Researchers from the University of Michigan, VA Ann Arbor Healthcare System, and Tufts Medical Center in Boston have created a risk model for patients with pre-diabetes they hope will aid in customizing the most effective preventive treatments for them.
The new 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.
The researchers hope to turn the model, published in The British Medical Journal, 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.
The team developed the model using data from the Diabetes Prevention Program, which randomly assigned people with an elevated risk of diabetes to placebo, the drug metformin, or a lifestyle-modification program. After narrowing the risk factors to the most important seven, the researchers developed a scoring scale using the clinical trial data, assigning points to each measure to calculate total score.
They discovered 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 two diabetes-preventing approaches, administration of metformin or lifestyle modifications, had.
The team found that metformin benefited only the people who the model showed had the very highest risk of developing diabetes. But for them, it really made a difference, bringing down their risk of the disease by 21 percentage points.
By contrast, exercise and weight loss, with encouragement from a health coach, benefited everyone in the DPP study to some extent, the new model shows.
For the one-quarter of study participants who the model says had the highest risk of diabetes, this lifestyle intervention cut their chance of developing the disease by 28 percentage points. For those who had the lowest diabetes risk, this same intensive lifestyle change brought down their risk too but only by 5 points.
The researchers hope their paper serves as a proof of principle to researchers studying other disease and prevention strategies, showing the power of multivariate risk prediction to understand heterogeneity of treatment effect.
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