1,3-Butadiene (BD) is a monomer produced in petrochemical production facilities and from several combustion sources. The United States Environmental Protection Agency has defined BD as a probable human carcinogen. Methods for assessing exposure and internal dose are therefore of critical interest, and one technique is the measurement of urinary metabolites. Here we describe methods for measuring two urinary metabolites, N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine (referred to as MI) and an isomeric mixture of the regio- and stereoisomers (R)/(S)-N-acetyl-S-(1-(hydroxymethyl)-2-propen-yl)-L-cysteine and (R)/(S)-N-acetyl-S-(2-hydroxy-3-butenyl)-L-cysteine (referred to as MII). The method is based on isolation of the metabolites by solid-phase extraction and measurement using liquid chromatography and triple quadrupole mass spectrometry (LC-MS(3)). The LC-MS(3) allowed good selectivity with minimal sample preparation. Assay accuracy was within 10% or better, with substantial improvement in accuracy accompanying the commercial availability of deuterated internal standards for both compounds. Assay precision and linearity passed rigorous validation criteria, and precision-based limits of quantitation values were 12 and 1 ng/mL for MI and MII, respectively. Data are shown from analysis of human urine from occupationally exposed individuals and rat urine from BD exposures conducted to investigate rodent metabolic profiles. Both of these data sets clearly show that this assay can discern previously described relationships between BD exposure and the production of MI/MII.
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http://dx.doi.org/10.1093/jat/28.3.168 | DOI Listing |
Rheumatoid arthritis (RA) is closely associated with environmental factors. Volatile organic compounds (VOCs) are a common environment pollutant which can induce autoimmune diseases. However, studies on the relationship between VOCs and RA are still unclear.
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December 2024
Nephrology Department, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Zhejiang, China.
The risk of infertility is progressively escalating over the years, and it has been established that exposure to environmental pollutants is closely linked to infertility. As a prevalent environmental pollutant in daily life, there is still a lack of substantial evidence on the association between volatile organic compounds (VOCs) exposure and infertility risk. This study aimed to examine the association between VOCs exposure and the risk of female infertility in the United States.
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December 2024
Instituto de Ciencias de la Vid y del Vino-ICVV (Consejo Superior de Investigaciones Científicas-CSIC, Universidad de La Rioja, Gobierno de La Rioja), Finca La Grajera, Ctra. de Burgos Km. 6 (LO-20, salida 13), Logroño E-26007, La Rioja, Spain. Electronic address:
The epidemiological assessment of wine consumption usually has been obtained using self-reporting questionnaires. In this study, two metabolomic approaches, targeted and untargeted, were applied to 24-h urine samples from a cohort of La Rioja (Spain) (aged 52-78), comparing moderate and daily wine consumers (20 males and 13 females) without diet intervention, versus non-consumers (8 males and 35 females). Results showed that the non-targeted metabolomics approach has allowed for the annotation of sixteen compounds in 24-h urine samples from regular wine-consumers that were not detected in the urine of non-wine consumers.
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December 2024
School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei 230032, China.
Background: Our previous study showed that antibiotic exposure was linked to depressive symptomatology in community-dwelling older adults in China. Our current study aims to explore the underlying mechanisms by assessing the intermediated effects of circulating short-chain organic acids (SCOAs) on this association.
Methods: Depressive symptoms were screened by the 30-item Geriatric Depression Scale (GDS-30).
Metabolites
December 2024
Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles.
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