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Grant: Project Grant

Research Institute of the McGill University Health Centre

$1,231,652.00

early Prediction of preeclampsia using Urinary biomarkers anaLyseS in high-risk prEgnancies (PULSE-U)

EF 2024-25

Résumé
Preeclampsia is a pregnancy disorder characterized by high blood pressure, protein in the urine, and organ damage. It affects 1 out of every 12 pregnancies and can lead to serious issues for the parent and their baby. Preeclampsia starts in the first few weeks of pregnancy, but its symptoms only appear in the second half of pregnancy. This provides a window of opportunity to predict it before the symptoms start. However, past attempts have not been able to predict preeclampsia early and accurately enough. The kidneys are organs that filter your blood and produce urine. They are central in preeclampsia development. Patients with preeclampsia have more proteins (tiny structures that perform tasks in our body) in their urine compared to healthy pregnancies. The types of proteins are also different. Proteins may help to predict preeclampsia, and there are many more to discover. Urine is an untouched, easy-to-collect, and rich source of information. Our study aims to look at urine proteins to develop a new predictive test for preeclampsia. We will recruit pregnant people at high risk for preeclampsia across 10 hospitals worldwide and collect urine in the first and second trimesters. Then, we will use specialized protein analysis techniques to identify and measure the quantity of each protein in the urine. Our goal is to identify the difference between urine proteins in healthy and pregnancies affected by preeclampsia. Next, using artificial intelligence, we will identify proteins that predict who will develop preeclampsia. Finally, we will assess this strategy by itself and when combined with other current tests to determine its ability to predict preeclampsia. Effective prediction of preeclampsia would allow doctors to concentrate their care efforts and limited healthcare resources to people truly at high risk for preeclampsia. Our findings could help the development of new predictive tests and treatments for this devastating disorder.
Programme
Project Grant
Emplacement
Quebec, Canada
Type
Grant
Source
CIHR Funding Decisions Database

Tel que publié

Id
510845
Name
Daskalopoulou Stella S
Region
Québec
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Health Services and Policy Research
Theme2
Clinical
Prcname2
Clinical Investigation - A: Reproduction, Maternal, Child and Youth Health 2
Projecttitle
early Prediction of preeclampsia using Urinary biomarkers anaLyseS in high-risk prEgnancies (PULSE-U)
Programtype2
Operating Grants
Orgnameinp2
Research Institute of the McGill University Health Centre
Cihramount2
$1,231,652
Cihrequipment2
$0
Programname2
Project Grant
Keyworddelim
High-Risk Pregnancies; Machine Learning; Multicentre; Multinational; Prediction; Preeclampsia; Urinary Biomakers; Validation
Abstract
Preeclampsia is a pregnancy disorder characterized by high blood pressure, protein in the urine, and organ damage. It affects 1 out of every 12 pregnancies and can lead to serious issues for the parent and their baby. Preeclampsia starts in the first few weeks of pregnancy, but its symptoms only appear in the second half of pregnancy. This provides a window of opportunity to predict it before the symptoms start. However, past attempts have not been able to predict preeclampsia early and accurately enough. The kidneys are organs that filter your blood and produce urine. They are central in preeclampsia development. Patients with preeclampsia have more proteins (tiny structures that perform tasks in our body) in their urine compared to healthy pregnancies. The types of proteins are also different. Proteins may help to predict preeclampsia, and there are many more to discover. Urine is an untouched, easy-to-collect, and rich source of information. Our study aims to look at urine proteins to develop a new predictive test for preeclampsia. We will recruit pregnant people at high risk for preeclampsia across 10 hospitals worldwide and collect urine in the first and second trimesters. Then, we will use specialized protein analysis techniques to identify and measure the quantity of each protein in the urine. Our goal is to identify the difference between urine proteins in healthy and pregnancies affected by preeclampsia. Next, using artificial intelligence, we will identify proteins that predict who will develop preeclampsia. Finally, we will assess this strategy by itself and when combined with other current tests to determine its ability to predict preeclampsia. Effective prediction of preeclampsia would allow doctors to concentrate their care efforts and limited healthcare resources to people truly at high risk for preeclampsia. Our findings could help the development of new predictive tests and treatments for this devastating disorder.
Competitiondate
202409
Approvedterm2
4 yrs 0 mth
Pinamesdelim
Daskalopoulou, Stella S
Approvedterm
4 ans 0 mois
Cihrcontribution2
~ $1,231,652~ $0~