Highly Accurate Method Developed Predicts Postpartum Diabetes
By LabMedica International staff writers Posted on 06 Jul 2016 |
Image: Professor Michael Wheeler with Ashley St. Pierre of the Hospital for Sick Children, conducting additional tests in women with gestational diabetes to evaluate racial and ethnic differences in prediction, and investigate high-risk groups with prediabetes to learn if metabolomics will predict type 2 diabetes in the general population (Photo courtesy of the University of Toronto).
Gestational diabetes is defined as glucose intolerance that is first identified during pregnancy and it occurs in 3% to 13% of all pregnant women, and increases a woman's risk of developing type 2 diabetes by 20% to 50% percent within five years after pregnancy.
A simple, accurate new way to predict which women with gestational diabetes will develop type 2 diabetes after delivery has been discovered which would allow health care providers to identify women at greatest risk and help motivate women to make early lifestyle changes and follow other strategies that could prevent them from developing the disease later in life.
An international team of scientists working with those at the University of Toronto (ON, Canada) obtained fasting blood samples from 1,035 women diagnosed with gestational diabetes and enrolled in the Kaiser Permanente's Study of Women, Infant Feeding and Type 2 Diabetes after GDM Pregnancy, also known as the SWIFT Study. The SWIFT study screened women with oral glucose tolerance tests at two months after delivery and then annually thereafter to evaluate the impact of breastfeeding and other characteristics on the development of type 2 diabetes after a pregnancy complicated by gestational diabetes.
The team conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident type 2 diabetes (T2D) status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, being far superior to fasting plasma glucose alone. The new method may also be able to predict individuals who may develop type 2 diabetes in the general population which would be a major advance at a time when more than 300 million people suffer from the preventable form of this disease. A next-generation blood test that's more simple and accurate than the current options could help to identify individuals who would benefit most from more timely and effective interventions to prevent type 2 diabetes.
Michael B. Wheeler, PhD, a professor in the Department of Physiology and a senior author of the study said, “After delivering a baby, many women may find it very difficult to schedule two hours for another glucose test. What if we could create a much more effective test that could be given to women while they're still in the hospital? Once diabetes has developed, it's very difficult to reverse.” The study was published in the June 2016 issue of the journal Diabetes.
Related Links:
University of Toronto
A simple, accurate new way to predict which women with gestational diabetes will develop type 2 diabetes after delivery has been discovered which would allow health care providers to identify women at greatest risk and help motivate women to make early lifestyle changes and follow other strategies that could prevent them from developing the disease later in life.
An international team of scientists working with those at the University of Toronto (ON, Canada) obtained fasting blood samples from 1,035 women diagnosed with gestational diabetes and enrolled in the Kaiser Permanente's Study of Women, Infant Feeding and Type 2 Diabetes after GDM Pregnancy, also known as the SWIFT Study. The SWIFT study screened women with oral glucose tolerance tests at two months after delivery and then annually thereafter to evaluate the impact of breastfeeding and other characteristics on the development of type 2 diabetes after a pregnancy complicated by gestational diabetes.
The team conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident type 2 diabetes (T2D) status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, being far superior to fasting plasma glucose alone. The new method may also be able to predict individuals who may develop type 2 diabetes in the general population which would be a major advance at a time when more than 300 million people suffer from the preventable form of this disease. A next-generation blood test that's more simple and accurate than the current options could help to identify individuals who would benefit most from more timely and effective interventions to prevent type 2 diabetes.
Michael B. Wheeler, PhD, a professor in the Department of Physiology and a senior author of the study said, “After delivering a baby, many women may find it very difficult to schedule two hours for another glucose test. What if we could create a much more effective test that could be given to women while they're still in the hospital? Once diabetes has developed, it's very difficult to reverse.” The study was published in the June 2016 issue of the journal Diabetes.
Related Links:
University of Toronto
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