Work

Temporal variability of urinary microcystin-LR concentrations in healthy adult men: Recommendations for sampling strategy

Year: 2026

Type: article

Abstract: INTRODUCTION: Microcystin-LR (MC-LR) is widely distributed in the environment, and humans can be exposed to it through a variety of routes. OBJECTIVES: This study aimed to characterize the exposure levels and temporal variability of MC-LR in the urine of a healthy population. METHODS: Spot urine samples were collected from 20 healthy males over five consecutive days in both the cold and warm seasons. Urinary MC-LR was measured by UPLC-MS/MS and adjusted using four dilution methods. Model fits across correction methods were compared using the Akaike Information Criterion (AIC). Variability was assessed by intraclass correlation coefficients (ICCs). Sensitivity and specificity analyses were conducted to assess the accuracy of single and multiple randomly selected spot or first morning void (FMV) urine samples in identifying individuals with high MC-LR exposure. RESULTS: MC-LR was detected in 66.12% of urine samples, with significantly higher concentrations observed in the cold season than in the warm season (P < 0.001). The AIC results showed that the creatinine-adjusted model had an optimal fit. There was poor reproducibility (ICC < 0.40) of spot, FMV, and 24-hour urine MC-LR concentrations in both the long and short terms. Sensitivity and specificity analyses showed that the sensitivity for identifying individuals in the high-exposure group increased significantly with the number of urine samples included in the analysis. The minimum number of samples (k) required to estimate the participant-specific mean MC-LR concentration within 20% of the "true" value ranged from 3 in the warm season to 18 in the cold season. CONCLUSION: Our study suggests that predicting an individual's long-term average MC-LR exposure level from a single spot urine sample is likely to result in misclassification of exposure. Collecting two or more urine samples from individuals at different time points substantially improves exposure assessment accuracy.

Source: Journal of Advanced Research

DOI: 10.1016/j.jare.2026.02.017