Preventing premature births
Dr Mande and her team have researched and developed an algorithm that can predict the likelihood of a premature birth by assessing the microbiome diversity of women in their early stages of pregnancy.
They analysed data taken from 1,621 publicly available vaginal microbiomes, sampled from 303 pregnant women in the US and China, and identified certain diversity patterns consistent with pre- term delivery.
This analysis was done using a novel algorithm developed by Dr Mande and her team, which has been dubbed the Taxonomical Composition Skew (TCS).
In over 1,000 cross-validation tests of the microbiome data using the TCS algorithm, Dr Mande and her team were able to predict with more than 95% accuracy the likelihood of a woman carrying to full term or delivering prematurely.
What is more interesting is that this high level of accuracy showed up in ‘first-trimester’ samples
i.e. predictions were most accurate with samples taken in the first trimester.
Changes in the mother’s body later in the pregnancy made it more difficult to predict whether they would carry to full term.
Dr Mande says being able to predict a preterm delivery so early on in the pregnancy will help at-risk mothers get the appropriate treatment / management they need as early as possible.
“This is the most satisfying project I have worked on because at the end of 10 years of working on algorithm development, analysing real-world datasets and coming up with the microbiome markers, this finding in particular has the potential to prevent a lot of deaths,” says Dr Mande.
“Preterm deliveries can be avoided with this. I am very excited when I see that our research has a real impact on human health. A lot of lives will be able to be saved in a very cost-effective way.”