臺灣博碩士論文加值系統

背景
已有許多研究證實家戶內的胃幽門螺旋桿菌感染，但尚未有研究利用基因流行病學研究去量化家族聚集的影響。此外，盡管胃幽門螺旋桿菌的家族聚集已被推廣至胃部癌前病變如：萎縮性胃炎、胃黏膜腸上皮化生，卻忽略了家族聚集對疾病進程的動態影響。

目的
本論文的目的為利用Correa模式與先證者(Proband)的病例對照研究設計及其推展，配合不同統計模式去推估家族聚集對於胃幽門螺旋桿菌傳染及胃部癌前病變(萎縮性胃炎、胃黏膜腸上皮化生)進程的動態影響。

材料與方法
在此論文中，我們利用兩個資料集來研究家族聚集對胃幽門螺旋桿菌與和胃幽門螺旋桿菌相關之胃部癌前病變的影響。第一個資料集採用35個日本被感染胃幽門螺旋桿菌兒童的家族研究，其中包含兩種基因分型結果：多位點序列分型(MLST)及隨機擴增多態性DNA標記(RAPD)，用於檢測胃幽門螺旋桿菌的家族聚集與傳播途徑。第二個資料集是利用Correa模式與馬祖的胃部癌前病變的社區預防資料，包含三個時間段：1996-2003為篩檢介入之前，2004-2007為篩檢介入之後，2008為全面性投藥之化學預防。
我們利用兩階段先證者的病例對照研究去推估在相同MLST、RAPD家族聚集的影響，與貝式有向無環圖(DAG)計算在給定觀察資料與未知資料之下的條件機率，利用隨機效應模式計算家庭內部成員胃幽門螺旋桿菌感染的家族聚集影響。
我們利用多元邏輯斯迴歸分析、離散狀態及時間之馬可夫鍊模式與連續時間下馬可夫過程三種模型估計在Correa模式之下家族聚集對胃幽門螺旋桿菌感染與胃部癌前病變動態轉移的影響。
結果
第一部份：日本基因序列與胃幽門螺旋桿菌感染的家族聚集研究
當使用第一個孩子作為指標個案時，貝式DAG模型的DNA指紋序列的估計結果顯示匹配序列的家族聚集在MLST為56倍（95％CI：3.99-1878.07），及RAPD為68.10倍（95％CI：4.85-2171.12）。
第二部分：家族聚集對胃幽門螺旋桿菌感染及胃部癌前病變動態轉移平衡的影響
應用馬祖資料與多元邏輯斯迴歸模式，在調整抽菸與飲食習慣之下，家族聚集的影響在胃幽門螺旋桿菌感染、萎縮性胃炎與胃黏膜腸上皮化生皆有統計上的顯著，其估計值自1.39 (95% CI: 1.09-1.77) 至 2.02 (95% CI: 1.26-3.55)。
符合遍歷理論的馬可夫鍊的估計結果表明，在Correa模式之下於1996年存在強烈的家族聚集趨勢，其上三角形與下三角形的比為2.10。然而在2004與2008的相應數值為0.91與0.87。
連續時間馬可夫過程的估計結果證明家族聚集與Correa模式的動態轉移平衡存在高度相關。主要貢獻為從正常狀態到胃幽門螺旋桿菌感染，指標個案疾病狀態為胃幽門螺旋桿菌感染、萎縮性胃炎、及胃黏膜腸上皮化生其倍數分別為：1.30 (95% CI: 1.18-1.44), 1.21 (95% CI: 1.07-1.36), 和1.35 (95% CI: 1.14-1.59)。而在第二階段自胃幽門螺旋桿菌感染轉移至萎縮性胃炎，其倍數根據不同指標個案狀態為0.74 (95% CI: 0.62-0.88), 1.33 (95% CI: 1.10-1.60), 和1.06 (95% CI: 0.82-1.37)。在第三階段萎縮性胃炎轉移至胃黏膜腸上皮化生結果未達統計上顯著，倍數分別為1.28 (95% CI: 0.94-1.75), 0.95 (95% CI: 0.68-1.34), 和 1.14 (95% CI: 0.72-1.81)。
結論
本論文利用統計模式去評估胃幽門螺旋桿菌感染與Correa模式下胃部癌前病變動態轉移的影響。所提出的模式應用於一個具有基因分型結果的資料集，與一基於社區的胃部癌前病變資料在預防措施介入前與介入後。

Introduction
Although intra-familial aggregation of helicobacter pylori infection has been well studied before, the effect size of family aggregation has been scarcely elucidated by using a well-designed genetic epidemiological study. While the familial aggregation of HP has been extended to include pre-cancerous lesions like atrophy gastritis (AG) and intestine metaplasia (IM), the effect size of familial aggregation accounting for the dynamic transition study has been even neglected.
Aims
The objective of this thesis is to employ a case-control proband study design and its variants in conjunction with different statistical models to estimate the effect size of familial aggregation of genetic typing associated with HP infection and the dynamic transitions between HP, AG, and IM under the context of the Correa model.
Material and Methods
Two data sources were used for studying familial aggregation of HP and HP-related pre-cancerous lesions. The first dataset was derived from a 35 index Japanese pediatric patients familial study with the available information on two genetic typing procedures, Multilocus Sequence Typing (MLST) and random amplified polymorphic DNA (RAPD) fingerprinting used for detecting transmission route of familial aggregation. The second dataset was descended from the Matsu community-based prevention of gastric neoplasm under the context of Correa model with three periods, 1996-2003 before intervention, 2004-2007 after screening, and 2008 after chemoprevention. The case-control proband study design with two-state was designed to estimate the effect size of familial aggregation associated with the same sequence of MLST and also RAPD. Bayesian directed acyclic graphic (DAG) model was built up to develop fully conditional distribution given the observed data and unknown quantity to estimate the effect size of family aggregation of HP infection making allowance for the correlation of HP infection across the same family members with the random effect model.
To model how family aggregation affects the dynamic transition of HP and gastric pre-cancerous neoplasm under the context of the Correa multistate model, three statistical approaches were used, including the multi-nominal logistic regression model, the discrete-state and discrete-time Markov chain model, and continuous-time Markov process.
Results
Part I Japanese familial aggregation study on genotyping associated with HP infection
When using first child as index case the estimated results on the DNA fingerprint sequence with Bayesian DAG model show the tendency of familial aggregation for the matched sequence of RAPD was 56-fold (95% CI: 3.99-1878.07) for RAPD and 68.10-fold (95% CI: 4.85-2171.12) for MLST compared with the unmatched sequence.
Part II Family aggregation for the dynamic transition of HP and gastric neoplasm
With the application of multi-nominal logistic regression model to the Matsu data, the effect sizes of familial aggregation of HP infection, AG, and IM were statistically significant with the range from 1.39 (95% CI: 1.09-1.77) to 2.02 (95% CI: 1.26-3.55) after adjustment for smoking and dietary factors.
The estimated results of Markov chain with the ergodicity theory show there was a strong tendency of familial aggregation for reaching equilibrium with the property of the Correa model in 1996 but not in the two periods after interventions as the ratio of the summation of transition probability in the upper triangle to that of in the lower triangle was around 2.24 whether the corresponding values in 2004 and 2008 were 0.91 and 0.87.
The estimated results of continuous-time Markov process demonstrate family aggregation was highly associated with the dynamic transition of the Correa model mainly in the transition from normal to HP infection with the order of 1.30 (95% CI: 1.18-1.44), 1.21 (95% CI: 1.07-1.36), and 1.35 (95% CI: 1.14-1.59) for the index case of HP infection , AG, and IM. The influence of family aggregation dwindled from HP infection to AG with the order of 0.74 (95% CI: 0.62-0.88), 1.33 (95% CI: 1.10-1.60), and 1.06 (95% CI: 0.82-1.37) to AG to IM with insignificant findings, 1.28 (95% CI: 0.94-1.75), 0.95 (95% CI: 0.68-1.34), and 1.14 (95% CI: 0.72-1.81)
Conclusions
Statistical models are proposed here to model familial aggregation of HP infection and the dynamic transition of HP-related gastric precancerous lesion under the context of the Correa model. The proposed models were applied to one data with genotyping HP infection and the community-based data before and after prevention of gastric neoplasm.

vii
CONTENTS
中文摘要 ........................................................................................................................... i
ABSTRACT .................................................................................................................... iv
CONTENTS ................................................................................................................... vii
List of Figures .................................................................................................................. 1
List of Tables 2
Chapter 1 Introduction .............................................................................................. 4
Chapter 2 Literature Review .................................................................................... 6
2.1 Familial aggregation of Helicobacter pylori Infection ................................... 6
2.1.1 Introduction of Helicobacter pylori ....................................................... 6
2.1.2 Infection and transmission of Helicobacter pylori-Familial aggregation
............................................................................................................... 6
2.1.3 DNA-fingerprinting of H pylori. infection ............................................ 7
2.1.4 Critiques ................................................................................................ 9
2.2 Model for familial aggregation ..................................................................... 11
2.2.1 Case-control proband study on Familial aggregation data .................. 11
2.2.2 Population-based proband-oriented pedigree information system ...... 12
Chapter 3 Material and Method ............................................................................. 14
3.1 Data .................................................................................................................... 14
3.1.1 Japan HP infection Data ...................................................................... 14
3.1.2 Matsu gastric cancer screening program ............................................. 27
3.2 Case-control proband method ....................................................................... 28
3.2.1 Generalized estimated equation model (GEE) .................................... 28
3.2.2 Bayesian DAG (directed acyclic graphic model) ................................ 30 viii
3.3 Matsu gastric cancer screening program ...................................................... 33
3.3.1 Multi-nominal logistic regression model ............................................ 35
3.3.2 Bayesian DAG (directed acyclic graphic model) ................................ 36
3.3.3 Family aggregation with Markov Chain Model .................................. 38
3.3.4 Continuous-time Discrete-state Markov Model .................................. 41
Chapter 4 Result ....................................................................................................... 45
4.1 Case-control proband analysis for family aggregation of HP infection
considering DNA fingerprinting matching ................................................... 45
4.2 Case-control proband analysis for family aggregation of HP infection and
gastric pre-malignance .................................................................................. 51
4.2.1 Family aggregation analysis based on full family data ....................... 51
4.2.2 Family aggregation analysis based on sampling family data .............. 52
4.2.3 Family aggregation analysis based on sampling family data in 1996 . 52
4.2.4 Family aggregation analysis based on sampling family data in 2004 . 52
4.2.5 Family aggregation analysis based on sampling family data in 2008 . 53
4.2.6 Family aggregation analysis based on sampling family data with three
periods ................................................................................................. 53
4.2.7 Trace Plots of DAG Model ................................................................. 83
4.3 Family aggregation with Markov chain model ............................................. 86
4.4 Continues Markov multistate model ............................................................. 89
Chapter 5 Discussion................................................................................................ 94
5.1 Quantitative approaches to studying family aggregation on HP infection and
gastric neoplasm ........................................................................................... 94
5.2 Generalized estimating equation model (GEE) ............................................ 96
5.3 Bayesian DAG (directed acyclic graphic model) ......................................... 97 5.4 Multi-nominal logistic regression model ...................................................... 97
5.5 Family aggregation with Markov Chain Model ........................................... 98
5.6 Continuous-time Discrete-state Markov Model ........................................... 98
REFERENCE ................................................................................................................ 100

Aziz RK, Khalifa MM, Sharaf RR. Contaminated water as a source of Helicobacter pylori infection: A review. J Adv Res. 2015;6(4):539-47.

Bamford KB, Bickley J, Collins JS, Johnston BT, Potts S, Boston V, Owen RJ, Sloan JM. Helicobacter pylori: comparison of DNA fingerprints provides evidence for intrafamilial infection. Gut 1993;34:1348–50.

Blaser MJ. The bacteria behind ulcers. Sci Am 1996;274:104–7.

Chiu YH, Lin WY, Wang PE, Chen YD, Wang TT, Warwick J, Chen TH. Population-based family case-control proband study on familial aggregation of metabolic syndrome: finding from Taiwanese people involved in Keelung community-based integrated screening (KCIS no. 5). Diabetes Res Clin Pract. 2007;75(3):348-56. Epub 2006 Aug 28.

Chiu SY, Chen LS, Yen AM, Chen HH. Population-based proband-oriented pedigree information system: application to hypertension with population-based screening data (KCIS No. 25). J Am Med Inform Assoc. 2012;19(1):102-10.

Cox, D.R. and Miller, H.D. (1965) The Theory of Stochastic Process. Chapman & Hall, London.

Dominici P, Bellentani S, Di Biase AR, Familial clustering of Helicobacter pylori infection: population based study. BMJ. 1999 Aug 28;319(7209):537-40.

Fukase K, Kato M, Kikuchi S, et al. Effect of eradication of Helicobacter pylori on incidence of metachronous gastric carcinoma after endoscopic resection of early gastric cancer: an open-label,randomised controlled trial. Lancet 2008;372:392–7.

Goodman KJ, Correa P. The transmission of Helicobacter pylori. A critical review of the evidence. Int J Epidemiol 1995;24:875–87.

Hashinaga M, Okimoto T, Kodama M, Mizukami K, Ogawa R, Okamoto K, et al. count report – of present conditions – 2013–2014 year resistant bacteria surveillance of drug-resistant Helicobacter pylori in our country [Japanese]. Jpn J Helicobacter Res. 2016;17:45–9.

IARC Helicobacter pylori Working Group. Helicobacter pylori Eradication as a Strategy for Gastric Cancer Prevention. Lyon, France: International Agency for Research on Cancer (IARC Working Group Reports, No. 8). International Agency for Research on Cancer 2014. 978-92-832-2454-9

Japanese Society for Helicobacter Research. Guidelines for the management of Helicobacter pylori infection in Japan. 2016 ed, revised. Tokyo: Sentan Igaku-Sha; 2016.

Kivi M, Tindberg Y. Helicobacter pylori occurrence and transmission: a family affair? Scand J Infect Dis. 2006;38(6-7):407-17.

Kobayashi I, Murakami K, Kato M, Kato S, Azuma T, Takahashi S, et al. Changing antimicrobial susceptibility epidemiology of Helicobacter pylori strains in Japan between 2002 and 2005. J Clin Microbiol. 2007;45(12):4006–10.

Kobayashi I, Azuma T, Ikeda F, Uemura T, Kato S, Kato M, et al. Count report of the present conditions 2010-2011 year resistant bacteria surveillance of drug-resistant Helicobacter pylori in our country [Japanese]. Jpn J Helicobacter Res. 2013;14:102–6.

Lee YC, Chiang TH, Chou CK, Tu YK, Liao WC, Wu MS, Graham DY. Association Between Helicobacter pylori Eradication and Gastric Cancer Incidence: A Systematic Review and Meta-analysis. Gastroenterology. 2016;150(5):1113-1124.e5.

Liang KY, Pulver AE. Analysis of case-control/family sampling design. Genet Epidemiol. 1996;13(3):253-70.

Mamishi S, Eshaghi H, Mahmoudi S, Bahador A, Hosseinpour Sadeghi R, Najafi M, Farahmand F, Khodadad A, Pourakbari B. Intrafamilial transmission of Helicobacter pylori: genotyping of faecal samples. Br J Biomed Sci. 2016;73(1):38-43.

McMillan M, Mackay WG, Williams CL, Shepherd AJ, Malcolm C, Weaver LT. Intrafamilial Genotyping of Helicobacter pylori from Faecal DNA. Gastroenterol Res Pract. 2011 2011:491035

Nahar S, Kibria KM, Hossain ME, Sultana J, Sarker SA, Engstrand L, Bardhan PK, Rahman M, Endtz HP. Evidence of intra-familial transmission of Helicobacter pylori by PCR-based RAPD fingerprinting in Bangladesh. Eur J Clin Microbiol Infect Dis. 2009;28(7):767-73.

Nomura A, Stemmermann GN, Chyou PH, Kato I, Perez-Perez GI, Blaser MJ. Helicobacter pylori infection and gastric carcinoma among Japanese Americans in Hawaii. N Engl J Med. 1991;325(16):1132-6.

Osaki T, Okuda M, Ueda J, Konno M, Yonezawa H, Hojo F, Yagyu K, Lin Y, Fukuda Y, Kikuchi S, Kamiya S. Multilocus sequence typing of DNA from faecal specimens for the analysis of intra-familial transmission of Helicobacter pylori. J Med Microbiol. 2013;62(Pt 5):761-5.

Osaki T, Konno M, Yonezawa H, Hojo F, Zaman C, Takahashi M, Fujiwara S, Kamiya S. Analysis of intra-familial transmission of Helicobacter pylori in Japanese families. J Med Microbiol. 2015;64(Pt 1):67-73.

Parsonnet J, Friedman GD, Vandersteen DP, Chang Y, Vogelman JH, Orentreich N, Sibley RK. Helicobacter pylori infection and the risk of gastric carcinoma. N Engl J Med. 1991;325(16):1127-31.

Pryczynicz A, Guzińska-Ustymowicz K, Kemona A, Czyzewska J. Helicobacter pylori infection and expressions of EGF, EGFR and c-erbB-2 proteins in gastric carcinoma. Folia Histochem Cytobiol 2009; 47: 447-51.Suerbaum S, Michetti P. Helicobacter pylori infection. N Engl J Med 2002;347:1175-86.

Puz S, Innerhofer A, Ramharter M, Haefner M, Hirschl AM, Kovách Z, Rotter M, Makristathis A. A novel noninvasive genotyping method of Helicobacter pylori using stool specimens. Gastroenterology. 2008;135(5):1543-51.

Rajindrajith S, Devanarayana NM, de Silva HJ. Helicobacter pylori infection in children. Saudi J Gastroenterol. 2009;15(2):86-94.

Roma-Giannikou E, Karameris A, Balatsos B, Panayiotou J, Manika Z, Van-Vliet C, Rokkas T, Skandalis N, Kattamis C. Intrafamilial spread of Helicobacter pylori: a genetic analysis. Helicobacter. 2003;8(1):15-20.

Suerbaum S, Michetti P. Helicobacter pylori infection. N Engl J Med. 2002 10;347(15):1175-86.

Vale FF, Vítor JM. Transmission pathway of Helicobacter pylori: does food play a role in rural and urban areas? Int J Food Microbiol. 2010;138(1-2):1-12.

Watari J, Chen N, Amenta PS, et al. Helicobacter pylori associated chronic gastritis, clinical syndromes, precancerous lesions, and pathogenesis of gastric cancer development. World J Gastroenterol. 2014 14;20(18):5461-73.

Yokota S, Konno M, Fujiwara S, Toita N, Takahashi M, Yamamoto S, Ogasawara N, Shiraishi T. Intrafamilial, Preferentially Mother-to-Child and Intraspousal, Helicobacter pylori Infection in Japan Determined by Mutilocus Sequence Typing and Random Amplified Polymorphic DNA Fingerprinting. Helicobacter. 2015;20(5):334-42.