臺灣博碩士論文加值系統

研究背景及目的：骨質疏鬆症是重要的公共衛生議題，骨質疏鬆症會增加骨折風險及隨後的罹病率與死亡率。目前所知有多個因子與低骨密度有關。過去研究著重於建立停經後婦女骨質疏鬆症的預測模型，然而骨少症的預測模型有其需要性。本研究目的乃針對40-55歲女性，建立一個預測骨少症風險的模型。
研究方法：本研究為橫斷式研究，招募了1350位在西元2009至2010年間參加某健檢中心健康檢查之40到55歲女性。本研究的主要依變項為骨少症(-1 ≥ 骨密度T質 > -2.5)。本研究的潛在干擾因子包括年齡、體重或身體質量指數、腰圍、吸菸狀態、酒精攝取、規則運動與否、血壓(收縮壓/舒張壓)、血清空腹血糖、膽固醇、三酸甘油脂、肌酸酐、鹼性磷酸酶的濃度、補充鈣與否、以及停經狀態。
研究結果：本研究建立「骨質疏鬆症臨床前期評估工具」(Osteoporosis Preclinical Assessment Tool, OPAT)，此預測模型包括數個在生物學上與骨少症有關的重要變數，以及多變項分析有統計上顯著關係(p < 0.05)的變數。這些變數包括年齡、停經狀態、體重及血清鹼性磷酸酶濃度。OPAT的總分介於0到7分，可將女性區分為低、中及高風險等三組，此工具的預測力(receiver operating characteristic curve, 曲線下的面積)為0.77，比亞太骨質疏鬆症風險評估工具(Osteoporosis Self-assessment Tool for Asians)的預測力0.69還要好。本預測模型中的血清總鹼性磷酸酶濃度易於常規之健康檢查時取得，能顯著增加預測40-55歲的女性骨少症的敏感性(McNemar檢定，p = 0.004)。
結論：我們的研究結果建立了一個重要的骨少症預測模型，可用於臨床前期評估40-55歲婦女罹患骨質疏鬆症之風險。

Background: Osteoporosis is a major public health issue because it has been associated with increased fracture risk and the subsequent morbidity and mortality. Several factors have been related to low bone mineral density (BMD). Previous prediction models have focused on osteoporosis in postmenopausal women. A prediction tool for osteopenia is needed. The objective of the study was to establish a prediction model for osteopenia risk in women aged 40-55 years.
Methods: This was a cross-sectional study. A total of 1,350 Taiwanese women aged 40 to 55 years old were recruited from a certain health check-up center between 2009 and 2010. The outcome was osteopenia (-1 ≥ BMD T-score > -2.5). Factors considered as potential confounders included age, body weight or body mass index, waist circumference, smoking, alcohol intake, regular exercise, blood pressure (systolic/diastolic), fasting glucose, cholesterol, triglyceride, creatinine, alkaline phosphatase (ALP), calcium supplement and menopause status.
Results: The Osteoporosis Preclinical Assessment Tool (OPAT) developed in this study was based on variables with biological importance to osteopenia and variables that remained significant (p < 0.05) in the multivariable analysis, which included age, menopausal status, weight, and alkaline phosphatase level. The OPAT had a total score that ranged from 0 to 7, and categorized women into high-, moderate-, and low-risk groups. The predictive ability of the OPAT (area under the receiver operating characteristic curve (AUC) = 0.77) was significantly better than that of the Osteoporosis Self-assessment Tool for Asians (AUC = 0.69). The inclusion of serum total alkaline phosphatase level in the model, which was easy to obtain from routine health checkups, significantly enhanced the sensitivity (McNemar test, p=0.004) for detecting osteopenia in women aged 40-55 years.
Conclusion: The findings provide an important tool for identifying women at risk of osteoporosis at the preclinical phase.

Verification letter from the oral examination committee I
Acknowledgements II
Chinese abstract III
English abstract V
Table of contents VII
List of figures IX
List of tables X
List of appendices XI
Chapter 1. Introduction 1
Chapter 2. Literature Review 3
2.1 Importance of osteoporosis 3
2.2 Classification of osteoporosis 5
2.3 Bone mineral density and its measurement 6
2.4 Factors affecting bone mineral density 7
2.4.1 Age 7
2.4.2 Ethnicity 7
2.4.3 Anthropometric measures 7
2.4.4 Nutrients 8
2.4.5 Hormones 8
2.4.6 Medical comorbidities 9
2.4.7 Medications 10
2.4.8 Physical activity 11
2.4.9 Smoking and alcohol drinking 11
2.5 Bone turnover markers 12
2.6 Prediction of osteopenia 13
2.7 Aims 15
Chapter 3. Materials and Methods 16
3.1 Study population 16
3.2 Measurement of BMD 17
3.3 Measurement of demographic factors 18
3.4 Measurement of clinical factors and comorbidities 19
3.5 Statistical analyses 20
3.6 Point-based prediction model 21
Chapter 4. Results 23
4.1 Characteristics of the study population 23
4.2 Variable selection and the prediction model for osteopenia risk 24
4.3 Predictive performance of the model 25
Chapter 5. Discussion 26
5.1 Main findings 26
5.2 Comparison with prior studies 27
5.3 Strengths and limitations 29
5.4 Conclusions 31
References 32
Figure 1. Flowchart of participant recruitment 44
Figure 2. The distributions of bone mineral density 45
Figure 3. The distributions of bone mineral density by menopause status 46
Figure 4. The distributions of age 47
Figure 5. The distributions of age by menopause status 48
Figure 6. The distributions of weight 49
Figure 7. The distributions of weight by menopause status 50
Figure 8. The distributions of alkaline phosphatase 51
Figure 9. The distributions of alkaline phosphatase by menopause status 52
Figure 10. Splines curves of bone mineral density against age, weight and alkaline phosphatase 53
Figure 11. Splines curves of bone mineral density against age, weight and alkaline phosphatase by menopause status 54
Figure 12. Distribution of women with osteopenia and normal BMD by low, moderate and high risk. 55
Figure 13. ROC curves (a) and (b) calibration plots for the classification of women aged 40-55 with osteopenia and normal BMD. 56
Table 1. Comparisons of different prediction tools for osteoporosis, low BMD, and osteopenia 57
Table 2. Characteristics of study subjects 59
Table 3. Final multivariable logistic regression model for predicting osteopenia risk in women aged 40 to 55 years (OPAT) 60
Table 4. Summary statistics for different models 61
Appendix 1. Certificates of institutional review board 62
Appendix 2. Published paper 64

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