Common and divergent dynamic changes of the human plasma metabolome across the first week of life in the Gambia and Papua New Guinea 3859
Bibliographic record
Abstract
Abstract Description Background The plasma metabolome changes markedly over the first week of life, yet little is known regarding how these trajectories might differ across geographically distinct populations. Methods To gain insight into potential differences in ontogeny between geographic sites, newborn participants were enrolled in The Gambia (n = 45) and Papua New Guinea (n = 45). All participants had samples collected at birth and were randomized to have their second sample collected either on the first, third, or seventh day following birth. Global untargeted plasma metabolomics employed ultra-performance liquid chromatography mass spectrometry (Metabolon). Results PCA showed plasma metabolites clustering by day of sample collection and by site. The majority (62%) of metabolite features showed a significant difference based on time point: 46% of these were lipids and 17% were amino acids. Additionally, 24% of the metabolite features showed a significant difference between sites, with the most common being lipids (28.2%). Xenobiotics including caffeine and food metabolites were distinct in GAM vs. PNG, suggesting differences in maternal diet and environmental factors. Conclusions The plasma metabolome undergoes marked changes during the first week of human life, with distinct features across geographic locations, and warrants further exploration with respect to potential correlations to immune status and clinical outcomes, with particular emphasis on lipid pathways. Funding Sources Supported by NIH/NIAID grants: U19AI118608 and U19AI168643–01, as well as the BCH Precision Vaccines Program. Topic Categories Computational and Systems Immunology (COMP)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".