Postpartum alterations following inflammation in rat pregnancy: a discovery proteomic analysis
Bibliographic record
Abstract
Women with a history of preeclampsia have an increased risk of subsequent cardiovascular and metabolic disease. While aberrant inflammation during pregnancy is associated with the development of preeclampsia, whether maternal inflammation increases the risk of disease later in life is unclear. Using a rat model we determined whether aberrant inflammation in pregnancy alters the levels of plasma proteins associated with cardiovascular and metabolic disease risk in the postpartum period. Pregnant rats were administered lipopolysaccharide (LPS) or saline on gestational days 13.5-16.5 to induce inflammation. Non-pregnant controls consisted of age-matched female rats subjected to similar administration of LPS or saline. Examination of the proteomic profile of plasma collected 16 weeks after delivery or from non-pregnant controls using liquid chromatography-tandem mass spectrometry revealed 100 differentially expressed proteins. Moreover, we identified 188 proteins in pregnant rats, of which 49 were differentially expressed in saline- vs LPS-treated dams. Of the 49 proteins regulated by LPS, 28 were pregnancy specific. PANTHER classification software, DAVID database and Ingenuity Pathways analysis revealed that the differentially expressed proteins in pregnant saline vs LPS-treated rats are associated with alterations in lipid and glucose metabolism and atherosclerosis, all of which may contribute to cardiovascular and metabolic disease risk. Results from proteomic and pathway analyses were validated by immunoassay of three serum proteins selected a priori and by assessment of serum metabolites. This discovery study demonstrates that aberrant inflammation during pregnancy results in long-lasting postpartum physiological alterations known to be associated with metabolic and cardiovascular disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".