Comparison of the effects of urinary flow on adjusted and non‐adjusted excretion of heavy metals and organic substances in ‘healthy’ men

Abstract

The effect of variation in urinary flow rate (ml urine min-1, UF) on adjusted and non-adjusted urinary excretion of metals and organic substances was examined in ten 'healthy' men. The effect of UF was found to be eliminated when urinary concentrations of lead (Pb), hippuric acid (HA), delta-aminolevulinic acid (ALA) and coproporphyrin (CP) were adjusted to urinary specific gravity and to UF. Similarly, the effect of UF on inorganic mercury (Hg) was eliminated when the concentration was adjusted to timed excretion and to UF. The effect on zinc (Zn) and copper (Cu) was eliminated when adjusted to urinary creatinine (Cn) and to UF. The difference in the effect of UF on the adjusted urinary excretion was explained by the effect of UF on timed excretion of substance, which was ranked in the following order: Pb, HA, total urinary solutes, ALA and CP; Zn; Cu; Cn; and Hg. It was concluded that 'UF-adjusted concentration' is applicable to measurement of all substances in urine as a method of minimizing UF effects, while the other three adjustment methods have only limited uses.

Assessing urinary flow rate, creatinine, osmolality and other hydration adjustment methods for urinary biomonitoring using NHANES arsenic, iodine, lead and cadmium data

“…1 ) significant ( p < 0.001) negative, log-linear relationships with UFR. This confirmed previous findings [ 20 ]. The weak (Pb, Cd, As IMM and I in Fig.…”

“…hydration, at that time. Strong positive Spearman’s correlation coefficients (r s ) have been reported between ERs of urinary analytes and UFR [ 20 , 21 ], indicating that the adjustment in Eq. 2 is not theoretically robust.…”

“…where Araki’s b values were derived for a number of analytes using multiple voids from single individuals subjected to water loading and water restrictive conditions. This UFR adjustment was found to be more effective than ER, creatinine and SG adjustment in removing UFR-dependent variation from adjusted urinary analyte concentrations [ 20 , 21 ]. For datasets, such as NHANES, that do not contain extensive analyte concentration data for multiple voids from single individuals, a previously reported iterative method [ 22 ] may be used to calculate population-level Araki’s b values by optimising appropriate performance criteria.…”

“…Suggested criteria are summarised in Table 1 . For this work, the primary assessment criterion was the extent of removal of systematic dependence on UFR of adjusted urinary analyte concentrations (Criterion A, Table 1 ) as has been used previously [ 20 , 22 , 23 ]. Additionally, since blood biomonitoring is the preferred measure of exposure for several chemicals, notably lead (Pb), and is not as susceptible to the same level of variation as urinary concentrations [ 24 ], the correlation between adjusted urinary concentrations and blood concentrations was also used as a secondary assessment criterion (Criterion B, Table 1 ) and has been previously explored [ 25 ] for Pb.…”

On the distribution of urine output in normally cycling women

“…The requirements of users of such assays are rather different from those of a woman monitoring her own fertility every day. In other cases, such as the monitoring of heavy metal exposure by urinalysis, not only is correction advocated, but it is demonstrated that the best means of achieving this is to correct using the urine production rate (Araki et al, 1986a;Araki et al, 1986b;Weaver et al, 2014). We have shown previously that urine production rate correction of E1G concentrations is necessary if the intention is to obtain an estimate of the corresponding serum analyte concentration (Brown et al, 2018).…”