Multi-matrix biomonitoring approach to assess exposure to metals and trace elements in the Lebanese population and associations with drinking water consumption.

This study is the first attempt to assess exposure to metals and trace elements in subgroups of the Lebanese population using a multi-matrix biomonitoring approach. Concentrations of 11 metals and trace elements (aluminum (Al), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), selenium (Se), uranium (U), zinc (Zn)) were measured in urine, hair and toenails. Biological levels were compared according to age, sex, smoking status, socioeconomic status, geographical area and drinking water source. While most urinary and toenail concentrations of metals and trace elements were not different between males and females, measured concentrations of several elements in hair were higher in females compared to males. Urinary concentrations of some metals (Al, Cu, Se and Zn) were higher in children compared to teenagers and adults. Hair and toenail concentrations of several elements (As, Cd, Pb, Mn, Se in hair and toenails plus Al, Fe in toenails) were also significantly higher in children compared to teenagers and/or adults. Smoking status had no influence on metal and trace element concentrations. Levels of Cd, Pb and Mn were also higher in samples from subgroups with lower economic status (Cd and Pb in the three matrices and Mn in hair and toenails). Very few correlations were identified between sources of drinking water and urine, hair, and toenail concentrations of metals and trace elements. However, a correlation was observed between hair and toenails levels of As, Cd and Pb. Overall, results highlight that a special attention should be given to metal and trace element exposure in this population (including Pb, As, Cd, Mn, and Se). It could be relevant to scale up this kind of investigation with a large human biomonitoring initiative in the Lebanese population in order to generalize results, and assess trends over time.

Study of selenium intake and disposition in various matrices based on mathematical algorithms derived from pooled biomonitoring data.

Biomonitoring is increasingly used to assess exposure to selenium (Se) in the population. However, there is little harmonization among protocols used in the different studies (varying biological matrices, differences in expression of results (concentrations versus amounts, units)). This makes inter-comparison of biomonitoring results across studies difficult. From a public health risk perspective, it also becomes challenging to estimate baseline levels in biological matrices for populations exposed by various sources. The aim of this study was thus to perform a systematic analysis of the relationship between Se intakes and biological concentrations based on published data. Inclusion and exclusion criteria were used and led to select 75 published biomonitoring data in humans from an extended review of Se biomonitoring studies. This represents 8 628 individuals who provided biological samples aiming at documenting Se exposure and/or Se concentrations in two or more biological matrices. Mathematical algorithms that relate Se intakes to biological concentrations and establish matrix-to-matrix associations were derived from these pooled biomonitoring data. Logarithmic regressions showed good correlations between Se intakes and whole blood concentrations (R(2)=0.884), plasma concentrations (R(2)=0.863) and urinary excretion rates (R(2)=0.958). Blood and plasma concentrations were also strongly related (R(2)=0.874), as were whole blood concentrations and urinary excretion rates (R(2)=0.953). The interpretation of the log-regression coefficients allowed illustrating Se physiology. Se concentrations in plasma tend to plateau when daily intake exceed 150 µg/d, whereas Se in urine increases rapidly above this threshold. The application of the algorithms to other independent data sets in order to reconstruct past Se intakes confirmed that interpretation of results on the basis of Se in integuments may be misleading if external contamination is not avoided. This approach based on pooled data covered a wide range of exposure and the large number of data integrated increased the level of confidence of results.