Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models.

BACKGROUND: Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia. OBJECTIVE: This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index. STUDY DESIGN: This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate. RESULTS: First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes. CONCLUSION: Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

Fetal Cardiac Function at Midgestation and Subsequent Development of Preeclampsia.

OBJECTIVE: To assess differences in cardiac morphology and function at midgestation in fetuses from pregnancies that subsequently developed preeclampsia (PE) or gestational hypertension (GH). METHODS: This was a prospective study in 5,801 women with singleton pregnancies attending for a routine ultrasound examination at midgestation, including 179 (3.1%) who subsequently developed PE and 149 (2.6%) who developed GH. Conventional and more advanced echocardiographic modalities, such as speckle-tracking, were used to assess fetal cardiac function in the right and left ventricle. The morphology of the fetal heart was assessed by calculating the right and left sphericity index. RESULTS: In fetuses from the PE group (vs the no PE or GH group) there was a significantly higher left ventricular global longitudinal strain and lower left ventricular ejection fraction that could not be accounted for by fetal size. All other indices of fetal cardiac morphology and function were comparable between groups. There was no significant correlation between fetal cardiac indices and uterine artery pulsatility index multiple of the median or placental growth factor multiple of the median. CONCLUSION: At midgestation, fetuses of mothers at risk of developing PE, but not those at risk of GH, have mild reduction in left ventricular myocardial function. Although absolute differences were minimal and most likely not clinically relevant, these may suggest an early programming effect on left ventricular contractility in fetuses of mothers who develop PE.

Fetal Cardiac Function at Midgestation in Women Who Subsequently Develop Gestational Diabetes.

Importance: Fetuses in women with gestational diabetes (GD) compared with those without GD show evidence of subclinical cardiac functional and morphological changes. However, it is uncertain whether glycemia or the adverse maternal underlying risk factor profile is the main driver for fetal cardiac remodeling. Objective: To assess cardiac morphology and function at midgestation in fetuses of mothers prior to development of GD and compare them with those of unaffected controls. Design, Setting, and Participants: During this prospective nonintervention screening study at 19 to 23 weeks' gestation, fetal cardiac morphology and function were assessed in all participants. Pregnancy complications were obtained from the medical records of the women. Fetal cardiac morphology and function were assessed in all participants at Harris Birthright Research Institute at King's College Hospital, London, United Kingdom. Participants included pregnant women with singleton pregnancy who attended their routine fetal ultrasound examination at midgestation and agreed to participate in the Advanced Cardiovascular Imaging Study in pregnancy. Main Outcome and Measures: Comparison of fetal cardiac morphology and function between mothers who subsequently developed GD and those who did not develop GD. Methods: This was a prospective nonintervention screening study of 5620 women with singleton pregnancies at 19 to 23 weeks' gestation. Conventional and more advanced echocardiographic modalities, such as speckle tracking, were used to assess fetal cardiac function in the right and left ventricle. The morphology of the fetal heart was assessed by calculating the right and left sphericity index. Results: The 5620 included patients had a mean age of 33.6 years. In 470 cases, the women were diagnosed with GD after the midgestation echocardiographic assessment (8.4%). Women with GD, compared with the non-GD group, were older, had higher BMI, higher prevalence of family history of diabetes, non-White ethnicity, chronic hypertension, and GD in a previous pregnancy. In fetuses of the GD group compared with the non-GD group, there was mild increase in interventricular millimeter thickness (0.04; 95% CI, 0.03-0.06 mm) and left atrial area (0.04; 95% CI, 0.04-0.05), whereas left and right functional indices were comparable between groups with the exception of left ventricular ejection fraction, which was marginally improved in the GD group (0.02; 95% CI, 0.03-0.03). Conclusions and Relevance: This study demonstrates that prior to development of GD, there was mild alteration in fetal cardiac morphology without affecting cardiac function. This suggests that the adverse maternal risk factor profile and not only the glycemia might contribute to cardiac remodeling noted in fetuses of women with GD.