Biomonitoring of Urinary Benzene Metabolite SPMA in the General Population in Central Italy

Contamination of groundwater by petroleum hydrocarbons: Impact of fuel stations in residential areas

Anthropogenic factors such as leakages from fuel storage facilities contribute to the release of petroleum hydrocarbons into groundwater. Following the proliferation of fuel stations in residential areas, this research assessed physicochemical parameters, salinity, and levels of total petroleum hydrocarbons (TPH) in groundwater sources within selected residential areas. From the study, mean values of temperature (30.5 °C), pH (5.8), EC (181.5 μs/cm), TDS (90.7 mg/L), and salinity (0.1 ppm) were recorded. The highest mean concentration of TPH (9.5 mg/L) was recorded at location A, while three sampling points (J, L, and M) exhibited 0.0 mg/L. Notably, TPH concentrations exceeding permissible limits were observed at three sampling points (A, B, and R). Strong positive correlations were observed between EC and TDS (r = 0.9), as well as salinity and EC (r = 0.9) and TDS (r = 0.9). Matrix plots demonstrated non-linear relationships, except for TDS and EC, although TPH and temperature exhibited a slightly linear pattern. The distance from USTs to the groundwater sources varied in the area. At location H, this distance (25 m) was measured as the shortest, where the mean TPH concentration was 3.71 mg/L. However, site Q exhibited the longest distance of 535 m, accompanied by a mean TPH concentration of 1.1 mg/L. Though the proximity of USTs to groundwater sources exerted some level of influence on the groundwater system, multiple linear regression, ANOVA, and cluster analysis showed that this did not pose direct and major impacts on the concentrations of TPH. However, approaches are needed to remediate the affected groundwater sources.

False positives and false negatives in benzene biological monitoring

Benzene is a commonly used industrial chemical that is a significant environmental pollutant. Occupational health specialists and industrial toxicologists are concerned with determining the exact amount of exposure to chemicals in the workplace. There are two main approaches to assess chemical exposure; air monitoring and biological monitoring. Air monitoring has limitations, which biological monitoring overcomes and could be used as a supplement to it. However, there are several factors that influence biological monitoring results. It would be possible to assess exposure more accurately if these factors were taken into account. This study aimed to review published papers for recognizing and discussing parameters that could affect benzene biological monitoring. Two types of effects can be distinguished: positive and negative effects. Factors causing positive effects will increase the metabolite concentration in urine more than expected. Furthermore, the parameters that decrease the urinary metabolite level were referred to as false negatives. From the papers, sixteen influential factors were extracted that might affect benzene biological monitoring results. Identified factors were clarified in terms of their nature and mechanism of action. It is also important to note that some factors influence the quantity and quality of the influence of other factors. As a result of this study, a decision-making protocol was developed for interpreting the final results of benzene biological monitoring.

Characterization of the internal working-life exposome using minimally and non-invasive sampling methods - a narrative review

During recent years, we are moving away from the 'one exposure, one disease'-approach in occupational settings and towards a more comprehensive approach, taking into account the totality of exposures during a life course by using an exposome approach. Taking an exposome approach however is accompanied by many challenges, one of which, for example, relates to the collection of biological samples. Methods used for sample collection in occupational exposome studies should ideally be minimally invasive, while at the same time sensitive, and enable meaningful repeated sampling in a large population and over a longer time period. This might be hampered in specific situations e.g., people working in remote areas, during pandemics or with flexible work hours. In these situations, using self-sampling techniques might offer a solution. Therefore, our aim was to identify existing self-sampling techniques and to evaluate the applicability of these techniques in an occupational exposome context by conducting a literature review. We here present an overview of current self-sampling methodologies used to characterize the internal exposome. In addition, the use of different biological matrices was evaluated and subdivided based on their level of invasiveness and applicability in an occupational exposome context. In conclusion, this review and the overview of self-sampling techniques presented herein can serve as a guide in the design of future (occupational) exposome studies while circumventing sample collection challenges associated with exposome studies.

Synthesis and application of quaternary amine-functionalized core-shell-shell magnetic polymers for determination of metabolites of benzene, toluene and xylene in human urine samples and study of exposure assessment

As production processes have evolved, airborne concentrations of benzene, toluene and xylene in many workplaces are already well below the occupational exposure limits. However, studies have shown that low levels of exposure to benzene, toluene and xylene can still cause health effects in people exposed occupationally. However, there is no literature on health risk assessment of internal exposure. In view of this, an analytical method based on quaternary amine-functionalized core-shell-shell magnetic polymers (QA-CSS-MPs) was developed for the determination of seven metabolites in urine by MSPE-UPLC-DAD-HRMS. Furthermore, an improved QuEChERS method for the extraction of seven metabolites from human urine samples was introduced for the first time and satisfactory extraction rates were achieved. In addition, QA-CSS-MPs microspheres with core-shell-shell structure were designed and synthesized, and the morphology, composition and magnetic properties of the materials were fully characterized to verify the rationality of the synthetic route. Subsequently, QA-CSS-MPs microspheres were used as magnetic solid-phase extraction (MSPE) adsorbents for the purification of urine extracts, and UPLC-DAD-HRMS was used for the detection of seven metabolites. As a result, this method allows the accurate determination of seven metabolites in urine samples over an ultra-wide concentration range (0.001-100 mg/L). Under optimal experimental conditions, i.e., 2% hydrochloric acid in urine for the hydrolysis and 20 mg of QA-CSS-MPs for 5 min purification, the spiked recoveries of the seven target metabolites ranged from 81.5% to 117.7% with RSDs of 1.0%-9.4%. The limits of detection (LODs, S/N≥3) for the established method were in the range of 0.2-0.3 μg/L. The developed method was applied to 254 human urine samples for the determination of seven metabolites. The results showed that the concentration distributions of three xylene metabolites in urine, 2-MHA, 3-MHA, 4-MHA and total MHA, showed statistically significant differences for occupational exposure (p<0.001). In addition, the results of the internal exposure assessment showed that there is a high potential health risk associated with occupational exposure processes.

Hematological Effects and Benchmark Doses of Long-Term Co-Exposure to Benzene, Toluene, and Xylenes in a Follow-Up Study on Petrochemical Workers

Benzene, toluene, and xylenes (BTX) commonly co-exist. Exposure to individual components and BTX-rich mixtures can induce hematological effects. However, the hematological effects of long-term exposure to BTX are still unclear, and respective reference levels based on empirical evidence should be developed. We conducted a follow-up study in BTX-exposed petrochemical workers. Long-term exposure levels were quantified by measuring cumulative exposure (CE). Generalized weighted quantile sum (WQS) regression models and Benchmark Dose (BMD) Software were used to evaluate their combined effects and calculate their BMDs, respectively. Many hematologic parameters were significantly decreased at the four-year follow-up (p < 0.05). We found positive associations of CE levels of benzene, toluene, and xylene with the decline in monocyte counts, lymphocyte counts, and hematocrit, respectively (β > 0.010, Ptrend < 0.05). These associations were stronger in subjects with higher baseline parameters, males, drinkers, or overweight subjects (Pinteraction < 0.05). BTX had positive combined effects on the decline in monocyte counts, red-blood-cell counts, and hemoglobin concentrations (Ptrend for WQS indices < 0.05). The estimated BMDs for CE levels of benzene, toluene, and xylene were 2.138, 1.449, and 2.937 mg/m3 × year, respectively. Our study demonstrated the hematological effects of long-term BTX co-exposure and developed 8h-RELs of about 0.01 ppm based on their hematological effects.

Biomonitoring of Exposure to Urban Pollutants and Oxidative Stress during the COVID-19 Lockdown in Rome Residents

Background: The objective of this study is to evaluate the effects of traffic on human health comparing biomonitoring data measured during the COVID-19 lockdown, when restrictions led to a 40% reduction in airborne benzene in Rome and a 36% reduction in road traffic, to the same parameters measured in 2021. Methods: Biomonitoring was performed on 49 volunteers, determining the urinary metabolites of the most abundant traffic pollutants, such as benzene and PAHs, and oxidative stress biomarkers by HPLC/MS-MS, 28 elements by ICP/MS and metabolic phenotypes by NMR. Results: Means of s-phenylmercaputric acid (SPMA), metabolites of naphthalene and nitropyrene in 2020 are 20% lower than in 2021, while 1-OH-pyrene was 30% lower. A reduction of 40% for 8-oxo-7,8-dihydroguanosine (8-oxoGuo) and 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodGuo) and 60% for 8-oxo-7,8-dihydroguanine (8-oxoGua) were found in 2020 compared to 2021. The concentrations of B, Co, Cu and Sb in 2021 are significantly higher than in the 2020. NMR untargeted metabolomic analysis identified 35 urinary metabolites. Results show in 2021 a decrease in succinic acid, a product of the Krebs cycle promoting inflammation. Conclusions: Urban pollution due to traffic is partly responsible for oxidative stress of nucleic acids, but other factors also have a role, enhancing the importance of communication about a healthy lifestyle in the prevention of cancer diseases.

Differences in the sperm metabolomes of smoking and nonsmoking men†

Currently, spermiogram analysis is the most relevant method used to clarify the potential infertility of a couple. However, in some cases, the reasons for infertility remain obscure. Smoking is among the factors that have been described to adversely affect male fertility. Smoking increases oxidative stress and thus promotes various pathological processes. Comparative studies, particularly those on metabolomic changes in sperm and seminal plasma caused by smoking, have not yet been published. Thus, the present pilot study aimed at the mass spectrometric characterization of the metabolomes of specimens from both smoking and nonsmoking subjects and the comparison of the evaluated data in terms of sperm apoptosis and spermiogram parameters. The results provided evidence that the conventional spermiogram is not altered in smokers compared to nonsmokers. However, a more careful investigation of sperm cells by metabolomic profiling reveals profound effects of smoking on sperm: first, nitrogen oxide synthase, a marker of oxidative stress, is activated. Second, the uptake of fatty acids into sperm mitochondria is reduced, leading to an impaired energy supply. Third, phenylalanine hydroxylation and tryptophan degradation, which are both indications of altered tetrahydrobiopterin biosynthesis, are reduced. Moreover, flow cytometry approaches indicated increased sperm caspase-3 activity, a sign of apoptosis. The present study clearly shows the negative effects of smoking on semen quality. Especially for idiopathic cases, metabolomic profiling can help to shed light on male subfertility or infertility.

Magnetic molecularly imprinted polymers for extraction of S-phenylmercapturic acid from urine samples followed by high-performance liquid chromatography

In this study, magnetic molecularly imprinted polymers (MMIPs) were prepared and used as sorbents for extraction of S-phenylmercapturic acid (S-PMA) from urine samples, followed by high-performance liquid chromatography ultraviolet-visible (HPLC-UV/Vis) analysis. The MMIPs were synthesized by the copolymerization reaction of (phenylthio) acetic acid (template molecule), methacrylic acid (functional monomers) and ethylene glycol dimethacrylate (cross-linkers). The morphology, structure property and surface groups of the prepared MMIPs were characterized by scan electron microscopy, transmission electron microscopy, infrared spectroscopy, X-ray diffraction pattern, thermogravimetric analyses, Brunauer-Emmett-Teller and vibrating sample magnetometer. The selectivity of the MMIPs was investigated in the presence of interferents. Various parameters affecting the S-PMA extraction efficiency were investigated, including MMIPs amount, pH, sample volume, desorption solvent, as well as extraction and desorption time. The obtained optimal parameters were as follows: MMIPs amount (20 mg), pH (3.0), sample volume (5 mL), desorption solvent (methanol/acetic acid [9/1, v/v]), extraction time (30 minutes) and desorption time (2 minutes). The method was validated according to the Food and Drug Administration Guidance for Industry on Bioanalytical Method Validation. The calibration curve for the analyte was linear in the concentration range of 0.030-1.0 mg/L (r = 0.9995). The LOD and LOQ of the method were 0.0080 and 0.0267 mg/L, respectively. The enrichment factor of the MMIPs was 5. The relative standard deviations of intra- and inter-day tests were in the range of 3.8-5.1% and 3.9-6.3%, respectively. The recoveries at three different concentrations of 0.10, 0.50 and 0.80 mg/L ranged between 95.2% and 98.6%. In addition, the MMIPs could be reused for at least eight times. The proposed method was successfully applied to the determination of S-PMA in urine samples. In addition, this developed method could be used as a tool in the early screening and clinical diagnosis of benzene intoxication.

The shape of low-concentration dose-response functions for benzene: implications for human health risk assessment

Are dose-response relationships for benzene and health effects such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) supra-linear, with disproportionately high risks at low concentrations, e.g. below 1 ppm? To investigate this hypothesis, we apply recent mode of action (MoA) and mechanistic information and modern data science techniques to quantify air benzene-urinary metabolite relationships in a previously studied data set for Tianjin, China factory workers. We find that physiologically based pharmacokinetics (PBPK) models and data for Tianjin workers show approximately linear production of benzene metabolites for air benzene (AB) concentrations below about 15 ppm, with modest sublinearity at low concentrations (e.g. below 5 ppm). Analysis of the Tianjin worker data using partial dependence plots reveals that production of metabolites increases disproportionately with increases in air benzene (AB) concentrations above 10 ppm, exhibiting steep sublinearity (J shape) before becoming saturated. As a consequence, estimated cumulative exposure is not an adequate basis for predicting risk. Risk assessments must consider the variability of exposure concentrations around estimated exposure concentrations to avoid over-estimating risks at low concentrations. The same average concentration for a specified duration is disproportionately risky if it has higher variance. Conversely, if chronic inflammation via activation of inflammasomes is a critical event for induction of MDS and other health effects, then sufficiently low concentrations of benzene are predicted not to cause increased risks of inflammasome-mediated diseases, no matter how long the duration of exposure. Thus, we find no evidence that the dose-response relationship is supra-linear at low doses; instead sublinear or zero excess risk at low concentrations is more consistent with the data. A combination of physiologically based pharmacokinetic (PBPK) modeling, Bayesian network (BN) analysis and inference, and partial dependence plots appears a promising and practical approach for applying current data science methods to advance benzene risk assessment.

Benzene metabolite SPMA and acrylamide metabolites AAMA and GAMA in urine of children and adolescents in Germany - human biomonitoring results of the German Environmental Survey 2014-2017 (GerES V)

Benzene and acrylamide are carcinogenic substances contained inter alia in tobacco smoke. The mercapturic acid metabolites of benzene, N-acetyl-S-phenyl-L-cysteine (SPMA), and of acrylamide, N-acetyl-S-(3-amino-3-oxopropyl)-cysteine (AAMA) and N-acetyl-S-(3-amino-2-hydroxy-3-oxopropyl)-cysteine (GAMA), were analysed in 2260 first-morning void urine samples from children and adolescents aged 3-17 years, participating in the population-representative German Environmental Survey on Children and Adolescents, GerES V 2014-2017. SPMA was detected in 98% of the participants with a geometric mean (GM) of 0.097 μg/L urine. Smokers had about 10-fold higher levels of the benzene metabolite SPMA than non-smokers. The sample comprises of 48 self-reported smokers, mainly in the oldest age group (14-17-year-olds). Second-hand smoke exposure, living near busy or very busy roads, and using domestic fuels for heating were additionally associated with higher benzene metabolite levels. SPMA levels in GerES V were lower compared to levels found in other countries, which in part however may reflect different proportions of smokers. The acrylamide metabolites AAMA and GAMA were detected in 100% of the participants with a GM of 72.6 μg/L urine for AAMA and 15.0 μg/L urine for GAMA. Smoking children and adolescents had about 2.5-fold higher AAMA levels than non-smoking ones. The frequency of consumption of french-fried potatoes and potato crisps consumption was also positively associated with urinary AAMA and GAMA levels. Compared to the urinary AAMA and GAMA levels in Germany and other countries, levels in GerES V tended to be higher than in the few studies reported. The urinary levels of the benzene biomarker SPMA, and the acrylamide biomarkers AAMA and GAMA build the basis to derive reference values for the exposure of children and adolescents in Germany. The results reveal options for exposure reduction mainly in personal choices regarding smoking and diet, but also requiring policy to maintain efforts in non-smoking regulations and improving ambient air quality. Providing these results also to the European HBM Initiative HBM4EU will contribute to gain knowledge on the exposure of the European population, the health impact of carcinogens and thus providing support for substantiated exposure assessment.

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

Analysis of Multiple Biomarkers Using Structural Equation Modeling

Objectives

When examining the relationship between smoking intensity and toxicant exposure biomarkers in an effort to understand the potential risk for smoking-related disease, individual biomarkers may not be strongly associated with smoking intensity because of the inherent variability in biomarkers. Structural equation modeling (SEM) offers a powerful solution by modeling the relationship between smoking intensity and multiple biomarkers through a latent variable.

Methods

Baseline data from a randomized trial (N = 1250) were used to estimate the relationship between smoking intensity and a latent toxicant exposure variable summarizing five volatile organic compound biomarkers. Two variables of smoking intensity were analyzed: the self-report cigarettes smoked per day and total nicotine equivalents in urine. SEM was compared with linear regression with each biomarker analyzed individually or with the sum score of the five biomarkers.

Results

SEM models showed strong relationships between smoking intensity and the latent toxicant exposure variable, and the relationship was stronger than its counterparts in linear regression with each biomarker analyzed separately or with the sum score.

Conclusions

SEM is a powerful multivariate statistical method for studying multiple biomarkers assessing the same class of harmful constituents. This method could be used to evaluate exposure from different combusted tobacco products.

Biomarkers of Environmental Toxicants: Exposure and Biological Effects

Biomarkers of environmental toxicants are measures of exposures and effects, some of which can serve to assess disease risk and interindividual susceptibilities.[...].

Effect of Benzene Exposure on the Urinary Biomarkers of Nucleic Acid Oxidation in Two Cohorts of Gasoline Pump Attendants

(1) Background: The oxidized guanine derivatives excreted into urine, products of DNA and RNA oxidation and repair, are used as biomarkers of oxidative damage in humans. This study aims to evaluate oxidative damage in gasoline pump attendants occupationally exposed to benzene. Benzene is contained in the gasoline but it is also produced from traffic and from smoking. (2) Methods: Twenty-nine gasoline pump attendants from two major cities of Saudi Arabia and 102 from Italy were studied for urinary 8-oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodGuo), 8-oxo-7,8-dihydroguanosine (8-oxoGuo), and S-phenyl-mercapturic acid (SPMA) for benzene exposure and urinary cotinine for smoking status assessment by liquid chromatography-tandem mass spectrometry. Airborne benzene was also assessed in the Italian group by gas-chromatography with flame ionization detector (GC-FID). (3) Results: The results suggest that high levels of benzene exposure can cause an accumulation of SPMA and bring about the formation of the oxidation biomarkers studied to saturation. At low exposure levels, SPMA and oxidation biomarker levels were correlated among them and were associated with the smoking habit. (4) Conclusions: The study confirms the association between benzene exposure and the excretion of nucleic acid oxidation biomarkers and enhances the importance of measuring the smoking habit, as it can significantly influence oxidative damage, especially when the exposure levels are low.