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研究生：	沈德依 Shen, Te-Yi
論文名稱：	台灣停經婦女心血管危險因子長期追蹤研究 LONGITUDINAL STUDY OF CARDIOVASCULAR RISK FACTORS ACROSS THE MENOPAUSAL TRANSITION IN TAIWANESE WOMEN
指導教授：	余聰 Yu, Tsung
學位類別：	博士 Doctor
系所名稱：	醫學院 - 公共衛生學系 Department of Public Health
論文出版年：	2022
畢業學年度：	111
語文別：	英文
論文頁數：	134
中文關鍵詞：	停經年齡、心臟血管疾病危險因子、長期追蹤資料分析
外文關鍵詞：	age at menopause, CVD risk factors, longitudinal data analysis
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停經過渡期對女性來說是一段重要的生命歷程。在此期間，與停經相關的生理變化與老化有關的變化同時出現，增加發生心臟代謝疾病的風險。因此，了解停經和老化是獨立或共同對 CVD 危險因子變化的影響相當重要。為了釐清其間的複雜性，本論文的目的是研究停經年齡的危險因子及其對健康的影響，並應用長期追蹤資料分析方法研究心血管疾病危險因子的長期變化及其與台灣女性停經年齡的關係。
自然停經年齡 (ANM) 及其相關因子引起公共衛生的關注，因為ANM可能是晚年心臟代謝疾病風險的指標。研究發現ANM因地理區域分布和種族而不同。在本論文的第二章中，我們分析了來自橫斷面調查（國民營養健康狀況變遷調查）的4111名35歲及以上台灣女性的數據。利用Cox比例風險模型研究ANM 與其相關因子的關聯性，並使用生命表方法估計ANM中位數。研究結果顯示，受過高等教育、已婚且曾懷孕過的較年輕出生世代的女性可能比較晚停經。因此，ANM可能受到早期與所經歷的社會經濟/營養狀況的影響。
在第三章節中，我們分析台灣美兆健康數據庫的 36,931 名停經後的婦女世代。利用存活分析評估停經年齡與全死因死亡率、糖尿病相關死亡率、心血管疾病相關死亡率和癌症相關死亡率之間的關係。研究結果顯示，停經年齡 <40-44 歲女性的糖尿病死亡率顯著較高，且45-49 歲停經女性的全因死亡率高於參考組女性（停經年齡50-54 歲）。這些女性也與 CVD 死亡率增加有關，而 55-60 歲停經女性族群的 CVD 死亡率風險則降低。因此，停經年齡可能是中年女性心臟代謝疾病的重要指標，代表著未來的健康老化。
在第四章中，我們使用來自美兆健康數據庫探究一項縱向研究，本研究討論 CVD 風險因子隨時間變化的態樣。我們計算出停經過渡期間 CVD 危險因子的平均變化，並探討停經和老化對 CVD 危險因子變化率的影響。三酸甘油酯 (TG)、總膽固醇 (TC) 和低密度脂蛋白膽固醇 (LDL-C)呈現與停經相關的軌跡模式，在最後一次月經(FMP)前後2 年內快速增加。血糖的增加情形始於 FMP 之前，接近FMP 時加速變化， FMP 之後則輕微減速。收縮壓(SP)在FMP前2年上升較快，並在老年時達到穩定狀態。低密度脂蛋白膽固醇(HDL-C) 和身體質量指數 (BMI) 在 FMP 之前顯著增加，之後則減緩。我們研究證據顯示，早停經與停經過渡期間 TG 和 BMI 的較快增加有關。相比之下，晚停經的女性（55-59 歲）其TC、LDL-C 和 HDL-C 的變化比停經年齡較早的女性來的緩慢。
綜上所述，本論文利用台灣停經過渡期女性重覆測量CVD危險因子的數據證實停經期對 CVD 危險因子的影響，尤其是血脂，而對於停經期提前的女性來說，這種影響尤其顯著。此研究結果顯示出早停經女性對心血管疾病風險管理認知的需要。
關鍵字:停經年齡心臟血管疾病危險因子長期追蹤資料分析

The menopausal transition is a critical period for women. During this time, menopause-related physiological changes coexist with age-related changes, increasing the risk of developing cardiometabolic disease. Thus, it is important to understand the impact of menopause and chronological ageing, either alone or together, on cardiovascular disease (CVD) risk factors. To disentangle such complexity, the goal of this dissertation was to study the risk factors of the age at menopause and their effect on health, as well as apply a longitudinal data analysis approach to study the long-term changes in CVD risk factors and their association with age at menopause in Taiwanese women.
Age at natural menopause (ANM) and its associated factors have attracted much interest because ANM might be an indicator for cardiometabolic disease risk in later life. Research suggests that ANM varies across geographic regions and ethnicities. In chapter two of this dissertation, we analyzed the data of 4,111 women aged 35 years and above from cross-sectional surveys (Nutrition and Health Survey in Taiwan). A Cox proportional hazards model was used to assess the association of ANM with relevant factors, and a life table method was used to estimate the median ANM. Our findings showed that women in the younger cohorts with higher educational levels, who are parous and married, may have a later ANM. Thus, ANM may reflect the influences of socioeconomic/nutritional status of women in their early life or throughout their lifetime.
In chapter three of this dissertation, we analyzed a cohort of 36,931 postmenopausal women from the MJ Health Database in Taiwan. Survival analysis was used to assess the association of the age at menopause with all-cause, diabetes-related, CVD-related, and cancer-related mortality. Our findings revealed that women aged <40‒44 years at menopause showed significantly higher diabetes mortality, while women experiencing menopause at 45‒49 years showed higher all-cause mortality than the reference category (50‒54 years). These women were also associated with increased CVD mortality, while the CVD mortality risk declined in the 55‒60 years’ category. Thus, the age at menopause could be an important cardiometabolic disease marker for women in midlife that indicates future longevity.
In chapter four of this dissertation, we describe a longitudinal study using data from the MJ Health Database characterizing the changes in CVD risk factors over time. We calculated the mean changes in CVD risk factors across the menopausal transition and considered the effect of menopause and ageing on the rates of changes in the CVD risk factors. The trajectories of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein-cholesterol (LDL-C) followed a menopause-related pattern with rapid acceleration within the 2-year period before and after the final menstrual period (FMP). The pattern of change for blood glucose began with an increase before the FMP, an accelerated change around the FMP, and a slight deceleration after the FMP. The systolic blood pressure (SP) increased faster 2 years before the FMP and reached stable levels in older age. The HDL-C and body mass index (BMI) showed a significant increase before the FMP but slowed down afterward. We found evidence that an early age at menopause was associated with faster increases in TG and BMI across the menopausal transition. In contrast, women experiencing menopause at a later age (55‒59 years) had a slower change in TC, LDL-C, and HDL-C than those with younger age at menopause.
In summary, data from the Taiwanese cohort of women transitioning through menopause with repeated measures of CVD risk factors confirm the impact of menopause on CVD risk factors, particularly the lipids. The impact is especially significant for women experiencing early menopause. These findings indicate the need for awareness of CVD risk management in women with early menopause.
Keyword: age at menopause, CVD risk factors, longitudinal data analysis

ABSTRACT i 
ACKNOWLEDGEMENTS vi 
TABLE OF CONTENTS vii 
LIST OF TABLES x 
LIST OF FIGURES xii
CHAPTER ONE
INTRODUCTION 1
CHAPTER TWO 7
CHAPTER THREE 34
CHAPTER FOUR 55
CHAPTER FIVE 115
REFERENCES 120
Table 2-1. Baseline characteristics of the study population (sample size = 4,111) 22
Table 2-2. Baseline characteristics of the study population at each survey	25
Table 2-3. Baseline characteristics of the study population, by birth cohort 27
Table 2-4. Estimated median age at natural menopause (ANM) by sociodemographic, lifestyle, and clinical characteristics of the study sample 30
Table 2-5. Hazard ratios (95% confidence interval) for natural menopause from Cox proportional hazards models 32
Table 3-1. Baseline characteristics stratified by age at menopause (N=36,931) 49
Table 3-2. Hazard ratios of age at menopause and mortality 51
Table 3-3. Hazard ratios of age at menopause and mortality among women who had never used contraceptives or hormone therapies, or had not underwent gynecology surgeries (n=21,621) 52
Table 3-4. Hazard ratios of age at menopause and mortality after excluding women with follow-up time less than three years (n=36,421) 53
Table 3-5. Hazard ratios of age at menopause and mortality by attained age 54
Table 4-1. Characteristics of participants stratified by age at menopause (N=2,163) 96
Table 4-2. Sample distribution of risk factors measured at different intervals from the final menstrual period (N=2,163) 98
Table 4-3. Associations between age at menopause and biomarkers (unadjusted model) 100
Table 4-4. Associations between age at menopause and biomarkers, adjusted for 
Covariates 104
Table 4-5. Adjusted CVD risk factors rate of change (unite/year) by different categories of age at menopause 108
Table 4-6. Segment-Specific Slope for Years Before/After the FMP for Cardiovascular Risk Factors by age at menopause categorization (unadjusted model) 109
Table 4-7. Segment-Specific Slope for Years Before/After the FMP for Cardiovascular Risk Factors, adjusted for covariates covariates 112
Figure 2-1. Cumulative age at menopause distribution stratified by birth year21
Figure 3-1. Restricted cubic spline plots with adjusted hazard ratios (95% CI) for all-cause (a), cardiovascular (b), all-cancer (c) and diabetes (d) mortality associated with women’s age at menopause 48
Figure 4-1. CVD risk factors mean change by 0.5 year before and after the date of final menstrual period 85
Figure 4-2. Predicted mean confounder adjusted trajectories of CVD risk factors by mixed effect model, by age at menopause86
Figure 4-3. Predicted mean confounder adjusted trajectories of CVD risk factors by spline model, by age at menopause 91
Appendix 1-1. Schematic of follicle-stimulating hormone (FSH) and estradiol (E2) changes around the final menstrual period (FMP) 133
Appendix 1-2. Stages of Reproductive Ageing workshop staging system (STRAW) 134
                                    

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