慢性腎臟病患者腹高與Framingham風(fēng)險(xiǎn)評(píng)分、內(nèi)皮特異性分子1相關(guān)性探討
發(fā)布時(shí)間：2018-10-21 14:33
【摘要】：目的:1、分析在慢性腎臟病(chronic kidney disease CKD)患者中腹高(Sagittal Abdominal Diameter,SAD)與 CKD 并發(fā)心血管疾病(cardiovascular disease CVD)危險(xiǎn)因素的相關(guān)性及SAD與Framingham風(fēng)險(xiǎn)評(píng)分相關(guān)性探討;2、分析 SAD 與內(nèi)皮特異性分子 1(endothelial specific molecule-1,ESM-1)的相關(guān)性以及內(nèi)臟脂肪組織(visceral adipose tissue VAT)與炎癥、內(nèi)皮細(xì)胞功能紊亂的機(jī)制探討。方法:1.收集2011年11月至2016年7月在我院腎臟內(nèi)科住院CKD患者307例。測(cè)量身高及體重,計(jì)算體重指數(shù)(body mass index BMI);測(cè)量SAD;收集患者病史、臨床指標(biāo)計(jì)算Framingham風(fēng)險(xiǎn)評(píng)分。采用SPSS15.0進(jìn)行統(tǒng)計(jì)學(xué)分析:Kolmogorov Smirnov檢驗(yàn)所有計(jì)量資料是否為正態(tài)分布。符合正態(tài)分布以均數(shù)±標(biāo)準(zhǔn)差(x±s)表示,非正態(tài)分布以中位數(shù)和四分位數(shù)表示。分組間變量比較計(jì)數(shù)資料采用卡方檢驗(yàn)(X2),計(jì)量資料采用方差分析(ANOVA)。各變量之間采用Spearman相關(guān)性分析。多重線性回歸分析用于明確變量間依存關(guān)系,P0.05可認(rèn)為差異有統(tǒng)計(jì)學(xué)意義。2.收集2015年至2016年昆明醫(yī)科大學(xué)第一附屬醫(yī)院腎內(nèi)科非透析CKD5期患者共69例,測(cè)量患者SAD、收集一般資料、病史,各項(xiàng)檢查指標(biāo);血清ESM-1水平采用酶聯(lián)免疫吸附法(ELISA)測(cè)定。采用SPSS15.0進(jìn)行統(tǒng)計(jì)學(xué)分析:Kolmogorov Smirnov檢驗(yàn)所有計(jì)量資料是否為正態(tài)分布。符合正態(tài)分布以均數(shù)±標(biāo)準(zhǔn)差(x±s)表示。分組間變量比較計(jì)數(shù)資料采用卡方檢驗(yàn)(X2),計(jì)量資料采用T檢驗(yàn)。各變量之間采用Spearman相關(guān)性分析。多重線性回歸分析用于明確變量間依存關(guān)系,P0.05可認(rèn)為差異有統(tǒng)計(jì)學(xué)意義。結(jié)果:1、Framingham 風(fēng)險(xiǎn)評(píng)分與年齡(P0.01)、BMI(P0.01)和SAD(P0.01)成顯著正相關(guān),與GFR呈負(fù)相關(guān)(P=0.068);采用多重線性回歸模型對(duì)變量進(jìn)行調(diào)整(校正R2=0.178)后SAD與Framingham仍呈現(xiàn)顯著正相關(guān)(P0.01)。SAD 與年齡(P0.05)、GFR(P=0.068)、BMI(P0.05)、UA(P=0.055)呈正相關(guān);采用多重線性回歸模型對(duì)變量進(jìn)行調(diào)整分析后(校正R2=0.409)顯示,SAD 與 BMI(P0.01)、糖尿病病史(P0.01)、GFR(P0.01)和年齡(P0.01)呈顯著正相關(guān)。2、在 69 例 CKD5 患者中 SAD(P0.05)與 LgESM-1 正相關(guān),HGB(P0.05)與LgESM-1負(fù)相關(guān)。采用多重線性回歸模型對(duì)年齡、吸煙等變量進(jìn)行調(diào)整(校正R2為0.121),SAD(P0.05)與LgESM-1正相關(guān),HGB(P0.05)與LgESM-1負(fù)相關(guān)。結(jié)論:1、在CKD患者中,CKD并發(fā)CVD的危險(xiǎn)因素年齡、GFR、CRP與SAD呈正相關(guān),Framingham風(fēng)險(xiǎn)評(píng)分與SAD正相關(guān),SAD可作為未透析CKD患者Framingham風(fēng)險(xiǎn)評(píng)分潛在的預(yù)測(cè)因子。2、在非透析CKD5期患者中,SAD與血管內(nèi)皮損傷標(biāo)志物ESM-1正相關(guān),可作為非透析CKD5期患者血管內(nèi)皮損傷潛在的預(yù)測(cè)因子。
[Abstract]:Objective: 1 to analyze the correlation between high abdominal (Sagittal Abdominal Diameter,SAD and (cardiovascular disease CVD) risk factors in patients with chronic kidney disease (chronic kidney disease CKD) complicated with cardiovascular disease and the correlation between SAD and Framingham risk score; 2, to analyze the relationship between SAD and endothelium-specific molecules. (1) correlation of (endothelial specific molecule-1,ESM-1), (visceral adipose tissue VAT) of visceral adipose tissue and inflammation, Mechanism of endothelial cell dysfunction. Methods: 1. From November 2011 to July 2016, 307 patients with CKD were hospitalized in the Department of Renal Medicine in our hospital. Height and weight were measured, body mass index (body mass index BMI);) was calculated, SAD; was collected and Framingham risk score was calculated. SPSS15.0 was used to analyze the statistics and: Kolmogorov Smirnov was used to test whether all the metrological data were normal distribution. The normal distribution is expressed as mean 鹵standard deviation (x 鹵s), and the non-normal distribution is expressed as median and quartile. The counting data between groups were compared by chi-square test (X2), and the data were measured by analysis of variance (ANOVA). Spearman correlation analysis was used among the variables. Multiple linear regression analysis was used to clarify the relationship between variables, P0.05 can be considered the difference is statistically significant. 2. A total of 69 non-dialysis CKD5 patients were collected from 2015 to 2016 in the Department of Renal Medicine of the first affiliated Hospital of Kunming Medical University. The general data, medical history and various examination indexes of SAD, were measured. The serum ESM-1 level was determined by Elisa (ELISA). SPSS15.0 was used to analyze the statistics and: Kolmogorov Smirnov was used to test whether all the metrological data were normal distribution. The normal distribution is expressed as mean 鹵standard deviation (x 鹵s). The counting data were compared by chi-square test (X 2) and T test was used. Spearman correlation analysis was used among the variables. Multiple linear regression analysis was used to clarify the relationship between variables, P0.05 can be considered the difference is statistically significant. Results: (1) there was a significant positive correlation between SAD risk score and age (P0.01), BMI (P0.01) and SAD (P0.01), and a negative correlation between SAD and GFR (P0.068), and a significant positive correlation between SAD and Framingham (P0.01). SAD and age (P0.05), GFR (P0.068), BMI (P0.05), UA (P0.055) after adjusting variables with multiple linear regression model (R2C0.178). The multiple linear regression model was used to analyze the variables (adjusted R2P 0.409). SAD was positively correlated with BMI (P0.01), history of diabetes (P0.01), GFR (P0.01) and age (P0.01). In 69 cases of CKD5, SAD (P0.05) was negatively correlated with LgESM-1 (P 0.05) and LgESM-1 was negatively correlated with LgESM-1 (P 0.05). The multiple linear regression model was used to adjust the age, smoking and other variables (adjusted R2 was 0.121), SAD (P0.05) and LgESM-1 was positively correlated with, HGB (P0.05 and negatively correlated with LgESM-1. Conclusion: 1. In CKD patients, the age of risk factors of CKD complicated with CVD, GFR,CRP and SAD were positively correlated, and Framingham risk score was positively correlated with SAD. SAD could be used as a potential predictor of Framingham risk score in non-dialysis CKD patients. In non-dialysis CKD5 patients, SAD was associated with Framingham risk score. The vascular endothelial injury marker ESM-1 was positively correlated. It can be used as a potential predictor of vascular endothelial injury in non-dialysis CKD5 patients.
【學(xué)位授予單位】：昆明醫(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)】：R692
本文編號(hào)：2285388
[Abstract]:Objective: 1 to analyze the correlation between high abdominal (Sagittal Abdominal Diameter,SAD and (cardiovascular disease CVD) risk factors in patients with chronic kidney disease (chronic kidney disease CKD) complicated with cardiovascular disease and the correlation between SAD and Framingham risk score; 2, to analyze the relationship between SAD and endothelium-specific molecules. (1) correlation of (endothelial specific molecule-1,ESM-1), (visceral adipose tissue VAT) of visceral adipose tissue and inflammation, Mechanism of endothelial cell dysfunction. Methods: 1. From November 2011 to July 2016, 307 patients with CKD were hospitalized in the Department of Renal Medicine in our hospital. Height and weight were measured, body mass index (body mass index BMI);) was calculated, SAD; was collected and Framingham risk score was calculated. SPSS15.0 was used to analyze the statistics and: Kolmogorov Smirnov was used to test whether all the metrological data were normal distribution. The normal distribution is expressed as mean 鹵standard deviation (x 鹵s), and the non-normal distribution is expressed as median and quartile. The counting data between groups were compared by chi-square test (X2), and the data were measured by analysis of variance (ANOVA). Spearman correlation analysis was used among the variables. Multiple linear regression analysis was used to clarify the relationship between variables, P0.05 can be considered the difference is statistically significant. 2. A total of 69 non-dialysis CKD5 patients were collected from 2015 to 2016 in the Department of Renal Medicine of the first affiliated Hospital of Kunming Medical University. The general data, medical history and various examination indexes of SAD, were measured. The serum ESM-1 level was determined by Elisa (ELISA). SPSS15.0 was used to analyze the statistics and: Kolmogorov Smirnov was used to test whether all the metrological data were normal distribution. The normal distribution is expressed as mean 鹵standard deviation (x 鹵s). The counting data were compared by chi-square test (X 2) and T test was used. Spearman correlation analysis was used among the variables. Multiple linear regression analysis was used to clarify the relationship between variables, P0.05 can be considered the difference is statistically significant. Results: (1) there was a significant positive correlation between SAD risk score and age (P0.01), BMI (P0.01) and SAD (P0.01), and a negative correlation between SAD and GFR (P0.068), and a significant positive correlation between SAD and Framingham (P0.01). SAD and age (P0.05), GFR (P0.068), BMI (P0.05), UA (P0.055) after adjusting variables with multiple linear regression model (R2C0.178). The multiple linear regression model was used to analyze the variables (adjusted R2P 0.409). SAD was positively correlated with BMI (P0.01), history of diabetes (P0.01), GFR (P0.01) and age (P0.01). In 69 cases of CKD5, SAD (P0.05) was negatively correlated with LgESM-1 (P 0.05) and LgESM-1 was negatively correlated with LgESM-1 (P 0.05). The multiple linear regression model was used to adjust the age, smoking and other variables (adjusted R2 was 0.121), SAD (P0.05) and LgESM-1 was positively correlated with, HGB (P0.05 and negatively correlated with LgESM-1. Conclusion: 1. In CKD patients, the age of risk factors of CKD complicated with CVD, GFR,CRP and SAD were positively correlated, and Framingham risk score was positively correlated with SAD. SAD could be used as a potential predictor of Framingham risk score in non-dialysis CKD patients. In non-dialysis CKD5 patients, SAD was associated with Framingham risk score. The vascular endothelial injury marker ESM-1 was positively correlated. It can be used as a potential predictor of vascular endothelial injury in non-dialysis CKD5 patients.
【學(xué)位授予單位】：昆明醫(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)】：R692
【參考文獻(xiàn)】
中國(guó)期刊全文數(shù)據(jù)庫(kù) 前1條
1 嚴(yán)艷;汪年松;嚴(yán)海東;簡(jiǎn)桂花;張曉光;王曉霞;薛勤;高許萍;;雙能X線評(píng)價(jià)慢性腎臟病患者營(yíng)養(yǎng)狀況的研究[J];中國(guó)中西醫(yī)結(jié)合腎病雜志;2009年02期
，本文編號(hào)：2285388
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