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| DOI | Trouver le DOI : https://doi.org/10.1016/j.patter.2022.100655 |
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| Auteur | Rechercher : Marić, Ivana; Rechercher : Contrepois, Kévin; Rechercher : Moufarrej, Mira N.; Rechercher : Stelzer, Ina A.; Rechercher : Feyaerts, Dorien; Rechercher : Han, Xiaoyuan; Rechercher : Tang, Andy; Rechercher : Stanley, Natalie; Rechercher : Wong, Ronald J.; Rechercher : Traber, Gavin M.; Rechercher : Ellenberger, Mathew; Rechercher : Chang, Alan L.; Rechercher : Fallahzadeh, Ramin; Rechercher : Nassar, Huda; Rechercher : Becker, Martin; Rechercher : Xenochristou, Maria; Rechercher : Espinosa, Camilo; Rechercher : De Francesco, Davide; Rechercher : Ghaemi, Mohammad S.1; Rechercher : Costello, Elizabeth K.; Rechercher : Culos, Anthony; Rechercher : Ling, Xuefeng B.; Rechercher : Sylvester, Karl G.; Rechercher : Darmstadt, Gary L.; Rechercher : Winn, Virginia D.; Rechercher : Shaw, Gary M.; Rechercher : Relman, David A.; Rechercher : Quake, Stephen R.; Rechercher : Angst, Martin S.; Rechercher : Snyder, Michael P.; Rechercher : Stevenson, David K.; Rechercher : Gaudilliere, Brice; Rechercher : Aghaeepour, Nima |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | preeclampsia; machine learning; predictive modeling; multiomics; biomarkers |
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| Résumé | 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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| Date de publication | 2022-12-09 |
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| Maison d’édition | Cell Press |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 841122ab-4753-4aa7-b735-61124c7dc478 |
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| Enregistrement créé | 2022-12-15 |
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| Enregistrement modifié | 2023-01-04 |
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