請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99917完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 簡國龍 | zh_TW |
| dc.contributor.advisor | Kuo-Liong Chien | en |
| dc.contributor.author | 許馨尹 | zh_TW |
| dc.contributor.author | Hsin-Yin Hsu | en |
| dc.date.accessioned | 2025-09-19T16:17:33Z | - |
| dc.date.available | 2025-09-20 | - |
| dc.date.copyright | 2025-09-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-24 | - |
| dc.identifier.citation | References
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Response: FACEing reality: productive tensions between our epidemiological questions, methods and mission. International Journal of Epidemiology. 2017;45:1852-1865. doi: 10.1093/ije/dyw330 127. Burgess S, Ference BA, Staley JR, Freitag DF, Mason AM, Nielsen SF, Willeit P, Young R, Surendran P, Karthikeyan S, et al. Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis. JAMA Cardiology. 2018;3:619-627. doi: 10.1001/jamacardio.2018.1470 128. Kronenberg F, Mora S, Stroes ESG. Consensus and guidelines on lipoprotein(a) - seeing the forest through the trees. Curr Opin Lipidol. 2022;33:342-352. doi: 10.1097/mol.0000000000000855 129. Boffa MB. Beyond fibrinolysis: The confounding role of Lp(a) in thrombosis. Atherosclerosis. 2022;349:72-81. doi: 10.1016/j.atherosclerosis.2022.04.009 130. Tsimikas S, Witztum JL. The role of oxidized phospholipids in mediating lipoprotein(a) atherogenicity. Curr Opin Lipidol. 2008;19:369-377. doi: 10.1097/MOL.0b013e328308b622 131. Riches K, Porter KE. Lipoprotein(a): Cellular Effects and Molecular Mechanisms. Cholesterol. 2012;2012:923289. doi: 10.1155/2012/923289 132. Coassin S, Kronenberg F. Lipoprotein(a) beyond the kringle IV repeat polymorphism: The complexity of genetic variation in the LPA gene. Atherosclerosis. 2022;349:17-35. doi: 10.1016/j.atherosclerosis.2022.04.003 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99917 | - |
| dc.description.abstract | 背景:
動脈粥樣硬化疾病會因血脂的異常導致一系列與血管阻塞相關的疾病,如缺血性心臟病、缺血性腦血管疾病及周邊動脈疾病。糖尿病不僅是動脈粥樣硬化性疾病的關鍵風險因素,還顯著增加大腸直腸癌的風險,對預防和管理此類疾病具有重要意義。 目的: (一)探討在不同糖尿病狀態和嚴重程度的大腸直腸癌患者中,接受治癒性切除手術後的大腸直腸癌預後的差異 (二)檢查大腸直腸癌與後續糖尿病風險之間的關係,並進一步探討化學治療對大腸直腸癌患者糖尿病風險的影響情形 (三)利用觀察性和孟德爾隨機分派試驗方法探索脂蛋白(a)與粥狀動脈硬化疾病和全死因風險之間的非線性關係。 方法: (一) 利用世代追蹤研究,2007年至2015年間癌症登記資料庫串聯健保資料庫和死亡登記檔。在接受治癒性切除手術的I至III期大腸直腸癌患者,根據其糖尿病狀態被分為三組:無糖尿病、有糖尿病但無併發症和有糖尿病且有併發症。使用Cox回歸模型來探討糖尿病嚴重性與大腸直腸癌預後之間的關聯,包括總體存活率(OS)、無病存活率(DFS)、復發和癌症特異性存活率(CSS) (二) 利用2007年至2018年間癌症登記資料庫與健保資料庫,86,268名大腸直腸癌患者和86,268名由傾向分數配對的沒有大腸直腸癌的一般族群,探討大腸直腸癌是否增加新發糖尿病的風險。進一步分析2007年至2016年間癌症登記資料庫中的37,277名大腸直腸癌患者,以評估其化學治療暴露與糖尿病風險之間的關聯。化學治療暴露被分為四個類別:無化學治療、少於90天、90至180天以及超過180天。研究評估了這些化學治療暴露類別中糖尿病風險的差異 (三) 通過觀察性研究和孟德爾隨機分派試驗,利用2006年3月至2023年5月期間的UK Biobank,探討脂蛋白(a)與不同種族atherosclerotic cardiovascular disease (ASCVD) 風險之間的潛在線性和非線性關係。排除已確立缺血性心臟病或遺傳數據缺失的個案。參與者根據其脂蛋白(a)分為四組:低於70百分位數、70至80百分位數之間以及高於90百分位數,並作為連續變數進行分析。主要有興趣的結果為動脈粥樣硬化性心血管疾病,包括缺血性心臟病、缺血性中風和周邊動脈病的住院和死亡事件,以及全因死亡率。 結果: (一) 在對 71,747 名接受治癒性切除手術(第 I 至第 III 期)大腸直腸癌患者的分析中,依照糖尿病狀態分為三組:44,944 名無糖尿病、8,864 名有糖尿病但無併發症、5,394 名有糖尿病且有併發症。最終納入 59,202 名大腸直腸癌患者。與無糖尿病組比較,雖然「糖尿病但無併發症」組在整體存活率(OS;HR:1.05,95% CI:1.01–1.09)、無病存活率(DFS;HR:1.08,95% CI:1.04–1.12)及癌症特異存活率(CSS;HR:0.98,95% CI:0.93–1.03)表現略差,但差異並未達顯著。相較之下,「有併發症的糖尿病」組則顯著增加不良存活風險(OS:HR:1.85,95% CI:1.78–1.92;DFS:HR:1.75,95% CI:1.69–1.82;CSS:HR:1.41,95% CI:1.33–1.49)。此外,患有糖尿病之大腸直腸癌患者的復發風險也高於無糖尿病者。 (二) 本架構分析涵蓋 89,900 名第 I–IV 期大腸直腸癌患者,並與一般族群中 86,268 名非大腸直腸癌者進行 1:1 傾向分數配對。經配對後,每組皆保留 86,268 名參與者。整體結果顯示,大腸直腸癌患者罹患糖尿病的風險比配對後之一般人口高出 14%(HR:1.14;95% CI:1.09–1.20),且在確診後第一年達到最高,之後則維持在較高的糖尿病的風險。此外,本研究亦探討 37,277 名第 II–IV 期大腸直腸癌患者接受不同化學治療時長與後續糖尿病風險的關聯。患者依照化學治療持續時間分為:未接受化學治療(15,018 例)、化學治療三個月內(12,538 例)、化學治療三至六個月(5,313 例)、以及化學治療超過六個月(4,408 例)。結果顯示,若在三年內化學治療總天數超過 180 天,後續糖尿病風險將提高約 60% 至 70%(HR:1.64;95% CI:1.07–2.49)。性別與 TNM 分期則為重要的效應修飾因子。 (三) 本研究依據脂蛋白 (a) 濃度將受試者分成四組,結果顯示:低於第 70 百分位(n = 261,486)發生 2,682 起缺血性心臟病事件及 22,220 例全因死亡;第 70–80 百分位(n = 37,318)發生 445 起缺血性心臟病事件及 3,358 例全因死亡;第 80–90 百分位(n = 37,330)發生 503 起缺血性心臟病事件及 2,999 例全因死亡;而高於第 90 百分位(n = 37,267)則觀察到最高比例之 547 起缺血性心臟病事件及 2,312 例全因死亡。Kaplan–Meier 存活曲線分析顯示,四組之缺血性心臟病事件累積發生率存在顯著差異(log–rank test < 0.001),且脂蛋白 (a) 濃度越高,事件風險亦越高。非線性模型分析(restricted cubic splines)進一步指出脂蛋白 (a) 與缺血性心臟病風險之間具有非線性關係。為釐清此關係的因果性,本研究利用孟德爾隨機分派試驗,並使用候選基因分析(candidate SNPs approach)及UK Biobank 的關鍵 SNP(lead SNP),運用殘差法(residual method)與雙排名法(doubly rank method)以確認脂蛋白 (a) 與心血管疾病風險之間的非線性因果關聯。 結論: (一) 在接受治癒性切除手術的大腸直腸癌患者中,糖尿病的嚴重程度與長期結果之間呈負相關,尤其於女性以及較早期階段的大腸直腸癌患者中更為顯著。 (二) 大腸直腸癌患者的糖尿病風險明顯升高,尤其長期化學治療(特別是含 capecitabine 的療程)會進一步增加糖尿病的發生機率。因此,對於大腸直腸癌患者而言,尤其在化學治療期間,密切監測血糖極為重要。 (三) 高濃度脂蛋白 (a) 顯著提升缺血性心臟病與全因死亡風險,且邊緣性呈現非線性關係;臨床上宜納入常規風險評估並及早預防。隨著針對脂蛋白 (a) 的新藥物持續研發,搭配基因檢測與精準醫療策略,未來或能更有效降低此族群的心血管事件與死亡風險。 | zh_TW |
| dc.description.abstract | Background
Atherosclerotic disease results from abnormal blood lipid levels and encompasses various vascular obstruction–related conditions, such as ischemic heart disease, ischemic cerebrovascular disease, and peripheral arterial disease. Diabetes mellitus is not only a key risk factor for atherosclerotic conditions but also substantially raises the risk of colorectal cancer, underscoring its importance in the prevention and management of these diseases. The proposal includes three study projects: (1)Using the Taiwan Cancer Registry database to explore the prognostic variations following curative resection of CRC among patients with differing levels of diabetic control; (2) Examining the association between CRC and the risk of subsequent diabetes mellitus and investigating the impact of chemotherapy on diabetes mellitus risk in CRC patients; (3) Using the UK Biobank to explore the nonlinear relationship between lipoprotein(a) levels and the risks of ASCVD and all–cause mortality, utilizing both observational and Mendelian randomization methods. Objectives 1. To investigate differences in colorectal cancer prognosis among patients with varying diabetes status and severity following curative resection. 2. To examine the relationship between colorectal cancer and subsequent diabetes risk and further evaluate the impact of chemotherapy on diabetes risk in patients with colorectal cancer. 3. To use both observational and Mendelian randomization approaches to explore potential nonlinear associations between lipoprotein(a) and atherosclerotic cardiovascular disease (ASCVD). Methods 1. We conducted a cohort study using cancer registry data from 2007 to 2015 linked with the National Health Insurance database and death registry. Patients with stage I to III colorectal cancer who underwent curative resection were classified into three groups based on diabetes status: no diabetes, diabetes without complications, and diabetes with complications. We applied Cox proportional hazards models to analyze the relationship between diabetes severity and colorectal cancer outcomes, including overall survival (OS), disease–free survival (DFS), recurrence, and cancer–specific survival (CSS). 2. Using cancer registry data from 2007 to 2018 linked with the National Health Insurance database, we identified 86,268 patients with colorectal cancer and 86,268 propensity score–matched individuals without colorectal cancer to investigate whether colorectal cancer increases the risk of new–onset diabetes. We then further examined 37,277 patients with stage II to IV colorectal cancer (2007–2016) to assess the association between chemotherapy exposure—categorized as none, <90 days, 90–180 days, and >180 days—and diabetes risk. 3. Utilizing both observational analyses and Mendelian randomization, this study used data from the UK Biobank between March 2006 and May 2023 to investigate potential linear and nonlinear relationships between lipoprotein(a) and ASCVD risk, excluding those already diagnosed with ischemic heart disease or lacking genetic data. Participants were stratified by lipoprotein(a) levels into four groups—below the 70th percentile, 70th–80th percentile, 80th–90th percentile, and above the 90th percentile—and analyzed as a continuous variable. The primary outcomes of interest included hospital admissions and death events related to ASCVD (ischemic heart disease, ischemic stroke, and peripheral arterial disease). Results 1. Among 71,747 patients with stage I–III colorectal cancer who underwent curative resection, three groups were defined by diabetes status: 44,944 without diabetes, 8,864 with diabetes but no complications, and 5,394 with diabetes and complications, ultimately including 59,202 patients in the analysis. Compared with patients without diabetes, those with diabetes but no complications showed a marginally worse prognosis (OS: HR:1.05, 95% CI:1.01–1.09; DFS: HR:1.08, 95% CI:1.04–1.12; CSS: HR:0.98, 95% CI:0.93–1.03) without reaching statistical significance. By contrast, diabetes with complications was associated with a markedly higher risk of unfavorable outcomes (OS: HR:1.85, 95% CI:1.78–1.92; DFS: HR:1.75, 95% CI:1.69–1.82; CSS: HR:1.41, 95% CI:1.33–1.49). Colorectal cancer patients with diabetes also experienced a higher recurrence risk than those without diabetes. 2. In the analysis involving 89,900 patients with stage I–IV colorectal cancer matched 1:1 with 86,268 individuals from the general population, each group retained 86,268 participants after propensity score matching. Overall, colorectal cancer conferred a 14% higher risk of diabetes compared with matched individuals (HR:1.14, 95% CI:1.09–1.20), peaking within the first-year post–diagnosis and remaining elevated thereafter. Further investigation among 37,277 patients with stage II–IV colorectal cancer revealed that those who underwent more than 180 days of chemotherapy within three years were 60–70% more likely to develop diabetes (HR:1.64, 95% CI:1.07–2.49). Sex and TNM stage emerged as important effect modifiers. 3. Based on lipoprotein(a) levels, participants were allocated to four groups: below the 70th percentile (n=261,486; 2,682 ischemic heart disease events and 22,220 all–cause deaths), 70th–80th percentile (n=37,318; 445 ischemic heart disease events and 3,358 all–cause deaths), 80th–90th percentile (n=37,330; 503 ischemic heart disease events and 2,999 all–cause deaths), and above the 90th percentile (n=37,267; 547 ischemic heart disease events and 2,312 all–cause deaths). Kaplan–Meier curves indicated significant differences in the cumulative incidence of ischemic heart disease across groups (log–rank test <0.001), with higher lipoprotein(a) levels associated with greater risk. Nonlinear modeling (restricted cubic splines) suggested a nonlinear association between lipoprotein(a) and ischemic heart disease risk. To clarify causality, the study employed Mendelian randomization combined with a candidate SNPs approach and lead SNPs from the UK Biobank, utilizing the residual method and doubly rank method to confirm the nonlinear causal relationship between lipoprotein(a) and cardiovascular disease risk. Conclusion 1.Among patients with stage I–III colorectal cancer undergoing curative resection, diabetes severity was adversely associated with long–term outcomes, especially in women and those with earlier–stage disease. 2.Colorectal cancer patients face a significantly higher risk of developing diabetes, which is further elevated by prolonged chemotherapy, particularly regimens involving capecitabine. Close glycemic monitoring is therefore crucial for these patients, especially during chemotherapy. 3.Elevated lipoprotein(a) levels substantially increase the risks of ischemic heart disease, showing a marginally nonlinear pattern. Incorporating lipoprotein(a) into routine risk assessment and initiating early preventive measures are advisable. As new therapeutic agents targeting lipoprotein(a) continue to emerge, integrating genetic testing and precision medicine may more effectively reduce cardiovascular risk in this high–risk population. | en |
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| dc.description.tableofcontents | 目次
論文口試委員審定書 i 謝辭 ii 中文摘要 iii 英文摘要 viii Abbreviations viii 第一章 研究背景 1 1.1 研究架構 1 1.2 主題一、糖尿病嚴重程度與大腸直腸癌治癒性切除後的腫瘤相關預後:台灣族群為基礎的世代追蹤研究 2 1.2.1 大腸直腸癌的疾病負擔和風險因子 2 1.2.2 大腸直腸癌的預後 4 1.2.3 糖尿病和大腸直腸癌預後的相關性 5 1.3 主題二、大腸直腸癌與後續糖尿病風險:台灣族群為基礎的世代追蹤研究 9 1.3.1 糖尿病和大腸直腸癌的關係 9 1.3.2 大腸直腸癌的治療方式 9 1.3.3 大腸直腸癌和糖尿病發生風險的文獻回顧 11 1.3.4 大腸直腸癌和糖尿病發生風險的生理機轉 12 1.3.5 觀察性研究中的輔助和評估因果推論的工具 14 1.4 主題三、脂蛋白(a)與動脈粥樣硬化心血管疾病及全因死亡風險之間的線性和非線性關聯 23 1.4.1 動脈粥樣硬化性心血管疾病負擔和風險因子 23 1.4.2 脂蛋白(a) 24 1.4.3 脂蛋白(a)和動脈粥樣硬化性心血管疾病的關係 27 1.4.4 脂蛋白(a)和動脈粥樣硬化性心血管疾病風險的生理機轉 31 1.4.5 孟德爾隨機分派試驗 32 第二章研究目的與假說 36 2.1 主題一、糖尿病嚴重程度與大腸直腸癌治癒性切除後的腫瘤相關預後:台灣族群為基礎的世代追蹤研究 36 2.2 主題二、大腸直腸癌與後續糖尿病風險:台灣族群為基礎的世代追蹤研究 37 2.3主題三、脂蛋白(a)與動脈粥樣硬化心血管疾病及全因死亡風險之間的線性和非線性關聯 38 第三章 研究方法 39 3.1 主題一、糖尿病嚴重程度與大腸直腸癌治癒性切除後的腫瘤相關預後:台灣族群為基礎的世代追蹤研究 39 3.1.1 研究架構 39 3.1.2 納入排除條件 39 3.1.3 資料來源 40 3.1.4 暴露定義 42 3.1.5 結果定義 43 3.1.6 統計方法 43 3.2 主題二、大腸直腸癌與後續糖尿病風險:台灣族群為基礎的世代追蹤研究 47 3.2.1 研究架構 47 3.2.2 納入條件與排除條件 47 3.2.3 暴露定義 48 3.2.4 結果定義 49 3.2.5 對照組及共變項定義 49 3.2.6 統計方法 50 3.3 主題三、脂蛋白(a)與動脈粥樣硬化心血管疾病及全因死亡風險之間的線性和非線性關聯 55 3.3.1 研究架構 55 3.3.2 納入條件與排除條件 56 3.3.3 暴露定義 56 3.3.3 結果定義 56 3.3.4 共變項定義 57 3.3.5 統計方法 58 第四章 研究結果 65 4.1 主題一、糖尿病嚴重程度與大腸直腸癌治癒性切除後的腫瘤相關預後:台灣族群為基礎的世代追蹤研究 65 4.1.1 基本人口學特徵 65 4.1.2 描述性統計 66 4.1.3 分析性統計 67 4.2 主題二、大腸直腸癌與後續糖尿病風險:台灣族群為基礎的世代追蹤研究 73 4.2.1 研究族群 73 4.2.2 基本人口學特徵和其他描述性統計 73 4.2.3 分析性統計 75 4.3 主題三、脂蛋白(a)與動脈粥樣硬化心血管疾病及全因死亡風險之間的線性和非線性關聯 80 4.3.1 基本人口學特徵 80 4.3.2 描述性統計 82 4.3.3 分析性統計 84 第五章 討論 90 5.1 主題一、糖尿病嚴重程度與大腸直腸癌治癒性切除後的腫瘤相關預後:台灣族群為基礎的世代追蹤研究 90 5.1.1 主要結果 90 5.1.2 比較過去文獻 90 5.1.3 致病機轉 92 5.1.4 公共衛生與臨床應用 92 5.1.5 優點與限制 93 5.1.6 結論 96 5.2 主題二、大腸直腸癌與後續糖尿病風險:台灣族群為基礎的世代追蹤研究 98 5.2.1 主要結果 98 5.2.2 比較過去文獻 98 5.2.3 致病機轉 99 5.2.4 公共衛生與臨床應用 100 5.2.5 優點與限制 101 5.2.6 結論 106 5.3 主題三、脂蛋白(a)與動脈粥樣硬化心血管疾病及全因死亡風險之間的線性和非線性關聯 108 5.3.1 主要結果 108 5.3.2 比較過去文獻 108 5.3.3 致病機轉 109 5.3.4 公共衛生與臨床應用 110 5.3.5 優點與限制 110 5.3.6 結論 111 References 112 Tables 124 Table I–1. Literature review of diabetes and colorectal cancer prognosis. 124 Table I–2. Definitions of the colorectal cancer population the study cohort 127 Table I–3. The definition of curative surgery of colorectal cancer 128 Table I–4. International Classification of Diseases codes used in the study cohort (study project I) 129 Table I–5. Definition of complications of diabetes mellitus 130 Table II–1. Literature review of colorectal cancer and diabetes risk. 132 Table II–2. Comparisons of propensity score matching and inverse propensity score weighting 134 Table II–3. The drug categories used to define the medication–based comorbidities in the study cohort (study project I, II) 136 Table III–1. Definitions of the clinical outcomes in the study project III 137 Table III–2. Different methods for Mendelian randomization analyses 138 Table V. Patient Characteristics by Diabetes Status 139 Table VI. Hazard Ratios (95% CI) of Colorectal Cancer Prognosis by Diabetes Status 141 Table VII. Competing Analyses of the Association Between Different Diabetes Statuses and CRC Prognosis 143 Table VIII. Subgroup Analyses for the Association Between Diabetic Status and Overall Survival 144 Table IX. Subgroup Analyses for the Association Between Diabetic Status and Disease–Free Survival 145 Table X. Subgroup Analyses for the Association Between Diabetic Status and Recurrence 146 Table XI. Subgroup Analyses for the Association Between Diabetic Status and Cancer–Specific Survival 147 Table XII. Unweighted and inverse propensity score–weighted differences of baseline characteristics of different diabetic status 148 Table XIII. Sensitivity analysis results of colorectal cancer prognosis according to diabetic status after inverse propensity score weighting 149 Table XIV. Sensitivity analyses of colorectal cancer prognosis by diabetic status using the 2011 colorectal cancer cohort with more detailed baseline characteristics 150 Table XV. Sensitivity analyses of colorectal cancer prognosis by diabetic status using different classification criteria for diabetic status 152 Table XVI. Distribution of baseline demographics in the study population, specified by cancer status 154 Table XVII. Distribution of baseline demographics in the study population according to chemotherapy usage. 155 Table XVIII. Hazard Ratios (HRs) for incident diabetes associated with colorectal cancer compared with the matched general population. 157 Table XVIIII. Hazard ratios (HRs) for incident diabetes associated with cumulative chemotherapy usage 158 Table XX. Hazard ratios (HRs) for incident diabetes mellitus associated with cumulative capecitabine usage 159 Table XXI. Patients with colorectal cancer since 2011 with more baseline covariates 160 Table XXII. Excluding patients with stage IV colorectal cancer 161 Table XXIII. Cumulative chemotherapy exposure period changed to 2 years 162 Table XXIV. Using a multinomial regression model for propensity score estimation 163 Table XXV. Baseline characteristics of the UKB participants included in this study according to lipoprotein (a) levels 164 Table XXVI. Hazard ratios (HRs) for incident ASCVD associated with lipoprotein(a) levels 167 Table XXVII. Candidate SNPs approach 168 Table XXVIII. selected SNPs meeting core instrumental variable (IV) assumptions 169 Table XXVIIII. Genetically predicted LP(a) based on a polygenic risk score constructed from SNPs identified in literature reviews 170 Table XXX. Association of the LP(a) levels with the risk of ASCVD via mendelian randomization analysis 171 Table XXXI. Examining the nonlinear association between lipoprotein(a) and ASCVD Risk using the Doubly Rank method in nonlinear Mendelian Randomization analysis 172 Table XXXII. Five–year all–cause mortality, absolute risk difference, and survival ratio using internal reference group in colorectal cancer patients stratified by diabetes status 173 Figures 174 Figure 1. Study scheme 174 Figure 2. Biological mechanisms between diabetes and colorectal cancer prognosis (Project I) 175 Figure 3. Treatment Regimen of Colorectal Cancer According to Stages 176 Figure 4. Biological mechanisms between colorectal cancer and subsequent diabetes mellitus risk (Project II) 177 Figure 5. Structure of lipoprotein(a) (Project III) 178 Figure 6. Biological mechanisms between lipoprotein(a) and atherosclerotic cardiovascular disease (Project III) 179 Figure 7. Directed Acyclic Graphs (DAGs) of Mendelian Randomization and Randomized Controlled Trials 180 Figure 8. Flowchart of the study (Project I) 181 Figure 9. Survival curves and cumulative incidence rates. Kaplan–Meier survival curves of (A) overall survival and (B) disease–free survival by diabetic status. Cumulative incidence rates of (C) recurrence and (D) cancer–specific mortality by diabetic status 182 Figure 10. The propensity score distribution of each diabetic mellitus status 186 Figure 11–1. Flowchart of the study for the assessment for the association between colorectal cancer and diabetes risk (Project II–1) 187 Figure 11–2. Flowchart of the study for the assessment for the association between chemotherapy usage and diabetes risk (Project II–2) 188 Figure 12. Survival curves. Kaplan–Meier survival curves of (A) incident diabetes mellitus according to CRC status. (B) incident diabetes mellitus according to CRC status adjusted by annual hospital visits. CRC, colorectal cancer 189 Figure 13. Subsequent diabetes mellitus risk according to time since diagnosis among patients with colorectal cancer. (A) Hazard ratios for diabetes mellitus according to time since diagnosis among patients with colorectal cancer (CRC). (B)Adjusted hazard ratios for diabetes mellitus according to time since diagnosis among patients with colorectal cancer (CRC). 191 Figure 14. The propensity score distribution of each chemotherapy usage group 193 Figure 15. Flowchart of the study for the association between lipoprotein(a) levels and risks of atherosclerotic cardiovascular disease (ASCVD) and all–cause death (Project III) 194 Figure 16. The Kaplan–Meier survival curves of incident ASCVD according to lipoprotein(a) level 195 Figure 17. The Kaplan–Meier survival curves of incident coronary artery disease according to lipoprotein(a) level 196 Figure 18. The Kaplan–Meier survival curves of incident peripheral artery disease according to lipoprotein(a) level 197 Figure 19. The Kaplan–Meier survival curves of incident ischemic stroke according to lipoprotein(a) level 198 Figure 20. Nonlinear relationship with lipoprotein(a) of ASCVD risk 199 Figure 21. Flowchart for SNP selection according to instrumental variable core assumptions 200 Figure 22. MR-Egger Scatter Plot of SNP Effects for Lipoprotein(a) on ASCVD 201 Figure 23. Leave-One-Out sensitivity analysis for the causal effect of lipoprotein(a) on ASCVD 202 Figure 24. Examining the nonlinear association between lipoprotein(a) and ASCVD Risk using the Doubly Rank method in nonlinear Mendelian Randomization analysis 203 Figure 25. Examining the nonlinear association between lipoprotein(a) and ASCVD Risk using the Residual method in nonlinear Mendelian Randomization analysis 204 Figure 26. A nonlinear Mendelian Randomization analysis of lipoprotein(a) and ASCVD Risk: comparison of the Residual vs. Doubly Rank methods 205 Figure 27. Evolution of causal inference theories: a historical timeline 206 Appendix 207 i. R code for Inverse propensity score weighting 207 ii. R code for non-linear Mendelian randomization analyses 210 博士論文口試問題和回答 212 研究目的與主題之間的連貫性問題 212 糖尿病的定義與討論不夠清楚 214 統計分析方法之使用與細節說明 217 結果詮釋與統計細節 218 非線性 Mendelian Randomization分析之探討 219 研究結果之臨床意義與實務應用 220 論文架構、撰寫細節及閱讀性問題 220 其他技術細節與格式問題 221 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 大腸直腸癌 | zh_TW |
| dc.subject | 糖尿病 | zh_TW |
| dc.subject | 糖尿病合併併發症 | zh_TW |
| dc.subject | 總體存活率 | zh_TW |
| dc.subject | 無病存活率 | zh_TW |
| dc.subject | 癌症化學治療 | zh_TW |
| dc.subject | 整合醫學 | zh_TW |
| dc.subject | 脂蛋白(a) | zh_TW |
| dc.subject | 動脈粥狀硬化疾病 | zh_TW |
| dc.subject | 孟德爾隨機分派試驗 | zh_TW |
| dc.subject | integrated medicine | en |
| dc.subject | colorectal cancer | en |
| dc.subject | diabetes mellitus | en |
| dc.subject | diabetes with complication | en |
| dc.subject | disease‐free survival | en |
| dc.subject | overall survival | en |
| dc.subject | cancer chemotherapy | en |
| dc.subject | Mendelian randomization analysis | en |
| dc.subject | atherosclerotic cardiovascular disease | en |
| dc.subject | lipoprotein(a) | en |
| dc.title | 探討大腸直腸癌和血脂異常對動脈粥狀硬化相關疾病風險的關聯性 | zh_TW |
| dc.title | Exploring the Association Between Colorectal Cancer, Dyslipidemia, and the Risk of Atherosclerosis–Related Diseases | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 杜裕康;李文宗;張慶國;盧子彬;黃麗卿;白其卉 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Kang Tu;Wen-Chung Lee;Chin-Kuo Chang;Tzu-Pin Lu;Lee-Ching Hwang;Chyi-Huey Bai | en |
| dc.subject.keyword | 大腸直腸癌,糖尿病,糖尿病合併併發症,總體存活率,無病存活率,癌症化學治療,整合醫學,脂蛋白(a),動脈粥狀硬化疾病,孟德爾隨機分派試驗, | zh_TW |
| dc.subject.keyword | colorectal cancer,diabetes mellitus,diabetes with complication,disease‐free survival,overall survival,cancer chemotherapy,integrated medicine,lipoprotein(a),atherosclerotic cardiovascular disease,Mendelian randomization analysis, | en |
| dc.relation.page | 222 | - |
| dc.identifier.doi | 10.6342/NTU202502397 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-07-24 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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