Journal of environmental and Sanitary engineering (환경위생공학)

Korean Society for Environmental Sanitary Engineers (대한환경위생공학회)
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Patterns of Bone Mineral Density and Its Causal Factors among Korean Adult Women

한국 성인여성의 골밀도와 관련요인에 관한 연구(II)

  • You, Moo-Hyun (Department of Environmental Health Science Soonchunhyang University) ;
  • Son, Bu-Soon (Department of Environmental Health Science Soonchunhyang University) ;
  • Park, Jong-An (Department of Environmental Health Science Soonchunhyang University) ;
  • Yang, Won-Ho (Department of Occupational HealthCatholic University of Daegu) ;
  • Kim, Jong-O (Department of Environmental Hygiene, Dong Nam Health College) ;
  • Jang, Bong-Ki (Department of Environmental Health Science Soonchunhyang University)
  • 유무현 (순천향대학교 환경보건학과) ;
  • 손부순 (순천향대학교 환경보건학과) ;
  • 박종안 (순천향대학교 환경보건학과) ;
  • 양원호 (대구가톨릭대학교 산업환경보건학과) ;
  • 김종오 (동남보건대학 환경위생과) ;
  • 장봉기 (순천향대학교 환경보건학과)
  • Published : 2006.06.01
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Abstract

This study was to investigate relationship between osteoporosis and various factors. We compared the result in 390 women with under -2.5 bone mineral density (BMD, T-scores) with those in 370 women with over -1.0 BMD. According to WHO criteria, women with -2.5 BMD are regarded as patient with osteoporosis, while with above -1.0 BMD are healthy. We obtained the BMD(T-scores) data of 9.704 adult women over 20 year old. The following are the results of this study: The mean age of interviewed women was 45.7 years and their menopausal age was 48.6 years. Means of height and weight were 157.3cm and 56.9Kg respectively. The BMD was the highest in 30-34 year-old women, an average ranges were in the 35-44 year old group. The BMD decreased after 45 year-old. The early sixties women began to experience thiness of the BMD and the mid-sixties showed symptoms of osteoporosis. The formular showing the relationship between age and BMD can be represented as $Y=9.71X^2-2.71X+0.06$ (p<0.001). The bone mineral density decreased significantly with increases of age(p<0.001) and decreases of weight(p<0.001) in multiple regression analysis using age, weight, menopause age, height, number of pregnancies, number of children, and age as independent variables.

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