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Prediction Model for Reduced Bone mass in Women using Individual Characteristics & Life Style Factors

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KMID : 0604719980050010083
ÀÌÀº³² ( Lee Eun-Nam ) - µ¿ÀÇ´ëÇб³ ÇÑÀÇ°ú´ëÇÐ

ÀÌÀº¿Á (  ) - ¼­¿ï´ëÇб³ °£È£´ëÇÐ ±³¼ö

Abstract

°á ·Ð
º» ¿¬±¸´Â °ñ¹Ðµµ ¿¹ÃøÀÎÀÚ¿¡ ´ëÇÑ Æ÷°ýÀûÀÎ ºÐ¼®À» ÅëÇØ ÁÖ¿ä ¿¹Ãø¿äÀÎÀ» ±Ô¸íÇÏ°í, °ñ
·®ÀÌ °¨¼ÒµÈ »ç¶÷°ú Á¤»óÀûÀÎ »ç¶÷À» ±¸ºÐÇÒ ¼ö ÀÖ´Â ¿¹Ãø¸ðÇüÀ» ±¸ÃàÇϹǷμ­ È¿À²ÀûÀÎ °ñ
´Ù°øÁõ°ü¸® ÇÁ·Î±×·¥°³¹ßÀ» ¸ð»öÇϱâ À§ÇØ ½ÃµµµÇ¾ú´Ù.
À̸¦ À§ÇÏ¿© 1997³â 7¿ù 15ÀϺÎÅÍ 10¿ù¸»±îÁö 4°³¿ù°£ ºÎ»ê ¹× ¼­¿ï¿¡ À§Ä¡ÇÑ 3°³ÀÇ Á¾ÇÕ
º´¿ø°ú 1°³ Á¾ÇÕ°ËÁø¼¾ÅÍ¿¡¼­ 28-76¼¼ÀÇ 417¸í ¿©¼ºÀ» ´ë»óÀ¸·Î °ñ¹Ðµµ¿¡ ¿µÇâÀ» ÁÖ´Â À§
Çè¿äÀο¡ ´ëÇÑ Æ÷°ýÀûÀÎ Á¤º¸¸¦ ¼öÁýÇÏ¿´°í, ÀÌÁß¿¡³ÊÁö X¼± Èí¼ö°èÃø¹ý(DXA)À» ÀÌ¿ëÇÏ¿©
¿äÃß°ñ°ú ´ëÅð°ñ Ward »ï°¢ºÎÀ§¿¡¼­ °ñ¹Ðµµ¸¦ ÃøÁ¤ÇÏ¿´´Ù.
º» ¿¬±¸¸¦ ÅëÇØ ¾òÀº °á°ú´Â ´ÙÀ½°ú °°´Ù.
1) ¿äÃß°ñ °ñ·®°¨¼Ò ¿©ºÎÀÇ ÁÖ¿ä ¿¹Ãø¿äÀÎÀº ¿¬·É, üÁß, Æó°æ¿©ºÎ, 1ÀÏ È°µ¿´ë»ç·®, °ú°Å
¿ìÀ¯¼·Ãë ½À°ü ¹× °ú°Å ¿îµ¿½À°üÀ̾ú°í ´ëÅð°ñ Ward »ï°¢ºÎÀ§ÀÇ °ñ·®°¨¼Ò ¿©ºÎÀÇ ÁÖ¿ä ¿¹
Ãø¿äÀÎÀº ¿¬·É, üÁß, ±³À°Á¤µµ, °ú°Å ¿ìÀ¯¼·Ãë ½À°ü°ú °ú°Å ¿îµ¿½À°üÀ̾ú´Ù(p<.0001).
2) º» ¿¬±¸°á°ú ±¸ÃàµÈ ¿¹ÃøÀÇÇüÀÇ ¿äÃß°ñ¿¡¼­ÀÇ °ñ·®°¨¼Ò ¿©ºÎ¿¡ ´ëÇÑ ¿¹ÃøÀûÁß·üÀº
80.6%ÀÌ¸ç ´ëÅð°ñ Ward »ï°¢ºÎÀ§¿¡¼­ÀÇ ¿¹ÃøÀûÁß·üÀº 79.4%ÀÎ °ÍÀ¸·Î ³ªÅ¸³µ´Ù.
3) °³ÀÎÀû Ư¼º Áß ½ÅÀå(¿äÃß°ñ : r=.25, p<.001, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ : r=.22, p<.001)°ú
üÁß(¿äÃß°ñ : r=19, p<.01, ´ëÅð°ñ Ward »ï°¢µÎÀ§ : r=.24, p<.001)Àº °ñ¹Ðµµ¿Í À¯ÀÇÇÑ »ó°ü
°ü°è¸¦ º¸¿´´Ù.
4) °³ÀÎÀû Ư¼º Áß ¿¬·É±º¿¡ µû¶ó ¿äÃß°ñ°ú ´ëÅð°ñ Ward »ï°¢ºÎÀ§ÀÇ °ñ·®°¨¼Ò ¿©ºÎ´Â À¯
ÀÇÇÑ Â÷ÀÌ°¡ ÀÖ´Â °ÍÀ¸·Î ³ªÅ¸³ª ¿¬·ÉÀÌ ³ôÀº ±º¿¡¼­ ¿¬·ÉÀÌ ³·Àº ±ºº¸´Ù °ñ·®°¨¼Ò ¿©¼ºÀÌ
À¯ÀÇÇÏ°Ô ¸¹¾Ò´Ù(¿äÃß°ñ : X2=77.15, P<.001, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ :
X2=48.20, p<.001).
5) °³ÀÎÀû Ư¼º Áß ±³À°Á¤µµ¿¡ µû¶ó ¿äÃß°ñ°ú ´ëÅð°ñ Ward »ï°¢ºÎÀ§ÀÇ °ñ·®°¨¼Ò ¿©ºÎ´Â
À¯ÀÇÇÑ Â÷ÀÌ°¡ ÀÖ´Â °ÍÀ¸·Î ³ªÅ¸³ª ±³À°Á¤µµ°¡ ³ôÀº ±º¿¡¼­ °ñ·® °¨¼Ò¿©¼ºÀÌ Àû¾ú´Ù(¿äÃß°ñ
: X2=21.49, p<.001, ´ëÅð°ñ Ward»ï°¢ºÎÀ§ : X2=12.99, p<.05).
6) °³ÀÎÀû Ư¼º Áß °¡Á··Â°ú ¿äÃß°ñ ¹× ´ëÅð°ñ Ward »ï°¢ºÎÀ§ÀÇ °ñ·®°¨¼Ò ¿©ºÎ¿Í´Â ¹«°ü
ÇÑ °ÍÀ¸·Î ³ªÅ¸³µ´Ù(¿äÃß°ñ : X2=1.93, p=0.38, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ :
X2=1.57, p=0.46).
7) ¿ù°æ·Â¿¡¼­´Â Ãʰ濬·ÉÀÌ ´ÊÀ»¼ö·Ï(¿äÃß°ñ : r=-.14, p<.01, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ :
r=-.12, p<.05), Æó°æ °æ°ú±â°£ÀÌ ±æ¼ö·Ï(¿äÃß°ñ : r=-.32, p<.001, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ :
r=-.35, p<.001)°ñ¹Ðµµ°¡ ³·Àº °ÍÀ¸·Î ³ªÅ¸³µ´Ù.
8)Ãâ»ê·ÂÀº °ñ¹Ðµµ¿Í À¯ÀÇÇÑ »ó°ü°ü°è¸¦ º¸¿© Ãâ»êÈܼö°¡ ¸¹À»¼ö·Ï °ñ¹Ðµµ°¡ ³·Àº °ÍÀ¸·Î
³ªÅ¸³µÀ¸¸ç(¿äÃß°ñ : r=-.29, p<.001, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ : r=-.33, p<.001), ¸ðÀ¯¼öÀ¯±â°£
ÀÌ ±ä ±×·ì¿¡¼­ ªÀº ±×·ìº¸´Ù °ñ·®°¨¼Ò ¿©¼ºÀÌ À¯ÀÇÇÏ°Ô ¸¹¾Ò´Ù(¿äÃß°ñ
X2=6.39, p<.05, ´ëÅð°ñ Ward »ïÀÚºÎÀ§ : X2=.751, p<.05).
9) Æó°æ¿©ºÎ¿¡ µû¶ó °ñ·®°¨¼Ò ¿©°Ý´Â À¯ÀÇÇÑ Â÷ÀÌ°¡ ÀÖ¾î Æó°æ±×·ì¿¡¼­ Æó°æµÇÁö ¾ÊÀº ±×
·ì º¸´Ù °ñ·®°¨¼Ò ¿©¼ºÀÌ À¯ÀÇÇÏ°Ô ¸¹¾Ò´Ù(¿äÃß°ñ : X2=60.68, p<.001, ´ëÅð°ñ
Ward »ï°¢ ºÎÀ§ : X2=22.73, p<.001).
10) »ýÈ°¾ç½Ä¿äÀÎ Áß ÇöÀçÀÇ È°µ¿Á¤µµ¸¦ ³ªÅ¸³»´Â È°µ¿´ë»ç·®Àº °ñ¹Ðµµ¿Í ³ôÀº »ó°ü¼ºÀ»
º¸¿´´Ù(¿äÃß°ñ : r=.52, p<.001, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ : r=.54, p<.001).
11) Çе¿±â, û¼Ò³â±â, ¼º³âÃʱ⿡ ¿ìÀ¯¸¦ ÀÚÁÖ ¼·ÃëÇÑ ±×·ìÀº ÀÚÁÖ ¼·ÃëÇÏÁö ¾ÊÀº ±×·ìº¸
´Ù °ñ·®°¨¼Ò ¿©¼ºÀÌ À¯ÀÇÇÏ°Ô Àû¾ú´Ù(¿äÃß°ñ : X2=59.76, p<.001, ´ëÅð°ñ
Ward »ï°¢ºÎÀ§ : X2=62.63, p<.001).
12) Çе¿±â, û¼Ò³â±â, ¼º³âÃʱ⿡ ¿îµ¿À» ÀÚÁÖ ½Ç½ÃÇÑ ±×·ìÀº ÀÚÁÖ ½Ç½ÃÇÏÁö ¾ÊÀº ±×·ìº¸
´Ù °ñ·®°¨¼Ò ¿©¼ºÀÌ À¯ÀÇÇÏ°Ô Àû¾ú´Ù(¿äÃß°ñ : X2=98.06, p<.001, ´ëÅð°ñ
Ward »ï°¢ºÎÀ§ : X2=121.18, p<.001).
13) »ýÈ°¾ç½Ä¿äÀÎ Áß Èí¿¬±â°£(¿äÃß°ñ : X2=0.72, p=0.40, ´ëÅð°ñ Ward »ï°¢
ºÎÀ§ : X2=2.45, p=0.11), À½ÁֱⰣ(¿äÃß°ñ : X2=0.32, p=0.57, ´ë
Åð°ñ Ward »ï°¢ºÎÀ§ : X2=0.82, p=0.37) ¹× Ä¿ÇǼ·ÃëÁ¤µµ(¿äÃß°ñ :
X2=5.75, p=0.12, ´ëÅð°ñ Ward »ï°¢ºÎÀ§ : X2=3.68, p=0.30)´Â
°ñ·®°¨¼Ò ¿©ºÎ¿Í ¹«°üÇÑ °ÍÀ¸·Î ³ªÅ¸³µ´Ù.

This study was carried out to identify the Important modifiable risk factors for
reduced bone mass and to construct prediction model which can classify women with
either low or high bone mass.
Through the literature review, individual characteristics such as age, body weight,
height, education level, family history, age of menarche, postmenopausal period, gravity,
partly, menopausal status, and breast feeding period were identified and factors of life
style such as past milk consumption, past physical activity, present daily activity,
present calcium intake, alcohol intake, cigarette smoking, coffee consumption were
identified as influencing factors of reduced bone mass in women.
Four hundred and eighty women aged between 28 and 76 who had given
measurement bone mineral density by dual energy x-ray absortiometry in lumbar
vertebrae and femur from July to October, 1997 at 4 general hospitals in Seoul and
Pusan were selected for this study. Women were excluded if they had a history of any
chronic illness such as rheumatoid arthritis, diabetes mellitus, hyperthroidism, &
gastrointestinal disorder and any medication such as calcium supplements, calcitonin,
estrogen, thyroxine, antacids, & corticosteroids known affect bone.
As a result of these exclusion criteria, four hundred and seventeen women were used
for analysis.
Multiple logistic regression model was developed for estimating the likelihood of the
presence or absence of reduced bone mass. A SAS procedure was used to estimate risk
factor coefficient. The results are as follows :
For lumbar spine, the variables significant were age, body weight, menopause status,
daily activity, past milk consumption, and past physical activity(p<0.01), while for
femoral Ward's triangle, age, body weight, level of education, past milk consumption,
past physical activity (p<0.001).
Past physical activity, present daily activity and past milk consumption are the most
powerful modifiable predictors in vertebrae and femur among the predictors. When the
model performance was evaluated by comparing the observed outcome with predicted
outcome, the model correctly identified 74.1% of persons with reduced bone mass and
84.5% of persons with normal bone mass in the lumbar vertebrae and 82.9% of persons
with reduced bone mass and 75.0% of persons with normal bone mass in the femoral
Ward's triangle.
On the basis of these results, a number of recommendations for the management of
reduced bone mass may be model.
First, those woman who are classified as high risk group of the reduced bone mass in
the prediction model should examine the bone mineral density to further examine the
usefulness of this model.
Second, the optimal amount of milk consumption and a regular weight bearing
exercise in childhood, adolescence, and early adult should be ensured.

KeyWords

women, osteoporosis, risk factor, logistic regression model,
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