How can we keep more people from joining the ranks of the 29 million Americans already diagnosed with diabetes? What if we could tell with precision who has the highest risk of developing the disease, and figure out which preventive steps are most likely to help each of them individually?<\/p>\n
Researchers have just released a \"precision medicine\" approach to diabetes prevention that could do just that -- using existing information like blood sugar levels and waist-to-hip ratios, and without needing new genetic tests. <\/p>\n
Their newly published model examined 17 different health factors, in an effort to predict who stands to gain the most from a diabetes-preventing drug, or from lifestyle changes like weight loss and regular exercise. Seven of those factors turned out to matter most. <\/p>\n
The model is published in the British Medical Journal by a team from the University of Michigan, VA Ann Arbor Healthcare System and Tufts Medical Center in Boston. <\/p>\n
They hope to turn it into a tool for doctors to use with patients who have \"pre-diabetes,\" currently defined by abnormal results on a test of blood sugar after fasting. They also hope their approach could be used to develop similar precise prediction models for other diseases and treatments. <\/p>\n
\"Simply having pre-diabetes is not everything,\" says lead author Jeremy Sussman, M.D., M.S. \"This really shows that within the realm of pre-diabetes there's a lot of variation, and that we need to go beyond single risk factors and look holistically at who are the people in whom a particular approach works best.\" Sussman is an assistant professor of general medicine at the U-M Medical School and a research scientist at the VA Center for Clinical Management Research. <\/p>\n
The team developed the model using data from a gold-standard clinical trial of diabetes prevention: the Diabetes Prevention Program, which randomly assigned people with an elevated risk of diabetes to placebo, the drug metformin, or a lifestyle-modification program. <\/p>\n
The team developed and tested their model by carefully analyzing data from more than 3,000 people in the study, all of whom had a high body mass index and abnormal results on two fasting blood sugar tests. Most also had a family history of diabetes, and more than a third were African American or Latino -- all known to be associated with higher risks of diabetes. In all, they looked at 17 factors that together predicted a person's risk of diabetes -- and his or her chance of benefiting from diabetes-preventing steps. They found seven factors were most useful. <\/p>\n
The seven were: fasting blood sugar, long-term blood sugar (A1C level), total triglycerides, family history of high blood sugar, waist measurement, height, and waist-to-hip ratio. They developed a scoring scale using the clinical trial data, assigning points to each measure to calculate total score. <\/p>\n
Fewer than one in 10 of trial participants who scored in the lowest quarter would develop diabetes in the next three years, while almost half of those in the top quarter would develop diabetes in that time. Then, the team looked at what impact the two diabetes-preventing approaches had. <\/p>\n
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Precision medicine to prevent diabetes? Researchers develop personalized way to steer prevention efforts<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" How can we keep more people from joining the ranks of the 29 million Americans already diagnosed with diabetes? What if we could tell with precision who has the highest risk of developing the disease, and figure out which preventive steps are most likely to help each of them individually? Continue reading