Analysis of the Distribution of Urine Color and Its Relationship With Urine Dry Chemical Parameters Among College Students in Beijing, China – A Cross-Sectional Study

Objectives: 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. Methods: 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 L(*)a(*)b(*) values and routine urine chemical examination were performed through digital imaging colorimetry and a urine chemical analyzer, respectively. Results: 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 L(*)a(*)b(*); 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, p < 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. Conclusions: 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.