In a recent study, preterm preeclampsia was seen more often in women with specific metabolite biomarkers.
According to a recent study published in the American Journal of Obstetrics & Gynecology, preterm preeclampsia (PE) risk at 11 to 13 weeks’ gestation is greater from single metabolites and amino acid ratios associated with arginine bioavailability and nitric oxide synthase pathways.
Prediction and prevention of PE has not significantly improved from better understanding of pathophysiology, possibly because maternal syndrome development occurs in distinct pathophysiological pathways. If this is the case, subtypes of PE will need to be recognized to achieve greater clinical unity.
PE prediction models currently determine risk based on Doppler velocimetry of the uterine arteries, maternal risk factors, blood levels of the proteins placental growth factor (PlGF) and pregnancy-associated plasma protein A (PAPP-A) and mean arterial pressure. The detection rate (DR) of these models enables preventive strategies, but adding biomarkers may increase the DR for specific PE risk profiles.
Interactions between genetic makeup and external influences are reflected by a person’s metabolome, making it potentially effective for measuring maternal risk related to the environment. However, there is little data on how metabolome can be used as a PE risk predictor.
To determine whether metabolite biomarkers can accurately predict PE in early pregnancy, investigators conducted an observational case-control study. Data was collected from a large prospective screening study on early prediction of complications in pregnancy among women during their first routine hospital visit.
Participants had their first routine hospital visit between 2010 and 2015 at King’s College Hospital, London, United Kingdom. Protocols from the Fetal Medicine Foundation were followed for first-trimester evaluations. This included collecting blood samples and biobanking.
Pregnancy outcome data was also collected, with PE determined by American College of Obstetricians and Gynecologists 2019 criteria. Major pregnancy outcomes included PE, fetal growth restriction, gestational diabetes mellitus, and spontaneous preterm birth.
A targeted tandem liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was used to evaluate first-trimester plasma samples. Metabolite analysis occurred for 38 consecutive days, with data reviewed by 2 independent analysts.
Multiples of the median (MoM) were used to normalize biomarker data, and the Spearman rank correlation test was used to calculate pairwise correlations across normalized biomarker readouts. Prediction analyses were performed for the following: all subjects, body mass index (BMI) of under25 kg/m2, BMI of 25 to under 30 kg/m2, and BMI of 30 kg/m2 or more.
Preterm PE was seen more often in Black patients, along with patients who had an increased body weight or BMI. Those with preterm PE had a median gestation at delivery of 34.2 weeks and median birthweight of 1771 g, significantly lower than the control group at a median gestation at delivery of 39.2 weeks and median birthweight of 3295 g.
Biomarkers significantly associated with decreased MoM levels included PIGF, PAPP-A, 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine, bilirubin, and glutamine. On average, individuals with preterm PE had increased metabolite levels.
Differences in PIGF and PAPP-A levels were not observed based on BMI. However, a preterm PE prediction for ornithine was found in patients with a BMI below 25 kg/m2, and predictions for decanoylcarnitine, dodecanoylcarnitine and symmetric dimethylarginine were found in patients with a BMI of 30 kg/m2 or more.
These results indicated metabolites were effective in predicting preterm PE risk at 11 to 13 weeks of gestation. Investigators recommended future studies on preterm PE prediction consider different maternal risk profiles to improve the clinical utility of screening tests.
Tuytten R, Syngelaki A, Thomas G, et al. First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index. American Journal of Obstetrics & Gynecology. 2023;223(1):55.E1-55.E10. doi:10.1016/j.ajog.2022.12.012