title: Improving diabetes prevention with benefit based tailored treatment: risk based reanalysis of Diabetes Prevention Program.
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.
  
  
  METHODS
  Post hoc analysis of the Diabetes Prevention Program, a randomized controlled trial.
  
  
  METHODS
  Ambulatory care patients.
  
  
  METHODS
  3060 people without diabetes but with evidence of impaired glucose metabolism.
  
  
  METHODS
  Intervention groups received metformin or a lifestyle modification program with the goals of weight loss and physical activity.
  
  
  METHODS
  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.
authors.affiliation: Department of Veterans Affairs Center for Clinical Management Research, Ann Arbor, MI 48109-2800, USA Division of General Internal Medicine, University of Michigan, NCRC, 2800 Plymouth Road, Building 16/343E, Ann Arbor jeremysu@med.umich.edu.
authors.forename: Jeremy B
authors.initials: JB
authors.lastname: Sussman
authors.affiliation: Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, 35 Kneeland Street, Boston, MA 02111, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Kent
authors.affiliation: Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, 35 Kneeland Street, Boston, MA 02111, USA.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Nelson
authors.affiliation: Department of Veterans Affairs Center for Clinical Management Research, Ann Arbor, MI 48109-2800, USA Division of General Internal Medicine, University of Michigan, NCRC, 2800 Plymouth Road, Building 16/343E, Ann Arbor RWJ Foundation Clinical Scholars Program, University of Michigan, 2800 Plymouth Road, NCRC B10-G016, Ann Arbor.
authors.forename: Rodney A
authors.initials: RA
authors.lastname: Hayward
mesh_terms: D001786:Blood Glucose / Q000378:metabolism; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000517:prevention & control*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D008687:Metformin / Q000627:therapeutic use; D008875:Middle Aged; D011322:Primary Prevention; D058996:Quality Improvement; D018570:Risk Assessment
chemical_list: D001786:Blood Glucose; D008687:Metformin