Maternal Diet and the Serum Metabolome in Pregnancy: Robust Dietary Biomarkers Generalizable to a Multiethnic Birth Cohort
Bibliographic record
Abstract
Advances in metabolomics are anticipated to decipher associations between dietary exposures and health. Replication biomarker studies in different populations are critical to demonstrate generalizability. To identify and validate robust serum metabolites associated with diet quality and specific foods in a multiethnic cohort of pregnant women. In this cross-sectional analysis of 3 multiethnic Canadian birth cohorts, we collected semiquantitative FFQ and serum data from 900 women at the second trimester of pregnancy. We calculated a diet quality score (DQS), defined as daily servings of “healthy” minus “unhealthy” foods. Serum metabolomics was performed by multisegment injection-capillary electrophoresis-mass spectrometry, and specific serum metabolites associated with maternal DQSs were identified. We combined the results across all 3 cohorts using meta-analysis to classify robust dietary biomarkers (r > ± 0.1; P < 0.05). Diet quality was higher in the South Asian birth cohort (mean DQS = 7.1) than the 2 white Caucasian birth cohorts (mean DQS <3.2). Sixty-six metabolites were detected with high frequency (>75%) and adequate precision (CV <30%), and 47 were common to all cohorts. Hippuric acid was positively associated with healthy diet score in all cohorts, and with the overall DQS only in the primarily white Caucasian cohorts. We observed robust correlations between: 1) proline betaine—citrus foods; 2) 3-methylhistidine—red meat, chicken, and eggs; 3) hippuric acid—fruits and vegetables; 4) trimethylamine N-oxide (TMAO)—seafood, meat, and eggs; and 5) tryptophan betaine—nuts/legumes. Specific serum metabolites reflect intake of citrus fruit/juice, vegetables, animal foods, and nuts/legumes in pregnant women independent of ethnicity, fasting status, and delays to storage across multiple collection centers. Robust biomarkers of overall diet quality varied by cohort. Proline betaine, 3-methylhistidine, hippuric acid, TMAO, and tryptophan betaine were robust dietary biomarkers for investigations of maternal nutrition in diverse populations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".