Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study
Bibliographic record
Abstract
Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26-28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".