Predicting preeclampsia

March 14, 2016 § 1 Comment

Researchers are developing new ways to predict preeclampsia using serum lipid profiles. Photo credit: Wikimedia Commons

Preeclampsia affects roughly three percent of pregnant women in the U.S., bringing on a host of complications that include premature births and even death. Unfortunately, there is no effective diagnostic test to predict the onset of disease.

In a recent paper published in the Journal of Lipid Research, Steven Graves of Brigham Young University and colleagues described a set of biomarkers that could help in the early detection of preeclampsia.

In spite of efforts to identify the mechanisms surrounding the disease, researchers haven’t been able to pinpoint a causative factor. When attempting to develop predictive assays for prenatal complications, scientists place the safety of the mothers’ and their unborn babies first. While a sampling of the placenta may provide critical information about preeclamptic processes, the placenta sampling procedure is risky. Scientists interested in developing prenatal diagnostic tests need to consider methods that are both informative and reasonable to use in a clinical setting.

Graves and colleagues decided to look at lipids although proteins tend to be the more conventional class of biomarker. Graves says the team focused on lipids in the blood because lipids tend to be more forgiving than their protein counterparts. Lipids, “are not particularly heat-sensitive compared to a protein or peptide and they’re not degraded rapidly by proteolytic enzymes which exist in the serum,” explains Graves. Additionally, serum samples can be collected in the clinic relatively easily with blood draws, keeping the risk to patients low.

The researchers took samples collected for another trial that was studying the early events of Down’s syndrome. Of the serum samples available, they used those collected at the earliest available time point which was 12–14 weeks into the pregnancy.

Using mass spectrometry data, the team compared the serum lipid profiles of women who went on to develop preeclampsia and those who did not. After an initial analysis and a second confirmatory run in another sample set, the team identified a set of 23 biomarkers in the form of mass spectral profiles that were able to predict those women who would go on to have a preeclamptic event.

Any biomarker on its own can’t provide sufficient predictive value, but combining the markers together into sets increased predictability. For their sample population, the investigators found that using six biomarkers helped with predicting preeclampsia; combining more than six markers failed to show an increase in predictive value. When the lipid test becomes publically available, Graves advises using all 23 biomarkers together to better account for individual patient factors.

Though the lipid biomarkers are intriguing, Graves is careful to point out these biomarkers aren’t ready for the clinic just yet. “What should happen now is one should establish a clear hypothesis that this set of markers would be useful and then carry out studies” focused on these markers. A lipid-based test will only be available after it passes through all the necessary studies and approval of a clinical test by the U.S. Food and Drug Administration.

Currently, the true advantage of this research isn’t in its immediate clinical value but the potential use of the biomarkers for streamlining the research process. Because the disease is so rare, one of the biggest issues with prospective studies for preeclampisa is the sheer number of women that need to be enrolled in order to have adequate numbers of preeclamptic cases. However, if researchers first can narrow down the population, using a set of predictive biomarkers such as the one proposed in the paper, fewer women would need to be enrolled. Graves proposes, “It could save time and allow for more things to be tested more efficiently.”

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Bree Yanagisawa, the guest author of this blog post, is a science writing intern at ASBMB Today. You can follow her on Twitter @BreeTalksSci .

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