臺灣博碩士論文加值系統

許多的研究指出糖尿病會加速動脈粥狀硬化，後者為引起心血管疾病的主因。目前動脈粥狀硬化的假說為低密度脂蛋白氧化、內皮細胞功能變異以及發炎反應。本研究為了瞭解多重危險因子對糖尿病加速動脈粥狀硬化進展的貢獻度和交互作用關係，故使用 apoE-/- 小鼠 (自發性動脈粥狀硬化動物模式) 處理 streptozotocin (STZ) 為模式來探討問題。本研究利用代謝體學 (metabolomics) 方法去探討上述疾病進展過程中多種危險因子的貢獻度和交互作用。氫核磁共振 (1H-NMR) 光譜法用於血漿和肝臟中脂質代謝物廣泛的分析，液相層析-質譜法 (LC-MS) 則用於水溶物質廣泛的分析，再藉主成分分析 (principal component analysis, PCA) 和資料庫的搜尋，尋找有顯著改變的代謝物，並評估作為生物指標的潛力。結果顯示: STZ 誘導的 (第一型) 糖尿病組 (n=6) 和控制組 (n=10) 比較，前者血漿膽固醇、游離脂肪酸 (FFAs)、多元不飽和脂肪酸 (PUFAs) 和膽鹼磷脂 (phosphatidylcholine, PC) 值皆較控制組顯著增加 (P<0.05)。肝臟中脂質代謝物的1H-NMR圖譜顯示，控制組脂質代謝異常的情形為高三酸甘油酯 (triacylglycerol, TG) 型式的脂肪肝，糖尿病組則呈現為高膽固醇型式的脂肪肝。顯示 STZ 誘導 apoE-/- 小鼠糖尿病的發生，所造成的脂質異常屬於更嚴重且更趨動脈粥狀硬化的型式，肝臟為主要代謝異常的位置，肝臟和血漿的膽固醇和血漿 PC 之間呈高正相關，故推測糖尿病組肝臟膽固醇生合成增加亦促使 PC 生合成增加，釋放至血漿中造成糖尿病組更高膽固醇和高 PC 的情況。肝臟中的水溶代謝物譜型分析結果顯示，lactate、alanine 和 acetate 在糖尿病組中皆顯著增加 (P<0.05)。液相層析-質譜法分析的結果透過 relative frequency filter (100%)、ANOVA 和 PCA 分析，可在肝臟和血漿中確認出糖尿病組和控制組間具有顯著差異的 100 多個質量點。本研究可得以下結論：糖尿病加速動脈粥狀硬化的進展，可能是藉由造成更嚴重且更趨動脈粥狀硬化的脂質代謝異常所致。血漿 PC 值增加可能作為一項有潛力的生物指標，用以偵測糖尿病加速動脈粥狀硬化的進展。

Many studies have strongly suggested that diabetes mellitus (DM) may accelerate atherosclerosis, the underlying mechanism of cardiovascular disease (CVD). Strong evidence also supports that LDL oxidation, endothelial dysfunction, and inflammations are involved in the pathogenesis of atherosclerosis. To better understand the contribution and interaction of the multiple risk factors in diabetes-accelerated atherosclerosis, apolipoprotein E-deficient (apoE-/-) mice (a spontaneous atherosclerosis animal model) treated with streptozotocin (STZ) were used as an animal model. This study also incorporated metabolomics as an approach to explore the trend and contribution of lipid disorders in diabetes-accelerated atherosclerosis. Proton-nuclear magnetic resonance (1H-NMR) spectroscopy was used as the tool for global lipid metabolite profiling in plasma and liver, whereas liquid chromatography-mass spectrometry (LC-MS) was used as a tool for global water-soluble metabolite profiling. Principal component analysis (PCA) and database (METLIN and Human Metabolome Database) search were used for finding candidate metabolites, which were significantly changed. Results demonstrated that lipid disorders causing by STZ-induced diabetes (type 1 DM) were more atherogenic (hypercholesterolemia). In DM group, plasma cholesterol, free fatty acids (FFAs), polyunsaturated fatty acids (PUFAs) and phosphatidylcholine (PC) were significantly elevated (P<0.05). Hepatic lipid metabolite profiling demonstrated that the lipid disorders shifted from high triacylglycerol (TG) in control group to high cholesterol in DM group. Liver was the major site where metabolic disturbance occurred. The highly positive correlations between cholesterol and PC in liver and plasma suggested that increased biosynthesis of cholesterol also stimulated the biosynthesis of PC in the liver. Water-soluble metabolite analysis by 1H-NMR demonstrated that lactate, alanine and acetate were significantly elevated (P<0.05) in the liver in DM group. This study concludes that the underlying mechanism in diabetes-accelerated atherosclerosis may be due to more severe and atherogenic lipid disorders in liver and plasma, namely from elevated TG to more atherogenic hypercholesterolemia. Elevation of plasma PC may be a potential biomarker to predict the progression of diabetes-accelerated atherosclerosis.

目錄
指導教授推薦書
口試委員會審定書
授權書 iii
誌謝 iv
中文摘要 vi
Abstract viii
目錄 x
表目錄 xiv
圖目錄 xvi
縮寫表 xviii
第一章、緒論 (Introduction) 1
1.1 糖尿病 (Diabetes mellitus) 1
1.2 肥胖、胰島素抗拒性與代謝症候群 3
1.3 動脈粥狀硬化 (Atherosclerosis) 4
1.4 內皮細胞功能變異 (Endothelial cell dysfunction) 6
1.5 血脂異常 7
1.6 發炎反應 (Inflammation) 9
1.7 動物模式 11
1.8 生物指標 (Biomarkers) 12
1.9 代謝體學 (Metabolomics) 13
2.0 核磁共振光譜法 (Nuclear magnetic resonance spectroscopy) 16
第二章、研究目的 18
第三章、實驗材料和方法 19
3.1 實驗材料 19
3.1.1 實驗動物 19
3.1.2 飼料 19
3.1.3 試劑套組 20
3.1.4 分析試藥、溶劑 20
3.1.5 實驗設備 21
3.2 實驗方法 22
3.2.1 動物處理 22
3.2.2 血漿生化數值之測定 22
3.2.2.1 測量血漿中膽固醇 22
3.2.2.2 測量血漿中三酸甘油酯 23
3.2.2.3 測量血糖 23
3.2.2.4 測量血漿游離脂肪酸 24
3.2.2.5 測量血漿 CRP 24
3.2.3 同時粹取水溶、脂溶代謝物 25
3.2.4 氫核磁共振光譜 (1H-NMR) 測定 26
3.2.5 液相層析質譜法實驗 27
3.3 統計分析 29
第四章、結果 31
4.1 血漿生化數值分析 31
4.2 核磁共振光譜分析 31
4.2.1 血漿代謝物核磁共振光譜分析 31
4.2.2 肝臟代謝物核磁共振光譜分析 35
4.3 生物指標間的相關性 37
4.3.1 血漿中生物指標間的相關性 (Correlation coefficient, r) 37
4.3.2 血漿和肝臟中生物指標間的相關性 37
4.3.3 肝臟中生物指標間的相關性 39
4.3.4 血漿生物指標間normalize TC後之相關性 39
4.3.5 血漿和肝臟生物指標間normalize TC後之相關性 40
4.3.6 肝臟生物指標間normalize TC後之相關性 41
4.4 液相層析質譜儀分析 41
第五章、討論 43
第六章、結論 49
參考文獻 50
表圖 59
附錄 111

表目錄
Table 1. Plasma concentrations of glucose, FFAs, TG, TC and CRP. 59
Table 2. 1H NMR assignments with chemical shift, multiplicity for signals identified in the lipid extract of blood plasma. 60
Table 3. Comparison of integration values of plasma lipid metabolites in control (CT) and STZ-treated (DM) mice. 61
Table 4. Lipid metabolites found in the plasma of control (CT) and STZ- treated (DM) mice 62
Table 5. Concentrations of lipid metabolites in the plasma of CT (control) and STZ-treated (DM) mice 63
Table 6. Comparison of integration values of liver lipid metabolites in CT (control) and STZ-treated (DM) mice. 64
Table 7. Lipid metabolites found in the liver of control (CT) and STZ-treated (DM) mice. 65
Table 8. Concentrations of lipid metabolites in the liver of CT (control) and STZ-treated (DM) mice. 66
Table 9. 1H NMR assignments with chemical shift, multiplicity for signals identified in the water-soluble metabolites of blood plasma. 67
Table 10. Water soluble-metabolites found in the plasma of control (CT) and STZ-treated (DM) mice. 68
Table 11. Water-soluble metabolites found in the liver of control (CT) and STZ-treated (DM) mice. 69
Table 12. Correlation between biomarkers of plasma and liver in total mice (control and diabetes groups). 70
Table 13. Correlation between biomarkers of plasma and liver normalized with cholesterol of plasma and liver in total mice (control and diabetes groups). 71

圖目錄
Fig. 1. The progression of atherosclerosis. 72
Fig. 2. The metabolism of lipoproteins. 73
Fig. 3. Diagram of lesion formation time period in apoE-deficient mice fed with western type diet or chow diet. 74
Fig. 4. Time table of animal experiment. 75
Fig. 5. Experimental flow chart. 76
Fig. 6. 1H NMR spectra of lipid extracts from plasma of A. control (CT) and B. diabetes (DM) group. 77
Fig. 7. 1H NMR spectra of lipid extracts from liver of A. control (CT) and B. diabetes (DM) group. 78
Fig. 8. 1H NMR spectra of plasma water-soluble metabolites from diabetes group (A) without water suppression (B) with water suppression. 79
Fig. 9. 1H NMR spectra of plasma water-soluble metabolites in A. control (CT) and B. STZ-treated (DM) mice. 80
Fig. 10. 1H NMR spectra of liver water soluble-metabolites in A. control (CT) and B. STZ-treated (DM) mice. 81
Fig. 11. Correlation between biomarkers of plasma 82
Fig. 12. Correlation between biomarkers of liver and plasma 87
Fig. 13. Correlation between biomarkers of liver 94
Fig. 14. Correlation between TG and PUFA of plasma normalized with cholesterol of plasma 96
Fig. 15. Correlation between biomarkers of plasma and liver normalized with cholesterol respectively 97
Fig. 16. Correlation between biomarkers of liver normalized with cholesterol of liver 98
Fig. 17. BPC chromatograms of water-soluble metabolites extracted from plasma of A. control (CT) and B. diabetes (DM) group. 99
Fig. 18. BPC chromatograms of water-soluble metabolites extracted from liver of A. control (CT) and B. diabetes (DM) group. 100
Fig. 19. PCA plot (PC1 18.83% vs. PC2 7.687%) of plasma water-soluble metabolites from control (CT) and diabetes (DM) group 101
Fig. 20. PCA plot (PC1 43.32% vs. PC2 15.72%) of plasma water-soluble metabolites from control (CT) and diabetes (DM) group. 102
Fig. 21. Ordered list of plasma water-soluble metabolites 103
Fig. 22. PCA plot (PC1 5.483% vs. PC2 4.671%) of liver water-soluble metabolites from control (CT) and diabetes (DM) group. 104
Fig. 23. PCA plot (PC1 34.83% vs. PC2 13.74%) of liver water-soluble metabolites from control (CT) and diabetes (DM) group. 105
Fig. 24. Ordered list of liver water-soluble metabolites 106
Fig. 25. Metabolites significantly disturbed in the liver and plasma. 107
Fig. 27. Correlation between plasma biomarkers and lipid core 109

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