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Investigators hoped to use large-scale proteomics to help predict hypertensive disorders of pregnancy (HDP), using blood proteins obtained from individuals in their first trimester of pregnancy—but success has been elusive.
The search continues for a way to accurately predict the development of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia, in pregnant individuals. Results of a recent US nested case-control study, described by the authors in JAMA Cardiology, show that the use of a large-scale aptamer-based proteomics panel added no significant predictive utility beyond that of normally available clinical and demographic factors.1
The study authors accessed blood plasma samples of close to 2000 nulliparous pregnant individuals seen at 8 medical centers who were in their first trimester between 2010 and 2013, as well as detailed clinical data about the patients through birth or other pregnancy outcome. Of the cohort, 753 individuals developed HDP and 1097 had no adverse pregnancy outcome—specifically, no HDP, preterm birth, small-for-gestational-age infant, or birth before 37 weeks.
Beyond clinical and demographic variables, the primary predictive model included 6481 unique human proteins, but not one was able to improve the model’s predictive performance. The available covariates were body mass index, diabetes, health insurance, fetal sex, age (mean, cases and controls, 26.9 years), and self-reported race and ethnicity (Asian, 2.8%; non-Hispanic Black, 14.9%; Hispanic, 14.9%; non-Hispanic White, 62.8%; other, 4.8%).
The primary predictive model in this study included 6481 unique human proteins, but not one was able to improve the model’s predictive performance | Image Credit: Andrey Popov-stock.adobe.com
The primary measure of predictive performance was area under the receiver operating characteristic curve (AUC), and with AUCs of 0.64 for the study’s training set and 0.62 for its test set. The authors described the model’s performance as “modest,” with the large-scale proteomics adding no “meaningful discriminatory value.”
As such, the investigators concluded, “protein markers measured by an aptamer-based platform from plasma in early pregnancy are unlikely to improve risk prediction for HDP, and other approaches need to be considered to improve early identification of those at increased risk.”
The team noted an “intriguing…apparently paradoxical finding” of their study: that higher levels of the protein QSOX1 in the first trimester are associated with lower risk of HDP, despite the fact that higher levels are known to be associated with higher risk in late pregnancy.
“Sorting out this paradox may lead to useful mechanistic understanding,” they wrote.
They also championed the creation of additional studies that would build on their efforts to identify HDP risk during early pregnancy—or possibly before. Perhaps, they suggested as well, work could focus on proteomic prediction of HDP in different patient subgroups, or for those patients with the most difficult outcomes, such as preterm preeclampsia with severe features.
The team found that many of the plasma proteins assayed in the first-trimester samples were significantly associated with the eventual occurrence of HDP, but of these, most did not substantively aid in accurately predicting HDP. Ultimately, the researchers kept several proteins in the elastic net modeling that evaluated all 6481 proteins assayed: OMG, amyloid P component, serum (APCS), TFPI, QSOX1, iINHBC.1, NOTUM, SERPINF1.1, and FHIT. Only QSOX1—a marker of cellular growth and extracellular matrix remodeling—has previously been reported to have a role in pregnancy, they stated.
“This proteins-only model resulted in modest discriminatory capability,” the investigators went on. Other modeling approaches that they tried resulted in similar AUC results.
They added that they were aware of 2 previous studies that had tried to use the large-scale proteomics approach to predict HDP using first-trimester samples.2,3 None of the proteins included in those studies’ predictive models turned out to be among the 8 “top hits” in their analysis.
References
1. Greenland P, Segal MR, McNeil RB, et al.Large-scale proteomics in early pregnancy and hypertensive disorders of pregnancy. JAMA Cardiol. Published online July 3, 2024. doi:10.1001/jamacardio.2024.1621
2. Erez O, Romero R, Maymon E, et al. The prediction of late-onset preeclampsia: results from
a longitudinal proteomics study. PLoS One. 2017;12(7):e0181468. doi:10.1371/journal.pone.0181468
3. Tarca AL, Romero R, Benshalom-Tirosh N, et al. The prediction of early preeclampsia: results from a longitudinal proteomics study. PLoS One. 2019;14(6):e0217273. doi:10.1371/journal.pone.0217273
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