# Integrative risk modeling for early prediction of endometriosis and its long-term health outcomes

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $660,720

## Abstract

More than 200,000 women are diagnosed with endometriosis every year and over half of those women
do not receive a definitive diagnosis until 8.5 years after the onset of symptoms and many times when
they present with additional comorbidities. While several studies have suggested that genomic markers,
environmental risk factors and inflammatory markers play crucial roles in endometriosis symptomatology,
there are no effective tools available to predict an individual's risk of developing endometriosis or to
predict its downstream effects. The long-term goal is to develop effective and non-invasive early
screening tools to identify patients at risk of developing endometriosis and predict long-term effects. The
main objective of this project is the development of models to predict the risk of endometriosis across
varied clinical manifestations and associated long-term health outcomes. Our central hypothesis is that
integrative risk models will successfully identify patients at risk of developing endometriosis and
associated diseases that occur either concurrently with endometriosis (reproductive age) or after
endometriosis development (long-term health outcomes), enabling early diagnosis and prevention. This
general hypothesis will be tested via the following specific aims:(1) Develop an integrative risk model to
predict patients at high risk of developing endometriosis; (2) Develop an integrative risk model combining
genetic and nongenetic risk factors to predict clinical manifestations among women with endometriosis ;
(3) Create a lifelong chronological map of endometriosis to identify individuals at risk of developing
associated comorbidities. In aim 1, we will integrate genetic and non-genetic risk factors extracted from
Electronic Health Records in linear and non-linear fashion to generate an EndoRisk model. In aim 2, we
will generate a catalog of additional risk factors linked to various clinical manifestations of endometriosis
and develop risk model for varied manifestations. In aim 3, we will evaluate mediating risk of
endometriosis on associated comorbidities and develop a mediator risk prediction model for concomitant
conditions and long-term health outcomes. At the successful completion of the proposed research, the
expected outcomes will be rigorously evaluated non-invasive computational methods for screening and
diagnosing endometriosis across various clinical manifestations and its long-term effects based on
genetic and non-genetic factors. The proposed research is innovative because our novel methodology
for integrated risk models will have immediate translational implications. These results will provide a
strong basis for future development of strategies for improving patient outcomes and translating the
knowledge to clinical practice by providing support for identifying patients at high, moderate, and mild
risk of endometriosis, which is expected to have a significant impact on women suffering from
endometriosis or its long-term effe...

## Key facts

- **NIH application ID:** 10837883
- **Project number:** 5R01HD110567-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** SHEFALI Setia VERMA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $660,720
- **Award type:** 5
- **Project period:** 2023-05-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10837883

## Citation

> US National Institutes of Health, RePORTER application 10837883, Integrative risk modeling for early prediction of endometriosis and its long-term health outcomes (5R01HD110567-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10837883. Licensed CC0.

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