Biomarkers for the ‘Great Obstetrical Syndromes’

About the Program

One of the features of the “Great Obstetrical Syndromes” is that treatment at the time of disease is ineffective because clinical manifestations are adaptive. The long preclinical phase creates an opportunity for prediction and intervention to prevent disease. This, however, requires the development of biomarkers for early identification of women at risk. Biomarkers are measurable indicators of biological events that represent a subclinical stage or manifestation of disease. They have been used to advance the understanding of pathological processes, detect early disease, target prevention strategies and monitor treatment. Challenges for the discovery of biomarkers in perinatal medicine include the syndromic nature of obstetrical disease (multiple etiologies), low prevalence, and incomplete understanding of the mechanisms of disease. To overcome the challenges in the field, we have approached biomarker discovery using novel approaches such as i) the disaggregation of obstetrical disease by placental histopathology (divide and conquer), ii) target the search for biomarkers based on information derived from single cell genomic studies of the placenta, and iii) use crowdsourcing to involve the community and enable robust assessment of omics methods and data. Over the years, the Perinatology Research Branch has proposed biophysical and biochemical markers for several conditions, such as fetal death, acute funisitis, clinical chorioamnionitis, preeclampsia and spontaneous preterm birth, among others. This work has enabled the development of preventive strategies, such as the vaginal progesterone treatment for women with a midtrimester short cervix for reducing the rate and impact of prematurity.


  • Design and conduct studies aimed at the discovery of biomarkers for prediction of the Great Obstetrical Syndromes
  • Determine the optimal gestational age window when biomarkers are predictive and have value for patient management
  • Develop novel ways to improve prediction performance by personalized assessment of existing and novel biochemical and biophysical markers
  • Evaluate the performance of existing biomarkers for known and novel applications
  • Leverage machine learning to combine predictive information from omics-based data
  • Use placental single cell genomics to inform the discovery of biomarker for obstetrical disease
  • Provide training to Maternal-Fetal and Postdoctoral fellows, residents and medical students

Research Highlights

  • Proposed a sonographic short cervix during mid-gestation as a biomarker for prediction of spontaneous preterm birth, and demonstrated that vaginal progesterone reduced the rate of preterm birth
  • Found that a low angiogenic index-1 (PlGF/sVEGFR-1 ratio) at 20-24 weeks of gestation predicts subsequent diagnosis with fetal death
  • Reported the use of rapid MMP-8 and IL-6 bedside tests to detect intra-amniotic inflammation and to identify women at risk for imminent preterm delivery
  • Introduced a maternal blood test for identification of women at risk for developing placental lesions consistent with maternal vascular underperfusion
  • Proposed than an imbalance in angiogenic/anti-angiogenic factors is predictive of massive perivillous fibrin deposition, which results in recurrent fetal death and growth restriction.
  • Described amniotic fluid RNA signatures to assess fetal development and organ maturity
  • Identified plasma proteomic markers of early and late preeclampsia and determined the window of gestation when the biomarkers have optimal predictive value.
  • Proposed a molecular clock of pregnancy based on maternal plasma proteins and whole blood RNAs
  • Identified a chemokine (CXCL10) which is associated with maternal anti-fetal rejection in preterm labor
  • Conducted a longitudinal study of angiogenic and anti-angiogenic factors in uncomplicated pregnancy and pregnancy destined to develop preeclampsia or small-for-gestational age fetus

    Select Publications