Incidence Rate of Prediabetes Progression to Diabetes: Modeling an Optimum Target Group for Intervention.
Thirty-seven percent of US adults have prediabetes. Various interventions can delay diabetes progression; however, the optimum target group for risk reduction is uncertain. This study estimated rate of progression to diabetes at 1 and 5 years among a cohort of patients from 3 primary care clinics and modeled the potential magnitude in diabetes incidence risk reduction of an intervention program among specific subgroups. Records of 106,821 empaneled patients in 2005 were reviewed. Generalized population attributable risk (PAR) statistics were calculated to estimate the impact of reducing fasting blood glucose on diabetes progression. Multiple intervention effects (varying levels of glucose reduction along with multiple adherence rates) were examined for those with baseline glucose from 110 to 119 mg/dL and ≥120 mg/dL. Ten percent of patients (n = 10,796) met criteria for prediabetes. The 1- and 5-year diabetes incidence rate was 38.6 and 40.24 per 1000 person-years, respectively. Age and obesity were independent predictors of increased progression rate. The generalized PAR for a 10-point reduction in the 110-119 mg/dL subgroup with 25% adherence was 7.6%. The generalized PAR for similar percent reduction and adherence level in patients with baseline glucose of ≥120 mg/dL was only 3.0%. Rate of progression to diabetes increased over time and with associated independent risk factors. Greater risk reduction in diabetes progression within the target population can be achieved when the intervention is successful in those with baseline glucose of 110-119 mg/dL. Modeling an optimum target group for a diabetes prevention intervention offers a novel and useful guide to planning and allocating resources in population health management.
1 Robert D and Patricia E Kern Center for the Science of Health Care Delivery , Mayo Clinic, Rochester, Minnesota.|2 Division of Primary Care Internal Medicine, Department of Medicine, Mayo Clinic , Rochester, Minnesota.
Ramona S
RS
DeJesus
3 Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.
Carmen Radecki
CR
Breitkopf
1 Robert D and Patricia E Kern Center for the Science of Health Care Delivery , Mayo Clinic, Rochester, Minnesota.|4 Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.
Lila J
LJ
Rutten
5 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.
Debra J
DJ
Jacobson
5 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.
Patrick M
PM
Wilson
1 Robert D and Patricia E Kern Center for the Science of Health Care Delivery , Mayo Clinic, Rochester, Minnesota.|4 Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.
Jennifer St
JS
Sauver
D000328:Adult; D000368:Aged; D001786:Blood Glucose; D015992:Body Mass Index; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology* / Q000517:prevention & control*; D018450:Disease Progression; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011236:Prediabetic State / Q000453:epidemiology*; D012189:Retrospective Studies; D012307:Risk Factors; D055815:Young Adult
D001786:Blood Glucose