Gestational and Postpartum Exposure to PM2.5 Components and Glucose Metabolism in Chinese Women: A Prospective Cohort Study.

Pregnant women are physiologically prone to glucose intolerance, while the puerperium represents a critical phase for recovery. However, how air pollution disrupts glucose homeostasis during the gestational and early postpartum periods remains unclear. This prospective cohort study conducted an oral glucose tolerance test and measured the insulin levels of 834 pregnant women in Guangzhou, with a follow-up for 443 puerperae at 6-8 weeks postpartum. Residential PM2.5 and five chemical components were estimated by an established spatiotemporal model. The adjusted linear model showed that an IQR increase in gestational PM2.5 exposure was associated with an increase of 0.17 mmol/L (95% CI: 0.06, 0.28) in fasting plasma glucose (FPG) and 0.24 (95% CI: 0.05, 0.42) in the insulin resistance index. Postpartum PM2.5 exposure was linked to a 0.17 mmol/L (95% CI: 0.05, 0.28) elevation in FPG per IQR, with a strengthened association found in women with gestational diabetes (Pinteraction = 0.003). In the quantile-based g-computation model, NO3- consistently contributed to the combined effect of PM2.5 components on gestational and postpartum FPG. This study was the first to suggest that PM2.5 components were associated with exacerbated gestational insulin resistance and elevated postpartum FPG. Targeted interventions reducing the emissions of toxic PM2.5 components are essential to improving maternal glucose metabolism.

The Associations of Prenatal Exposure to Fine Particulate Matter and Its Chemical Components with Allergic Rhinitis in Children and the Modification Effect of Polyunsaturated Fatty Acids: A Birth Cohort Study.

BACKGROUND: Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter <2.5µm in aerodynamic diameter (PM2.5)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring. OBJECTIVES: We aimed to investigate the association of prenatal exposure to PM2.5 and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs. METHODS: This prospective birth cohort study involved 657 mother-child pairs from Guangzhou, China. Prenatal exposure to residential PM2.5 mass and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of PM2.5 components and examine the modification effects of maternal PUFA levels. RESULTS: Approximately 5.33% and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal PM2.5 was 35.50±5.31 µg/m3. PM2.5 was positively associated with the risk of developing AR [hazard ratio (HR)=1.85; 95% confidence interval (CI): 1.16, 2.96 per 5 µg/m3] and its symptoms (HR=1.79; 95% CI: 1.22, 2.62 per 5 µg/m3) after adjustment for confounders. Similar associations were observed between individual PM2.5 components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to PM2.5 and its components. CONCLUSION: Prenatal exposure to PM2.5 and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of PM2.5 on respiratory allergy. https://doi.org/10.1289/EHP13524.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study.

BACKGROUND: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth. OBJECTIVE: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic. METHODS: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases. RESULTS: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74). CONCLUSIONS: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

Spatio-temporal interpolation and delineation of extreme heat events in California between 2017 and 2021.

Robust spatio-temporal delineation of extreme climate events and accurate identification of areas that are impacted by an event is a prerequisite for identifying population-level and health-related risks. In prior research, attributes such as temperature and humidity have often been linearly assigned to the population of the study unit from the closest weather station. This could result in inaccurate event delineation and biased assessment of extreme heat exposure. We have developed a spatio-temporal model to dynamically delineate boundaries for Extreme Heat Events (EHE) across space and over time, using a relative measure of Apparent Temperature (AT). Our surface interpolation approach offers a higher spatio-temporal resolution compared to the standard nearest-station (NS) assignment method. We show that the proposed approach can provide at least 80.8 percent improvement in identification of areas and populations impacted by EHEs. This improvement in average adjusts the misclassification of about one million Californians per day of an extreme event, who would be either unidentified or misidentified under EHEs between 2017 and 2021.

Systematic comparisons between Lyme disease and post-treatment Lyme disease syndrome in the U.S. with administrative claims data.

BACKGROUND: Post-treatment Lyme disease syndrome (PTLDS) is used to describe Lyme disease patients who have the infection cleared by antibiotic but then experienced persisting symptoms of pain, fatigue, or cognitive impairment. Currently, little is known about the cause or epidemiology of PTLDS. METHODS: We conducted a data-driven study with a large nationwide administrative dataset, which consists of more than 98 billion billing and 1.4 billion prescription records between 2008 and 2016, to identify unique aspects of PTLDS that could have diagnostic and etiologic values. We defined PTLDS based on its symptomatology and compared the demographic, longitudinal changes of comorbidity, and antibiotic prescriptions between patients who have Lyme with absence of prolonged symptoms (APS) and PTLDS. FINDINGS: The age and temporal distributions were similar between Lyme APS and PTLDS. The PTLDS-to-Lyme APS case ratio was 3.42%. The co-occurrence of 3 out of 19 chronic conditions were significantly higher in PTLDS versus Lyme APS-odds ratio and 95% CI for anemia, hyperlipidemia, and osteoarthrosis were 1.46 (1.11-1.92), 1.39 (1.15-1.68), and 1.62 (1.23-2.12) respectively. We did not find significant differences between PTLDS and Lyme APS for the number of types of antibiotics prescribed (incidence rate ratio = 1.009, p = 0.90) and for the prescription of each of the five antibiotics (FDR adjusted p values 0.72-0.95). INTERPRETATION: PTLDS cases have more codes corresponding to anemia, hyperlipidemia, and osteoarthrosis compared to Lyme APS. Our finding of hyperlipidemia is consistent with a dysregulation of fat metabolism reported by other researchers, and further investigation should be conducted to understand the potential biological relationship between the two. FUNDING: Steven & Alexandra Cohen Foundation, Global Lyme Alliance, and the Pazala Foundation; National Institutes of Health R01ES032470.

The demographic and socioeconomic correlates of behavior and HIV infection status across sub-Saharan Africa.

Background: Predisposition to become HIV positive (HIV + ) is influenced by a wide range of correlated economic, environmental, demographic, social, and behavioral factors. While evidence among a candidate handful have strong evidence, there is lack of a consensus among the vast array of variables measured in large surveys. Methods: We performed a comprehensive data-driven search for correlates of HIV positivity in >600,000 participants of the Demographic and Health Survey across 29 sub-Saharan African countries from 2003 to 2017. We associated a total of 7251 and of 6,288 unique variables with HIV positivity in females and males respectively in each of the 50 surveys. We performed a meta-analysis within countries to attain 29 country-specific associations. Results: Here we identify 344 (5.4% out possible) and 373 (5.1%) associations with HIV + in males and females, respectively, with robust statistical support. The associations are consistent in directionality across countries and sexes. The association sizes among individual correlates and their predictive capability were low to modest, but comparable to established factors. Among the identified associations, variables identifying being head of household among females was identified in 17 countries with a mean odds ratio (OR) of 2.5 (OR range: 1.1-3.5, R2 = 0.01). Other common associations were identified, including marital status, education, age, and ownership of land or livestock. Conclusions: Our continent-wide search for variables has identified under-recognized variables associated with being HIV + that are consistent across the continent and sex. Many of the association sizes are as high as established risk factors for HIV positivity, including male circumcision.

Characterising the relationships between physiological indicators and all-cause mortality (NHANES): a population-based cohort study.

BACKGROUND: Mortality risk stratification based on dichotomising a physiological indicator with a cutoff point might not adequately capture increased mortality risk and might not account for non-linear associations. We aimed to characterise the linear and non-linear relationships of 27 physiological indicators with all-cause mortality to evaluate whether the current clinical thresholds are suitable in distinguishing patients at high risk for mortality from those at low risk. METHODS: For this observational cohort study of the US non-institutionalised population, we used data from adults (≥18 years) included in the 1999-2014 National Health and Nutrition Examination Survey (NHANES) linked with National Death Index mortality data collected from Jan 1, 1999, up until Dec 31, 2015. We used Cox proportional hazards regression models adjusted for age, sex, and race or ethnicity to assess associations of physiological indicators with all-cause mortality. We assessed non-linear associations by discretising the physiological indicator into nine quantiles (termed novemtiles) and by using a weighted sum of cubic polynomials (spline). We used ten-fold cross validation to select the most appropriate model using the concordance index, Nagelkerke R2, and Akaike Information Criterion. We identified the level of each physiological indicator that led to a 10% increase in mortality risk to define our cutoffs used to compare with the current clinical thresholds. FINDINGS: We included 47 266 adults of 82 091 assessed for eligibility. 25 (93%) of 27 indicators showed non-linear associations with substantial increases compared with linear models in mortality risk (1·5-2·5-times increase). Height and 60 s pulse were the only physiological indicators to show linear associations. For example, participants with an estimated glomerular filtration rate (GFR) of less than 65 mL/min per 1·73 m2 or between 90-116 mL/min per 1·73 m2 are at moderate (hazard ratio 1-2) mortality risk. Those with a GFR greater than 117 mL/min per 1·73 m2 show substantial (hazard ratio ≥2) mortality risk. Both lower and higher values of cholesterol are associated with increased mortality risk. The current clinical thresholds do not align with our mortality-based cutoffs for fat deposition indices, 60 s pulse, triglycerides, cholesterol-related indicators, alkaline phosphatase, glycohaemoglobin, homoeostatic model assessment of insulin resistance, and GFR. For these indicators, the misalignment suggests the need to consider an additional bound when only one is provided. INTERPRETATION: Most clinical indicators were shown to have non-linear associations with all-cause mortality. Furthermore, considering these non-linear associations can help derive reliable cutoffs to complement risk stratification and help inform clinical care delivery. Given the poor alignment with our proposed cutoffs, the current clinical thresholds might not adequately capture mortality risk.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery.

BACKGROUND: Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown. OBJECTIVES: We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics. DISCUSSION: The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships. CONCLUSIONS: Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.

Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families.

Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case-control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD (P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband-parents) were -0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.

Characteristics of undiagnosed diabetes in men and women under the age of 50 years in the Indian subcontinent: the National Family Health Survey (NFHS-4)/Demographic Health Survey 2015-2016.

OBJECTIVE: Prior studies examining diabetes prevalence in India have found that nearly 50% of the diabetes population remains undiagnosed; however, the specific populations at risk are unclear. RESEARCH DESIGN AND METHODS: First, we estimated the prevalence of undiagnosed diabetes in India for 750 924 persons between the ages of 15 years and 50 years who participated in the National Family Health Survey (NFHS-4)/Demographic Health Survey (2015-2016), a cross-sectional survey of all 29 states and 7 union territories of India. We defined 'undiagnosed diabetes' as individuals who did not know about their diabetes status but had high random (≥200 mg/dL) or fasting (≥126 mg/dL) blood glucose levels. Second, using Poisson regression, we associated 10 different factors, including the role of healthcare access, and undiagnosed diabetes. Third, we examined the association of undiagnosed diabetes with other potential comorbid conditions. RESULTS: The crude prevalence of diabetes for women and men aged 15-50 years was 2.9%, 95% CI 2.9% to 3.1%, with self-reported diabetes prevalence at 1.7%, 95% CI 1.6 to 1.8. The overall prevalence of undiagnosed diabetes for 15-50 year olds was at 1.2%, 95% CI 1.2% to 1.3%. Forty-two per cent, 95% CI 40.7% to 43.4% of the individuals with high glucose levels were unaware of their diabetes status. Approximately 45%, 95% CI 42.9% to 46.4% of undiagnosed diabetes population had access to healthcare. Men, younger individuals, and those with lower levels of education were most at risk of being undiagnosed. Geographically, the Southern states in India had a significantly higher prevalence of undiagnosed diabetes despite having nearly universal access to healthcare. Risk factors combined with random glucose could predict undiagnosed diabetes (area under the curve of 97.8%, 95% CI 97.7% to 97.8%), Nagelkerke R2 of 66%). CONCLUSION: Close to half (42%) of the people with diabetes in India are not aware of their disease status, and a large subset of these people are at risk of poor detection, despite having health insurance and/or having access to healthcare. Younger age groups and men are the most vulnerable.

Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes.

OBJECTIVES: Exposome-wide association studies (EWAS) are a systematic and unbiased way to investigate multiple environmental factors associated with phenotype. We applied EWAS to study semen quality and queried the sample size requirements to detect modest associations in a reproductive cohort. STUDY DESIGN AND SETTING: We conducted 1) a multivariate EWAS of 128 endocrine disrupting chemicals (EDCs) from 15 chemical classes measured in urine/serum relative to 7 semen quality endpoints in a prospective cohort study comprising 473 men and 2) estimated the sample size requirements for EWAS etiologic investigations. RESULTS: None of the EDCs were associated with semen quality endpoints after adjusting for multiple tests. However, several EDCs (e.g., polychlorinated biphenyl congeners 99, 105, 114, and 167) were associated with raw p < 0.05. In a post hoc statistical power analysis with the observed effect sizes, we determined that EWAS research in male fertility will require a mean sample size of 2696 men (1795-3625) to attain a power of 0.8. The average size of four published studies is 201 men. CONCLUSION: Existing cohort studies with hundreds of participants are underpowered (<0.8) for EWAS-related investigations. Merging cohorts to ensure a sufficient sample size can facilitate the use of EWAS methods for assessing EDC mixtures that impact semen quality.

Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment.

Many factors affect the variation in the exposome. We examined the influence of shared household and partner's sex in relation to the variation in 128 endocrine disrupting chemical (EDC) exposures among couples. In a cohort comprising of 501 couples trying for pregnancy, we measured 128 (13 chemical classes) persistent and nonpersistent EDCs and estimated 1) sex-specific differences; 2) variance explained by shared household; and 3) Spearman's rank correlation coefficients ( rs) for females, males, and couples' exposures. Sex was correlated with 8 EDCs including per- and polyfluoroalkyl substances (PFASs) ( p < 0.05). Shared household explained 43% and 41% of the total variance for PFASs and blood metals, respectively, but less than 20% for the remaining 11 EDC classes. Coexposure patterns of the exposome were similar between females and males, with within-class rs higher for persistent than for nonpersistent chemicals. Median rss of polybrominated compounds and urine metalloids were 0.45 and 0.09, respectively, for females (0.41 and 0.08 for males; 0.21 and 0.04 for couples). Our findings suggest that individual, rather than shared environment, could be a major factor influencing the covariation of the exposome. Understanding the correlations of exposures has important analytical and sampling implications for exposomics research.

Cardiopulmonary effects of overnight indoor air filtration in healthy non-smoking adults: A double-blind randomized crossover study.

BACKGROUND: More than 90% of the world's population lives in areas where outdoor air pollution levels exceed health-based limits. In these areas, individuals may use indoor air filtration, often on a sporadic basis, in their residences to reduce exposure to respirable particles (PM2.5). Whether this intervention can lead to improvements in health outcomes has not been evaluated. METHODS: Seventy non-smoking healthy adults, aged 19 to 26 years, received both true and sham indoor air filtration in a double-blinded randomized crossover study. Each filtration session was approximately 13 h long. True and sham filtration sessions were separated by a two-week washout interval. The study was carried out in a suburb of Shanghai. RESULTS: During the study period, outdoor PM2.5 concentrations ranged from 18.6 to 106.9 µg/m3, which overlapped with levels measured in Western Europe and North America. Compared to sham filtration, true filtration on average decreased indoor PM2.5 concentration by 72.4% to 10.0 µg/m3 and particle number concentration by 59.2% to 2316/cm3. For lung function measured immediately after the end of filtration, true filtration significantly lowered airway impedance at 5 Hz (Z5) by 7.1% [95% CI: 2.4%, 11.9%], airway resistance at 5 Hz (R5) by 7.4% [95% CI: 2.4%, 12.5%], and small airway resistance (R5-R20) by 20.3% [95% CI: 0.1%, 40.5%], reflecting improved airway mechanics especially for the small airways. However, no significant improvements for spirometry indicators (FEV1, FVC) were observed. True filtration also significantly lowered von Willebrand factor (VWF) by 26.9% [95% CI: 7.3%, 46.4%] 24 h after the end of filtration, indicating reduced risk for thrombosis. Stratified analysis in male and female participants showed that true filtration significantly decreased pulse pressure by 3.3% [95% CI: 0.8%, 7.4%] in females, and significantly reduced VWF by 42.4% [95% CI: 17.4%, 67.4%] and interleukin-6 by 22.6% [95% CI: 0.4%, 44.9%] in males. Effect modification analyses indicated that filtration effects in male and female participants were not significantly different. CONCLUSION: A single overnight residential air filtration, capable of reducing indoor particle concentrations substantially, can lead to improved airway mechanics and reduced thrombosis risk.

Metabolic profiling of residents in the vicinity of a petrochemical complex.

No previous studies have simultaneously measured the biomarkers of environmental exposure and metabolome perturbation in residents affected by industrial pollutants. This study aimed to investigate the metabolic effects of environmental pollutants such as vanadium and polycyclic aromatic hydrocarbons (PAHs) on residents in the vicinity of a petrochemical complex. The study subjects were 160 residents, including 80 high-exposure subjects exposed to high levels of vanadium and PAHs and 80 age- and gender-matched low-exposure subjects living within a 40-km radius of a petrochemical complex. The exposure biomarkers vanadium and 1-hydroxypyrene and four oxidative/nitrosative stress biomarkers were measured in these subjects. Plasma samples from the study subjects were also analyzed using (1)H NMR spectroscopy for metabolic profiling. The results showed that the urinary levels of vanadium and 1-hydroxypyrene in the high-exposure subjects were 40- and 20-fold higher, respectively, than those in the low-exposure subjects. Higher urinary levels of stress biomarkers, including 8-OHdG, HNE-MA, 8-isoPF2α, and 8-NO2Gua, were also observed among the high-exposure subjects compared with the low-exposure subjects. Partial least squares discriminant analysis of the plasma metabolome demonstrated a clear separation between the high- and low-exposure subjects; the intensities of amino acids and carbohydrate metabolites were lower in the high-exposure subjects compared with the low-exposure subjects. The exposure to vanadium and PAHs may cause a reduction in the levels of amino acids and carbohydrates by elevating PPAR and insulin signaling, as well as oxidative/nitrosative stress.

Antibody enrichment and mass spectrometry of albumin-Cys34 adducts.

Untargeted analyses of tryptic peptides of human serum albumin (HSA) have been used to investigate unknown exposures to reactive electrophiles (adductomics). To reduce the complexity of the analytical matrix and thereby enhance identification of adducts by liquid chromatography-high-resolution mass spectrometry (LC-HRMS), a polyclonal anti-T3 antibody was designed to capture Cys34 adducts in tryptic digests of HSA (T3 is the third largest tryptic peptide). Epitopes were selected from sequences at both C- and N-termini based on the three-dimensional structure of the T3 peptide to minimize the influence of modified Cys34 residues. The assay was simplified by attaching magnetic beads to the anti-T3 antibody. When applied to commercial HSA and to plasma samples from healthy humans and analyzed by LC-HRMS, antibody treatment greatly reduced the background of non-T3 peptides in the sample matrix. Although other lipophilic HSA peptides were still present, presumably due to nonspecific binding to the antibody-magnetic-bead surfaces, their concentrations in antibody-treated samples were reduced about 6-fold compared to the same samples that had not been treated with the antibody. Analysis of antibody-enriched HSA digests from human plasma samples revealed 10 modified T3 peptides of which 8 were identified from accurate masses. Identified peptides included Cys34 oxidation and cysteinylation products and modifications representing losses of water and Lys and transpeptidation of Arg.

A sandwich ELISA for measuring benzo[a]pyrene-albumin adducts in human plasma.

Exposure to polycyclic aromatic hydrocarbons has often been quantified via DNA or human serum albumin (HSA) adducts of the carcinogenic metabolite benzo[a]pyrene diol epoxide (BPDE). We previously reported a sandwich ELISA, using 8E11 as capture antibody and anti-HSA as detection antibody, that detected intact BPDE adducts in HSA isolated from plasma. After confirming that BPDE binds to HSA at His146 and Lys195, we modified the ELISA to measure intact BPDE-HSA directly in human plasma. To adjust for interference due to nonspecifically bound HSA on well surfaces and to cross-reactivity of the antibodies, the ELISA employs paired wells with and without addition of BPDE tetrols to deactivate 8E11. By performing assays in quadruplicate, a series of sample-specific adjustments and screening steps are used to reduce measurement errors that are a consequence of detecting low BPDE-HSA concentrations in the general population. ELISA measurements of BPDE-HSA in plasma from smoking and nonsmoking subjects (range 0.280-2.88 ng BPDE-HSA/mg HSA) and from highway workers with and without exposure to asphalt emissions (range 0.346-13.9 ng BPDE-HSA/mg HSA) detected differences in BPDE-HSA levels in the a priori expected directions.

A sandwich enzyme-linked immunosorbent assay for adducts of polycyclic aromatic hydrocarbons with human serum albumin.

Adducts of benzo[a]pyrene-diolepoxide (BPDE) with blood nucleophiles have been used as biomarkers of exposure to polycyclic aromatic hydrocarbons (PAHs). The most popular such assay is a competitive enzyme-linked immunosorbent assay (ELISA) that employs monoclonal antibody 8E11 to detect benzo[a]pyrene tetrols following hydrolysis of BPDE adducts from lymphocyte DNA or human serum albumin (HSA). Here we used 8E11 as the capture antibody in a sandwich ELISA to detect BPDE-HSA adducts directly in 1-mg samples of HSA or 20 microl of serum/plasma. The assay employs an anti-HSA antibody for detection, and this is amplified by an avidin/biotinylated horseradish peroxidase complex. The sandwich ELISA has advantages of specificity and simplicity and is approximately 10 times more sensitive than the competitive ELISA. To validate the assay, HSA samples were assayed from three populations with known high PAH exposures (coke oven workers), medium PAH exposures (steel factory control workers), and low PAH exposures (volunteer subjects) (n=30). The respective geometric mean levels of BPDE-HSA adducts--67.8, 14.7, and 1.93 ng/mg HSA (1010, 220, and 28.9 fmol BPDE equiv/mg HSA)--were significantly different (P<0.05). The sandwich ELISA will be useful for screening PAH exposures in large epidemiologic studies and can be extended to other adducts for which capture antibodies are available.