Algorithm developed to predict gestational diabetes in women


Researchers have developed a new computer algorithm which will be able to identify women who are at high risk of gestational diabetes — even before they get pregnant.

For the study, published in the journal Nature Medicine, research team from Weizmann Institute of Science, analysed data on nearly 6,00,000 pregnancies available from Israel’s largest health organisation, Clalit Health Services.

“Our ultimate goal has been to help the health system take measures so as to prevent diabetes from occurring in pregnancy,” said senior author Eran Segal from Weizmann Institute of Science in Israel.

Gestational diabetes is characterised by high blood sugar levels that develop during pregnancy in women who did not previously have diabetes.

It occurs in three to nine per cent of all pregnancies and is fraught with risks for both mother and baby.

Typically, gestational diabetes is diagnosed between the 24th and 28th weeks of pregnancy, with the help of a glucose tolerance test in which the woman drinks a glucose solution and then undergoes a blood test to see how quickly the glucose is cleared from her blood.

In the study, the research team started out by applying a machine learning method to Clalit’s health records on some 4,50,000 pregnancies in women who gave birth between 2010 and 2017.

Gestational diabetes had been diagnosed by glucose tolerance testing in about four per cent of these pregnancies.

After processing big data – an enormous dataset made up of more than 2,000 parameters for each pregnancy, including the woman’s blood test results and her and her family’s medical histories.

The scientists’ algorithm revealed that nine of the parameters were sufficient to accurately identify the women who were at a high risk of developing gestational diabetes.

The nine parameters included the woman’s age, body mass index, family history of diabetes and results of her glucose tests during previous pregnancies (if any).

Next, to make sure that the nine parameters could indeed accurately predict the risk of gestational diabetes, the researchers applied them to Clalit’s health records on about 1,40,000 additional pregnancies that had not been part of the initial analysis.

The results validated the study’s findings: The nine parameters helped to accurately identify the women who ultimately developed gestational diabetes.

These findings suggest that by having a woman answer just nine questions, it should be possible to tell in advance whether she is at a high risk of developing gestational diabetes.