Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries.
Bibliographic record
Abstract
Maternal lipid levels change dynamically during gestation to support normal fetal growth. To obtain a detailed footprint of these changes and their differences in pregnancies with preterm or small-for-gestational-age (SGA) neonates, we analyzed 641 lipids and 639 metabolites in plasma from women by 24 weeks of pregnancy from three cohorts from low- and middle-income countries: Bangladesh, Zimbabwe, and Kenya. We consistently found a significant lipid imbalance with increased lipid levels that preceded preterm birth and decreased levels that preceded SGA births. Changes were most pronounced in triglycerides, including triglycerides containing proinflammatory omega-6 polyunsaturated fatty acids (PUFAs) in pregnancies with preterm infants. A machine learning model for prediction of preterm birth had modest performance [area under the receiver operator curve (AUC) = 0.69, 95% confidence interval (CI) = (0.68, 0.70)] and lower performance for predicting SGA [AUC = 0.64, CI 95% = (0.62, 0.65)]. Increased triglycerides containing proinflammatory PUFAs provide further evidence in favor of a previously considered dietary supplementation with the long-chain fatty acids.
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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 | 0.000 | 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".