Untargeted ‘SWATH’ mass spectrometry-based metabolomics for studying chronic and intermittent exposure to xenobiotics in cohort studies
Bibliographic record
Abstract
Humans are exposed to numerous chemicals daily, for example through nutrition, therapies, and lifestyle choices, which may exert beneficial or toxicological responses. In cohort studies, exposures are frequently assessed using questionnaires, although mass spectrometry-based metabolomics has recently emerged as complementary technique capable of yielding molecular evidence of exposures. Corresponding data processing workflows, however, have been mostly developed for detecting (omnipresent) endogenous metabolites, whereas detection of exogenous chemicals would benefit from fit-for-purpose strategies. In this work, we describe novel strategies for improved exposure detection and their application to data from an untargeted metabolomics study on urine samples from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier 'NCT02811835'), which includes kidney transplant recipients, potential living kidney donors, and living kidney donors (post-donation). Specifically, we describe a reference spectra generation workflow using exposure-positive samples to detect more and also previously-undetected chronic exposures, and we present a novel approach to establish detection limits based on targeted signal extraction for more reliable and lower-level detection of intermittent exposures. These approaches can contribute to unlocking additional exposure-related information from small-molecule profiling datasets thus increasing data usefulness in metabolomics research and in environmental, food, clinical, and forensic toxicology.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".