Comprehensive Blood Metabolome and Exposome Analysis, Annotation, and Interpretation in E-Waste Workers
Bibliographic record
Abstract
Background: Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. Methods: To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017–2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, asari and centWave, were applied. Results: In comparison to centWave, asari showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. Conclusions: This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".