Quantitative characterization of the urine and serum metabolomes of children is essential for ‘omics’ studies
Bibliographic record
Abstract
Understanding the long-term health impacts of the early-life exposome requires the characterization and assimilation of multi 'omics' data to ultimately link molecular changes to exposures. In this way, markers associated with negative health outcomes, such as increased disease risk, can be ascertained. However, determining the extent and direction of metabolic perturbations relies on comparisons to existing metabolomic reference profiles. While such resources are increasingly available for adult populations, analogous tools for children are decidedly lacking. Lau et al. have compiled robust, translatable quantitative metabolomics data on urine and serum samples for European children across six study locations. Metabolites were associated with body mass index, diet and demographics, and correlated within and between biofluids. As a result, a novel association between urinary 4-deoxyerythronic acid and body mass index was uncovered. This work serves as a crucial reference for future studies in exposomics, and - more broadly - represents a significant step forward for metabolomics by creating the foundation for a comprehensive reference metabolome for children.Please see related article: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1190-8.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".