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S-phenylmercapturic acid (S-PMA) levels in urine as an indicator of exposure to benzene in the Kinshasa population
International Journal of Hygiene and Environmental Health 216 (2013) 494–498
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International Journal of Hygiene and Environmental Health journal homepage: www.elsevier.com/locate/ijheh
Short communication
S-phenylmercapturic acid (S-PMA) levels in urine as an indicator of exposure to benzene in the Kinshasa population J. Tuakuila a,b,∗ a b
Environmental Chemistry, Faculty of Sciences, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo Louvain Center for Toxicology and Applied Pharmacology (LTAP), UCL, Brussels, Belgium
a r t i c l e
i n f o
Article history: Received 28 April 2012 Received in revised form 20 February 2013 Accepted 25 March 2013 Keywords: Biomonitoring Environmental pollution Benzene S-phenylmercapturic acid Reference values
a b s t r a c t Background and objectives: Data on human exposure to chemicals in Africa are scarce. A biomonitoring study was conducted in a representative sample of the population in Kinshasa (Democratic Republic of Congo) to document exposure to benzene. Methods: S-phenylmercapturic acid (S-PMA) was measured by LC–MS/MS in spot urine samples from 220 individuals (50.5% women), aged 6–70 years living in the urban area and from 50 additional subjects from the sub-rural area of Kinshasa. Data were compiled as arithmetic means, geometric means, percentile 95th and range expressed in g/L. Results: Overall, living in urban Kinshasa was associated with increased levels of S-PMA in urine as compared to a population living in the sub-rural area. Increased levels were also found by comparison with some date from literature. Conclusions: This study reveals the high benzene exposure of the Kinshasa population requiring the determination of benzene concentrations in ambient air of Kinshasa and limit values for the protection of human health. © 2013 Elsevier GmbH. All rights reserved.
Introduction Environmental pollution and other aspects of poor environmental quality have well established effects on human health and quality of life. Benzene is a known human carcinogen (IARC group 1) (IARC, 1987) and has been assigned the “skin” notation by the American Conference of Governmental Industrial Hygienists (ACGIH), who recommends for the biological monitoring of benzene the determination of S-phenylmercapturic acid (S-PMA) (ACGIH, 2010). S-PMA is minor metabolite of benzene, excreted in urine, which have been demonstrated to be suitable biomarker for monitoring benzene exposure in occupational and environmental settings at levels as low as and even below 1 ppm (Boogaard and Van Sittert, 1996; Melikian et al., 2002; Qu et al., 2003). Other authors, however, did not always find a significant correlation between exposure to benzene and S-PMA excretion data at such low levels of exposure (Carrieri et al., 2006; Manini et al., 2006). A number of reasons are envisaged for these conflicting results. Inoue et al. (2000, 2001) showed the existence in urine of N-acetyl-S-(1,2-dihydro2hydroxyphenyl)-l-Cysteine, a precursor of S-PMA (i.e. pre-S-PMA) that can be turned into S-PMA by acid hydrolysis. The amount of
∗ Correspondence address at: Local A-4.2, Faculté des Sciences, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo. Tel.: +243 819347828. E-mail address: [email protected] 1438-4639/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.ijheh.2013.03.012
S-PMA actually measured in urine also depends, therefore, on the degree of hydrolysis of its precursor, that changes as a function of both the pH, the storage conditions and the time of collection of urine samples. This is one of the possible causes for the miscorrelation between airborne benzene concentration and levels of S-PMA measured in the urine of the exposed workers in some studies (Carrieri et al., 2010; Protano et al., 2010). Another important confounding factor is active and passive smoking exposure, as cigarette smoking causes the inhalation of significant amounts of benzene (Polzin et al., 2007; Protano et al., 2012). The objective of the present study was to evaluate benzene exposure by measuring the levels of its metabolite S-PMA in the urine of the Kinshasa population.
Methods Study design In the absence of reliable population registers and in view of the practical difficulties of conducting a truly random sampling in the population of Kinshasa, we applied a two-stage systematic sampling approach (Ancelle, 2002). In the first stage, the 22 administrative entities of Kinshasa were listed in alphabetical order and 11 out of them were selected as follows: a first entity was drawn randomly from the list and every other subsequent entity was then
J. Tuakuila / International Journal of Hygiene and Environmental Health 216 (2013) 494–498
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Fig. 1. Maps of Africa (left above), the Democratic Republic of Congo (left under) and the Kinshasa City (right). The study took place in 11 urban areas and 2 sub-rural areas (N’sele and Maluku). Source: Adapted from Kinshasa. Wikipédia, l’encyclopédie libre. http://fr.wikipedia.org/w/index.php?title=Kinshasa&oldid=54218607 (accessed 12.06.12).
included, thus ensuring a comprehensive coverage of the entire urban area of Kinshasa (Fig. 1). In the second stage, we aimed to recruit about 25 healthy male and female subjects between 6 and 70 years from each of the 11 entities. In a mobilization campaign (mainly by word of mouth), healthy subjects were invited to come to the local health center to provide an urine sample (the health status was evaluated by questionnaire). After exclusion of 13 individuals because of possible direct occupational exposure to chemicals, 220 individuals not occupationally exposed to benzene provided an urine sample and were included in the present study (80% of the target number was reached). Informed consent was obtained from each subject and information on age, sex, place of residence and smoking habits were recorded. With the same methods of mobilization campaign, fifty additional subjects living in the sub-rural area of Kinshasa were also included. The characteristics of the two areas (urban/rural) selected: urban area had high percentage of population density, motorization, old second hand vehicles and car traffic whereas sub-rural area had high percentage of green area (Tuakuila et al., 2010). Laboratory methods Great care was taken to avoid contamination during all the steps of collection, transport and analysis. Spot urine specimens were collected in metal-free polystyrene containers and stored at −20 ◦ C. The samples were then kept frozen and transported in a cool box to
be analyzed by the Louvain Center for Toxicology and Applied Pharmacology (Brussels, Belgium). Isolute SAX 500 mg 3 mL SPE (Solid Phase Extraction) columns were used for S-phenylmercapturic acid (S-PMA) extraction. Briefly, we loaded 1 mL of centrifuged and diluted (1:1 with nanopure water) urine sample spiked with 25 l of IS working solution (1 mg/L of D5 -SPMA). SPE columns (generous gift from SOPACHEM) were previously conditioned with 2.5 mL of MeOH (methanol) and 2.0 mL of nanopure water. The stationary phase was then washed with 1.5 mL of nanopure water, 1.5 mL of buffer (PO4 3− 5 mM/pH 7.0) and 1.5 mL of aqueous formic acid 1% (v/v). The analytes were eluted with 2.0 mL of aqueous formic acid 10% (v/v). The eluate was evaporated to dryness under vacuum at 37 ◦ C. The residue was redissolved in 125 l of aqueous MeOH 9:1 (v/v). Fifty l of the extract was injected into the LC–MS/MS system, equipped with Waters Alliance 2795 LC Column: C 18 Supersples 100 (125 mm × 4 mm). Gradient chromatography was with a flow rate of 0.40 mL/min at 50 ◦ C using 0.5% (v/v) aqueous acetic acid (A) and methanol with 0.5% (v/v) acetic acid (B). ESI-negative mode was applied with the MS/MS system run in the multiple reaction monitoring (MRM). The retention time (RT) for SPMA and D5 -SPMA were 7.46 and 7.49 min, respectively. Quantifer (qualifier) mass transitions were m/z 238 → 109 (240 → 111) and m/z 243 → 114 (245 → 116) for SPMA and D5 -SPMA, respectively. The limit of detection (LOD) was 0.10 g/L. The method meets all the required validation criteria (Angerer et al., 1998; Melikian et al., 1999; Liao et al., 2002; Perbellini et al., 2002; Schettgen et al.,
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J. Tuakuila / International Journal of Hygiene and Environmental Health 216 (2013) 494–498
Table 1 Demographic characteristics of the participants and areas.
Number of subjects Age, yearsa 6–14, n (%) >14, n (%)
Urban
Sub-rural
P
220 31 ± 18 [6–70] 50 (22.7%) 170 (77.3%)
50 36 ± 15 [6–60] 10 (20.0%) 40 (80.0%)
0.55 0.67 0.93
Sex Male, n (%) Female, n (%)
109 (49.5%) 111 (50.5%)
21 (42.0%) 29 (58.0%)
Current smokers, n (%)
79 (35.9%)
6 (12.0%)
a
0.001
Arithmetic mean ± SD [range].
2008; Sabatini et al., 2008). For quality control, internal controls and reference materials were run together with the samples on a daily basis. This study was approved by the Congolese committee of medical ethics and the study results will be informed back to individuals sample donors with proper explanations. Cotinine was determined by HPLC (LOD = 50 g/L) according to the methods previously described by Benowitz (1996). Statistical analyses Data analysis was performed with the NCSS version 2004 (NCSS Institute Inc. 2004). Any normal distribution of the different variables was verified with the Kolmogorov-Smirnov test. Parametric tests were used for the analysis of normally distributed variables. The results were expressed as Arithmetic means (AM), Geometric means (GM), median, percentile 95th and range, as indicated. The limit of detection (LOD) divided by 2 was used for imputation of values lower than the LOD. Differences between samples with normal distribution were examined by the Student’s t-test, Chi-square test. Stepwise multiple linear regression analyses of log-transformed data were used to estimate the influence of independent variables (age, sex, smoking habits and zone) on the S-PMA (stepwise procedure, criteria: F probability to enter ≤0.05 and F probability to remove ≥0.10). A p-value lower than 0.05 was considered as statistically significant for all tests. Results and discussion Age of these 220 urban subjects was between 6 and 70 years and 31 years on average (standard deviation: 18). Most participants were adults (77.3%) and nearly half (50.5%) were female. Among adults, thirty-six percent (36%) were current smokers. The characteristics of sub-rural subjects are also presented in Table 1. The congested traffic with the widespread use of motorbikes, a popular mode of transportation in developing countries, as well as old automobiles is a potential source of benzene emission. The dominating trend of two-stroke engines is one of the significant factors for high benzene concentrations in ambient air (Leong et al., 2002), and moreover poorly maintained cars emit blue smoke with high content of volatile organic compounds, including benzene. In large cities involving such traffic, average benzene concentrations were estimated in the range of 15–120 g/m3 (Avogbe et al., 2005; Leong et al., 2002) largely exceeding the limit value set by the European Directive 2000/69/EC for the protection of human health (5 g/m3 , year average). Average half-hour values of 250 g/m3 and maximum half-hour values of 500 g/m3 were documented in Lagos, Nigeria (Baumbach et al., 1995). SPMA-U is a sensitive and specific biomarker of exposure to benzene (Boogaard and Van Sittert, 1995; Ghittori et al., 1999; Hoet et al., 2009). One potential problem in any study or survey of low level environmental exposure is how one deals statistically with those results below the LOD. For some substances, there may be a
Table 2 Urinary concentrations of SPMA (g/L) in the Kinshasa population (n = 220; 6–70 years). Group of population (n)
AM
GM
Median
P95th
Total (220)a
1.3
1.0
1.0
8.1
(0.1–9.6)
Sexb Female (111) Male (109)
1.4 1.1
1.2 0.9
1.3 0.9
8.2 8.0
(0.1–9.6) (0.1–9.0)
Ageb 6–14 years (50) >14 years (170)
0.8 1.9
0.4 1.6
0.6 1.5
7.0 9.3
(0.1–8.2) (0.1–9.6)
Smoking habitsb Non-smokers (141) Current smokers (79)
0.6 2.1
0.3 1.7
0.5 1.6
7.2 9.0
(0.1–8.2) (0.8–9.6)
Range
a
86 (39.9%) were below LOD. p-value < 0.05 (two-tailed t-test for independent samples, female vs. male, 6–14 years vs. > 14 years, current smokers vs. non-smokers), AM: arithmetic mean, GM: geometric mean, P95th: percentile 95th. b
high proportion of results below the LOD but the same problem can occur with larger surveys as well as the one reported here, eightysix (39.9%) SPMA measurements were less than their respective LOD (Table 2). This potential problem may be avoided by simply calculating the 95% percentile and stating the number of samples below the LOD (Bevan et al., 2012). In addition, pre-analytical conditions (storage conditions, time of collection of urine samples, etc.) may explain low amounts of SPMA observed (Carrieri et al., 2010; Protano et al., 2010). It should also be noted that the formation of S-PMA is GST (gluthathione Stransferases)-dependent (Dahabreh et al., 2010; Garte et al., 2008; Lin et al., 2008; Wormhoudt et al., 1999). GSTs are a family of dimeric enzymes which pay an important role in the detoxification of several industrial and/or environmental toxicant. For the cystosolic GSTs, two polymorphic genes have been largely investigated, i.e. GSTM1 and GSTT1, and it has been estimated that about 73 and 23% of US black population lack the gene, respectively (Wormhoudt et al., 1999). It is, therefore, possible that African populations also present a high frequency of this deletion, and this information may need to be taken into account in future studies for a correct interpretation of the S-PMA measured values. Median (range; P95th) urinary S-phenylmercapturic acid (SPMA) was 1.0 g/L (0.1–9.6; 8.1) (Table 2). Significant difference was found between women and men (0 for female, GM: 1.2 g/L vs. 1 for male, GM: 0.9 g/L; p = 0.01), indicating another source of exposure to benzene than tobacco smoke and vehicles emissions. Kerosene, which is used for cooking and energy supply can be a source of benzene emission. In a kitchen using a kerosene stove for cooking, a mean concentration of 103.4 g/m3 (range: 43.9–166.4) was measured (Pandit et al., 2001). Women who often spend considerable time indoors, mostly in cooking activities, are probably at greatest risk for exposures to this indoor air pollution. This can be considered as a reason to support the importance of monitoring air quality and biomonitoring human exposure for protecting public health in Kinshasa (DRC). In agreement with other studies, we found higher S-PMA levels in current smokers than in non-smokers (0 for non-smokers, GM: 0.3 g/L vs. 1 for current smokers, GM: 1.7 g/L; p < 0.01) (Table 2), which is not surprising since tobacco smoke represents an important source benzene exposure (Melikian et al., 2002; Bono et al., 2005; Polzin et al., 2007; Hoet et al., 2009; Protano et al., 2012). There was also statistically significant difference (0 for 6–14 years, GM: 0.4 g/L vs. 1 for >14 years, GM: 1.6 g/L; p < 0.01). Probably, because of all children were non-smokers. A 2.5-fold higher level was noted in residents of the urban area compared with those in sub-rural settings (0 for sub-rural individuals, P95th: 2.8 g/L vs. 1 for urban individuals, P95th: 8.1 g/L;
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Table 3 Comparison with sub-rural population and other biomonitoring surveys. Kinshasa (this study)
S-PMA (g/L)a a b c
Sub-rural area
Urban area
p
2.8 (n = 50)
8.1 (n = 220)
<0.01
UKc
2.5 (n = 705)
9.1 (n = 426)
Derived from 95th percentile value. Fustinoni et al. (2010). Bevan et al. (2012).
p < 0.01) (Table 3). The high percentage of smokers (Table 1) and air pollution in the urban area could, at least partly, explain this difference. A cross-sectional study conducted by Protano et al. (2010) in 243 Italian children (5–11 years) showed also that S-PMA concentrations were about 1.5-fold higher in children living in urban areas than in those in the rural group (p < 0.01). The S-PMA levels reported in our study are much higher than those recorded in a group of 705 Italian subjects either belonging to the general urban population living and working in Milan area or being gasoline station attendants, or refinery workers, in which the median S-PMA concentration was 2.5 g/L (P95th) (Fustinoni et al., 2010). However, S-PMA levels in adults (P95th: 9.3 g/L) (Table 2) were quite similar to 9.1 g/L (P95th) reported in 426 adults (>18 years) from the general UK population (Bevan et al., 2012). In Stepwise multivariable analyses, age (continuous variable), sex (0 for female/1 for male), smoking habits (cotinine as continuous variable) and zone (0 for sub-rural/1 for urban) were the independent parameters significantly associated with urinary SPMA excretion (dependent parameter) with values of partial R2 ranging between 0.010 (Sex), 0.012 (Age), 0.103 (Smoking habits) and 0.014 (Zone) (Table 4). The variance explained by the model resulted very low (0.139). This can be explained by the low level of Cotinine-U measured in smokers (GM [95%CI]: 140 [115–171] g/L), indicates that they were light smokers. In addition, the concentration of biomarkers in urine may vary between individuals due to differences in systemic exposure and metabolism. Two major limitations should be considered in evaluating present results. With regard to sample collection, selection of urinary sample donors did not follow rigid sampling strategy (such as random sampling) but by chance, which was practically inevitable under present survey conditions. Passive smoking exposure is an important factor affecting benzene exposure, this factor did not evaluate. Despite such limitations, however, it is prudent to conclude that data from the present study constitutes levels generally exceeded in the Kinshasa population. Living in urban area of Kinshasa is associated with increased levels of S-PMA in urine as compared to a reference population living in a sub-rural area of the same region. Increased levels were also found by comparison with some values in literature. This study reveals the high benzene exposure of the Kinshasa population requiring the determination of benzene concentrations in ambient air of Kinshasa and limit values for the protection of human health. Table 4 Multiple regression analysis model. Parameter (dependent variable)
SPMA (g/L) a
Italyb
Partial R2 (independent variables) a
b
Total R2 d
Age
Sex
Smoking habitsc
Zone
0.012
0.010
0.103
0.014
0.139
Age represented as continuous log-variable. b Sex represented as 0 for female and 1 for male. c Smoking habits: cotinine represented as continuous log-variable. d Zone represented as 0 for rural area and 1 for urban area, R2 : explained variance (i.e. the square of the correlation coefficient). Results are given for those variables that correlated, and when the regression was significant (p < 0.05).
Acknowledgments I am highly indebted to the study participants and to the staff of investigators, as well as all the local health services and health centers of the Kinshasan Public Health System that supported the field work. I also thank Professors Lison, Hoet, Haufroid and Mr. Boesmans for their collaboration. The financial support of the Belgian Technical Cooperation (Coopération Technique BelgeCTB/Belgische Technische Coöperatie-BTC), SOPACHEM and LTAP (Louvain Center for Toxicology and Applied Pharmacology) are gratefully acknowledged.
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International Journal of Hygiene and Environmental Health journal homepage: www.elsevier.com/locate/ijheh
Short communication
S-phenylmercapturic acid (S-PMA) levels in urine as an indicator of exposure to benzene in the Kinshasa population J. Tuakuila a,b,∗ a b
Environmental Chemistry, Faculty of Sciences, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo Louvain Center for Toxicology and Applied Pharmacology (LTAP), UCL, Brussels, Belgium
a r t i c l e
i n f o
Article history: Received 28 April 2012 Received in revised form 20 February 2013 Accepted 25 March 2013 Keywords: Biomonitoring Environmental pollution Benzene S-phenylmercapturic acid Reference values
a b s t r a c t Background and objectives: Data on human exposure to chemicals in Africa are scarce. A biomonitoring study was conducted in a representative sample of the population in Kinshasa (Democratic Republic of Congo) to document exposure to benzene. Methods: S-phenylmercapturic acid (S-PMA) was measured by LC–MS/MS in spot urine samples from 220 individuals (50.5% women), aged 6–70 years living in the urban area and from 50 additional subjects from the sub-rural area of Kinshasa. Data were compiled as arithmetic means, geometric means, percentile 95th and range expressed in g/L. Results: Overall, living in urban Kinshasa was associated with increased levels of S-PMA in urine as compared to a population living in the sub-rural area. Increased levels were also found by comparison with some date from literature. Conclusions: This study reveals the high benzene exposure of the Kinshasa population requiring the determination of benzene concentrations in ambient air of Kinshasa and limit values for the protection of human health. © 2013 Elsevier GmbH. All rights reserved.
Introduction Environmental pollution and other aspects of poor environmental quality have well established effects on human health and quality of life. Benzene is a known human carcinogen (IARC group 1) (IARC, 1987) and has been assigned the “skin” notation by the American Conference of Governmental Industrial Hygienists (ACGIH), who recommends for the biological monitoring of benzene the determination of S-phenylmercapturic acid (S-PMA) (ACGIH, 2010). S-PMA is minor metabolite of benzene, excreted in urine, which have been demonstrated to be suitable biomarker for monitoring benzene exposure in occupational and environmental settings at levels as low as and even below 1 ppm (Boogaard and Van Sittert, 1996; Melikian et al., 2002; Qu et al., 2003). Other authors, however, did not always find a significant correlation between exposure to benzene and S-PMA excretion data at such low levels of exposure (Carrieri et al., 2006; Manini et al., 2006). A number of reasons are envisaged for these conflicting results. Inoue et al. (2000, 2001) showed the existence in urine of N-acetyl-S-(1,2-dihydro2hydroxyphenyl)-l-Cysteine, a precursor of S-PMA (i.e. pre-S-PMA) that can be turned into S-PMA by acid hydrolysis. The amount of
∗ Correspondence address at: Local A-4.2, Faculté des Sciences, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo. Tel.: +243 819347828. E-mail address: [email protected] 1438-4639/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.ijheh.2013.03.012
S-PMA actually measured in urine also depends, therefore, on the degree of hydrolysis of its precursor, that changes as a function of both the pH, the storage conditions and the time of collection of urine samples. This is one of the possible causes for the miscorrelation between airborne benzene concentration and levels of S-PMA measured in the urine of the exposed workers in some studies (Carrieri et al., 2010; Protano et al., 2010). Another important confounding factor is active and passive smoking exposure, as cigarette smoking causes the inhalation of significant amounts of benzene (Polzin et al., 2007; Protano et al., 2012). The objective of the present study was to evaluate benzene exposure by measuring the levels of its metabolite S-PMA in the urine of the Kinshasa population.
Methods Study design In the absence of reliable population registers and in view of the practical difficulties of conducting a truly random sampling in the population of Kinshasa, we applied a two-stage systematic sampling approach (Ancelle, 2002). In the first stage, the 22 administrative entities of Kinshasa were listed in alphabetical order and 11 out of them were selected as follows: a first entity was drawn randomly from the list and every other subsequent entity was then
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Fig. 1. Maps of Africa (left above), the Democratic Republic of Congo (left under) and the Kinshasa City (right). The study took place in 11 urban areas and 2 sub-rural areas (N’sele and Maluku). Source: Adapted from Kinshasa. Wikipédia, l’encyclopédie libre. http://fr.wikipedia.org/w/index.php?title=Kinshasa&oldid=54218607 (accessed 12.06.12).
included, thus ensuring a comprehensive coverage of the entire urban area of Kinshasa (Fig. 1). In the second stage, we aimed to recruit about 25 healthy male and female subjects between 6 and 70 years from each of the 11 entities. In a mobilization campaign (mainly by word of mouth), healthy subjects were invited to come to the local health center to provide an urine sample (the health status was evaluated by questionnaire). After exclusion of 13 individuals because of possible direct occupational exposure to chemicals, 220 individuals not occupationally exposed to benzene provided an urine sample and were included in the present study (80% of the target number was reached). Informed consent was obtained from each subject and information on age, sex, place of residence and smoking habits were recorded. With the same methods of mobilization campaign, fifty additional subjects living in the sub-rural area of Kinshasa were also included. The characteristics of the two areas (urban/rural) selected: urban area had high percentage of population density, motorization, old second hand vehicles and car traffic whereas sub-rural area had high percentage of green area (Tuakuila et al., 2010). Laboratory methods Great care was taken to avoid contamination during all the steps of collection, transport and analysis. Spot urine specimens were collected in metal-free polystyrene containers and stored at −20 ◦ C. The samples were then kept frozen and transported in a cool box to
be analyzed by the Louvain Center for Toxicology and Applied Pharmacology (Brussels, Belgium). Isolute SAX 500 mg 3 mL SPE (Solid Phase Extraction) columns were used for S-phenylmercapturic acid (S-PMA) extraction. Briefly, we loaded 1 mL of centrifuged and diluted (1:1 with nanopure water) urine sample spiked with 25 l of IS working solution (1 mg/L of D5 -SPMA). SPE columns (generous gift from SOPACHEM) were previously conditioned with 2.5 mL of MeOH (methanol) and 2.0 mL of nanopure water. The stationary phase was then washed with 1.5 mL of nanopure water, 1.5 mL of buffer (PO4 3− 5 mM/pH 7.0) and 1.5 mL of aqueous formic acid 1% (v/v). The analytes were eluted with 2.0 mL of aqueous formic acid 10% (v/v). The eluate was evaporated to dryness under vacuum at 37 ◦ C. The residue was redissolved in 125 l of aqueous MeOH 9:1 (v/v). Fifty l of the extract was injected into the LC–MS/MS system, equipped with Waters Alliance 2795 LC Column: C 18 Supersples 100 (125 mm × 4 mm). Gradient chromatography was with a flow rate of 0.40 mL/min at 50 ◦ C using 0.5% (v/v) aqueous acetic acid (A) and methanol with 0.5% (v/v) acetic acid (B). ESI-negative mode was applied with the MS/MS system run in the multiple reaction monitoring (MRM). The retention time (RT) for SPMA and D5 -SPMA were 7.46 and 7.49 min, respectively. Quantifer (qualifier) mass transitions were m/z 238 → 109 (240 → 111) and m/z 243 → 114 (245 → 116) for SPMA and D5 -SPMA, respectively. The limit of detection (LOD) was 0.10 g/L. The method meets all the required validation criteria (Angerer et al., 1998; Melikian et al., 1999; Liao et al., 2002; Perbellini et al., 2002; Schettgen et al.,
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Table 1 Demographic characteristics of the participants and areas.
Number of subjects Age, yearsa 6–14, n (%) >14, n (%)
Urban
Sub-rural
P
220 31 ± 18 [6–70] 50 (22.7%) 170 (77.3%)
50 36 ± 15 [6–60] 10 (20.0%) 40 (80.0%)
0.55 0.67 0.93
Sex Male, n (%) Female, n (%)
109 (49.5%) 111 (50.5%)
21 (42.0%) 29 (58.0%)
Current smokers, n (%)
79 (35.9%)
6 (12.0%)
a
0.001
Arithmetic mean ± SD [range].
2008; Sabatini et al., 2008). For quality control, internal controls and reference materials were run together with the samples on a daily basis. This study was approved by the Congolese committee of medical ethics and the study results will be informed back to individuals sample donors with proper explanations. Cotinine was determined by HPLC (LOD = 50 g/L) according to the methods previously described by Benowitz (1996). Statistical analyses Data analysis was performed with the NCSS version 2004 (NCSS Institute Inc. 2004). Any normal distribution of the different variables was verified with the Kolmogorov-Smirnov test. Parametric tests were used for the analysis of normally distributed variables. The results were expressed as Arithmetic means (AM), Geometric means (GM), median, percentile 95th and range, as indicated. The limit of detection (LOD) divided by 2 was used for imputation of values lower than the LOD. Differences between samples with normal distribution were examined by the Student’s t-test, Chi-square test. Stepwise multiple linear regression analyses of log-transformed data were used to estimate the influence of independent variables (age, sex, smoking habits and zone) on the S-PMA (stepwise procedure, criteria: F probability to enter ≤0.05 and F probability to remove ≥0.10). A p-value lower than 0.05 was considered as statistically significant for all tests. Results and discussion Age of these 220 urban subjects was between 6 and 70 years and 31 years on average (standard deviation: 18). Most participants were adults (77.3%) and nearly half (50.5%) were female. Among adults, thirty-six percent (36%) were current smokers. The characteristics of sub-rural subjects are also presented in Table 1. The congested traffic with the widespread use of motorbikes, a popular mode of transportation in developing countries, as well as old automobiles is a potential source of benzene emission. The dominating trend of two-stroke engines is one of the significant factors for high benzene concentrations in ambient air (Leong et al., 2002), and moreover poorly maintained cars emit blue smoke with high content of volatile organic compounds, including benzene. In large cities involving such traffic, average benzene concentrations were estimated in the range of 15–120 g/m3 (Avogbe et al., 2005; Leong et al., 2002) largely exceeding the limit value set by the European Directive 2000/69/EC for the protection of human health (5 g/m3 , year average). Average half-hour values of 250 g/m3 and maximum half-hour values of 500 g/m3 were documented in Lagos, Nigeria (Baumbach et al., 1995). SPMA-U is a sensitive and specific biomarker of exposure to benzene (Boogaard and Van Sittert, 1995; Ghittori et al., 1999; Hoet et al., 2009). One potential problem in any study or survey of low level environmental exposure is how one deals statistically with those results below the LOD. For some substances, there may be a
Table 2 Urinary concentrations of SPMA (g/L) in the Kinshasa population (n = 220; 6–70 years). Group of population (n)
AM
GM
Median
P95th
Total (220)a
1.3
1.0
1.0
8.1
(0.1–9.6)
Sexb Female (111) Male (109)
1.4 1.1
1.2 0.9
1.3 0.9
8.2 8.0
(0.1–9.6) (0.1–9.0)
Ageb 6–14 years (50) >14 years (170)
0.8 1.9
0.4 1.6
0.6 1.5
7.0 9.3
(0.1–8.2) (0.1–9.6)
Smoking habitsb Non-smokers (141) Current smokers (79)
0.6 2.1
0.3 1.7
0.5 1.6
7.2 9.0
(0.1–8.2) (0.8–9.6)
Range
a
86 (39.9%) were below LOD. p-value < 0.05 (two-tailed t-test for independent samples, female vs. male, 6–14 years vs. > 14 years, current smokers vs. non-smokers), AM: arithmetic mean, GM: geometric mean, P95th: percentile 95th. b
high proportion of results below the LOD but the same problem can occur with larger surveys as well as the one reported here, eightysix (39.9%) SPMA measurements were less than their respective LOD (Table 2). This potential problem may be avoided by simply calculating the 95% percentile and stating the number of samples below the LOD (Bevan et al., 2012). In addition, pre-analytical conditions (storage conditions, time of collection of urine samples, etc.) may explain low amounts of SPMA observed (Carrieri et al., 2010; Protano et al., 2010). It should also be noted that the formation of S-PMA is GST (gluthathione Stransferases)-dependent (Dahabreh et al., 2010; Garte et al., 2008; Lin et al., 2008; Wormhoudt et al., 1999). GSTs are a family of dimeric enzymes which pay an important role in the detoxification of several industrial and/or environmental toxicant. For the cystosolic GSTs, two polymorphic genes have been largely investigated, i.e. GSTM1 and GSTT1, and it has been estimated that about 73 and 23% of US black population lack the gene, respectively (Wormhoudt et al., 1999). It is, therefore, possible that African populations also present a high frequency of this deletion, and this information may need to be taken into account in future studies for a correct interpretation of the S-PMA measured values. Median (range; P95th) urinary S-phenylmercapturic acid (SPMA) was 1.0 g/L (0.1–9.6; 8.1) (Table 2). Significant difference was found between women and men (0 for female, GM: 1.2 g/L vs. 1 for male, GM: 0.9 g/L; p = 0.01), indicating another source of exposure to benzene than tobacco smoke and vehicles emissions. Kerosene, which is used for cooking and energy supply can be a source of benzene emission. In a kitchen using a kerosene stove for cooking, a mean concentration of 103.4 g/m3 (range: 43.9–166.4) was measured (Pandit et al., 2001). Women who often spend considerable time indoors, mostly in cooking activities, are probably at greatest risk for exposures to this indoor air pollution. This can be considered as a reason to support the importance of monitoring air quality and biomonitoring human exposure for protecting public health in Kinshasa (DRC). In agreement with other studies, we found higher S-PMA levels in current smokers than in non-smokers (0 for non-smokers, GM: 0.3 g/L vs. 1 for current smokers, GM: 1.7 g/L; p < 0.01) (Table 2), which is not surprising since tobacco smoke represents an important source benzene exposure (Melikian et al., 2002; Bono et al., 2005; Polzin et al., 2007; Hoet et al., 2009; Protano et al., 2012). There was also statistically significant difference (0 for 6–14 years, GM: 0.4 g/L vs. 1 for >14 years, GM: 1.6 g/L; p < 0.01). Probably, because of all children were non-smokers. A 2.5-fold higher level was noted in residents of the urban area compared with those in sub-rural settings (0 for sub-rural individuals, P95th: 2.8 g/L vs. 1 for urban individuals, P95th: 8.1 g/L;
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Table 3 Comparison with sub-rural population and other biomonitoring surveys. Kinshasa (this study)
S-PMA (g/L)a a b c
Sub-rural area
Urban area
p
2.8 (n = 50)
8.1 (n = 220)
<0.01
UKc
2.5 (n = 705)
9.1 (n = 426)
Derived from 95th percentile value. Fustinoni et al. (2010). Bevan et al. (2012).
p < 0.01) (Table 3). The high percentage of smokers (Table 1) and air pollution in the urban area could, at least partly, explain this difference. A cross-sectional study conducted by Protano et al. (2010) in 243 Italian children (5–11 years) showed also that S-PMA concentrations were about 1.5-fold higher in children living in urban areas than in those in the rural group (p < 0.01). The S-PMA levels reported in our study are much higher than those recorded in a group of 705 Italian subjects either belonging to the general urban population living and working in Milan area or being gasoline station attendants, or refinery workers, in which the median S-PMA concentration was 2.5 g/L (P95th) (Fustinoni et al., 2010). However, S-PMA levels in adults (P95th: 9.3 g/L) (Table 2) were quite similar to 9.1 g/L (P95th) reported in 426 adults (>18 years) from the general UK population (Bevan et al., 2012). In Stepwise multivariable analyses, age (continuous variable), sex (0 for female/1 for male), smoking habits (cotinine as continuous variable) and zone (0 for sub-rural/1 for urban) were the independent parameters significantly associated with urinary SPMA excretion (dependent parameter) with values of partial R2 ranging between 0.010 (Sex), 0.012 (Age), 0.103 (Smoking habits) and 0.014 (Zone) (Table 4). The variance explained by the model resulted very low (0.139). This can be explained by the low level of Cotinine-U measured in smokers (GM [95%CI]: 140 [115–171] g/L), indicates that they were light smokers. In addition, the concentration of biomarkers in urine may vary between individuals due to differences in systemic exposure and metabolism. Two major limitations should be considered in evaluating present results. With regard to sample collection, selection of urinary sample donors did not follow rigid sampling strategy (such as random sampling) but by chance, which was practically inevitable under present survey conditions. Passive smoking exposure is an important factor affecting benzene exposure, this factor did not evaluate. Despite such limitations, however, it is prudent to conclude that data from the present study constitutes levels generally exceeded in the Kinshasa population. Living in urban area of Kinshasa is associated with increased levels of S-PMA in urine as compared to a reference population living in a sub-rural area of the same region. Increased levels were also found by comparison with some values in literature. This study reveals the high benzene exposure of the Kinshasa population requiring the determination of benzene concentrations in ambient air of Kinshasa and limit values for the protection of human health. Table 4 Multiple regression analysis model. Parameter (dependent variable)
SPMA (g/L) a
Italyb
Partial R2 (independent variables) a
b
Total R2 d
Age
Sex
Smoking habitsc
Zone
0.012
0.010
0.103
0.014
0.139
Age represented as continuous log-variable. b Sex represented as 0 for female and 1 for male. c Smoking habits: cotinine represented as continuous log-variable. d Zone represented as 0 for rural area and 1 for urban area, R2 : explained variance (i.e. the square of the correlation coefficient). Results are given for those variables that correlated, and when the regression was significant (p < 0.05).
Acknowledgments I am highly indebted to the study participants and to the staff of investigators, as well as all the local health services and health centers of the Kinshasan Public Health System that supported the field work. I also thank Professors Lison, Hoet, Haufroid and Mr. Boesmans for their collaboration. The financial support of the Belgian Technical Cooperation (Coopération Technique BelgeCTB/Belgische Technische Coöperatie-BTC), SOPACHEM and LTAP (Louvain Center for Toxicology and Applied Pharmacology) are gratefully acknowledged.
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