Liu Jingnan, Zhang Zijuan, Pang Xiaohan, Cheng Yaxing, Man Da, He Xinyi, Zhao Huihui, Zhao Ruizhen, Wang Wei
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Institute of National Medicine, Beijing University of Chinese Medicine, Beijing, China.
Front Nutr. 2021 Oct 4;8:719260. doi: 10.3389/fnut.2021.719260. eCollection 2021.
The objective of this study was to provide a new classification method by analyzing the relationship between urine color (Ucol) distribution and urine dry chemical parameters based on image digital processing. Furthermore, this study aimed to assess the reliability of Ucol to evaluate the states of body hydration and health. A cross-sectional study among 525 college students, aged 17-23 years old, of which 59 were men and 466 were women, was conducted. Urine samples were obtained during physical examinations and 524 of them were considered valid, including 87 normal samples and 437 abnormal dry chemistry parameters samples. The urinalysis included both micro- and macro-levels, in which the CIE Lab values and routine urine chemical examination were performed through digital imaging colorimetry and a urine chemical analyzer, respectively. The results showed that L (53.49 vs. 56.69) in the abnormal urine dry chemistry group was lower than the normal group, while b (37.39 vs. 33.80) was greater. Urine color can be initially classified based on shade by grouping b. Abnormal urine dry chemical parameter samples were distributed more in the dark-colored group. Urine dry chemical parameters were closely related to Ucol. Urine specific gravity (USG), protein, urobilinogen, bilirubin, occult blood, ketone body, pH, and the number of abnormal dry chemical parameters were all correlated with Ucol CIE Lab; according to a stepwise regression analysis, it was determined that more than 50% of the variation in the three-color space values came from the urine dry chemical parameters, and the b value was most affected by USG (standardized coefficient β = 0.734, < 0.05). Based on a receiver operating characteristic curve (ROC) analysis, Ucol ≥ 4 provided moderate sensitivity and good specificity (AUC = 0.892) for the detection of USG ≥ 1.020. Our findings on the Ucol analysis showed that grouping Ucol based on b value is an objective, simple, and practical method. At the same time, the results suggested that digital imaging colorimetry for Ucol quantification is a potential method for evaluating body hydration and, potentially, health.
本研究的目的是通过基于图像数字处理分析尿液颜色（Ucol）分布与尿液干化学参数之间的关系，提供一种新的分类方法。此外，本研究旨在评估Ucol评估身体水合状态和健康状况的可靠性。对525名年龄在17 - 23岁的大学生进行了一项横断面研究，其中男性59名，女性466名。在体检期间采集尿液样本，其中524份被认为有效，包括87份正常样本和437份干化学参数异常样本。尿液分析包括微观和宏观层面，其中CIE Lab值通过数字成像比色法进行测定，常规尿液化学检查则通过尿液化学分析仪进行。结果显示，异常尿液干化学组的L值（53.49对56.69）低于正常组，而b值（37.39对33.80）则更高。尿液颜色可根据b值分组初步按深浅分类。异常尿液干化学参数样本在深色组中分布更多。尿液干化学参数与Ucol密切相关。尿比重（USG）、蛋白质、尿胆原、胆红素、隐血、酮体、pH值以及异常干化学参数的数量均与Ucol CIE Lab相关；根据逐步回归分析，确定三色空间值中超过50%的变异来自尿液干化学参数，且b值受USG影响最大（标准化系数β = 0.734，P < 0.05）。基于受试者工作特征曲线（ROC）分析，Ucol≥4对于检测USG≥1.020具有中等敏感性和良好特异性（AUC = 0.892）。我们对Ucol分析的结果表明，基于b值对Ucol进行分组是一种客观、简单且实用的方法。同时，结果表明用于Ucol定量的数字成像比色法是评估身体水合状态以及潜在健康状况的一种潜在方法。
