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研究生:蔡明松
研究生(外文):Min-Sung Tsai
論文名稱:慢性腎臟病患體內骨髓過氧化酶和體脂肪的關係
論文名稱(外文):The relationship between myeloperoxidase and adiposity in chronic kidney disease patients
指導教授:陳奇祥陳奇祥引用關係蕭慧美蕭慧美引用關係
指導教授(外文):Khee-Siang ChanHuey-Mei Shaw
學位類別:碩士
校院名稱:嘉南藥理科技大學
系所名稱:保健營養系
學門:醫藥衛生學門
學類:營養學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:28
中文關鍵詞:慢性腎衰竭骨髓過氧化酶維生素EC反應蛋白體脂肪
外文關鍵詞:Chronic kidney disease (CKD)Body fat (BF)Polymorphonuclear leukocyte (PMN)MacrophageMyeloperoxidase (MPO)Bioelectrical impedance analysis (BIA)tocopherolhigh sensitive CRP (hsCRP)
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研究目的:肥胖是許多慢性病發生的危險因子,研究指出內臟脂肪和發炎指標及代謝症候群的相關性比皮下脂肪還強。脂肪組織容易處於慢性發炎與氧化壓力下,當這些發炎細胞被激活時便會釋出發炎相關物質,其中包括骨髓過氧化酶(Myeloperoxidase, MPO)。MPO除了與體內慢性發炎反應有關外亦會造成體內氧化壓力,目前亦被認為與預測心血管疾病之發生率有關聯。然而,在慢性腎功能不全的患者,其體內脂肪細胞和發炎反應的關係仍未有深入之研究。因此本實驗欲探討慢性腎衰竭患者和健康族群其體內MPO (發炎細胞激活能力指標) 和體內體脂肪含量 (Body fat, BF) 的關係性。同時也一併分析其他系統性發炎指標C反應蛋白(CRP)與脂溶性抗氧化維生素E在慢性腎衰竭病患的分布情形。方法:本實驗共收集60位健康族群和66位慢性腎衰竭患者,以生物電阻原理測定體脂肪,同時收集其尿液與血液以進行各項生化指標分析。在統計方面,以Mann-Whitney U或Kruskal-Walls統計法比較組間的連續數值,而組間個數的比較則使用卡方檢測。不同指標間的相關性研究採用spearman correlation test or pearson test。全部統計資料皆採用spss.17版本來分析。統計p值設定在0.05以下為具顯著意義。結果:資料顯示??tocopherol、MPO和CRP在健康和慢性腎衰竭兩族群之間有顯著不同,而且這些指標在排除預估腎絲球過濾率 (eGFR) 的影響之後,MPO和BF或和瘦體素 (Leptin) 之間呈現顯著相關。接著依據MPO數值分布區分為三群組以分析組間相關因素,結果指出年齡、BF、收縮壓、eGFR有顯著差異。進一步使用單變數與多變數迴歸分析,檢測與骨髓過氧化酶最具相關性的變數,結果發現在慢性腎衰竭病患組其MPO與BF具顯著相關。結論:本研究發現,研究族群體內的體脂肪含量和體內骨髓過氧化酶有關,且以慢性腎衰竭病患族群有顯著相關,在健康族群則無此現象。因此推測在慢性腎衰竭患者其脂肪組織較易引發發炎細胞之激活反應,原因是否與尿毒素之浸潤有關仍待進一步探討。
Background and purpose:Chronic inflammation and oxidative stress that has arisen due to nearby fat tissue. Visceral body fat (BF) is significantly correlated to the inflammation markers and the oxidative stress level better than to subcutaneous fat. The major inflammation reaction has arisen from a polymorphonuclear leukocyte (PMN) ,and to a lesser degree from monocytes and macrophages. Myeloperoxidase (MPO)is one of inflammatory signals that is secreted after PMN is stimulated, additionally, it reflects the inflammations. MPO is also linked to oxidative stress and increases the predictive power for cardiovascular disease. However, there was no further study in order to discuss the correlation between body fat tissue and the inflammatory response in CKD patients. Recently, a non-invasive medical device was developed that valuated the BF by the using bioelectrical impedance analysis (BIA). We verified the association between the activated polymorphonuclear leukocyte marker (MPO) and the BF in patients with chronic kidney disease as well as healthy participants. In addition, we also analyzed the distribution of plasma tocopherol and high sensitive C-Reactive protein in CKD patients. Methods : There was a total 66 CKD patients and 60 healthy participants enrolled. The baseline patient characteristics and the body appearance data were collected. BF was obtained by BIA (Tanita BC519, Tanita Co., Ltd.,). Continuous data between groups was compared by the Mann-Whitney U test and the categorical data was compared by the chi-squared test. Correlations between variables were applied to Spearman’s correlation or Pearson test if appropriate. Tertiles stratification and the natural logarithm transfer of the MPO were done for its nonparametric distribution. Traditional risk factors for MPO were put into a univariable linear regression model, and factors with a statistic p value less than 0.1 were enrolled into a multivariable regression model. All the data was put into statistics by SPSS.17. and any p < 0.05 was interpreted as being significant. Result: Baseline characteristics showed a difference in tocopherol, hsCRP and MPO between groups, and only MPO was correlated with BF or leptin levels after adjusted with eGFR. MPO tertiles disclosed differences in age, body fat, systolic blood pressure, and eGFR. The univariable linear regression model revealed that age, systolic blood pressure, neutrophil count, waist circumference, eGFR, serum albumin level, BF, HbA1c, and LDL predicted serum LnMPO(p < 0.1). Furthermore multivariable analysis disclosed that the neutrophil count, BF and serum albumin levels were significant(p = 0.009; 0.034, and 0.049, in respectively) predictors of plasma LnMPO in the CKD group, but not in the control subjects. Conclusion:body fat percentage obtained from BIA strongly predicts plasma MPO in CKD patients, but not in healthy participants. It did partly explained by primed and activated status of neutrophils in the CKD environment. Whether uremic toxins take the role of irritated neutrophils in obesity situations needs further investigation.
Chapter 1 Introduction
1.1 Background
1.1.1 Definition of Obesity
1.1.2 Obesity and its correlation with health problems
1.2 Literature review
1.2.1 Clinical evidence of increase prevalence of CKD in obesity
1.2.2 Obesity linked to increase inflammation
1.2.3 Obesity is also linked to increased oxidative stress
1.2.4 Visceral fat closely linked to metabolic syndrome and insulin
resistance
1.2.5 Myeloperoxidase as a marker of inflammation, oxidative stress and
atherosclerosis
1.3 Research purpose
1.4 Value of the research
Chapter 2 Methods
2.1 Research design
2.2 Study subjects
2.2.1 Case subjects
2.2.2 Control subjects
2.3 Material
2.3.1 Blood sample
2.3.2 Urine sample
2.3.3 Body fat measurement using Bioelectrical Impedance Analysis
2.3.4 Inflammatory biomarkers
2.4 Statistics
Chapter 3 Results
3.1 Baseline patient characteristics between groups
3.2 Different expressions over the inflammation markers between the two
groups
3.3 Patient transplantation characteristics according to the MPO
tertiles
3.4 Neutrophil count and Body fat predicted MPO in the CKD
group
Chapter 4 Discussion
4.1 Different body fat compositions, biomarkers of inflammation and
oxidative stress status among the groups
4.2 Disproportional increase in oxidative stress and acute-phase
inflammation markers in CKD
4.3 The relationship between body fat or leptin to the biomarkers of
inflammation and oxidative stress status
4.4 A higher visceral fat predicts MPO concentration
4.5 The role of Uremia toxins in adipocyte related inflammation: The
breaking the systemic at local tissues
4.6 Limitations : Body composition estimated by BIA
Chapter 5 Conclusion
Reference
Table
Figure
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