Metabolomics and proteomics in occupational medicine: a comprehensive systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Occupational biomonitoring is essential for assessing health risks linked to workplace exposures. The use of 'omics' technologies, such as metabolomics and proteomics, has become crucial in detecting subtle biological alterations induced by occupational hazards, thereby opening novel avenues for biomarker discovery. AIMS: This systematic review aims to evaluate the application of metabolomics and proteomics in occupational health. METHODS: Following the PRISMA guidelines, we conducted a comprehensive search on PubMed, Scopus, and Web of Science for original human studies that use metabolomics or proteomics to assess occupational exposure biomarkers. The risk of bias was assessed by adapting the Cochrane Collaboration tool and the Newcastle-Ottawa Quality Assessment Scale. RESULTS: Of 2311 initially identified articles, 85 met the eligibility criteria. These studies were mainly conducted in China, Europe, and the United States of America, covering a wide range of occupational exposures. The findings revealed that metabolomics and proteomics approaches effectively identified biomarkers related to chemical, physical, biomechanical, and psychosocial hazards. Analytical methods varied, with mass spectrometry-based techniques emerging as the most prevalent. The risk of bias was generally low to moderate, with specific concerns about exposure measurement and confounding factors. CONCLUSIONS: Integrating metabolomics and proteomics in occupational health biomonitoring significantly advances our understanding of exposure effects and facilitates the development of personalized preventive interventions. However, challenges remain regarding the complexity of data analysis, biomarker specificity, and the translation of findings into preventive measures. Future research should focus on longitudinal studies and biomarker validation across diverse populations to improve the reliability and applicability of occupational health interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it