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?
University of Michigan 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. The new model may allow better targeting of drugs and lifestyle changes to those who can benefit most.
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.
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. 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.
The team developed the model using data from a gold-standard clinical trial of diabetes prevention: the Diabetes Prevention Program, which more than 3,000 people with an elevated risk of diabetes were randomly assigned to placebo, the drug metformin, or a lifestyle-modification program.
From that research, the team found seven factors were the most useful in predicting a person’s risk of diabetes – and his or her chance of benefiting from diabetes-preventing steps: 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.
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.
“Our research has found that it is common that, although the average benefit in a clinical trial might be moderate, in reality those patients at high risk for a bad outcome get a lot of benefit, the average patient has modest chance of benefiting, and lower-risk patients may have little to no chance of benefitting, or are being harmed,” remarks co-author Dr. Rod Hayward, a professor of medicine at the U-M School of Medicine and a professor of health management and policy at the U-M School of Public Health.
The researchers hope their paper serves as a proof of principle to researchers studying other disease and prevention strategies, showing the power of the approach, technically called multivariate risk prediction to understand heterogeneity of treatment effect.
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