臺灣博碩士論文加值系統

根據美國腎臟資料系統（The US Renal Data System, USRDS）最新統計臺灣地區末期腎臟病（End Stage Renal Disease, ESRD）之發生率（Incidence rate）及盛行率（Prevalence rate）皆高居世界首位。目前對於慢性腎臟病（Chronic kidney disease, CKD）的診斷，評估腎功能是藉血液檢測血清肌酸酐（Creatinine）、腎絲球過濾率（Glomerular filtration rate, GFR）或尿液檢測微量白蛋白（Microalbumin）及尿液常規檢查（Urine routine），也可配合腎切片及影像醫學檢查來輔助確診。本研究的目的為研究分析腎小管上皮細胞（Renal tubular epithelial Cells, RTEC）是否可作為慢性腎臟病早期預測方法之一。自2017年01月23日起，收案576名病患並依據有無腎病分成二組進行分析，血液檢測Triglyceride、Total cholesterol、Cholesterol-LDL、BUN、Creatinine、eGFR、Uric acid、Albumin及Glucose，尿液檢測Protein、Creatinine、Microalbumin、Urine strip及鏡檢RTEC，並統計各項數據於腎病或RTEC間之差異性、相關性比較及疾病勝算比與概似比。實驗結果顯示，576名收案對象以女性較多（51.9%；299/576）。二組平均年齡皆呈現大於60歲之高齡化，且各年齡層皆有罹患腎病之可能性。當發生糖尿病或/且高血壓時，以有腎病者（25.1%；145/576）較無腎病者（23.3%；134/576）多。各項腎功能（BUN、Creatinine、eGFR、Uric acid、Albumin）數據於腎病是否發生間皆有顯著差異性（p值< 0.05），然而對於血脂類（Triglyceride、Total cholesterol、Cholesterol-LDL）、血糖及尿液Creatinine檢測卻無達到統計上之差異（p值 = 0.675、0.279、0.097、0.412）。依據CKD分期其314名無腎病者以G2期佔多數（151名，26.2%），而262名有腎病者中以G4期為主（60名，10.4%），顯示分別具有輕度及重度腎臟功能障礙。藉由Sternheimer stain染色鏡檢之RTEC呈現長形鋸齒狀，細胞質為紅紫色，偏心的細胞核為藍紫色，顆粒性的細胞質具有鋸齒狀的邊緣很明顯。RTEC出現於各個年齡層，且不論有無腎病或腎功能相關指標異常時，皆可於尿液發現0-2/HPF的分佈，與尿液十項化學反應的蛋白質（Protein, PRO）、白血球酯酶（Leukocyte esterase, LEU）及亞硝酸鹽（Nitrite, NIT）有較高的陽性率。各變數之相關性比較顯示當Creatinine值偏高、eGFR值偏低或尿液試紙PRO反應愈異常而呈現腎功能愈異常時，其RTEC數量的分佈就愈少。以RTEC作為腎病診斷工具時，從ROC曲線判別優於Pro（spot）及mAlb（spot），但還是不及血液檢測Creatinine、BUN 及eGFR。改以由多檢驗項目診斷腎病有無是可提升檢測該病之機率，其陽性概似比從0.565提升至3.027。總結，將RTEC新增於尿沉渣細胞鏡檢報告中，建議透過Sternheimer stain染色以增加對比易於判別及從尿液試紙之蛋白質、白血球酯酶及亞硝酸鹽作邏輯性判斷RTEC檢出率，另以多次採檢或檢體量增加以獲取細胞量。在臨床照護上，藉由醫師病況詢問和配合血液檢測Creatinine及eGFR與尿液篩檢微量白蛋白等多個腎病標誌才能評估出早期腎病的發生。

According to the latest statistics of the US Renal Data System (USRDS), the incidence rate and prevalence rate of end stage renal disease (ESRD) in Taiwan are among the highest in the world. Currently, the diagnosis of chronic kidney disease (CKD) was assessed by serum creatinine, glomerular filtration rate (GFR) or urinalysis of microalbumin and urine routine examination, but also with the kidney biopsy and medical imaging to assist the diagnosis. The purpose of this study was to investigate whether renal tubular epithelial cells (RTEC) can be used as one of the early prediction methods for chronic kidney disease. Since Jan. 23, 2017, 576 patients were enrolled and divided into two groups according to their previous history of kidney disease for statistical analysis. Blood tests included Triglyceride, Total cholesterol, Cholesterol-LDL, BUN, Creatinine, eGFR, Uric acid, Albumin, Glucose, and Urinalysis included Protein, Creatinine, Microalbumin, Urine strip, and RTEC. Statistics of the correlation and disease odds ratio (OR) and the likelihood ratio (LR) in the presence of kidney disease or RTEC. The results of the study showed that the majority of 576 patients were women (51.9%; 299/576). The average age of both men and women showed an age of over 60 years, and all age groups were at risk of developing kidney disease. When patients with diabetes and/or hypertension, those with nephropathy (25.1%; 145/576) were more than those without nephropathy (23.3%; 134/576). The data of renal function (BUN, Creatinine, eGFR, Uric acid, Albumin) were significantly different between the occurrence of nephropathy (p value < 0.05). However, no statistically significant differences were found in the lipids (Total cholesterol, Cholesterol-LDL), blood glucose and urine Creatinine (p value = 0.675、0.279、0.097、0.412). According to CKD staging, 314 patients without nephropathy accounted for the majority of G2 (151, 26.2%), while 262 patients with nephropathy were predominantly G4 (60, 10.4%) , showing mild and severe renal dysfunction. The RTECs showed elongated serrations with a cytoplasm of purplish red, an eccentric nucleus of blue-purple, and a clear jagged edge of the granular cytoplasm that was evident by Sternheimer stain staining. RTEC appeared in all age groups, and no matter whether there is kidney disease or abnormal renal related indicators, can be found in the urine 0-2 / HPF distribution, and the urine strips of chemical reaction protein (Protein, PRO), leukocyte Enzyme (Leukocyte esterase, LEU) and Nitrate reduction (Nitrite, NIT) have a higher positive rate. The correlation of variables showed that the distribution of RTEC was less when creatinine value was higher, eGFR value was lower, or urine strip PRO reaction was more abnormal, which showed more abnormal renal function. When RTEC was used as a diagnostic tool for nephropathy, it was better than urine Pro (spot) and urine mAlb (spot) from the ROC curve, but not as good as blood creatinine, BUN and eGFR. Multiple test items for diagnosis of nephropathy is to improve the probability of detection of the diseases, the positive likelihood ratio increased from 0.565 to 3.027. In conclusion, if RTEC is added to urinary sediment reports, it is recommended that Sternheimer staining be used to increase contrast and be easily discriminated, and to logically determine RTEC detection rates from the urine strip tests of protein, leukocyte esterase and nitrite. In addition to multiple collections or increased the urine volume to obtain more RTEC. Moreover, in clinical care, physicians inquired about the disease and assessed by serum creatinine, eGFR and urine screening of microalbumin or other indicators of nephropathy, can effectively assess the incidence of early kidney disease.

標題...............................................................i
審定書.............................................................ii
電子暨紙本學位論文書目同意公開申請書.................................iii
學位考試保密同意書暨簽到表...........................................iv
誌謝...............................................................v
目錄...............................................................vi
表目錄............................................................viii
圖目錄.............................................................ix
縮寫表.............................................................x
中文摘要...........................................................xii
英文摘要...........................................................xiv
第一章 緒論.......................................................1
第一節 慢性腎臟病（Chronic kidney disease, CKD）...................1
第一項 慢性腎臟病之簡介............................................1
1.1 慢性腎臟病之定義............................................1
1.2 慢性腎臟病之分期............................................1
第二項 慢性腎臟病之流行病學........................................2
2.1 臺灣慢性腎臟病之流行病學.....................................2
2.2 世界各國慢性腎臟病之流行病學.................................3
第三項 慢性腎臟病之臨床表徵........................................3
3.1 臨床症狀...................................................4
3.2 高危險族群.................................................4
3.2.1 糖尿病.................................................4
3.2.2 高血壓.................................................5
3.2.3 老年人.................................................5
第四項 慢性腎臟病的篩檢診斷........................................6
4.1 黃金診斷方法................................................6
4.2 其他診斷方法................................................7
4.3 臨床診斷篩檢建議............................................8
第二節 腎小管上皮細胞（Renal tubular epithelial cell, RTEC）.......9
第一項 腎小管上皮細胞之簡介........................................9
1.1 解剖位置、細胞形態..........................................9
1.2 生理功能...................................................10
第二項 腎小管上皮細胞之疾病探討....................................11
第三項 研究動機..................................................12
第四項 研究目的..................................................13
第二章 研究材料與方法..............................................15
第一節 研究對象..................................................15
第二節 研究材料與試劑.............................................15
第三節 研究方法...................................................16
第一項 檢體收集.................................................16
第二項 檢體保存.................................................16
第三項 檢體分析.................................................17
3.1 血液生化分析..............................................17
3.2 尿液生化分析.............................................20
3.3 尿液十項化學分析..........................................20
3.4 尿液沉渣細胞分析..........................................23
第四項 統計分析.................................................23
第三章 結果......................................................24
第一節 收案對象之背景資料及臨床數據................................24
第二節 收案對象之慢性腎臟病分期進展................................26
第三節 RTEC於染色前後之鏡檢形態...................................26
第四節 RTEC之各年齡層分佈.........................................27
第五節 RTEC之各生化數據比較.......................................28
第六節 RTEC與Creatinine之陽性比率.................................30
第七節 尿液化學十項之RTEC陽性率....................................30
第八節 RTEC之相關性比較...........................................30
第九節 ROC曲線判別各腎病指標之表現..................................31
第十節 腎病之RTEC與eGFR、CREA及PRO之勝算比及概似比..................31
第四章 討論.......................................................33
第五章 結論.......................................................38
第六章 參考文獻...................................................39
第七章 附錄.......................................................43
附錄一 人體試驗委員會同意臨床研究證明書..............................43
附錄二 MULTISTIX® 10 SG 100 STRIPS BAYER..........................44

表目錄 List of Tables
表1. 有無罹患腎病患者之背景資料......................................45
表2. 有無罹患腎病患者之臨床數據......................................46
表3. 有無罹患腎病患者之CKD分期......................................47
表4. RTEC之各年齡層分佈............................................48
表5. 各年齡層分佈之RTEC陽性率.......................................49
表6. 有無罹患腎病患者之RTEC差異.....................................50
表7. Creatinine之RTEC差異..........................................51
表8. eGFR之RTEC差異................................................52
表9. BUN之RTEC差異.................................................53
表10. Protein（spot urine）之RTEC差異..............................54
表11. Microalbumin（spot urine）之RTEC差異.........................55
表12. RTEC與Creatinine之陽性比率...................................56
表13. 尿液化學十項之RTEC陽性率......................................57
表14. RTEC之相關性比較.............................................58
表15. ROC曲線判別各腎病指標之表現...................................59
表16. 腎病之RTEC與eGFR、CREA及PRO之勝算比及概似比....................60

圖目錄 List of Figures
圖1. RTEC於染色前後之細胞形態鏡檢...................................61
圖2. RTEC之各年齡層分佈............................................62
圖3. 各年齡層分佈之RTEC陽性率.......................................63
圖4. eGFR與RTEC之ROC曲線...........................................64

1. Dahabreh, I.J., et al., Systematic review: Anti-epidermal growth factor receptor treatment effect modification by KRAS mutations in advanced colorectal cancer. Ann Intern Med, 2011. 154（1）: p. 37-49.
2. Kuo, H.W., et al., Epidemiological features of CKD in Taiwan. Am J Kidney Dis, 2007. 49（1）: p. 46-55.
3. Eckardt, K.U., et al., Definition and classification of CKD: the debate should be about patient prognosis--a position statement from KDOQI and KDIGO. Am J Kidney Dis, 2009. 53（6）: p. 915-20.
4. Glassock, R.J. and C. Winearls, An epidemic of chronic kidney disease: fact or fiction? Nephrol Dial Transplant, 2008. 23（4）: p. 1117-21.
5. System, U.R.D., 2015 USRDS Annual Data Report-Vol2 Chapter 13: International Comparisons. American Journal of Kidney Diseases, 2015. 67（3）: p. S291-S334.
6. Wen, C.P., et al., All-cause mortality attribu表le to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet, 2008. 371（9631）: p. 2173-82.
7. Jha, V., et al., Chronic kidney disease: global dimension and perspectives. Lancet, 2013. 382（9888）: p. 260-72.
8. Alicic, R.Z., M.T. Rooney, and K.R. Tuttle, Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clin J Am Soc Nephrol, 2017.
9. Liwa, C. and H. Jaka, Renal Diseases and Use of Medicinal Herbal Extracts: A Concise Update of Reported Literature in Africa. J Nephrol Renal Ther, 2016. 2（008）.
10. Murphy, D., et al., TRends in prevalence of chronic kidney disease in the united states. Annals of Internal Medicine, 2016. 165（7）: p. 473-481.
11. Lewis , E.J., et al., The Effect of Angiotensin-Converting-Enzyme Inhibition on Diabetic Nephropathy. New England Journal of Medicine, 1993. 329（20）: p. 1456-1462.
12. 林明彥 and 黃尚志, 台灣慢性腎臓病卅末期腎臓病流行病學過去、現在與未來. 腎臟與透析, 2007. 19（1）: p. 1-5.
13. R, S., R. B, and A. KC, US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States. American Journal of Kidney Diseases, 2017. 69（3）: p. （suppl 1）:S1-S688.
14. Rossing, P., et al., [Diabetic nephropathy. Unchanged occurrence in patients with insulin-dependent diabetes mellitus]. Ugeskr Laeger, 1996. 158（42）: p. 5940-3.
15. Caramori, M.L., P. Fioretto, and M. Mauer, The need for early predictors of diabetic nephropathy risk: is albumin excretion rate sufficient? Diabetes, 2000. 49（9）: p. 1399-408.
16. 蕭匡智, et al., 糖尿病腎病變. 腎臟與透析, 2013. 25（1）: p. 31-34.
17. Kearney, P.M., et al., Global burden of hypertension: analysis of worldwide data. Lancet, 2005. 365（9455）: p. 217-23.
18. National Kidney, F., K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis, 2002. 39（2 Suppl 1）: p. S1-266.
19. Barrett, B.J., Applying multiple interventions in chronic kidney disease. Semin Dial, 2003. 16（2）: p. 157-64.
20. Coresh, J., et al., Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis, 2003. 41（1）: p. 1-12.
21. Madhan, K., The epidemic of elderly patients with dialysis-requiring end-stage renal disease in New Zealand. N Z Med J, 2004. 117（1195）: p. U912.
22. Harding, M.B., et al., Renal artery stenosis: prevalence and associated risk factors in patients undergoing routine cardiac catheterization. J Am Soc Nephrol, 1992. 2（11）: p. 1608-16.
23. 丁小?, 王一梅, and 上. 200032, 复旦大学附属中山医院肾内科, 老年人肾脏生理学特点与肾功能衰竭. 老年医学与保健, 2006（2006年 02）: p. 67-69.
24. Rowe, J.W., et al., The effect of age on creatinine clearance in men: a cross-sectional and longitudinal study. J Gerontol, 1976. 31（2）: p. 155-63.
25. Roberfroid, M.B., Inulin-type fructans: functional food ingredients. J Nutr, 2007. 137（11 Suppl）: p. 2493s-2502s.
26. Ma, Y.C., et al., Comparison of 99mTc-DTPA renal dynamic imaging with modified MDRD equation for glomerular filtration rate estimation in Chinese patients in different stages of chronic kidney disease. Nephrol Dial Transplant, 2007. 22（2）: p. 417-23.
27. Traynor, J., et al., How to measure renal function in clinical practice. Bmj, 2006. 333（7571）: p. 733-7.
28. Perrone, R.D., N.E. Madias, and A.S. Levey, Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem, 1992. 38（10）: p. 1933-53.
29. Stevens, L.A., et al., Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med, 2006. 354（23）: p. 2473-83.
30. Levey, A.S., et al., A new equation to estimate glomerular filtration rate. Ann Intern Med, 2009. 150（9）: p. 604-12.
31. Earley, A., et al., Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med, 2012. 156（11）: p. 785-95, w-270, w-271, w-272, w-273, w-274, w-275, w-276, w-277, w-278.
32. Matsushita, K., et al., Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. Jama, 2012. 307（18）: p. 1941-51.
33. Vanmassenhove, J., et al., Management of patients at risk of acute kidney injury. The Lancet. 389（10084）: p. 2139-2151.
34. Lameire, N., W. Van Biesen, and R. Vanholder, Acute renal failure. The Lancet, 2005. 365（9457）: p. 417-430.
35. Nash, K., A. Hafeez, and S. Hou, Hospital-acquired renal insufficiency. American Journal of Kidney Diseases, 2002. 39（5）: p. 930-936.
36. Soares, D.M., et al., Delayed Nephrology Consultation and High Mortality on Acute Kidney Injury: A Meta-Analysis. Blood Purif, 2017. 43（1-3）: p. 57-67.
37. Pereira, P.C.B., et al., Molecular Pathophysiology of Renal Tubular Acidosis. Current Genomics, 2009. 10（1）: p. 51-59.
38. 中央健康保險署, 101 年門診透析總額專業醫療服務品質報告. 2013.
39. Chu-Su, Y., et al., Enhancing the Detection of Dysmorphic Red Blood Cells and Renal Tubular Epithelial Cells with a Modified Urinalysis Protocol. Sci Rep, 2017. 7: p. 40521.
40. 陳苓怡, et al., 臨床評估腎臟功能方法之優缺點. 內科學誌, 2012. 23（1）: p. 34-41.
41. Shulman, N.B., et al., Prognostic value of serum creatinine and effect of treatment of hypertension on renal function. Results from the hypertension detection and follow-up program. The Hypertension Detection and Follow-up Program Cooperative Group. Hypertension, 1989. 13（5 Suppl）: p. I80-93.
42. Yang, W., et al., Association of kidney disease outcomes with risk factors for CKD: findings from the Chronic Renal Insufficiency Cohort （CRIC） study. Am J Kidney Dis, 2014. 63（2）: p. 236-43.
43. Jafar, T.H., et al., Progression of chronic kidney disease: the role of blood pressure control, proteinuria, and angiotensin-converting enzyme inhibition: a patient-level meta-analysis. Ann Intern Med, 2003. 139（4）: p. 244-52.
44. Ejerblad, E., et al., Association between smoking and chronic renal failure in a nationwide population-based case-control study. J Am Soc Nephrol, 2004. 15（8）: p. 2178-85.
45. Grubbs, V., et al., Body mass index and early kidney function decline in young adults: a longitudinal analysis of the CARDIA （Coronary Artery Risk Development in Young Adults） study. Am J Kidney Dis, 2014. 63（4）: p. 590-7.
46. 鈕聖文 and 黃尚志, 馬兜鈴酸相關惡性腫瘤與腎病變之簡介與研究新進展. 內科學誌, 2013. 24（3）: p. 189-197.
47. Chen, S.C., et al., Association of dyslipidemia with renal outcomes in chronic kidney disease. PLoS One, 2013. 8（2）: p. e55643.
48. Lin, J.L., et al., Environmental lead exposure and progression of chronic renal diseases in patients without diabetes. N Engl J Med, 2003. 348（4）: p. 277-86.
49. Yeh, C.H., et al., The risk of diabetic renal function impairment in the first decade after diagnosed of diabetes mellitus is correlated with high variability of visit-to-visit systolic and diastolic blood pressure: a case control study. BMC Nephrol, 2017. 18（1）: p. 99.
50. Vassalotti, J.A., et al., Kidney early evaluation program: a community-based screening approach to address disparities in chronic kidney disease. Semin Nephrol, 2010. 30（1）: p. 66-73.
51. Lu, C., et al., Prevalence and risk factors associated with chronic kidney disease in a Uygur adult population from Urumqi. J Huazhong Univ Sci Technolog Med Sci, 2010. 30（5）: p. 604-10.
52. Chan, T.C., et al., Addressing health disparities in chronic kidney disease. Int J Environ Res Public Health, 2014. 11（12）: p. 12848-65.
53. Garg, A.X., et al., Albuminuria and renal insufficiency prevalence guides population screening: results from the NHANES III. Kidney Int, 2002. 61（6）: p. 2165-75.
54. Perazella, M.A., et al., Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol, 2010. 5（3）: p. 402-8.
55. Scivittaro, V., M.B. Ganz, and M.F. Weiss, AGEs induce oxidative stress and activate protein kinase C-beta（II） in neonatal mesangial cells. Am J Physiol Renal Physiol, 2000. 278（4）: p. F676-83.
56. Gebremichael, Y., et al., Multi-scale mathematical model of drug-induced proximal tubule injury: Linking urinary biomarkers to epithelial cell injury and renal dysfunction. Toxicol Sci, 2017.
57. Chao, C.T., et al., Advanced age affects the outcome-predictive power of RIFLE classification in geriatric patients with acute kidney injury. Kidney Int, 2012. 82（8）: p. 920-7.
58. Skowron, B., et al., Experimental model for acute kidney injury caused by uropathogenic Escherichia coli. Postepy Hig Med Dosw （Online）, 2017. 71（0）: p. 520-529.