ArticlePatterns (New York, N.Y.)2022
Early prediction and longitudinal modeling of preeclampsia from multiomics.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Epigenetic landscape of placental tissue in preeclampsia: a systematic review of DNA methylation profiles.BMC pregnancy and childbirth · 2025Pooled it
- The relative importance of neighborhood environment features in explaining preeclampsia risk using machine learning.Pregnancy (Hoboken, N.J.) · 2026Article
- Role of microbial metabolites in the pathogenesis of hypertensive disorders of pregnancy: From short‑chain fatty acids to tryptophan metabolites (Review).Molecular medicine reports · 2026Review
- A Machine Learning Framework for Preeclampsia Prediction at Isidro Ayora Hospital, Ecuador.Diagnostics (Basel, Switzerland) · 2026Article
- First-trimester plasma targeted metabolomics for eicosanoids reveals predictive potential and preventive targets for severe preeclampsia: a nested case-control study.BMC pregnancy and childbirth · 2026Article
- Eclampsia risk prediction across diverse U.S. populations using CDC data: machine learning versus ACOG checklists.AJOG global reports · 2026Article
- Multiomics characterization of acute child illness and mortality in Africa and South Asia.Nature communications · 2026Article
- Multi-Omics for Mothers and Infants (MOMI) Consortium: a global initiative to study adverse pregnancy outcomes.Journal of global health · 2026Article
- An encyclopedia of the cord blood metabolome reveals maternal-fetal interactions and disease risk.Cell reports. Medicine · 2026Article
- Special Issue "Molecular and Cellular Research in Pregnancy-Related Complications".International journal of molecular sciences · 2026Article
- Multi-omics insights into immune tolerance at the maternal-fetal interface in recurrent pregnancy loss: mechanisms, integration challenges, and translational perspectives.Frontiers in immunology · 2026Review
- Extracellular Vesicle-Associated Non-Coding RNAs in Preeclampsia: Mechanistic Insights, Biomarker Discovery, and Emerging Nanomedicine Concepts.International journal of nanomedicine · 2026Review
- Triglyceride profiles in preterm infants reveal dynamic and age-specific trajectories.Frontiers in pediatrics · 2026Article
- Single-cell mapping of maternal-fetal cross-talk in preeclampsia.Research square · 2025Article
- Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries.Science advances · 2025Article
- Cell type inference in cell-free nucleic acid liquid biopsy.Nature biotechnology · 2025Review
- Cell-free RNAs in maternal peripheral blood as potential biomarkers of preeclampsia: a review.Reproductive biology and endocrinology : RB&E · 2025Review
- Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia.European journal of medical research · 2025Article
- PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.Bioinformatics (Oxford, England) · 2025Article
- Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025Review
Corrections and comments
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Authors and funding
33 authors at 3 institutions in 2 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.