本文關鍵詞：心臟自主神經病變診斷評估、相關危險因素分析及數(shù)學模型構建研究　出處：《復旦大學》2014年博士論文　論文類型：學位論文

  更多相關文章： 心臟自主神經病變 診斷實驗 無金標準 Bayesian估計 危險因素 Logistic回歸 基因-環(huán)境因素交互作用 篩查模型 人工神經網絡 風險模型

【摘要】：研究背景心臟自主神經病變通常被認為是糖尿病比較常見的慢性并發(fā)癥之一,其發(fā)病機制尚不十分明確。現(xiàn)已證實,老年人、冠心病患者、高血壓病患者及全身免疫系統(tǒng)疾病患者亦常并發(fā)現(xiàn)心臟自主神經病變。心臟自主神經病變具有較高的患病率和發(fā)病率。心臟自主神經病變診斷沒有真正的金標準存在。Ewing's試驗為半定量試驗,被用于評價心臟自主神經功能的方法。心率變異性檢測是通過記錄一定長度的心電圖,計算正常竇性心率相鄰心搏之間R-R間期的差異程度來反映心率的變化,定量地反映自主神經功能。心率變異性分析方法具有量化、簡單可行、客觀、安全等優(yōu)點。無金標準試驗下Bayesian方法可以評估診斷實驗的性能(如敏感性和特異性)。然而,目前在中國人群中無金標準試驗下Bayesian方法評估基于心率變異性標準值心臟自主神經病變診斷性能研究未見報道。人類復雜疾病的發(fā)病模型可認為是多基因疾病機制,根據(jù)其復雜疾病模型,心臟自主神經病變應該由環(huán)境因素、遺傳因素及遺傳與環(huán)境因素交互作用決定。遺傳流行病學的大量證據(jù)表明心臟自主神經病變是遺傳因素和環(huán)境因素共同導致的結果。全基因組關聯(lián)分析方法發(fā)現(xiàn)并鑒定了大量與心臟自主神經病變關聯(lián)的遺傳變異。但是中國人群的相關研究鮮有報道,更未見基因-環(huán)境交互作用的分析報道。數(shù)學模型是使用數(shù)學語言描述和抽象事物,是關于部分現(xiàn)實世界和為一種特殊目的而作的一個抽象的、簡化的結構。數(shù)學模型在臨床中的應用通常包括：疾病的篩查模型,風險評估模型及預后模型。疾病定量評估和預測是運用統(tǒng)計方法和數(shù)學模型,通過對過去一些歷史數(shù)據(jù)的統(tǒng)計分析,對事物未來的發(fā)展趨勢、增減速度以及可能達到的發(fā)展水平做出數(shù)量的說明,并且以數(shù)學模型來表達基本規(guī)律,對目前狀況的評估及對未來發(fā)展進行預測。然而,目前沒有發(fā)現(xiàn)心臟自主神經病變相關的篩查和風險數(shù)學模型構建研究。目的本研究目的：1)明確基于短程頻率心率變異性標準值的心臟自主神經病變診斷價值評估；2)明確中國人群中心臟自主神經病變相關危險因素(環(huán)境、遺傳相關因素)；3)構建心臟自主神經病變篩查模型；及4)構建心臟自主神經病變風險模型。方法和結果建立中國人群心臟自主神經病變樣本數(shù)據(jù)庫。以多階段抽樣方法(整群抽樣及簡單隨機抽樣)征集2092例樣本,完成一般數(shù)據(jù)、生化檢查、糖耐量試驗、心率變異性檢查等臨床表型數(shù)據(jù)收集,提取DNA樣本。主要基于橫斷面研究設計的有關心臟自主神經病變的診斷標準評估,相關危險因素分析,篩查模型及其風險評估模型構建研究。1)無金標準診斷試驗下Bayesian分析對心臟自主神經病變診斷評估研究：該研究基于社區(qū)的大型橫斷面數(shù)據(jù),研究人群包括2,092受試者,所有受試者完成相應的基線數(shù)據(jù)收集和短程頻率心率變異性測試。同時我們從另一個人群中征集88名同時接受短程頻率心率變異性測試和Ewing's試驗的受試者。首先從2092個樣本人群中選取所有的健康人(371名)明確短程頻率心率變異性各組分的參考值。在無診斷金標準情況下,應用Bayesian方法在兩個樣本中估計基于短程頻率心率變異性檢測心臟自主神經病變診斷性能。結果顯示短程頻率心率變異性診斷模型具有很高靈敏度(80%)和特異性(80%)。非劣效性檢測表明,短程頻率心率變異性的診斷價值并不遜色于Ewing's試驗。普通人群中心臟自主神經病變患病率估計為14.92%。在糖尿病患者中,其患病率估計為29.17%。2)中國人群心臟自主神經病變危險因素分析：在心臟自主神經病變樣本數(shù)據(jù)庫中,基于診斷評估分析結果,分類患病人群何非患病人群；以Logistic回歸模型對收集的臨床表型數(shù)據(jù)進行多因素相關分析,篩選心臟自主神經病變環(huán)境相關危險因素；將心臟自主神經病變樣本DNA進行SNP分型,獲取遺傳相關數(shù)據(jù),進行基因關聯(lián)分析；在心臟自主神經病變樣本數(shù)據(jù)庫中抽取心臟自主神經病變患者數(shù)據(jù)進行基因-環(huán)境交互作用分析。單因素分析顯示,14個風險因素與心臟自主神經病變顯著關聯(lián)(P0.05)。多因素Logistic回歸分析5個獨立的危險因素：年齡(OR=1.47,95%C1:1.22-1.69, P0.001,表3),心率(OR=2.41,95%CI：2.04-2.71, P 0.001),高血壓病病程(O R=1.24,95%CI:1.08-1.41, P0.05),胰島素抵抗指數(shù)(OR=3.45,95%CI:2.12-5.82, P 0.001)和腰圍(OR=3.60,95%CI：1.12-6.25,P0.001)。而在本樣本中,基因-表型分析表明所選取5個候選基因與心臟自主神經病變無明顯關聯(lián)性,而基因-環(huán)境因素交互作用分析表明肥胖表型體重指數(shù)與SANIOA (rs7375036)存在交互作用(ORGEI= 5.404,95%CI:1.355-21.558,P=0.017)；糖尿病與SANIOA (rs7375036)存在交互作用(ORGEI=3.453,95%CI:0.973-12.254,P=0.055)；及代謝綜合征與ESRI (rs9340799)存在交互作用(ORGEI=1.505,95%CI：0.98-2.312,P=0.062)。3)中國人群心臟自主神經病變篩查模型構建：總樣本(2092例樣本)被平分為模型生成集和驗證集。篩查模型在模型生成集中,由逐步多元Logistic回歸分析生成。最終回歸模型的變量為心臟自主神經病變篩查模型的組成部分。篩查模型的性能在驗證集和總樣本中進行評估。最終的篩查模型變量包括年齡、體重指數(shù)、高血壓病和心率,這些變量與心臟自主神經病變存在顯著相關性(P0.05)。模型性能分析表明,在模型的生成集和驗證集中,其系統(tǒng)的ROC曲線下面積分別為0.726(95%CI為0.686-0.766)和0.784(95%CI為0.749-0.818)。在驗證集中,最佳臨界分數(shù)為6(風險積分分數(shù)范圍是0-15),該風險評分系統(tǒng)的敏感性,特異性和需要后續(xù)標準測試的比例分別為74.63%、67.50%和39.88%。4)心臟自主神經病變風險模型構建及對比研究：研究目的是應用人工神經網絡和多因素Logistic回歸模型在自然人群中構建心臟自主神經病變的風險模型,并用比較這兩種方法所構建的風險模型的相關性能。將研究樣本分為模型生成集和驗證集。在同一模型生成集中分別應用神經網絡和L ogistic回歸模型分析構建相應的心臟自主神經病變風險模型,并在相同的驗證集進行評估和預測性能分析。最后將這兩類風險模型的性能進行比較。單因素分析顯示,14個風險因素與心臟自主神經病變顯著關聯(lián)(P0.05)。Logistic回歸構建風險模型的ROC曲線下面積為0.758(95%CI為0.724-0.793),神經網絡風險模型ROC曲線下面積為0.762(95%CI為0.732-0.793。結論：研究表明1)短程頻率心率變異性參考值可應用于心臟自主神經病變診斷測試,并具有較高靈敏度和特異性。短程頻率心率變異性測試對心臟自主神經病變的診斷價值不劣于傳統(tǒng)的Ewing's試驗,可以應用于心臟自主神經病變的診斷,特別適合于大規(guī)模人群診斷應用。心臟自主神經病變在中國人群中具有較高患病率,并在糖尿病、高血壓和代謝綜合征患者中的患病率更高。2)多因素Logistic回歸分析表明心臟自主神經病變的環(huán)境相關危險因素表明年齡、心率、高血壓病病程及代謝性因素(腰圍和胰島素抵抗指數(shù))與該疾病相關。在本樣本中,基因-表型分析表明候選基因與心臟自主神經病變無明顯的相關性,但是基因-環(huán)境交互分析表明SCNIOA和ESR1基因與代謝性因素存在交互作用。3)我們開發(fā)了基于一組簡單變量(不需要實驗室檢查或復雜的臨床檢查)的心臟自主神經病變的篩查模型。該模型是一種簡單、快速、便宜、非侵入性的、可靠的篩查工具。在中國人群中,可應用于該疾病的早期預防,用以延緩疾病的進展。4)本研究有效的應用人工神經網絡和Logistic回歸構建具有更高區(qū)分度和精準度的心臟自主神經病變風險模型,非劣效性檢測發(fā)現(xiàn)人工神經網絡預測模型的靈敏度,特異性和預測值不劣于Logistic回歸構建的風險模型。表明這兩類風險模型都是有效的評估和預測工具。
[Abstract]:The research background of cardiac autonomic neuropathy is often considered one of the more common chronic complications of diabetes, its pathogenesis is still unclear. It has been confirmed that elderly patients with coronary heart disease, and patients with hypertension and systemic diseases of the immune system is often found and cardiac autonomic neuropathy. Cardiac autonomic neuropathy has a high prevalence and incidence the rate of diagnosis of cardiac autonomic neuropathy. There is no real gold standard.Ewing's test for semi quantitative test method to be used for evaluation of cardiac autonomic function. HRV detection is by ECG recording length and calculate the degree of difference between normal sinus heart rate adjacent cardiac R-R interval to reflect the changes in heart rate, reflect the autonomic nerve function. The heart rate variability analysis method is simple and feasible, quantitative, objective, security and other advantages. No gold standard test Bayesian method can evaluate the performance of diagnostic tests (such as sensitivity and specificity). However, at present in the China crowd no gold standard test method of Bayesian assessment of heart rate variability in the standard value of cardiac autonomic neuropathy diagnosis performance has not been reported. Based on the complex human disease model of disease disease is considered to be a mechanism of polygenic disease, according to the the model of complex diseases, cardiac autonomic neuropathy should be decided by environmental factors, genetic factors and the interaction of genetic and environmental factors. A lot of evidence of Genetic Epidemiology showed that cardiac autonomic neuropathy is a common cause of genetic and environmental factors. The results of a genome-wide association analysis method and found substantial genetic variability and cardiac autonomic neuropathy associated. Identification of Chinese population. But there are few reports on related analysis reports, no more gene environment interaction. The model is the use of mathematical language to describe and abstract things, is on the part of the real world and for a special purpose of an abstract, simplified structure. The application of mathematical model in clinical practice usually includes: screening models of disease, risk assessment model and the prognosis of disease model. The quantitative assessment and prediction is the use of statistics method and mathematical model of the past through some statistical analysis of historical data, the development trend of the things, and increase or decrease the speed may reach the development level of the number of instructions to make, and the expression of basic rules on the mathematical model, the assessment of the current situation and predict the future development. However, there is no research on the construction of screening found the mathematical model and the risk of cardiac autonomic neuropathy related. Purpose: the purpose of this study: 1) based on the standard of God clear cardiac autonomic heart rate variability short frequency value The diagnostic value evaluation; 2) determine the related risk China center crowd dirty autonomic neuropathy (environmental factors, genetic factors); 3) construction of cardiac autonomic neuropathy screening model; and 4) the construction of cardiac autonomic neuropathy risk model. Methods and results to establish China groups of cardiac autonomic neuropathy in multistage sample database. Sampling methods (cluster sampling and simple random sampling) for 2092 samples, the completion of the general data, biochemical examination, glucose tolerance test, heart rate variability examination of clinical phenotype data collection, extraction of DNA samples. The main diagnostic criteria of cross-sectional study about design of cardiac autonomic neuropathy based on the assessment, analysis of the related risk factors. Study on.1 model screening model and risk assessment) no gold standard diagnostic test Bayesian analysis and evaluation study on the diagnosis of cardiac autonomic neuropathy: the study Large cross section data based on community, the study population consisted of 2092 subjects, all subjects completed baseline data collection and short frequency of heart rate variability in the corresponding test. At the same time we from another group of 88 to solicit and accept short frequency heart rate variability test and Ewing's test subjects. The first selection of healthy people all from a sample of 2092 people (371) were clear HRV frequency short-range reference value. In the absence of gold standard in the diagnosis of cases, the application of Bayesian method to estimate the frequency of short-range HRV detection of cardiac autonomic neuropathy diagnosis based on performance in two samples. The results showed that the frequency of short heart rate variability diagnosis model it has a very high sensitivity (80%) and specificity (80%). The results showed noninferiority testing, short frequency of heart rate variability in diagnostic value is inferior to Ewing's in the general population test. Cardiac autonomic neuropathy prevalence estimates of 14.92%. in patients with diabetes, the prevalence rate is estimated at 29.17%.2) analysis of the risk of cardiac autonomic neuropathy factors Chinese crowd: in cardiac autonomic neuropathy sample database, diagnosis and evaluation based on the results of the analysis, classification of population where the prevalence of non Logistic regression; clinical phenotype data collection model multi factor correlation analysis, to screen the environmental risk of cardiac autonomic neuropathy factors; the cardiac autonomic neuropathy samples DNA SNP types, access to genetic data, genetic correlation analysis; data extraction in patients with cardiac autonomic neuropathy in cardiac autonomic neuropathy in the sample database for analysis of gene environment interaction by single factor analysis. Show that the 14 risk factors associated with cardiac autonomic neuropathy (P0.05). Multivariate Logistic Regression analysis of 5 independent risk factors: age (OR=1.47,95%C1:1.22-1.69, P0.001, table 3), heart rate (OR=2.41,95%CI:2.04-2.71, P 0.001), hypertension (O R=1.24,95%CI:1.08-1.41 P0.05), insulin resistance index (OR=3.45,95%CI:2.12-5.82, P 0.001) and waist circumference (OR=3.60,95%CI:1.12-6.25, P0.001). In this sample, genotype phenotype analysis show that the selected 5 candidate genes and cardiac autonomic neuropathy had no obvious relevance, and gene environment interaction analysis showed that BMI and obesity phenotypes of SANIOA (rs7375036) interaction (ORGEI= 5.404,95%CI:1.355-21.558, P=0.017); diabetes mellitus (rs7375036) and SANIOA interaction (ORGEI=3.453,95%CI:0.973-12.254, P=0.055); and the metabolic syndrome ESRI (rs9340799) and interaction (ORGEI= 1.505,95%CI:0.98-2.312, P=0.062).3) China people heart The dirty construction of autonomic neuropathy screening model: the total sample (2092 samples) were divided into model generation and validation set. The screening model generation focused on model analysis. By stepwise Logistic regression to generate part of the final regression model variables for cardiac autonomic neuropathy screening model. The performance of the model is evaluated in the validation screening set and the total sample. The final screening model variables including age, BMI, hypertension disease and heart rate, these variables and cardiac autonomic neuropathy (P0.05). There was significant correlation analysis showed that the performance of the model and focus on generating set and validation of the model, the system under the ROC curve were 0.726 (95%CI to 0.686-0.766) and 0.784 (95%CI 0.749-0.818). In the validation set, the best critical score of 6 (0-15 risk score score range), the sensitivity of risk scoring system, and the specific needs The following standard test respectively 74.63%, 67.50% and 39.88%.4) of cardiac autonomic neuropathy risk model and comparison. The purpose of this study is the application of artificial neural network and multi factor Logistic regression model to construct risk model of cardiac autonomic neuropathy in natural populations, and the related performance risk model constructed by the two methods. The study sample was divided into model generation and validation set. Generation focus respectively using neural network and L ogistic regression model to analyze the construction of cardiac autonomic neuropathy risk model in the same model, and in the same test set to evaluate and predict the performance analysis. Finally compare the performance of the two kinds of risk models the single factor analysis showed that 14 risk factors significantly associated with cardiac autonomic neuropathy (P0.05) ROC curve regression to establish the risk model under.Logistic The area is 0.758 (95%CI 0.724-0.793), ROC curve area of neural network risk model was 0.762 (95%CI to 0.732-0.793. conclusion: 1) short frequency of heart rate variability in the reference value can be used in the diagnosis of cardiac autonomic neuropathy test, and has high sensitivity and specificity. Ewing's test frequency short heart rate variability test diagnostic value the cardiac autonomic neuropathy is not inferior to the traditional, can be used in the diagnosis of cardiac autonomic neuropathy, especially suitable for large populations. Diagnosis of cardiac autonomic neuropathy has a higher prevalence in China populations, and in diabetes, hypertension and metabolic syndrome in patients with a higher prevalence of.2 multi factor Logistic) the regression analysis shows that the environmental risk factors of cardiac autonomic neuropathy showed that age, heart rate, hypertension and metabolic factors (waist circumference and insulin Resistance index) associated with this disease. In this sample, genotype phenotype analysis indicated that the candidate gene and cardiac autonomic neuropathy showed no obvious correlation, but the gene environment interaction analysis showed that SCNIOA and ESR1 genes and metabolic factors interaction.3) we developed based on a simple set of variables (do not need clinical examination laboratory or complex) screening model of cardiac autonomic neuropathy. This model is a simple, fast, inexpensive, non-invasive and reliable screening tool. In the Chinese crowd, can be used in the early stage of the disease prevention, with the progress of.4 to delay disease) this study of the application of artificial neural network and Logistic regression of cardiac autonomic neuropathy risk models more discriminative and accurate, non inferiority detection sensitivity prediction model of artificial neural network, specificity and prediction The value is not inferior to the risk model constructed by Logistic regression. It shows that these two types of risk models are effective evaluation and prediction tools.

【學位授予單位】：復旦大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R541

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本文編號：1420515