Assessing Welding Fume Exposure Among Professional Welders: Exploring Biomarkers of Exposure and Markers of Health Effects
Bibliographic record
Abstract
Welding is widely used in various industrial settings. Exposure to welding fumes is associated with various health conditions, such as cardiopulmonary diseases and cancer. This dissertation comprises four studies that collectively assess exposure to welding fumes among professional welders using an integrative biomonitoring and metabolomics approach. Study 1 aimed to synthesize the existing evidence on the associations between welding fume exposure and changes in oxidative stress [superoxide dismutase (SOD) and malondialdehyde (MDA)] and DNA damage [8-hydroxy-2′-deoxyguanosine (8-OHdG) and DNA-protein crosslink (DPC)] markers in professional welders. A thorough search was performed in Embase, Web of Science, Scopus, Medline, and CINAHL, supplemented by grey literature. Data were analyzed using narrative synthesis and random-effects meta-analysis. Of the 450 retrieved studies, 14 met the inclusion criteria and were included in the review. Meta-analyses showed significant differences in the 8-OHdG (MD = 9.38; 95% CI, 0.55–18.21) and DPC (MD = 1.07; 95% CI, 0.14–2) levels between welders and controls. However, no significant difference was observed in MDA levels (MD = 0.26; 95% CI, -0.03, 0.55) between the groups, whereas narrative synthesis showed an inconsistent trend in SOD levels. The included studies had a high risk of exclusion and confounding biases. The results suggest an association between welding fume exposure and DNA damage in professional welders, although the evidence is limited. Further studies are warranted to evaluate the potential of other biomarkers for assessing oxidative stress and DNA damage in welders and the underlying mechanisms. Study 2 aimed to evaluate the field effectiveness of respirators against metal particle exposure through a comprehensive systematic review of major bibliographic databases and grey literature sources. Of the 463 references, 70 underwent full-text screening, and eight papers satisfied the inclusion criteria and were included in the review. Studies have reported significant differences in metal particle levels between workers who wore respirators and those who did not (p˂0.05). We also found that N95 respirators provided significantly less protection than elastomeric and powered air-purifying respirators (p˂0.001). The results underscore the need to implement more field studies, including biomonitoring and metabolomics approaches, to better assess the protective role of different types of respirators in protecting welders from the detrimental health effects of exposure to welding fumes. Study 3 aimed to assess exposure to welding fumes in professional welders using biomarkers of exposure and metabolomics. 38 welders and 36 power line technicians as the non-exposed group were recruited from various facilities across the province of Alberta, Canada. Air sampling was performed throughout the shift. Fasting urine samples were collected from the participants the day after the air sampling. Metals and metabolites in air and urine samples were quantified using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Liquid Chromatography with tandem Mass Spectrometry (LC-MS-MS), respectively. Welders exhibited significantly higher urinary levels of As, Cr, Fe, Mn, and Ni compared to the non-exposed group (p˂0.05). A receiver operating characteristic (ROC) curve analysis showed that Mn and Ni could be potential biomarkers for welding fume exposure (AUC > 0.7). Metabolomics analysis identified urinary beta-hydroxybutyric acid, arginine, asparagine, choline, ornithine higher in welders compared to the non-exposed group (AUC > 0.7). The ROC analysis also identified urinary metabolites associated with welding experience and smoking in welders. The linear mixed models (LMMs) results identified welding experience and smoking as the main predictors of urinary metals and metabolites in welders. The results underscore potential of using biomonitoring and metabolomics for assessing welding fume exposure, suggesting further research into underlying mechanisms. Study 4 aimed to investigate the potential of using metals in the exhaled breath condensate (EBC) as biomarkers of welding fume exposure. The cohort included 33 welders and 30 non-exposed participants. Air sampling was conducted throughout shifts in the welder group. EBC sampling was conducted using R-Tubes. Metals in the collected samples were quantified using ICP-MS. Post-shift levels of Al, Co, Cu, Mn, Fe, Ni, Pb, and Zn in the welders' EBC were significantly higher than those in the non-exposed group (p<0.05). Among welders, V levels were consistently higher post-shift than pre-shift, while Pb levels dropped, especially in smokers. LMMs identified smoking as the main predictor for pre-shift metal levels, while welding experience and exposure to high levels of welding fumes drove post-shift metal levels in EBC. Keywords: Biomarker, Exposure, Welding Fumes, Oxidative Stress, Metabolomics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".