Use of cell-free signals as biomarkers for early and easy prediction of preeclampsia

Gekas, Jean; Boomer, Theresa; Rodrigue, Marc-André; Jinnett, Kristine N.; Bhatt, Sucheta

doi:10.3389/fmed.2023.1191163

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…It lacks information on the length of cfDNA fragments. A study verified that pregnant women with late-onset PE had a significantly higher cfDNA fragment size distribution compared to controls ( 25 ). The potential of combined this cfDNA signal with pTSS coverages for PE prediction needs to be further explored.…”

Section: Discussionmentioning

confidence: 83%

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Yu,

Xu,

Zhang

et al. 2024

Front. Med.

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BackgroundPreeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors.MethodsWe retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24–45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher’s exact test and Mann–Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors.ResultsBy using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.ConclusionIncorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

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Section: Discussionmentioning

confidence: 83%

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Yu,

Xu,

Zhang

et al. 2024

Front. Med.

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Cell-free DNA methylome analysis for early preeclampsia prediction

De Borre,

Che,

et al. 2023

Nat Med

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Fetal Fraction of Cell‐Free DNA in the Prediction of Adverse Pregnancy Outcomes: A Nationwide Retrospective Cohort Study

Becking,

Bekker,

Henrichs

et al. 2024

BJOG

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ObjectiveTo assess the added value of fetal fraction of cell‐free DNA in the maternal circulation in the prediction of adverse pregnancy outcomes.DesignRetrospective cohort study.SettingNationwide implementation study on non‐invasive prenatal testing (NIPT; TRIDENT‐2 study).PopulationPregnant women in the Netherlands opting for NIPT between June 2018 and June 2019.MethodsTwo logistic regression prediction models were constructed for each adverse pregnancy outcome. The first model (base model) included prognostic clinical parameters that were selected from existing first‐trimester prediction models for adverse pregnancy outcomes. The second model (fetal fraction model) included fetal fraction as a predictor on top of the prognostic clinical parameters included in the base model. The added prognostic value of fetal fraction was assessed by comparing the base and fetal fraction models in terms of goodness of fit and predictive performance.Main Outcome MeasuresLikelihood ratio test (LRT), area under the curve (AUC) and Integrated Discrimination Improvement (IDI) index.ResultsThe study cohort consisted of 56 110 pregnancies. The incidence of adverse pregnancy outcomes was 5.7% for hypertensive disorders of pregnancy (HDP; n = 3207), 10.2% for birthweight < p10 (n = 5726), 3.2% for birthweight < p2.3 (n = 1796), 3.4% for spontaneous preterm birth (sPTB; n = 1891), 3.4% for diabetes (n = 1902) and 1.3% for congenital anomalies (n = 741). Adding fetal fraction to the base model improved model fit for HDP, birthweight < p10, birthweight < p2.3, all sPTB, and diabetes, but not for congenital anomalies (LRT p < 0.05). For HDP, the AUC improved from 0.67 to 0.68 by adding fetal fraction to the base model (p = 0.14) with an IDI of 0.0018 (p < 0.0001). For birthweight < p10, the AUC improved from 0.65 to 0.66 (p < 0.0001) with an IDI of 0.0023 (p < 0.0001). For birthweight < p2.3, the AUC improved from 0.67 to 0.69 (p < 0.0001) with an IDI of 0.0011 (p < 0.0001). For all sPTB, the AUC was similar for both models (AUC 0.63, p = 0.021) with an IDI of 0.00028 (p = 0.0023). For diabetes, the AUC was similar (AUC 0.72, p = 0.35) with an IDI of 0.00055 (p = 0.00015).ConclusionsFetal fraction has statistically significant but limited prognostic value in the prediction of adverse pregnancy outcomes in addition to known prognostic clinical parameters.

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Use of cell-free signals as biomarkers for early and easy prediction of preeclampsia

Cited by 4 publications

References 34 publications

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Cell-free DNA methylome analysis for early preeclampsia prediction

Fetal Fraction of Cell‐Free DNA in the Prediction of Adverse Pregnancy Outcomes: A Nationwide Retrospective Cohort Study

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