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Observational Study
. 2024 Sep 1;9(9):791-799.
doi: 10.1001/jamacardio.2024.1621.

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Philip Greenland  1 , Mark R Segal  2 , Rebecca B McNeil  3 , Corette B Parker  3 , Victoria L Pemberton  4 , William A Grobman  5   6 , Robert M Silver  7 , Hyagriv N Simhan  8 , George R Saade  9   10 , Peter Ganz  11 , Priya Mehta  12 , Janet M Catov  13 , C Noel Bairey Merz  14 , Jasmina Varagic  4 , Sadiya S Khan  15 , Samuel Parry  16 , Uma M Reddy  17 , Brian M Mercer  18 , Ronald J Wapner  19 , David M Haas  20
Affiliations
Observational Study

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Philip Greenland et al. JAMA Cardiol. .

Abstract

Importance: There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia.

Objective: To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP.

Design, setting, and participants: This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates.

Exposures: Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models.

Main outcomes and measures: Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC).

Results: This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. 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 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs.

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

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Greenland reported receiving grants from the National Institutes of Health (NIH) and the American Heart Association during the conduct of the study. Dr McNeil reported receiving grants from the NIH/National Heart, Lung, and Blood Institute (NHLBI) during the conduct of the study. Dr Silver reported receiving grants from the NIH during the conduct of the study. Dr Simhan reported receiving consultant fees from Organon and Naima Health. Dr Ganz reported serving on the medical advisory board for SomaLogic for which no financial renumeration of any kind was received. Dr Bairey Merz reported receiving personal fees from iRhythm, Abbott Diagnostics, and Sanofi outside the submitted work. Dr Khan reported receiving the NHLBI outside the submitted work. Dr Parry reported receiving grants from the NIH during the conduct of the study. Dr Mercer reported receiving grants from the NIH during the conduct of the study. Dr Haas reported receiving grants from the NHLBI during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Flow Diagram of Inclusions and Exclusions in Ancillary Study of Cases and Controls for Study of Proteomics Predictors of Hypertensive Disorders of Pregnancy
APO indicates adverse pregnancy outcome; HDP, hypertensive disorders of pregnancy; HHS, Heart Health Study; nuMoM2b, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be.
Figure 2.
Figure 2.. Volcano Plot of Proteins Associated With Hypertensive Disorders of Pregnancy Using First-Trimester Plasma Samples, After Covariate Adjustment
The 3 most strongly associated proteins were quiescin sulfhydryl oxidase1 (QSOX1), matrix metalloproteinase 12 (MMP12), and inhibin subunit beta C (INHBC).
Figure 3.
Figure 3.. Area Under the Receiver Operating Characteristic Curves (AUCs) for Training Set and Test Set in a Proteins-Only Model for Prediction of Hypertensive Disorders of Pregnancy
Figure 4.
Figure 4.. Area Under the Receiver Operating Characteristic Curves (AUCs) for Training Set and Test Set in a Model That included Prespecified Clinical Covariates and Proteins for Prediction of Hypertensive Disorders of Pregnancy
See this image and copyright information in PMC

References

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