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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84112完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 簡國龍(Kuo-Liong Chien) | |
| dc.contributor.author | Min-Kuang Tsai | en |
| dc.contributor.author | 蔡旻光 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:04:55Z | - |
| dc.date.copyright | 2022-10-03 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-14 | |
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Nephrol Dial Transplant 2011;26(3):963-9. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84112 | - |
| dc.description.abstract | 背景與目標 自從1995年全民健康保險開辦以來,台灣的末期腎臟病,包括透析的發生率與盛行率都在快速增加,台灣的透析盛行率是世界第一位。過去針對一般民眾以運動來預防末期腎臟病或透析的研究並不多。本研究評估不同運動量與運動強度,對於預防末期腎臟病的效果。 方法 計畫一:本研究世代總共有543,667名參加健康檢查的參與者,他們在1996-2017年間,接受多次的健康檢查,追蹤時間的中位數為13年,在檢查時參與者會填寫一份關於其疾病史和生活方式的自填式問卷,其中包括詳細的運動資料,根據每週運動量,我們將參與者分為五組不同的運動量MET (metabolic equivalent of task)-hour。同時蒐集參與者的生化健檢資料,透過串聯全國重大傷病檔的檔案,找到2,520位接受長期透析或換腎的參與者。本研究以多變量COX比率風險模式來計算不同運動量與運動強度對於末期腎臟病的風險,同時也對其他共變項包括教育程度、臨床檢查和生活習慣等變項進行調整。 計畫二:本研究建立了三個不同的慢性透析預測模型來進行比較,模型1:腎衰竭風險模型 (Kidney failure risk equation, KFRE) (納入:年齡、性別、腎絲球過濾率和蛋白尿);模型2:模型1 + 疾病史 + 生活方式等變項;模型3:完整模型+所有重要的臨床健康檢查變項。本研究也會依COX比率風險模式的結果來發展末期腎臟病的預測模式,以C-index來比較不同模式的預測能力。 結果 計畫一:有達到中度運動量以上,也就是每週約150分鐘以上運動量者,其末期腎臟病風險減少了12% (Hazard ratio:0.88, 95% confidence interval: 0.80, 0.98),此風險已校正基線的腎絲球過濾率及蛋白尿在內的不同共變項。我們也觀察到不同運動量與末期腎臟病風險之間有劑量效應關係。與不運動的人相比,平均每增加30分鐘的運動,末期腎臟病風險減少5% (HR:0.95, 95% CI: 0.92, 0.98),另外,在相同運動量中,與中等強度相比,高強度的運動降低末期腎臟病風險達35% (HR:0.65, 95% CI: 0.52, 0.81)。每週約150分鐘以上的運動量降低末期腎臟病風險的效果在男性、60歲以下以及糖尿病或高血脂症的參與者中更為明顯。 計畫二:在末期腎臟病預測模式中,除了年齡、性別、腎絲球過濾率及蛋白尿之外,再加入生活習慣及疾病史等因子可以顯著提高Harrell’s concordance index預測值(C 統計量),從 0.91 提高到 0.94 (95%, CI: 0.94, 0.95)。在全模式的模式三中,更進一步將 C 統計量提高到 0.95 (95%, CI: 0.95, 0.96)。若參與者在全模式中的分數為33以上,在10 年會有達到3%的透析風險。而在本研究世代中,這個風險族群者有超過一半最後發展為需接受透析治療(敏感性為:0.53,95% CI: 0.51, 0.55),這個敏感性比慢性腎臟病第三期以上還要更高(敏感性為:0.48,95% CI: 0.46, 0.50)。 結論 計畫一:每週約150分鐘以上的運動量能夠顯著降低末期腎臟病的。這個結果表示,那些有心血管疾病風險的人,包括慢性腎臟病者、糖尿病者、高血壓或高血脂者,都應該從事更多的運動來減少他們之後得到末期腎臟病的風險。 計畫二:我們所發展的末期腎臟病/透析預測模式,可以應用在一般沒有慢性腎臟病者或是有慢性腎臟病1~5期者來使用,本研究指出,納入生活習慣及疾病史等資料,可以顯著提高末期腎臟病的預測能力。 | zh_TW |
| dc.description.abstract | Background and Objectives The incidence and prevalence of end-stage renal disease (ESRD)/kidney dialysis have increased significantly in Taiwan since the initiation of the National Health Insurance in 1995. As a result, Taiwan has the world's highest dialysis prevalence per million population. Limited studies have been conducted on the effect of physical activity (PA) in preventing ESRD and dialysis in the Asian population. This prospective study examines the impact of amounts and the intensity of PA engagement per week on the risk of ESRD/dialysis in later life. Methods Project (1): The study cohort comprising 543,667 individuals participated in a standard health examination program in Taiwan. Participants received multiple medical screenings between 1996 and 2017, with a median of 13 years. At each visit, participants completed a self-administered questionnaire on their medical history and lifestyle risk factors, including detailed physical activity engagement, which was later converted into five amounts of MET (metabolic equivalent of task)-hour categories. Specimens for clinical tests were also collected. By linking ID data to the national registry for Catastrophic Illness Patients, we identified a total of 2,520 participants under long-term dialysis or kidney transplantation. A multivariate Cox proportional hazards model was used to assess the hazard ratio (HR) of different levels and intensity of physical activity engagement, adjusting for education, clinical, and lifestyle covariates. Project (2): We built three models for comparison: model 1: kidney failure risk equation (KFRE) model (age, sex, estimated glomerular filtration rate, and proteinuria); model 2: model 1 + medical history + lifestyle risk factors; and model 3: full model + all significant biochemical factors. We used the Cox proportional hazards model to develop points-based models and applied the C statistic to compare the predictive ability of different models. Results Project (1): The fully active group had a 12% lower hazard of ESRD compared with the no reported LTPA group (HR: 0.88, 95% CI: 0.80, 0.98) adjusting for covariates including baseline estimated glomerular filtration rate and proteinuria. We observed a dose-response relationship between LTPA and the risk of ESRD. For each additional 30 minutes of physical activity, the risk of end-stage renal disease was reduced by 5% (HR: 0.95, 95% CI: 0.92, 0.98). Within the exact amounts of LTPA, vigorous-intensity PA carried a 35% lower HR for ESRD than moderate-intensity (HR: 0.65, 95% CI: 0.52, 0.81). The effect was more substantial among men, younger participants, and participants with diabetes or hyperlipidemia. Project (2): Adding lifestyle factors to the basic model significantly improved the C statistic from 0.91 to 0.94 (95% CI: 0.94, 0.95). The full model improved the C statistic to 0.95 (95% CI: 0.95, 0.96). With a cut-off score of 33 of a 10-year ESRD risk (ESRD risk > 3%), this full model detected over half of the individuals progressing to ESRD (sensitivity: 0.53, 95% CI: 0.51, 0.55), which was higher than the sensitivity of cohort participants by the criteria with CKD stage 3 or higher (sensitivity: 0.48, 95% CI: 0.46, 0.50). Conclusions Project (1): More than 150 minutes of LTPA per week had a significant effect on lowering the ESRD risk. This finding suggested that no reported LTPA patients with cardio-vascular risks should engage more LTPA to lower their risk of ESRD. Project (2): Our ESRD prediction model, including medical history and lifestyle factors, significantly improved the predictive ability for long-term dialysis in a large cohort with or without chronic kidney diseases. | en |
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| dc.description.tableofcontents | 致謝 I 縮寫表 II 中文摘要 III 英文摘要 V 第一章 研究背景 1 第一節 末期腎臟病的流行病學現況 1 第二節 末期腎臟病的定義及危險因子 1 第三節 運動的文獻回顧 2 1.3.1 運動的定義 2 1.3.2 運動的測量 2 1.3.3 運動的建議量 3 1.3.4 台灣的運動盛行率 4 第四節 運動與末期腎臟病的文獻回顧 (計畫一) 5 1.4.1 慢性腎臟病的運動指引 5 1.4.2 運動對於腎臟的短期影響 6 1.4.3 運動對於慢性腎臟病的長期影響 6 1.4.4 運動對於末期腎臟病的長期影響 7 1.4.5 運動對於末期腎臟病的影響機轉 8 第五節 末期腎臟病的預測模式文獻回顧 (計畫二) 10 1.5.1 以慢性腎臟病第三期到第五期為主的末期腎臟病預測模式 10 1.5.2 以一般人口族群為主的慢性腎臟病預測模式 11 1.5.3 以一般人口族群為主的末期腎臟病預測模式 11 第二章 研究目的 13 第三章 材料與方法 14 第一節 研究參與者、納入及排除條件 14 第二節 末期腎臟病的定義 14 第三節 運動量與運動強度的定義 14 第四節 共變項 15 第五節 統計分析 16 3.5.1不同運動量與末期腎臟病的統計分析 (計畫一) 16 3.5.2 研究樣本數的估計 17 3.5.3 建立末期腎臟病的預測模型 (計畫二) 17 第四章 結果 20 4.1.1 研究世代的描述 (計畫一) 20 4.1.2 運動與慢性腎臟病的關聯性 (計畫一) 21 4.1.3 運動減少末期腎臟病的風險 (計畫一) 21 4.1.4 運動的變化量減少末期腎臟病的風險 (計畫一) 23 4.2.1 研究世代發展末期腎臟病風險預測模式的人口學描述 (計畫二) 23 4.2.2 末期腎臟病風險預測模式計分 (計畫二) 24 4.2.3 末期腎臟病風險預測模式結果 (計畫二) 24 4.2.4 本預測模式與不同的風險族群,包括:慢性腎臟病期別、不同eGFR、蛋白尿嚴重度、糖尿病及高血壓等,比較透析風險的結果 (計畫二) 25 4.2.5 本預測模式的應用範例 (計畫二) 26 第五章 討論 28 5.1 運動降低末期腎臟病的風險 (計畫一) 28 5.2 發展末期腎臟病的預測模式 (計畫二) 34 參考文獻 39 表目錄 Table 1. Predicted variables in prediction models for ESRD 54 Table 2. Characteristics of participants by leisure-time physical activity 55 Table 3. Risk of chronic kidney disease or proteinuria by leisure-time physical activity 57 Table 4. Kidney function of participants by leisure-time physical activity and by age 58 Table 5. Risk of ESRD by amount of leisure-time physical activity 59 Table 6. Risk of ESRD by amount and intensity of leisure-time physical activity 60 Table 7. Hazard ratios of ESRD by amount of leisure-time physical activity and stages of chronic kidney disease 61 Table 8. Sensitivity analysis: hazard ratios of ESRD by amount of leisure-time physical activity (competing sub-hazard ratio) 62 Table 9. Sensitivity analysis: hazard ratios of ESRD by amount of leisure-time physical activity (excluding participants with ESRD in 2 years, among participants had 2nd visits) 63 Table 10. Competing sub-hazard ratios of ESRD by amount and intensity of leisure-time physical activity 64 Table 11. Risk of ESRD for medium or above amount of leisure-time physical activity stratified by selected risk groups 65 Table 12. Risk of end-stage renal disease for cohort participants with two visits-change by leisure-time physical activity 67 Table 13. Characteristics of study participants by ESRD status and identified ESRD predictors in the total cohort 68 Table 14. Hazard ratios of ESRD for risk factors for competing risk model (Fine and Grey model) 71 Table 15. Hazard ratios of ESRD for risk factors for the prediction models 74 Table 16. Scores for ESRD prediction for the three models 77 Table 17. Harrell’s C index of the full, training and validation datasets by model 79 Table 18. Predictive effectiveness of selected subgroups for prediction of participants in the total cohort progressing to ESRD 80 Table 19. Distribution of ESRD cases during follow-up by prediction scores and by CKD definition 81 Table 20. Distribution of ESRD cases for those with high ESRD risk defined in this model with or without CKD 82 Table 21. Application of the prediction models for different risk profiles 83 Table 22. The ESRD cases, all-cause mortality in total and validation cohort during the 21 year follow-up 84 圖目錄 Figure 1. (A) Prevalence of physical activity in Taiwan by sex and by age and (B) incidence rate of treated ESRD in Taiwan by age 85 Figure 2. Physical activity guideline for chronic kidney disease 86 Figure 3. Mechanism for exercise or physical activity to prevent chronic kidney disease, end-stage renal disease and mortality 87 Figure 4. Research scheme of this study 88 Figure 5. Flowchart of participants included in this study 89 Figure 6. Risk of ESRD by amount and intensity of physical activity 90 Figure 7. Risk of ESRD by amount of physical activity and CKD stages 91 Figure 8. Adjusted ESRD hazard ratio for the fully active group compared with the no reported LTPA group, by participant characteristics and cardio-vascular risk factors 92 Figure 9. Flowchart of participants for ESRD prediction model 93 Figure 10. Discriminatory accuracy of the models in 10 years in the validation set 94 Figure 11. Calibration plot of the risk prediction models in the validation set 95 Figure 12. Distribution of identified RRT cases (n=2,212) during follow-up by prediction scores and by CKD stages 96 附錄 Appendix table 1. Literature review of physical activity and kidney diseases (chronic kidney disease or end-stage renal disease) 97 Appendix table 2. Literature review of end-stage renal disease prediction models 109 Publication 112 附件一、發表於Mayo Clinic Proceedings的文章 113 附件二、發表於Clinical Kidney Journal的文章 126 | |
| dc.language.iso | zh-TW | |
| dc.subject | 預測模式 | zh_TW |
| dc.subject | 末期腎臟病 | zh_TW |
| dc.subject | 透析 | zh_TW |
| dc.subject | 運動 | zh_TW |
| dc.subject | 世代研究 | zh_TW |
| dc.subject | cohort study | en |
| dc.subject | prediction model | en |
| dc.subject | dialysis | en |
| dc.subject | end-stage renal disease | en |
| dc.subject | leisure-time physical activity | en |
| dc.title | 運動降低末期腎臟病的風險- 五十萬人的世代研究 | zh_TW |
| dc.title | The role of physical activity in lowering the risk of end-stage renal disease - A cohort study based on half-a-million adults | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0002-2528-3911 | |
| dc.contributor.advisor-orcid | 簡國龍(0000-0003-4979-8351) | |
| dc.contributor.oralexamcommittee | 温啟邦(Chi-Pang Wen),許志成(Chih-Cheng Hsu),吳泓彥(Hon-Yen Wu),李文宗(Wen-Chung Lee),林先和(Hsien-Ho Lin),洪冠予(Kuan-Yu Hung) | |
| dc.contributor.oralexamcommittee-orcid | 温啟邦(0000-0001-7896-2558) | |
| dc.subject.keyword | 運動,末期腎臟病,透析,預測模式,世代研究, | zh_TW |
| dc.subject.keyword | leisure-time physical activity,end-stage renal disease,dialysis,prediction model,cohort study, | en |
| dc.relation.page | 138 | |
| dc.identifier.doi | 10.6342/NTU202201429 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-07-15 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-10-03 | - |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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