| Download | - View final version: Early prediction and longitudinal modeling of preeclampsia from multiomics (PDF, 3.2 MiB)
- View supplementary information: Early prediction and longitudinal modeling of preeclampsia from multiomics (PDF, 2.5 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.patter.2022.100655 |
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| Author | Search for: Marić, Ivana; Search for: Contrepois, Kévin; Search for: Moufarrej, Mira N.; Search for: Stelzer, Ina A.; Search for: Feyaerts, Dorien; Search for: Han, Xiaoyuan; Search for: Tang, Andy; Search for: Stanley, Natalie; Search for: Wong, Ronald J.; Search for: Traber, Gavin M.; Search for: Ellenberger, Mathew; Search for: Chang, Alan L.; Search for: Fallahzadeh, Ramin; Search for: Nassar, Huda; Search for: Becker, Martin; Search for: Xenochristou, Maria; Search for: Espinosa, Camilo; Search for: De Francesco, Davide; Search for: Ghaemi, Mohammad S.1; Search for: Costello, Elizabeth K.; Search for: Culos, Anthony; Search for: Ling, Xuefeng B.; Search for: Sylvester, Karl G.; Search for: Darmstadt, Gary L.; Search for: Winn, Virginia D.; Search for: Shaw, Gary M.; Search for: Relman, David A.; Search for: Quake, Stephen R.; Search for: Angst, Martin S.; Search for: Snyder, Michael P.; Search for: Stevenson, David K.; Search for: Gaudilliere, Brice; Search for: Aghaeepour, Nima |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | preeclampsia; machine learning; predictive modeling; multiomics; biomarkers |
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| Abstract | Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia. |
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| Publication date | 2022-12-09 |
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| Publisher | Cell Press |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 841122ab-4753-4aa7-b735-61124c7dc478 |
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| Record created | 2022-12-15 |
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| Record modified | 2023-01-04 |
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