e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 9 ( 2 0 1 5 ) 70–76

Available online at www.sciencedirect.com

ScienceDirect journal homepage: www.elsevier.com/locate/etap

Short communication

Coverage intervals for trace elements in human scalp hair are site specific E. Tamburo ∗ , D. Varrica, G. Dongarrà Dip. Scienze della Terra e del Mare (DiSTeM), via Archirafi 36, 90123 Palermo, Italy

a r t i c l e

i n f o

a b s t r a c t

Article history:

Coverage intervals for trace elements in human scalp hair commonly provide the basis for

Received 13 June 2014

interpreting laboratory results and also in comparative decision-making processes regarding

Received in revised form

exposure risk assessment. This short communication documents, by some examples, that

30 October 2014

those computed for human hair are to be considered site specific, as they reflect local envi-

Accepted 4 November 2014

ronmental conditions; also each geographic area has a typical profile of hair elemental

Available online 18 November 2014

composition of its inhabitants. Therefore, the levels of trace elements in hair are not strictly

Keywords:

identification of anomalous environmental exposures are requested or even in detecting

Coverage intervals

physiological disorders.

comparable between different areas of the world. This issue is particularly relevant when

Hair composition

© 2014 Elsevier B.V. All rights reserved.

Biomonitoring Risk assessment Trace elements

1.

Introduction

In human biomonitoring (HBM), aimed to assess the environmental exposure to metals or toward the analysis of metabolic disorders and nutritional status, extensive use is made of coverage intervals. They reflect the variability of a single chemical element in a well-defined human matrix (blood, urine, hair, nails, sweat, etc.), describing the concentration range observed in a statistically significant sample of reference subjects considered “normal” or not particularly affected by occupational exposure or personal causes of risk. The term “normal” is generally not associated with the health status but it is indicative of the background or baseline level for a population.



Corresponding author. Tel.: +39 9123861644. E-mail address: [email protected] (E. Tamburo).

http://dx.doi.org/10.1016/j.etap.2014.11.005 1382-6689/© 2014 Elsevier B.V. All rights reserved.

Coverage interval is commonly defined as that interval with a given level of confidence enclosing a specified percentage (usually 95%) of the values of a population from which the reference subjects have been drawn. Estimation of coverage intervals takes into account the type of data distribution (normal, log-normal), the confidence intervals of the extreme limits and the approach (parametric or non-parametric) to be used (Poulsen et al., 1997). Coverage intervals commonly provide the basis for interpreting laboratory results and, if reliable, may become important tools in comparative decision-making processes regarding exposure risk assessment and also forensic and clinic considerations. Conversely, embarking on interpretation of HBM data in absence of reference data is meaningless.

e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 9 ( 2 0 1 5 ) 70–76

Hair represents a pathway of excretion of substances from the human body. It accumulates trace elements in higher concentrations than serum, blood or urine and, being a painless, non-invasive method of biomonitoring, allows reliable and widespread screening analysis (ATSDR, 2001; Gellein et al., 2008). In HBM, multielemental analysis of human hair (often indicated as HMA) is widely used as a mean for identifying and quantifying exposure to trace metals and metalloids, in the reasonable belief that hair of unexposed and healthy individuals generally contains trace elements within a well-defined concentration range; when the concentration of a chemical element falls outside the computed coverage interval, this occurrence is often taken as indication of a possible anomaly for which an individual or group of subjects deserves special attention and investigation. However, it is worth keeping in mind that metal concentrations in hair depend on several factors, some of which are intrinsic to subjects: gender, hair color, eating habits, age and lifestyle. Furthermore, reliable coverage intervals for comparative purposes are still scarce (Harkins and Susten, 2003; Barbosa et al., 2005; Mikulewicz et al., 2013). Even though coverage intervals have been properly computed taking into account the above confounding factors affecting their reliability, the extreme levels of an interval cannot be assumed tout court as cut-off levels within which the concentration of a chemical element has to be considered normal and, on the contrary, anomalous when it is above the upper limit of the confidence interval. It is important to first evaluate whether the interval is adequate to represent the ˜ individual or group of subjects to be tested (Pena-Fernández et al., 2014). Our opinion is that coverage intervals cannot always be extended to environmental contexts distinct from those for which they were calculated, because they are strongly affected by geographical differences in environmental metal concentrations leading to different levels for accumulation of chemical elements in hairs. This possibility is very often ignored, especially in the commercial use of hair analysis where each laboratory uses own reference intervals, often built on the base of results from specimens handled by the lab. Therefore it may be expected that a given hair trace element level is considered low by some laboratories, normal by others and high by others. The purpose of this short communication is to document by some examples that coverage intervals computed for human hair are site specific. Here the reference distributions of some elements in children of the same age (11–14 years old) residing in sites characterized by different environmental conditions, such as volcanic, mining, urban and industrial areas, are used to discuss the effects this peculiarity may have in practice. Whether the observed differences are environmental acceptable or suggesting risk of some adverse health conditions has not been evaluated here as beyond the scope of this work.

2.

71

Materials and methods

Donors were chosen from children, 11–14 year old, of both gen˜ ders, using the following exclusion criteria (Pena-Fernández et al., 2014): -

non-Caucasian ethnicity; age over 14 years; habitual use of cigarettes; recent surgery or orthodontic treatment; colored hair or use of hairstyling products.

Each participant to this study was therefore within the defined characteristics. Coverage intervals (at a confidence level of 0.95) were computed with lower and upper limits fixed at 2.5th and 97.5th percentiles of the interval, according to the non-parametric approach recommended by the International Union of Pure and Applied Chemistry (IUPAC). The data considered for the comparative purpose have been taken from literature (Dongarrà et al., 2011, 2012; Varrica et al., 2014a,b). They regard hair samples collected within the urban area of Palermo (PA, Sicily), in several small towns located around Mt. Etna (ET, Sicilia) and in Sardinia (SAR) where sampling was conducted in two different sites: at Iglesias, close to the mining district of Sulcis-Iglesiente and at the island of Sant’Antioco. Unpublished data (Varrica, pers. com.) from the industrial area of Gela (GL, Sicily) have also been used. This choice is due to the fact that the hair samples were analyzed in the same laboratory with the same analytical procedure. The town of Palermo (PA) is located in NW Sicily, facing the Tyrrhenian Sea and surrounded by mountains reaching 500–1000 m above sea level. Lithologically, the study area consists of sedimentary rocks: limestone, clay, marly-clay and white or yellow Quaternary biocalcarenite. Potential local pollutants are limited to emissions from vehicular traffic, house heating and small manufacturing industries. Mt. Etna (ET) is an active volcano characterized by persistent degassing, with frequent explosions and lava flows from summit craters, producing significant amounts of ash. The volcano is an important source of trace elements in the local environment, contributing around 16% to the global budget during eruptive periods and around 2% during normal degassing (Gauthier and Le Cloarec, 1998). Such contributions are significantly higher than local emissions of anthropogenic origin (Aiuppa et al., 2006). The town of Gela is located in south Sicily along the Mediterranean coastline and, from a geological point of view, it lies on sedimentary rocks represented by limestone, clay, marly-clay, white or yellow quaternary biocalcarenites and some gypsum. The town of Gela suffers a severe anthropogenic pressure, due to the petrochemical plant located close to the town, several mechanical industries related to the refinery, numerous greenhouses for the production of early fruits and vegetables and high traffic density. Southwestern Sardinia (Iglesias and Sant’Antioco study areas) is one of the oldest and most important polymetallic mining areas in Italy. The geology is largely dominated

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by Palaeozoic lithotypes, of sedimentary and igneous origin. The ubiquitous presence of large outcrops of sulfide and oxide ores, as well as the products of the long lasting mining activity, characterize the whole district with unusual concentrations of metals and metalloids. The largest concentration of mine wastes in Sardinia is close to the town of Iglesias. Sant’Antioco Island has different geologic and environmental features, being made up of pyroclastic volcanic rocks and lacking of significant base metal mineralization events.

3.

Results and discussion

Fig. 1 shows the computed coverage intervals for PA, ET, GL and SAR along with the subsets W-Etna and Iglesias (Appendices A.1 and A.2). It may be noted that for many elements they are clearly not equivalent for the different sites, but rather, because of the different elemental variability, the interval of an element for a site extends far beyond that one calculated for another site. This suggests that a more or less large number of individuals from one site may represent an extreme value of the distribution calculated for another site or, put differently, what is normal for one site may be categorized as abnormally high for another site. Therefore, it was decided to calculate the number of samples of each element that (at each site) exceed the 97.5th percentile (the upper coverage limit) of concentrations calculated at the other sites. Let 15% be the minimum number of samples exceeding the 97.5th percentile considered of significance for the purpose of the present note. If we take into account as reference distribution Mt. Etna’s sample group (total number of samples 367) it is possible to note that: • 57% of the analyzed subjects residing in Gela exceed the 97.5th percentile of the concentrations of Sr; • 18–31% of the analyzed subjects residing in Sardinia exceed the 97.5th percentile of the concentrations of Zn (18%), Se (25%) and Cd (31%); if only the residents of the mining district of Sulcis-Iglesiente were taken into account (59 samples) the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 97.5th percentile, would rise to five, Pb(19%), Ba (33%), Se (34%), Zn (43%) and Cd (76%) • 15–28% of the analyzed subjects residing within the urban area of Palermo exceed the 97.5th percentile of the concentrations of Sr (15%), Li (19%), Ni (22%), Mo (26%), Cd (28%). If we take into account as reference distribution Gela’s sample group (total number of samples 134) it is possible to note that: • 61% of the subjects analyzed residing in the Etna area exceed the 97.5th percentile of the concentrations of V and 16% exceed the 97.5th percentile of the concentrations of U; if only the residents of the western sector of the volcano were taken into account (155 samples) the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 95th percentile, would rise to three, including V (77%) Li (28%) and Rb (27%), whilst uranium shows a preponderance in the other sectors.

• 23 and 32% of the subjects analyzed residing in Sardinia exceed the 97.5th percentile of the concentrations of V and Cd, respectively. If only the residents of the mining district of Sulcis-Iglesiente were taken into account the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 97.5th percentile of the concentrations, would result the following two: Zn (17%) and Cd (79%); vanadium would remain a peculiarity of Sant’Antioco (35%); • 19–39% of the subjects analyzed residing within the urban area of Palermo exceed the 97.5th percentile of the concentrations of Ni (19%), Mo (24%), Cd (26%) and Li (39%). If we take into account as reference distribution Palermo’s sample group (total number of samples 136) it is possible to note that: • 15% and 64% of the analyzed subjects residing in the Etna area exceed the 97.5th percentile of the concentrations of As and V, respectively; if only the residents of the western sector of the volcano were taken into account the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 97.5th percentile of the concentrations would rise to three, including As (20%), Mn (21%) and V(80%); • 28–50% of the analyzed subjects residing in the town of Gela exceed the 97.5th percentile of the concentrations of As (28%), Sr (45%), Mn (50%); • 15–46% of the analyzed subjects residing in Sardinia exceed the 97.5th percentile of the concentrations of Zn (15), Cd (23), V (33), As (38) and Se (46); if only the residents of the mining district of Sulcis-Iglesiente were taken into account the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 97.5th percentile, would rise to the following six: Pb (17%), Ba (29%), Zn (34%), As (41%), Se (55%) and Cd (57). If we take into account as reference distribution Sardinia’s sample group (number of samples 143) it is possible to note that: • 33–78% of the subjects analyzed residing in Gela exceed the 97.5th percentile of the concentrations of Al (33%), Sr (77%), and Mn (78%); • 25–71% of the analyzed subjects residing within the urban area of Palermo exceed the 97.5th percentile of the concentrations of Mn (25%), Ni (25%), Rb (28%), Mo (32%), Al (44%), Li (45%) and Sr (71%); • 16–28% of the analyzed subjects residing in the Etna area exceed the 97.5th percentile of the concentrations of Li (16%), Rb (16%), Sb (19%), Al (20%), U (22%), Mn (27%), Sr (27%) and V (28%); if only the residents of the western sector of the volcano were taken into account the number of chemical elements for which at least 15% of subjects exhibits a surplus, with respect to the 97.5th percentile, would also include U (15%). Some of these discrepancies are the direct result of the different residence sites of donors. The highest metal levels are thus not an indication of anomalous internal incorporation

e n v i r o n m e n t a l t o x i c o l o g y a n d p h a r m a c o l o g y 3 9 ( 2 0 1 5 ) 70–76

73

Fig. 1 – Comparison of the coverage intervals observed at the study sites (ET: Mt. Etna; GL: town of Gela; PA: town of Palermo; SAR: Southwestern Sardinia). W-Etna and Iglesias are subsets of ET and SAR, respectively. Data points from literature are: (䊉) Senofonte et al. (2000); () Pereira et al. (2004); () Barbieri et al. (2011).

but rather derive from different specific local exposures. Strontium anomalies are due to the presence of carbonate rocks as well as Al, Cr, Li and Rb reflect the influence of crustal material (Dongarrà et al., 2011). The presence of Cr, Mn, Pb, Sb, U and V, at the Etna site, is attributable to the activity of the volcano and in particular to the direct influence of fallout and leaching of basaltic material (Varrica et al., 2014a). As, Ba, Cd, Cu, Pb, Se and Zn, in Sardinia reflect the presence of widespread mining activities (Varrica et al., 2014b). Sb, Cr and Ni have been related to vehicular traffic or oil combustion (Dongarrà et al., 2011). To further verify the specificity of the reference intervals we calculated a global coverage interval for each analyte (considering all the data together) and then we computed how many subjects were outside these intervals, for each site. It was observed that the new global coverage intervals hide all of the observed differences. Even though we reduce to 10% the minimum number of samples exceeding the 97.5th percentile considered of significance for the purpose of the present note, only 5 elements showed higher percentages: Mo (14%), Li (13%) and Ni (11%) at Palermo, Sr (14%) at Gela and Cd (13%) for subjects residing in Sardinia. Also shown in Fig. 1 are some data points regarding average concentrations of trace elements in hair samples reported in literature, to give the reader a further evidence of

conflicting results if appropriate coverage intervals are not used. Some notable examples are: aluminum average concentration for the urban area of Rome, as reported by Senofonte et al. (2000), well compares with the data from the towns of Palermo and Gela but not with those of Sardinia and Mt. Etna; Mn average concentrations from hair samples collected from the mining areas of S. Domingo (Portugal) and Bolivian Altiplano (Pereira et al., 2004; Barbieri et al., 2011) fall within the intervals observed in Sardinia and Palermo but outside those for Gela and Mt. Etna. From these results it follows that the geochemical environment is an important parameter in the evaluation of metals content in hair. The variable composition of soil, as well as anthropogenic activities, can result in different concentrations of trace metals in airborne particulate matter, groundwater and vegetation which in turn are capable of influencing the human intake of metals.

4.

Conclusions

Hair analysis is widely accepted as giving a good reflection of direct and indirect exposure to metals and metalloids and specific literature provides several examples of both scientific

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and commercial use of hair analysis. Coverage intervals are employed to interpret the obtained results. It would be ideal if they were unique with full overlapping, even when computed from people living in different environmental contexts. The findings presented here clearly demonstrate that this is rarely the case and the use of inappropriate reference intervals can lead to misleading interpretations. Therefore, occurrences of local variations in environmental metal concentrations (due to geological factors or industrialization) may contribute in comparative studies to substantial limitations with regard to interpretation of results and to mask potential risk of exposure to toxic elements. The data demonstrate that the reference intervals are valid only for the studied area and can only be extended to other areas with different characteristics with difficulty. This is particularly relevant when it is needed to draw conclusions regarding particular exposures to metals and metalloids of individuals or in medical implications dealing with health conditions. In these cases, the test result from one individual is compared with the reference interval computed for a population that was probably exposed to different environmental conditions giving, if above the limits of the reference interval, what is generally named a false positive. This is also true when commercial laboratories are involved in such evaluations or in clinical diagnoses, and it may also be of value in forensic, civillegal and risk assessment. This does not mean that coverage intervals are of doubtful merit but they must be considered site-specific as they reflect local environmental conditions. Nevertheless, a positive note is that when metal concentrations data from hair analysis are used as a screening procedure where environmental exposures are of concern, the coverage intervals contribute to distinguish the various sites,

ET

N

Al As Ba Cd Co Cr Cu Li Mn Mo Ni Pb Rb Sb Se Sr U V Zn

359 365 352 345 362 362 364 360 363 367 359 358 361 358 364 367 362 361 358

Mean

Std. Dev.

5.1 2.5 0.03 0.02 0.94 0.73 0.02 0.02 0.07 0.13 0.15 0.10 17 13 0.04 0.08 0.89 2.4 0.05 0.04 0.27 0.27 0.84 0.80 0.02 0.03 0.04 0.03 0.52 0.19 3.2 2.8 0.05 0.05 0.48 0.47 203 48

Median CV (%)

4.9 0.03 0.81 0.01 0.02 0.12 13 0.01 0.30 0.04 0.19 0.58 0.01 0.02 0.52 2.4 0.04 0.31 199

Coverage interval

50 0.05–10.8 79 0.001–0.08 78 0.005–2.5 101 0.0003–0.08 190 0.003–0.45 67 0.02–0.37 76 7.0–66 206 0.002–0.26 268 0.01–9.8 93 0.0003–0.16 101 0.04–0.84 95 0.001–3.0 147 0.001–0.11 82 0.01–0.10 37 0.21–1.05 88 0.22–9.9 89 0.004–0.18 98 0.06–1.8 24 129–323

highlighting anomalies of geographical, geological and anthropic characteristics. This allows us to identify and document the impact on humans of critical conditions due to industrial plants and natural phenomena, for example the presence of active volcanoes, mine sites or even the simple difference of lithology.

Conflict of interest The authors declare that there are no conflicts of interest.

Transparency document The Transparency document associated with this article can be found in the online version.

Acknowledgement The research was financially supported by Miur(funds FFR 2012 ex 60%).

Appendix A.1. Basic statistics of metal and metalloids (in g/g dry-weight basis) in hair samples from the study populations (ET: Mt. Etna; GL: town of Gela). N indicates the number of analyzed samples; coverage interval and coverage uncertainty computed at 0.95 level of confidence; the coefficient of variation (CV%) has been calculated as: CV(%) = 100 × standard dev./mean.

Coverage GL uncertainty 0.022 0.022 0.023 0.023 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022

Al As Ba Cd Co Cr Cu Li Mn Mo Ni Pb Rb Sb Se Sr U V Zn

N

132 132 132 132 132 134 132 132 134 134 132 132 134 132 132 134 134 134 134

Mean

Std. Dev.

6.5 3.4 0.05 0.05 1.5 1.2 0.02 0.02 0.11 0.15 0.10 0.09 20 25 0.02 0.01 1.3 0.99 0.07 0.04 0.31 0.25 0.75 1.0 0.01 0.02 0.03 0.03 0.61 0.37 13 9.1 0.04 0.04 0.11 0.06 211 67

Median CV (%)

5.8 0.03 1.35 0.01 0.05 0.07 13 0.02 1.03 0.06 0.22 0.43 0.01 0.02 0.53 11 0.03 0.09 199

Coverage interval

52 2.7–12 112 0.002–0.17 76 0.28–4.3 105 0.001–0.07 136 0.01–0.42 92 0.02–0.22 123 7.2–73 69 0.004–0.06 76 0.24–2.9 60 0.03–0.12 80 0.001–0.92 137 0.12–2.3 124 0.003–0.03 96 0.008–0.09 62 0.281.4 69 1.6–27 92 0.002–0.09 55 0.03–0.21 32 114–312

Coverage uncertainty

0.037 0.037 0.037 0.036 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037

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Appendix A.2. Basic statistics of metal and metalloids (in g/g dry-weight basis) in hair samples from the study populations (PA: town of Palermo; SAR: Southwestern Sardinia, including Iglesias and Sant’Antioco study areas). N indicates the number of analyzed samples; coverage interval and coverage uncertainty computed at 0.95 level of confidence; the coefficient of variation (CV%) has been calculated as: CV(%) = 100 × standard dev./mean. PA

N

Al As Ba Cd Co Cr Cu Li Mn Mo Ni Pb Rb Sb Se Sr U V Zn

131 130 130 132 136 131 135 131 133 135 131 129 129 135 134 132 130 132 130

Mean

Std. mediana CV Coverage Coverage SAR Dev. (%) interval uncertainty

6.3 3.6 6.3 0.01 0.01 0.002 1.4 0.65 1.43 0.05 0.05 0.03 0.19 0.33 0.08 0.15 0.14 0.11 22 10 20 0.14 0.22 0.04 0.38 0.27 0.35 0.20 0.37 0.06 0.59 0.53 0.46 1.0 0.71 0.82 0.02 0.02 0.01 0.03 0.02 0.02 0.40 0.20 0.37 6.5 3.2 6.6 0.03 0.03 0.02 0.09 0.05 0.08 191 61 180

59 164 47 102 176 109 54 147 82 212 88 75 105 102 88 49 126 59 31

0.01–13 0.0003–0.03 0.18–2.7 0.0004–0.16 0.01–1.2 0.001–0.48 9.1–60 0.001–0.56 0.002–0.91 0.0001–1.8 0.036–1.8 0.28–3.0 0.0002–0.06 0.0002–0.11 0.13–1.3 1.1–13 0.0001–0.11 0.001–0.21 97–329

0.037 0.037 0.037 0.037 0.036 0.037 0.036 0.037 0.036 0.036 0.037 0.037 0.037 0.036 0.036 0.037 0.037 0.037 0.037

Al As Ba Cd Co Cr Cu Li Mn Mo Ni Pb Rb Sb Se Sr U V Zn

N

142 143 140 142 141 141 139 141 142 141 141 140 143 142 140 142 143 142 138

Mean Std. Dev. mediana CV (%)

4.2 1.4 0.05 0.03 1.2 0.94 0.09 0.15 0.08 0.10 0.14 0.12 16 8.6 0.02 0.01 0.22 0.12 0.04 0.03 0.24 0.19 1.0 1.2 0.01 0.01 0.02 0.02 0.87 0.35 1.9 1.2 0.02 0.02 0.19 0.13 257 89

4.1 0.04 0.84 0.01 0.03 0.12 13 0.02 0.20 0.04 0.17 0.48 0.01 0.01 0.78 2 0.02 0.15 231

Coverage Coverage interval uncertainty

34 1.7–7.3 68 0.005–0.15 80 0.11–3.50 161 0.0003–0.48 131 0.01–0.39 80 0.04–0.47 53 7.4–37 70 0.003–0.04 55 0.03–0.52 70 0.01–0.13 80 0.06–0.83 118 0.09–5.0 80 0.0004–0.03 87 0.002–0.06 41 0.38–1.6 64 0.30–4.6 88 0.001–0.08 72 0.04–0.62 34 149–529

0.035 0.035 0.035 0.035 0.035 0.035 0.036 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.036

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Coverage intervals for trace elements in human scalp hair are site specific.

Coverage intervals for trace elements in human scalp hair commonly provide the basis for interpreting laboratory results and also in comparative decis...
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