Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program.

Sussman JB, Kent DM, Nelson JP, Hayward RA. Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program. BMJ (Clinical research ed.). 2015 Feb 19; 350:h454.

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Abstract:

OBJECTIVE: To determine whether some participants in the Diabetes Prevention Program were more or less likely to benefit from metformin or a structured lifestyle modification program. DESIGN: Post hoc analysis of the Diabetes Prevention Program, a randomized controlled trial. SETTING: Ambulatory care patients. PARTICIPANTS: 3060 people without diabetes but with evidence of impaired glucose metabolism. INTERVENTION: Intervention groups received metformin or a lifestyle modification program with the goals of weight loss and physical activity. MAIN OUTCOME MEASURE: Development of diabetes, stratified by the risk of developing diabetes according to a diabetes risk prediction model. RESULTS: Of the 3081 participants with impaired glucose metabolism at baseline, 655 (21%) progressed to diabetes over a median 2.8 years' follow-up. The diabetes risk model had good discrimination (C statistic = 0.73) and calibration. Although the lifestyle intervention provided a sixfold greater absolute risk reduction in the highest risk quarter than in the lowest risk quarter, patients in the lowest risk quarter still received substantial benefit (three year absolute risk reduction 4.9% v 28.3% in highest risk quarter; numbers needed to treat of 20.4 and 3.5, respectively). The benefit of metformin, however, was seen almost entirely in patients in the top quarter of risk of diabetes. No benefit was seen in the lowest risk quarter. Participants in the highest risk quarter averaged a 21.4% three year absolute risk reduction (number needed to treat 4.6). CONCLUSIONS: Patients at high risk of diabetes have substantial variation in their likelihood of receiving benefit from diabetes prevention treatments. Using this knowledge could decrease overtreatment and make prevention of diabetes far more efficient, effective, and patient centered, provided that decision making is based on an accurate risk prediction tool.