Liu J, Cheng Y, Zhang Z, Zhu L, Pan L, Zhou H, Zhao H, Ren X. A novel method and classification criteria for analyzing urine turbidity and its relationship with urine dry chemical parameters.
PLoS One 2025;
20:e0323351. [PMID:
40334222 PMCID:
PMC12058184 DOI:
10.1371/journal.pone.0323351]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Accepted: 04/08/2025] [Indexed: 05/09/2025] Open
Abstract
BACKGROUND
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.
METHODS
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 L*a*b* (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.
RESULTS
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.
CONCLUSION
This study provides a novel and objective approach for assessing urine turbidity, advancing the modernization of urine diagnostic practices in traditional medicine.
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