UC San Diego

AbstractBackgroundClinical risk factors for preeclampsia (PE), including previous PE, chronic hypertension, and pregestational diabetes, are poorly predictive of PE. Preterm PE, defined as diagnosis of PE with delivery prior to 37 weeks’ gestational age (GA), is more likely to be associated with serious morbidities and difficult clinical decision making. Therefore, there remains an urgent clinical need to develop a safe, feasible, and accurate predictor of preterm PE that integrates molecular biomarkers and relevant clinical factors into a single risk assessment score that can be used to guide clinical management.Objective(s)To discover, verify, and validate a mid-trimester proteomic biomarker risk predictor for preterm PE, comprised of a composite clinical variable and a small number of maternal serum analytes.Study DesignThis was a secondary analysis of data from two large clinical trials (PAPR,NCT02787213; TREETOP,NCT01371019). PAPR subjects’ eligibility was limited to those who had consented to research into preterm birth and pregnancy complications and who had blood drawn between 180/7– 226/7weeks’ gestation. TREETOP subjects were limited to those who had blood drawn between 180/7– 206/7weeks’ gestation. PAPR subjects were assigned to a discovery cohort, and TREETOP subjects were randomly assigned to a first-phase cohort for verification (comprised of one-third of eligible subjects) and to a separate second-phase cohort for validation (comprised of the remaining two-thirds of eligible subjects). Peptides were analyzed by liquid chromatography-multiple reaction monitoring mass spectrometry measuring 77 pregnancy-related proteins and quality control proteins. Models were limited to a maximum of one additional protein ratio and a composite clinical variable, referred to as Clin3, which was deemed positive if any of three factors was true for the subject: prior PE; pre-existing hypertension; and/or pregestational diabetes. Overall classifier performance was assessed via area under the receiver operating characteristic curve (AUC).ResultsVerification yielded nine multi-component classifier models for prediction of preterm PE, all of which were subsequently validated. Classifiers exhibited greater predictive performance than clinical factors alone. Example performance metrics across a range of classifier score thresholds and GA at birth cutoffs of 37, 34 and 32 weeks for the Clin3 + inhibin subunit beta c (INHBC)/SHBG classifier, which showed the highest AUC, demonstrating a sensitivity of 89% at a specificity of 75% for prediction of early-onset preeclampsia (<34 weeks’ GA).Conclusion(s)Here, we report on discovery, verification, and validation of models for prediction of preterm PE. The log ratio of INHBC/SHBG along with any one of three clinical risk factors demonstrated high sensitivity and specificity. This combination of protein biomarkers and clinical factors has the potential to be used in the mid-trimester of pregnancy to guide clinical management to avoid both unnecessary medical procedures and the most serious complications of early-onset PE.