Research led by the University of California San Diego (UCSD) shows that a panel of micro (mi)RNA biomarkers can predict preeclampsia and also indicate how severe the condition is likely to be.
The researchers initially identified 110 extracellular miRNAs, which can move between cells, that were linked to preeclampsia before narrowing it down to a panel of three pairs of linked miRNAs with the help of machine learning.
The panel of miRNA biomarkers was able to differentiate between milder and more severe cases of preeclampsia and performed even better when combined with the preexisting placental growth factor (PlGF) and soluble FMS-like tyrosine kinase 1 (sFlt1) ratio.
Preeclampsia is a type of placental dysfunction impacting up to 8% of pregnancies. Symptoms include high maternal blood pressure and protein levels, and the condition can be very dangerous for both the mother and baby. There is no available treatment for preeclampsia and the only way of stopping the condition progressing is by delivering the baby early.
“Currently, early diagnosis of and/or risk assessment for the later development of preeclampsia is problematic due to the lack of assays that are highly specific for this disease. Accurate evaluations are important when planning the intensity of pregnancy surveillance or determining the timing of delivery,” write senior author Louise Laurent, a professor at UCSD, and colleagues in the journal Science Advances.
“If delivery is induced too early, the neonate may be unnecessarily exposed to complications associated with prematurity. However, if the decision to deliver is made too late, the mother and neonate may be exposed to an increased risk of severe manifestations of preeclampsia, which can lead to serious morbidity or death.”
There are biomarkers available to predict preeclampsia, but the accuracy for predicting the onset of the condition is not high enough. These include PlGF, which is important for maintaining placental health, and sFlt1, which inhibits the action of PlGF.
If sFlt1 levels are high and PlGF levels low then it can be an indicator of preeclampsia. For this reason the sFlt1:PlGF ratio is used as a predictor of the condition. It can be useful for predicting who will not develop preeclampsia, but the positive predictive value of this test tends to be 65% or less so it is more difficult to accurately predict who will develop the condition.
In the current study, Laurent and colleagues searched for new biomarkers linked to preeclampsia onset and prognosis in 71 women with preeclampsia and 52 controls without the condition.
They found that the combination of three sets of extracellular miRNA biomarkers (miR-522-3p/miR-4732-5p, miR-516a-5p/miR-144-3p, and miR-27b-3p/let-7b-5p) was able to distinguish cases from controls and also indicate the severity of preeclampsia in women who developed it with a sensitivity of 93% and specificity of 79%. The positive predictive value was 55% and negative predictive value was 89%.
Notably, when the miRNA biomarkers were combined with the sFlt1:PlGF ratio the accuracy was better than for either test alone with a sensitivity of 89.4%, specificity of 91.3%, positive predictive value of 95.5%, and negative predictive value of 80.8%.
“Future validation studies are required to establish the clinical utility of this approach for early diagnosis and prognosis of preeclampsia in the obstetrical triage setting and to better define performance limits of the approach regarding factors such as the interval from blood draw to preeclampsia diagnosis,” conclude the authors.
“We believe that validation and clinical application of our candidate biomarkers will allow for better clinical resource allocation, prevent low-risk patients from unnecessary admission and procedures, and increase understanding of their roles in preeclampsia disease pathogenesis, which may help develop therapies for patients at high risk.”
