University Bundang HospitalKyeongi-doRepublic of Korea
  2. 2.Department of Internal MedicineKangwon National University HospitalChuncheon, Gangwon-doRepublic of Korea

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