請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50074
標題: | 整合科學統計模型探討端粒長度對於第二型糖尿病、心血管疾病以及肥胖之影響 Statistical Model for Synthesis Science Assessing the Effect of Telomere Length on Type 2 Diabetes Mellitus, Cardiovascular Disease, and Obesity |
作者: | Hsin-Mei Chang 張心玫 |
指導教授: | 陳秀熙(Hsiu-Hsi Chen) |
關鍵字: | 端粒長度,糖尿病,心血管疾病,肥胖, telomere length,diabetes melitus,cardiovascular disease,obesity, |
出版年 : | 2016 |
學位: | 碩士 |
摘要: | 白血球端粒長度對於老化以及其相關疾病(如第二型糖尿病、心血管疾病、肥胖以及癌症)在目前的研究中已顯示其良好的預測能力,因此有許多研究致力於評估較短的端粒對於各種疾病之風險的影響。此外,亦有許多整合各研究之統合性分析將影響各研究結果之因素納入評估其效果之考量,期得到白血球端粒之真實影響。在個別研究以及統合性研究中皆遭遇如下之議題:利用單一疾病作為結果評估端粒長度之影響是否合宜?此一議題之基礎源於端粒對於基因以及表觀基因之功能具有廣泛之影響,因此端粒亦可能同時成為多種疾病之危險因子。若能以多種疾病做為評估端粒長度對於健康效益之影響程度將提供對於端粒長度做為健康危險因子上更為豐富的訊息。
<研究目的> 本研究論文之目的為: (1)利用蒙地卡羅模擬方式,利用已發表之文獻提供的相關變項產生個人資料; (2)以貝氏階層模型隨機效應模式,利用(1)項產生的數據評估較短的端粒長度對第二型糖尿病、心血管疾病與肥胖之影響; (3)以貝氏廣義線性模式為基礎,結合觀察到之結果與不同特性的連續或類別變項,合成產生證據; (4)延伸利用貝氏證據整合統計模型,利用已發表文獻產生的個人數據,加以評估端粒長度對這三種疾病產生的統合效果。 <材料與方法> 藉由系統性回顧已發表文獻所提供之端粒長度對此三種疾病之影響,在具有與不具有較短端粒長度的兩個族群,我們摘取文獻中與較短的端粒長度相關疾病的充分統計量及其相關因子,再利用每一研究所摘錄資料以微模擬方式進行每一研究個人資料產出。本論文首先運用傳統的固定效應及隨機效應模式,由上述文獻中找到端粒過短對糖尿病、心血管疾病及肥胖的未調整效應。接著考量個人相關危險因子與研究間之異質性,利用本論文提出的貝氏階層模型評估端粒長度之影響。接著,在貝氏階層模式下,以多變項常態分布考量三種疾病間之相關性,估計較短之端粒長度對糖尿病、心血管疾病及肥胖三種疾病的統合效應。 <結果> 本分析由8篇第二型糖尿病及7篇心血管疾病文獻萃取資料,端粒長度及肥胖相關性是由上述15篇資料取得,最後此三種疾病納入分析的個案人數分別為18376, 7781, 及26157。利用傳統的證據整合分析方法,端粒長度過短對糖尿病、心血管疾病及肥胖的粗勝算比分別為1.38 (95%CI: 1.25, 1.52), 1.51 (95% CI: 1.28, 1.78)及1.01 (95%CI: 0.96, 1.06)。利用貝氏階層模式可得到端粒長度過短對糖尿病、心血管疾病及肥胖的調整勝算比分別為1.46 (95%CI: 1.36, 1.56), 1.59 (95%CI: 1.35, 1.84), 及1.06 (95%CI: 1.00, 1.13)。研究間異質性在不同疾病需應用不同模式加以考量,對糖尿病而言,最佳模式為隨機截距模式(DIC: 17605.1),心血管疾病為隨機截距及隨機斜率模式(DIC: 9031.7),肥胖則為隨機截距模式(DIC: 2874.1)。此外,本研究利用隨機效應模式,以多變量貝氏階層模式,同時考量糖尿病、心血管疾病及肥胖三種慢病相關性後,結果顯示端粒長度對於糖尿病、心血管疾病及肥胖之危險對比值分別為1.23 (95%CI: 1.21, 1.24)、1.54 (95%CI: 1.51, 1.57)及0.99 (95%CI: 0.99, 1.00)。利用統合分析結果預測臺灣地區40歲以上人口糖尿病及心血管疾病個案數,並輔以基隆整合式篩檢死亡資料的死亡率推估全死因人數,結果發現端粒較短的族群其死亡數會多出約10,438人,相對危險性為1.021(95%信賴區間:1.017-1.025)。 <結論> 根據統合分析科學準則,本研究利用蒙地卡羅模擬方式產生以個人為主的實證資料,考量異質性後探討端粒長度對於三種慢性病的影響。本研究根據這些實證資料發現在這三種慢性病中,端粒長度對於心血管疾病之影響最為顯著(增加60%危險性)、糖尿病次之(增加46%危險性)、肥胖最低(增加6%危險性)。本研究最有趣且最顯著的貢獻是同時考量三種慢性病相關性,探討端粒長度之影響,結果顯示對於心血管疾病其影響效力降低10% (增加54%危險性)、糖尿病降低50%影響效力(增加23%危險性),而對於肥胖則無增加危險性。 <Background> Leucocyte telomere length (LTL) has been recognized as a predictor for aging and age-related diseases, including T2DM, cardiovascular disease (CVD), obesity, and cancer. Numerous studies have been conducted to report the effect sizes regarding the influence of short LTL on the risk of each disease of interest with individual studies and with meta-analysis. Moreover, such a kind of associated study is faced with the interesting question: Is it adequate to investigate the influence of short LTL on single disease regardless of single study or meta-analysis? The rationale is that as LTL is genetically or epigenetically inherited the influence may be pervasive in the involvement of multiple diseases rather than single disease. It seems more attractive to study the effect of short LTL with multiple outcomes of interest. <Aims> The objectives of this thesis were: (1)to use Monte Carlo micro-simulation to generate empirical data from published articles with relevant covariates; (2)to estimate the effect size of short LTL on T2DM, CVD, and obesity with the Bayesian hierarchical random-effect model based on simulated data as indicated in (1); (3)to incorporate observed outcomes with different characteristics including both categorical and continuous variables with the Bayesian generalized linear model underpinning in the process of deriving synthesis evidence; and (4)to assess the evidence of integrated effect of LTL on the three diseases by extending the Bayesian hierarchical model for synthetic science based on the simulated data according to the published articles. <Material and methods> From the published articles on effect of LTL on the three diseases, sufficient statistics of relevant covariates for each study were abstracted, and used for micro-simulation to generate individual data. The effect of LTL on T2DM, CVD, and obesity was derived by using DerSimonian and Laird method and generalized linear model of fixed effect and random effect. The effect size was evaluated after considering individual heterogeneity and the heterogeneity at study level by the proposed Bayesian hierarchical model with random intercept and random slope parameters. The integrated effect of LTL on T2DM, CVD, and obesity was assessed by extending the proposed Bayesian hierarchical model for synthetic science to include the three category of disease and modelled by multivariate normal distribution. <Results> Considering the outcome of T2DM and CVD, a total of 8 and 7 articles were enrolled for data extraction. The association of LTL and obesity were explored by these 15 studies. This comprised a total study subjects of 18376, 7781, and 26157, respectively. By using DerSimonian and Laird method the cOR of short TL on T2DM, CVD and obesity were 1.38 (95%CI: 1.25, 1.52), 1.51 (95%CI: 1.28, 1.78), and 1.01 (95%CI: 0.96, 1.06), respectively. By using the Bayesian hierarchical model the aOR of LTL on T2DM, CVD, and obesity were 1.46 (95%CI: 1.36, 1.56), 1.59 (95%CI: 1.35, 1.84), and 1.06 (95%CI: 1.00, 1.13) respectively. The most appropriate model for T2DM, CVD, and obesity were random intercept model (DIC: 17605.1), random slope and random intercept model (DIC: 9031.7), and random intercept model (DIC: 2874.1) respectively. Using the random-effect model treating T2DM, CVD, obesity as multiple correlated outcome with Bayesian underpinning, the integrated effect of aOR of short LTL on T2DM, CVD, and obesity were 1.23 (95%CI:1.21, 1.24), 1.54 (95% CI: 1.51, 1.57), and 0.99 (95%CI: 0.99, 1.00), respectively. The number of DM and CVD cases and the number of all-cause deaths for the Taiwanese population aged 40 years and older from Keelung Community-based integrated screening (KCIS) study. The results show that there were10,438 extra all-cause deaths projected in the group with shorten LTL given relative rate of 1.021 (95% CI: 1.017-1.025). <Conclusion> Based on the principle of synthesis science, the current thesis made expedient use of Monte Carlo simulation that was used to generate individual empirical data in conjunction with Bayesian hierarchical random-effect model for modelling on the effect of LTL on three common chronic diseases, allowing for heterogeneity explained by relevant covariates and the unexplained variation due to target populations, study designs, the variation of measurement in LTL, and other variations. The conclusion based on the empirical findings is that most notable effect of LTL is seen in CVD (60% increased risk), followed by type 2 DM (46% increased risk) and the least (only 6% increased risk) for obesity among three chronic diseases. It is interesting to note that the magnitude of contribution still remained for the joint effect of three diseases but the effect sizes were reduced by 10 % for CVD (54% increased risk), 50% for Type 2 DM (23% increased risk) and almost lacking of elevated risk for obesity when correlation across three diseases has been considered. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50074 |
DOI: | 10.6342/NTU201601932 |
全文授權: | 有償授權 |
顯示於系所單位: | 流行病學與預防醫學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf 目前未授權公開取用 | 1.27 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。