ATSDR evaluation of potential for human exposure to tungsten

Keith, L. Samuel; Wohlers, David; Moffett, Daphne B.; Rosemond, Zemoria A.

doi:10.1177/0748233707081906

Cited by 22 publications

(12 citation statements)

References 59 publications

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“…Tungsten is commonly used in metal alloys (e.g., cemented carbide) and in numerous tools, automotive parts, and other products. The widespread use of tungsten alloys makes low-level occupational exposure common, but it usually involves coexposure with other metals ( 30 ). Environmental exposure to tungsten also occurs commonly through water, food, and ambient air.…”

Section: Discussionmentioning

confidence: 99%

Metals in Urine and Diabetes in U.S. Adults

2015

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Our objective was to evaluate the relationship of urine metals including barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium, tungsten, and uranium with diabetes prevalence. Data were from a cross-sectional study of 9,447 participants of the 1999–2010 National Health and Nutrition Examination Survey, a representative sample of the U.S. civilian noninstitutionalized population. Metals were measured in a spot urine sample, and diabetes status was determined based on a previous diagnosis or an A1C ≥6.5% (48 mmol/mol). After multivariable adjustment, the odds ratios of diabetes associated with the highest quartile of metal, compared with the lowest quartile, were 0.86 (95% CI 0.66–1.12) for barium (Ptrend = 0.13), 0.74 (0.51–1.09) for cadmium (Ptrend = 0.35), 1.21 (0.85–1.72) for cobalt (Ptrend = 0.59), 1.31 (0.90–1.91) for cesium (Ptrend = 0.29), 1.76 (1.24–2.50) for molybdenum (Ptrend = 0.01), 0.79 (0.56–1.13) for lead (Ptrend = 0.10), 1.72 (1.27–2.33) for antimony (Ptrend < 0.01), 0.76 (0.51–1.13) for thallium (Ptrend = 0.13), 2.18 (1.51–3.15) for tungsten (Ptrend < 0.01), and 1.46 (1.09–1.96) for uranium (Ptrend = 0.02). Higher quartiles of barium, molybdenum, and antimony were associated with greater HOMA of insulin resistance after adjustment. Molybdenum, antimony, tungsten, and uranium were positively associated with diabetes, even at the relatively low levels seen in the U.S. population. Prospective studies should further evaluate metals as risk factors for diabetes.

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Section: Discussionmentioning

confidence: 99%

Metals in Urine and Diabetes in U.S. Adults

2015

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“…Agarwal et al [ 50 ] also reported a significant positive association between cardiovascular and cerebrovascular disease and tungsten which was selected as a main effect as well as interaction with lead in AENET-I and as the fourth most important variable in BART. Tungsten, commonly used in metal alloys [ 59 ], has also been positively associated with diabetes [ 60 ] and peripheral artery disease [ 54 ]. Barium, a silvery-white metal used for many different purposes in industry [ 61 ], was also selected in our multipollutant models.…”

Section: Discussionmentioning

confidence: 99%

Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES

2017

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Background: There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutantpollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints. Methods: We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003(NHANES -2004(NHANES to 2013(NHANES -2014. We randomly split the data evenly into training and testing sets and constructed ERS's of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. Results: ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS's showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS's showed non-significant positive associations with mortality outcomes. Conclusions: ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

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“…Cathepsin B mediates direct NLRP3 oligomerization or ROS formation via mitochondrial membrane disruption (5). ROS in turn facilitates the dissociation of TXNIP from its inhibitor TRX (6). Newly dissociated TXNIP, binds to inactive NLRP3 (7) resulting in NLRP3 activation and subsequent oligomerization (8) which activates caspase-1 (9).…”

Section: Discussionmentioning

confidence: 99%

“…Further, increased use of tungsten may potentially contaminate surrounding air, water, and soil near tungsten mines and industrial sites. 6 Tungsten has been identified at several hazardous waste sites that have been proposed for inclusion on the environmental protection agency's (EPA) national priority list. 4 Regulatory agencies from various countries (USA, Australia, Sweden, New Zealand, China) have issued regulatory guidelines with respect to tungsten exposure.…”

Section: Introductionmentioning

confidence: 99%

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Original Research: Evaluation of pulmonary response to inhaled tungsten (IV) oxide nanoparticles in golden Syrian hamsters

Prajapati

Adebolu

Morrow

et al. 2016

Exp Biol Med (Maywood)

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Extensive industrial and military uses of tungsten have raised the possibilities of human occupational and environmental exposure to nanoparticles of this metal, with concomitant health concerns. The goal of this study was to investigate the potential mechanism of pulmonary toxicity associated with inhaled tungsten (IV) oxide nanoparticles (WO NPs) in Golden Syrian Hamsters. Animals exposed to WO NPs via inhalation were divided into three groups - control and two treatment groups exposed to either 5 or 10 mg/m of aerosolized WO NPs for 4 h/day for four days. A long-term exposure study (4 h/day for eight days) was also carried out using an additional three groups. Pulmonary toxicity assessed by examining changes in cell numbers, lactate dehydrogenase activity, alkaline phosphatase activity, total protein content, TNF-α, and HMGB1 levels in bronchoalveolar lavage fluids showed a significant difference when compared to control (P < 0.05). The molecular mechanism was established by assessing protein expression of cathepsin B, TXNIP, NLRP3, ASC, IL-1β and caspase-1. Western blot analysis indicated a 1.5 and 1.7 fold changes in NLRP3 in treatment groups (5 mg/m, P < 0.05 and 10 mg/m, P < 0.01, respectively), whereas levels of cathepsin B were 1.3 fold higher in lung tissue exposed to WO NPs suggesting activation of inflammasome pathway. Morphological changes studied using light and electron microscopy showed localization of nanoparticles and subsequent perturbation in airway epithelia, macrophages, and interstitial areas of alveolar structures. Results from the current study indicate that inhalation exposure to WO NPs may induce cytotoxicity, morphological changes, and lung injury via pyroptotic cell death pathway caused by activation of caspase-1.

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ATSDR evaluation of potential for human exposure to tungsten

Cited by 22 publications

References 59 publications

Metals in Urine and Diabetes in U.S. Adults

Metals in Urine and Diabetes in U.S. Adults

Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES

Original Research: Evaluation of pulmonary response to inhaled tungsten (IV) oxide nanoparticles in golden Syrian hamsters

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