Predicting Occupational Exposures to Carbon Nanotubes and Nanofibers Based on Workplace Determinants Modeling

Quantile regression for longitudinal data with values below the limit of detection and time-dependent covariates-application to modeling carbon nanotube and nanofiber exposures

BACKGROUND

In studies of occupational health, longitudinal environmental exposure, and biomonitoring data are often subject to right skewing and left censoring, in which measurements fall below the limit of detection (LOD). To address right-skewed data, it is common practice to log-transform the data and model the geometric mean, assuming a log-normal distribution. However, if the transformed data do not follow a known distribution, modeling the mean of exposure may result in bias and reduce efficiency. In addition, when examining longitudinal data, it is possible that certain covariates may vary over time.

OBJECTIVE

To develop predictive quantile regression models to resolve the issues of left censoring and time-dependent covariates and to quantitatively evaluate if previous and current covariates can predict current and/or future exposure levels.

METHODS

To address these gaps, we suggested incorporating different substitution approaches into quantile regression and utilizing a method for selecting a working type of time dependency for covariates.

RESULTS

In a simulation study, we demonstrated that, under different types of time-dependent covariates, the approach of multiple random value imputation outperformed the other approaches. We also applied our methods to a carbon nanotube and nanofiber exposure study. The dependent variables are the left-censored mass of elemental carbon at both the respirable and inhalable aerosol size fractions. In this study, we identified some potential time-dependent covariates with respect to worker-level determinants and job tasks.

CONCLUSION

Time dependency for covariates is rarely accounted for when analyzing longitudinal environmental exposure and biomonitoring data with values less than the LOD through predictive modeling. Mistreating the time-dependency as time-independency will lead to an efficiency loss of regression parameter estimation. Therefore, we addressed time-varying covariates in longitudinal exposure and biomonitoring data with left-censored measurements and illustrated an entire conditional distribution through different quantiles.

Validation of a full-shift benzene exposure empirical model developed for work on offshore petroleum installations on the Norwegian continental shelf

Workers on offshore petroleum installations might be exposed to benzene, a carcinogenic agent. Recently, a full-shift benzene exposure model was developed based on personal measurements. This study aimed to validate this exposure model by using datasets not included in the model. The exposure model was validated against an internal dataset of measurements from offshore installations owned by the same company that provided data for the model, and an external dataset from installations owned by another company. We used Tobit regression to estimate GM (geometric mean) benzene exposure overall and for individual job groups. Bias, relative bias, precision, and correlation were estimated to evaluate the agreement between measured exposures and the levels predicted by the model. Overall, the model overestimated exposure when compared to the predicted exposure level to the internal dataset with a factor of 1.7, a relative bias of 73%, a precision of 0.6, a correlation coefficient of 0.72 (p = 0.019), while the Lin's Concordance Correlation Coefficient (CCC) was 0.53. The model underestimated exposure when compared to the external dataset with a factor of about 2, with a relative bias of -45%, a precision of 1.2, a correlation coefficient of 0.31 (p = 0.544), and a Lin's CCC of 0.25. The exposure model overestimated benzene exposure in the internal validation dataset, while the precision and the correlation between the measured and predicted exposure levels were high. Differences in measurement strategies could be one of the reasons for the discrepancy. The exposure model agreed less with the external dataset.

A harmonized protocol for an international multicenter prospective study of nanotechnology workers: the NanoExplore cohort

Nanotechnology applications are fast-growing in many industrial fields. Consequently, health effects of engineered nanomaterials (ENMs) should be investigated. Within the EU-Life project NanoExplore, we developed a harmonized protocol of an international multicenter prospective cohort study of workers in ENM-producing companies. This article describes the development of the protocol, sample size calculation, data collection and management procedures and discusses its relevance with respect to research needs. Within this protocol, workers' ENM exposure will be assessed over four consecutive working days during the initial recruitment campaign and the subsequent follow-up campaigns. Biomonitoring using noninvasive sampling of exhaled breath condensate (EBC), exhaled air, and urine will be collected before and after 4-day exposure monitoring. Both exposure and effect biomarkers, will be quantified along with pulmonary function tests and diagnosed diseases reported using a standardized epidemiological questionnaire available in four languages. Until now, this protocol was implemented at seven companies in Switzerland, Spain and Italy. The protocol is well standardized, though sufficiently flexible to include company-specific conditions and occupational hygiene measures. The recruitment, to date, of 140 participants and collection of all data and samples, enabled us launching the first international cohort of nanotechnology workers. All companies dealing with ENMs could join the NanoExplore Consortium, apply this harmonized protocol and enter in the cohort, concieved as an open cohort. Its protocol meets all requirements of a hypotheses-driven prospective study, which will assess and reassess effects of ENM exposure on workers' health by updating the follow-up of the cohort. New hypothesis could be also considered.

Serum peptidome: diagnostic window into pathogenic processes following occupational exposure to carbon nanomaterials

BACKGROUND

Growing industrial use of carbon nanotubes and nanofibers (CNT/F) warrants consideration of human health outcomes. CNT/F produces pulmonary, cardiovascular, and other toxic effects in animals along with a significant release of bioactive peptides into the circulation, the augmented serum peptidome. While epidemiology among CNT/F workers reports on few acute symptoms, there remains concern over sub-clinical CNT/F effects that may prime for chronic disease, necessitating sensitive health outcome diagnostic markers for longitudinal follow-up.

METHODS

Here, the serum peptidome was assessed for its biomarker potential in detecting sub-symptomatic pathobiology among CNT/F workers using label-free data-independent mass spectrometry. Studies employed a stratified design between High (> 0.5 µg/m3) and Low (< 0.1 µg/m3) inhalable CNT/F exposures in the industrial setting. Peptide biomarker model building and refinement employed linear regression and partial least squared discriminant analyses. Top-ranked peptides were then sequence identified and evaluated for pathological-relevance.

RESULTS

In total, 41 peptides were found to be highly discriminatory after model building with a strong linear correlation to personal CNT/F exposure. The top-five peptide model offered ideal prediction with high accuracy (Q2 = 0.99916). Unsupervised validation affirmed 43.5% of the serum peptidomic variance was attributable to CNT/F exposure. Peptide sequence identification reveals a predominant association with vascular pathology. ARHGAP21, ADAM15 and PLPP3 peptides suggest heightened cardiovasculature permeability and F13A1, FBN1 and VWDE peptides infer a pro-thrombotic state among High CNT/F workers.

CONCLUSIONS

The serum peptidome affords a diagnostic window into sub-symptomatic pathology among CNT/F exposed workers for longitudinal monitoring of systemic health risks.

Occupational Exposure to Carbon Nanotubes and Carbon Nanofibres: More Than a Cobweb

Carbon nanotubes (CNTs) and carbon nanofibers (CNFs) are erroneously considered as singular material entities. Instead, they should be regarded as a heterogeneous class of materials bearing different properties eliciting particular biological outcomes both in vitro and in vivo. Given the pace at which the industrial production of CNTs/CNFs is increasing, it is becoming of utmost importance to acquire comprehensive knowledge regarding their biological activity and their hazardous effects in humans. Animal studies carried out by inhalation showed that some CNTs/CNFs species can cause deleterious effects such as inflammation and lung tissue remodeling. Their physico-chemical properties, biological behavior and biopersistence make them similar to asbestos fibers. Human studies suggest some mild effects in workers handling CNTs/CNFs. However, owing to their cross-sectional design, researchers have been as yet unable to firmly demonstrate a causal relationship between such an exposure and the observed effects. Estimation of acceptable exposure levels should warrant a proper risk management. The aim of this review is to challenge the conception of CNTs/CNFs as a single, unified material entity and prompt the establishment of standardized hazard and exposure assessment methodologies able to properly feed risk assessment and management frameworks.

Evaluation of total and inhalable samplers for the collection of carbon nanotube and carbon nanofiber aerosols

A growing number of carbon nanotubes and nanofibers (CNT/F) exposure and epidemiologic studies have utilized 25-mm and 37-mm open-faced cassettes (OFC) to assess the inhalable aerosol fraction. It has been previously established that the 37-mm OFC under-samples particles greater than 20 μm in diameter, but the size-selective characteristics of the 25-mm OFC have not yet been fully evaluated. This article describes an experimental study conducted to determine if the 25- and 37-mm OFCs performed with relative equivalence to a reference inhalable aerosol sampler when challenged with CNT/F particles. Side-by-side paired samples were collected within a small Venturi chamber using a 25-mm styrene OFC, 37-mm styrene OFC, 25-mm aluminum OFC, and Button Inhalable Aerosol Sampler. Three types of CNT/F materials and an Arizona road dust were used as challenge aerosols for the various sampler configurations. Repeated experiments were conducted for each sampler configuration and material. The OFC samplers operated at flow rates of 2 and 5 liters per minute. Results showed that the 25-mm OFC performed comparably to the Button Sampler when challenged with CNT/F aerosols, which was demonstrated in five of the six experimental scenarios with an average error of 20%. Overall, the results of this study indicate that the sampling efficiency of the 25- and 37-mm OFCs adequately followed the ISO/ACGIH/CEN inhalable sampling convention when challenged with CNT/F aerosols. Past exposure and epidemiologic studies that used these OFC samplers can directly compare their results to studies that have used other validated inhalable aerosol samplers.