Liu Jingnan, Cheng Yaxing, Zhang Zijuan, Zhu Lanxin, Pan Liping, Zhou Hang, Zhao Huihui, Ren Xiaoqiao
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Institute of Ethnic Medicine and Pharmacy, Beijing University of Chinese Medicine, Beijing, China.
PLoS One. 2025 May 7;20(5):e0323351. doi: 10.1371/journal.pone.0323351. eCollection 2025.
Urine turbidity is a significant diagnostic marker for early screening of urinary tract infections, kidney stones, and other related conditions. However, current methods for analyzing urine turbidity often rely on subjective assessments. This study aims to investigate the relationship between urine turbidity and the urine color values measured by spectrophotometer, providing an objective quantification method for both urine turbidity and urine color, while also exploring the underlying causes of urine turbidity.
A cross-sectional study was conducted among newly enrolled university students undergoing physical examination in Beijing. Basic demographic information and morning urine samples were collected. Urine turbidity was assessed using human visual evaluation and a urine chemical analyzer, while urine color CIE Lab* (International Commission on illumination) was measured using a spectrophotometer. Routine urine chemical examination was also performed Correlations among urine turbidity, urine color, and urine dry chemical parameters were analyzed.
A total of 1220 participants (68.7% female, mean age: 23.66 years) were included in the study. Spearman correlation analysis showed that urine turbidity was significantly negatively correlated with L* (lightness) and significantly positively correlated with a* (redness) and b* (yellowness). Regression analysis identified L* as the most affected parameter by urine turbidity (standardized coefficient β=-1.030, p < 0.05). Receiver operating characteristic (ROC) analysis showed that L* was highly effective in distinguishing different urine turbidity levels, with L* < 89.165 achieving excellent sensitivity and specificity (AUC = 0.984) and 96% accuracy in identifying turbid urine. In addition, urine turbidity was positively correlated with urine specific gravity, protein, and urine color (p < 0.05), while its relationship with pH was nonlinear. These findings suggest that multiple factors collectively influence urine turbidity.
This study provides a novel and objective approach for assessing urine turbidity, advancing the modernization of urine diagnostic practices in traditional medicine.
尿液浑浊是早期筛查尿路感染、肾结石及其他相关病症的重要诊断标志物。然而，目前分析尿液浑浊度的方法往往依赖主观评估。本研究旨在探究尿液浑浊度与分光光度计测量的尿液颜色值之间的关系，为尿液浑浊度和尿液颜色提供一种客观量化方法，同时探究尿液浑浊的潜在原因。
对在北京新入学参加体检的大学生进行横断面研究。收集基本人口统计学信息和晨尿样本。采用人工视觉评估和尿液化学分析仪评估尿液浑浊度，同时使用分光光度计测量尿液颜色的CIE Lab*（国际照明委员会）值。还进行了常规尿液化学检查，分析尿液浑浊度、尿液颜色和尿液干化学参数之间的相关性。
本研究共纳入1220名参与者（女性占68.7%，平均年龄：23.66岁）。Spearman相关性分析表明，尿液浑浊度与L*（明度）显著负相关，与a*（红色度）和b*（黄色度）显著正相关。回归分析确定L是受尿液浑浊度影响最大的参数（标准化系数β=-1.030，p<0.05）。受试者工作特征（ROC）分析表明，L在区分不同尿液浑浊度水平方面非常有效，L*<89.165时在识别浑浊尿液方面具有出色的灵敏度和特异性（AUC=0.984），准确率达96%。此外，尿液浑浊度与尿比重、蛋白质和尿液颜色呈正相关（p<0.05），而其与pH的关系呈非线性。这些发现表明多种因素共同影响尿液浑浊度。
本研究为评估尿液浑浊度提供了一种新颖且客观的方法，推动了传统医学尿液诊断实践的现代化。
