本文選題：阿爾茲海默 + 血漿蛋白��； 參考：《首都醫(yī)科大學(xué)》2017年碩士論文

【摘要】：背景阿爾茲海默(Alzheimer’s Disease,AD)是一種慢性的神經(jīng)系統(tǒng)退行性疾病,該疾病的臨床表現(xiàn)主要為為記憶方面的退化以及認(rèn)知功能障礙,嚴(yán)重威脅老年人的生活質(zhì)量和生命安全。歐美國(guó)家中65歲以上老年人的癡呆癥患病率約為4~8%,我國(guó)的癡呆患病率為7.8%,其中AD的患病率為4.8%。作為世界上人口數(shù)量最為龐大的國(guó)家,我國(guó)將面臨愈發(fā)嚴(yán)峻的人口老齡化問(wèn)題,AD將給患者,家庭和社會(huì)帶來(lái)極大沉重的經(jīng)濟(jì)和生活負(fù)擔(dān)。AD從出現(xiàn)臨床癥狀至首次確診時(shí)間平均大于一年,且病情多為中重度(67%),早期AD診斷的研究一直是國(guó)內(nèi)外的熱點(diǎn)和難點(diǎn)問(wèn)題之一。AD發(fā)展涉及腦部結(jié)構(gòu)微小且復(fù)雜的變化,通過(guò)圖像微觀紋理特征預(yù)測(cè)AD進(jìn)展具有潛在價(jià)值。磁共振成像(Magnetic Resonance Imaging,MRI)的使用已被證明在預(yù)測(cè)輕度認(rèn)知功能損害(Mild Cognitive Impairment,MCI)到AD的轉(zhuǎn)換以及老年人認(rèn)知功能下降方面上有很大的提示作用。同樣,血漿蛋白組學(xué)已經(jīng)被證明具有診斷AD以及預(yù)測(cè)MCI轉(zhuǎn)換為AD的珍貴價(jià)值。結(jié)合血漿蛋白質(zhì)組學(xué)和MRI成像作為生物標(biāo)志物在早期AD診斷與預(yù)測(cè)中具有潛在優(yōu)勢(shì)。在MRI圖像用于臨床疾病識(shí)別或預(yù)測(cè)的模型研究中,高斯過(guò)程(Gaussian Processes,GP)分類表現(xiàn)出強(qiáng)大的能力。高斯過(guò)程是基于統(tǒng)計(jì)學(xué)習(xí)理論和貝葉斯理論發(fā)展起來(lái)的一種有監(jiān)督的機(jī)器學(xué)習(xí)算法,高斯過(guò)程泛化能力強(qiáng),超參數(shù)設(shè)置靈活、具有非參數(shù)推斷以及概率輸出等優(yōu)點(diǎn),適用于處理非線性和高維度等復(fù)雜回歸問(wèn)題。目的通過(guò)基于Contourlet變換提取腦部圖像微觀紋理特征,結(jié)合血漿蛋白組學(xué)生物標(biāo)志物,采用高斯過(guò)程建立阿爾茨海默癥的早期預(yù)測(cè)模型,為AD的早期診斷提供相關(guān)證據(jù)。方法本次研究共收集420例數(shù)據(jù),其中AD患者84例,MCI患者287例,正常對(duì)照組49例。采用區(qū)域增長(zhǎng)法從冠狀位的腦部MRI圖像中分割得到海馬區(qū)域,采用Contourlet變換處理對(duì)海馬區(qū)域圖像進(jìn)行處理并且計(jì)算14個(gè)紋理值參數(shù)。基于基線數(shù)據(jù),學(xué)采用t檢驗(yàn)或方差分析進(jìn)行組間差異性比較,對(duì)血漿蛋白組間差異有統(tǒng)計(jì)學(xué)意義的變量采用LASSO(Least Absolute Shrinkage and Selection Operator)回歸進(jìn)行變量篩選,然后采用高斯過(guò)程模型以及支持向量機(jī)模型進(jìn)行分類建模,考慮組合核函數(shù)選擇最佳分類模型并做交叉驗(yàn)證;基于MCI患者基線基本信息,血漿蛋白數(shù)據(jù)和腦圖像數(shù)據(jù),以隨訪期內(nèi)是否轉(zhuǎn)換為AD作為結(jié)局標(biāo)簽進(jìn)行建模,建立早期AD的分類預(yù)測(cè)模型。結(jié)果對(duì)于AD和健康對(duì)照組基線血漿蛋白濃度的t檢驗(yàn)比較得到Apo AII,FSH,FASLG receptor等18種血漿蛋白組間差異有統(tǒng)計(jì)學(xué)意義;AD組以及健康對(duì)照組隨訪1年期前后,64種血漿蛋白有組間差異有統(tǒng)計(jì)學(xué)意義。LASSO回歸分析得到20種血漿蛋白可作為早期AD診斷潛在的生物標(biāo)志物,靈敏度76.2%,特異度81.3%。ROC曲線下面積為80.4%(95%CI:86.2%~79%);以MCI組是否轉(zhuǎn)換為AD為結(jié)局,對(duì)有組間差異的血漿蛋白進(jìn)行LASSO回歸,并校正了性別和年齡,得到BNP,IL16,TBG,APOE,PLGF,TFF3等6種血漿蛋白,靈敏度91.2%,特異度78.4%,ROC曲線下面積為84.1%(95%CI:91.8%~81.6%)。結(jié)合研究對(duì)象的基本信息,分別基于左右測(cè)以及雙側(cè)海馬腦圖像紋理特征建立高斯過(guò)程分類模型�；贏D和健康對(duì)照兩組構(gòu)建的分類模型,右側(cè)海馬區(qū)的靈敏度為91.2%,特異度為81.6%,大于左側(cè)海馬區(qū)域模型;基于雙側(cè)海馬的分ROC曲線下面積(0.922)大于基于左右側(cè)海馬區(qū)圖像單獨(dú)建立的模型(0.851和0.901)�；贛CI基線數(shù)據(jù)和隨訪結(jié)局建立的預(yù)測(cè)模型中,GR模型預(yù)測(cè)MCI轉(zhuǎn)化的準(zhǔn)確率達(dá)到88.4%,預(yù)測(cè)MCI轉(zhuǎn)歸為正常的準(zhǔn)確率達(dá)到80.0%。SVM模型預(yù)測(cè)MCI轉(zhuǎn)化的準(zhǔn)確率達(dá)到81.0%,預(yù)測(cè)MCI轉(zhuǎn)歸為正常的準(zhǔn)確率達(dá)到60.0%。結(jié)論血漿蛋白水平的IL-16,TBG,BNP,TFF3,PLGF和ApoE的組合可以區(qū)分AD患者和健康個(gè)體可以用于早期診斷和監(jiān)測(cè)AD以及預(yù)測(cè)MCI轉(zhuǎn)化為AD;具有高斯徑向基核函數(shù)的組合核函數(shù)高斯過(guò)程方法預(yù)測(cè)效果較好;基于MR圖像紋理以及血漿蛋白數(shù)據(jù)構(gòu)建預(yù)測(cè)模型,對(duì)AD的早期預(yù)測(cè)具有積極作用。
[Abstract]:Background Alzheimer 's Disease (AD) is a chronic neurodegenerative disease. The clinical manifestations of this disease are mainly memory degradation and cognitive impairment, which seriously threaten the quality of life and life safety of the elderly. The prevalence rate of dementia in older people over 65 years in Europe and America is about 4~8%, The prevalence rate of dementia in the country is 7.8%, of which the prevalence of AD is 4.8%. as the largest population in the world. Our country will face the increasingly severe problem of population aging. AD will bring great heavy economic and living burden to patients, families and society, and the.AD from the appearance of bed symptoms to the first diagnosis is more than one year, and Most of the disease is moderate to severe (67%), the early AD diagnosis has been one of the hot and difficult problems at home and abroad..AD development involves small and complex changes in the brain structure. It is of potential value to predict the progress of AD through the microscopic texture features of the image. The use of Magnetic Resonance Imaging (MRI) has been proved to be a mild recognition in the prediction. The Mild Cognitive Impairment (MCI) has a great hint in the conversion of AD and the decline of cognitive function in the elderly. Similarly, plasma proteomics has been proved to have a valuable value in the diagnosis of AD and the prediction of MCI conversion to AD. Combined plasma proteomics and MRI imaging as a biomarker in early AD It has a potential advantage. In the study of MRI images for clinical disease identification or prediction, the Gauss process (Gaussian Processes, GP) classification shows strong ability. The Gauss process is a supervised machine learning algorithm based on statistical learning theory and Bias theory, and the generalization ability of Gauss process. Strong, super parameter setting is flexible, has the advantages of non parametric inference and probability output. It is suitable for dealing with complex regression problems such as nonlinear and high dimension. Objective to establish the early precondition of Alzheimer's disease by using the Gauss process to extract the microscopic texture features of brain images based on Contourlet transform and combine the biomarkers of plasma proteomics. The test model provided relevant evidence for the early diagnosis of AD. Methods a total of 420 data were collected in this study, including 84 cases of AD patients, 287 cases of MCI patients and 49 normal controls. The hippocampus region was segmented by regional growth method from the MRI image of the coronal position, and the hippocampal region images were processed by Contourlet transformation treatment and 1 were calculated. 4 texture value parameters. Based on the baseline data, t test or ANOVA were used to compare the differences between groups. The variables with significant differences in plasma protein groups were selected by LASSO (Least Absolute Shrinkage and Selection Operator) regression, and then the Gauss process model and support vector machine model were used. The optimal classification model of the combined kernel function was selected and the cross validation was taken into consideration. Based on the basic information of the MCI baseline, the plasma protein data and the brain image data, the model was modeled as the outcome label for the conversion of AD in the follow-up period, and the early AD classification prediction model was established. The results were for the baseline plasma eggs of the AD and the healthy control groups. The t test of white concentration showed that there were significant differences between the 18 plasma protein groups, such as Apo AII, FSH, FASLG receptor, and so on, and in the AD group and the healthy control group, before and after the 1 years of follow-up, the differences between the 64 plasma proteins were statistically significant and the 20 plasma proteins could be used as a potential biomarker for early AD diagnosis. Degree 76.2%, the area under the specificity 81.3%.ROC curve was 80.4% (95%CI:86.2%~79%), and if the group MCI was converted to AD, the plasma protein with different groups was returned by LASSO, and the sex and age were corrected, and 6 plasma proteins, such as BNP, IL16, TBG, APOE, PLGF, TFF3, were obtained. The sensitivity was 91.2%, the specificity was 78.4%, and the area under the ROC curve was 84.1%. 91.8%~81.6%). Based on the basic information of the subjects, the Gauss process classification model was established on the basis of the left and right tests and the texture features of the bilateral hippocampal images. The sensitivity of the right hippocampal region was 91.2% and the specificity was 81.6%, which was greater than the left hippocampal region model based on the two groups of AD and the healthy control groups. The sub R based on the bilateral hippocampus was divided into two groups. The area under the OC curve (0.922) is larger than the model based on the left and right hippocampal images (0.851 and 0.901). In the prediction model based on the MCI baseline data and the follow-up outcome, the GR model predicts the accuracy of MCI conversion to 88.4%, and the prediction of MCI to the normal accuracy reaches the 80.0%.SVM model to predict the MCI transformation accuracy of 81. %, the prediction of MCI to the normal accuracy rate of 60.0%. to the plasma protein level of IL-16, TBG, BNP, TFF3, PLGF and ApoE can distinguish between AD patients and healthy individuals can be used for early diagnosis and monitoring AD and predictive MCI into AD; Gauss radial basis kernel function of the combined kernel function Gauss process method prediction effect is better; The prediction model based on MR image texture and plasma protein data has a positive effect on the early prediction of AD.

【學(xué)位授予單位】：首都醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.16;R445.2

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