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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳秀熙 | |
| dc.contributor.author | Pei-Hua Wang | en |
| dc.contributor.author | 王珮樺 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:40:40Z | - |
| dc.date.available | 2017-09-13 | |
| dc.date.copyright | 2017-09-13 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-16 | |
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Association between adenomas of rectosigmoid colon and metabolic syndrome features in a Chinese population. J Gastroenterol Hepatol 2005;20:1410 – 5. Wannamethee, S.G., Shaper, A.G., Durrington, P.N., Perry, I.J., 1998. Hypertension, serum insulin, obesity and the metabolic syndrome. Journal of Human Hypertension 12, 735–741. Wassertheil-Smoller S, Applegate W, Berge K, Chang C, Davis B, Grimm R, Kostis J, Pressel S, Schron E. Change in depression as a precursor of cardiovascular events. SHEP Cooperative Research Group (Systoloc Hypertension in the elderly). Arch Intern Med. 1996; 156: 553–561. Wassertheil-Smoller S, Shumaker S, Ockene J, Talavera GA, Greenland P, Cochrane B, Robbins J, Aragaki A, Dunbar-Jacob J: Depression and cardiovascular sequelae in postmenopausal women. The Women's Health Initiative (WHI). Arch Intern Med 2004; 164(3): 289–298. Welin C, Lappas G, Wilhelmsen L. Independent importance of psychosocial factors for prognosis after myocardial infarction. J Intern Med. 2000; 247: 629–639. Whang W, Kubzansky LD, Kawachi I, Rexrode KM, Kroenke CH, Glynn RJ, Garan H, Albert CM: Depression and risk of sudden cardiac death and coronary heart disease in women: results from the Nurses' Health Study. J Am Coll Cardiol 2009; 53(11): 950–958. Whooley MA, Browner WS. Study of Osteoporotic Fractures Research Group. Association between depressive symptoms and mortality in older women. Arch Intern Med. 1998; 158: 2129–2135. Wu Q, Kling JM. Depression and the risk of myocardial infarction and coronary death: a meta-analysis of prospective cohort studies. Medicine. 2016; 95(6). Wulsin LR, Evans JC, Vasan RS, Murabito JM, Kelly-Hayes M, Benjamin EJ: Depressive symptoms, coronary heart disease, and overall mortality in the Framingham Heart Study. Psychosom Med 2005; 67(5): 697–702. Yasuda N, Mino Y, Koda S, Ohara H. The differential influence of distinct clusters of psychiatric symptoms, as assessed by the general health questionnaire, on cause of death in older persons living in a rural community of Japan. J Am Geriatr Soc. 2002; 50: 313–320. Yen, M.F., Chen, H.H., 2013. Stochastic models for multiple pathways of temporal natural history on co-morbidity of chronic disease. Computational Statistics and Data Analysis 57, 570–588. Yumru M, Savas HA, Kurt E, et al. Atypical antipsychotics related metabolic syndrome in bipolar patients. J Affect Disord. 2007;98(3):247–252. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68892 | - |
| dc.description.abstract | 研究背景
儘管代謝症候群由五個生物標誌所組合而成已被確立,然而對於疾病如何由一個組成進展至多於兩個的組成,意即共病發展的危險性,或是兩個組成由哪一個先發生、哪一個後發生的問題,是值得研究的問題。此外,是否發生第一個疾病的風險和發展成後續疾病的風險,可被其他代謝症候群組成或受其他因素(如憂鬱症、腺瘤及尿酸)所影響,這些不同類型的疾病組合及其他因素,可能較僅探討代謝症候群,對推定心血管疾病和腦血管疾病發生之危險性有所助益。 研究目的 本論文以高血壓和糖尿病為例,探討單一疾病和兩種疾病共病的疾病路徑,研究目的包含: (1) 估計族群中會發展高血壓和糖尿病共病所佔的比例。 (2) 估計由第一個(或單一)疾病的發病危險和轉變到第二個疾病的速率 (3) 比較第一個疾病具共病潛力與單一疾病無共病潛力之不同風險。 (4) 比較在給定第一個疾病情形下,第二個續發疾病之不同風險。 (5) 評估代謝症候群組成及其他因子對第一個(或單一)疾病及第二個續發疾病發病風險的效應。 (6) 考量人口學、憂鬱和腺瘤等因子下,估算不同共病路徑發病機率下之心血管疾病和中風疾病風險。 材料及方法 本研究用於估計慢性病共病路徑相關資料是取自1999年至2015年基隆社區整合式篩檢計畫。除了人口特徵和生活型態的問卷調查,總共有104,898位代代謝症候群篩檢參加者納入分析。腺瘤資料是透過免疫法大腸癌糞便潛血檢查篩檢取得。本研究追蹤參加者後續就醫,以了解包含憂鬱症、心血管疾病和中風之罹病情形。 本研究利用混合性馬可夫共同廻歸模型評估高血壓和糖尿病之共病可能性,以及評估兩個疾病間發生的先後關係。並利用比例風險回歸模式評估其他代謝症候群組成及其他因素對共病路徑的影響。Cox比例風險回歸模型用以評估上述不同共病路徑發病風險對心血管疾病和中風罹病風險之影響。。 結果 研究結果顯示族群中有23.1%(95%CI:22.6%-23.6%)具有潛力形成高血壓和糖尿病之共病。這23%的高血壓或糖尿病患者中,有86%(95%CI:85%-87%)第一個疾病是先有高血壓而之後發展為糖尿病共病。具共病可能性的患者,其先發病為糖尿病是無共病者的36.2倍,先發病為高血壓則是無共病者的11.9倍。相較於第一次發病為高血壓者其發展第二個續發疾病的風險,是第一次發病為糖尿病者的69.7倍。 關於代謝症候群的三種其他組成對第一次(單一)疾病(對糖尿病的OR 從2.49倍到8.17倍和對高血壓的OR從1.25倍到1.54倍),和同時有高血壓和糖尿病的風險(OR 倍1.47到1.63倍)的效應,皆具統計上顯著意義。尿酸對血壓(OR=1.62, 95%CI: 1.50-1.76),糖尿病(OR=5.43, 95%CI: 4.08-7.23),和同時有高血壓及糖尿病(OR=1.35, 95%CI: 1.15-1.59)的風險皆顯著效應。然而,腺瘤和憂鬱症對糖尿病與高血壓發展及共同效應均不具統計上顯著意義。 調整人口特徵和其他變項後,共病路徑發病危險分數對於心血管疾病的風險(aOR=1.03, 95%CI:1.02-1.05)和中風(aOR=1.05, 95%CI:1.02-1.08)據統計上顯著意義。憂鬱症為心血冠疾病(aOR = 2.19,95%CI:1.81-2.65及中風(aOR = 2.52,95%CI:1.79-3.56)發生之獨立因子),腺瘤也有一致性的效應,但接近統計上顯著意義。 結論 透過高血壓及糖尿病的先後發病時序,和兩種疾病的共同可能性評估,研究結果顯示與代謝症候群其三個組成(高密度脂蛋白、三酸甘油酯和肥胖)及尿酸有相關。這種共病路徑對心血管疾病和中風罹病風險的影響不能予以忽略。本研究提供一種嶄新的量化模式,並考量其他共病因子對共病潛在性及不同的疾病發病時序影響,有助於心血管和中風等疾病預防。 | zh_TW |
| dc.description.abstract | Introduction
Despite the well-established metabolic syndrome (MetS) with the clustering of five components of biomarkers, it is still elusive whether and how the risk of developing one component (disease) of MetS is affected by the potential of being co-morbidity (more than two components), a latent variable, and also the temporal sequence between two diseases. Moreover, whether the risk of onset of first disease and the risk of developing subsequent disease is affected by the other components of MetS and extraneous variable (such as depression, adenoma, and uric acid) that are putative factors for the risk of cardiovascular disease and cerebrovascular disease (e.g. stroke) other than the components of MetS. Aims This thesis is intended to elucidate the pathway, taking hypertension and diabetes mellitus (DM) for example, leading to single disease and comorbidity with both of diseases (1) to estimate the proportion of being susceptible to hypertension and DM; (2) to estimate the force of being the first (single) disease and the force of transition to the second subsequent disease; (3) to compare the risk of being first disease with the potential of having co-morbidity and that of single disease without potential of having co-morbidity based on (1) and (2); (4) to compare the risk of being second subsequent disease given the first disease by two reverse orders of both hypertension and DM based on (1) and (2); (5) to assess the effect size of other components of MetS and that of extraneous variables on the risk of first (single) disease and the risk of second subsequent disease; (6) to relate the probability of risk score based on the pathway of (1) and (2) to the risk of CVD and stroke with adjustment for demographic features and other extraneous variables. Material and Methods Data sources: The empirical data used for estimating parameters related to the bivariate pathway on two components of MetS are derived from the Keelung community-based integrated screening (KCIS) since 1999 until 2015. In addition to questionnaire on demographic features and life-style factors, annual health check-up for five components of MetS have been offered for approximately 104, 898 residents. Information on adenoma has been collected through one of colorectal cancer screening with fecal immunochemical test (FIT). Moreover, depression and the outcomes on CVD and stroke during has been obtained by linking the entire cohort with claimed data on national health insurance during follow-up. Mixture Markov-based co-morbidity model: The previously proposed bivariate co-morbidity pathway was applied to the model the potential of being both hypertension and DM and also the temporal sequence of onset of disease between hypertension and DM. The effects of other components of MetS and extraneous variables such as adenoma, depression, and uric acid were evaluated by using proportional hazards regression form. The probability of being each mode built in bivariate pathway of hypertension and DM was further incorporated into the Cox proportional hazards regression model to assess the influence of bivariate comorbidity pathway on the risk for CVD and stroke with adjustment for the variables that were associated with the risk for CVD and stroke. Results The estimated results on bivariate co-morbidity pathway show 23.1% (95% CI: 22.6 %-23.6%) of the underlying population had potential of developing both hypertension and DM. Approximately 86% (95% CI: 85%-87%) out of these 23% with the potential of having both diseases developed first hypertension and then DM later. It is very interesting not that those taking the pathway of co-morbidity were at great risk of being first disease by 36.2-fold for DM and by 11.9-foldfor hypertension compared with those taking the pathway of no potential of co-morbidity. The risk of developing the second subsequent disease was greater for the first disease with DM than that with hypertension by 69.7-fold. The statistical significance was noted regarding the effects of all three other components of MetS on the first (single) disease (ORs from 2.49 to 8.17 for DM and ORs from 1.25 to 1.54 for hypertension) and also on the risk for having both hypertension and DM (ORs from 1.47 to 1.63). The significant effects of uric acid on hypertension (OR=1.62, 95% CI: 1.50-1.76), on DM (OR=5.43, 95% CI: 4.08, 7.23), and also on the risk of having both hypertension and DM (OR: 1.35, 95%CI: 1.15-1.59) were also noted. However, there was lacking of statistical significance regarding the effect of adenoma and depression for each of two risks. After adjustment for demographic features and other significant variables, the effect of the probability of being each mode of bivariate co-morbidity was statistically significant for the risk for CVD (aOR=1.03, 95%CI:1.02-1.05) and stroke (aOR=1.05, 95%CI:1.02-1.08). It is very interesting to note that the independent effects of depression on the risk for CVD (aOR=2.19, 95%CI: 1.81-2.65) and stroke (aOR=2.52, 95%CI: 1.79-3.56) were found whereas the corresponding effect of adenoma was of borderline statistical significance. Conclusions Bivariate co-morbidity pathway for hypertension and DM in relation to three other components of Mets (HDL, Triglyceride, and Obesity) and also extraneous variables such as adenoma and depression has been quantitatively assessed by providing a clear picture of temporal sequence and the potential of having both disease. The influence of such a bivariate co-morbidity pathway on the risk for CVD and stroke has been noted making allowance for the independent effects demographic features and depression. The findings provide a new quantitative insight into prevention of CVD and stroke by considering the potential of being co-morbidity and also the temporal sequence of first hypertension and then second DM. | en |
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| dc.description.tableofcontents | 中文摘要 i
Abstract iv LIST OF FIGURES viii LIST OF TABLES ix Chapter 1. Introduction 1 1.1 Time dimension on co-morbidity and their clinical management 1 1.2 Clinical applications to co-morbidity of MetS 1 1.3 Hypotheses on co-morbidity pathway related to MetS 2 1.4 Mixture Markov-based co-morbidity regression model 3 1.5 Objectives 4 Chapter 2. Literature Review 6 2.1 Co-morbidity of hypertension and diabetes 6 2.2 Metabolic syndrome and colorectal neoplasia 9 2.3 Metabolic syndrome and depression 12 2.4 Metabolic syndrome and cardiovascular disease (CVD) 13 2.5 Depression, myocardial infarction (MI) and coronary heart disease (CHD) 14 2.6 Depression and cardiovascular disease (CVD) 16 Chapter 3. Material and Methods 18 3.1 Study population 18 3.2 Data collection 18 3.3 Study design 20 3.4 The Mixture of Markov models 21 3.5 Clinical weights for diabetes, hypertension, and comorbidity 25 3.6 Statistical analysis 26 Chapter 4. Results 27 4.1 The multiple pathway of temporal natural history on bivariate co-morbidity of MetS 27 4.2 The risk factors in association with the incidence of CVD 34 Chapter 5. Discussion 38 5.1 Neglected aspect of temporal sequence of time to have onset of co-morbid disease 38 5.2 Methodological Concerns over bivariate co-morbidity pathway 39 5.3 Clinical Applications to co-morbidity of MetS 39 5.4 Methodological considerations and limitations 41 References 103 LIST OF FIGURES Figure 3.1. Bivariate co-morbidity pathway model for partial MetS associated with CVD and Stroke considering uric acid, depression, and adenoma 90 Figure 4.1.1 Relative risks of adenoma associated with single and second subsequent disease 91 Figure 4.1.2 Relative risks of depression associated with single and second subsequent disease 92 Figure 4.1.3 Relative risks of uric acid associated with single and second subsequent disease 93 Figure 4.1.4 Relative risks of obesity associated with single and second subsequent disease 94 Figure 4.1.5 Relative risks of hyperlipidaemia associated with single and second subsequent disease 95 Figure 4.1.6 Relative risks of HDL associated with single and second subsequent disease 96 Figure 4.1.7 Relative risks of obesity, hyperlipidaemia, and HDL associated with single and second subsequent disease 97 Figure 4.1.8. The predictive probabilities of multiple pathway in patients with Mets, adenoma, and comorbidity 98 Figure 4.1.9. 20-year Cumulative probabilities by different groups 99 Figure 4.1.10. 20-year Cumulative probabilities by different groups 100 Figure 4.1.11. 30-year Cumulative probabilities by different groups 101 Figure 4.1.12. The predictive probabilities of multiple pathway in patients with diabetes, hypertension, and comorbidity 102 LIST OF TABLES Table 2.1 The summary of associated study between MetS and colorectal neoplasia 43 Table 2.2 The summary of associated study between MetS and depression 56 Table 4.1.1 Transition history and observed counts for Mets and Adenoma 67 Table 4.1.2: Transition history and observed counts for diabetes and hypertension 69 Table 4.1.3. Estimated incidence of Mets, and adenoma, and transition rates to co-morbidity (Mets + adenoma) 71 Table 4.1.4. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM). 72 Table 4.1.5. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering adenoma factor 73 Table 4.1.6. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering depression factor 74 Table 4.1.7 Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering uric acid factor 75 Table 4.1.8. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering obesity factor. 76 Table 4.1.9. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering hyperlipidemia factor. 77 Table 4.1.10. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering HDL factor. 78 Table 4.1.11. Estimated incidence of hypertension, and diabetes mellitus (DM), and transition rates to co-morbidity (hypertension + DM) considering other three MetS components. 79 Table 4.2.1. Selected characteristics by CVD 81 Table 4.2.2. The factors associated with the risk of CVD by univariate and multivariable Cox regression analyses 82 Table 4.2.3. The factors associated with the risk of CVD by univariate and multivariable Cox regression analyses 83 Table 4.2.4. The factors associated with the risk of CVD by univariate and multivariable Cox regression analyses 84 Table 4.2.5. The interaction test for the risk of CVD 85 Table 4.2.6. The factors associated with the risk of Stroke by univariate and multivariable Cox regression analyses 86 Table 4.2.7 The factors associated with the risk of Stroke by univariate and multivariable Cox regression analyses 87 Table 4.2.8. The factors associated with the risk of Stroke by univariate and multivariable Cox regression analyses 88 Table 4.2.9. The interaction test for the risk of Stroke 89 | |
| dc.language.iso | en | |
| dc.subject | 中風 | zh_TW |
| dc.subject | 心血管疾病 | zh_TW |
| dc.subject | 共病 | zh_TW |
| dc.subject | 代謝症候群 | zh_TW |
| dc.subject | 混合性馬可夫?歸模型 | zh_TW |
| dc.subject | Metabolic Syndrome | en |
| dc.subject | Cardiovascular Disease | en |
| dc.subject | Stroke | en |
| dc.subject | Co-morbidity | en |
| dc.subject | Mixture Markov-based Regression Model | en |
| dc.title | 以混合性馬可夫共病廻歸模型探討代謝症候群與心血管疾病及中風之相關性 | zh_TW |
| dc.title | Mixture Markov-based co-morbidity regression model for Metabolic Syndrome Associated with Cardiovascular Disease and Stroke | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張榮珍,潘信良,陳立昇 | |
| dc.subject.keyword | 代謝症候群,心血管疾病,中風,共病,混合性馬可夫?歸模型, | zh_TW |
| dc.subject.keyword | Metabolic Syndrome,Cardiovascular Disease,Stroke,Co-morbidity,Mixture Markov-based Regression Model, | en |
| dc.relation.page | 114 | |
| dc.identifier.doi | 10.6342/NTU201703557 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-17 | |
| 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 |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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