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標題: | 台灣女性之腎功能、停經狀態與骨質密度關聯性研究 Association of Renal Function, Menopausal Status, with Bone Mineral Density in Taiwanese Women |
作者: | Yueh-Hsuan Sheng 盛業瑄 |
指導教授: | 程蘊菁(Yen-Ching Chen) |
關鍵字: | 腎功能,停經狀態,中藥使用,骨質密度, renal function,menopausal status,use of Chinese herb,bone mineral density, |
出版年 : | 2012 |
學位: | 碩士 |
摘要: | 背景介紹
研究發現慢性腎臟疾病與骨質疏鬆症相關聯。少數幾個研究曾探討輕度慢性腎臟疾病與骨質密度間關聯性,但是停經作用是否影響此關聯卻從未被探討過且無研究同時探討停經前及之後的個案。 材料與方法 此為一個橫斷性研究。於2009-2010年間在台灣台北市美兆健檢中心收案,共招募1,575位受試者,年紀40-55歲停經前與停經後婦女。骨質密度使用雙光子骨密度檢測法 (dual-energy x-ray absorptiometry, DXA)測量受試者的腰椎骨質密度。腎功能估計使用Modification of Diet in Renal Disease (MDRD)公式和Cockcroft-Gault公式。統計方式使用多變項邏輯式迴歸分析及一般線性迴歸分析探討腎功能與骨質密度間關聯性。另外,根據停經狀態與是否使用中藥分別做分層分析。 結果 較低的估計腎絲球過濾速率,其低骨質密度的風險會降低 (估計腎絲球過濾速率: <80 vs. ≥ 80 毫升/分鐘/1.73平方公尺: 校正勝算比=0.63, 95%信賴區間=0.48-0.82)。較低的肌酸酐清除率則會增加低骨質密度的風險(肌酸酐清除率: <78 vs. ≥ 78 毫升/分鐘,校正勝算比=1.42,95%信賴區間 =1.10-1.84)。分層之後,較低的估計腎絲球過濾速率比起較高者在停經前婦女(估計腎絲球過濾速率< 80 vs. ≥ 80 毫升/分鐘/1.73平方公尺:校正勝算比=0.55,95%信賴區間=0.40-0.75),以及沒有使用中藥的婦女(估計腎絲球過濾速率: <80 vs. ≥ 80毫升/分鐘/1.73平方公尺,校正勝算比=0.59,95%信賴區間=0.45-0.77),低骨質密度的風險會降低。使用中藥與否會影響估計腎絲球過濾速率與骨質密度間的關聯性(P值=0.03)。於停經後婦女卻觀察到與停經前婦女相反的結果(估計腎絲球過濾速率: <80 vs. ≥ 80毫升/分鐘/1.73平方公尺,校正勝算比=2.21,95% 信賴區間=1.43-3.41; < 80毫升/分鐘/1.73平方公尺,校正勝算比=2.05,95%信賴區間=1.30-3.23)。較低的肌酸酐清除率則在停經前的婦女(肌酸酐清除率: <78 vs. ≥ 78 毫升/分鐘,校正勝算比=1.36, 95%信賴區間= 1.02-1.81),停經後的婦女 (肌酸酐清除率: ≥ 78 毫升/分鐘,校正勝算比=2.49,95%信賴區間=1.52-4.07; <78毫升/分鐘: 校正勝算比=3.96,95%信賴區間=2.57-6.12),以及沒有使用中藥的婦女 (校正勝算比=1.46,95%信賴區間= 1.12-1.90),均會增加低骨質密度的風險。骨質密度在停經前婦女較停經後顯著的較高 (1.21 vs. 1.10 克/平方公尺, p值<0.0001)。 結論 較低的肌酸酐清除率會增加低骨質密度的風險,尤其在停經前、停經後婦女與沒有使用中藥的婦女。估計腎絲球過濾速率有著相反的結果,可能是由於公式中沒有包含體重,而體重是骨質密度的一個很重要預測因子。 Introduction: Chronic kidney disease has been associated with osteoporosis. Few studies have explored the association between mild renal dysfunction and bone mineral density (BMD). In addition, menopausal status was not considered while assessing this association as previous studies only included postmenopausal women. Material and Methods: This is a cross-sectional study. A total of 1,575 women aged 40 to 55 were recruited from MJ health screening center in Taipei between 2009 and 2010. Spinal BMD was assessed by dual-energy x-ray absorptiometry. Renal function was estimated by Modification of Diet in Renal Disease equation and Cockcroft-Gault equation. Multivariate logistic regression model and generalized linear model were applied to assess the association between renal function and BMD. Stratification analyses were performed by menopausal status and use of Chinese herb, respectively. Results: Low estimated glomerular filtration rate (eGFR) was associated with decreased risk of low BMD [eGFR :<80 vs. ≥ 80 mL/mins/1.73m2, adjusted odds ratio (AOR) =0.63, 95% confidence interval (CI)=0.48-0.82]. In contrast, low creatinine clearance rate (CCr) was associated with increased risk of low BMD (CCr : <78 vs. ≥ 78 mL/mins, AOR=1.42, 95% CI=1.10-1.84). BMD level was significantly different between pre- and postmenopausal women (1.21 vs. 1.10 g/m2, p<0.0001). Chinese herb, but not menopausal status, significantly modify the association between eGFR and the risk of low BMD (P interaction=0.03). After stratification, low eGFR was associated with low BMD in premenopausal women (eGFR < 80 vs. ≥ 80 mL/mins/1.73m2: AOR=0.55, 95% CI=0.40-0.75), and in women who did not use of Chinese herb (eGFR: <80 vs. ≥ 80 mL/mins/1.73m2, AOR=0.59, 95% CI=0.45-0.77). An opposite association was observed in postmenopausal women (eGFR; ≥ 80 mL/mins/1.73m2: AOR=2.21, 95% CI=1.43-3.41; < 80 mL/mins/1.73m2: AOR=2.05, 95% CI=1.30-3.23). Low CCr was associated with increased risk of low BMD in premenopausal women (CCr <78 vs. ≥ 78 mL/mins AOR=1.36, 95% CI= 1.02-1.81), postmenopausal women ( CCr ≥ 78 mL/mins: AOR=2.49, 95% CI=1.52-4.07; <78 mL/mins: AOR=3.96, 95% CI=2.57-6.12) , and women who did not use of Chinese herb (CCr < 78 vs. ≥ 78 mL/mins: AOR=1.46, 95% CI= 1.12-1.90). Conclusion: Low CCr was associated with increased risk of low BMD, especially in premenopausal, postmenopausal women, and women who did not use of Chinese herb, respectively. In contrast, eGFR showed opposite association because the important predictor of BMD, body weight, was not incorporated in it. |
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