Polygenic prediction of preeclampsia and gestational hypertension

Honigberg, Michael C.; Truong, Buu; Khan, Rehan; Xiao, Brenda; Bhatta, Laxmi; Vy, Ha My T.; Guerrero, Rafael F.; Schuermans, Art; Selvaraj, Margaret Sunitha; Patel, Aniruddh P.; Koyama, Satoshi; Cho, So Mi Jemma; Vellarikkal, Shamsudheen Karuthedath; Trinder, Mark; Urbut, Sarah; Gray, Kathryn J.; Brumpton, Ben; Patil, Snehal; Zoellner, Sebastian; Antopia, Mariah C.; Saxena, Richa; Nadkarni, Girish N.; Do, Ron; Yan, Qi; Pe’er, Itsik; Verma, Shefali S.; Gupta, Rajat M.; Haas, David M.; Martin, Hilary C.; Heel, David A. van; Laisk, Triin; Natarajan, Pradeep

doi:10.1038/s41591-023-02374-9

Cited by 45 publications

(33 citation statements)

References 77 publications

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“…In line with prior studies, [22][23][24] we demonstrate that SBP PRS is associated with clinically measured blood pressure and risk of hypertensive disorders. The heritability of hypertensive disorders based on PRS is well established, and recent studies have demonstrated that these findings also translate to hypertensive disorders of pregnancy.…”

Section: Discussionsupporting

confidence: 92%

“…The heritability of hypertensive disorders based on PRS is well established, and recent studies have demonstrated that these findings also translate to hypertensive disorders of pregnancy. 18,23,24 Several recent studies of preeclampsia and blood pressure PRS have shown a strong disease correlation in patients with higher PRS scores. 18,23,24 The maximal blood pressure measured during pregnancy was elevated in the group with the top 25% SBP PRS.…”

Section: Discussionmentioning

confidence: 99%

“…18,23,24 Several recent studies of preeclampsia and blood pressure PRS have shown a strong disease correlation in patients with higher PRS scores. 18,23,24 The maximal blood pressure measured during pregnancy was elevated in the group with the top 25% SBP PRS. We were not able to find a significant relationship between SBP PRS and preeclampsia, chronic hypertension, hypertensive disorders of pregnancy, antihypertensive medication use, and family history.…”

Section: Discussionmentioning

confidence: 99%

“…As SBP is the trait with the highest genetic correlation with maternal preeclampsia genetics and has the highest predictive power for future hypertensive disorders and cardiovascular disease, [22][23][24] we created an SBP PRS using the open-source PRS-CS tool. 25 PRS-CS computes single-nucleotide polymorphism effect sizes by high-dimensional Bayesian regression using GWAS summary statistics and a linkage disequilibrium reference panel.…”

Section: Polygenic Risk Scoresmentioning

confidence: 99%

“…While maternal preeclampsia GWAS data are available, 15 the GWAS remains underpowered, and the PRSs developed using these results were weaker compared with SBP PRS. 23,24 To increase the likelihood of improving prediction using genetic risk factors, we selected the largest blood pressure GWAS meta-analysis to date, with over 1 million individuals, 17 and used a European linkage disequilibrium reference panel with 1.1 million variants derived from samples from the 1000 Genomes Project to create an SBP PRS in our study population. We categorized the PRS into quartiles of risk ranging from the lowest genetic risk to the highest genetic risk: <25%, 25% to 49%, 50% to 75%, and >75%.…”

Section: Polygenic Risk Scoresmentioning

confidence: 99%

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Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

Kovacheva,

Eberhard,

Cohen

et al. 2024

Hypertension

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Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-week gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. Methods: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk. Results: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score. Conclusions: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Polygenic Risk Scoresmentioning

confidence: 99%

Section: Polygenic Risk Scoresmentioning

confidence: 99%

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Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

Kovacheva,

Eberhard,

Cohen

et al. 2024

Hypertension

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Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia

Schuermans,

Truong,

Ardissino

et al. 2024

JAMA Cardiol

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ImportanceHypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease.ObjectiveTo identify proteins in the circulation associated with HDPs.Design, Setting, and ParticipantsTwo-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease–related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins’ dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP–related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease–related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023.ExposuresGenetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs).Main Outcomes and MeasuresGestational hypertension and preeclampsia.ResultsGenetic association data for cardiovascular disease–related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro–brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP–related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins.Conclusions and RelevanceStudy findings suggest genetic associations of 4 cardiovascular disease–related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

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Cardiovascular Genetics in Women

Lin,

Bouatia-Naji

2024

Manual of Cardiovascular Disease in Women

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Polygenic prediction of preeclampsia and gestational hypertension

Cited by 45 publications

References 77 publications

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies

Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia

Cardiovascular Genetics in Women

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