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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊立民 | zh_TW |
dc.contributor.advisor | Lee-Ming Chuang | en |
dc.contributor.author | 林志弘 | zh_TW |
dc.contributor.author | Chih-Hung Lin | en |
dc.date.accessioned | 2023-09-22T16:14:47Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-02 | - |
dc.identifier.citation | 1. Ahmed AM. History of diabetes mellitus. Saudi Med J 2002;23(4):373–8.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89817 | - |
dc.description.abstract | 隨著糖尿病腎病變盛行率的快速成長,其對個別病患健康及整體醫療系統均構成了重大的挑戰。如何能早期診斷,早期介入一直是糖尿病腎病變臨床照護上的重點。然而,糖尿病腎病變的致病機轉十分複雜;而現行的臨床診斷方式有其侷限存在。因此,本研究的目的,除了藉由文獻回顧,梳理近期糖尿病腎病變診斷方式的進展外,亦希望探索出有潛力的新穎生物標記;並且由現行的臨床常規檢查中,找出新的數據應用方式,以期能建立預測第 2 型糖尿病病患腎功能惡化的新型模式,進而有助於糖尿病腎病變的早期防治。
第一部分:以血紅素糖化指數做為低慢性腎臟病風險第 2 型糖尿病患腎功能惡化之預測方法 現行對於慢性腎臟病的預測因子的了解多半來自風險較高的族群,其是否能完全應用在低風險族群先前並不清楚。在一個有 780 位低慢性腎臟病風險第 2 型糖尿病患研究世代中,吾人發現到利用描述個體糖化血色素的實測值以及估計值之間差距的血紅素糖化指數,可預測腎功能快速下降的情形;同時縱向資料分析結果,亦顯示血紅素糖化指數與腎絲球濾過率估算值預測變化呈負相關性。 第二部分:以追蹤間白蛋白尿的變異度做為第 2 型糖尿病患腎功能惡化之預測方法 個體白蛋白尿嚴重程度的變異在糖尿病患是常見的現象;然而先前並不確定此種現象在第 2 型糖尿病患是否具有臨床重要性。利用一個有 1008 位第 2 型糖尿病患的回溯性世代,吾人設計出了蛋白尿變異度積分來描述個體追蹤間白蛋白尿的變異度,並發現蛋白尿變異度積分可增進預測模式對於腎功能惡化研究終點的鑑別能力;而在另一個有 310 位第 2 型糖尿病患的獨立驗證世代中,吾人則展示出了較高的蛋白尿變異度積分,可預測 5 年後的腎功能惡化狀況。 吾人至目前的研究成果,雖然對現行糖尿病腎病變預測模式的改進有所助益,但仍有許多有待完善之處。今後若要繼續發展理想的預測模式,則需將整個疾病的病程發展納入考量—包括背景因素、與致病機轉有關的指標(包含開發新穎之生物標記)、以及臨床表現型的測量,經由全面性的評估,方能對病患的腎功能變化做出更及時、更準確的預測,最終進而能達成對糖尿病腎病變防治的早期預防目標。 | zh_TW |
dc.description.abstract | The rapid-growing prevalence of diabetic kidney disease (DKD) has become a major burden to patient health as well as global healthcare system. It is of great clinical importance to have prompt diagnosis and intervention of DKD. However, the pathogenesis of DKD is extremely complicated, and there are limitations in current diagnostic methods. The aim of the present study is to establish new models for prediction of renal function deterioration in patient with type 2 diabetes (T2D). In addition to reviewing recent literature about new biomarkers for the diagnosis of DKD, we also attempted to develop new applications of routine clinical parameters, as well as exploring the potential of novel diagnostic biomarkers, to build more comprehensive prediction models for DKD.
Part one: using hemoglobin glycation index (HGI) as a predictor of renal function deterioration in patients with T2D and a low risk of chronic kidney disease (CKD) Current knowledge about predictors of CKD mostly came from individual in higher risk stratifications. It was not clear whether these predictors could be extrapolated to patients with low CKD risk. In a cohort of 780 patients with T2D and a low risk of CKD, we demonstrated that HGI, which is used to describe the discrepancy between and measured glycated hemoglobin, can independently predict renal function deterioration. The analysis of longitudinal data also showed that HGI correlates negatively to estimated annual change of estimated glomerular filtration rate (eGFR). Part two: using visit-to-visit variability of albuminuria as a predictor of renal function deterioration in patients with T2D. Variation in the severity of albuminuria is a common finding in patients with diabetes. However, the clinical importance of such phenomenon in patients with T2D was not clear. In a retrospective cohort of 1008 patients with T2D, we developed the albuminuria variability score (AVS) to describe the visit-to-visit variability of albuminuria in a single individual. We found that AVS improves the discriminative power of existing models for renal function deterioration endpoints. In another independent validation cohort of 310 patients with T2D, we demonstrated that high AVS can independently predict 5-year renal function deterioration. In summary, the above-mentioned findings have shed light on the improvement of prediction models of DKD. To have a more comprehensive evaluation, the development of future prediction model should consider predictors from aspects of background genetics, environment factors, pathogenesis, novel biomarkers and clinical phenotypes, so that prompt diagnosis and early intervention of DKD can be made possible. | en |
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dc.description.tableofcontents | 口試委員會審定書 i
謝辭 ii 中文摘要 iii 英文摘要 v 目錄 vii 表目錄 x 圖目錄 xi 第一章 研究背景及目的 1 1.1 糖尿病的慢性併發症及其臨床重要性 1 1.2 糖尿病腎病變:致病機轉 2 1.3 糖尿病腎病變:現行臨床診斷方式及預測模式的侷限 3 1.4 研究目的 5 第二章 新型糖尿病腎病變診斷方式之回顧 6 2.1 單一生物標記 6 2.2 特定基因之先天變異或後天表觀遺傳改變 6 2.3 微核糖核酸 7 2.4 蛋白質體學及代謝體學之應用 7 第三章 以血紅素糖化指數做為低慢性腎臟病風險第 2 型糖尿病患腎功能惡化之預測方法 9 3.1 背景 9 3.2 研究方法 10 3.2.1 研究世代的建立 10 3.2.2 數值測量及資料收集 10 3.2.3 血紅素糖化指數 11 3.2.4 研究定義及試驗終點 11 3.2.5 統計方法 12 3.3 結果 13 3.3.1 研究世代於基準點的臨床特徵 13 3.3.2 基準點血紅素糖化指數對腎功能變化的預測能力 13 3.3.3 血紅素糖化指數與腎絲球濾過率估算值預測變化在縱向資料中的關聯性 15 3.4 討論 16 第四章 以追蹤間白蛋白尿的變異度做為第 2 型糖尿病患腎功能惡化之預測方法 19 4.1 背景 19 4.2 研究方法 20 4.2.1 研究世代的建立 20 4.2.2 數值測量及資料收集 20 4.2.3 白蛋白尿變異度積分 21 4.2.4 研究定義及試驗終點 21 4.2.5 統計方法 22 4.3 結果 23 4.3.1 研究世代於基準點的臨床特徵 23 4.3.2 白蛋白尿變異度積分計算標準之訂定 24 4.3.3 白蛋白尿變異度積分與試驗終點的相關性 25 4.3.4 白蛋白尿變異度積分與腎絲球過濾率估算值年平均變動值的關係 25 4.3.5 高白蛋白尿變異度積分可預測 5 年後的腎功能惡化狀況 26 4.3.6 白蛋白尿變異度積分與血紅素糖化指數的關聯性與合併運用可能性之探索 27 4.4 討論 27 第五章 結論及未來展望 30 參考文獻 31 附錄 93 Group-based trajectory modeling (GBTM) 的執行方法 93 中英對照表 94 發表論文清冊 97 | - |
dc.language.iso | zh_TW | - |
dc.title | 第2型糖尿病病患腎功能惡化之新型預測模式的發展 | zh_TW |
dc.title | Development of novel prediction models for renal function deterioration in patients with type 2 diabetes | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 博士 | - |
dc.contributor.oralexamcommittee | 周祖述;王治元;姜至剛;黃建寧;胡啟民 | zh_TW |
dc.contributor.oralexamcommittee | Tzuu-Shuh Jou;Chih-Yuan Wang;Chih-Kang Chiang;Chien-Ning Huang;Chii-Min Hwu | en |
dc.subject.keyword | 第 2 型糖尿病,糖尿病腎病變,血紅素糖化指數,蛋白尿變異度積分,新穎生物標記, | zh_TW |
dc.subject.keyword | type 2 diabetes,diabetic kidney disease,hemoglobin glycation index,albuminuria variability score,novel biomarkers, | en |
dc.relation.page | 97 | - |
dc.identifier.doi | 10.6342/NTU202302707 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-08-02 | - |
dc.contributor.author-college | 醫學院 | - |
dc.contributor.author-dept | 臨床醫學研究所 | - |
顯示於系所單位: | 臨床醫學研究所 |
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