運用基因標記之臨床有效性預測台灣老年人晚發型阿茲海默氏病之風險

Clinical validity of genetic markers for predicting the risk of late-onset Alzheimer’s disease in Taiwanese elders

蕭琪蓉(Chi-Jung Hsiao)

指導教授 ： 程蘊菁

國立臺灣大學/公共衛生學院/流行病學與預防醫學研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.10165

摘要

背景
晚發型阿茲海默氏病是一種神經退化性疾病，且其為在失智症中最常見的子型。先前的研究顯示基因多型性在晚發型阿茲海默症中扮演了重要的角色。然而，近來探討晚發型阿茲海默氏病的全基因關聯研究使用勝算比及P值做關聯分析產生之結果不一致，由於全基因關聯研究有較高的偽陽性率及偽陰性率，因此本研究旨在運用臨床有效性來挑選出可以預測晚發型阿茲海默氏病的單一核苷酸多型性以解決上述之問題。

方法
本研究是一個病例對照研究。於2007至2010年間在台灣北部三家醫院的神經科門診招募了294位晚發型阿茲海默氏病的患者，以及從成人健康檢查和醫院志工招募了503位的健康受試者。所有受試者的年齡皆大於等於65歲，本研究排除有憂鬱症、帕金森氏症、出血性及缺血性腦中風、腦梗塞或腦瘤等病症之長者。此外，晚發型阿茲海默氏病及對照組分別對於年齡、性別、父母親與祖父母的籍貫以及共病症 (例如:高血脂、高血壓、糖尿病及頭部外傷) 進行配對。最後，40組配對(含55位晚發型阿茲海默氏病患者與40位對照組) 被納入研究，並且進行全基因體微陣列掃描。接著以顯性遺傳模式計算每一個單一核苷酸多型性的臨床有效性。並且以條件式邏輯斯回歸模型調整ApoE e4狀態、教育程度以及挑選的單一核苷酸多型性來建立一個預測晚發型阿茲海默氏病的極簡模型。除此之外，同步篩檢ApoE e4及挑選的單一核甘酸多型性基因標記可以解決只使用ApoE e4時低敏感度的問題。最後，利用逐一剔除交叉驗證法來驗證最終的基因模型。

結果
挑出來預測晚發型阿茲海默氏病兩支位於ZNF521基因上的單一核苷酸
(rs12965520 或rs7230380)，由於其約登指數的數值為最高 (0.42)。加上兩者任一者與ApoE e4進行同步篩檢時，淨敏感度將提升到0.95。因此受試者帶有對偶基因的ZNF521單一核苷酸 (rs12965520或rs7230380) 者相較於野生型者有較高的晚發型阿茲海默氏病風險 (調整後勝算比= 23.7, 95%信賴區間: 4.5-126.2)。比起只調整教育程度 (曲線下方面積= 0.68) 或只調整教育程度、ApoE e4攜帶狀態 (曲線下方面積= 0.81)，同時調整教育程度、ApoE e4攜帶狀態以及ZNF521單一核苷酸 (rs12965520與rs7230380) 之最終統計模型有顯著較佳的預測力 (曲線下方面積= 0.89)。此外，逐一剔除交叉驗證法顯示出此最終模型有較好的模型適宜度 (曲線下方面積= 0.89)。

結論
就我們所知，本研究為第一篇運用臨床有效性來挑選能預測晚發型阿茲海默氏病的基因標記。此研究挑選出的基因標記與ApoE e4進行，可以解決ApoE e4低敏感度的問題。本研究建立的極簡模型可能可以應用在大規模的社區篩檢。然而，我們結果仍然需要其他大型的中國人研究來驗證。

關鍵字

單一核苷酸多型性 ； 晚發型阿茲海默氏病 ； 臨床有效性

並列摘要

Background
Late-onset Alzheimer’s disease (LOAD) is a neurodegenerative disease and is the most common form of dementia. Previous studies showed that genetic polymorphisms may play an important role in LOAD. However, recent genome-wide association studies (GWAS) used odds ratio (OR) and P value to identify genetic markers for LOAD. However, results were inconsistent probably due to high false positive and negative rates. Therefore, this study was aimed to use clinical validity to identify single nucleotide polymorphisms (SNPs) for predicting LOAD risk.

Material and Methods
This was a case-control study. A total of 294 LOAD cases were recruited from the neurology clinics of three teaching hospitals in northern Taiwan from 2007 to 2010. Healthy controls (n = 503) were recruited from elderly health checkup program and volunteers of the hospital during the same period of time. All participants were Taiwanese aged 65 years or older. In addition, LOAD cases and controls were matched on age, gender, birthplace of parents/grandparents and comorbidities (e.g., hypercholesterolemia, hypertension, diabetes mellitus, and head injury). Forty matched pairs (55 LOAD patients and 40 controls) were identified for further genome-wide microarray scan. Dominant genetic model was used to obtain clinical validity for each SNP. A parsimonious model for predicting LOAD risk was developed by using conditional logistic regression models adjusted for education, Apolipoprotein E (ApoE) e4 status and SNPs with highly clinical validity. Simultaneous screening of identified SNP and ApoE e4 for predicting LOAD risk was performed to improve the low sensitivity of using ApoE e4 alone. Leave-one-out cross validation was used to validate the final genetic model.

Results
When ApoE e4 was added simultaneously for prediciting LOAD risk, two ZNF521 SNPs, rs12965520 and rs7230380, were identified. Participants carrying variant allele “A” of these two SNPs had a higher risk of LOAD as compared with wild type (adjusted odds ratio = 23.7; 95% confidence interval = 4.5-126.2). After model comparison, the final model included education years, ApoE e4 status, and either of the ZNF521 SNPs (rs12965520 and rs7230380) with a sound prediction ability [Area under the receiver operating characteristic curve (AUC) = 0.89]. In addition, LOOCV shows an ideal model-fit (AUC = 0.89).

Conclusions
To the best of our knowledge, this study, for the first time, used clinical validity to identify genetic markers for predicting LOAD risk. Simultaneous screening of the identified SNP and ApoE e4 significantly improve the low sensitivity of using ApoE e4 alone for prediction. The parsimonious model established in this study may be applicable to large-scale screening in the community for predicting LOAD. However, larger studies in Chinese ethnic group are warranted to confirm our finding.

並列關鍵字

single nucleotide polymorphism ； late-onset Alzheimer’s disease ； clinical validity

參考文獻

1. Eric Lander (1996). "The new genomics: global views of biology." Science 274 (1): 536–539

2. Abildayeva, K., J. F. Berbee, A. Blokland, P. J. Jansen, F. J. Hoek, O. Meijer, D. Lutjohann, T. Gautier, T. Pillot, J. De Vente, L. M. Havekes, F. C. Ramaekers, F. Kuipers, P. C. Rensen and M. Mulder (2008). "Human apolipoprotein C-I expression in mice impairs learning and memory functions." J Lipid Res 49(4): 856-869.

3. Alfonso, P., M. Canamero, F. Fernandez-Carbonie, A. Nunez and J. I. Casal (2008). "Proteome analysis of membrane fractions in colorectal carcinomas by using 2D-DIGE saturation labeling." J Proteome Res 7(10): 4247-4255.

4. Alzheimer’s Association (2014). Alzheimer’s Disease Facts and Figures: [http://www.alz.org/alzheimers_disease_facts_figures.asp?type=homepage].

5. Behrens, M. I., C. Lendon and C. M. Roe (2009). "A common biological mechanism in cancer and Alzheimer's disease?" Curr Alzheimer Res 6(3): 196-204.