# Epigenetic phenotyping from liquid biopsy for preeclampsia risk classification during the first trimester

> **NIH NIH R41** · KANONA, INC. · 2024 · $306,223

## Abstract

PROJECT SUMMARY
Preeclampsia (PE), a pregnancy-specific condition characterized by hypertension and other organ system
dysfunction, is a major contributor to worldwide morbidity and mortality for both the mother and offspring.
During PE, pregnant individuals may experience life-threatening complications and they harbor lifelong risks for
cardiovascular disease, while the offspring may experience complications related to prematurity. High-quality
evidence supports the use of low-dose aspirin to reduce the risk of PE in those at high-risk, and emerging data
suggests that tighter blood pressure control may also mitigate the risks. Identification of pregnancies at
high-risk, however, currently relies on crude assessment of clinical risk factors alone with poor positive
predictive values. Although screening programs outside of the United States (US) report high detection rates,
many include maternal clinical factors, serum biomarkers, and ultrasound measures. Testing of performance in
a US population has not been possible as these included metrics are not routinely measured as part of
standard prenatal care. Although PE is rooted in placental dysfunction, with aberrations, such as epigenetic
modifications, occurring early in pregnancy, the placenta remains inaccessible during pregnancy. Recent data
suggests that placental derivatives (RNA, cell-free DNA) circulating in maternal circulation can be interrogated
to understand developing pregnancy complications, including PE. Our key insight is that the sequencing data
generated via cell-free DNA based non-invasive prenatal testing (NIPT) for aneuploidy screening can be
leveraged to infer epigenetic signatures specific to the placenta. With testing of over 1000 samples, we have
found that epigenetic signatures (in this case, nucleosome positioning) can be reliably inferred based on
available sequencing data, and that machine learning modeling can be used to predict early-onset PE. In the
proposed studies, Kanona, Inc and collaborators will build upon this work to refine/correct technical and
analytic variables, perform validation on a separate and distinct cohort, and optimize the methodology for PE
risk classification from cfDNA through sequencing of placental samples and deeper sequencing of first
trimester cfDNA samples. With the studies proposed here, and follow-up phase II external validation studies,
Kanona, Inc. will develop a test for PE prediction that can be easily performed in conjunction with cell-free DNA
based NIPT without the need for additional blood samples or dedicated laboratory workflows. This has the
potential to significantly alter the landscape of prenatal care, providing an element of precision medicine with
potentially actionable information that can prompt initiation of interventions or tailored monitoring programs to
ultimately decrease the incidence and potentially severity of PE.

## Key facts

- **NIH application ID:** 10822733
- **Project number:** 1R41HL172464-01
- **Recipient organization:** KANONA, INC.
- **Principal Investigator:** Jonathan Reichel
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $306,223
- **Award type:** 1
- **Project period:** 2024-01-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10822733

## Citation

> US National Institutes of Health, RePORTER application 10822733, Epigenetic phenotyping from liquid biopsy for preeclampsia risk classification during the first trimester (1R41HL172464-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10822733. Licensed CC0.

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