Obesogenic diet exposure alters uterine natural killer cell biology and impairs vasculature remodeling in mice
Bibliographic record
Abstract
Pre-pregnancy obesity associates with adverse reproductive outcomes that impact maternal and fetal health. While obesity-driven mechanisms linked to poor pregnancy outcomes remain unclear, it is possible that obesity affects local uterine immune cells. Uterine immune cells, particularly uterine natural killer cells (uNK), play central roles in orchestrating developmental events in pregnancy. However, the effect of obesity on uNK biology is poorly understood. Using a high fat/high sugar diet (HFD) obesogenic 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. Within HFD mice, both weight gain-dependent (diet-induced obese; DIO) and -independent (DIO-resistant; DIO-R) effects on natural cytotoxicity receptor-1 frequency and uNK activity were observed. Importantly, HFD-associated changes in uNK coincided with impairments in uterine artery remodeling in mid but not late pregnancy. Comparison of uNK mRNA transcripts in control diet mice to gene signatures in DIO or DIO-R identified differentially expressed genes that play roles in promoting activity/cytotoxicity and impeding vascular biology. Together, this work provides new insight into how obesity, and particularly how diet-induced weight-gain, may impact uNK processes important in pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".