Maternal obesity and offspring neurodevelopment are associated with hypoxic gene expression in term human placenta
Bibliographic record
Abstract
One third of women in the United States are affected by obesity during pregnancy. Maternal obesity (MO) is associated with an increased risk of neurodevelopmental and metabolic disorders in the offspring. The placenta, located at the maternal-fetal interface, is a key organ determining fetal development and likely contributes to programming of long-term offspring health. We profiled the term placental transcriptome in humans (pre-pregnancy BMI 35+ [MO condition] or 18.5-25 [lean condition]) using single-nucleus RNA-seq to compare expression profiles in MO versus lean conditions, and to reveal potential mechanisms underlying offspring disease risk. We recovered 62,864 nuclei of high quality from 10 samples each from the maternal-facing and fetal-facing sides of the placenta. On both sides in several cell types, MO was associated with upregulation of hypoxia response genes. On the maternal-facing side only, hypoxia gene expression was associated with offspring neurodevelopmental measures, in Gen3G, an independent pregnancy cohort with bulk placental tissue RNA-seq. We leveraged Gen3G to determine genes that correlated with impaired neurodevelopment and found these genes to be most highly expressed in extravillous trophoblasts (EVTs). EVTs further showed the strongest correlation between neurodevelopment impairment gene scores (NDIGSs) and the hypoxia gene score. We reanalyzed gene expression of cultured EVTs, and found increased NDIGSs associated with exposure to hypoxia. Among EVTs, accounting for the hypoxia gene score attenuated 44% of the association between BMI and NDIGSs. These data suggest that hypoxia in EVTs may be a key process in the neurodevelopmental programming of fetal exposure to MO.
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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 | 0.001 |
| 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".