Early Effect Markers and Exposure Determinants of Metalworking Fluids Among Metal Industry Workers: Protocol for a Field Study
Bibliographic record
Abstract
BACKGROUND: Exposure to aerosols from metalworking fluids (MWF) has previously been related to a series of adverse health outcomes (eg, cancer, respiratory diseases). Our present epidemiological study focuses on occupational exposures to MWF and a panel of exposure and effect biomarkers. We hypothesize that these health outcomes are caused by particle exposure that generates oxidative stress, leading to airway inflammation and ultimately to chronic respiratory diseases. We aimed to assess whether MWF exposure, in particular as characterized by its oxidative potential, is associated with biomarkers of oxidative stress and inflammation as well as genotoxic effects. OBJECTIVE: The ultimate goal is to develop exposure reduction strategies based on exposure determinants that best predict MWF-related health outcomes. The following relationships will be explored: (1) exposure determinants and measured exposure; (2) occupational exposure and preclinical and clinical effect markers; (3) exposure biomarkers and biomarkers of effect in both exhaled breath condensate and urine; and (4) biomarkers of effect, genotoxic effects and respiratory symptoms. METHODS: At least 90 workers from France and Switzerland (30 controls, 30 exposed to straight MWF and 30 to aqueous MWF) were followed over three consecutive days after a nonexposed period of at least two days. The exposure assessment is based on MWF, metal, aldehyde, and ultrafine particle number concentrations, as well as the intrinsic oxidative potential of aerosols. Furthermore, exposure biomarkers such as metals, metabolites of polycyclic aromatic hydrocarbons and nitrosamine are measured in exhaled breath condensate and urine. Oxidative stress biomarkers (malondialdehyde, 8-isoprostane, 8-hydroxy-2'-deoxyguanosine, nitrates, and nitrites) and exhaled nitric oxide, an airway inflammation marker, are repeatedly measured in exhaled breath condensate and urine. Genotoxic effects are assessed using the buccal micronucleus cytome assay. The statistical analyses will include modelling exposure as a function of exposure determinants, modelling the evolution of the biomarkers of exposure and effect as a function of the measured exposure, and modelling respiratory symptoms and genotoxic effects as a function of the assessed long-term exposure. RESULTS: Data collection, which occurred from January 2018 until June 2019, included 20 companies. At the date of writing, the study included 100 subjects and 29 nonoccupationally exposed controls. CONCLUSIONS: This study is unique as it comprises human biological samples, questionnaires, and MWF exposure measurement. The biomarkers collected in our study are all noninvasive and are useful in monitoring MWF exposed workers. The aim is to develop preventative strategies based on exposure determinants related to health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13744.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".