Plasma Proteome in Early Pregnancy Falls Short in Predicting Hypertensive Disorders: JAMA

In this case-control study involving 753 individuals with HDP and 1097 individuals without adverse pregnancy outcomes, the primary predictive model incorporating clinical and demographic variables did not identify any of over 6000 unique human proteins that enhanced the model's predictive capability. Furthermore, all predictive models exhibited only moderate discriminatory performance.

There is no consensus regarding the best method for predicting hypertensive disorders of pregnancy, including preeclampsia and gestational hypertension. Philip Greenland, Northwestern University, Feinberg School of Medicine, Chicago, Illinois, and colleagues aimed to determine the predictive ability of large-scale proteomics in early pregnancy for HDP prediction.

For this purpose, the researchers conducted a nested case-control study from 2022 to 2023 using plasma samples and clinical data collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. The multicenter observational study took place at eight medical centers in the US. It included nulliparous individuals during first-trimester clinical visits. Participants with HDP were cases; controls were selected from those who delivered at or after 37 weeks without any preterm birth, HDP, or small-for-gestational-age infant. Self-reported race and ethnicity, age, diabetes, body mass index (BMI), fetal sex, and health insurance were available covariates.

Proteomics analysis utilizing an aptamer-based assay assessed 6481 unique human proteins in stored plasma, with covariates incorporated into predictive models.

Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset including 80% of study participants, with the remaining 20% used as an independent testing dataset. The main metric used to assess predictive performance was the area under the receiver operating characteristic curve (AUC).

The following were the key findings of the study:

• This study included 753 HDP cases and 1097 controls with a mean age of 26.9 years.
• The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates.
• The predictive performance of the resulting model was modest, with a training set AUC of 0.64 and a test set AUC of 0.62. Further adjustment for the study site yielded only minimal changes in AUCs.

In conclusion, in the case-control study, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above routinely available demographic and clinical factors, with detailed clinical data and stored plasma samples available in the first trimester.

Reference:

Greenland P, Segal MR, McNeil RB, et al. Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy. JAMA Cardiol. Published online July 03, 2024. doi:10.1001/jamacardio.2024.1621