# The role of maternal obesity-driven inflammation and adverse pregnancy outcomes in a mouse model of preeclampsia

> **NIH NIH P20** · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · 2020 · $222,000

## Abstract

Project Summary:
Pregnancy is a physiological state of inflammation. However, heightened inflammation during pregnancy is linked
to adverse outcomes, such as preeclampsia (PE). The clinical signs of PE include maternal hypertension and
proteinuria during the second half of gestation. While PE presents later in pregnancy, its origins are thought to
begin early in pregnancy or even before conception. Importantly, maternal hypertension only resolves after
delivery of the placenta; therefore, it is widely accepted that abnormal placentation plays a causal role in PE
pathogenesis, though the etiology of this is unknown. A number of maternal characteristics, including obesity,
are known risk factors for developing PE. It is hypothesized maternal adiposity may contribute to heightened
inflammation and subsequent abnormal placental vascular development. The overarching goal of these
proposed studies is to test the hypothesis that pro-inflammatory mediators produced by specific immune cells
within maternal adipose tissue reduce pro-angiogenic factors at the maternal-fetal interface. We will conduct our
studies using the BPH/5 mouse model of PE.

## Key facts

- **NIH application ID:** 9854386
- **Project number:** 1P20GM135002-01
- **Recipient organization:** LSU PENNINGTON BIOMEDICAL RESEARCH CTR
- **Principal Investigator:** Jennifer Liford Sones
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $222,000
- **Award type:** 1
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9854386

## Citation

> US National Institutes of Health, RePORTER application 9854386, The role of maternal obesity-driven inflammation and adverse pregnancy outcomes in a mouse model of preeclampsia (1P20GM135002-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9854386. Licensed CC0.

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