Maternal obesity drives functional alterations in uterine NK cells
Bibliographic record
Abstract
Over one-fifth of North American women of childbearing age are obese, putting these women at risk for a variety of detrimental chronic diseases. In addition, obesity increases the risk for developing major complications during pregnancy. The mechanisms by which obesity contributes to pregnancy complications and loss remain unknown. Increasing evidence indicates that obesity results in major changes to adipose tissue immune cell composition and function; whether or not obesity also affects immune function in the uterus has not been explored. Here we investigated the effect of obesity on uterine natural killer (uNK) cells, which are essential for uterine artery remodeling and placental development. Using a cohort of obese or lean women, we found that obesity led to a significant reduction in uNK cell numbers accompanied with impaired uterine artery remodeling. uNK cells isolated from obese women had altered expression of genes and pathways associated with extracellular matrix remodeling and growth factor signaling. Specifically, uNK cells were hyper-responsive to PDGF, resulting in overexpression of decorin. Functionally, decorin strongly inhibited placental development by limiting trophoblast survival. Together, these findings establish a potentially new link between obesity and poor pregnancy outcomes, and indicate that obesity-driven changes to uterine-resident immune cells critically impair placental development.
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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 | 0.000 |
| 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) | 0.002 | 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".