Profiles of gene expression in maternal blood predict offspring birth weight in normal pregnancy
Bibliographic record
Abstract
The association between lower birth weight and increased disease risk in adulthood has drawn attention to the physiological processes that shape the gestational environment. We implement genome-wide transcriptional profiling of maternal blood samples to identify subsets of genes and associated transcription control pathways that predict offspring birth weight. Female participants (N = 178, mean = 27.0 years) in a prospective observational birth cohort study were contacted between 2009 and 2014 to identify new pregnancies. An in-home interview was scheduled for early in the third trimester (mean = 30.3 weeks) to collect pregnancy-related information and a blood sample, and birth weight was measured shortly after delivery. Transcriptional activity in white blood cells was determined with a whole-genome gene expression direct hybridization assay. Fifty transcripts were differentially expressed in association with offspring birth weight, with 18 up-regulated in relation to lower birth weight, and 32 down-regulated. Examination of transcription control pathways identified increased activity of NF-κB, AP-1, EGR1, EGR4, and Gfi families, and reduced the activity of CEBP, in association with lower birth weight. Transcript origin analyses identified non-classical CD16+ monocytes, CD1c+ myeloid dendritic cells, and neutrophils as the primary cellular mediators of differential gene expression. These results point toward a systematic regulatory shift in maternal white blood cell activity in association with lower offspring birth weight, and they suggest that analyses of gene expression during gestation may provide insight into regulatory and cellular mechanisms that influence birth outcomes.
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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 | 0.000 | 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) | 0.000 | 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".