本文選題：腦白質(zhì)疏松 + 紅細胞分布寬度��； 參考：《廣西醫(yī)科大學(xué)》2017年碩士論文

【摘要】：目的:研究腦白質(zhì)疏松(white matter hypertensities,WMH)的危險因素及其與紅細胞分布寬度(red blood cell distribution width,RDW)的相關(guān)性。方法:選取2015年1月至2016年10月在廣西醫(yī)科大學(xué)第一附屬醫(yī)院住院完善頭顱MRI的患者,根據(jù)納入及排除標準最終納入研究的患者共146例,其中MRI正常的為共58例(非WMH組),MRI上僅表現(xiàn)為腦白質(zhì)疏松的88例(WMH組),收集數(shù)據(jù)包括:(1)一般資料:性別、年齡、身高及體重并計算體重指數(shù)BMI;(2)既往史:高血壓、糖尿病、腦卒中、冠心病、吸煙、飲酒史;(3)實驗室數(shù)據(jù):紅細胞分布寬度、血紅蛋白、平均紅細胞體積、白細胞、中性粒細胞比例、總膽固醇、低密度脂蛋白膽固醇、高密度脂蛋白膽固醇、甘油三酯、同型半胱氨酸。(4)根據(jù)Fazekas評分標準對腦白質(zhì)疏松嚴重程度進行評分。計量資料采用獨立樣本T檢驗,計數(shù)資料采用卡方c2檢驗,以單因素分析中有統(tǒng)計學(xué)意義(P0.05)的因素為自變量,以是否腦白質(zhì)疏松為因變量應(yīng)用二分類logistic回歸分析探討腦白質(zhì)疏松的獨立危險因素;以腦白質(zhì)疏松嚴重程度為因變量,應(yīng)用有序多分類logistic回歸分析研究腦白質(zhì)疏松及嚴重程度的獨立危險因素。應(yīng)用Spearman相關(guān)性檢驗分析腦白質(zhì)疏松嚴重程度與紅細胞分布寬度的相關(guān)性。應(yīng)用Pearson相關(guān)性檢驗及Spearman相關(guān)性檢驗分析紅細胞分布寬度與其他危險因素的相關(guān)性。結(jié)果:(1)與非WMH組相比,WMH組的年齡(37-86歲,64.40±10.15 vs.30-81歲,51.50±14.22)、高血壓病史比例(52.8%vs.29.3%)、同型半胱氨酸(13.45±5.41 vs.11.69±4.82)、RDW水平(13.7%±1.29%vs.13.29%±0.83%)升高,而血紅蛋白水平(125.89±13.64 vs.131.13±11.11)降低,差異有統(tǒng)計學(xué)意義(P㩳0.05)。進一步進行二分類Logistic回歸分析提示年齡是腦白質(zhì)疏松的獨立危險因素(OR 1.080,95%CI 1.039~1.123,P㩳0.05)。(2)非WMH組、輕度WMH組、中-重度WMH組高血壓病史比例(分別為29.3%、48.5%、70%,P=0.004),年齡(分別為51.50±14.22、62.25±9.99、71.70±6.84,P=0.000),RDW(分別為13.29±0.84、13.60±1.34、14.05±1.05,P=0.031)呈遞增趨勢,Hb(131.13±11.11、126.63±13.90、123.41±12.70,P=0.034)呈遞減趨勢,差異有統(tǒng)計學(xué)意義。進一步進行有序多分類Logistic回歸分析,結(jié)果顯示腦白質(zhì)疏松嚴重程度的獨立危險因素(OR 1.095,95%CI 1.057~1.135,P=0.000)。(3)Spearman相關(guān)性分析顯示腦白質(zhì)疏松嚴重程度與RDW(r=0.207,P=0.012)呈正相關(guān)。(4)Pearson相關(guān)性分析顯示RDW與血紅蛋白、MCV呈負相關(guān)(r分別為-0.390,-0.458,P0.05)。結(jié)論:(1)年齡是腦白質(zhì)疏松發(fā)生和嚴重程度的獨立危險因素。(2)紅細胞分布寬度在腦白質(zhì)疏松患者中升高,并與腦白質(zhì)疏松嚴重程度呈正相關(guān),紅細胞分布寬度與腦白質(zhì)疏松相關(guān)的機制還需要更大規(guī)模的、設(shè)計良好的實驗進一步探究。
[Abstract]:Objective: To study the risk factors of white matter hypertensities (WMH) and its correlation with the distribution width of red blood cells (red blood cell distribution width, RDW). Methods: to select the head MRI patients at the First Affiliated Hospital of Guangxi Medical University from January 2015 to October 2016, according to the inclusion and exclusion criteria. A total of 146 patients were enrolled in the study, of which 58 cases (non WMH) were normal MRI and 88 cases of leukoaraiosis (group WMH) on MRI. The data included: (1) general data: sex, age, height and weight and calculate body mass index BMI; (2) history of hypertension, diabetes, stroke, coronary heart disease, smoking, drinking history; (3) laboratory number According to the distribution of red blood cells, hemoglobin, average red blood cell volume, white blood cell, neutrophils ratio, total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol, triglyceride, homocysteine. (4) the severity of leukoaraiosis was evaluated according to the Fazekas score. The measurement data were measured by independent sample T The counting data were determined by chi square C2 test. The independent variables were statistically significant (P0.05) in the single factor analysis. The independent risk factors of leukoaraiosis were investigated by two classified logistic regression analysis on whether the leukoaraiosis was used as a dependent variable. With the severity of leukoaraiosis as the dependent variable, an orderly multi classification logistic regression was used. Analysis and study of independent risk factors of leukoaraiosis and severity. Spearman correlation test was used to analyze the correlation between the severity of leukoaraiosis and the distribution width of red blood cells. The correlation between Pearson correlation test and Spearman correlation test was used to analyze the correlation between red blood cell distribution width and other risk factors. (1) and non WMH The age of group WMH (37-86 years, 64.40 + 10.15 vs.30-81 years, 51.50 + 14.22), the history of hypertension (52.8%vs.29.3%), homocysteine (13.45 + 5.41 vs.11.69 + 4.82), RDW level (13.7% + 1.29%vs.13.29% + 0.83%), and the level of hemoglobin (125.89 + 13.64 vs.131.13 + 11.11) decreased, the difference was statistically significant (P? 0.05). Further two classification Logistic regression analysis showed that age was an independent risk factor for leukoaraiosis (OR 1.080,95%CI 1.039~1.123, P? 0.05). (2) non WMH group, mild WMH group, moderate to severe WMH group (29.3%, 48.5%, 70%, P=0.004), age (respectively 51.50 + 14.22,62.25 + 9.99,71.70 + 6.84 respectively, P=0.000). Do not be 13.29 + 0.84,13.60 + 1.34,14.05 + 1.05, P=0.031) increasing trend, Hb (131.13 + 11.11126.63 + 13.90123.41 + 12.70, P=0.034) showed a decreasing trend, and the difference was statistically significant. Further analysis of sequential multiple classification Logistic regression showed the independent risk factor of the severity of leukoaraiosis (OR 1.095,95%CI 1.057~1.135,) (3) (3) Spearman correlation analysis showed that the severity of leukoaraiosis was positively correlated with RDW (r=0.207, P=0.012). (4) Pearson correlation analysis showed that RDW was negatively correlated with hemoglobin and MCV (R is -0.390, -0.458, P0.05). (1) age is an independent risk factor for the occurrence and severity of leukoaraiosis. (2) the wide distribution of red blood cells The degree of leukoaraiosis is higher in patients with leukoaraiosis and is positively correlated with the severity of leukoaraiosis. The mechanism of red cell distribution and leukoaraiosis needs to be more large-scale, and well designed experiments are further explored.
【學(xué)位授予單位】：廣西醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R743

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