Analysis of salivary components as non-invasive biomarkers for monitoring chronic kidney disease

Bhaskar Navaneeth, M. Suchetha
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引用次数: 6

Abstract

Saliva, a valuable source of biochemical information, is a potential diagnostic substance that helps to identify many diseases. Studies have revealed that saliva tests help identify many diseases. Saliva test has excellent advantages over blood test as the former can be collected non-invasively using simple equipment. This paper explores how salivary components can be used as diagnostic tool to identify chronic kidney disease (CKD). Experimental analysis was conducted to assess the levels of salivary components in whole saliva of CKD patients in contrast with healthy people. Urea and creatinine are the most accepted biomarkers of CKD. The correlation between creatinine and urea levels in human saliva and blood were analysed. Unstimulated saliva flow rate and pH levels were also monitored in this study. The results obtained from this study give concrete evidence that there is a positive correlation between creatinine and urea levels in blood and saliva. From the derived regression line equations, serum urea and creatinine values can be predicted from salivary values. Receiver operating characteristics (ROC) performance analysis was performed and area under the curve (AUC) of 0.95 and 0.89 was obtained for salivary creatinine and urea.
唾液成分作为监测慢性肾脏疾病的非侵入性生物标志物的分析
唾液是一种宝贵的生化信息来源,是一种潜在的诊断物质,有助于识别许多疾病。研究表明,唾液测试有助于识别许多疾病。唾液检测相对于血液检测有很大的优势,因为唾液检测可以使用简单的设备进行无创采集。本文探讨了如何利用唾液成分作为诊断工具来识别慢性肾脏疾病(CKD)。通过实验分析来评估CKD患者与健康人全唾液中唾液成分的水平。尿素和肌酐是最被接受的CKD生物标志物。分析了人唾液和血液中肌酐和尿素水平的相关性。本研究还监测了未受刺激的唾液流速和pH值。本研究的结果为血液和唾液中的肌酐和尿素水平之间存在正相关关系提供了具体证据。从推导的回归线方程,血清尿素和肌酐值可以预测唾液值。进行受试者工作特征(ROC)分析,唾液肌酐和尿素的曲线下面积(AUC)分别为0.95和0.89。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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