Browsing by Author "Raja, Anita"

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    A Comprehensive and Bias-Free Machine Learning Approach for Risk Prediction of Preeclampsia with Severe Features in a Nulliparous Study Cohort
    (Research Square, 2023-04-10) Lin, Yun; Mallia, Daniel; Clark-Sevilla, Andrea; Catto, Adam; Leshchenko, Alisa; Yan, Qi; Haas, David; Wapner, Ronald; Pe'er, Itsik; Raja, Anita; Salleb-Aouissi, Ansaf; Obstetrics and Gynecology, School of Medicine
    Objective: Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort. Materials and methods: The prospective study cohort to which we applied machine learning is the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) study, which contains information from eight clinical sites across the US. Maternal serum samples were collected for 1,857 individuals between the first and second trimesters. These patients with serum samples collected are selected as the final cohort. Results: Our prediction models achieved an AUROC of 0.72 (95% CI, 0.69-0.76), 0.75 (95% CI, 0.71-0.79), and 0.77 (95% CI, 0.74-0.80), respectively, for the three visits. Our initial models were biased toward non-Hispanic black participants with a high predictive equality ratio of 1.31. We corrected this bias and reduced this ratio to 1.14. The top features stress the importance of using several tests, particularly for biomarkers and ultrasound measurements. Placental analytes were strong predictors for screening for the early onset of preeclampsia with severe features in the first two trimesters. Conclusion: Experiments suggest that it is possible to create racial bias-free early screening models to predict the patients at risk of developing preeclampsia with severe features or eclampsia nulliparous pregnant study cohort.
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    Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas
    (Springer Nature, 2024-05-07) Khan, Raiyan R.; Guerrero, Rafael F.; Wapner, Ronald J.; Hahn, Matthew W.; Raja, Anita; Salleb‑Aouissi, Ansaf; Grobman, William A.; Simhan, Hyagriv; Silver, Robert M.; Chung, Judith H.; Reddy, Uma M.; Radivojac, Predrag; Pe’er, Itsik; Haas, David M.; Obstetrics and Gynecology, School of Medicine
    Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.
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    Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b
    (Oxford University Press, 2023) Yan, Qi; Guerrero, Rafael F.; Khan, Raiyan R.; Surujnarine, Andy A.; Wapner, Ronald J.; Hahn, Matthew W.; Raja, Anita; Salleb-Aouissi, Ansaf; Grobman, William A.; Simhan, Hyagriv; Blue, Nathan R.; Silver, Robert; Chung, Judith H.; Reddy, Uma M.; Radivojac, Predrag; Pe’er, Itsik; Haas, David M.; Obstetrics and Gynecology, School of Medicine
    Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWASs) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display extensive results, we developed a web-based tool GnuMoM2b (https://gnumom2b.cumcobgyn.org/) for searching, visualizing, and sharing results from a GWAS of 479 pregnancy traits as well as phenome-wide association studies of more than 17 million single nucleotide polymorphisms. The genetic results from 3 ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.