Fok, Tsz Nam = 香港二型糖尿病併發症風險評估模型 / 霍梓楠. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 71-72). / Abstracts in English and Chinese. / Fok, Tsz Nam = Xianggang er xing tang niao bing bing fa zheng feng xian ping gu mo xing / Huo Zinan. / Abstract --- p.i / 概要 --- p.iii / Acknowledgements --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Dataset Information --- p.3 / Chapter 3 --- Background and Literature Review --- p.9 / Chapter 3.1 --- The Idea of Risk Model --- p.9 / Chapter 3.2 --- Discrimination Problem --- p.10 / Chapter 3.3 --- Receiver Operating Characteristic (ROC) Curve --- p.11 / Chapter 3.4 --- Summary Indices of the ROC Curve --- p.13 / Chapter 3.4.1 --- Area Under ROC Curve (AROC) --- p.14 / Chapter 3.4.2 --- Maximum Vertical Distance --- p.16 / Chapter 3.5 --- Discrimination Performance in Prognostic Model --- p.18 / Chapter 3.5.1 --- Survival Data --- p.18 / Chapter 3.5.2 --- Survival Function --- p.20 / Chapter 3.5.3 --- Time-dependent ROC Curve for Censored Data --- p.21 / Chapter 3.6 --- Earlier Work on Diabetic Complications Risk Models --- p.22 / Chapter 3.6.1 --- Maximization of the AROC --- p.28 / Chapter 4 --- Model Development --- p.29 / Chapter 4.1 --- Overview --- p.29 / Chapter 4.2 --- Estimating the ROC curve and the AROC --- p.29 / Chapter 4.3 --- Choosing Suitable Risk Factors --- p.30 / Chapter 4.4 --- Mixing Risk Factors and Optimizing Coefficients --- p.31 / Chapter 4.5 --- Validation of Risk Equations Using Test Set --- p.33 / Chapter 5 --- Results and Validation --- p.34 / Chapter 5.1 --- Performance of the Risk Factor Candidates --- p.34 / Chapter 5.2 --- Estimation of the Coefficients --- p.37 / Chapter 5.3 --- Checking the Uniqueness of the Solution --- p.41 / Chapter 5.4 --- Validation Using Test Set --- p.46 / Chapter 5.5 --- Comparison of AROC-optimized and MVD-optimized Risk Equations --- p.56 / Chapter 6 --- Comparison of our Results with Earlier Work --- p.58 / Chapter 7 --- "Discussion, Outstanding Issues and Future Works" --- p.66 / Chapter 7.1 --- Comparison Between the AROC and the MVD --- p.66 / Chapter 7.2 --- Applications of Risk Models --- p.68 / Chapter 7.3 --- Limitations of the study --- p.69 / Chapter 7.4 --- Outstanding Issues and Future Works --- p.69 / Chapter 7.4.1 --- The Estimation of Error Due to Sampling Variance --- p.69 / Chapter 7.4.2 --- Time-dependent Coefficients --- p.69 / Chapter 7.4.3 --- Extending the Idea to other Datasets --- p.70 / Chapter 7.5 --- Conclusion --- p.70 / Bibliography --- p.71

Indiana University-Purdue University Indianapolis (IUPUI) / Obesity increases cardiovascular disease risk and is associated with factors of the “metabolic syndrome” (MetS), a disorder including hypertension, hypercholesterolemia and/or impaired glucose tolerance. Expanding adipose and subsequent inflammation is implicated in vascular dysfunction in MetS. Perivascular adipose tissue (PVAT) surrounds virtually every artery and is capable of releasing factors that influence vascular reactivity, but the effects of PVAT in the coronary circulation are unknown. Accordingly, the goal of this investigation was to delineate mechanisms by which lean vs. MetS coronary PVAT influences vasomotor tone and the coronary PVAT proteome. We tested the hypothesis that MetS alters the functional expression and vascular contractile effects of coronary PVAT in an Ossabaw swine model of the MetS. Utilizing isometric tension measurements of coronary arteries in the absence and presence of PVAT, we revealed the vascular effects of PVAT vary according to anatomical location as coronary and mesenteric, but not subcutaneous adipose tissue augmented coronary artery contractions to KCl. Factors released from coronary PVAT increase baseline tension and potentiate constriction of isolated coronary arteries relative to the amount of adipose tissue present. The effects of coronary PVAT are elevated in the setting of MetS and occur independent of endothelial function. MetS is also associated with substantial alterations in the coronary PVAT proteome and underlying increases in vascular smooth muscle Ca2+ handling via CaV1.2 channels, H2O2-sensitive K+ channels and/or upstream mediators of these ion channels. Rho-kinase signaling participates in the increase in coronary artery contractions to PVAT in lean, but not MetS swine. These data provide novel evidence that the vascular effects of PVAT vary according to anatomic location and are influenced by the MetS phenotype.