Variability of urinary metal in short-, mid-, long-term periods and its optimal sampling strategy: A novel...

The health effects of metals are well-documented, but relying on single urine samples may inadequately reflect short-, mid-, or long-term exposure, leading to potential misclassification. Variability in urinary metal concentrations and its implications for exposure assessment across different timeframes and epidemiological study designs remain underexplored. Identifying optimal sampling strategies and minimum sample sizes is crucial for exposure assessment of enhancing environmental health research. In a two-year repeated-measures study of healthy adults across 4 visits (2019-2021), first morning void (FMV) urine samples were collected to measure 22 metals. Variance apportionments and intraclass correlation coefficients (ICCs) evaluated metal reproducibility over short-, mid-, and long-term intervals. Surrogate category analyses were conducted to determine the minimum sample size needed for accurate exposure classification. For the long-term variability, four epidemiological scenarios were considered and compared to assess their ability in improving exposure classification. In total, 3,541 FMV samples were collected from 60 participants during all visits. We observed daily variations in metal levels at both the group and individual levels, with fluctuations ranging from several-fold to several tens of times. Co and Zn showed the highest reproducibility, requiring only 2-3 samples to accurately classify exposure across short-, mid-, and long-term periods. Other metals, such as As, Cu, Rb, Sr, Cs, and V, demonstrated good predictive ability, requiring approximately 5 and 10 samples to characterize exposure levels over one month and two years. Conversely, Al, Cr, Sb, and Se consistently failed to meet specificity thresholds of 0.7. Study designs that account for "visits apart" and involve subjects sampling on the same day performed better in exposure classification. Future studies examining the health effects of urinary metals with high temporal variability should carefully consider sampling dates, intervals, and sample size when designing their study to ensure accurate exposure classification across the population.