Obesogenic diet exposure alters uterine natural killer cell biology and impairs vasculature remodeling in mice†
Bibliographic record
Abstract
Prepregnancy obesity associates with adverse reproductive outcomes that impact maternal and fetal health. While obesity-driven mechanisms underlying adverse pregnancy outcomes remain unclear, local uterine immune cells are strong but poorly studied candidates. Uterine immune cells, particularly uterine natural killer cells (uNKs), play central roles in orchestrating developmental events in pregnancy. However, the effect of obesity on uNK biology is poorly understood. Using an obesogenic high-fat/high-sugar diet (HFD) mouse model, we set out to examine the effects of maternal obesity on uNK composition and establishment of the maternal-fetal interface. HFD exposure resulted in weight gain-dependent increases in systemic inflammation and rates of fetal resorption. While HFD did not affect total uNK frequencies, HFD exposure did lead to an increase in natural cytotoxicity receptor-1 expressing uNKs as well as overall uNK activity. Importantly, HFD-associated changes in uNK coincided with impairments in uterine artery remodeling in mid but not late pregnancy. Comparison of uNK mRNA transcripts from control and HFD mice identified HFD-directed changes in genes that play roles in promoting activity/cytotoxicity and vascular biology. Together, this work provides new insight into how obesity may impact uNK processes central to the establishment of the maternal-fetal interface in early and mid pregnancy. Moreover, these findings shed light on the cellular processes affected by maternal obesity that may relate to overall pregnancy health.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".